Multi-hazard Loss Estimation Methodology 
Flood Model 
HAZUS.MH MR3 

Technical Manual 

Developed by: 
Department of Homeland Security
Federal Emergency Management Agency 
Mitigation Division 
Washington, D.C. 



Under a contract with: 
National Institute of Building Sciences 
Washington, D.C. 



Foreword 

The research and development and studies that provided the basis for this 
publication were conducted pursuant to a contract with the Federal Emergency 
Management Agency (FEMA) by the: 

The National Institute of Building Sciences (NIBS) is a non-governmental, 
non-profit organization, authorized by Congress to encourage a more rational 
building regulatory environment, to accelerate the introduction of existing 
and new technology into the building process and to disseminate technical 
information. 
Copies of this report are available through the Federal Emergency Management 
Agency.  For information contact FEMA @ 
http://www.fema.gov/plan/prevent/hazus/ or: 
FEMA Distribution Center 
P.O. Box 2012 Jessup, Maryland 20794-2012 Tel.: 1 800-480-2520  Fax: 301-362-
5335 
HAZUS. is a trademark of the Federal Emergency Management Agency. 

Acknowledgments

HAZUS-MH, HAZUS-MH MR1, HAZUS-MH MR2 and HAZUS-MH MR3 
Flood Model Methodology Development 
Flood Committee 
Chairman, Doug Plasencia, AMEC, Phoenix, Arizona 
Richard Boisvert, Cornell University, Ithaca, New York 
Dave Canaan, Mecklenburg County, Charlotte-Mecklenburg, North Carolina 
John Costa, Unites States Geological Survey, Portland, Oregon 
Joe Coughlin, DHS, EP&R Directorate (FEMA), Federal Insurance Administration, 
Washington, 
D.C. 
Darryl Davis, Corps of Engineers Hydrologic Engineering Center, Davis, 
California 
Neil Grigg, Colorado State University, Fort Collins, Colorado 
Howard Leikin, DHS, EP&R Directorate (FEMA), Federal Insurance 
Administration, 

Washington, D.C. 
Kenneth Lewis, KVL Consultants, Inc., Scottsdale, Arizona 
Randy Reiman, National Weather Service, Silver Spring, Maryland 
Spencer Rogers, North Carolina Sea Grant, Wilmington, North Carolina  
Ming Tseng, U.S. Army Corps of Engineers, Washington, D.C. 
Gilbert White, University of Colorado, Boulder, Colorado 

ABS Consulting, Irvine, California 
Charles Scawthorn, Principal-in-Charge; Paul Flores, Project Manager; Neil 
Blais, Assistant 
Project Manager; Hope Seligson, Donald Ballantyne, Stephanie Chang, Eric 
Tate, Fatih Dogan, 
Rick Ranous, Charles Huyck, Nancy Nishimoto, Jennifer Trudeau 
Consultants 
Michael Baker Jr., Inc., Glen Burnie, Maryland; Ed Mifflin, Adam Simcock, Kim 
Mueller, Katie 
Field, Claiborne Ashby, Will Thomas, Jim Murphy, Rebecca Quinn, Brendan 
Smith, Erin 
Lawrence; Jack Faucett Associates, Inc., Lafayette, California; Christopher 
Jones, Christopher 
P. Jones, P.E., Durham, North Carolina; Michael F. Lawrence, Kristin Noyes, 
H. Vicky Hsu 
Other Consultants Adam Rose, Penn State University, University Park, 
Pennsylvania; Harold Cochrane, Colorado State University, Fort Collins, 
Colorado; Jerry Steenson, Colorado State University, Fort Collins, Colorado; 
Kim Shoaf, University of California Los Angeles, Los Angeles, California 
Proof-of-Concept Communities The City of Austin, Texas; The City of Charlotte 
and Mecklenburg County, North Carolina; The City of Fort Collins and Larimer 
County, Colorado; The City and County of Grand Forks, North Dakota; The City 
of Pensacola Beach and Escambia County, Florida; The City and County of San 
Diego, California; The City of Scottsdale and Maricopa County, Arizona; The 
City of Wrightsville Beach and New Hanover County, North Carolina; Story 
County, Iowa; The Heinz Center 
Flood Model Software Development 
Software Committee Chairman, Dick Bilden, Consultant, Reston, Virginia Co-
Chairman, Mike Haecker, Consultant, Austin, Texas Dan Cotter, Terrapoint, The 
Woodlands, Texas Gerry Key, Computer Sciences Corporation, San Diego, 
California Tracy Lenocker, Lenocker and Associates, Inc., Orange, California 
Kenneth Lewis, KVL and Associates, Inc., Scottsdale, Arizona Frank Opporto, 
DHS, EP&R Directorate (FEMA),Information Services Technology Division, 
Washington, D.C. Dirk Vandervoort, POWER Engineers, Inc., Boise Idaho Leslie 
Weiner-Leandro, DHS, EP&R Directorate (FEMA), Information Services Technology 
Division, Washington, D.C. 
ABS Consulting, Irvine, California Andrew Petrow, Project Manager; Nikolay 
Todorov, William Talamaivao, Y-Co Nguyen, Yamini Manickam,, Liyuan Wu, Ray 
Kincaid, Phil Burtis, Kerry Zimmerman, Linda Wrigley, Laura Samant, Tom 
Montoya 
Consultants Fatih Dogan, Eric Tate, Neil Blais, Kavin Keratickasama, Sheetal 
Gawalpanchi, Dennis Singer, 
Srivatsava Sunkara, Vidya Prasad, Jacob Varghese, Nilanjin Battacharya, Raj 
Cherikuri, Narayan Sakhamuri  Special thanks to ESRI for its assistance in 
coordinating ArcGIS and Spatial Analyst with the 
Flood Model. Special thanks to the Flood Model User Group, consisting of 
ASFPM and other community officials, who provided guidance to the methodology 
and software design process.  Their assistance was invaluable, but the list 
is too extensive to identify all of the participants individually. 
Beta Test Subcommittee   HAZUS-MH Darryl Davis, Corps of Engineers Hydrologic 
Engineering Center, Davis, California Neil Grigg, Colorado State University, 
Fort Collins, Colorado Charles Kircher, Kircher & Associates, Palo Alto, 
California Tracy Lenocker, Lenocker and Associates, Inc., Orange, California 
Ken Lewis, KVL and Associates, Inc., Scottsdale, Arizona Masoud Zadeh, 
Consultant, San Jose, California 
Beta Test Communities   HAZUS-MH Division of Emergency Management, 
Tallahassee, Florida; Washington State Emergency Management, Camp Murray, 
Washington; Whatcom County Public Works, Bellingham, Washington; Johnson 
County, Olathe, Kansas; Mecklenburg County Stormwater Services, Charlotte, 
North Carolina; Louisiana State University, Baton Rouge, Louisiana; Charleson 
County Building Services, North Charleston, South Carolina 
Beta Test Subcommittee   HAZUS-MH MR1 
Douglas Bausch, Department of Homeland Security, Emergency Preparedness and 
Response Directorate (FEMA), Washington, D.C. Richard Eisner, Governor's 
Office of Emergency Services, Oakland, California John Knight, South Carolina 
Emergency Management Division, Columbia, South Carolina Kevin Mickey, The 
Polis Center, Indianapolis, Indiana Mark Neimeister, Delaware Geological 
Survey, Newark, Delaware Lynn Seirup, New York City Office of Emergency 
Management, New York, New York 
Beta Test Subcommittee   HAZUS-MH MR2 Douglas Bausch, Department of Homeland 
Security, Federal Emergency Management Agency, Washington, D.C. John Knight, 
South Carolina Emergency Management Division, Columbia, South Carolina Kevin 
Mickey, The Polis Center, Indianapolis, Indiana Joe Rachel, Department of 
Homeland Security, Federal Emergency Management Agency, Washington, D.C. Ken 
Wallace, Department of Homeland Security, Federal Emergency Management 
Agency, Washington, D.C. Bryan Siltanen, Advanced Systems Development, Inc., 
Arlington, VA 
Beta Test Subcommittee   HAZUS-MH MR3 Douglas Bausch, Department of Homeland 
Security, Federal Emergency Management Agency, 
Washington, D.C. John Buechler, The Polis Center, Indianapolis, IN Susan 
Cutter, Hazards and Vulnerability Research Institute, University of South 
Carolina, 
Columbia, SC Aiju Ding, CivilTech Engineering, Inc., Cypress, TX Craig 
Eissler, Geo-Tech Visual Power Tec  Texas Geographic Society, Austin, TX 
Melanie Gall, Hazards and Vulnerability Research Institute, University of 
South Carolina, 
Columbia, SC 
Shane Hubbard, The Polis Center, Indianapolis, IN 

David Maltby, Geo-Tech Visual Power Tech/First American Flood Data Services, 
Austin, TX Kevin Mickey, The Polis Center, Indianapolis, IN David Niel, 
APPTIS Inc., McLean, VA Jack Schmitz, The Polis Center, Indianapolis, IN 
Bryan Siltanen, APPTIS Inc., McLean, VA Hilary Stephens, Greenhorne & O'Mara, 
Inc., Laurel, MD Eric Tate, Hazards and Vulnerability Research Institute, 
University of South Carolina, Columbia, 
SC 
HAZUS-MH and HAZUS-MH MR2 Shell Development 
PBS&J, Atlanta, Georgia Mourad Bouhafs, Program Manager; Pushpendra Johari, 
Sandeep Mehndiratta Special thanks to ESRI for its assistance in coordinating 
ArcGIS with HAZUS-MH MR2. 
Project Sponsorship and Oversight 
Department of Homeland Security, FEMA, Mitigation Division, Washington, D.C. 
Frederick Sharrocks, Section Chief, Assessment & Plan for Risk; Cliff Oliver, 
Chief, Risk Assessment Branch; Edward Laatsch, Chief, Building Science and 
Technology; Eric Berman, Project Officer; Claire Drury, Project Officer; Paul 
Tertell, Michael Mahoney, Stuart Nishenko, Scott McAfee, Paul Bryant 
FEMA Technical Monitors Douglas Bausch, FEMA Region 8; Mark Crowell, Physical 
Scientist; John Ingargiola, Douglas Bellemo, Allyson Lichtenfels, Divisional 
Coordination 
Project Management 
National Institute of Building Sciences, Washington, D.C.  Philip Schneider, 
Director, Multihazard Loss Estimation Methodology Program; Barbara Schauer, 
Senior Project Manager 


Message to Users 
HAZUS is designed to produce loss estimates for use by federal, state, 
regional and local governments and private enterprises in planning for risk 
mitigation, emergency preparedness, response and recovery. HAZUS comes 
complete with methodology to analyze earthquakes, flood and hurricane winds.  
The methodology deals with nearly all aspects of the built environment, and a 
wide range of different types of losses.  Extensive national databases are 
embedded within HAZUS, containing information such as demographic aspects of 
the population in a study region, square footage for different occupancies of 
buildings, and numbers and locations of bridges. Embedded parameters have 
been included as needed.  Using this information, users can carry out general 
loss estimates for a region.  The HAZUS methodology and software are flexible 
enough so that locally developed inventories and other data that more 
accurately reflect the local environment can be substituted, resulting in 
increased accuracy. 
Uncertainties are inherent in any loss estimation methodology.  They arise in 
part from incomplete scientific knowledge concerning each of the three 
hazards and their effects upon buildings and facilities.  They also result 
from the approximations and simplifications that are necessary for 
comprehensive analyses.  Incomplete or inaccurate inventories of the built 
environment, demographics and economic parameters add to the uncertainty.  
These factors can result in a range of uncertainty in loss estimates produced 
by HAZUS, possibly at best a factor of two or more. 
The methodology has been tested against the judgement of experts and, to the 
extent possible, against records from several past earthquakes, floods and 
hurricanes.  However, limited and incomplete data about damage from these 
events precludes complete calibration of the methodology.  Nevertheless, when 
used with embedded inventories and parameters, HAZUS has provided a credible 
estimate of such aggregated losses as the total cost of damage and numbers of 
casualties. HAZUS has done less well in estimating more detailed results - 
such as the number of buildings or bridges experiencing different degrees of 
damage.  Such results depend heavily upon accurate inventories.  Of course, 
the geographic distribution of damage may be influenced markedly by local 
conditions.  In the few instances where HAZUS has been partially tested using 
actual inventories of structures plus correct local condition maps, it has 
performed reasonably well. 
Users should be aware of the following specific limitations: 
 	
While HAZUS can be used to estimate losses for an individual building, the 
results must be considered as average for a group of similar buildings.  It 
is frequently noted that nominally similar buildings have experienced vastly 
different damage and losses during a natural hazard. 

 	
When using embedded inventories, accuracy of losses associated with lifelines 
may be less than for losses from the general building stock.  The embedded 
databases and assumptions 

used to characterize the lifeline systems in a study region are necessarily 
incomplete and oversimplified. 

 	
The Flood Model performs its analysis at the census block level with small 
numbers of buildings.  Damage analysis of these small numbers makes the Flood 
Model more sensitive to rounding errors. These results should be used with 
suitable caution. 


HAZUS should still be regarded as a work in progress.  Additional damage and 
loss data from actual earthquakes, wind or flood events, and further 
experience in using the software will contribute to improvements in future 
releases.  To assist us in further improving HAZUS, users are invited to 
submit comments on methodological and software issues by letter, fax or e-
mail to: 
Philip Schneider    Eric Berman National Institute of Building Sciences 
Department of Homeland Security 1090 Vermont Ave., N.W. Federal Emergency 
Management Agency Washington, DC 20005    Mitigation Division Tel: 202-289-
7800 ext. 127 500 C Street, S.W. Fax: 202-289-1092     Washington, DC 20472 
E-Mail: HAZUSGenHelp@nibs.org  Tel: 202-646-3427 
Fax: 202-646-2787 E-Mail: Eric.Berman@dhs.gov 

Limitations of the HAZUS-MH MR3 Software 
Installation 
 	
HAZUS-MH MR3 is certified to run on ArcGIS 9.2 SP2. Tests have shown that 
HAZUS-MH MR3 is unable to fully function on the ArcGIS 9.2 platform only, SP1 
is required, but SP2 is preferable. ArcGIS 9.2 SP2 is available from the ESRI 
website. 

 	
HAZUS-MH MR3 is certified to run on MS Windows 2000 SP2, SP3 and SP 4 and 
Windows XP SP1 and SP2.  A user is allowed to install HAZUS-MH MR3 on MS 
Windows 2000 and XP for Service Packs higher than SP4 and SP2 respectively, 
but HAZUS-MH MR3 is not certified to work flawlessly with those service 
packs. 

 	
HAZUS-MH MR3 must be uninstalled only with the Windows Add/Remove Programs 
utility. For details on uninstalling, please consult the User Manuals. 

 	
Users who plan to operate HAZUS-MH MR3 in a network environment will be able 
to perform HAZUS operations, such as importing, but not study region 
creation. 


Study Region Size 
 	
The database management system of HAZUS-MH MR3 is SQL Server 2005 Express 
Edition. This system has a size limit of 4 GB per database, which limits the 
size of the regions to 90,000 census blocks for a Riverine or Coastal flood 
analysis.  Ninety thousand census blocks approximates four counties and is 
equivalent to an area with a population of about 9 million.  For a multi-
hazard study region that includes data for all three hazards, the 4 GB limit 
will permit an even smaller study region.  To work around this, the full 
version of Microsoft SQL Server must be used (see Appendix H in the User 
Manual). 

 	
Multihazard loss analysis capability is limited to the 23 states that 
experience hurricane, flood and earthquake hazards and requires that the user 
first run annualized losses for each of the three hazards. 

 	
To maximize the size of the study region that may be analyzed, set the 
virtual memory size from a minimum of 2048 MB to a  maximum of 4096 MB.  For 
the earthquake model, the virtual memory size may be increased from a minimum 
of 1024 MB to a maximum of 2048 MB for optimal operation.  Here are the steps 
for setting the virtual memory size (in Windows XP.  See page 2-17 in User 
Manual for Windows 2000): 


1 - Click on Start | Settings | Control Panel | System  
2 - Click on the Advanced Tab 3 - Click on the Settings button under the 
Performance group 
4 - Click on the Advanced tab 
5 - Click the Change button under Virtual Memory 
6 - Replace the initial and maximum values 
7   Click Set then OK three times to exit to the main screen 
 	To speed up the study region aggregation process, state databases can 
be copied to the local hard-disk.  If necessary, the Registry can be updated 
so that HAZUS-MH looks to the location where you copied the data on the local 
hard disk rather than to the default DVD location. 
The HAZUS-MH MR3 installation allows the user to specify the folder where the 
state data will be copied through the "Choose Data Path" dialog in the 
installation wizard.  If, at the time of installation, the user specifies the 
folder where the data will be copied after installation, they only need to 
perform Step 1 as described below.  If at the time of installation the User 
does not specify the folder where the state data will be copied by the user 
after installation, or if they want to change the folder specified during 
installation, then Steps 2 through 4 for updating the Registry should be 
completed. 
NOTE: The "Choose Data Path" dialog in the installation process only 
specifies the folder where the state data will be copied by the user from the 
DVD after installation has completed.  This dialog doesn't copy the data from 
the DVD to the specified folder; that has to be done manually by the user 
after installation. 
1 - Copy one or more of the state data folders (e.g., NC1), both the DVD 
identification files (e.g., D1.txt ^ 4.txt) and "syBoundary.mdb" from the 
Data DVD to a folder on your hard drive (e.g., D:\HAZUSData\). As an example, 
the following graphic illustrates how the data for the state of South 
Carolina would be organized under the HAZUS Data folder. 
2 - Next, point the program to the data folder on your local hard drive. To 
do this, click the "Start" button and select "Run" to open the Run  window, 
type "regedit" in the Run window edit box and click the "OK" button to open 
the Registry Editor.  Navigate through the folders listed in the Registry 
Editor to the following location: 
HKEY_LOCAL MACHINE | SOFTWARE | FEMA | HAZUS-MH | General  
3 - Now look at the right side of the window and find the entry called 
"DataPath1".  Double click on "DataPath1" to open the Edit String window and 
enter the full name of the folder on the hard drive that contains the data 
copied from the DVDs in the edit box.  Click the OK button to update the 
DataPath1 value. 
IMPORTANT: Make sure the path ends with a "\" and do not change any of the 
other registry settings. 
4 - Close the Registry Editor by choosing Exit from the File menu of the 
Registry Editor. 
Capabilities 
 	
Transferring data, including importing study regions, from HAZUS99, HAZUS99-
SR1, HAZUS99-SR2, HAZUS-MH and HAZUS-MH MR1 to HAZUS-MH MR3 will require the 
assistance of technical support. 

 	
Except for Puerto Rico, inventory data are unavailable for the US held 
territories. 

 	
Due to lack of default data, riverine users in the States of Hawaii and 
Alaska and Puerto Rico will be unable to perform hydrologic analyses.  These 
users may still compute riverine flood hazard; however, options of specific 
return period and suite of return periods will be unavailable. Instead 
specific discharge should be selected. 

 	
Components of independently developed data sets in the default inventory data 
might not line up on maps, for example, the placement of bridges and roads, 
and facilities.  This situation can be addressed by updating the default 
inventory data with user supplied data. 

 	
When running the hydrology analysis the recommended limitation is 150-reaches 
to ensure function completion. 

 	
When running the hydraulic (Delineate Floodplain) analysis, the recommended 
limitation is 24-reaches for the suite and 90-reaches for single discharge or 
single return period, assuming the machine has 2 GB of RAM. 

 	
Running the General Building Stock analysis might require 1 to 3 hours 
analysis time. 

 	
The flood date (Analysis -> Parameters -> Agricultural...) needs to be set 
before running the agricultural analysis (Analysis -> Run -> Agricultural 
Products).  Failure to do so will result in a message during analysis that 
will require interaction and will likely delay completing the batch of 
selected analyses. 

 	
The coastal What Ifs, Long-term Erosion and Shore Protection features are 
disabled. 


BIT and InCAST 
 	
In the Flood Model, BIT does not allow mapping from specific to general 
building types. 

 	
Since InCAST development predated the development of the Flood Model in 
HAZUS-MH, data types used for different types of hazard specific data in 
InCAST are not 


compatible with those used in HAZUS-MH MR3.  Additionally, InCAST does not 
capture all hazard specific attributes used in HAZUS-MH MR3. 
InCAST can be used to capture flood hazard data which can be imported into 
HAZUS-MH MR3 from hzIncast table.  However, the following fields should not 
be imported: BldgType, Kitchen, Dinning and Sleeping. 
Technical Support 
 	Technical support is available via telephone, e-mail, or FAX.  The 
numbers and addresses are listed on the HAZUS software package and under the 
Help menu in the software.  Information on HAZUS updates, software patches, 
and FAQs are available at http://www.fema.gov/plan/prevent/hazus/index.shtm/. 

What s New in HAZUS   Flood Model 
What s New in HAZUS-MH MR1 Data 
 	Updated valuations for the general building stock. 
Methodology 
 	Optimized riverine and coastal flooding hazard characterization 
Other Features 
 	
Operation on the new ArcGIS 9.0 SP1 platform 

 	
Improved integration with the underlying ArcGIS platform. 

 	
Capability to run HAZUS-MH MR1 without administrative rights. 

 	
Flood Information Tool (FIT) integrated into the HAZUS-MH MR1 installation. 

 	
Capability to utilize third-party tools. 



What s New in HAZUS-MH MR2 Data 
 	
2005 valuation data for all occupancy classes. 

 	
Means location factors for residential and non-residential occupancies on a 
county basis.   

 	
Updated and validated valuation data for single-family residential housing 
and manufactured housing based on comparisons with other national databases. 

 	
Zeros substituted for any negative values calculated for the daytime, 
nighttime, working commercial, working industrial and commuting populations. 

 	
Construction age and values by decade for every census block with floor area 
(square footage). 


Methodology 
 	Capability to create study regions larger than four counties using the 
full version of SQL Server. (The 90,000 census block limit, which 
approximates four counties, is still applicable to study regions created with 
SQL Server MSDE with its 2 GB size limit.) 
Other Features 
 	
Keyboard operation of all user interface operations with some exceptions. 

 	
Operation on the ArcView 9.1 SP1 platform. 

 	
Certified on Windows XP SP2. 

 	
Operation on the MDAC 2.8 data access engine from Microsoft. 


What s New in HAZUS-MH MR3 Data 
 	
Commercial data updated to Dun & Bradstreet 2006. 

 	
Building valuations updated to R.S. Means 2006. 

 	
Building counts based on census housing unit counts for RES1 (single-family 
dwellings) and RES2 (manufactured housing) instead of calculated building 
counts. 


Methodology 
 	
User-supplied flood maps and flood depth grids import into the  quick look  
and  enhanced quick look  functions. 

 	
Optimized building analysis. 

 	
Revised hazard menu graphic user interface. 


Other Features 
 	
Operation on the ArcView 9.2 SP2 platform. 

 	
Operation on Microsoft SQL Server 2005 Express. 

 	
Operation on the MDAC 2.8 SP1 data access engine from Microsoft. 

 	
Operation on FarPoint Spread 7   Spreadsheet. 

 	
Operation on Crystal Reports 11. 

 	
Enhancements to the existing aggregation routines to reflect improved 
procedures for processing study region information. 




Table of Contents 
Foreword 
.............................................................................
.............................................................................
.......... iii
Acknowledgments..............................................................
.............................................................................
.............v
Message to 
Users........................................................................
.............................................................................
....ix
Limitations of the HAZUS-MH MR3 Software 
.............................................................................
..........................xi
What s New in HAZUS   Flood 
Model........................................................................
............................................xv
Chapter 1.  Introduction to FEMA Flood Loss Estimation Methodology 
.......................................................... 1-1

1.1 HAZUS:  FEMA s Multi-Hazard Loss Estimation Methodology & Software 
........................................... 1-1

1.2 Technical Manual 
Background...................................................................
................................................. 1-2

1.3 Technical Manual Scope 
.............................................................................
................................................ 1-2

1.4 Technical Manual Organization 
.............................................................................
..................................... 1-3

Chapter 2.  Overall Approach and Framework of Methodology 
........................................................................ 2-1

2.1 Vision Statement 
.............................................................................
............................................................ 2-1

2.2 Project Objectives 
.............................................................................
.......................................................... 2-1

2.2.1 Accommodation of User Needs 
.............................................................................
.............................. 2-4

2.2.2 State-of-the-Art 
.............................................................................
....................................................... 2-4

2.2.3 
Balance......................................................................
........................................................................... 
2-4

2.2.4 Uses of Methodology 
Data.........................................................................
.......................................... 2-5

2.2.5 Accommodation of Different Levels of Funding 
.............................................................................
.... 2-5

2.2.6 
Standardization..............................................................
....................................................................... 2-5

2.2.7 Non-
Proprietary..................................................................
.................................................................. 2-5

2.3 Description of Flood Loss Estimation 
Methodology..................................................................
................. 2-6

2.3.1 Level of Analysis 
.............................................................................
.................................................... 2-8

2.3.2 Loss Estimation 
Analysis.....................................................................
.............................................. 2-12

2.4 Integration of the three HAZUS Loss Estimation 
Models...................................................................... 
2-13

Chapter 3.  Inventory Data: Collection and Classification 
.............................................................................
.... 3-1

3.1 Introduction 
.............................................................................
.................................................................... 3-1

3.2 Direct Damage Data   Buildings and Facilities 
.............................................................................
............. 3-1

3.2.1 General Building Stock 
.............................................................................
........................................... 3-1

3.2.1.1 
Classification...............................................................
.................................................................. 3-2

3.2.1.2 The Default General Building Stock Database 
............................................................................. 
3-5

3.2.1.3 Specific Occupancy-to-Model Building Type Mapping 
............................................................. 3-28

3.2.2 Essential 
Facilities...................................................................
........................................................... 3-28

3.2.2.1 
Classification...............................................................
................................................................ 3-28

3.2.3 High Potential Loss Facilities 
.............................................................................
............................... 3-31

3.2.4 User Defined 
Facilities...................................................................
.................................................... 3-33

3.3 Direct Damage Data - Transportation Systems 
.............................................................................
............ 3-33

3.3.1 Highway 
Systems......................................................................
......................................................... 3-34

3.3.1.1 
Classification...............................................................
................................................................ 3-34

3.3.2 Railway Systems 
.............................................................................
................................................... 3-34

3.3.2.1 
Classification...............................................................
................................................................ 3-35

3.3.3 Light Railway Systems 
.............................................................................
......................................... 3-35

3.3.3.1 
Classification...............................................................
................................................................ 3-36

3.3.4 Bus Systems 
.............................................................................
.......................................................... 3-36

3.3.4.1 
Classification...............................................................
................................................................ 3-37

3.3.5 Ports and 
Harbors......................................................................
......................................................... 3-37

3.3.5.1 
Classification...............................................................
................................................................ 3-37

3.3.6 Ferry Transportation Systems 
.............................................................................
............................... 3-38

3.3.6.1 
Classification...............................................................
................................................................ 3-38

3.3.7 Airports 
.............................................................................
................................................................. 3-39

3.3.7.1 
Classification...............................................................
................................................................ 3-39

xviii 
3.4 Direct Damage Data   Lifeline Utility 
Systems......................................................................
.................. 3-41

3.4.1 Potable Water 
Systems......................................................................
................................................. 3-41

3.4.1.1 
Classification...............................................................
................................................................ 3-41

3.4.2 Wastewater 
Systems......................................................................
..................................................... 3-42

3.4.2.1 
Classification...............................................................
................................................................ 3-43

3.4.3 Oil Systems 
.............................................................................
........................................................... 3-43

3.4.3.1 
Classification...............................................................
................................................................ 3-44

3.4.4 Natural Gas Systems 
.............................................................................
............................................. 3-44

3.4.4.1 
Classification...............................................................
................................................................ 3-44

3.4.5 Electric Power 
Systems......................................................................
................................................ 3-45

3.4.5.1 
Classification...............................................................
............................................................... 3-45

3.4.6 Communication 
Systems......................................................................
.............................................. 3-46

3.4.6.1 
Classification...............................................................
................................................................ 3-46

3.5 Direct Damage Data Agricultural 
Products.....................................................................
.......................... 3-46

3.6 Direct Damage Data   Vehicles 
.............................................................................
................................... 3-48

3.6.1 Building inventory 
.............................................................................
................................................ 3-49

3.6.1.1 Parking Generation 
Rates........................................................................
.................................... 3-49

3.6.2 Parking Supply and Parking Occupancy 
.............................................................................
...............3-52

3.6.3 Vehicle Population by Age Group and Type 
.............................................................................
........ 3-53

3.6.4 Vehicle Value 
Estimation...................................................................
................................................ 3-53

3.7 Hazardous Materials Facilities 
.............................................................................
..................................... 3-54

3.8 Direct Economic and Social 
Loss.........................................................................
..................................... 3-54

3.8.1 Demographics 
Data.........................................................................
................................................... 3-54

3.9 Indirect Economic 
Data.........................................................................
.................................................... 3-56

3.10 References 
.............................................................................
.................................................................... 3-56

Chapter 4.  Potential Earth Science Hazards 
(PESH).......................................................................
................... 4-1

4.1 Flood 
Hazard.......................................................................
........................................................................ 4-1

4.2 Riverine Flood Hazard 
.............................................................................
................................................... 4-1

4.2.1 Identifying Stream Reaches 
.............................................................................
.................................... 4-2

4.2.2 Hydrologic 
Analysis.....................................................................
...................................................... 4-16

4.2.2.1 Reach Identification and Watershed 
Association..................................................................
...... 4-28

4.2.2.2 Define Drainage Area 
.............................................................................
.................................... 4-32

4.2.2.3 Default Gage 
Identification...............................................................
.......................................... 4-34

4.2.2.4 Automated 
Adjustments..................................................................
............................................ 4-35

4.2.3 Hydraulic Analyses 
.............................................................................
............................................... 4-36

4.2.3.1 Mapping the Flood 
Surface......................................................................
................................... 4-37

4.2.3.2 Default Hydraulic Analyses 
.............................................................................
........................... 4-49

4.2.3.3 Non-conveyance Areas 
.............................................................................
.................................. 4-65

4.2.3.4 Calculating Flood Depths for Other Return 
Frequencies............................................................ 4-68

4.2.4 Exploring Scenarios 
.............................................................................
.............................................. 4-72

4.3 Coastal Flood Hazard 
.............................................................................
................................................... 4-72

4.3.1 
Introduction.................................................................
....................................................................... 4-72

4.3.2 User Inputs and Overview of Coastal Flood Hazard 
Model............................................................... 4-73

4.3.3 Terms and 
Definitions..................................................................
...................................................... 4-77

4.3.3.1 Coast 
.............................................................................
.............................................................. 4-77

4.3.3.2 Depth-Limited 
Wave.........................................................................
.......................................... 4-77

4.3.3.3 Dune Peak 
.............................................................................
...................................................... 4-77

4.3.3.4 Dune Reservoir 
.............................................................................
.............................................. 4-77

4.3.3.5 Dune 
Toe..........................................................................
........................................................... 4-77

4.3.3.6 
Fetch........................................................................
.................................................................... 4-78

4.3.3.7 Flood Elevation Ratio 
.............................................................................
.................................... 4-78

4.3.3.8 Freeboard 
.............................................................................
....................................................... 4-78

4.3.3.9 Reference Elevation 
.............................................................................
....................................... 4-78

4.3.3.10 
Roughness....................................................................
............................................................... 4-78

4.3.3.11 Shore 
Type.........................................................................
......................................................... 4-78

4.3.3.12 Shoreline 
.............................................................................
........................................................ 4-79

4.3.3.13 Slope 
.............................................................................
.............................................................. 4-79

4.3.3.14 Stillwater Depth 
.............................................................................
............................................. 4-79

4.3.3.15 
SWEL.........................................................................
................................................................. 4-79

4.3.3.16 
Transect.....................................................................
.................................................................. 4-79

4.3.3.17 Wave Exposure 
.............................................................................
.............................................. 4-79

4.3.3.18 Wave Height 
.............................................................................
.................................................. 4-80

4.3.3.19 Wave Overtopping 
.............................................................................
......................................... 4-80

4.3.3.20 Wave 
Period.......................................................................
......................................................... 4-80

4.3.3.21 Wave 
Regeneration.................................................................
.................................................... 4-80

4.3.3.22 Wave 
Runup........................................................................
........................................................ 4-80

4.3.3.23 Wave Setup 
.............................................................................
.................................................... 4-80

4.3.4 Shorelines and 
Transects....................................................................
................................................ 4-80

4.3.5 Shoreline 
Segmentation.................................................................
..................................................... 4-83

4.3.6 Shoreline Characterization 
.............................................................................
.................................... 4-83

4.3.6.1 Wave Exposure 
.............................................................................
.............................................. 4-84

4.3.6.2 Shore 
Type.........................................................................
......................................................... 4-85

4.3.6.3 100-Year Stillwater 
Elevation....................................................................
................................. 4-87

4.3.6.4 Wave Setup 
.............................................................................
.................................................... 4-87

4.3.6.5 Other Stillwater Elevations 
.............................................................................
............................ 4-88

4.3.6.6 Significant Wave Height at 
Shore........................................................................
....................... 4-91

4.3.6.7 Peak Wave Period 
.............................................................................
.......................................... 4-91

4.3.7 Erosion Assessment 
.............................................................................
.............................................. 4-93

4.3.7.1 Dune Reservoir Size and Erosion (Retreat or 
Removal)............................................................. 4-93

4.3.7.2 Erosion Model Treatment of Multiple Dunes 
............................................................................. 
4-95

4.3.7.3 Erosion Treatment on Interior 
Fetches......................................................................
.................. 4-97

4.3.8 Wave Height Model 
.............................................................................
.............................................. 4-97

4.3.9 Wave Runup Model 
.............................................................................
.............................................. 4-99

4.3.9.1 Description of Wave Runup Model 
.............................................................................
............... 4-99

4.3.9.2 Wave Overtopping 
.............................................................................
....................................... 4-102

4.3.10 Wave Regeneration 
.............................................................................
............................................. 4-104

4.3.11 What-If 
Scenarios....................................................................
......................................................... 4-106

4.3.11.1 Long-term Erosion 
.............................................................................
....................................... 4-106

4.3.11.2 Shore 
Protection...................................................................
..................................................... 4-107

4.4 Other Types of Flooding: 
.............................................................................
........................................... 4-107

4.4.1 Sheet Flooding 
.............................................................................
.................................................... 4-107

4.4.2 Great Lakes 
Flooding.....................................................................
.................................................. 4-107

4.5 References 
.............................................................................
.................................................................. 4-108

Chapter 5.  Direct Physical Damage - General Building Stock 
........................................................................... 
5-1

5.1 Introduction 
.............................................................................
.................................................................... 5-1

5.1.1 
Scope........................................................................
............................................................................ 
5-2

5.1.2 Input Requirements and Output Information 
.............................................................................
..........5-2

5.1.3 Form of Damage 
Functions....................................................................
.............................................. 5-2

5.2 Building Parameters Related to Flood Damage 
.............................................................................
............. 5-3

5.2.1 Building 
Age..........................................................................
.............................................................. 5-3

5.2.2 Foundation Type and First Floor 
Elevation....................................................................
...................... 5-3

5.2.3 Model Building 
Types........................................................................
.................................................. 5-4

5.3 Building and Contents Damage Due to 
Flooding.....................................................................
................... 5-6

5.3.1 Compilation of Depth-Damage Functions 
.............................................................................
..............5-6

5.3.1.1 FIMA (FIA) Residential Depth-Damage Curves - Riverine 
......................................................... 5-6

5.3.1.2 FIMA (FIA) Residential Depth-Damage Curves   Coastal 
........................................................ 5-14

5.3.1.3 USACE Depth-Damage Curves (Residential and Non-
Residential)........................................... 5-14

5.3.1.4 Other Coastal Depth-Damage 
Functions....................................................................
................. 5-17

5.3.2 Default Structure and Contents Damage 
Curves.......................................................................
......... 5-17

5.3.2.1 Commentary on the Assignment and Implementation of Coastal Damage 
Functions................ 5-20

5.4 Building Damage Due to Velocity 
.............................................................................
............................... 5-21

5.5 Consideration of Warning and Associated Damage 
Reduction................................................................. 5-
28

xx 
5.6 Consideration of Uncertainty 
.............................................................................
....................................... 5-30

5.7 Guidance for Expert Users 
.............................................................................
........................................... 5-31

5.7.1 Selection of Alternate Depth-Damage 
Functions....................................................................
........... 5-31

5.7.2 Sources of Additional Depth-Damage 
Functions....................................................................
........... 5-31

5.7.3 Development of Custom Depth-Damage 
Functions....................................................................
....... 5-31

5.8 References 
.............................................................................
.................................................................... 5-31

Chapter 6.  Direct Physical Damage - Essential and High Potential Loss 
Facilities.......................................... 6-1

6.1 Introduction 
.............................................................................
.................................................................... 6-1

6.1.1 
Scope........................................................................
............................................................................ 
6-1

6.1.2 Essential Facilities 
Classification...............................................................
.......................................... 6-1

6.1.3 Input Requirements and Output Information 
.............................................................................
..........6-3

6.1.4 Form of Damage 
Functions....................................................................
.............................................. 6-3

6.2 Description of Occupancies and Model Building 
Types........................................................................
.....6-4

6.3 Building Damage Due to 
Flooding.....................................................................
......................................... 6-4

6.4 Guidance for Expert Users 
.............................................................................
............................................. 6-5

6.4.1 Selection of Alternate Depth-Damage 
Functions....................................................................
............. 6-5

6.4.2 Development of New Depth-Damage functions 
.............................................................................
..... 6-5

6.4.3 Velocity-Damage 
Functions....................................................................
............................................. 6-5

6.4.4 High Potential Loss Facilities 
.............................................................................
................................. 6-5

6.4.4.1 
Introduction.................................................................
.................................................................. 6-5

6.4.4.2 Input Requirements and Output Information 
.............................................................................
... 6-5

6.4.4.3 Form of Damage Functions and Damage 
Evaluation...................................................................
. 6-6

6.5 Essential Facility and HPL Damage References 
.............................................................................
............ 6-6

Chapter 7. Lifelines:  Transportation and Utilities 
.............................................................................
................ 7-1

7.1 Introduction 
.............................................................................
.................................................................... 7-1

7.2 
Scope........................................................................
.............................................................................
...... 7-6

7.2.1 Input Requirements and Output 
Format.......................................................................
........................ 7-8

7.2.2	Form of the Damage 
Functions....................................................................
........................................ 7-8

7.2.3	Transportation 
Bridges......................................................................
................................................... 7-8

7.2.4	Treatment of Plants, Pump Stations, Vaults, Substations, and 
Telecommunication Facilities - Inundation 
.............................................................................
............................................................. 7-10 
7.2.5	Utility 
Systems......................................................................
............................................................. 7-11

7.2.6	Lifeline Classifications, Functionality Thresholds and Damage Functions 
....................................... 7-11

7.2.7
	References.............................................................
............................................................................. 
7-21

Chapter 8.  Direct Damage to Vehicles 
.............................................................................
..................................... 8-1

8.1 Introduction 
.............................................................................
.................................................................... 8-1

8.1.1 Motor Vehicle Damage 
Estimation...................................................................
................................... 8-3

8.1.2 
Classification...............................................................
......................................................................... 8-4

8.1.3 Input Requirements and Output Information 
.............................................................................
..........8-4

8.1.4 Form of Damage 
Functions....................................................................
.............................................. 8-4

8.2 Damage Due to 
Inundation...................................................................
....................................................... 8-4

8.2.1 
Overview.....................................................................
......................................................................... 8-4

8.2.2 Depth-Damage 
Functions....................................................................
................................................. 8-5

8.2.3 Consideration of Warning and Associated Damage Reduction 
........................................................... 8-6

8.3 Guidance for Expert Users 
.............................................................................
............................................. 8-6

Chapter 9.  Direct Damage to Agriculture (Crops) 
.............................................................................
................. 9-1

9.1 Introduction 
.............................................................................
.................................................................... 9-1

9.1.1 
Scope........................................................................
............................................................................ 
9-3

9.1.2 Classification of Agriculture Products 
.............................................................................
.................... 9-3

9.1.3 Input Requirements and Output Information 
.............................................................................
..........9-4

9.1.4 Form of Damage 
Functions....................................................................
.............................................. 9-4

9.2 Damage Due to 
Inundation...................................................................
....................................................... 9-5

9.3 Damage due to Collateral Hazards (duration) 
.............................................................................
................ 9-6

9.4 Benefits of 
Flooding.....................................................................
............................................................... 9-6

9.4.1 Flood 
Attenuation..................................................................
............................................................... 9-6

9.4.2 Soil Quality 
.............................................................................
............................................................. 9-7

9.4.3 Water 
Quality......................................................................
................................................................. 9-7

9.4.4 Water 
Supply.......................................................................
................................................................. 9-7

9.4.5 Wildlife 
Habitat......................................................................
.............................................................. 9-7

9.4.6 Recreational Setting 
.............................................................................
................................................ 9-7

9.4.7 
Others.......................................................................
............................................................................ 
9-7

9.5 Ecological Assessment of Natural Floodplain 
Functions....................................................................
........ 9-8

9.5.1 Wetland Evaluation Technique 
.............................................................................
............................... 9-9

9.5.2 Environmental Monitoring Assessment Program 
.............................................................................
... 9-9

9.5.3 Hydrogeomorphic 
Approach.....................................................................
........................................... 9-9

9.6 Economic Valuation of Natural Floodplain 
Functions....................................................................
..........9-10

9.6.1 Market Approaches 
.............................................................................
............................................... 9-10

9.6.2 Indirect Market 
Methods......................................................................
.............................................. 9-10

9.6.3 Expressed Preference Models 
.............................................................................
............................... 9-11

9.6.4 Benefit Transfer 
.............................................................................
.................................................... 9-11

9.7 Guidance for Expert Users 
.............................................................................
........................................... 9-14

9.8 References 
.............................................................................
.................................................................... 9-14

Chapter 10.  Induced Damage Models - Hazardous Materials 
Release............................................................ 10-1

10.1 Introduction 
.............................................................................
.................................................................. 10-1

10.1.1 
Scope........................................................................
.......................................................................... 
10-3

10.1.2 Classification of Hazardous Materials 
.............................................................................
.................. 10-3

10.1.3 Input Requirements and Output Information 
.............................................................................
........ 10-3

10.2 Description of Methodology 
.............................................................................
........................................ 10-7

10.3 Guidance for Expert-Generated Estimates 
.............................................................................
................... 10-7

10.4 References 
.............................................................................
.................................................................... 10-8

Chapter 11.  Induced Damage Methods   Debris 
.............................................................................
.................. 11-1

11.1 Introduction 
.............................................................................
.................................................................. 11-1

11.1.1 
Scope........................................................................
.......................................................................... 
11-2

11.1.2 Form of Damage 
Estimate.....................................................................
............................................. 11-3

11.1.3 Input Requirements and Output Information 
.............................................................................
........ 11-3

11.2 Description of Methodology 
.............................................................................
........................................ 11-3

11.2.1 Debris Generated From Damaged 
Structures...................................................................
.................. 11-3

11.2.2 Natural Debris Carried by 
Floodwaters..................................................................
............................ 11-9

11.3 Guidance for Expert-Generated Estimates 
.............................................................................
................... 11-9

11.4 References 
.............................................................................
.................................................................... 11-9

Chapter 12.  Direct Social Losses   
Casualties...................................................................
................................. 12-1

12.1 Introduction 
.............................................................................
.................................................................. 12-1

12.2 Summary of Collected Casualty Data 
.............................................................................
.......................... 12-3

12.3 Proposed Form of Casualty Models for Eventual Inclusion into HAZUS Flood 
...................................... 12-7

12.4 Documentation Displayed in the HAZUS Flood Model 
........................................................................... 
12-8

12.5 References 
.............................................................................
.................................................................. 12-10

Chapter 13.  Direct Social Losses - Displaced Households Due to Loss of 
Housing Habitability and Short-term Shelter 
Needs........................................................................
.............................................................................
. 13-1 
13.1 Introduction 
.............................................................................
.................................................................. 13-1

13.2 Displaced Households - Form of Loss Estimate 
.............................................................................
.......... 13-3

13.2.1 Input Requirements 
.............................................................................
............................................... 13-3

13.2.2 Description of Methodology 
.............................................................................
................................. 13-4

13.2.2.1 Displaced Persons As A Result Of Utility Damage 
.................................................................... 13-5

13.2.2.2 Shelter Category 
Weight.......................................................................
...................................... 13-7

13.2.2.3 Shelter Relative Modification 
Factors......................................................................
................... 13-7

13.2.3 User-defined Changes to Weight and Modification Factors 
.............................................................. 13-8

13.3 References 
.............................................................................
.................................................................... 13-9

Chapter 14.  Direct Economic 
Losses.......................................................................
............................................ 14-1

14.1 Introduction 
.............................................................................
.................................................................. 14-1

14.1.1 
Scope........................................................................
.......................................................................... 
14-3

14.1.2 Form of Direct Economic Loss 
Estimates....................................................................
......................14-4

14.1.3 Input Requirements 
.............................................................................
............................................... 14-5

xxii 
14.2 Description of Methodology: 
Buildings....................................................................
................................ 14-5

14.2.1 Full Building Replacement 
Costs........................................................................
............................... 14-5

14.2.1.1 Default Values for Building Replacement 
Cost.........................................................................
. 14-5

14.2.2 Contents Replacement Cost 
.............................................................................
................................ 14-15

14.2.3 Default Values for Regional Cost Variation 
.............................................................................
....... 14-16

14.2.4 Procedure for Updating Building Cost Estimates 
............................................................................ 
14-16

14.2.5 Depreciated Building Replacement Cost 
.............................................................................
............ 14-16

14.2.5.1 Single Family Residential 
.............................................................................
............................ 14-16

14.2.5.2 Other Residential and Non-
Residential..................................................................
................... 14-17

14.2.6 Building Contents 
Losses.......................................................................
.......................................... 14-18

14.2.7 Business Inventory Losses 
.............................................................................
.................................. 14-18

14.2.8 Relocation Expenses 
.............................................................................
........................................... 14-22

14.2.9 Loss of 
Income.......................................................................
.......................................................... 14-25

14.2.9.1 Capital Related, Wage, Output and Employment 
Losses.......................................................... 14-33

14.2.10 Rental Income Losses 
.............................................................................
......................................... 14-37

14.2.11 Guidance for Estimates Using Advanced Data and Models 
Analysis.............................................. 14-37

14.3 Description of Methodology:  Lifelines 
.............................................................................
..................... 14-38

14.3.1 Bridges 
.............................................................................
................................................................ 14-38

14.3.2 Utility 
Systems......................................................................
........................................................... 14-39

14.3.2.1 Potable Water Facilities 
.............................................................................
............................... 14-39

14.3.2.2 Wastewater Systems 
.............................................................................
.................................... 14-39

14.3.2.3 Petroleum 
Systems......................................................................
.............................................. 14-40

14.3.2.4 Natural Gas Systems 
.............................................................................
.................................... 14-40

14.3.2.5 Electric Power 
Systems......................................................................
....................................... 14-40

14.3.2.6 Communication 
Systems......................................................................
.....................................14-41

14.4 Description of Methodology: Vehicles 
.............................................................................
...................... 14-41

14.5 Description of Methodology: Agriculture 
(Crops)......................................................................
............ 14-42

14.6 References 
.............................................................................
.................................................................. 14-42

Chapter 15.  Indirect Economic Losses 
.............................................................................
.................................. 15-1

15.1 Introduction 
.............................................................................
.................................................................. 15-1

15.2 Background: What are Indirect 
Losses?......................................................................
............................. 15-3

15.2.1 Principles of Natural Hazard Loss Estimation 
.............................................................................
...... 15-3

15.2.2 How Indirect Losses 
Occur........................................................................
........................................ 15-5

15.2.3 Regional vs. National Losses 
.............................................................................
................................ 15-7

15.3 Background: How are Indirect Losses 
Modeled?.....................................................................
................ 15-7

15.3.1 A Primer on Input-Output Loss Modeling 
Techniques...................................................................
... 15-8

15.3.2 An Illustration of Input-Output 
Techniques...................................................................
.................. 15-11

15.3.3 The Stimulative Impact of Reconstruction 
Aid..........................................................................
...... 15-12

15.3.4 Alternative Modeling 
Techniques...................................................................
................................. 15-13

15.4 Methodology of the Indirect Loss 
Module.......................................................................
....................... 15-15

15.4.1 
Overview.....................................................................
..................................................................... 15-15

15.4.2 Required Economic Data 
.............................................................................
.................................... 15-17

15.4.3 Damage-Related 
Inputs.......................................................................
............................................. 15-17

15.4.4 Reconstruction-Related Inputs 
.............................................................................
............................ 15-18

15.4.5 Model Algorithms for Rebalancing the 
Economy......................................................................
...... 15-20

15.4.5.1 Core 
Algorithms...................................................................
..................................................... 15-20

15.4.5.2 The Time Dimension 
.............................................................................
................................... 15-24

15.4.5.3 The Effect of Borrowing for 
Rebuilding...................................................................
................ 15-24

15.4.5.4 Tax Revenue 
Impacts......................................................................
.......................................... 15-26

15.4.5.5 Distributional 
Impacts......................................................................
......................................... 15-28

15.4.6 Special Sectors 
.............................................................................
.................................................... 15-30

15.4.6.1 Agriculture 
.............................................................................
................................................... 15-30

15.4.6.2 
Tourism......................................................................
............................................................... 15-35

15.4.7 
Results......................................................................
........................................................................ 15-
39

15.4.8 A Note Regarding Small Study 
Areas........................................................................
......................15-39

15.5 Running the Indirect Loss 
Module.......................................................................
................................... 15-40

15.5.1 Default Data Analysis (Level 
1)...........................................................................
............................ 15-40

15.5.1.1 User Inputs and Default 
Data.........................................................................
...........................15-40

15.5.1.2 Calculation of Indirect 
Impacts......................................................................
...........................15-43

15.5.1.3 The Formats of the 
Outputs......................................................................
................................. 15-46

15.5.2 User-Supplied Data Analysis (Level 
2)...........................................................................
................. 15-47

15.5.2.1 IMPLAN Input-Output 
Data.........................................................................
............................ 15-47

15.5.2.2 Specifying Indirect Loss 
Factors......................................................................
......................... 15-48

15.6 Example 
Solutions....................................................................
............................................................... 15-52

15.7 References 
.............................................................................
.................................................................. 15-54

Chapter 16.  Additional 
Capabilities.................................................................
................................................... 16-1

16.1 Levees 
.............................................................................
.......................................................................... 
16-1

16.2 Flow Regulation 
.............................................................................
........................................................... 16-4

16.3 
Velocities...................................................................
.............................................................................
... 16-7

16.4 Policy 
Analysis.....................................................................
..................................................................... 16-9

16.4.1 Floodplain Regulation   BFE+1 Foot 
.............................................................................
................... 16-9

16.4.2 Flood Mapping 
Restudies....................................................................
............................................. 16-13

16.4.3 Building Acquisition and Removal 
.............................................................................
..................... 16-14

16.4.4 Flood 
Forecasting..................................................................
........................................................... 16-15

16.5 References 
.............................................................................
.................................................................. 16-16

Appendix A. Limitations of Use for the Flood 
Model........................................................................
.................. 0-1

A.1 Introduction 
.............................................................................
.................................................................... 0-1

A.1.1 Freeing Memory Using SQL Server Manager 
.............................................................................
........ 0-2

A.1.2 Increasing Virtual Memory to Run Large Study 
Regions.................................................................... 
0-4

List of Tables 
Table 2.1  Attributes of the HAZUS Flood Model 
.............................................................................
..................... 2-10
Table 3.1  HAZUS Building Occupancy Classes 
.............................................................................
......................... 3-3
Table 3.2  HAZUS Building Occupancy Classes 
.............................................................................
......................... 3-4
Table 3.3 Typical Square Footage Per Unit (Main Living Area) by Census 
Division (R)1..................................... 3-7
Table 3.4  Income Ratio and Basement Distribution by Census Region 
................................................................. 3-10
Table 3.5  Floor Areas for Multi-Family Dwellings (RES2 & RES3A-
RES3F)..................................................... 3-12
Table 3.6  Distribution of Floors for Single Family 
Residences...................................................................
........... 3-16
Table 3.7  Distribution of Floors for Multi-Family 
Residences...................................................................
............ 3-17
Table 3.8 Association of US Census  Year Built  with Commercial Building  
Characteristics  Year 

Constructed .................................................................
............................................................................. 
3-17
Table 3.9  Distribution of Number of Floors by Year Built Commercial 
Buildings ............................................... 3-18
Table 3.10  Distribution of Foundation Types for Single Family and Multi-
Family* Residences.......................... 3-19
Table 3.11  Default Floor Heights Above Grade to Top of Finished Floor 
(Riverine)............................................ 3-20
Table 3.12  Distribution of Pre-FIRM Foundation Types 
.............................................................................
.......... 3-22
Table 3.13  Distribution of Post-FIRM Foundation Types by Coastal Zones 
.........................................................3-23
Table 3.14  Default Floor Heights Above Grade to Top of Finished Floor 
(Coastal) ............................................. 3-24
Table 3.15  Distribution of Garages for Single Family Residential 
Structures........................................................ 3-26
Table 3.17  Essential Facilities Classification 
.............................................................................
............................ 3-29
Table 3.18  Essential Facilities Inventory Occupancy Classification and Flood 
Model Default Parameters .......... 3-31
Table 3.19  High Potential Loss Facilities 
Classifications..............................................................
......................... 3-32
Table 3.20  Highway System Classifications 
.............................................................................
............................. 3-34
Table 3.21  Railway System 
Classifications..............................................................
.............................................. 3-35
Table 3.22  Light Rail System 
Classifications..............................................................
........................................... 3-36
Table 3.23  Bus System 
Classifications..............................................................
..................................................... 3-37
Table 3.24  Ports and Harbors 
Classifications..............................................................
........................................... 3-38
Table 3.25  Ferry System Classifications 
.............................................................................
................................... 3-39
Table 3.26  Airports Classifications 
.............................................................................
........................................... 3-40
Table 3.27  Potable Water System Classifications 
.............................................................................
..................... 3-42
Table 3.28  Wastewater System Classifications 
.............................................................................
......................... 3-43

Table 3.29  Oil System 
Classifications..............................................................
...................................................... 3-44
Table 3.30  Natural Gas System 
Classifications..............................................................
........................................ 3-45
Table 3.31  Electric Power System Classifications 
.............................................................................
.................... 3-45
Table 3.32  Communication System Classifications 
.............................................................................
.................. 3-46
Table 3.33  Expected Daily Utilization Commercial 
Parking......................................................................
............ 3-51
Table 3.34  Estimated Parking Distribution by Parking Area Type 
........................................................................ 3-52
Table 3.35  Vehicle Age Distribution by Vehicle 
Classification...............................................................
.............. 3-53
Table 3.36  Demographics Data and Utilization within HAZUS 
............................................................................ 
3-55
Table 4.1  Coastal Flood Hazard Modeling Process (U = User action; R = 
Required; O = Optional; P = 

Program completes activity; N/A = Activity not supported or 
undertaken).............................................. 4-76 Table 4.2  
Shoreline Wave Exposure Classification for Coastal Flood Model1 
...................................................... 4-85 Table 4.3  
Coastal Flood Model Selection by Shoreline Type 
.............................................................................
... 4-86 Table 4.4  Coastal Flood Elevation Ratios for Atlantic, Gulf of 
Mexico and Pacific Coastal Areas (based on 
46 locations:  27 Atlantic, 11 Gulf of Mexico, and 8 
Pacific)...................................................................4-
89 Table 4.5  Flood Elevation Ratios for Great Lakes (based on 53 locations: 
5 Superior, 10 Michigan, 8 
Huron, 25 Erie and 5 
Ontario).....................................................................
.............................................. 4-90
Table 4.6  Reference Elevation (Chart Datum) for Great 
Lakes........................................................................
...... 4-91
Table 4.7  Required Dune Reservoir Sizes (sq ft above SWEL, seaward of peak) 
to Prevent Dune Removal ....... 4-95
Table 4.8  Upper Limit for Rave Permitted by Coastal 
Model........................................................................
........ 4-102
Table 4.9  Coastal Flood Model Wave Overtopping as a Function of Wave 
Runup............................................. 4-103
Table 4.10 Wave Height Fetch Factor Coefficients for Use in Eq. 4-28 
............................................................... 4-105
Table 4.11  Wave Period Fetch Factor Coefficients for Use in Eq. 4-
30............................................................... 4-106
Table 5.1  Model Building Types 
.............................................................................
................................................. 5-4
Table 5.2  Basement Component Cost Expressed as a Percent of Total Structure 
Replacement Cost  (two

floors total, including the basement, assuming 1600 SF main structure) 
.................................................. 5-10
Table 5.3 Default Damage Functions for Estimation of Structure Damage 
............................................................ 5-17
Table 5.4  Default Damage Functions for Estimation of Contents 
Damage............................................................ 5-19
Table 5.5  Velocity-Depth Damage Relationship for Wood Buildings 
................................................................... 5-26
Table 5.6  Velocity-Depth Damage Relationship for Masonry and Concrete 
Buildings......................................... 5-26
Table 5.7  Velocity-Depth Damage Relationship for Steel Buildings 
..................................................................... 5-27
Table 5.8  Velocity-Depth Damage Relationship for Manufactured 
Housing......................................................... 5-28
Table 6.1  Essential Facilities Occupancy Classes 
.............................................................................
....................... 6-3
Table 6.2  Essential Facilities Classification and Model Building 
Types.................................................................. 6-4
Table 7.1  Lifeline System Components, Vulnerability to Flood Sub-hazards, 
Criticality and Potential 

Dollar Loss  and Outage Time 
.............................................................................
....................................... 7-3
Table 7.2  Highway Single-span Bridge Damage 
Relationship.................................................................
................ 7-9
Table 7.3  Highway Continuous-Span Bridge Damage 
Relationship.................................................................
....... 7-9
Table 7.4  Potable Water Classifications, Functionality Thresholds and Damage 
Function ................................... 7-12
Table 7.5  Wastewater classifications, Functionality Thresholds and Damage 
Functions ...................................... 7-15
Table 7.6  Crude and Refined Oil Classifications, Functionality Thresholds 
and Damage Functions .................... 7-17
Table 7.7  Crude and Refined Oil Classifications, Functionality Thresholds 
and Damage Functions .................... 7-18
Table 7.8  Natural Gas Classifications, Functionality Thresholds and Damage 
Functions ..................................... 7-19
Table 7.9  Electric Power Classifications, Functionality Thresholds and 
Damage Functions................................. 7-20
Table 8.1  Vehicle Depth Damage Relationships 
.............................................................................
......................... 8-4
Table 9.1  Crop Types Currently Available Within the HAZUS Flood Model 
......................................................... 9-3
Table 9.2  Economic Methods for Valuing Natural Floodplain 
Functions.............................................................. 9-12
Table 9.3  Summary of Non-market Values  ($ 
1998)........................................................................
..................... 9-13
Table 10.1 Classification of Hazardous Materials and Permit Amounts 
................................................................ 10-4
Table 11.1  Debris Weight by Occupancy Class 
.............................................................................
........................ 11-4
Table 11.2  Mapping Of Detailed HAZUS Foundation Types Into General 
Foundation Types For Debris

Estimation 
.............................................................................
.................................................................... 11-8
Table 12.1  Flood Events with Available Data Casualty 
.............................................................................
............ 12-4
Table 12.2  Fatalities, By 
Event........................................................................
....................................................... 12-5
Table 12.3  Deaths, By Cause   All 
Events.......................................................................
...................................... 12-6
Table 12.4  Deaths, By Cause   Flood Events 
Only.........................................................................
....................... 12-7
Table 13.1  The Shelter Category 
Weights......................................................................
........................................ 13-7

Table 13.2  Relative Modification Factors 
.............................................................................
................................. 13-7 Table 14.1  Default Full Replacement 
Cost Models (Means, 
2006)...................................................................... 
14-11 Table 14.2  Replacement Costs (and Basement Adjustment) for RES1 
Structures by Means Constructions 
Class (Means, 
2006)........................................................................
........................................................ 14-13
Table 14.3  Single Family Residential Garage Adjustment (Means, 
2006)...........................................................14-14
Table 14.4  Weights (percent) for Means Construction/Condition 
Models...........................................................14-15
Table 14.5  Default HAZUS Contents Value Percent of Structure Value 
.............................................................14-15
Table 14.6  Consumer Price Index 1990 - 2006 (Source: 
http://www.bls.gov/cpi/home.htm).............................. 14-20
Table 14.7  Annual Gross Sales or Production (Dollars per Square Foot) 
............................................................ 14-21
Table 14.8  Business Inventory (% of Gross Annual Sales) (ref: NIBS/FEMA 
HAZUS Technical Manual, 

Table 15.8) 
.............................................................................
................................................................. 14-21
Table 14.9  Rental Costs and Disruption Costs 
.............................................................................
........................ 14-23
Table 14.10 Percent Owned Occupied (ref: NIBS/FEMA HAZUS Technical Manual, 
Table 15.14) ................. 14-24
Table 14.11 Flood Restoration Time by Occupancy 
.............................................................................
............... 14-27
Table 14.12 Elements Dominating Building and Service Interruption for Floods 
............................................... 14-32
Table 14.13 Proprietor s Income 
.............................................................................
............................................. 14-34
Table 14.13 Proprietor s Income 
(Continued)..................................................................
.................................... 14-35
Table 14.14 HAZUS99 Earthquake Table of Recapture 
Factors......................................................................
.... 14-36
Table 15.1  Intersectoral Flows of a Hypothetical Regional Economy 
(dollars)..................................................... 15-9
Table 15.2  Interindustry Transactions 
.............................................................................
..................................... 15-11
Table 15.3  Correspondence between Building Occupancy Classes and Economic 
Sectors................................. 15-18
Table 15.4  Initial Transactions 
.............................................................................
................................................ 15-21
Table 15.5  10% Direct Loss in 
Manufacturing................................................................
..................................... 15-22
Table 15.6  Response to Loss with Fully Constrained Economy 
.......................................................................... 
15-22
Table 15.7  Response to Loss with Relaxed Import and Export 
Constraints.........................................................15-23
Table 15.8  Annual Borrowing Costs 
.............................................................................
....................................... 15-25
Table 15.9  The Effect of Loan Repayment on Household 
Demands.................................................................... 
15-25
Table 15.10  Ratio of IBT to Sector Output for 1 Percent of the Nation s 
Counties and States........................... 15-27
Table 15.11  Livestock Sales as a Percent of Total Agricultural Sales, by 
State................................................... 15-31
Table 15.12  Level 1 Synthetic IMPLAN Tables 
.............................................................................
..................... 15-34
Table 15.13  National Personal Consumption for Tourism Sectors, 1997  
(millions of dollars)........................... 15-37
Table 15.14 Margins for Tourism Commodities 
.............................................................................
..................... 15-37
Table 15.15  RPCs for a Sample of Counties that Rely on Tourism 
..................................................................... 15-38
Table 15.16  User Supplied Inputs for Indirect Economic Loss 
Module............................................................... 15-41
Table 15.17 Rebuilding Expenditures Example 
.............................................................................
...................... 15-43
Table 15.18  Format of  Total Economic Impact  Summary Report 
.................................................................... 15-46
Table 15.19 Industry Classification Bridge Table 
.............................................................................
................... 15-48
Table 15.20 Suggested Indirect Economic Loss Factors (Percentage Points) 
...................................................... 15-49
Table 15.21 Transportation Restoration and Lifeline Rebuilding Parameters  
(Percentage Points)..................... 15-50
Table 15.22 Manufacturing Restoration Parameters (percentage 
points)............................................................. 15-51
Table 15.23  All Other Industries Restoration and Buildings Rebuilding 
Parameters (percentage points) ........... 15-51
Table 15A.1  Classification of Synthetic Economies 
.............................................................................
................. 15-1
Table 15A.2  
Manufacturing/Service........................................................
............................................................... 15-1
Table 15A.3  
Service/Manufacturing........................................................
............................................................... 15-2
Table 15A.4  
Service/Trade................................................................
..................................................................... 15-2
Table 15A.5 Manufacturing/Service Economy 
.............................................................................
......................... 15-3
Table 15A.6 Service/Manufacturing Economy 
.............................................................................
......................... 15-4
Table 15A.7  Service/Trade 
Economy......................................................................
............................................... 15-5
Table 15A.8  Agricultural Counties Used in Synthetic Tables by Region 
.............................................................. 15-5

List of Figures 
Figure 2.1  Flood Model 
Schematic....................................................................
....................................................... 2-2
Figure 2.2  Project Deliverables 
.............................................................................
................................................... 2-4
Figure 2.3 Overview of the Integration of the FIT and the HAZUS Flood 
Model................................................... 2-7
Figure 2.4  Levels of Analysis and User 
Sophistication...............................................................
............................. 2-9
Figure 2.5  HAZUS Software Architecture with HAZUS Data Tools 
.....................................................................2-15
Figure 3.1  Manufactured Housing Growth Over 
Time.........................................................................
...................3-14
Figure 3.2  Number of Mobile Home Units by Floor Area    1990 US Census Data 
Housing Characteristics .......3-15
Figure 3.3  Vehicle Location Estimation System 
.............................................................................
........................3-49
Figure 3.4  Commercial Shopping Center Parking Demand Ratios 
.........................................................................3-50
Figure 4.1  Stream Network Nomenclature 
.............................................................................
.................................. 4-3
Figure 4.2  Stream Networks in the Mid-Atlantic 
Region.......................................................................
.................. 4-4
Figure 4.3  Potomac River Basin Watersheds 
.............................................................................
.............................. 4-5
Figure 4.4  Watersheds Covering Shenandoah County 
.............................................................................
................ 4-5
Figure 4.5  Drainage Pattern from a 10-square-mile Threshold 
.............................................................................
... 4-6
Figure 4.6 Reaches Affecting Shenandoah County 
.............................................................................
..................... 4-7
Figure 4.7 Reaches and 
Nodes........................................................................
.......................................................... 4-8
Figure 4.8 Default Reaches Affecting Study Area 
.............................................................................
.....................4-10
Figure 4.9  Identifying Main 
Streams......................................................................
.................................................4-11
Figure 4.10  Main Stream 
Differences..................................................................
....................................................4-12
Figure 4.11  Drainage and Watershed Areas 
.............................................................................
...............................4-14
Figure 4.12  Gages In and Near Study Area 
.............................................................................
................................4-15
Figure 4.13  Hydrologic Regions Near Shenandoah 
County.......................................................................
.............4-17
Figure 4.14  Points Farthest from 
Outlets......................................................................
...........................................4-19
Figure 4.15  Selected Reaches on Main 
Streams......................................................................
................................4-26
Figure 4.16  Reaches and 
Nodes........................................................................
.......................................................4-29
Figure 4.17  Default Reaches Affecting Study Area 
.............................................................................
...................4-30
Figure 4.18  Identifying Main 
Streams......................................................................
...............................................4-31
Figure 4.19  Main Stream 
Differences..................................................................
....................................................4-32
Figure 4.20  Drainage and Watershed Areas 
.............................................................................
...............................4-34
Figure 4.21  Gages In and Near Study Area 
.............................................................................
................................4-35
Figure 4.22  Surfaces Used to Develop Flood Depth Grid 
.............................................................................
..........4-36
Figure 4.23  Sample Reach and Cross 
Sections.....................................................................
...................................4-37
Figure 4.24  Interpolation 
Error........................................................................
........................................................4-38
Figure 4.25  Example Data:  Floodplains from Q3 Map and Cross Sections from 
FIRM........................................4-39
Figure 4.26  Up-and Downstream Study 
Limits.......................................................................
...............................4-40
Figure 4.27  Converting a Polygon to a 
Polyline.....................................................................
.................................4-40
Figure 4.28  Clipping the 
Polyline.....................................................................
.......................................................4-41
Figure 4.29  Define Centerline 
.............................................................................
....................................................4-43
Figure 4.30  Identify Cross Sections with Centerline 
.............................................................................
..................4-44
Figure 4.31  Bounding 
Polygon......................................................................
..........................................................4-45
Figure 4.32  Extended Cross Sections 
.............................................................................
.........................................4-46
Figure 4.33  Irregularly Spaced Elevation 
Grid.........................................................................
...............................4-47
Figure 4.34 Flood Surface 
.............................................................................
..........................................................4-48
Figure 4.35  Flood Depth Grid 
.............................................................................
....................................................4-48
Figure 4.36  Buffered 
Reach........................................................................
.............................................................4-51
Figure 4.37  Initial Cross Sections along Tributary to Irwin 
Creek........................................................................
..4-56
Figure 4.38  Pool in Gravel Pit along Tributary to Irwin Creek 
.............................................................................
..4-56
Figure 4.39  Cross Sections Affected by Gravel Pit 
.............................................................................
....................4-57
Figure 4.40  Floodplain along Tributary to Irwin 
Creek........................................................................
...................4-58
Figure 4.41  Floodplain 
Polygons.....................................................................
........................................................4-59
Figure 4.42  Conveyance 
Limits.......................................................................
........................................................4-62
Figure 4.43  Polygon Centerlines 
.............................................................................
................................................4-63
Figure 4.44  Downstream Reach, North Fork of the Shenandoah River 
..................................................................4-66
Figure 4.45  Flood Depths within Conveyance 
Area.........................................................................
.......................4-67
Figure 4.46  Flood Depths in Conveyance Areas 
.............................................................................
........................4-67

Figure 4.47  Overview of HAZUS Coastal Flood Hazard Modeling Process 
..........................................................4-75
Figure 4.48  Shoreline Smoothing 
.............................................................................
...............................................4-81
Figure 4.49  Mainland and Large Island 
Transects....................................................................
...............................4-82
Figure 4.50  Small Island 
Transects....................................................................
......................................................4-82
Figure 4.51  Shore Type Tab of Shoreline Characteristics Dialog 
...........................................................................4-
83
Figure 4.52  100-Year Flood Conditions Tab of Shoreline Characteristics 
Dialog..................................................4-84
Figure 4.53  Wave Setup 
Sketch.......................................................................
........................................................4-88
Figure 4.54  Dune Reservoir for Erosion Assessment, Single Dune 
........................................................................4-93
Figure 4.55  Eroded Dune 
Profile......................................................................
.......................................................4-94
Figure 4.56  Dune Retreat 
Profile......................................................................
.......................................................4-94
Figure 4.57  Computation of Dune Reservoir for Closely Spaced Multiple 
Dunes..................................................4-96
Figure 4.58  Closely-Spaced Multiple Dunes with Dune Reservoir Greater than 
the Critical Value Required to 

Prevent Removal 
.............................................................................
...........................................................4-96 Figure 4.59  
Closely-Spaced Multiple Dunes with Dune Reservoir Less than the Critical 
Value Required to Prevent 
Removal 
.............................................................................
........................................................................4-97 
Figure 4.60 Wave Height Model   Relationship between Wave Crest Elevation, 
Stillwater Flood Depth and Wave 
Setup........................................................................
.............................................................................
......4-98
Figure 4.61  Wave Runup Model Definition Sketch 
.............................................................................
...................4-99
Figure 4.62  Composite Profile Modeled in Long-Term Erosion What-If 
.............................................................4-107
Figure 5.1  Building Damage Relationship to Other Components of the 
Methodology............................................ 5-1
Figure 5.2 FIA Credibility-Weighted Building Depth-Damage Curves as of 
12/31/1998 ...................................... 5-8
Figure 5.3  FIA-Based Structure Depth-Damage Curve 2 or More Stories,  
Basement-Modified ...........................5-12
Figure 5.4 FIA-Based Structure Depth-Damage Curve  Split Level, Basement-
Modified......................................5-13
Figure 5.5  FIA-based Residential Contents Damage Curves 
.............................................................................
.....5-13
Figure 5.6  Building Collapse Curve for Wood Frame Buildings developed by the 
USACE Portland District

(USACE, 1985) 
.............................................................................
.............................................................5-23 Figure 5.7  
Building Collapse Curve for Masonry and Concrete Bearing Wall Buildings 
developed by the USACE 
Portland District (USACE, 
1985)........................................................................
.......................................5-24 Figure 5.8  Building Collapse 
Curve for Steel Frame Buildings  developed by the USACE Portland District 
(USACE, 1985) 
.............................................................................
.............................................................5-25 Figure 5.9  
Day Curve for Residential Areas (Source: USACE, New York District, 1984) 
....................................5-29 Figure 6.1  Essential and High 
Potential Loss Facility Component Relationship to Other Components of the 
HAZUS 
Flood 
Methodology..................................................................
................................................................... 6-2
Figure 7.1 Transportation and Utility Systems Relationship to the 
Components..................................................... 7-7
Figure 8.1  Vehicles Relationship to the Components of the HAZUS Flood 
Methodology...................................... 8-2
Figure 8.2  Vehicle Depth Damage 
Functions....................................................................
....................................... 8-5
Figure 9.1  Agricultural Products Relationship to the Components of the HAZUS 
Flood Methodology ................. 9-2
Figure 9.2  Riverine floodplain sections (Source: Cowdin, 
1999)........................................................................
..... 9-8
Figure 10.1 Hazard Materials Relationship to Other Components of the HAZUS 
Flood Methodology.................10-2
Figure 11.1 Debris Estimation Relationship to Other Components of the HAZUS 
Flood Methodology................11-2
Figure 12.1  Social Losses Casualties Relationship to Other Components of the 
HAZUS Flood Methodology .....12-2
Figure 12.2 20th Century U.S. Flood Fatalities 
.............................................................................
...........................12-9
Figure 12.3  U.S. Flood Fatalities, 1988-
1997.........................................................................
.................................12-9
Figure 13.1 Shelter Relationship to Other Components in the HAZUS Flood 
Methodology .................................13-2
Figure 14.1 Direct Economic Loss Module Relationship to Other Components of 
the Methodology ....................14-2
Figure 14.2 Single Family Residential Depreciation Models (Means, 
2002)........................................................14-17
Figure 14.3  Means Commercial/Industrial/Institutional Depreciation Model 
(Means, 2002)...............................14-18
Figure 15.1  Relationship of Indirect Economic Loss to Other Components in 
the HAZUS Flood Methodology ..15-2
Figure 15.2 Indirect Losses and Adjustments to Lessen Them 
.............................................................................
..15-6
Figure 15.3 Illustrative 
Computation..................................................................
...................................................15-12
Figure 15.4 Indirect Loss Module 
Schematic....................................................................
....................................15-21
Figure 15.5 Farm Resource Regions Defined by 
ERS..........................................................................
.................15-33
Figure 15.6  Initial Effects of the 
Shock........................................................................
.........................................15-44
Figure 16.1 Flood Depths in Non-conveyance 
Areas........................................................................
......................16-2
Figure 16.2 User-supplied Levee 
Alignment....................................................................
.......................................16-3
Figure 16.3 Affects of Levee on Flood 
Depths.......................................................................
.................................16-3

Figure 16.4 Flood Frequency 
Curve........................................................................
................................................16-5
Figure 16.5  Distribution of Velocities in 
Floodplain...................................................................
............................16-8
Figure 16.6 Flood Specific Occupancy Mapping 
.............................................................................
.....................16-10
Figure 16.7 Flood Specific Occupancy Mapping Characteristics User 
Copy........................................................16-11
Figure 16.8  Building Characteristics 
.............................................................................
........................................16-12
Figure A.1  Browse to the sqlmangr.exe Application 
File.........................................................................
................ 0-2
Figure A.2  SQL Server Service 
Manager......................................................................
........................................... 0-3
Figure A.3  Click the Start/Continue Button to Restart 
Service......................................................................
.......... 0-3
Figure A.4  Control Panel Folder and the System Properties Dialog 
........................................................................ 0-4
Figure A.5  Advanced Page on the System Properties 
Dialog.......................................................................
............ 0-5
Figure A.6  Performance Options Dialog 
.............................................................................
..................................... 0-5
Figure A.7  Virtual Memory Settings 
.............................................................................
........................................... 0-6

Chapter 1.  Introduction to FEMA Flood Loss Estimation Methodology 
1.1 HAZUS:  FEMA s Multi-Hazard Loss Estimation Methodology & Software 
In the early 1990 s, FEMA embarked on an ambitious undertaking to expand the 
Nation s capacity to estimate losses from major types of natural hazards, 
including earthquakes, floods, and severe winds. This enhanced capacity for 
estimating losses from natural hazards will be embodied in HAZUS, an 
integrated software package.  Within HAZUS, there will be a separate module 
for estimating the losses from each hazard.  The earthquake module is now 
operational, and it is undergoing continual improvements.  Other modules for 
floods and wind are currently under development. 
This expanded analytical capacity will assist public officials at all levels 
of government in preparing estimates of losses from natural hazards, and in 
facilitating emergency response, planning, and hazard mitigation.  One can 
envision numerous private-sector applications as well, particularly by the 
insurance and construction industries and others interested in economic 
development. 
From a natural hazards policy perspective, the capacity of HAZUS to generate 
consistent loss estimates for these multiple hazards is particularly 
significant.  To achieve this consistency, HAZUS, to the extent possible, 
draws on shared national databases.  The national inventory of housing and 
commercial and industrial facilities is perhaps the best example of a shared 
database.  Because of the unique nature of each hazard, however, different 
attributes of the shared data are most critical in determining loss estimates 
from individual hazard.  For example, for flood loss estimation, knowing a 
building s first floor elevation and specific location within a community is 
more critical than in estimating earthquake losses.  In contrast, knowing the 
height of the building and certain of its structural characteristics is more 
critical in estimating earthquake losses. 
Within HAZUS, care is also being taken to guarantee that the loss estimation 
methodologies are consistent across modules.  The flood and wind committees, 
for example, are coordinating their efforts so that the separate 
methodologies do not double count the losses due to wind and storm surge 
during coastal storms. 
Another unique feature of HAZUS is its capacity to accommodate additional 
data and methods that are often available at the state and local level.  It 
is through this capacity of HAZUS that localities can use the tool to refine 
loss estimates for local emergency planning and to determine the effects of 
hazard mitigation strategies.  Where no current data on the flood hazard 
exist, HAZUS can also be used by localities as a platform for increasing 
awareness of the flood hazard and for generating interest in estimating 
losses based on these readily available  default  data and methods.  It 
provides other states and localities a platform for estimating losses based 
on readily available data bases, and can serve to demonstrate effectively the 
benefits of developing better data at the local level for hazard loss 
estimation, emergency response and mitigation planning. Perhaps in contrast 
to some other hazards, this capacity within HAZUS is particularly 
1-2 
critical for floods, given the local nature of the hazard and the capacity to 
affect the nature of the hazard through local structural works. 
From a national policy perspective, FEMA is responsible for providing 
national estimates of annualized losses due to these various natural hazards.  
At the most general level, these loss estimates document the magnitude of the 
natural hazards problems, as well as provide a benchmark against which 
progress toward reducing losses due to natural hazards through public policy 
can be assessed.  In its first application of HAZUS for this purpose, FEMA 
published a report in February 2001 entitled: HAZUS 99: Estimated Annualized 
Earthquake Losses for the United States. As other modules in HAZUS become 
available, FEMA anticipates using HAZUS to provide estimates of annualized 
losses from other major hazards on a basis consistent with those for 
earthquakes. 
1.2 Technical Manual Background 
The Technical Manual describes the methods for performing flood loss 
estimation.  It is based on a multi-year project to develop a nationally 
applicable methodology for estimating potential flood losses on a regional 
basis. The project has been conducted for the National Institute of Building 
Sciences (NIBS) under a cooperative agreement with the Federal Emergency 
Management Agency (FEMA). 
The primary purpose of the project is to develop guidelines and procedures 
for making flood loss estimation at a regional scale.  These loss estimates 
would be used primarily by local, state and regional officials to plan and 
stimulate efforts to reduce risks associated with flooding and to prepare for 
emergency response and recovery.  A secondary purpose of the project is to 
provide a basis for assessing nationwide risk of flood losses. 
The methodology development and software implementation has been performed by 
a team of flood loss experts composed of engineers, hydraulic and hydrology 
modelers, emergency planners, economists, social scientists, geographic 
information systems analysts, and software developers. The Flood Oversight 
Committee has provided technical direction and review of the work. 
1.3 Technical Manual Scope 
The scope of the Technical Manual includes documentation of all methods and 
data that are used by the methodology.  Loss estimation methods and data are 
obtained from referenced sources tailored to fit the framework of the 
methodology, or from new methods and data developed when existing methods and 
data were lacking or not current state of the art. 
The Technical Manual is a comprehensive, highly technical collection of 
methods and data covering a broad range of topics and disciplines, including 
hydraulics and hydrology, structural engineering, floodplain management, 
social science, and economics.  The Technical Manual is written for readers 
who are expected to have some degree of expertise in the technical topic of 
interest, and may be inappropriate for readers who do not have this 
background. 
As described in Chapter 2, a separate User Manual describes the flood loss 
estimation methodology in non-technical terms and provides guidance to users 
in the application of the methodology.  The methodology software is 
implemented using Geographic Information Systems (GIS) software (specifically 
ArcGIS 9.2 with SP2 with the Spatial Analyst extension as developed by 
Environmental System Research Institute (ESRI)) as described in the Technical 
Manual. 
1.4 Technical Manual Organization 
The HAZUS-MH Flood Model Technical Manual organization has been established 
by the existing Earthquake Model Technical Manual and so in some cases, it 
may not be as clear as a flood specific organization may have been.  This 
section has been written to help the flood user wade through the Technical 
Manual and locate items of interest.  The Technical Manual Chapters are as 
follows: 
Forward:  A short paragraph providing legal disclaimers and copyright 
information regarding the protection and rights of FEMA and the National 
Institute of Building Sciences (NIBS) with respect to this document and the 
HAZUS-MH model. 
Message to Users:  This section provides some caution and guidance to the 
users on how the results of the three models can be used and issues related 
to uncertainty resulting from using the default data and the assumptions 
necessary to produce functioning methodology. 
Acknowledgements:  A listing of people and organizations who committed their 
time and effort in the development of the HAZUS-MH Flood Model. 
Chapter 1:  Introduction to FEMA s flood loss estimation methodology.  This 
chapter provides a history of HAZUS and the development of HAZUS.  This 
section introduces the reader to the Technical Manual  scope and 
organization. 
Chapter 2:  This chapter provides the user with an overview of the HAZUS-MH 
framework, the project vision and the objectives.  This chapter will provide 
the reader with an understanding of key concepts related to HAZUS-MH such as 
the levels of analysis. 
Chapter 3:  Chapter 3 provides the reader with a description of the baseline 
or default data provided within HAZUS-MH. The chapter is organized to follow 
the menu organization within the three models with a discussion of the 
General Building Stock (GBS), Essential Facilities, High Potential Loss 
Facilities, User Defined Facilities, Transportation Systems, and Utility 
Systems.  The three models share a common valuation discussion and 
demographics data.  The Flood Model has unique data discussed in the sections 
on Agriculture Products and Vehicles. 
Chapter 4:  The Potential Earth Science Hazards (PESH) is the chapter where 
the user will find descriptions on hazard methodology.  In other words, this 
section will describe for the user how the methodology that has been coded 
within the Flood Model to develop the flood depth grids that are used in 
estimating losses.  This chapter will address the riverine and coastal 
hazards and the  What-if?  modeling that is available for the user.  A brief 
section will discuss other flood hazards that may be addressed in future 
versions of the Flood Model. 
Chapter 5:  This chapter addresses the heart of the loss estimation 
methodology, the damage analysis for the General Building Stock. In this 
chapter, the methodology for estimating the losses associated with the depth 
grids developed from the hazard models discussed in Chapter 4 is described. 
This chapter includes a discussion of the building damage functions, the 
function library and the application of these functions to the occupancy 
classifications. 
Chapter 6:  Similar to Chapter 5, this chapter discusses the application of 
the depth damage functions to the Essential Facilities.  This chapter will 
discuss the classification of the essential facilities, the default damage 
curves, and the facility functionality. 
Chapter 7:  The reader will find a detailed discussion of the development and 
application of damage functions for transportation facilities (Bridges only 
for the Flood Model) and the utility systems.  As with the previous two 
chapters, this section will describe the analysis capabilities of the flood 
model. 
Chapter 8:  This chapter provides a detailed discussion on the vehicle damage 
analysis.  Unique to the flood model, this chapter discusses the development 
of the damage functions for vehicles and the application of the functions to 
the vehicle inventory. 
Chapter 9:  Another Flood Model unique analysis, this chapter provides a 
detailed discussion of the damage methodology for agricultural products.  The 
chapter will provide an overview of the AGDAM models modified for use within 
the flood model. 
Chapter 10:  Although the flood model does not perform any direct analysis 
for the hazardous materials inventory, this chapter has been included to 
remain consistent with the earthquake model. 
Chapter 11:  While the earthquake model performs several analyses for induced 
damages, the Flood Model only analyzes debris related to building damages.  
This chapter will describe the overall process for the debris analysis and 
the methodology associated with the analysis. 
Chapter 12:  The Flood Model does not perform any direct analysis in support 
of casualty estimation, but this chapter provides a detailed discussion on 
the research performed for the HAZUS Flood Model and the resulting document 
available for the users review. 
Chapter 13:  Like the earthquake model, the Flood Model provides the user 
with an estimate of the shelter requirements.  The Flood Model does not make 
use of all the parameters the earthquake model does and accounts for the 
likelihood of evacuations due to flood warning. This chapter provides a 
detailed summary of the methodology created for the Flood Model. 
Chapter 14:  This chapter provides the reader with a detailed discussion on 
how the flood model transforms the damages estimated for buildings and 
contents into direct economic impacts such as the building, content, and 
inventory losses. 
Chapter 15:  The Indirect Economic Loss Module (IELM) is a standalone module 
closely related to the module within the earthquake model.  This chapter will 
provide the user with an understanding of the IELM and the applied 
methodology. 
Chapter 16:  This section provides the reader with a detailed discussion on 
the capabilities of the Flood Model and how the user can manipulate both 
Level 1 and Level 2 data to perform policy analyses. 








anticipated that at this level there will be extensive participation by local 
utilities and owners of special facilities. 
There is no standardized Advanced Data and Models Analysis study. The quality 
and detail of the results depend upon the level of effort. Six months to two 
years would be required to complete an Advanced Data and Models Analysis. 
Each subsequent level builds on and adds to the data and analysis procedures 
available in previous levels. 
Figure 2.4 provides a graphic representation of the various levels of 
analysis and the subsequent user sophistication to achieve that level of 
analysis. 

The attributes of the model for each level of analysis and examples of 
typical applications are presented in Table 2.1. 






Chapter 3.  Inventory Data: Collection and Classification 
3.1 Introduction 
An important requirement for estimating losses from floods is the 
identification and valuation of the building stock, infrastructure, and 
population exposed to flood hazard i.e., an inventory. Consequently, the 
HAZUS Flood Model uses a comprehensive inventory in estimating losses. This 
inventory serves as the default when the users of the model do not have 
better data available. The inventory consists of a proxy for the general 
building stock in the continental United States, Hawaii and the US held 
Territories.  Additionally, the model contains national data for essential 
facilities, high potential loss facilities, selected transportation and 
lifeline systems, demographics, agriculture, and vehicles.  This inventory is 
used to estimate damage and the direct economic losses for some elements 
(i.e., the general building stock) or the associated impact to functionality 
for essential facilities. 
The Earthquake Model s general building stock is currently available at the 
census tract level but increased resolution is needed to support the Flood 
Model.  The census block was chosen as the level of aggregation due to its 
relatively small geographic size and the capability of the census to identify 
data at that level of detail.  The census only provides information for the 
development of the residential structures data. Similar to the Earthquake 
Model, Dun & Bradstreet (D&B) has provided data for non-residential 
structures at the census block level. 
3.2 Direct Damage Data   Buildings and Facilities 
3.2.1 General Building Stock 
The General Building Stock (GBS) includes residential, commercial, 
industrial, agricultural, religious, government and education buildings.  
Damage is estimated in percent and is weighted by the area of inundation at a 
given depth for a given census block.  The entire composition of the general 
building stock within a given census block is assumed to be evenly 
distributed throughout the block.  The inventory information necessary for 
determining a given percent damage for the inundated area is given by 
relationships between the specific occupancy classifications and the building 
types. The square foot occupancy table is the table from which all the other 
tables are based. 
All three models (Earthquake, Wind and Flood) use key common data to ensure 
that the users do not have inventory discrepancies when switching from hazard 
to hazard.  Generally the Flood Model displays GBS data at the census block 
while the Hurricane and Earthquake Model displays GBS data at the census 
tract level.  In order to allow for future alignment between the Hurricane 
and Flood Models, the Hurricane Model will display and perform analysis at 
the census block level if the user has included the Flood Model in the study 
region.  Whenever the Flood Model is included in the study region, all three 
models require the user to edit the common inventory data at the census block 
level. The key GBS databases include the following: 
 	
Square footage by occupancy.  These data are the estimated floor area by 
specific occupancy (e.g., COM1). For viewing by the user, these data are also 
rolled up to the general occupancies (e.g., Residential). 

 	
Full Replacement Value by occupancy.  These data provide the user with 
estimated replacement values by specific occupancy (e.g., RES1).  For viewing 
by the user, these data are also rolled up to the general occupancies (e.g., 
Commercial). 

 	
Building Count by occupancy.  These data provide the user with an estimated 
building count by specific occupancy (e.g., IND1).  For viewing by the user, 
these data are also rolled up to the general occupancies (e.g., Government). 

 	
General Occupancy Mapping.  These data provide a general mapping for the GBS 
inventory data from the specific occupancy to general building type (e.g., 
Wood).  Generally, all three models will agree, however, a user can modify 
the general occupancy mapping at the census block level in the Flood Model 
thereby requiring them to select an  average  value at the tract level in the 
other two models, which will result in variances.  This should not be an 
issue for users making this type of change. 

 	
Demographics.  This table provides housing and population statistics for the 
study region. 


3.2.1.1 Classification 
In HAZUS99, 28 specific occupancy classifications were used in the baseline 
inventory.  The primary purpose of building classifications is to group 
buildings with similar valuation, damage and loss characteristics into a set 
of pre-defined groups for analysis.  For example, the damage and loss models 
represent a typical response of the occupancy classification to inundation. 
During the development of the HAZUS-MH and the Flood Model, it was 
recommended that the number of specific occupancy classifications increase 
from 28 to 33 to allow for an enhanced classification of the multi-family 
dwellings.  This was accepted by all three HAZUS contractors and therefore, 
all three models are using the same specific occupancy classifications.   
With respect to classifying buildings by their construction types, where 
Earthquake Model uses 36 specific construction types (e.g., S1L), the Flood 
Model uses the five general construction classifications: Wood, Concrete, 
Masonry, Steel, and Manufactured Housing (aka Mobile Home).  Table 3.1 shows 
the resulting specific occupancy classifications and the label used 
throughout the HAZUS Flood Model. The table also shows the SIC code 
classification used in the development of the non-residential facilities. 
Table 3.1 HAZUS Building Occupancy Classes 

HAZUS Label  Occupancy Class  Standard Industrial Codes (SIC)  
Residential  
RES1  Single Family Dwelling  
RES2  Mobile Home  
RES3A  Multi Family Dwelling - Duplex  
RES3B  Multi Family Dwelling   3-4 Units  
RES3C  Multi Family Dwelling   5-9 Units  
RES3D  Multi Family Dwelling   10-19 Units  
RES3E  Multi Family Dwelling   20-49 Units  
RES3F  Multi Family Dwelling   50+ Units  
RES4  Temporary Lodging  70  
RES5  Institutional Dormitory  
RES6  Nursing Home  8051, 8052, 8059  
Commercial  
COM1  Retail Trade  52, 53, 54, 55, 56, 57, 59  
COM2  Wholesale Trade  42, 50, 51  
COM3  Personal and Repair Services  72, 75, 76, 83, 88  
COM4   Business/Professional/Technical Services  40, 41, 44, 45, 46, 47, 49, 
61, 62, 63, 64, 65, 67, 73, 78 (except 7832), 81, 87, 89  
COM5  Depository Institutions  60  
COM6  Hospital  8062, 8063, 8069  
COM7  Medical Office/Clinic  80 (except 8051, 8052, 8059, 8062, 8063, 8069)  
COM8  Entertainment & Recreation  48, 58, 79 (except 7911), 84  
COM9  Theaters  7832, 7911  
COM10  Parking  
Industrial  
IND1  Heavy  22, 24, 26, 32, 34, 35 (except 3571, 3572), 37  
IND2  Light  23, 25, 27, 30, 31, 36 (except 3671, 3672, 3674), 38, 39  
IND3  Food/Drugs/Chemicals  20, 21, 28, 29  
IND4  Metals/Minerals Processing  10, 12, 13, 14, 33  
IND5  High Technology  3571, 3572, 3671, 3672, 3674  
IND6  Construction  15, 16, 17  
Agriculture  
AGR1  Agriculture  01, 02, 07, 08, 09  
Religion/Non-Profit  
REL1  Church/Membership Organizations  86  

Table 3.1 HAZUS Building Occupancy Classes (Continued) 

HAZUS Label  Occupancy Class  Standard Industrial Codes (SIC)  
Government  
GOV1  General Services  43, 91, 92 (except 9221, 9224), 93, 94, 95, 96, 97  
GOV2  Emergency Response  9221, 9224  
Education  
EDU1  Schools/Libraries  82 (except 8221, 8222)  
EDU2  Colleges/Universities  8221, 8222  

The Earthquake Model provided the initial guidelines for the development of 
the building classifications by building type. As stated earlier, the Flood 
Model does not require the two-dimension matrix for the building types as 
seen in the Earthquake Model.  With the exception of the velocity damage, for 
which the damage functions utilize the general building type, all of the 
Flood Model damage and loss calculations are performed based on the 33 
specific occupancies and the foundation distribution that is discussed later 
in this chapter.  Results for by building type are post-processed from the 
specific occupancy results. 
Table 3.2 shows the general building types as defined in the three models and 
the range of heights (number of stories) for these building types.  Because 
the damage functions are only marginally dependent on the number of floors, 
the typical floor information provided in the Earthquake Model Technical 
Manual is not provided here. 
Table 3.2 HAZUS Building Occupancy Classes 

Number  Label/Description  Height Name  Range of Stories  
1  Wood Frame  All  All  
2  Low-Rise  1  3  
3  Steel Frame  Mid-Rise  4   7  
4  High-Rise  8 and up  
5  Low-Rise  1   3  
6  Concrete Frame  Mid-Rise  4   7  
7  High-Rise  8 and up  
8  Low-Rise  1  3  
9  Masonry  Mid-Rise  4-7  
10  High-Rise  8 and up  
11  Manufactured Housing  All  

3.2.1.2 The Default General Building Stock Database 
The general building stock inventory was developed from the following 
information: 
 	
Census of Population and Housing, 2000: Summary Tape File 1B Extract on CD-
ROM / prepared by the Bureau of Census. 

 	
Census of Population and Housing, 2000: Summary Tape File 3 on CD-ROM / 
prepared by the Bureau of Census. 

 	
Dun & Bradstreet, Business Population Report aggregated by Standard 
Industrial Classification (SIC) and Census Block, May 2002. 

 	
Department of Energy, Housing Characteristics 1993.  Office of Energy Markets 
and End Use, DOE/EIA-0314 (93), June 1995. 

 	
Department of Energy, A Look at Residential Energy Consumption in 1997, 
DOE/EIA0632(97), November 1999. 

 	
Department of Energy, A Look at Commercial Buildings in 1995: 
Characteristics, Energy Consumption, and Energy Expenditures, DOE/EIA-
0625(95), October 1998. 


The US Census and the Dun & Bradstreet data were used to develop the general 
building stock inventory. The three reports from the Department of Energy 
(DOE) helped in defining regional variations in characteristics such as 
number and size of garages, type of foundation, and number of stories. The 
inventory s baseline floor area is based on a distribution contained in the 
DOE s Energy Consumption Report.  An approach was developed using the same 
report for determining the valuation of single-family residential homes by 
accounting for income as a factor on the cost of housing. 
Initially the methodology created the opportunity for the user to develop 
conflicting or discrepant square footage totals for single-family residential 
structures within a census block between the inventory database and the 
valuation database.  The solution was to integrate the regional DOE 
distributions with the income factors developed for determining valuation.  
To do this, default values for typical square footage per single-family home 
were developed from Energy Information Administration (EIA) data on heated 
floor space.  These default data, shown in Table 3.3, are provided by region 
and income group.  The breakdown reflects not only how typical housing size 
varies across the U.S., but also how in general, higher income areas tend to 
contain larger single-family homes. 
Consequentially, the default typical square footage data was derived from a 
detailed, unpublished database provided by the EIA.  Only information on 
families in single-family residences, aggregated across all 
foundation/basement types, was used.  The raw database included information 
on the number of households by region, income category, and housing floor 
space. Regional data were available by 9 multi-state census divisions (e.g., 
New England). 
The very nature of the default data, both in occupancy classifications and 
extent of coverage (national) requires the use of a baseline database 
collected in a consistent manner for the nation. The data source changes 
depending on the general use of the inventory being explored.  For example, 
to determine the total floor area (square feet) of single-family residences 
by census block, one uses a data source like the Census data.  While 
sufficient for residential occupancy, the Census data does not address non-
residential occupancy classifications. 
The development of the default inventory required two major datasets for the 
two main elements of the built environment.  To create the default inventory 
for residential structures, the US Department of Commerce s Census of Housing 
was used.  For commercial and industrial structures, a commercial supplier, 
Dun & Bradstreet (D&B) was contacted.  The project team performed the 
aggregation to the census data, while D&B performed the aggregation to their 
own data (due to its proprietary nature). 
The STF1B census extract at the census block level allows for the quick 
quantification of the single-family residential environment.  When combined 
with the STF3A census extract at the census block group level, the STF1B can 
provide a better proxy of the multi-family environment than using one extract 
alone.  In both the single-family and multi-family proxies, the proposed 
methodology represents an improvement over using single  average  values 
similar to the existing HAZUS99 data. 
The STF3A extract also provides information that is useful in developing 
distributions for the age of buildings within each census block group as well 
as valuable demographic data.  The age distribution, for example, can be used 
to infer the Pre-FIRM and Post-FIRM distribution which has an impact on the 
loss estimation. 
The D&B provides a realistic representation of the non-residential 
environment.  Based on the site specific data contained within their 
database, D&B s data is used to provide a reasonable assessment of the non-
residential environment.  The processing of the D&B data is discussed in more 
detail in Section 3.2.1.2.1. 
3.2.1.2.1 Specific Occupancy Square Footage by Census Block 
Single-Family Residences (RES1) 
The following discussion highlights the data development effort for the RES1 
square foot values by block. The Census Extract STF1B provides estimates of 
the single family attached and detached housing units on a block-by-block 
basis.  Several other sources of information were used to develop 
distributions of square footage relative to the income of the census block 
group. The DOE distributions of income factors were used to develop a ratio 
of the census block group income (STF3A field P08A001) and the average income 
for the region (the nine multi-state census divisions. 
The EIA data provided information regarding the heated floor area in 
relationship to income. Income was reported in 25 categories (e.g., $20,000-
$22,499) that were converted into five relative income groups for consistency 
with the inventory valuation methodology.  Housing floor space data were 
provided in 7 categories (e.g., 2,000-2,399 sq. ft.), which, for purposes of 
computing typical floor space, were represented by the midpoint of the range 
(e.g., 2,200 sq. ft.). This enabled average floor space to be calculated for 
the 9 census divisions and 5 relative income categories. 
Table 3.3 Typical Square Footage Per Unit (Main Living Area) by  
Census Division (R)1 

R = New England 
Income Ratio:  Basement  
No (j=1)  Yes2 (j=2)  
Ik < 0.5  1300  975  
0.5 = Ik < 0.85  1500  1125  
0.85 = Ik < 1.25  1800  1350  
1.25 = Ik < 2.0  1900  1425  
Ik = 2.0  2200  1650  

R = Middle Atlantic 
Income Ratio:  Basement  
No (j=1)  Yes2 (j=2)  
Ik < 0.5  1300  975  
0.5 = Ik < 0.85  1500  1125  
0.85 = Ik < 1.25  1700  1275  
1.25 = Ik < 2.0  1900  1425  
Ik = 2.0  2200  1650  

R = East North Central 
Income Ratio:  Basement  
No (j=1)  Yes2 (j=2)  
Ik < 0.5  1300  975  
0.5 = Ik < 0.85  1600  1200  
0.85 = Ik < 1.25  1700  1275  
1.25 = Ik < 2.0  1800  1350  
Ik = 2.0  2500  1875  

Table 3.3 Typical Square Footage Per Unit (Main Living Area) by  Census 
Division (R)1 (Continued) 
R = West North Central 
Income Ratio:  Basement  
No (j=1)  Yes2 (j=2)  
Ik < 0.5  1300  975  
0.5 = Ik < 0.85  1500  1125  
0.85 = Ik < 1.25  1800  1350  
1.25 = Ik < 2.0  1800  1350  
Ik = 2.0  2300  1725  

R = South Atlantic 
Income Ratio:  Basement  
No (j=1)  Yes2 (j=2)  
Ik < 0.5  1400  1050  
0.5 = Ik < 0.85  1600  1200  
0.85 = Ik < 1.25  1700  1275  
1.25 = Ik < 2.0  2000  1500  
Ik = 2.0  2300  1725  

R = East South Central 
Income Ratio:  Basement  
No (j=1)  Yes2 (j=2)  
Ik < 0.5  1300  975  
0.5 = Ik < 0.85  1400  1050  
0.85 = Ik < 1.25  1700  1275  
1.25 = Ik < 2.0  1900  1425  
Ik = 2.0  2500  1875  

R = West South Central 
Income Ratio:  Basement  
No (j=1)  Yes2 (j=2)  
Ik < 0.5  1300  975  
0.5 = Ik < 0.85  1700  1275  
0.85 = Ik < 1.25  1800  1350  
1.25 = Ik < 2.0  1900  1425  
Ik = 2.0  2500  1875  

R = Mountain 
Table 3.3 Typical Square Footage Per Unit (Main Living Area) by  
Census Division (R)1 (Continued) 


Income Ratio:  Basement  
No (j=1)  Yes2 (j=2) 
 Ik < 0.5  1200  900  
0.5 = Ik < 0.85  1500  1125  
0.85 = Ik < 1.25  1700  1275  
1.25 = Ik < 2.0  1800  1350  
Ik = 2.0  2600  1950  

R = Pacific 
Income Ratio:  Basement  
No (j=1)  Yes2 (j=2) 
 Ik < 0.5  1300  975  
0.5 = Ik < 0.85  1500  1125  
0.85 = Ik < 1.25  1700  1275  
1.25 = Ik < 2.0  1900  1425  
Ik = 2.0  2100  1575  

Notes: 
1 based on data from the Energy Information Administration, Housing 
Characteristics 1993;  
2 (Area of main living area if basement present) = 0.75 x (Area of main 
living area if no basement).  This 
adjustment allows consistent application of the Means cost models, in which 
basement areas are added-
on, and are assumed to be 1/3 of main living area. 
While the US Census data does have data defining the median income for each 
census block, there is data for the median income for each census block 
group.  This value will be applied to each block within the block group.  
With the median income for each census block, and the median income for the 
census region, it is possible to define an Income Ratio that can be used to 
determine the square footage for buildings with and without basements. Table 
3.4 below shows the 9 census regions, the states within those regions and the 
values used to compute the Income Ratio. The value from the Census STF3A 
field P08A001 is the median income for the census block group that will be 
applied to every census block within the group.  The distribution of 
basements is a summation or roll-up of the foundation type distribution 
discussed later in this section. 
Table 3.4 Income Ratio and Basement Distribution by Census Region 

Region (States)  Income Ratio  Percent with Basement  Percent without 
Basement  
AL  P053001 / 36,268  25  75  
AK  P053001 / 52,492  13  87  
AZ  P053001 / 39,653  32  68  
AR  P053001 / 30,082  5  95  
CA  P053001 / 45,070  13  87  
CO  P053001 / 49,216  32  68  
CT  P053001 / 50,647  81  19  
DE  P053001 / 47,438  23  77  
DC  P053001 / 38,005  23  77  
FL  P053001 / 37,305  23  77  
GA  P053001 / 41,481  23  77  
HI  P053001 / 45,657  13  87  
ID  P053001 / 37,760  32  68  
IL  P053001 / 46,649  68  32  
IN  P053001 / 41,315  68  32  
IA  P053001 / 41,560  75  25  
KS  P053001 / 38,393  75  25  
KY  P053001 / 36,826  25  75  
LA  P053001 / 32,500  5  95  
ME  P053001 / 39,815  81  19  
MD  P053001 / 52,846  23  77  
MA  P053001 / 45,769  81  19  
MI  P053001 / 46,034  68  32  
MN  P053001 / 50,088  75  25  
MS  P053001 / 31,963  25  75  
MO  P053001 / 44,247  75  25  
MT  P053001 / 32,553  32  68  
NE  P053001 / 39,029  75  25  
NV  P053001 / 43,262  32  68  
NH  P053001 / 48,029  81  19  
NJ  P053001 / 51,739  76  24  
NM  P053001 / 34,035  32  68  
NY  P053001 / 40,822  76  24  
NC  P053001 / 38,413  23  77  
ND  P053001 / 33,769  75  25  

Table 3.4 Income Ratio and Basement Distribution by Census Region (Continued) 

Region (States)  Income Ratio  Percent with Basement  Percent without 
Basement  
OH  P053001 / 41,972  68  32  
OK  P053001 / 34,020  5  95  
OR  P053001 / 41,915  13  87  
PA  P053001 / 41,394  76  24  
RI  P053001 / 43,428  81  19  
SC  P053001 / 36,671  23  77  
SD  P053001 / 35,986  75  25  
TN  P053001 / 35,874  25  75  
TX  P053001 / 39,296  5  95  
UT  P053001 / 46,539  32  68  
VT  P053001 / 40,908  81  19  
VA  P053001 / 47,701  23  77  
WA  P053001 / 46,412  13  87  
WV  P053001 / 29,217  23  77  
WI  P053001 / 45,441  68  32  
WY  P053001 / 38,291  32  68  

Once the parameters above had been defined, it is possible to develop an 
algorithm that allows for the estimation of the RES1 or single-family 
residential square footage for the entire nation. This algorithm is: 
RES1 (sq. ft.) = Total Single Family Units (STF1B H1BX0002) *[(Percent of 
units with basement) * (floor area w/basement based on income ratio and 
region) 
+ (Percent of units without basement)*(floor area w/o basement based on 
income
ratio and region] where Income Ratio = STF3A P08A001/regional income

For a sample New England census block, 81% Basement 19% no basement and an Ik 
of 0.67: 
RES1 (sq. ft.) = [STF1BX0002] * [(0.81)*(1,125) + (0.19)*(1,500)] 
Multi-Family and Manufactured Housing (RES3 and RES2) 
Developing the multi-family (RES3A through RES3F) and manufactured housing 
(RES2) inventory requires additional information and effort compared to the 
single-family occupancy classification.  In the 1999 census extract, the 
STF1B (census block data) extract identifies only those housing units within 
the 10 or more unit classification, unfortunately, the 2000 census extract no 
longer provided that information.  Therefore in order to define of the multi-
family units, it is necessary to utilize the STF3A extract.  The multi-family 
definition in the STF3A extract identifies Duplex, 3-4 Unit, 5-9 unit, 10-19 
unit, 20-49 unit, and 50+ dwellings. Additionally the STF3A census data 
provides a definition of the Manufactured Housing (MH) units within a block 
group and therefore the RES2 was processed at the same time.  The census data 
has an  other  classification for that will be ignored since this 
classification represent a very small portion of the universe of housing 
units and there is no  other  damage functions that can be assigned to these 
facilities.  Examples of the  Other  Census classification include vans and 
houseboats. 
Unlike the single family residential that used the Housing Characteristics 
1993 to define heated floor area, assessor data from around the United 
States, including that from the six Proof-of-Concept (POC) communities, was 
reviewed to develop preliminary estimates of average floor area for multi-
family housing.  This data was then peer reviewed by engineering experts to 
develop an average floor area per number of units for the unit ranges 
provided by the census data. Table 3.5 shows the distribution of the floor 
area by unit.  The associated equations provide an example of the 
calculations that have taken place. 
Table 3.5 Floor Areas for Multi-Family Dwellings (RES2 & RES3A-RES3F) 

Units  Duplex  3-4  5-9  10-19  20-49  50+  Manufactured Housing  Other  
Floor Area  1,500  750  800  750  700  650  Single Wide   950 Double Wide   
1,350  Not Required  

Previously, the flood model team had a complex process that allowed for a 
more accurate block level distribution. However, when the US Census Bureau 
modified the SF1 extract to eliminate information regarding the single-family 
and large multi-family fields, it became necessary to modify the data 
manipulation process.  The multi-family data was still available in the SF3 
extract at the census block group level. The only available process was to 
distribute the census block group data homogeneously throughout the census 
blocks.  The distribution process is facilitated by finding the ratio of 
total housing units per census block (H1BX0001) with respect to the total 
housing units per census block group (H0010001). This ratio was then used to 
as a multiplier to distribute the census block group level multi-family data 
into each census block. 
Step 1:  Develop the ratio of total housing units for each census block  
Unit Ratio = (H1BX0001)/(H0010001) 
Step 2:  Distribute the multi-family housing units throughout each census 
block 
For example: 
Duplex units per block = H0200003*Unit Ratio 
Step 3:  Derive Floor area per occupancy classification 
Manufactured Housing (sq. ft.) = Census Block RES2 (from Step 2)* (0.75 * 950 
+ 0.25 * 1,400) Duplex (sq. ft.) = (Census Block Duplex from Step 2) * 1,500 
3-4 Units (sq. ft.) = (Census Block 3-4 units from Step 2) * 750 
5-9 Units (sq. ft.) = (Census Block 5-9 units from Step 2) * 800 
10-19 Units (sq. ft.) = (Census Block 10-19 units from Step 2) * 750 
20-49 Units (sq. ft.) = (Census Block 20-49 units from Step 2) * 700 
50+ Units (sq. ft.) = (Census Block 50+ units from Step 2) * 700 
By using the above distribution, the valuation can be more specifically 
tailored to each floor plan. This has the potential future benefit of 
allowing the user to modify the floor area for multifamily units.  For 
example in future releases, it may be possible to provide the user the 
capability to modify the average floor area for duplexes to 2,000 square feet 
per unit if this more closely reflected the users  community.  This should 
then lead to a net decrease in the total number of units for the RES3A 
occupancy classification. 
The floor areas presented for manufactured housing are based on review of 
various internet websites for manufactured housing sales (new and used), 
housing manufacturers, and finally additional US Census Bureau data.  There 
was a great deal of information regarding sales and shipment of manufactured 
housing since the 1970 s, but there was very little information regarding the 
attrition rate experienced over the same 30-year span.  Charting information 
from the Manufactured Housing Institute, Figure 3.1 shows that there has been 
a general growth trend in the size of the units since the 1980 s for both the 
single wide and doublewide (also known as single-section and multi-section) 
manufactured housing. 

198019811982198319841985198619871988198919901991199219931994199519961997 
Year of Sale 
The recently released American Housing Survey for the US, 19971  (September, 
1999) contained estimated floor areas for manufactured housing (labeled 
Mobile Home in the Census tables) based on a surveyed population of over 8 
million manufactured homes across the United States. The survey does not 
differentiate between single-section and multi-section units, but when the 
values are charted the distribution presents natural points to estimate these 
dimensions. Figure 3.2 shows the distribution of floor area by number of 
structures from the survey.  Using this distribution, it is possible to 
estimate representative values for single-section and multi-section units of 
950 square feet and 1,400 square feet, respectively.  
1 US Department of Housing and Urban Development and US Census Bureau, 
American Housing Survey for the United States H150/97, Office of Policy 
Development and Research and the US Census Bureau, September 1999. 

02004006008001000120014001600180020002200240026002800300032003400360038004000
42004400 
Floor Area  (Square Feet) 
Figure 3.2 Number of Mobile Home Units by Floor Area    1990 US Census Data 
Housing Characteristics 
Non-Residential Occupancy Classifications 
The HAZUS99 Earthquake Model inventory used the D&B business inventory at the 
census tract level for all non-residential structures and those facilities 
that are commercial in nature but provide housing for people such as hotels 
(RES4) and nursing homes (RES6).  The D&B data represents approximately 76 
percent (approximately 14 million) of the total estimated businesses in the 
United States (approximately 19 million).  While initially this might seem 
like a low representation, the D&B database accounts for 98 percent of the 
gross national product.  D&B states that the remaining businesses are likely 
to be smaller and home-based.  If true, the proxy inventory established for 
the residential dwellings will account for these businesses in the total 
damage estimates. 
D&B provided the data aggregated on the SIC definitions used previously in 
the development of the HAZUS99 Earthquake Model (HAZUS99 Users Manual, 1997 
Table Appendix A.19, page A-23).  The D&B data obtained for the Flood Model 
provided floor area for businesses at the census block level. It should be 
noted that D&B performs regular random sampling of businesses in their 
database to obtain the actual floor area.  D&B then utilizes proprietary 
algorithms to estimate the floor area for the remaining businesses.  
According to D&B, floor area is sampled for approximately 25 percent of their 
business database and the remainder is modeled. 
With their data, D&B provided a count of businesses, the total floor area 
(modeled and sampled), and the total number of employees.  During a review of 
the data, it was discovered that D&B had some data aggregated at the census 
block groups and tracts level.  Review of the data determined that these 
errors were consistent with automated georeferencing processes and are likely 
to represent those businesses where the addresses did not match directly with 
D&B s reference street base. D&B performed an additional review and 
ascertained that this was in fact the cause of this aggregation.  It was 
felt, however, that the tract and block group data could be safely 
distributed to the census blocks based on weighted averages of commercial 
development within the blocks. Review of the results of this effort showed 
little net impact and continued agreement with ground truth data. 
The D&B data contained information on all non-residential uses including some 
agricultural facilities, general government offices, schools, and churches.  
Again, comparison with POC data and other available data showed relatively 
good agreement. 
Building Height (Number of Stories) 
Table 3.6 provides a distribution of the single-family residential structures 
by census region. When reviewing the new data within the Residential Energy 
Consumption 1997 report, there was sufficient data to develop the 
distributions in Table 3.5.  As can be seen in this table, the Northeast 
region has a larger percentage of 2-story single-family homes than any other 
census region with the South and West having a preponderance of 1-story 
single-family homes. 
Table 3.6 Distribution of Floors for Single Family Residences 

US Census  Number of Stories (% of Structures)  
Region  States within the Region  1-Story  2-Story  3-Story  Split Level  
Northeast  CT, MA, ME, NH, NJ, NY, PA, RI, VT  29  61  8  2  
Midwest  IA, IL, IN, KS, MI, MN, MO, NE, ND, OH, SD, WI  44  45  5  6  
South  AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN, TX, VA, WV  72  
23  3  2  
West  AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY  68  26  3  3  

Data Source   A Look at Residential Energy Consumption in 1997 (Nov 1999) 
Table HC1-13a converted to percent of total single family dwellings. 
For the multi-family residences, the Housing Characteristics 1993 report did 
not provide any distribution by number of floors.  This information was 
enhanced in the Residential Energy Consumption 1997 report with a broad 
distribution of number of floors that is best represented in Table 3.7.  The 
actual data is provides a little more detail than that seen in the table, but 
based on the current damage functions available, there is no real value in 
providing additional definition above 5 floors. 
Table 3.7 Distribution of Floors for Multi-Family Residences 

US Census  Number of Stories (% of Structures)  
Region  States within the Region  1-2 Stories  3-4 Stories  5+ Stories  
Northeast  CT, MA, ME, NH, NJ, NY, PA, RI, VT  29  26  45  
Midwest  IA, IL, IN, KS, MI, MN, MO, NE, ND, OH, SD, WI  49  29  22  
South  AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN, TX, VA, WV  66  
21  13  
West  AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY  58  25  17  

Data Source   A Look at Residential Energy Consumption in 1997 (Nov 1999) 
Table HC1-13a converted to percent of total multi-family dwellings. 
For commercial and industrial structures it is possible to develop a 
distribution of floors based on the Commercial Building Characteristics2 
(1997). The Commercial Building Characteristics Table 10   Year Constructed, 
Number of Buildings, 1995 provides for Low, Medium and High rise ranges 
(approximately consistent with the current HAZUS distributions) based on year 
built. The range of dates provided do not align exactly with the US Census 
ranges, but when aligned as seen in Table 3.8 it is possible to create a 
working relationship that will allow for the creation of a distribution of 
low, medium and high rise. 
Table 3.8 Association of US Census  Year Built  with Commercial Building  
Characteristics  Year Constructed  


US Census Year Built Ranges for Residential Units  Corresponding Commercial 
Building Characteristics Year Constructed Ranges  
1939 or Earlier and 1940-1949  1919 or Before and 1920-1945  
1950-1959  1946-1959  
1960-1969  1960-1969  
1970-1979  1970-1979  
1980-1988  1980-1989  
1989-Mar 1990  1990-1992 1993-1995  

When using Table 3.8 as the basis for relating the commercial development 
with the physical characteristic of number of floors, Table 3.9 is developed. 
2 Department of Energy, 1995.  A Look At Commercial Buildings in 1995 
DOE/EIA-0625(97), Washington, D.C., Energy Information Administration Office 
of Energy Markets and End Use, US Department of Energy, October, 1998. 
Table 3.9 Distribution of Number of Floors by Year Built Commercial Buildings 

Census Year Built  Low Rise  Mid-Rise  High Rise  
1939 or Earlier and 1940-1949  92  7  1  
1950-1959  98  1  1  
1960-1969  96  3  1  
1970-1979  98  1  1  
1980-1989  96  3  1  
Post 1990  96  3  1  

Data Source - Commercial Building Characteristics Table 10   Year 
Constructed, Number of Buildings, 1995 
3.2.1.2.2 Building Foundation Type 
The distribution of foundations, the associated first floor heights, and the 
Pre/Post-FIRM relationships are the key controlling parameters affecting 
flood damage within the model.  The flood model allows the user to define or 
control these parameters in the Flood Specific Occupancy Mapping dialogs, 
which are discussed in further detail in Section 5.2 of this Technical 
Manual. This section will focus on the process by which the foundation 
distributions and the first floor heights were defined. 
To properly develop a distribution of foundation types, the following 
foundation definitions are used: 
 	
Pile: An open foundation, composed of tall and slender members, embedded 
deeply into the ground. A pile is a single element, not built-up on site like 
a pier.  For our purposes, cast-inplace columns supported by a deep 
foundation (pile cap, or mat or raft below the anticipated scour depth) will 
be classified as a pile foundation.  In some pile-supported buildings, shear 
walls may be used to transfer shear from the upper building to the embedded 
foundation elements. 

 	
Pier: An open foundation (no load-bearing perimeter walls), usually built of 
masonry units and supported by shallow footings. Piers usually range from 
approximately 2 ft to 8 ft in height. 

 	
Solid Wall:  Load-bearing perimeter walls greater than 4 ft in height, 
usually supported by shallow footings. Floor beams or joists usually rest 
atop the walls, and may or may not be supported by interior piers or columns. 

 	
Basement or Garden Level Basement:  Any level or story, which has its floor 
subgrade on all sides. Usually load bearing, masonry or concrete walls around 
the perimeter of the building, supported on shallow footings. Floor beams or 
joists rest atop the walls.  Shallow basements with windows slightly above 
grade are defined as a garden level basement. 

 	
Crawlspace:  Usually short (less than 4 ft high), load bearing, masonry or 
concrete walls around the perimeter of the building footprint, supported on 
shallow footings.  Floor beams or joists rest atop the walls and may also 
rest on interior piers. 

 	
Fill: Soil built up above the natural ground elevation and used to support a 
slab or shallow footings. 

 	
Slab-on-Grade: Concrete slab resting on the ground. It may have its edges 
thickened or turned down, but does not rely on other walls or footings for 
support. 


Riverine Building Foundation Types 
Foundation type can be determined from either the Housing Characteristics 
report (1993) or the Residential Energy Consumption report (1997) with the 
exception of those areas subjected to coastal flood hazards.  Foundation 
types such as pilings are not considered or mentioned in the either report, 
but this information can be derived from the H. John Heinz III Center data 
collected for their report  The Hidden Cost of Coastal Hazards  (2000).  
Coastal hazard areas will be discussed later in this section. 
When the two reports were compared there seemed to be only moderate 
differences in the total percentages. For this reason, the Residential Energy 
Consumption (1997) census division reporting was used to enhance accuracy of 
the foundation distributions available to the user. While the Residential 
Energy Consumption report does not consider multi-family residences of five 
units or less, it will be assumed that this distribution can be applied to 
these structures since the numbers are so similar to the distributions found 
in the Housing Characteristics. 
For non-coastal development Table 3.10 provides the recommended distribution 
of foundation types (basement, crawlspace, or slab on grade) for single 
family and multi-family residences of less than 5 units. Riverine foundation 
distributions do not vary by Pre-FIRM or Post-FIRM. 
Table 3.10 Distribution of Foundation Types for Single Family and Multi-
Family* 
Residences 


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

Table 3.10 Distribution of Foundation Types for Single Family and Multi-
Family* Residences (Continued) 

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

Data Source:   A Look at Residential Energy Consumption in 1997 (Nov 1999) 
Table HC1-9b through HC112b as percent of single-family housing units. 
Riverine Building Floor Height Above Grade 
With the distribution of default foundation types determined it is necessary 
to determine what this means in terms of first floor elevation.  For the sake 
of consistency, it was determined that the measurement of floor height from 
grade to the top of the finished floor for both Pre-FIRM and Post-FIRM would 
be a good basis for default values.  Table 3.11 provides the default pre-FIRM 
or Post-FIRM elevations for each foundation type in riverine flood hazard 
areas.   
Table 3.11 Default Floor Heights Above Grade to Top of Finished Floor 
(Riverine) 
ID  Foundation Type  Pre-FIRM  Post-FIRM  
1  Pile  7 ft  8 ft  
2  Pier (or post and beam)  5 ft  6 ft  
3  Solid Wall  7 ft  8 ft  
4  Basement (or Garden Level)  4ft  4 ft1  
5  Crawlspace  3 ft  4 ft  
6  Fill  2 ft  2 ft  
7  Slab  1 ft  1 ft1  

Source Data: Expert Opinion Notes: 1 Typically not allowed, but may exist 
Note the heights shown here are default values.  In most cases, regulations 
are written to include a freeboard above the Base Flood Elevation (BFE).  
Additionally typical engineering design will shift from one foundation type 
to another depending on the height necessary to elevate the structure above 
BFE.  Therefore the user is recommended to use the following guidelines for 
Post-FIRM foundation distributions: 
 	
Piles: Utilized when the BFE plus freeboard is 8 feet or greater. 

 	
Piers: Utilized when the BFE plus freeboard is less than 6 feet.  If BFE plus 
freeboard is greater than 6 feet, typical construction practice is to use 
other foundation types such as solid walls or piles. 

 	
Solid Walls:  Utilized when the BFE plus freeboard is less than 8 feet.  If 
the BFE plus freeboard is greater than 8 feet, typical construction practice 
is to use piles. 

 	
Basements: Typically not allowed in Post-FIRM development within the mapped 
floodplain. The user should establish the Post-FIRM distribution to match 
what is actually occurring in the regulated areas. 

 	
Crawlspaces:  Utilized when the BFE plus freeboard is less than 4 feet.  If 
BFE plus freeboard is greater than 4 feet, typical construction practice is 
to use other foundation types such as piers, solid walls, or piles. 

 	
Fill: Utilized when the BFE plus freeboard is less than 2 feet.  If the BFE 
plus freeboard is greater than 2 feet, typical construction practice is to 
use other foundation types such as crawlspace, piers, solid walls, or piles. 

 	
Slab-on-Grade:  Typically not allowed in Post-FIRM development within the 
mapped floodplain. The user should establish the Post-FIRM distribution to 
match what is actually occurring in the regulated areas. 


Coastal Building Foundation Types 
The H. John Heinz III Center for Science, Economics and the Environment has 
developed a report discussing coastal erosion along the US coastline.  Part 
of this effort entailed collecting data from several coastal communities for 
the areas that front the actual coastline.  Their study included site visits 
to survey the areas of interest.  While the data they developed was collected 
for a different task, it contained detailed information on the structures 
that front coastlines from around the US. For additional information 
regarding the methodology of data collection and the complete metadata 
discussion, please refer to the Heinz Center s report3. 
3 The Heinz Center. 2000. Evaluation of Erosion Hazards.  Washington, D.C.: 
The H. John Heinz III Center for Science, Economics and the Environment. 
The Heinz Center s data was supplied with the necessary metadata to allow for 
analysis to identify potential usefulness for the HAZUS Flood Model.  The 
data contained information regarding foundation types and the structures 
flood zone (i.e., A Zone, V Zone, etc.).  The data was graphically plotted in 
order to find distinct construction features by geographic region and flood 
zone appropriate for the development of a modifier table.  Table 3.12 shows 
the table for Pre-FIRM structures that is applied to those census blocks that 
are within or intersect with the coastal FIRM zones. 
Table 3.12 Distribution of Pre-FIRM Foundation Types  

Coastline  Pile  Pier  Solid Wall  Basement  Crawl  Fill  Slab  
Pacific  7  7  1  2  46  0  37  
Great Lakes  0  1  0  0  29  0  70  
North Atlantic  47  7  2  0  34  0  10  
South Atlantic  34  7  2  0  20  0  37  
Gulf of Mexico  34  7  1  1  21  0  36  

Source Data: The H. John Heinz III Center for Science, Economics and the 
Environment study data, and expert opinion 
Table 3.12 shows that the Heinz Center did not find any structures that were 
located on elevated fill in any of their sample communities.  The field for 
elevated fill was kept so users could modify the foundation types to include 
this classification if it exists within their community.  The flood team felt 
that the communities investigated by the Heinz Center had an unusually high 
number of pile foundations due to modern hurricane experiences.  To 
accommodate this concern, the North Atlantic, South Atlantic and Gulf Coast 
V-zones were modified slightly to increase the use of pier foundations and 
reduce the pile foundations.  The team also reduced the slab-on-grade 
foundations and increased the use of the crawlspace foundations for the Great 
Lake A-zone. 
For Post-FIRM structures, Table 3.13 provides the default distribution for 
the flood model.  It should be noted that the Heinz Center data includes some 
foundation types that should not have been utilized within the flood zones 
indicated. For example, the North Atlantic V-zone data includes some slab-on-
grade structures.  This may be an indication that some of the structures were 
built just before or were under construction while the ordinances were being 
put in place. 
It is important to note that the flood model will apply the A-zone and V-
zones throughout a given census block based on the zone information available 
in the Q3. 
Table 3.13 Distribution of Post-FIRM Foundation Types by Coastal Zones 

Coastline  Post-FIRM Distribution of Coastal Foundation Type  
Pile  Pier  Solid Wall  Basement  Crawlspace  Fill  Slab on Grade  
V-Zone  
Pacific  60  25  0  0  10  0  5  
Great Lakes  5  0  10  0  30  0  55  
North Atlantic  75  15  5  0  0  0  5  
South Atlantic  80  15  2  0  1  0  2  
Gulf of Mexico   85  10  2  0  1  0  2  
A-Zone  
Pacific  20  5  0  0  55  0  20  
Great Lakes  5  0  10  0  30  0  55  
North Atlantic  40  10  5  0  30  0  15  
South Atlantic  50  15  2  0  20  0  13  
Gulf of Mexico   50  15  2  0  20  0  13  

Source Data: The H. John Heinz III Center for Science, Economics and the 
Environment study data and expert opinion 
Coastal Building Floor Height Above Grade 
For coastal flood areas a consistent measure of floor height from grade to 
the top of the finished floor was selected for both A-zone and V-zone 
heights.  While the FIA looks to the bottom of the lowest horizontal member 
it is believed that utilizing a constant reference point in the structures 
made the table clearer and easier for the user.  Within the flood model the 
floor height will automatically be adjusted to reference the lowest 
horizontal floor member to make the height consistent with the damage curves, 
which all follow the FEMA coastal approach.   
Table 3.14 provides the default elevations for each foundation type in 
coastal flood hazard areas. This table also shows the changes in foundation 
type and height by flood hazard zone and pre-FIRM or Post-FIRM. Typically, 
foundations like slab-on-grade, fill, and crawlspaces are not allowed in V-
zone construction, but there will be occasions within communities that these 
foundations exist in some numbers due to map revisions or delays in 
compliance enforcement. For this reason V-zone elevations are provided for 
these foundation types. 
Table 3.14 Default Floor Heights Above Grade to Top of Finished Floor 
(Coastal) 
ID  Foundation Type  Pre-FIRM  Post-FIRM  
A zone  V zone  
1  Pile (or column)  7 ft  8 ft  8 ft  
2  Pier (or post and beam)  5 ft  6 ft  8 ft  
3  Solid Wall  7 ft  8 ft  8 ft  
4  Basement (or Garden Level)  4ft  4 ft1  4 ft1  
5  Crawlspace  3 ft  4 ft  4 ft1  
6  Fill  2 ft  2 ft  2 ft1  
7  Slab  1 ft  1 ft1  1 ft1  

Source Data: Expert Opinion 
Notes: 
1 Typically not allowed, but may exist 
Note, the heights shown here are default values for coastal areas.  In most 
cases, regulations are written to include a freeboard above the Base Flood 
Elevation (BFE).  Additionally typical engineering design will shift from one 
foundation type to another depending on the height requirements to elevate 
the structure above BFE.  Therefore the user is recommended to use the 
following guidelines for Post-FIRM foundation distributions: 
 	
Pile:  Typically this foundation is utilized when the BFE plus freeboard is 8 
feet or greater. 

 	
Pier:  Typically this foundation is utilized when the BFE plus freeboard is 
less than 6 feet (A-zone) and 8 feet (V-zone). If BFE plus freeboard is 
greater than these heights, typical construction practice is to use other 
foundation types such as solid walls or piles. 

 	
Solid Wall:  Typically this foundation is utilized when the BFE plus 
freeboard is less than 8 feet.  If the BFE plus freeboard is greater than 8 
feet, typical construction practice is to use piles. 

 	
Basement:  This is typically not allowed in Post-FIRM development.  The user 
should establish the Post-FIRM distribution to match what is actually 
occurring in the regulated areas. 

 	
Crawlspace:  Typically this foundation is utilized when the BFE plus 
freeboard is less than 4 feet.  If BFE plus freeboard is greater than 4 feet, 
typical construction practice is to use other foundation types such as piers, 
solid walls, or piles.  This foundation type is typically not allowed in 
areas identified as V-zone. 

 	
Fill:  Typically this foundation is utilized when the BFE plus freeboard is 
less than 2 feet.  If the BFE plus freeboard is greater than 2 feet, typical 
construction practice is to use other foundation types such as crawlspace, 
piers, solid walls, or piles.  This foundation type is typically not allowed 
in areas identified as V-zone. 

 	
Slab-on-Grade:  This is typically not allowed in Post-FIRM development.  The 
user should establish the Post-FIRM distribution to match what is actually 
occurring in the regulated areas. 


As with the values in Table 3.11, the foundation results in Table 3.12 were 
slightly modified to account for the unusually high percentage of pile 
foundations. 
3.2.1.2.3 Building Year Built and Pre-FIRM/Post-FIRM Designation 
The U.S. Congress established the National Flood Insurance Program (NFIP) 
with the passage of the National Flood Insurance Act of 1968. Therefore, all 
buildings built before the community entered the NFIP should be designated as 
Pre-FIRM.  Post-FIRM designation should be based on the year that the 
community (viewed by Census Block in the Flood Specific Occupancy Mapping) 
started participating in the NFIP. Currently, HAZUS defaults all Census 
Blocks  entry date into the NFIP program as 2003 due to lack of data.  Users 
can edit the entry date and determine Pre-FIRM/Post-FIRM designations. 
The Comprehensive Data Management System (CDMS) tool will provide a more 
accurate Pre-FIRM/Post-FIRM distribution within the Census Blocks based on 
Tax Assessor s data aggregation.  This percentage, if available for the 
community, will automatically override the default entry dates in HAZUS. 
3.2.1.2.4 Garage Distributions 
The development of a distribution of garages within a census block assists in 
the assignment of valuation functions to the dwellings.  These valuation 
functions determine whether or not the structure is luxury, custom, average 
home, or economy.  Once again, both DOE reports were reviewed to determine if 
the data provided differed and if changes in the methodologies impacted the 
reporting of this information.  This review showed that percentages presented 
in the report are slightly different, but the methodology did not seem to 
have an impact on the quality of the reporting and therefore the data from 
the Residential Energy Consumption Report (1997) will be used. Initially it 
was hoped that the census divisions could be utilized, but upon review of the 
data it showed that some data was not reported because either not enough 
units were interviewed to properly report results or the Relative Standard of 
Error was greater than 50%.  It was therefore determined that Census regions 
would be used as seen in Table 3.15.  The user will have the option to modify 
the results. 
As with some of the other housing characteristics tables, multi-family 
housing units were not asked about garages, but the totals are reported as a 
portion of the universe of housing.  It is therefore necessary to adjust the 
table to represent a percentage of single family and small multifamily homes.  
Again, the housing characteristics tables provide the percentages of multi-
family units not asked so this adjustment could be made. 
Table 3.15 Distribution of Garages for Single Family Residential Structures 

US Census Region  States within the Region  Garage Types  
1-Car  2-Car  3-Car  Covered Carport  None  
Northeast  CT, MA, ME, NH, NJ, NY, PA, RI, VT  32  30  3  2  33  
Midwest  IA, IL, IN, KS, MI, MN, MO, NE, ND, OH, SD, WI  21  47  6  3  23  
South  AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN, TX, VA, WV  16  
29  1  13  41  
West  AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY  17  45  5  10  23  

Data Source:   A Look at Residential Energy Consumption in 1997 (Nov 1999) 
Table HC1-9b through HC12b as percent of single-family housing units. 
3.2.1.2.5 Building Count by Specific Occupancy 
Single-Family Residences and Manufactured Housing (RES1 and RES2) 
In HAZUS-MH MR-2 and earlier versions, building count data for each occupancy 
was estimated by dividing the total square footage for each census block (or 
tract) and occupancy by a single typical or standard value of building square 
foot by occupancy.  For example, RES1 structures were assumed to be 1,600 sq. 
ft., while COM1 structures were assumed to be 110,000 sq. ft., etc. Since the 
square footage for residential structures was originally derived from housing 
unit count data available in the U.S. Census, it made sense to utilize these 
housing unit counts directly for RES1 and RES2 occupancies, rather than to 
utilize data derived from a two step proxy (estimating square footage from 
housing unit count, then estimating building count from square footage). 
For HAZUS-MH MR-3, revised building count data were generated for RES1 and 
RES2 occupancies from block group and block level census data: 
 	
block group level census data - total count of all housing units, including 
the count of housing units in each housing category (1 unit detached, 1 unit 
attached, 2 units, 3 or 4 units, 5 - 9 units, 10 - 19 units, 20 - 49 units, 
50+ units, and mobile home) 

 	
block level data - total count of all housing units. 


For each census block group, the percent of all housing units assumed to be 
single family (RES1) was estimated as the sum of "1 unit detached" and "1 
unit attached" counts, divided by total census block group housing unit 
count. Similarly, the percent of all housing units assumed to be manufactured 
housing (RES2) was taken as the ratio of "mobile home" counts divided by 
total census block group housing unit count. This assumes that construction 
across the census block group is homogeneous - the same assumption that was 
made in developing the original HAZUS square footage databases. To estimate 
census block RES1 and RES2 counts, the census block group ratios identified 
above were multiplied by the total census block housing unit count to arrive 
at census block RES1 and RES2 counts. 
All Other Occupancy Classifications 
The building count data for all other occupancy classifications are derived 
by dividing total square footage (by occupancy and by census block/tract) by 
an assumed typical building size for each occupancy. The assumed building 
sizes are given in Table 3.16 below. 
Table 3.16 Assumed Typical Building Square Footage by Specific Occupancy 
Occupancy  Square Footage  
RES3A  3,000  
RES3B  3,000  
RES3C  8,000  
RES3D  12,000  
RES3E  40,000  
RES3F  60,000  
RES4  135,000  
RES5  25,000  
RES6  25,000  
COM1  110,000  
COM2  30,000  
COM3  10,000  
COM4  80,000  
COM5  4,100  
COM6  55,000  
COM7  7,000  
COM8  5,000  
COM9  12,000  
COM10  145,000  
IND1  30,000  
IND2  30,000  
IND3  45,000  
IND4  45,000  
IND5  45,000  
IND6  30,000  
AGR1  30,000  
REL1  17,000  
GOV1  11,000  
GOV2  11,000  
EDU1  130,000  
EDU2  50,000  

3-28 
3.2.1.3 Specific Occupancy-to-Model Building Type Mapping 
PBS&J should provide the text for this section. 
Default mapping schemes for specific occupancy classes to model building 
types by floor area percentage are provided in Table 3A-X through 3A-Y (PBS&J 
to provide tables) of Appendix 3A. 
3.2.2 Essential Facilities 
Essential facilities are those facilities that provide services to the 
community and should be functional after a flood. Essential facilities 
include hospitals, police stations, fire stations and schools. The damage for 
essential facilities is determined on a site-specific basis (i.e., the depth 
of flooding at the location of the facility based on the latitude and 
longitude provided).  The purpose of the essential facility module is to 
determine the expected loss of functionality for these critical facilities. 
The data required for the analysis include mapping of essential facility s 
occupancy classes to model building types or a combination of essential 
facilities building type and design level. The Flood Model has attempted to 
mirror the Earthquake Model as much as possible with this approach. Since the 
Flood Model does provide results in damage states, rather the inventory is 
simplified and the user can define the construction classification of the 
building to improve the model. 
3.2.2.1 Classification 
The essential facilities are also classified based on the building structure 
type and occupancy class.  The building structure types of essential 
facilities are the same as those for the general building stock presented in 
Table 3.1. The occupancy classifications are broken into general occupancy 
and specific occupancy classes.  For the methodology, the general occupancy 
classification system consists of three groups (medical care, emergency 
response, and schools). Specific occupancy consists of fourteen classes.  The 
occupancy classes are given in Table 3.17, where the general occupancy 
classes are identified in over each section. 
Table 3.17 Essential Facilities Classification 

HAZUS Label  Occupancy Class  Description  
Medical Care Facilities  
MDFLT  Default Hospital  Assigned features similar to EFHM  
EFHS  Small Hospital  Hospital with less than 50 Beds  
EFHM  Medium Hospital  Hospital with beds between 50 & 150  
EFHL  Large Hospital  Hospital with greater than 150 Beds  
EFMC  Medical Clinics  Clinics Labs Blood Banks  
Emergency Response  
FDFLT  Default Fire Station  
EFFS  Fire Station  
PDFLT  Default Police Station  
EFPS  Police Station  
EDFLT  Default EOC  
EFEO  Emergency Operation Centers  
Schools  
SDFLT  Default School  Assigned features similar to ESF1  
EFS1  Grade Schools Primary/ High Schools  
EFS2  Colleges/Universities  

Unlike the Earthquake Model, which had to deal with damage states and 
probabilities of having a certain building type, the Flood Model methodology 
is designed to allow the user to select he general building type for the 
facility and control the critical input for the estimation of losses.  At 
Level 1, the model will use the basic location information (name, latitude, 
longitude, general facility information) and identify the depth of flooding 
at the site.  The Flood Model has assigned a default building type, which the 
user can easily modify.  Additionally, an assumed foundation type is also 
presented to the user for their review and modification. 
Since the user can easily modify the foundation type, building type and other 
key parameters to match the actual facility, the Flood Model does not define 
a facility as Pre- or Post-FIRM.  The user can, however, identify if there is 
any flood protection surrounding the facility such as floodgates that prevent 
water from entering a basement. 
Critical assumptions are as follows: 
 	
Basement (Y/N):  The assumption developed from expert opinion is that EFFS, 
EFS1, and EFS2 do not have basements.  All other essential facilities are 
assumed to have basements. The user can modify this field if their facility 
is incorrect using the table dialog. 

 	
First Floor Elevation:  Assume EFEO, EFFS, EFS1, and EFS2 are at grade.  
Assume all other facilities are 3 feet above grade.  The user can adjust this 
field using the table dialog. 

 	
Number of Stories:  Assume all ESF1, EFHS, EFMC, EFFS, EFPS, and EFEO are all 
low rise structures, and the EFHM, and EFHL are mid rise.  The user can 
adjust this field using the table dialog. 

 	
Damage Functions:  Comparable damage functions from the General Building 
Stock should be used to determine the estimated damage (percent) from which a 
loss of function for essential facilities can be developed.  The user can 
change the damage functions in the analysis parameters dialogs. 


Using the information presented in Table 3.18 below, it is possible to 
determine an estimated damage for the facility in the baseline inventory.  
Unlike earthquake damage, where the facility may remain functional even after 
some damage has been sustained, the depth of flooding dictates whether a 
facility remains in operation or not and then clean-up time dictates when 
functionality is restored. As Table 3.18 shows, the general assumption is 
that when the depth of flooding at the facility reaches half a foot, 
typically the facility is closed and people evacuated.  In the case of some 
hospitals, this does not always mean the patients are evacuated, but the 
trauma center will typically refuse new patients. 
Since the essential facility is a point site, the depth can be determined 
from the grid cell in which the facility falls.  Based on the first floor 
elevation and the existence of a basement, the damage can be estimated and 
the functionality determined.  The user can override the basement, number of 
stories, and use an alternative damage function by changing the assignment.  
This approach removes the need for an occupancy mapping for essential 
facilities. 
Table 3.18 Essential Facilities Inventory Occupancy Classification and Flood 
Model Default Parameters 

HAZUS Label  Occupancy Class  Default Building Type  Basement  First Floor 
Height (ft)  No. of Stories  Functionality Depth (ft)  
Medical Care Facilities  
MDFLT  Default Hospital  Concrete  Yes  3  Mid  0.5  
EFHS  Small Hospital  Concrete  Yes  3  Low  0.5  
EFHM  Medium Hospital  Concrete  Yes  3  Mid  0.5  
EFHL  Large Hospital  Concrete  Yes  3  Mid  0.5  
EFMC  Medical Center  Concrete  Yes  3  Low  0.5  
Emergency Centers  
FDFLT  Default Fire Station  Concrete  No  0  Low  0.5  
EFFS  Fire Station  Concrete  No  0  Low  0.5  
PDFLT  Default Police Station  Concrete  Yes  0  Low  0.5  
EFPS  Police Station  Concrete  Yes  0  Low  0.5  
EDFLT  Default Emergency Center  Concrete  Yes  0  Low  0.5  
EFEO  Emergency Center  Concrete  Yes  0  Low  0.5  
Schools  
SDFLT  Default School  Masonry  No  0  Low  0.5  
EFS1  School  Masonry  No  0  Low  0.5  
EFS2  University  Concrete  No  0  Low  0.5  

3.2.3 High Potential Loss Facilities 
High potential loss facilities were defined during the development of the 
Earthquake Model as those facilities that are likely to cause heavy 
earthquake losses if damaged.  For the earthquake methodology, high potential 
loss (HPL) facilities include nuclear power plants, dams, and some military 
installations.  The inventory data required for HPL facilities include the 
geographical location (latitude and longitude) of the facility. 
The dam classifications are based on the National Inventory of Dams (NATDAM) 
database (FEMA 1993). 
Table 3.19 High Potential Loss Facilities Classifications 

HAZUS Label  General Occupancy  Specific Occupancy  
Dams  
DDFLT  Dam Default  Default  
HPDA  Dams  Arch  
HPDB  Dams  Buttress  
HPDC  Dams  Concrete  
HPDE  Dams  Earth  
HPDG  Dams  Gravity  
HPDM  Dams  Masonry  
HPDR  Dams  Rock fill  
HPDS  Dams  Stone  
HPDT  Dams  Timber Crib  
HPDU  Dams  Multi-Arch  
HPDZ  Dams  Miscellaneous  
Nuclear Power Plants  
NDFLT  Nuclear Plant Default  Default  
HPNP  Nuclear Power Facilities  Nuclear Power Facilities  
Military Installations  
MDFLT  Military Default  Default  
HPMI1  Military Installations  Barracks/Group Quarters  
HPMI2  Military Installations  Officer/Enlisted Quarters - Multi-Unit  
HPMI3  Military Installations  Officer/Enlisted Quarters - Detached  
HPMI4  Military Installations  Maintenance/Operations Shops  
HPMI5  Military Installations  Administrative Offices  
HPMI6  Military Installations  Mess Halls  
HPMI7  Military Installations  Officer/Enlisted Clubs  
HPMI8  Military Installations  Gymnasiums/Armory  
HPMI9  Military Installations  Gas/Services Stations  
HPMI10  Military Installations  PX/Retail Stores  
HPMI11  Military Installations  Arsenals  
HPMI12  Military Installations  Other  

Damage and loss estimation calculation for high potential loss facilities are 
not performed as part of the flood methodology.  The user can map the 
locations of the facilities over the flood hazard grid and ascertain the 
potential for problems associated with the scenario flood conditions and the 
facility. 
3.2.4 User Defined Facilities 
By default, there is no baseline inventory for User Defined facilities within 
HAZUS.  The user will be provided with a table definition and structure and 
the opportunity to either import a table that matches that structure, or to 
add individual records within the data table.  The Flood Model will use the 
damage functions from the General Building Stock damage library.  The damage 
functions are associated by the Occupancy code and key fields, such as  Num 
of Stories  and  Foundation.  For example, RES1 with 2 stories and a basement 
would be listed under R12B and a COM1 that is mid rise and has no basement 
would be listed under C1MN. The user can select another damage function from 
the Flood Model damage function library or build a function and assign it to 
the user-defined facility.  It is recommended that the user utilizes this 
functionality when they have facilities that do not fall within the normal 
occupancy classifications. An example of this might be those users who would 
like to perform a more detailed analysis on structures related to a railway 
system.  The user can create a listing of railway stations, assign the 
appropriate damage functions and perform the analysis.  It is recommended 
that the user utilizes the User Defined facilities feature when the InCAST 
Tool has been used to collect the inventory data. 
Damage and loss estimation calculation for User Defined facilities are 
performed as part of this methodology and are displayed in results tables, 
but are not aggregated in a summary report.   
3.3 Direct Damage Data - Transportation Systems 
The inventory classification scheme for lifeline systems separates components 
that makeup the system into a set of pre-defined classes.  The classification 
system used in this methodology was developed to provide an ability to 
differentiate between varying lifeline system components with substantially 
different damage and loss characteristics.  Transportation systems addressed 
in the methodology include highways, railways, light rail, bus, ports, 
ferries and airports.  The classification of each of these transportation 
systems is discussed in detail in the following sections. The inventory data 
required for the analysis of each system is also identified in the following 
sections. 
Effort has been made to classify the components based on their vulnerability 
to flooding.  At this time, the Flood Model does not account for flood borne 
debris impact or the loads resulting from flood borne debris trapped against 
transportation features such as bridges.  The initial release of the Flood 
Model will estimate the level of damage to the bridge network and the 
subsequent functionality of the bridge, but other transportation components 
have been deferred to later versions. 
3-34 
The Flood Model comes with a baseline bridge database compiled from the 
National Transportation Atlas and was last updated in 2001. The Flood Model 
uses the scour field within the database as discussed later in Section 7 of 
this document.  The user can add or remove bridges from this database. 
3.3.1 Highway Systems 
A highway transportation system consists of roadways, bridges and tunnels.  
The inventory data required for analysis include the geographical location, 
classification, and replacement cost of the system components.  The Flood 
Model has tailored the classification system to meet the needs of the flood 
community and reduce the overall data collection effort.   
The Flood Model has delayed the assessment of losses to street segments and 
other highway components, but will produce an estimate of the percent damage 
to a bridge and the probability of the bridge being functional depending on 
the estimated damage. 
3.3.1.1 Classification 
The classes of highway systems are presented in Table 3.20.  The table also 
provides a comparison of the Flood Model classification scheme to the 
Earthquake Model classification scheme to allow those users who have an 
earthquake database to aggregate the data appropriately into the flood 
requirements. 
Table 3.20 Highway System Classifications 

Flood Label  General Occupancy  Specific Occupancy  HAZUS Valuation1  
HRD1  Highway Roads  Major Roads (1km 4 lanes))  10,000  
HRD2  Highway Roads  Urban Roads (1 km 2 lanes)  5,000  
HTU  Highway Tunnel  Highway Tunnel  20,000  
HWBM  Highway Bridge  Major Bridge  20,000  
HWBO  Highway Bridge  Other Bridge (include all wood)  1,000  
HWBCO  Highway Bridge  Other Concrete Bridge  1,000  
HWBCC  Highway Bridge  Continuous Concrete Bridge  5,000  
HWBSO  Highway Bridge  Other Steel Bridge  1,000  
HWBSC  Highway Bridge  Continuous Steel Bridge  5,000  

Notes: 
1 All dollar amounts are in thousands of dollars. 
3.3.2 Railway Systems 
A railway transportation system consists of tracks, bridges, tunnels, 
stations, fuel, dispatch and maintenance facilities.  The inventory data 
required for analysis include the geographical location, classification and 
replacement cost of the system components.  The Flood Model has tailored the 
classification system to meet the needs of the flood community and reduce the 
overall data collection effort. 
The Flood Model has delayed the assessment of losses to railway segments and 
other railway components, but will produce an estimate of the percent damage 
to a bridge and the probability of the bridge being functional depending on 
the estimated damage. 
3.3.2.1 Classification 
The classes of railway systems are presented in Table 3.21.  The table also 
provides a comparison of the Flood Model classification scheme to the 
Earthquake Model classification scheme to allow those users who have an 
earthquake database to aggregate the data appropriately into the flood 
requirements. 
Table 3.21 Railway System Classifications 

Flood Label  General Occupancy  Specific Occupancy  HAZUS Valuation1  
RTR  Railway Tracks  Railway Tracks (per km)  1,500  
RBRU  Railway Bridge  Railway Bridge Unknown  5,000  
RBRC  Railway Bridge  Concrete Railway Bridge  5,000  
RBRS  Railway Bridge  Steel Railway Bridge  5,000  
RBRW  Railway Bridge  Wood Railway Bridge  5,000  
RTU  Railway Tunnel  Railway Tunnel  10,000  
RSTS  Railway Urban Station  Steel Railway Urban Station  2,000  
RSTC  Railway Urban Station  Concrete Railway Urban Station  2,000  
RSTW  Railway Urban Station  Wood Railway Urban Station  2,000  
RSTB  Railway Urban Station  Brick Railway Urban Station  2,000  
RFF  Railway Fuel Facility  Railway Fuel Facility (Tanks)  3,000  
RDF  Railway Dispatch Facility  Railway Dispatch Facility (Equip)  3,000  
RMFS  Railway Maintenance Facility  Steel Railway Maintenance Facility  2,800  
RMFC  Railway Maintenance Facility  Concrete Railway Maintenance Facility  
2,800  
RMFW  Railway Maintenance Facility  Wood Railway Maintenance Facility  2,800  
RMFB  Railway Maintenance Facility  Brick Railway Maintenance Facility  2,800  

Notes: 
1 All dollar amounts are in thousands of dollars. 
3.3.3 Light Railway Systems 
A light railway transportation system consists of tracks, bridges, tunnels, 
stations, fuel, dispatch and maintenance facilities.  The major difference 
between light rail and rail systems is the power supply, where light rail 
systems operate with DC power substations.  The inventory data required for 
analysis include the geographical location, classification and replacement 
cost of the system components.  The Flood Model has tailored the 
classification system to meet the needs of the flood community and reduce the 
overall data collection effort. 
The Flood Model has delayed the assessment of losses to railway segments and 
other light rail components, but will produce an estimate of the percent 
damage to a bridge and the probability of the bridge being functional 
depending on the estimated damage. 
3.3.3.1 Classification 
The classes of light rail systems are presented in Table 3.22.  The table 
also provides a comparison of the Flood Model classification scheme to the 
Earthquake Model classification scheme to allow those users who have an 
earthquake database to aggregate the data appropriately into the flood 
requirements. 
Table 3.22 Light Rail System Classifications 

Flood Label  General Occupancy  Specific Occupancy  HAZUS Valuation1  
LTR  Light Rail Track  Light Rail Track (per km)  1,500  
LBRU  Light Rail Bridge  Light Rail Bridge Unknown  5,000  
LBRC  Light Rail Bridge  Concrete Light Rail Bridge  5,000  
LBRS  Light Rail Bridge  Steel Light Rail Bridge  5,000  
LBRW  Light Rail Bridge  Wood Light Rail Bridge  5,000  
LTU  Light Rail Tunnel  Light Rail Tunnel  10,000  
LDC  DC Substation  DC Substation (equip)  2,000  
LDF  Dispatch Facility  Dispatch Facility (equip)  3,000  
LMFS  Maintenance Facility  Steel Maintenance Facility  2,600  
LMFC  Maintenance Facility  Concrete Maintenance Facility  2,600  
LMFW  Maintenance Facility  Wood Maintenance Facility  2,600  
LMFB  Maintenance Facility  Brick Maintenance Facility  2,600  

Notes: 
1 All dollar amounts are in thousands of dollars. 
3.3.4 Bus Systems 
A bus transportation system consists of urban stations fuel facilities, 
dispatch and maintenance facilities. The inventory data required for analysis 
include the geographical location, classification and replacement cost of the 
system components.  The Flood Model has tailored the classification system to 
meet the needs of the flood community and reduce the overall data collection 
effort. 
The Flood Model has delayed the assessment of losses to bus systems. 
3.3.4.1 Classification 
The classes of bus systems are presented in Table 3.23.  The table also 
provides a comparison of the Flood Model classification scheme to the 
Earthquake Model classification scheme to allow those users who have an 
earthquake database to aggregate the data appropriately into the flood 
requirements. 
Table 3.23 Bus System Classifications 

Flood Label  General Occupancy  Specific Occupancy  HAZUS Valuation1  
BPTS  Bus Urban Station  Steel Bus Urban Station  1,000  
BPTC  Bus Urban Station  Concrete Bus Urban Station  1,000  
BPTB  Bus Urban Station  Brick Bus Urban Station  1,000  
BPTW  Bus Urban Station  Wood Bus Urban Station  1,000  
BFF  Bus Fuel Facility  Bus Fuel Facility (tanks)  150  
BDF  Bus Dispatch Facility  Bus Dispatch Facility (equip)  400  
BMFW  Bus Maintenance Facility  Wood Bus Maintenance Facility  1,300  
BMFS  Bus Maintenance Facility  Steel Bus Maintenance Facility  1,300  
BMFC  Bus Maintenance Facility  Concrete Bus Maintenance Facility  1,300  
BMFB  Bus Maintenance Facility  Brick Bus Maintenance Facility  1,300  

Notes: 
1 All dollar amounts are in thousands of dollars. 
3.3.5 Ports and Harbors 
Port and harbor transportation systems consist of waterfront structures, 
cranes/cargo handling equipment, warehouses and fuel facilities.  The 
inventory data required for analysis include the geographical location, 
classification and replacement cost of the system components.  The Flood 
Model has tailored the classification system to meet the needs of the flood 
community and reduce the overall data collection effort. 
The Flood Model has delayed the assessment of losses to ports and harbors. 
3.3.5.1 Classification 
The classes of ports and harbors are presented in Table 3.24. The table also 
provides a comparison of the Flood Model classification scheme to the 
Earthquake Model classification scheme to allow those users who have an 
earthquake database to aggregate the data appropriately into the flood 
requirements. 
Table 3.24 Ports and Harbors Classifications 

Flood Label  General Occupancy  Specific Occupancy  HAZUS Valuation1  
PWS  Waterfront Structures  Waterfront Structures  1,500  
PEQ  Cranes/Cargo Handling Equipment  Cranes/Cargo Handling Equipment  2,000  
PWH W  Warehouses  Wood Port Warehouses  1,200  
PWHS  Warehouses  Steel Port Warehouses  1,200  
PWHC  Warehouses  Concrete Port Warehouses  1,200  
PWHB  Warehouses  Brick Port Warehouses  1,200  
PFF  Fuel Facility  Port Fuel Facility  2,000  

Notes: 
1 All dollar amounts are in thousands of dollars. 
3.3.6 Ferry Transportation Systems 
A ferry transportation system consists of waterfront structures, passenger 
terminals, warehouses, fuel facilities, and dispatch and maintenance 
facilities.  The inventory data required for analysis include the 
geographical location, classification and replacement cost of the system 
components. The Flood Model has tailored the classification system to meet 
the needs of the flood community and reduce the overall data collection 
effort. 
The Flood Model has delayed the assessment of losses to ferry transportation 
systems. 
3.3.6.1 Classification 
The classes of ferry transportation systems are presented in Table 3.25.  The 
table also provides a comparison of the Flood Model classification scheme to 
the Earthquake Model classification scheme to allow those users who have an 
earthquake database to aggregate the data appropriately into the flood 
requirements. 
Table 3.25 Ferry System Classifications 

Flood Label  General Occupancy  Specific Occupancy  HAZUS Valuation1  
FWS  Water Front Structures  Ferry Waterfront Structures  1,500  
FPTW  Ferry Passenger Terminals  Wood Ferry Passenger Terminals  1,000  
FPTS  Ferry Passenger Terminals  Steel Ferry Passenger Terminals  1,000  
FPTC  Ferry Passenger Terminals  Concrete Ferry Passenger Terminals  1,000  
FPTB  Ferry Passenger Terminals  Brick Ferry Passenger Terminals  1,000  
FFF  Ferry Fuel Facility  Ferry Fuel Facility  400  
FDF  Ferry Dispatch Facility  Ferry Dispatch Facility  200  
FMFW  Piers and Dock Facilities  Wood Piers and Dock Facilities  520  
FMFS  Piers and Dock Facilities  Steel Piers and Dock Facilities  520  
FMFC  Piers and Dock Facilities  Concrete Piers and Dock Facilities  520  
FMFB  Piers and Dock Facilities  Brick Piers and Dock Facilities  520  

Notes: 
1 All dollar amounts are in thousands of dollars. 
3.3.7 Airports 
An airport transportation systems consists of control towers, runways, 
terminal buildings, parking structures, fuel facilities, and maintenance and 
hanger facilities.  The inventory data required for analysis include the 
geographical location, classification and replacement cost of the system 
components.  The Flood Model has tailored the classification system to meet 
the needs of the flood community and reduce the overall data collection 
effort. 
The Flood Model has delayed the assessment of losses to airports. 
3.3.7.1 Classification 
The classes of airport transportation systems are presented in Table 3.26.  
The table also provides a comparison of the Flood Model classification scheme 
to the Earthquake Model classification scheme to allow those users who have 
an earthquake database to aggregate the data appropriately into the flood 
requirements. 
Table 3.26 Airports Classifications 

Flood Label  General Occupancy  Specific Occupancy  HAZUS Valuation1  
ACTW  Airport Control Towers  Wood Airport Control Towers  5,000  
ACTS  Airport Control Towers  Steel Airport Control Towers  5,000  
ACTC  Airport Control Towers  Concrete Airport Control Towers  5,000  
ACTB  Airport Control Towers  Brick Airport Control Towers  5,000  
APTR  Airport Runway  Airport Runway (total)  28,000  
AFF  Fuel Facilities  Fuel Facilities  5,000  
AFO  Seaport/Stolport/Gliderport/etc.  Seaport/Stolport/Gliderport/etc.  500  
AFH  Heliport Facilities  Heliport Facilities  2,000  
APS  Airport Parking Structure  Airport Parking Structure  1,400  
AMFW  Airport Maintenance & Hangar Facility  Wood Airport Maintenance & 
Hangar Facility  3,200  
AMFS  Airport Maintenance & Hangar Facility  Steel Airport Maintenance & 
Hangar Facility  3,200  
AMFC  Airport Maintenance & Hangar Facility  Concrete Airport Maintenance & 
Hangar Facility  3,200  
AMFB  Airport Maintenance & Hangar Facility  Brick Airport Maintenance & 
Hangar Facility  3,200  
ATBW  Airport Terminal Buildings  Wood Airport Terminal Buildings  8,000  
ATBS  Airport Terminal Buildings  Steel Airport Terminal Buildings  8,000  
ATBC  Airport Terminal Buildings  Concrete Airport Terminal Buildings  8,000  
ATBB  Airport Terminal Buildings  Brick Airport Terminal Buildings  8,000  
ATBU  Airport Terminal Unknown  Airport Terminal Buildings Unknown  8,000  

Notes: 
1 All dollar amounts are in thousands of dollars. 

3.4 Direct Damage Data   Lifeline Utility Systems 
The inventory classification scheme for lifeline systems separates components 
that makeup the system into a set of pre-defined classes.  The classification 
system used in this methodology was developed to provide an ability to 
differentiate between varying lifeline system components with substantially 
different damage and loss characteristics.  Utility systems addressed in the 
methodology include potable water, wastewater, oil, natural gas, electric 
power, and communication systems.  The classification of each of these 
utility systems is discussed in detail in the following sections.  The 
inventory data required for the analysis of each system is also identified in 
the following sections. 
Effort has been made to classify the components based on their vulnerability 
to flooding.  At this time, the Flood Model does not account for flood borne 
debris impact, or water borne debris loads, which can cause significant 
clean-up efforts for utility systems.  The Flood Model is analyzing those 
system components that are more vulnerable or costly to clean-up, repair or 
replace since they are likely to control the overall recovery costs and time. 
3.4.1 Potable Water Systems 
A potable water system consists of pipelines, water treatment plants, control 
vaults and control stations, wells, storage tanks and pumping stations.  The 
inventory data required for potable water systems analysis include the 
geographical location and classification of system components.  The analysis 
also requires the replacement cost for facilities and the repair cost for 
pipelines. 
The Flood Model will estimate damage, losses and functionality for select 
vulnerable components of the potable water system.  These include treatment 
plants, control vaults and control stations, and pumping stations. 
3.4.1.1 Classification 
The classes of potable water systems are presented in Table 3.27.  The table 
also provides a comparison of the Flood Model classification scheme to the 
Earthquake Model classification scheme to allow those users who have an 
earthquake database to aggregate the data appropriately into the flood 
requirements. 
Table 3.27 Potable Water System Classifications 

Flood Label  General Occupancy  Specific Occupancy  HAZUS Valuation1  
PWPE  Pipelines  Exposed Transmission Pipeline Crossing  1  
PWPB  Pipelines  Buried Transmission Pipeline Crossing  1  
PWP  Pipelines  Pipelines (non-crossing)  1  
PWSO  Water Treatment Plants  Small Water Treatment Plants Open/Gravity  
30,000  
PWMO  Water Treatment Plants  Medium Water Treatment Plants Open/Gravity  
100,000  
PWLO  Water Treatment Plants  Large Water Treatment Plants Open/Gravity  
360,000  
PWSC  Water Treatment Plants  Small Water Treatment Plants Closed/Pressure  
30,000  
PWMC  Water Treatment Plants  Medium Water Treatment Plants Closed/Pressure  
100,000  
PWLC  Water Treatment Plants  Large Water Treatment Plants Closed/Pressure  
360,000  
PPSB  Pumping Plants  Pumping Plants (Small) Below Grade  150  
PPMB  Pumping Plants  Pumping Plants (Med/Large) Below Grade  525  
PPSA  Pumping Plants  Pumping Plants (Small) Above Grade  150  
PPMA  Pumping Plants  Pumping Plants (Med/Large) Above Grade  525  
PCVS  Control Vaults and Stations  Control Vaults and Stations  50  
PSTC  Water Storage Tanks  Water Storage Tanks At Grade Concrete  1,500  
PSTS  Water Storage Tanks  Water Storage Tanks At Grade Steel  800  
PSTW  Water Storage Tanks  Water Storage Tanks At Grade Wood  30  
PSTE  Water Storage Tanks  Water Storage Tanks Elevated  800  
PSTB  Water Storage Tanks  Water Storage Tanks Below Grade (all)  1,500  
PWE  Wells  Wells  400  

Notes: 
1 All dollar amounts are in thousands of dollars. 
3.4.2 Wastewater Systems 
A wastewater system consists of pipelines, wastewater treatment plants, 
control vaults and control stations, and lift stations. The inventory data 
required for wastewater systems analysis include the geographical location 
and classification of system components.  The analysis also requires the 
replacement cost for facilities and the repair cost for pipelines. 
The Flood Model will estimate damage, losses, and functionality for select 
vulnerable components within the wastewater system including treatment 
plants, control vaults and stations, and lift stations. 
3.4.2.1 Classification 
The classes of wastewater systems are presented in Table 3.28.  The table 
also provides a comparison of the Flood Model classification scheme to the 
Earthquake Model classification scheme to allow those users who have an 
earthquake database to aggregate the data appropriately into the flood 
requirements. 
Table 3.28 Wastewater System Classifications 

Flood Label  General Occupancy  Specific Occupancy  HAZUS Valuation1  
WWPE  Sewers & Interceptors  Exposed Collector River Crossings  1  
WWPB  Sewers & Interceptors  Buried Collector River Crossings  1  
WWP  Sewers & Interceptors  Pipes (non-crossings)  1  
WWTS  Wastewater Treatment Plants  Small Wastewater Treatment Plants  60,000  
WWTM  Wastewater Treatment Plants  Medium Wastewater Treatment Plants  
200,000  
WWTL  Wastewater Treatment Plants  Large Wastewater Treatment Plants  720,000  
WWCV  Control Vaults and Control Stations  Control Vaults and Control 
Stations  50  
WLSW  Lift Stations  Lift Station (Small) Wet Well/Dry Well  300  
WLMW  Lift Stations  Lift Station (Med/Large) Wet Well/Dry Well  1,050  
WLSS  Lift Stations  Lift Station (Small) Submersible  300   
WLMS  Lift Stations  Lift Station (Med/Large) Submersible  1,050  

Notes: 
1 All dollar amounts are in thousands of dollars. 
3.4.3 Oil Systems 
An oil system consists of pipelines, refineries, control vaults and control 
stations, and tank farms.  The inventory data required for oil systems 
analysis include the geographical location and classification of system 
components.  The analysis also requires the replacement cost for facilities 
and the repair cost for pipelines. 
The Flood Model will estimate damage, losses and functionality for select 
vulnerable system components within the oil system.  This is limited to 
refineries and control vaults and stations. 
3.4.3.1 Classification 
The classes of oil systems are presented in Table 3.29.  The table also 
provides a comparison of the Flood Model classification scheme to the 
Earthquake Model classification scheme to allow those users who have an 
earthquake database to aggregate the data appropriately into the flood 
requirements. 
Table 3.29 Oil System Classifications 

Flood Label  General Occupancy  Specific Occupancy  HAZUS Valuation1  
OIPE  Pipelines  Exposed Transmission Pipelines River Crossings  1  
OIPB  Pipelines  Buried Transmission Pipelines River Crossings  1  
OIP  Pipelines  Pipelines (non-crossing)  1  
OPP  Pumping Plant  Pumping Plant  1,000  
OTF  Tank Farm  Tank Farm  2,000  
OCV  Oil Control Vault & Control Station  Oil Control Vault & Control Station  
50  
ORFS  Oil Refinery  Small Oil Refinery  175,000  
ORFM  Oil Refinery  Medium Oil Refinery  750,000  
ORFL  Oil Refinery  Large Oil Refinery  750,000  

Notes: 
1 All dollar amounts are in thousands of dollars. 
3.4.4 Natural Gas Systems 
A natural gas system consists of pipelines, control vaults and control 
stations, and compressor stations. The inventory data required for natural 
gas systems analysis include the geographical location and classification of 
system components.  The analysis also requires the replacement cost for 
facilities and the repair cost for pipelines. 
The Flood Model will estimate loses to select vulnerable system components 
within the natural gas system.  This includes the control vaults, control 
stations and compressor stations. 
3.4.4.1 Classification 
The classes of natural gas systems are presented in Table 3.30.  The table 
also provides a comparison of the Flood Model classification scheme to the 
Earthquake Model classification scheme to allow those users who have an 
earthquake database to aggregate the data appropriately into the flood 
requirements. 
Table 3.30 Natural Gas System Classifications 

Flood Label  General Occupancy  Specific Occupancy  HAZUS Valuation1  
NGPE  Pipelines  Exposed Transmission Pipelines River Crossings  1  
NGPB  Pipelines  Buried Transmission Pipelines River Crossings  1  
NGP  Pipelines  Pipelines (Non-crossing)  1  
NGCV  Control Valves and Control Stations  Control Valves and Control 
Stations  50  
NGC  Compressor Stations  Compressor Stations  1,000  

Notes: 
1 All dollar amounts are in thousands of dollars. 
3.4.5 Electric Power Systems 
An electric power system consists of generating plants, substations, 
distribution circuits, and transmission towers.  The inventory data required 
for electric power systems analysis include the geographical location and 
classification of system components.  The analysis also requires the 
replacement cost for facilities and the repair cost for transmission lines. 
The Flood Model will perform a limited analysis on select vulnerable electric 
power system components.  These components include the generating plants and 
substations. 
3.4.5.1 Classification 
The classes of electric power systems are presented in Table 3.31.  The table 
also provides a comparison of the Flood Model classification scheme to the 
Earthquake Model classification scheme to allow those users who have an 
earthquake database to aggregate the data appropriately into the flood 
requirements. 
Table 3.31 Electric Power System Classifications 

Flood Label  General Occupancy  Specific Occupancy  HAZUS Valuation1  
ESSL  Substations  Low Voltage Substation  10,000  
ESSM  Substations  Medium Voltage Substation  20,000  
ESSH  Substations  High Voltage Substation  50,000  
EDCE  Distribution Circuits  Distribution Circuits Elevated Crossings  3  
EDCB  Distribution Circuits   Distribution Circuits Buried Crossings  3  
EDC  Distribution Circuits   Distribution Circuits (non-crossing)  3  
EPPS  Generation Plants  Small Power Plants  100,000  
EPPM  Generation Plants  Medium Power Plants  500,000  
EPPL  Generation Plants  Large Power Plants  500,000  

Notes: 
1 All dollar amounts are in thousands of dollars. 

3-46 
3.4.6 Communication Systems 
A communication system consists of communications facilities, communications 
lines, control vaults, switching stations, Radio/TV station, weather station, 
or other facilities.  The inventory data required for communications systems 
analysis include the geographical location and classification of system 
components.  The analysis also requires the replacement cost for facilities 
and the repair cost for communications lines. 
The Flood Model has deferred estimating damage and losses for communications 
facilities. 
3.4.6.1 Classification 
The classes of communications systems are presented in Table 3.32.  The table 
also provides a comparison of the Flood Model classification scheme to the 
Earthquake Model classification scheme to allow those users who have an 
earthquake database to aggregate the data appropriately into the flood 
requirements. 
Table 3.32 Communication System Classifications 

Flood Label  General Occupancy  Specific Occupancy  HAZUS Valuation1  
CCTE  Communications Lines  Exposed Communications Lines River Crossings  N/A  
CCTB  Communications Lines  Buried Communications Lines River Crossings  N/A  
CCT  Communications Lines  Communications Lines (non-crossings)  N/A  
CCSV  Control Vault  Control Vault  50  
CCS1  Switching Stations  Central Offices/Switching Stations Below Grade  
5,000  
CCS2  Switching Stations  Central Offices/Switching Stations At or Above 
Grade   5,000  
CBR  Radio/TV Station  Radio Station/TV Station  2,000  
CBW  Weather Station  Weather Station  2,000  
CBO  Other Communication Facility  Other Communication Facility  2,000  

Notes: 
1 All dollar amounts are in thousands of dollars. 
3.5 Direct Damage Data Agricultural Products 
Based on the results from the proof-of-concept exercise and direction from 
the HAZUS Flood Model Oversight Committee, the methodology for developing the 
agriculture products inventory has been established.  Additionally, the flood 
model project team took the opportunity to review additional crop loss 
estimation approaches currently used by the US Army Corps of Engineers 
(USACE). This includes the current development within the Hydraulic 
Engineering Center s (HEC) Flood Impact Analysis (FIA) and Flood Damage 
Assessment (FDA) models.  The project team also reviewed a program called 
Computerized Agricultural Crop Flood Damage Assessment System (CACFDAS).  
None of these models varied significantly from the Agriculture Flood Damage 
Analysis (AGDAM) approach tested in the proof-of-concept effort, which has 
been selected for implementation.  The methodology discussed in the Final 
Task 2 report combines two nationally available datasets that provide 
sufficient data to develop a general distribution of crops by type, average 
yield by NRI polygon, the unit price, and the harvest price. As mentioned in 
the report, the two datasets are the National Resources Inventory (NRI) and 
the National Agriculture Statistical Service (NASS). 
The NRI dataset was created to allow the US Department of Agriculture (USDA) 
to   assess the status, conditions and trends of resources at 5-year 
intervals.   The NRI has conducted their survey in 1977, 1982, 1987, 1992 and 
1996 and 2001. The data for 2001 is not available for inclusion into the 
HAZUS Flood Model, but this does not pose any issue for the model based on 
the chosen approach. The NRI data is compiled and presented at the sub-county 
level.  The NRI data consists of point sample data taken throughout the 
county.  This is associated with soils data and  expansion factors  that 
identify what each sample point represents in terms of acres. 
The 1992 NRI dataset was used to develop sub-county polygons (essentially an 
intersection of the county boundary with the USGS 8-digit Hydrologic Unit 
codes or HUCS).  For example Story County, Iowa and the county are subdivided 
into six polygons (one of which had no data). For the entire nation, this 
resulted in an average of six polygons per county.  The NRI data also 
provides sample points taken throughout the county.  Each sample point is 
associated with specific HUC and county polygon, soils data and  expansion 
factors , statistical weighting factors that establish the total crop acreage 
and yields represented by the sample point.  The NRI data is averaged over 
each collection interval (5 intervals since 1977) to smooth variations in 
agriculture yields.  These variations include changes in crop types, crop 
rotation and seasonal or weather related changes in yield.  As with every 
inventory dataset within HAZUS, the user can modify or adjust the values 
based on more accurate local information.  The total yields for each polygon 
are then summed over the 5 collection intervals and averaged to produce the 
 average yield  that will be supplied to the user.  The user will be able to 
edit the value to account for their estimated or actual yields.  The NRI data 
provides definition of the units of measure for each crop type (such as 
bushels).  Due to data limitations, the agriculture inventory will not be 
available for Alaska and the US territories; however, the Flood Model is 
being developed such that users in those areas could easily input locally 
available data as needed. 
The NASS data is compiled annually by the NASS, a branch of the USDA, and 
covers nearly every aspect of the agriculture industry.  This rigorous 
collection of data is used primarily to assess and estimate crop yields and 
future industry planting.  The data is collected through a variety of 
sources. The key limitation of this data is that the crop yields are 
developed for the entire county and do not assess regional variations in crop 
types or yields.  It is believed that the NASS data is more perishable due to 
the variations in crop yields from year to year. Additionally, the countywide 
distribution limits the accuracy of the data.  The NASS data, however, 
provides the most up to date estimate on the unit price for each crop type 
since the data is collected annually. With the strong variations in crop 
price this field can easily be updated by the user with data from the NASS 
website. The User Manual will recommend that the user check the NASS website 
prior to performing an agriculture loss assessment. 
3-48 
By associating the crop types from the NASS data to the NRI, the project team 
was able to obtain the crop price per unit (e.g., $ per bushel).  The NASS 
data is compiled annually, and the most recent values were used. It is 
anticipated that the crop price per unit will be the data field most likely 
modified by users since this value fluctuates annually.  The software will 
make this data field easy to locate, identify, and modify. 
Based on conversations with various districts within the USACE and based on 
review of the previously mentioned crop loss models (FIA, AGDAM, and CACFDAS) 
used by the USACE, crop losses are substantially affected by the duration of 
the flood.  Since the flood model is not developing a hazard duration factor, 
the solution is to provide a table of results to the user for a range of 
durations. The USACE has a set of duration functions with factors for 0, 3, 
7, and 14 days of duration.  The flood model will provide a single table of 
losses by crop type for each duration period. 
The USACE has provided damage functions for several different crop types and 
the flood model will use many of these functions as the default.  A damage 
function library is being developed based on curves collected from the 
various USACE Districts.  Damage and duration curves have been obtained from 
the Sacramento, St. Paul, and Vicksburg Districts.  All the curves are based 
on a Julian calendar to account for the changing potential to loss from 
planting to harvest.  The user will need to provide a date for the flood 
scenario, and the flood model will determine the Julian date and identify the 
loss potential from the damage function.  The loss will be increased by the 
duration factors as discussed above. 
3.6 Direct Damage Data   Vehicles 
In order to utilize HAZUS to estimate flood damage of motor vehicles, 
procedures are required to: (1) calculate vehicle inventory within a study 
area; (2) allocate vehicles by time of day to different locations; (3) 
estimate the value of vehicles; and (4) apply a percent loss damage function 
according to the flood depth. These steps are divided into two parts.  The 
first part is a vehicle location estimator, and the other part is the value 
of damage calculator.  The vehicle location estimator is summarized in Figure 
3.3. 
Vehicle Location Estimation System 
INPUT 



PROCESS 
OUTPUT 

3.6.1 Building inventory 
Building inventory in a study area is provided in HAZUS, which categorizes 
all building structures into 33 occupancy groups. Among those major occupancy 
groups, 11 are residential, 10 are commercial, and 6 are industrial. 
3.6.1.1 Parking Generation Rates 
Parking generation rates are used to associate number of parked vehicles to 
square footages of different types of occupancy groups in HAZUS during a 
flood event.  Vehicle distributions are estimated for daytime and nighttime, 
with daytime assumed to be normal business hours.  The Institute of 
Transportation Engineers (ITE) has compiled the most comprehensive parking 
generation study. The latest version of the ITE Parking Generation manual was 
dated 1987, however, and a new edition is expected in early 2002.  Another 
comprehensive source of parking in relation to land use is the Off-Street 
Parking Requirements manual compiled by the American Planning Association 
(APA).  For the purpose of this report, various regional parking studies are 
referenced to update the ITE study and help determine the parked vehicle 
distribution according to time of day.  These regions include Austin, Denver, 
Indianapolis, Seattle, and Westfield. 
The following examples illustrate the process of assigning parked vehicles to 
specific occupancy groups. The first example is related to retail trade, 
which belongs to the HAZUS occupancy group COM1. The ITE parking generation 
report devotes land use code 810-850 and 870-890 to retail trade, which 
includes shopping centers, restaurants, supermarkets, and so on.  The ITE 
updated parking generation rate (data through April 2001) for shopping 
centers is available in a report from DKS Associates that utilizes results of 
940 parking studies to characterize generalized retail trade activities.  
During regular business hours, average vehicles per 1,000 square feet of 
shopping center space generally fall between 3-4; during nighttime, 
observations are limited and the rate falls sharply to between 0-1 vehicles 
per 1,000 square feet. Corresponding numbers for the 85 percentile are 
between 4.5-6 for the daytime and also between 0-1 during nighttime, as seen 
in Figure 3.4. 

Results from various parking studies show 2.5 (mid-day) at Austin, 1.74 at 
Indianapolis (for all land uses regardless of time), 1.2-3.8 (1.76-6.23 
supplied, with 40%-67% utilization rate) at Puget Sound, 1.7 at Seattle, and 
3 (mid-day, 3.33 in zoning) at Westfield.  
The 1995 Dollars and Cents Guide to Shopping Centers found median amount of 
parking supplied by developers to be 5.1 spaces per 1,000 square feet gross 
land area in American neighborhood and community-sized shopping centers; the 
Urban Land Institute recommends 4, 4.5, and 5 spaces per 1,000 square foot of 
retail for shopping centers under 400, 400-600, and over 600 thousand square 
feet. 
The off-street parking requirements manual from APA also indicates 5 spaces 
per thousand square feet floor area, while the minimum requirements for metro 
areas of Seattle, Portland, Tacoma, and San Francisco are 1-2.86, 2.5, 3, and 
1-2, respectively. 
Considering this information, 2.4 vehicles per 1,000 square feet is selected 
to be used as the mid-day rate, while 0.68 is selected as the night rate.  
Suppose that 4 is the peak parking demand, then parking utilization rates 
would be 60% and 17% of the peak rate respectively.  The expected utilization 
rates corresponding to time of day are developed from the updated parking 
generation informational report and the Westfield comprehensive parking plan.  
The latter is shown in Table 3.33. 
Table 3.33 Expected Daily Utilization Commercial Parking  

Hour of Day  % of Peak  
6:00  0%  
7:00  8%  
8:00  18%  
9:00  42%  
10:00  68%  
11:00  87%  
12:00  97%  
13:00  100%  
14:00  97%  
15:00  95%  

Hour of Day  % of Peak  
16:00  87%  
17:00  79%  
18:00  82%  
19:00  89%  
20:00  87%  
21:00  61%  
22:00  32%  
23:00  13%  
24:00  0%  

The second example deals with the multi-family dwelling, HAZUS category RES3.  
Since parking generation studies generally relate parked vehicles to 
residential units, average square footage of floor area shared by a unit 
needs to be estimated for the conversion.  For this purpose, multifamily 
properties owned by Associated Estate Realty Corporation are referred.  The 
company owns garden, townhome, ranch, mid-rise, and high-rise style 
properties across 12 Midwest states including Indiana, Michigan, and Ohio.  
Average unit size of these properties is slightly over 900 square feet, 
excluding those with government assistance, which is generally smaller.  This 
estimated parking requirement is compared with various residential 
construction projects and zoning requirements.  After taking into account the 
shared public space of multifamily dwellings, 1,000 square feet per unit is 
assumed. 
Peak parking generation per unit shown in ITE s study is around 1, while the 
Westfield study uses 0.88 with mid-day estimate of 0.75.  The range of 
minimum parking requirements for Seattle, Portland, Tacoma, and San Francisco 
is 0.25-2.  If we consider the fact that the number of average vehicles per 
household stabilizes around 1.8, these numbers seem low.  This phenomenon may 
be due to the bias found in urban areas, where crowded lands, convenient 
public transportation, and high-rise structures are prevalent.  For our 
estimation purpose, 1.5 (for 5-49 units) is chosen as this rate is closer to 
what is used in planning parking demand for new development. 
The distribution of vehicle occupancy for residential dwellings with regard 
to the time of day assumed in Westfield study is around 90% during daytime 
and 100% during nighttime, while the same study assumes 50% daytime occupancy 
for hotels.  The seemingly high occupancy during daytime may be due to the 
bias of urban areas, where people utilize other means of travel than 
3-52 
personal vehicles and parking of vehicles attracted by businesses.  Summing 
up the factors for multifamily parking generation, 0.3 is used for the 
daytime, while 1.35 is used for the nighttime. Similar processes were applied 
for each of the remaining occupancy groups in HAZUS.  More information is 
available for some of the groups than for others. Generally, ITE, parking 
studies of metropolitan areas, the National Personal Travel Survey (NPTS), 
and related projects of private organizations, are combined to develop a best 
estimate for this purpose.  The table for parking generation, column by 
column, contains labels and classes of occupancy groups in HAZUS, units other 
than thousand square foot, conversion factors that turn other units into 
square footage, peak parking rate used, and percentage of peak parking rates 
as average daytime/nighttime rate 
3.6.2 Parking Supply and Parking Occupancy 
Once the numbers of vehicles potentially at risk are determined, these 
vehicles are further distributed to various parking facilities, such as on-
street, surface lot, garage, or underground, in order to determine the 
impacts of flood water levels on vehicles.  This distribution is irrelevant 
to non-urban areas, where all vehicles can be assumed to be on the surface.  
In urban areas, population density and land values result in underground and 
multi-story parking facilities.  The elevation of the parked vehicle will 
determine the level of damage, with below ground vehicles having no salvage 
value and above ground vehicles being afforded a level of added protection. 
Parking supplied by each source and its respective occupancy in an area are 
taken together to distribute vehicles among four parking facility types.  
After consulting various parking studies, Table 3.34 shows the estimated 
distribution: 
Table 3.34 Estimated Parking Distribution by Parking Area Type 

Urban  On-Street  Surface Log  Garage  Underground  
Parking Spaces  12.5%  31.5%  33.6%  22.4%  
Occupancy  78%  65%  45%  45%  
Distribution  18%  37%  27%  18%  

While the actual number of levels varies, a parking garage can be represented 
by a five-floor structure, with the roof also available for parking. 
To estimate the impact of flood damage to vehicles in urban areas, it is 
assumed that 18% of vehicles are below ground level and under water during 
all flood events and, therefore, total losses. Another 60% of the vehicles 
(18% (on-street) + 37% (surface lot) + 5% (first floor from garage)) are 
subject to damage based on the appropriate flood damage equation.  The 
remainder is located at least one level above ground and are assumed to 
receive no damage. 
3.6.3 Vehicle Population by Age Group and Type 
To estimate the probability of damage and vehicle value, vehicles are further 
assigned to three vehicle types: automobiles, light trucks and heavy trucks.  
Vehicle class estimates are developed by compiling data from the National 
Automobile Dealers Association (NADA), the US Department of Transportation s 
comprehensive Truck Size and Weight Study (TSWS), and the 1995 National 
Personal Transportation Survey (NPTS).  The distribution of vehicle age and 
percentage of trucks versus cars were taken from NADA, with further 
distribution among trucks by size from TSWS.  The 1995 NPTS data is shown in 
Table 3.35 to fill in the details of vehicle age and type. 
Percentage Distribution 
Table 3.35 Vehicle Age Distribution by Vehicle Classification 

Age  Car  LiteTrk  HvyTrk  Total  
0-2  8.438%  4.631%  0.459%  13.53%  
3-6  17.500%  6.703%  1.969%  26.17%  
7-10  15.625%  5.241%  0.919%  21.78%  
10+  20.938%  7.800%  9.778%  38.52%  
Sum  62.500%  24.375%  13.125%  100%  

3.6.4 Vehicle Value Estimation 
In order to calculate the dollar loss from vehicle damages from flood events, 
research was done to calculate the average price of new and used vehicles 
under the three categories. According to the 2001 NADA data, the average 
selling price of a new light vehicle is $24,923, while that of a used light 
vehicle is $13,648. Thus the value of an average used vehicle is 
approximately 55% of the value of a new vehicle.  Consider the fact that 
vehicles sold at the dealership tend to be younger than the whole vehicle 
population. As such, average used vehicle values are assumed to be 50% of the 
value of average new vehicle. 
The vehicle values given by NADA data do not differentiate between cars and 
trucks.  The NADA estimates are actual dealer selling prices for NADA members 
and include all accessories and options sold with the vehicle. 
New vehicles are estimated to be seven percent for cars and nine percent for 
light- and heavy-duty trucks of all vehicles sold. These estimates are 
obtained by dividing new vehicles by total vehicles in use between 1990-2000 
in Car/Truck Scrappage and Growth in the US table of 2001 Ward s Automotive 
Yearbook.  From the same yearbook, the tables of US Light Vehicle Sales by 
Segment (2000) and Ward s  01 Light Vehicle US Market Segmentation and Prices 
are put together to come up with the average prices of new cars and light 
trucks, which are $22,618.47 and $20,969.21 respectively. These new car 
prices are applied in the vehicle value estimate. 
3-54 
Using the table of US Truck Shipments by GVW by Make from the Ward s 
Automotive Yearbook and researching websites such as Truck Paper, Truck 
Trader Online, Working Wheels, Trucks.com, and numerous individual dealers  
inventory lists, each Make and Class (4-8) category was assigned a value to 
calculate average price of a new heavy duty truck.  The estimate is 
$76,087.67. 
To compute the total value of vehicles in an area, the number of total 
vehicles will be multiplied by the percentage of car/light truck/heavy truck, 
percentage of new/used vehicles, and the average value of vehicles that match 
both categories. 
3.7 Hazardous Materials Facilities 
Hazardous material facilities contain substances that can pose significant 
hazards because of their toxicity, radioactivity, flammability, explosiveness 
or reactivity.  Significant casualties or property damage could occur from a 
small number or even a single hazardous materials release induced by a flood, 
and the consequence of a flood-caused release can vary greatly according to 
the type and quantity of substance released, meteorological conditions and 
timeliness and effectiveness of emergency response.  Similarly to the case of 
critical faculties with a potential for high loss, such as large dams, the 
methodology does not attempt to estimate losses caused by flood, which caused 
hazardous materials releases.  Thus, the hazardous materials module of HAZUS 
is limited to inventory data concerning the location and nature of hazardous 
materials located at various sites. Section 10.1.2 describes the scheme used 
to define the degree of danger of hazardous materials. 
3.8 Direct Economic and Social Loss 
In this section, information related to inventory data required to determine 
direct economic and social loss is presented. The two main databases used to 
determine direct economic and social loss are demographic and building square 
footage databases. 
3.8.1 Demographics Data 
The census data are used to estimate direct social loss due to displaced 
households, casualties due to floods, and, as discussed in previous sections, 
estimation quality of building space (square footage) for certain occupancy 
classes.  The Census Bureau collects and publishes statistics about the 
people of the United States based on the constitutionally required census 
every 10 years, which is taken in the years ending in "0" (e.g., 2000).  The 
Bureau's population census data describes the characteristics of the 
population including age, income, housing and ethnic origin. 
The census data were processed for all of the census blocks in the United 
States, and 37 fields of direct importance to the methodology were extracted 
and stored.  These fields are shown in Table 3.36 and are supplied as default 
information with the methodology.  The population information is aggregated 
to a census block level.  As stated previously, census blocks are divisions 
of land that are based on hard geographic features that allow for the 
designation of territory. Examples of these hard features include roads, 
rivers, and railway tracks.  Census blocks are the smallest unit of 
aggregation for the census data.  This small unit of aggregation was better 
suited for the flood model damage analysis because of its generally, small 
area of coverage (typically one square mile or smaller).  In those cases 
where the census data is aggregated to the census block group (20-40 census 
blocks) rather than the census block, the data is smoothed over every census 
block within the block group.  Generally, it is conceived that census blocks 
contain populations or land uses that have relatively homogeneous population 
characteristics, economic status and living conditions. 
Census block divisions and boundaries change once every ten years.  Census 
block boundaries never cross county boundaries, and all the area within a 
county is contained within one or more census blocks. This characteristic 
allows for a unique division of land from country to state to county to 
census tract to census block group to census block.  Each Census block is 
identified by a unique 15-digit number.  The first two digits represent the 
block s state (called the State FIPS), the next three digits represent the 
block s county (when combined with the State FIPS is called the County FIPS), 
the next 6 digits identify the census tract within the county, another 2 
digits are used to identify the block group and the final two - three digits 
identify the census block.  For example, a census block numbered 
06037575900702 would be located in California (06) in Los Angeles County 
(037), in census tract (575900), in census block group (7), Block (02). 
Table 3.36 Demographics Data and Utilization within HAZUS 

Description of Field  Module Usage  
Shelter  Casualty  Occupancy Class  Lifelines  
Total Population in Census Block   *  *  *  
Total Household in Census Block  *  *  
Total # of People in General Quarter   *  
Total # of People < 16 years old   *  *  
Total # of People 16-65 years old  *  
Total # of People > 65 years old   *  
Total # of People   White  *  
Total # of People   Black  *  
Total # of People - Native American  *  
Total # of People   Asian  *  
Total # of People   Hispanic  *  
Total # of Households with Income < $10,000  *  
Total # of Households with Income $10 - $15K  *  
Total # of Households with Income $15 - $25K  *  
Total # of Households with Income $25 - $35K  *  
Total # of Households with Income > $35,000  *  
Total in Residential Property during Day   *  
Total in Residential Property at Night   *  
Total Working Population in Commercial Industry   *  
Total Working Population in Industrial Industry   *  
Total Commuting at 5 PM   *  
Total Owner Occupied - Single Household Units  *  *  
Total Owner Occupied - Multi-Household Units  *  *  
Total Owner Occupied - Multi-Household Structure  *  *  
Total Owner Occupied - Mobile Homes   *  *  
Total Renter Occupied - Single Household Units  *  *  

Table 3.36 Demographics Data and Utilization within HAZUS (Continued) 

Description of Field  Module Usage  
Shelter  Casualty  Occupancy Class  Lifelines  
Total Renter Occupied - Multi-Household Units  *  *  
Total Renter Occupied - Multi-Household Structure   *  *  
Total Renter Occupied - Mobile Homes   *  *  
Total Vacant - Single Household Units  *  
Total Vacant - Multi-Household Units   *  
Total Vacant - Multi-Household Structure   *  
Total Vacant - Mobile Homes  *  
Structure Age <40 years  *  
Structure Age >40 years  *  
Median Income  *  
Median Age of Housing Units  *  

3.9 Indirect Economic Data 
The indirect economic data refers to the post-flood change in the demand and 
supply of products, change in employment and change in tax revenues.  The 
user can specify the levels of potential increase in imports and exports, 
supply and product inventories and unemployment rates. 
3.10 References 
1.	
Department of Energy, 1995.  A Look At Commercial Buildings in 1995 
DOE/EIA0625(97), Washington, D.C., Energy Information Administration Office 
of Energy Markets and End Use, US Department of Energy, October, 1998. 

2.	
The Heinz Center. 2000.  Evaluation of Erosion Hazards.  Washington, D.C.: 
The H. John Heinz III Center for Science, Economics and the Environment. 

3.	
US Department of Housing and Urban Development and US Census Bureau, American 
Housing Survey for the United States H150/97, Office of Policy Development an 
Research and the US Census Bureau, September 1999. 


Chapter 4.  Potential Earth Science Hazards (PESH) 
4.1 Flood Hazard 
Given the location of a point how does one determine the flood hazard at that 
point?  In different contexts, flood hazard may have different meanings.  
Hazard can mean risk in some contexts and it can mean a source of danger in 
others.  The hazard may be that an area is inundated about once every 10 
years (risk) or it may be that an area is subject to flood depths ranging 
from 5 to 10 feet (source of danger). Flood frequency studies combine those 
ideas and define flood hazard in terms of the chance that a certain magnitude 
of flooding is exceeded in any given year.   
Flood magnitude is usually measured as a discharge value or elevation or 
depth.  For example one may refer to the 100-year flood elevation.  It is the 
elevation, at the point of interest, that has a 1-percent annual chance of 
being exceeded by floodwater.  Using the flood frequency convention, flood 
hazard is defined by a relation between depth of flooding and the annual 
chance of inundation greater than that depth.  The relation is called a 
depth-frequency curve and is the primary output of the HAZUS-MH MR3 flood 
hazard modeling. 
The flood loss estimation methodology consists of two basic analytical 
processes:  flood hazard analysis and flood loss estimation analysis.  Hazard 
characterization in the HAZUS Flood Model produces estimated flood depths for 
riverine and coastal flooding sources.  The Level 1 user, with a minimum of 
input, has the capability of producing flood depth grids along any river 
reach or shoreline. The Level 2 user is required to use the Flood Information 
Tool (FIT) to develop the flood depth grids required by the model. The Flood 
Information Tool (FIT) is an ArcGIS extension designed to process user-
supplied flood hazard data into the format required by the HAZUS Flood Model. 
The FIT, when given user-supplied inputs (e.g., ground elevations, flood 
elevations, and floodplain boundary information), computes the extent, depth 
and elevation of flooding for riverine and coastal hazards. 
While using the FIT, the user is expected to have a greater knowledge of the 
local flood hazard and a working knowledge of Geographic Information Systems, 
specifically ESRI s ArcGIS. 
Fundamentally, the methodology for Level 1 is very similar to the one 
developed for FIT, except for the fact that the only requirement on the user 
will be to provide the Digital Elevation Model (DEM -- typically assumed to 
be the USGS 30-meter National Elevation Dataset or NED).   
4.2 Riverine Flood Hazard 
The flood hazard identification portion of the Preview Model accommodates 
user-supplied data. The goal is to accept user-supplied data, develop flood 
depth and/or flood depth frequency information, and establish the spatial 
distribution of that information.  The result of the analysis is a Geographic 
Information System (GIS) model in a grid format with each cell attributed 
with flood depth information.  That information can be either a given flood 
depth or depth frequency information such as the mean, standard deviation, 
and coefficient of skew of the probability density function that describes 
the depth-frequency relation.  
4-2 
Flood depth is the difference between flood and ground surface elevations at 
each grid cell.  The ground surface model is a grid-cell based Digital 
Elevation Model (DEM).  The flood surface model is also grid-cell based. 
Algorithms have been developed to define the extent of the floodplain and to 
interpolate flood elevations between user-supplied digital cross sections.  
The algorithms were developed to accommodate the most detailed digital 
topographic and flood elevation data available to the user while minimizing 
the user interaction required. 
The approach finds the elevation difference between two surfaces, the flood 
surface and the ground surface, at each cell in the grid.  Cells where the 
flood surface elevation exceeds the ground surface elevation are within the 
floodplain.  The collection of cells where the flood elevation equals the 
ground elevation forms the floodplain boundaries.  
Level 1 methodology discussion describes the methods to identify the stream 
reaches, the hydrologic and hydraulic analysis to be performed, the 
identification of non-conveyance or backwater areas, and exploring what-ifs 
scenarios. 
4.2.1 Identifying Stream Reaches 
For the purposes of this discussion, a DEM is a grid of evenly spaced ground 
elevation data.  The spacing between the elevation data is referred to as the 
posting or cell size.  A DEM with 10-meter posting is a grid containing 10-
by-10-meter cells.  That is, each cell has an area of 100 square meters. 
Except at the DEM boundaries, each cell has eight neighbors: one on each of 
its four sides and one on each of its four corners.  Identifying the neighbor 
where a straight line has the greatest descending slope establishes a flow 
direction for each cell.  Creating a grid with each cell attributed with a 
flow direction allows for the determination of flow paths throughout the 
entire grid and the computation of the number of cells that  flow to  or, the 
accumulation at, a particular cell. An accumulation grid is created by 
attributing each cell with its accumulation, or, the number of cells that 
 flow  through it. Similar to the accumulation grid, a flow distance grid is 
created with each cell attributed with the distance, in terms of flow 
direction, to an outlet (a grid outlet). 
Multiplying the accumulation at a cell by the area of a cell gives the size 
of the drainage area associated with the cell.  The collection of cells that 
exceed a given accumulation value defines a network of streams that drain 
more than a given threshold area.  Each such network is composed of reaches, 
or stream segments, the end points of which are referred to as nodes.  The 
composition of a stream network is shown on Figure 4.1. 


There are three types of nodes: sources, junctions, and outlets.  Junctions 
are points where two reaches join (confluence).  Sources are the  upper-most  
points in a network.  There are no reaches upstream of sources.  An outlet is 
the  lower-most  point in a network.  A network contains one outlet. There 
are no reaches downstream of an outlet.  Note that a network can be a single 
reach (no junctions).  Also note that, if no more than two reaches join to 
form junctions and there are N sources, there are 2N-1 reaches. That is, one-
half of the nodes are sources. Because more than two reaches can share the 
same downstream point forming a junction, 2N-1 is an upper limit on the 
number of reaches. 
The term drainage area is used to denote either the size, in square miles, or 
the spatial extent of the drainage basin above a given point. The drainage 
area at a cell is the collection of cells that  flow  to or through the cell. 
Its size is the accumulation at the cell times the area (size) of one cell. 
For the purposes of this discussion, a watershed is the drainage area at a 
node less the drainage area at the next upstream node.  Thus, watersheds are 
associated with either reaches or source nodes. If there are N sources, there 
are (no more than) 3N-1 watersheds.  Drainage areas are the union of all 
watersheds upstream of the node.  Note that watersheds and drainage areas are 
the same at sources.  Watershed grids are created by attributing each cell in 
the grid with the nearest, in the sense of flow direction, feature. 
Using a DEM with approximately 400-meter posting, the continental United 
States was subdivided into watersheds by creating accumulation grids and 
using a 100-square-mile threshold for defining stream reaches.  Those 
watersheds and the nodes and reaches associated with them are contained in 
databases within HAZUS.  The watersheds and reaches are stored in shape 
files; the drainage areas and default discharge values corresponding to the 
nodes are stored in a table. 
For example, the stream networks draining 100 or more square miles in the 
mid-Atlantic region of the eastern United States is shown in Figure 4.2.  The 
major systems are (from north to south):  the Delaware, Susquehanna, Potomac, 
and James.  The Potomac River basin is shown in red. There are 421 reaches 
shown in Figure 4.2.  Of those, 65 are within the Potomac River basin 
upstream of Washington, D.C.  The drainage area of the Potomac River at 
Washington D.C. is 11,560 square miles. 
The watersheds associated with the Potomac River network are shown in Figure 
4.3.  There are 98 watersheds associated with that network, 33 of which are 
source watersheds. 
Except for the stream reach associated with each (non-source) watershed, the 
drainage areas of all streams within a watershed are contained in the 
watershed.  Therefore, all information necessary to determine the flood 
hazards affecting a study region are geographically contained in the set of 
watersheds that cover the study region and the network reaches associated 
with those watersheds. 
After a study region has been chosen, those watersheds that cover the study 
region are identified and saved in a feature table. HAZUS looks for a DEM 
that contains those watersheds.  HAZUS will calculate the coordinates of the 
smallest rectangle containing the watersheds and advise the user of, in the 
absence of another DEM, the portion of the National Elevation Dataset (NED) 
prepared by and obtained from the U.S. Geological Survey (USGS) needed to 
cover the study region. NED has a posting of approximately 30 meters.  Once 
the DEM is loaded into HAZUS, the user may set the threshold drainage area 
for the network of possible study reaches. 


Figure 4.4 shows the 10 watersheds in Figure 4.3 that cover Shenandoah 
County, Virginia.  The major drainage feature in the county is the North Fork 
Shenandoah River.  The North Fork joins the South Fork Shenandoah River to 
form the Shenandoah River just downstream of the county line. The Shenandoah 
River continues northeast and flows into the Potomac River at Harpers Ferry, 
West Virginia. 

The portion of the NED that covers the watersheds is shown on Figure 4.5.  
The stream network shown within the watersheds was determined using the DEM 
information within the watersheds to create flow direction and accumulation 
grids. The network is based on a threshold drainage area of 10 square miles. 
Because of the decrease in the threshold drainage area, the drainage density 
has increased. Differences in detail of the stream alignments are a 
consequence of the increased resolution (posting and elevation) of the NED 
over that of the 400-meter posting DEM. 
Using the more detailed network and associated watersheds, the reaches 
affecting the study area and their drainage areas can be identified for 
hydrologic and hydraulic analyses.  The watersheds associated with the 
reaches shown in Figure 4.5 are shown on Figure 4.6.  Those reaches affecting 
Shenandoah County are depicted in red. 

There are 41 stream reaches shown on Figure 4.5.  The total stream length is 
approximately 310 miles.  Figure 4.6 indicates that 24 of those reaches have 
floodplains that affect Shenandoah County. The total length of those reaches 
is approximately 225 miles. 

The threshold drainage area for identifying stream reaches is limited only by 
the resolution of DEM covering the study area. For example, if the user in 
our example is interested in streams draining a smaller area, the pattern in 
Figure 4.5 would be preserved with many more reaches included as tributaries 
to that pattern.  
After choosing the threshold drainage area, the stream reaches are shown, as 
in Figure 4.5, and the user is prompted to select the reaches for hydrologic 
analyses.  Figure 4.6 indicates that the user chose all streams affecting the 
county.  The hydrologic analyses begin after the reaches are selected. 
The first step is to identify reaches and associated watersheds that flow 
into the chosen reaches; the reaches into which the chosen reaches flow; and 
the watersheds associated with those reaches. When created, each reach is 
attributed with an upstream node identifier and a downstream node identifier. 
Figure 4.7, shows the upper portion of the North Fork Shenandoah River. The 
numbers denote nodes. 

There is a reach that  flows  from node 1 to node 5; a reach that flows from 
node 5 to node 6; and so on. Using the  from node, to node  data we can 
construct a one-step transition matrix, T1, for navigating up- and downstream 
in the network.  If we identify each reach by the number of its upstream node 
then each entry, (r,c), for row r and column c in T1 is 1 if the downstream 
node of stream r is the upstream node of stream c and 0 otherwise. 
The entry (r,c) in T1 can be thought of as the probability that reach r flows 
into reach c.  The probability that reach r flows through reach k into reach 
c is the value at (r,k) times the value at (k,c), either 1 or 0. If trc is 
the value of T1 at (r,c), the probability that reach r flows through 
N 
another reach into reach c is the sum P =.tr,ktk ,c , where N is the number 
of reaches. P is 
k =1 either 1 or 0.  It is the value at (r,c) of the matrix T1 multiplied by 
itself.  The matrix T2=2T12 , where the superscript 2 denotes matrix 
multiplication with itself, is a two-step matrix with (r,c)=2 if the 
downstream node of reach r is the upstream node of any reach having a 
downstream node that is the upstream node of reach c.  Otherwise (r,c)=0.  
For the network shown in Figure 4.7, rows 1 and 2 of T2 will have 2 in column 
6 and 0 in all other columns.  Likewise, the n-step, Tn =nT 1n . Summing the 
n matrices, when n is large enough, yields a transition matrix that contains 
all information needed to readily navigate through the network. 
Note that, if there are 2N+1 reaches, no reach is more than N steps from an 
outlet.  The transition matrix, T, for the network shown in Figure 4.7 is: 
0 0 0 0 1 2 3 
0 0 0 0 1 2 3 
0 0 0 0 0 1 2 
0 0 0 0 0 0 1 
0 0 0 0 0 1 2 
0 0 0 0 0 0 1 
0 0 0 0 0 0 0 
Note that columns 1 through 4 contain all zeros.  They are source reaches.  
No reaches (rows) flow into them.  Also note that row 7 contains all zeros.  
It is the outlet.  There are no reaches downstream of reach 7.  In general, 
reach k flows through all reaches, c, for which the value in row k, column c, 
(k,c) is non-zero; and drains all reaches, r, for which (r,k) is non-zero. 
Rather than multiplying T1 by itself n times, T is constructed by initially 
setting all values to  0  and making lists from the feature table of reaches.  
Entries in the first list are the records of the downstream reach. If there 
is no downstream reach, the entry is the location in the list.  In our 
example, given the list starts at record 1, the first list would be 
{5,5,6,7,6,7,7}.  The  last  list is a copy of the first list. 
Each record in the  next  list gets the value found in the record of the 
first list corresponding to the value in the same record (as we started with 
in the  next  list) in the  last  list.  Thus, the first entry in the  next  
list in our example is the fifth entry in the first list. The third entry in 
the  next  list is the sixth entry in the first list.  The  last  list is 
saved in a list of lists and, then, the  next  list becomes the  last  list.  
The process continues until the  next  list is identical to the  last  list 
(i.e., n is large enough). In our example, that happens when the last and 
next lists are both {7,7,7,7,7,7,7}. 
Each list in the list of lists is retrieved and used to set values in T.  
Those values are equal to position of each list in the list of lists.  The 
values associated with the first list are all 1; those associated with the 
second list are all 2.  If the value for a record in a list (i.e., flows-to-
in-n-steps record), does not equal the corresponding value in the previous 
list, then the number of the position of the list is put in the row 
corresponding to the record in the column corresponding to the value.  
A transition matrix is created for all reaches meeting the threshold drainage 
area requirement. The rows and columns of the matrix associated with the 
selected reaches are searched to identify up- and downstream reaches.  
Corresponding lists are made.  
Watersheds in the reduced default watershed feature table that do not 
intersect any of the selected reaches or the reaches draining to selected 
reaches are deleted from the feature table, leaving only watersheds that 
drain to the selected reaches.  The union of those watersheds defines a 
polygon that is used to cull the default reaches.  If the midpoint of a 
default reach is not contained in the polygon, that reach is deleted from the 
reduced default reach feature table. 
The default reaches that fall within the watersheds of the selected reaches 
and those draining to the selected reaches are shown in Figure 4.8. The 
reaches are the four on the North Fork Shenandoah River and three 
tributaries, shown in red. 

The remaining default reaches that have no remaining reaches upstream are 
identified as potential source reaches.  The upstream node of those reaches 
is found in the default node table and those that are source reaches are 
deleted from the feature table.  A list is made of the potential source 
reaches that are not identified as source reaches.  They are the default 
reaches that drain areas beyond the polygon.  That is, they are parts of 
streams that originate outside of the study area. 
The upstream nodes of all three tributaries shown in Figure 4.8 are contained 
within the polygon defined by the union of the watersheds covering the 
selected reaches.  That is, those tributaries are source reaches and, 
therefore, deleted from the feature table. 
Deleting the source reaches creates another network that may, unlike the 
situation depicted in Figure 4.8, contain new  sources.   Those new sources 
are deleted. The potential sources saved in the list of reaches on streams 
that originate outside of the polygon are not deleted.  The process continues 
until no new sources are created at which point, all default reaches are 
parts of streams that originate outside of the study area.  Those streams 
will be referred to as main streams.  All other streams, including the 
default reaches deleted in the culling process, drain areas contained within 
the polygon. 
Figure 4.9 shows the various default reaches involved.  The area depicted is 
in the vicinity of Clackamas County, Oregon.  The major stream in Clackamas 
County is the Willamette River, which flows into the Columbia River at 
Portland.  The Clackmas River flows from the southeast corner of the county 
to the Willamette River in the northwest corner.  It is the shown in red on 
Figure 4.9.  The reach on the Tualatin River and the upstream reach of the 
Willamette River shown in dark blue and the reaches shown in yellow are 
potential default source reaches that affect Clackamas County.  Only the 
yellow reaches are  true  source reaches. 

After the yellow reaches are deleted, the reach on the Tualatin River and the 
upstream reach of the Willamette River shown in dark blue and the upstream 
reaches shown in red are the potential default sources among the remaining 
reaches that affect Clackamas County.  Those shown in dark blue were flagged 
and, so, only the potential sources shown in red are deleted.  After the 
culling process is completed, only reaches shown in dark blue (Tualatin and 
Willamette Rivers) remain.  
In addition to the reaches meeting the threshold drainage area that have been 
selected, there are, at this stage, three other categories of reaches: 
Reaches meeting the threshold drainage area that flow to the selected reaches 
Reaches meeting the threshold drainage area to which the selected reaches 
flow  
Default reaches that are on main streams 
Each category is used differently with the selected reaches to compute 
discharge values for the selected reaches. Upstream reaches are used to 
determine drainage areas and identify upstream gages. Downstream reaches are 
used to identify downstream gages.  The default flood frequency information 
must be used for reaches on the main streams. 
Cell values in the watershed grid created with the threshold reaches that are 
not associated with the selected or upstream reaches are set to null.  Thus, 
the watershed grid only contains watersheds that are within the drainage 
areas associated with the selected reaches. 
The selected reaches that are on main streams must be identified.  As 
described earlier, collection of default reaches comprising the main stream 
differs from the collection of selected reaches on the main stream both in 
number of reaches and configuration or detail.  The difference is evident for 
the North Fork Shenandoah River, the main stream shown in Figure 4.10.  The 
red stream in Figure 4.10 is comprises four default reaches; the blue 
meandering main stream comprises 15 of the selected reaches. 

A transition matrix is created for the remaining default reaches.  The 
 sources  of the networks defined by the remaining default reaches are 
identified using the transition matrix.  The watersheds remaining in the 
watershed grid are converted to polygons. The union of those polygons is a 
polygon covering the area draining to the selected reaches.  Note that 
because the DEM is not the same DEM as used to create the default reaches, 
the watershed boundaries may not coincide where expected. The covering 
polygon is buffered inward one cell size.  The boundary of the resulting 
polygon intersects the source reaches in the remaining default reaches. If 
the  source  reach and boundary intersect at more than one point, the most 
downstream of the intersections is identified and stored in a list. 
Using the flow direction grid, the flow path is determined from each of the 
points in that  source  points list. That path will intersect the selected 
reaches and coincide with selected reaches from that intersection downstream 
(to the outlet of the selected reaches).  Each path is buffered one-half a 
cell size.  Lists are made of the selected reaches contained in each of the 
buffered paths. Thus, any selected reach that is on a main stream is 
contained in one or more of those main stream lists.  The default flood 
frequency information is used on the reaches in the main stream lists. 
The next step is to define the drainage area associated with each node in the 
networks of selected reaches and the reaches up- and downstream of the 
selected reaches.  The step begins with defining the transition matrix for 
the selected reaches.  Recall that a watershed grid is created when the 
threshold is chosen and the threshold reaches created.  The cells in that 
grid are attributed with the nearest (in flow direction) reach.  The groups 
of cells having (associated with) the same attributes (reaches) are converted 
to polygons.  The conversion process may create more than one polygon for a 
given watershed. 
Each polygon in the resulting collection of polygons is compared to every 
other polygon in the collection. Whenever the two polygons have the same 
attribute (are associated with the same reach), one is replaced with their 
union the other is placed in a list for removal.  After each polygon has 
passed through the process, the polygons in the removal list are removed from 
the polygon feature table.  The resulting feature table contains one polygon 
for each reach.  Those polygons are the drainage areas associated with the 
corresponding reaches. 
Note that the polygons associated with the source nodes have not been found 
at this point in the algorithm.  That is, the watersheds associated with 
source reaches include the watersheds draining to the upstream points of the 
source reaches. They are not watersheds but are, instead, the drainage areas 
of the downstream nodes of the source reaches. 
The source reaches are identified using the transition matrix.  For each 
source reach, the upstream point (actually a point 0.1 percent of the reach 
length along the reach) is found.  A watershed grid is created by identifying 
the groups of cells that flow to each of the source points and associating 
each cell with the corresponding point.  Those groups are converted to 
polygons the same way as were the watersheds associated with the reaches.  
The number of cells in each group is divided by the number of cells in one 
square mile, yielding the watershed area.  The polygon and area for each 
source point is stored as the upstream watershed and drainage area for the 
corresponding reach. The same process is applied to the watersheds associated 
with the reaches. The resulting polygons and areas are the watersheds and 
watershed areas associated with each of the reaches. 
The drainage area of the downstream node of each reach is the sum of the 
watersheds upstream of the node. The reaches that flow through each 
downstream node are identified using the transition matrix.  The sum of the 
watershed areas associated with those reaches plus the watershed area 
associated with the reach being analyzed is the drainage area of the 
downstream node. The union of the polygons associated with those watersheds 
is the polygon associated with the drainage area of the node. The resulting 
polygons and areas are stored as the downstream drainage area polygon and 
value for the reach. 
Samples of the polygons described in the process are shown on Figure 4.11.  
The drainage areas associated with the upstream nodes of a source reach and 
an interior reach are shown in yellow. The watershed areas are shown in light 
blue. 

At this point in the algorithm, the drainage areas associated with the 
upstream nodes of the source reaches and those associated with the downstream 
nodes of all of the reaches have been determined.  Note that the drainage 
areas associated with the downstream nodes are the union of the blue and 
yellow polygons shown in Figure 4.11. 
The drainage areas of the upstream nodes of the interior (non-source) reaches 
are determined by subtracting the area associated with the watershed of 
corresponding reach from the drainage area of the corresponding downstream 
node.  Similarly, if a reach is an interior reach, the polygon associated 
with the drainage area of its upstream node is the difference between the 
polygons associated with drainage area and watershed at the downstream node. 
Before moving to the algorithms that perform the hydrologic analyses, the 
default gage dataset is reduced to the gages that may be used those analyses.  
Using the transition matrix of the reaches meeting the threshold requirement 
(i.e., selected reaches and those up- and downstream), the outlet reaches are 
identified. The union of the drainage area polygons associated with the 
downstream nodes of the outlet reaches contains all gages that will be used 
in the hydrologic analyses.  Note that gages affecting the analyses of the 
main streams were accounted for in developing the default reaches and 
watersheds files. 
The union of drainage areas covering the study reaches and the gages within 
and near that area are shown in Figure 4.12. Each gage within the identified 
area is associated with the nearest reach (the reach feature table is 
spatially joined to the gage feature table).  That association is checked 
using drainage areas. 

The points representing the gage locations are attributed with total and 
contributing drainage areas and the mean, standard deviation, and coefficient 
of skew of the flood frequency curve at the gage. If the total drainage area 
of the gage is less than the drainage area of the upstream node and greater 
than the drainage area of the downstream node of the reach associated with 
the gage, the gage is assumed to be on that reach.  If not, the gage is 
removed from the gage feature table. That is, the gage is not used in 
subsequent analyses.  For example, no gages with less than the threshold 
drainage are used in subsequent analyses. 
Note there are two categories of reaches for which the algorithms have or may 
have underestimated the drainage areas.  Drainage areas for reaches on the 
main streams do not include the area beyond the study region that drain to 
the reaches.  Drainage areas associated with those reaches are underestimated 
by at least 100 square miles (the default drainage area threshold).  
Therefore, gages located on main streams will not be used in subsequent 
hydrologic analyses.  Recall that the default flood frequency data 
incorporates the gage information and will be used for reaches on the main 
streams. 
Drainage areas associated with reaches downstream of the selected reaches may 
be underestimated.  Tributaries that are not selected but flow into reaches 
that are downstream of the selected reaches do not survive the culling 
algorithm.  Drainage area calculations on down stream reaches do not include 
areas draining to those tributaries.  Before comparing the gage drainage 
areas with the reach drainage areas, the drainage areas associated with 
downstream reaches identified as containing a gage are adjusted. 
The adjustment uses the watershed grid and the accumulation grid.  A new grid 
is created. Cells in the new grid corresponding to cells contained in the 
group in the watershed grid that are associated with the reach are given the 
value of the corresponding cell in the accumulation grid. All other cells in 
the new grid are given a value of zero.  The maximum cell value of the new 
4-16 
grid is the number of cells draining to the downstream node of the reach.  
That number minus the number of cells in the group identified in the 
watershed grid is the number of cells draining to the upstream node.  
Dividing each of those numbers by the number of cells in one square mile 
yields the respective drainage areas. 
Those drainage areas are compared to the drainage areas at the gages 
associated with the reach. Gages not satisfying the drainage area test are 
deleted from the feature table, leaving only those gages that will be used in 
the hydrologic analyses. 
4.2.2 Hydrologic Analysis 
Hydrologic analyses are performed for each node using the regional regression 
equations developed by the USGS. The results of applying the equations are 
adjusted using stream gage data where the drainage area at the gage is 
between 50- and 150-percent of the drainage area of the node. Discharge 
values for reaches on main streams are interpolated from the corresponding 
values in the default flood frequency database. 
The USGS has divided each state into hydrologic regions and developed a set 
of regional regression equations for each region.  The equations are 
generally of the form 
QT = Cfi (P1) f2(P2 )... fn (Pn ) 
where QT is the discharge value with a return period of T; C is a constant; 
and fi(Pi) denotes a function of the ith parameter of the equation.  The 
number and types of parameters vary from one equation to another. With few 
exceptions, the fs are power functions, such as the drainage area raised some 
exponent. 
A shape file of polygons representing the hydrologic regions is included in 
HAZUS.  Tables included with HAZUS contain the information necessary to apply 
the equations.  There is a table for each return period computed.  Each 
record in a table is associated with a region and each field is associated 
with a function.  For example, the first field in every record is the 
constant, C, the second field is the exponent of the drainage area.  If a 
region does not use a particular function, the corresponding field contains a 
zero. The hydrologic regions in the vicinity of Shenandoah County are shown 
in Figure 4.13. 

A list is made consisting of the reaches to be analyzed.  Because the 
algorithm for adjusting discharge value estimates with information from 
stream gages involves applying the regional regression equations for the 
drainage area at the gage, reaches containing gages are included in the list. 
Reaches that are on main streams are not included in the list.  Thus, the 
list consists of reaches not on a main stream that were either selected for 
study or are located up- or downstream of a selected reach and contain a 
gage. 
Topographic Parameters 
At each node of each reach in the list, topographic parameters required for 
the regression equations are derived and stored in a temporary table.  This 
table is deleted once the hydrologic analysis is complete.  Each record in 
the table corresponds to a node.  Fields in the table correspond to: 
 	
The record number of the reach in the reach feature table. 

 	
A value denoting whether the node is at the upstream or downstream end of the 
reach (0 or 99, respectively). 

 	
The drainage area at the node. 

 	
The average elevation of the drainage area (mean basin elevation). 

 	
The average slope in the drainage area (mean basin slope). 

 	
The straight line distance between the outlet of the basin and the point 
farthest away as measured along the drainage network (basin length). 

 	
The length (channel length) of the longest drainage path, i.e., between the 
two points used to define basin length. 

 	
The elevation at a point located at a distance along the longest drainage 
path 10 percent of its length from the outlet. 

 	
The elevation at a point located at a distance along the longest drainage 
path 85 percent of its length from the outlet. 


After the table is created but before data is added, the size of the to-be-
analyzed list is investigated.  If the list is empty, all of the selected 
reaches are on main streams and flood frequency data comes from the default 
databases.  In that situation processing skips to the last algorithm, adding 
data using the default reaches.  If the list is not empty, topographic 
parameters are derived from the DEM and associated grids. 
A grid can be thought of as a sequence of numbers.  The position within the 
sequence defines the coordinate of a cell; the number at that position is the 
value of the cell.  Finding statistics of a grid is, essentially, finding the 
statistics of a large set of numbers.  Thus, statistics such as the maximum, 
minimum, or mean value of the cells in a grid are readily available. 
Recall that the direction grid was created by finding, for each cell, the 
neighboring cell to which a straight line has the greatest descending slope.  
A slope grid is defined by attributing each cell with that slope. 
The point farthest away, in terms of flow direction, from the outlet is 
determined for each drainage area associated with a source node.  Recall that 
a length grid, containing flow distances to outlets, was created when the 
accumulation grid was created.  The polygon representing the drainage area 
associated with a source node is used to extract cells from the length grid. 
The extracted cells form a grid.  The maximum (cell) value of that grid is 
identified.  All cells in the grid that have values less than 0.999999 times 
the maximum value are set to nil.  The resulting grid is converted to a 
feature table of points.  All points in the feature table are the maximum 
flow distance from the node drainage area to the outlet of the network (that 
the node belongs to) of reaches meeting the threshold drainage area 
requirement.  Because all flow paths within that drainage area pass through 
the node, the points are also the maximum flow distance from the node. 
The first (usually the only) point in the feature table is saved in a list.  
That list contains the upstream point of the longest flow path to every 
source of interest.  It is assumed in the algorithm for finding the longest 
flow path that, within any drainage area, the longest flow path originates at 
one of the points in the list. 
The upstream points of the longest flow paths in the Shenandoah County 
example are shown in Figure 4.14. 

After the list of potential farthest upstream points is complete, the 
topographic parameters are assigned. For each reach in the to-be-analyzed 
list, a record is added to the topographic parameter table and the record 
number corresponding to the record of the reach in the reach feature table is 
put in the reach record number field.  The up-or-down field is set to zero, 
denoting that the node is the upstream end of the reach.  The drainage area 
computed after the study reaches were selected is put in the drainage area 
field. 
The polygon representing the drainage area is used to form grids by 
extracting cells from the DEM and slope grids. The average values of those 
grids are determined and put in the fields corresponding to the mean basin 
elevation and mean basin slope. 
The length parameters are found by sampling the paths from those points in 
the points-farthest-from-the-source-nodes list that fall within the drainage 
area of the reach.  The path from each such point is found and its length is 
determined.  The path with the greatest length is identified as the longest 
stream.  The straight line between the end points of the longest path is 
created and identified as the basin length line.  The lengths of those two 
lines are put into the channel and basin length fields, respectively. 
Points are identified at distances of 15 and 90 percent of the total length 
as measured from the upstream end and along the line longest stream.  Note 
that, following convention, the parameter name indicates length from outlet 
whereas measurements in the GIS are in percentage from the uppermost point on 
the stream.  The elevations at those points are found from the DEM and the 
difference between the up- and downstream elevations is computed.  If that 
difference is positive, the elevation values are put into the elevation 85 
and elevation 10 fields. 
If the difference is not positive, the elevation of the point at 89 percent 
of the total length is determined and used instead of the elevation of the 
point at 90 percent.  If the difference is still not positive, the downstream 
sample point is moved upstream another percentage.  The process continues 
until either the difference between the elevation of the upstream (15 
percent) point and that of the point being moved is positive or the sample 
point has moved to within 85 percent of the length from the upstream end.  If 
the difference is not positive after those iterations, the elevation fields 
are set at 1.0 foot above and below the average of the last two elevations 
tested (i.e., at 15 and 85 percent of the total length from uppermost point). 
Setting the elevation 10 and elevation 85 fields completes the first record 
for the reach.  A new record is formed.  The reach record number field is 
again set to the record of the reach in the reach feature table. The up-or-
down field is set to 99, denoting a downstream node.  The process just 
described is repeated to set the values for the remainder of the parameters. 
After records have been added for all of the reaches (two each), the 
transition matrix is used to find reaches that are not source reaches.  The 
values in up-or-down field in the topographic parameter table for records 
corresponding to the upstream nodes of those reaches are changed from 0 to 1, 
thereby distinguishing between interior and source upstream nodes. 
Flood Frequency Table 
A table containing flood frequency information associated with the default 
reaches is included with HAZUS. The table contains a record for each node in 
the default (drainage area greater than 100 square miles) reach shape file.  
Each record has fields denoting a node identification number, the drainage 
area at the node, and the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year 
flood the discharge values. If a particular frequency flood was not computed, 
the field associated with that frequency contains a zero. 
A similar table is created for the nodes contained in the topographic 
parameter table.  That is, the nodes at the ends of reaches from the to-be-
analyzed list.  The node identification number is formed by adding the up-or-
down value divided by 100 to the reach record number.  For example, 44.99 
would be the downstream node of the reach in the 44th record of the (culled 
threshold) reach feature table. If a source, the upstream node of that reach 
would be identified as 44.00; if not a source it would be identified as 
44.01.  The drainage area is copied from the topographic parameter table. 
Discharge values are calculated for each return period.  The calculation 
begins by identifying the hydrologic regions that cover the drainage area at 
the node being analyzed.  The intersection of the drainage and each region is 
found.  The result of applying each regional regression equation is weighted 
by the portion of the drainage area within the region.  The algorithm 
calculates the ratios of the area of the intersection divided by the drainage 
area and the area of the intersection divided by the area of the hydrologic 
region. If both ratios are less than one percent, the drainage area is 
assumed to be outside of the hydrologic region. 
If either ratio is greater than one percent, the drainage area is partially 
within the hydrologic region and regression equation from that region will be 
used.  The region identification is added to a list of regions covering the 
drainage area.  Additionally, the polygon defined by the intersection of the 
hydrologic region and the drainage area is added to a list and the ratio of 
the areas of the intersection and the drainage area is added to a weighting 
list.  A sum of the ratios in the weighting list is kept to calculate the 
final weights given to the results of each equation applied. 
Discharge values are computed for each region in the region list.  As 
mentioned earlier, a table is included with HAZUS for each return period.  
Each table contains the hydrologic region identifiers and information 
regarding the functions in the regression equation.  The tables are labeled 
with  Q  plus a three-digit return period identifier.  Q025 is the table 
associated with a 25-year flood. The record corresponding to the first region 
in the region list is found in the appropriate return period (Q) table. 
Some hydrologic regions have more than one set of equations.  The different 
sets pertain to different ranges of drainage area.  For example, the regions 
in south-central and southeast Pennsylvania each have one set of equations 
for drainage areas less than 15 square miles and one set for drainage areas 
greater than 15 square miles.  Connecticut has a set of equations for 
drainage areas less than 10 square miles; a set for drainage areas between 10 
and 100 square miles; and a set for drainage areas greater than 100 square 
miles. 
Two fields in the Q tables contain threshold drainage areas, allowing for 
three sets of equations for each region (record).  Depending on where the 
drainage area falls within those thresholds, the algorithm sets a range of 
fields that contain the correct function data.  If the drainage area is less 
than 15 square miles and a portion of it is in region PA06 (south-central 
Pennsylvania), the algorithm locates the threshold field and, finding the 
value 15, locates the next threshold field. Finding zero in that field (there 
are only two ranges, greater or less than 15) the algorithm sets the 
appropriate range of fields to be sampled.  If the drainage area is greater 
than 15 square miles, a different range is set. 
The first field in the range contains the constant, C. The discharge value is 
set equal to the constant. If the discharge value is zero, the region does 
not have a regression equation for the return period being investigated.  In 
that situation, the remaining fields are not read and the discharge value for 
the portion of the drainage area contained in the region is zero. 
If the discharge value is greater than zero, the next field in the range is 
read.  That field contains the exponent used in the power function of 
drainage area.  The drainage area value is raised to that power and the 
result is multiplied by the discharge value (the constant at this point in 
the process). The discharge value is set equal to the resulting product and 
the next field in the range is read. 
Subsequent fields correspond to different functions in the regression 
equation.  The position of the field in the table (first field, second field, 
third field, ...) identifies a subroutine that solves the function. If the 
18th field is read and contains a nonzero value, the subroutine BsnChr18 is 
called. Each subroutine corresponds to a basin or climatic characteristic. 
If the characteristic is topographic, the parameters needed to solve the 
function are contained in the topographic parameters table. For example, the 
100-year flood discharge value in Shenandoah County, Virginia is proportional 
to the slope of the longest stream raised to the 0.21 power. The slope is in 
feet per mile as measured at points 10 and 85 percent of the total stream 
length from the outlet of the drainage area being analyzed.  There is a field 
in the Q100 table with a value of 0.21 in the record corresponding to region 
VA03. Reading that value, the algorithm calls the subroutine associated with 
the field.  The routine finds the values in the elevation 10, elevation 85, 
and longest stream length fields in the topographic parameters table, and 
calculates the slope (the difference in the elevations divide by 85 percent 
of the length).  The routine returns the slope.  The result of the 
subroutine, slope in this example, is raised to the exponent found in the 
Q100 table. 
If the parameter is not topographic, the routine obtains a measure of the 
parameter within the intersection (of the drainage area and hydrologic 
region) polygon.  The parameters are measured from grids or shape files 
similar to the way the topographic parameters were derived.  If a parameter 
is represented as a grid, the intersection polygon is used to extract cells 
from the grid. The parameter, such as the mean annual precipitation or the 
percent of area of a particular soil type, is measured from the extracted 
cells.  The value of the parameter is returned and raised to the exponent 
found in the Q table in the corresponding field. 
If the parameter is represented by a shape file, the intersections of the 
intersection polygon and the features in the shape file are used to measure 
the parameter.  For example, HAZUS includes a shape file of polygons 
representing lakes, ponds, and other storage areas used in the regression 
analyses. The percentage of storage in an area is computed as the 100 times 
sum of the areas of the intersections of those polygons and the intersection 
polygon, divided by the area of the intersection polygon. The value of the 
parameter is returned and raised to the exponent found in the Q table in the 
corresponding field. 
A value of 1.0 is used in the Q tables for functions that cannot be expressed 
as a power function. The subroutine corresponding to those functions measures 
the parameter, applies the function and returns the result. The result is 
raised to the power 1.0 and, therefore, is unchanged. 
Some functions of parameters are treated as parameters themselves being used 
in power functions. Constants are added to parameters measured as 
percentages, such as percentage of area of a particular soil type or 
percentage of area covered in forest.  Those functions (parameters plus 
constants) are treated as different parameters in the program.  For example, 
the value1.0 is added to the percentage of drainage area in lakes and ponds 
(storage percentage) in regions in western Oregon; the value 0.5 is added to 
the storage percentage in regions in Vermont.  There are two fields in the Q 
tables and, therefore, two corresponding subroutines, representing those 
values: one for storage plus 0.5; another for storage plus 1.0. 
As each subroutine is called and completed, the returned value is raised to 
exponent found in the Q table and that result is multiplied by the 
(continuously increasing) discharge value. After all of the fields in the 
range have been visited, the discharge value represents the contribution from 
the hydrologic region. If that value is zero (e.g., no regression equation in 
that region for that return period), the sum of the region weights is reduced 
by the weight given to that particular region.  If the discharge value is 
greater than zero, the value is multiplied by the region weight.  The 
weighted discharge value is added to a sum being kept until values from all 
regions in the region list have been added. 
If the resulting sum of the region weights, the net sum, is less than 60 
percent of the original sum of region weights, the discharge value is set to 
zero indicating that there is not a regression equation for the return period 
under investigation.  If the net sum is equal to or greater than 60 percent 
of the original sum, the discharge value is divided by the net sum, 
completing the weighting process. That quotient, the discharge value, is put 
in the flood frequency table in the record corresponding to the node, and the 
field corresponding to the return interval. 
Stream Gage Adjustment 
Reaches up- and down stream of each node are searched for gage information.  
The maximum upstream gage area is initially set to zero as each new node is 
investigated.  Upstream reaches are identified using the transition matrix.  
Note that if the node identification ends in 0.99, the node is a downstream 
node and, therefore, the reach associated with the node is also identified as 
upstream of the node.  If a reach is upstream of the node, it is checked 
against the list of reaches containing gages.  If the reach is in the gage 
list and the drainage area of the gage is greater than the maximum upstream 
gage area, the reach is identified as the upstream gage reach and the 
drainage area at the gage becomes the maximum. After the last upstream reach 
is checked, the upstream reach with gage having the greatest drainage area 
has been identified.  Of course it may not exist, in which case the 
corresponding drainage is, as initially set, zero. 
The reach downstream of the node containing the gage with smallest drainage 
area is found similarly.  The minimum area is initially set to 1,000,000 
square miles and the downstream reaches are identified using the transition 
matrix.  The reach associated with an upstream node is also identified as 
downstream of that node.  As the reaches are checked against the list of 
reaches with gages, the reach containing the gage with the smallest drainage 
area is identified. 
If there is an upstream reach with a gage that drains more than 50 percent of 
the drainage area of the node and there is a downstream reach with a gage 
that drains less than 150 percent of the drainage area of the node, the 
discharge values at the node are interpolated. 
The discharge value corresponding to each return period is determined at both 
gages. 
HAZUS contains a table of normalized random variables corresponding to 
various probabilities of being exceeded. The variables are Pearson Type III 
distributed, covering a range of skew coefficients differing by 0.1. The 
normalized random variable corresponding to a return interval T is the number 
of standard deviations between the mean and the value of the random variable 
that has, in the context of flood frequency, a probability 1/T of being 
exceed in any given year. 
Recall that the feature table of gage locations contains the mean, standard 
deviation, and coefficient of skew of the flood frequency curve at the gage.  
For each return interval, the algorithm searches a list corresponding to the 
probability values of the normalized random variable table.  The list is 
arranged in descending order.  The search ends when a probability is 
encountered that is less than the reciprocal of the return period.  A 
normalized random variable corresponding to the reciprocal of the return 
period is linearly interpolated using that probability and the probability 
located just before that probability in the list, and the normalized random 
variables corresponding to those probabilities. 
Multiplying the normalized random variable by the standard deviation and, 
then, adding the mean, yields the value of the random variable.  It is the 
base 10 logarithm of the discharge value, corresponding to the return period, 
T.  The T-year discharge value is equal to10 raised to that value. Having 
calculated the T-year discharge at the up- and downstream gage locations, the 
value at the node is interpolated as a power function of the drainage area.  
That is, 
Q =Aa . (4-1)
node node 
where Qnode is the discharge value at the node; Anode is the drainage area at 
the node; and the exponent, a, equals the base 10 logarithm of the ratio of 
the discharge values at the gages divided by the base 10 logarithm of the 
ratio of the drainage areas at the gages: 
log10 (Qup ) 
log10 (Qdown ) . (4-2)
a= 

log10 (Aup )
log10 (Adown )

The value, Qnode, is put in the field associated with the return period of 
the record associated with the node. 
If there is either an upstream reach with a gage that drains more than 50 
percent of the drainage area of the node or there is a downstream reach with 
a gage that drains less than 150 percent of the drainage area of the node, 
but not both, the discharge values at the node are weighted with the values 
associated with the gage. As with the previous case, the discharge values 
corresponding to the return intervals at the gage are determined using 
statistics from the gage record (mean, standard deviation and coefficient of 
skew).  Additionally, discharge values at the gage location are interpolated, 
as a power function of drainage area, using the values computed with the 
regression equations at the up- and downstream nodes of the reach containing 
the gage. A weighting parameter, RW, is computed: 
2 
(R -1)
Agage - Anode
RW = R - . (4-3)
A
gage 
Where, R is the discharge value determined from the statistics divided by the 
interpolated value; A denotes drainage area; the subscripts denote values 
associated with the gage location or node; and | | denotes the absolute 
value.  If the discharge value at either end of the reach containing the gage 
is zero, RW = 1.0. 
The discharge value in the field associated with the return period and the 
record associated with node is multiplied by the weighting parameter and 
replaced by that product. 
Adding Main Stream Reaches 
Discharge values for nodes on mainstreams are interpolated as power functions 
of drainage area similar to the procedure used for nodes with gages up- and 
downstream.  The drainage areas and discharge values at the nodes of the 
default reaches are used to interpolate the discharge values for the selected 
reaches. 
Recall that when the study reaches were selected, the default (meeting a 100-
square-mile-drainage-area threshold) reaches were culled to a collection 
consisting of reaches within the study -region that drain areas beyond the 
study region.  The  source  reaches of the networks defined by that 
collection were used to identify the selected reaches that are on main 
streams. The drainage area calculations for those reaches did not consider 
portions of the drainage areas beyond the study region. Those portions are 
the drainage areas of the upstream nodes of the source reaches in the 
remaining default networks. 
There are two sets of reaches for each main stream: a set of default reaches 
and a set of selected reaches. Lists of default  source  reaches and the 
selected reaches that are on the main streams were created when the main 
streams were identified.  Corresponding lists of default reaches are 
identified using the list of default  source  reaches and the transition 
matrix for the remaining default reaches. 
Using the numbers of steps in the transition matrix, the default reaches are 
arranged from upstream to downstream in each list.  Recall that, in the 
record for a given reach, there is a 1 in the field corresponding to the 
reach one step downstream, a 2 in the field of the reach two steps 
downstream, and so on. As a consequence, the reaches are arranged by 
increasing drainage area. 
Using the default flood frequency table, the list of source reaches from the 
remaining default reaches is arranged by drainage area. Pairing the lists of 
selected reaches that are on mainstreams with the default source reaches 
creates a list of lists of selected reaches arranged by the unaccounted for 
drainage area.  That is, the first list corresponds to the main stream with 
the largest drainage area; the next list corresponds to the main stream with 
the next largest drainage area. That arrangement establishes a sense of which 
main streams are tributary to other main streams. 
For each list of selected reaches on main streams, the unaccounted for 
drainage area is added to the drainage area of each node of each reach in the 
list.  Because main streams are defined from the upstream node to the outlet 
of the study region, several main streams may coincide (if they are in the 
same network within the study region).  Selected reaches on portions of main 
streams that coincide are members of each of the coinciding main streams  
reach lists.  The drainage area associated with one of those reaches is 
increased the appropriate amount each time the reach is found in one of the 
lists.  The idea is illustrated in Figure 4.15. 

Figure 4.15 shows the remaining default reaches in Clackamas County and the 
reaches affecting Clackamas County that meet a 10-square mile drainage area 
threshold. The two reaches shown in yellow are on the Willamette River: one 
upstream of and one downstream of the confluence with the Tualatin River.  
Recall both of those rivers were main streams. 
The drainage area in the default flood frequency table at the node labeled W 
(the upstream end of the Willamette River  source ) in the figure is added to 
both of the selected reaches shown in yellow. Additionally, the drainage area 
at the node labeled T (the upstream end of the Tualatin River  source ) is 
added to the downstream reach shown in yellow. 
After the drainage areas are adjusted, reaches contained in more than one 
list are deleted from all lists except the list associated with the largest 
drainage area.  Culling the lists that way establishes a unique stream (list) 
for each selected reach and, consequently, node. Discharge drainage area 
interpolations are confined to the stream (list) unique to the node being 
analyzed. 
The algorithm addresses one main stream at a time, making parallel lists of 
drainage areas at nodes of selected reaches and node identifier numbers from 
the default reach flood frequency table. One pair of lists is made for 
upstream nodes; another pair is made for downstream nodes. A reach is read 
from the main stream list and the drainage area at its upstream node is added 
to the upstream drainage area list. 
Default reaches are read (in downstream order) from the default reach list 
corresponding to the same main stream.  If the drainage area at upstream node 
of the default reach is greater than the drainage area of the selected reach 
node, the node identification number is added to the upstream node 
identification number list. If not, the downstream node of the default reach 
is test.  If its drainage area is greater than the drainage area of the 
selected reach node, its identification number is added to the upstream node 
identification number list.  If not, the process is repeated using the next 
default reach in the list.  If no default node tested has a drainage area 
greater than the target drainage area, the node identification number of the 
downstream node of the last reach in the default reach list is added to the 
upstream node identification number list.  In that situation, the discharge 
values for the node will be extrapolated. 
Next, the drainage area at the downstream node of the selected reach is added 
to the downstream drainage area list.  Starting with the reach containing the 
node just added to the upstream node identification number list, the default 
reaches are searched for the node with a drainage area greater than the 
target downstream drainage area.  When that node is found its identification 
number is added to the downstream node identification number list.  If no 
default node tested has a drainage area greater than the target drainage 
area, the node identification number of the downstream node of the last reach 
in the default reach list is added to the downstream node identification 
number list.  The discharge values for the node will be extrapolated. 
Note that the algorithm does not determine which selected reaches fall within 
which default reaches. Instead, the algorithm uses the default flood 
frequency table to establish discharge-drainage area relations for each main 
stream and, as just described, finds the positions, within the domain of 
drainage areas, of each node of a selected reach on a main stream.  The 
discharge values at each node is interpolated assuming the discharge-drainage 
area relations are piecewise log-linear (power functions). 
Records are added to the flood frequency table for selected reaches starting 
with the first entries in the pair of upstream lists just created.  The 
identification number of the first node added is the record of the reach in 
the feature table of reaches in the study region meeting the threshold 
drainage area requirement plus 0.01.  Any reach on a main stream cannot be a 
source reach.  The drainage area from the drainage area list is put in the 
drainage area field. 
The record in the default flood frequency table containing the node 
identification number from the upstream node identification number list and 
the record just before that record are identified. The drainage areas in 
those records are, respectively, the smallest drainage area greater than and 
the greatest drainage area smaller than that at the node of the selected 
reach (unless the latter drainage area is greater than those at all default 
nodes associated with the main stream).  The ratio of the difference of the 
base 10 logarithms of the three areas is used to interpolate.  That ratio is: 
A

...


...

log
10 
R

=

(4-4)

A

...


...

log
10 
A

where A denotes drainage area and the subscripts denote drainage areas from 
the node on the selected reach, or nodes on the default reaches having the 
smaller or greater drainage area.  Note that if Aselected is greater than 
Agreater, R is greater than 1.0. 
Discharge values are read from the default flood frequency table records for 
each return interval. If either of the values (smaller or greater) are zero, 
a zero is put in the corresponding field in the flood frequency table for 
selected reaches.  If both values are greater than zero, the base 10 
logarithm of the discharge value for the node on the selected reach is 
log10 (Qselected )= log10 (Qsmaller )+ R log10 ... Qgreater 
Qsmaller ... (4-5) 
where Q denotes discharge and the subscripts are as above.  If R is greater 
than 1.0 log10(Qselected) is extrapolated (beyond the largest default value). 
Raising 10 to the log10(Qselected) power yields the discharge value that is 
placed in the appropriate field in the flood frequency table for selected 
reaches.  The process is repeated for each return interval completing the 
record for the upstream node. 
The next node is the downstream node of the first selected reach in the 
mainstream list being analyzed. The node identification number is the reach 
record in the feature table plus 0.99.  The drainage area is the first 
drainage area in the downstream drainage area list.  The discharge values are 
assigned using the same procedure as used for the upstream node. 
The remaining nodes in the two lists are processed in order to complete the 
main stream.  The lists are emptied and new pairs of lists are created to 
analyze the next main stream (with the next largest drainage area). The flood 
frequency table is complete when the nodes of last main stream list of 
selected reaches have been included. 
4.2.2.1 Reach Identification and Watershed Association 
The model identifies reaches and associated watersheds that flow into the 
chosen reaches; the reaches into which the chosen reaches flow; and the 
watersheds associated with those reaches. When created, each reach is 
attributed with an upstream node identifier and a downstream node identifier. 
Figure 4.16, shows the upper portion of the North Fork Shenandoah River.  The 
numbers denote nodes. 

There is a reach that  flows  from node 1 to node 5; a reach that flows from 
node 5 to node 6; and so on. Using the  from node, to node  data the model 
constructs a one-step transition matrix, for navigating up- and downstream in 
the network.  If the model identifies each reach by the number of its 
upstream node then each entry, (r,c), for row r and column c in the matrix is 
1 if the downstream node of stream r is the upstream node of stream c and 0 
otherwise. 
A transition matrix is created for all reaches meeting the threshold drainage 
area requirement. The rows and columns of the matrix associated with the 
selected reaches are searched to identify up- and downstream reaches.  
Corresponding lists are made. 
Watersheds in the reduced default watershed feature table that do not 
intersect any of the selected reaches or the reaches draining to selected 
reaches are deleted from the feature table, leaving only watersheds that 
drain to the selected reaches.  The union of those watersheds defines a 
polygon that is used to cull the default reaches.  If the midpoint of a 
default reach is not contained in the polygon, that reach is deleted from the 
reduced default reach feature table. 
The default reaches that fall within the watersheds of the selected reaches 
and those draining to the selected reaches are shown in Figure 4.17. The 
reaches are the four on the North Fork Shenandoah River and three 
tributaries, shown in red. 

The remaining default reaches that have no remaining reaches upstream are 
identified as potential source reaches.  The upstream node of those reaches 
is found in the default node table and those that are source reaches are 
deleted from the feature table.  A list is made of the potential source 
reaches that are not identified as source reaches.  They are the default 
reaches that drain areas beyond the polygon.  That is, they are parts of 
streams that originate outside of the study area. 
The upstream nodes of all three tributaries shown in Figure 4.17 are 
contained within the polygon defined by the union of the watersheds covering 
the selected reaches.  That is, those tributaries are source reaches and, 
therefore, deleted from the feature table. 
Deleting the source reaches creates another network that may, unlike the 
situation depicted in Figure 4.17, contain new  sources.  Those new sources 
are deleted.  The potential sources saved in the list of reaches on streams 
that originate outside of the polygon are not deleted.  The process continues 
until no new sources are created at which point, all default reaches are 
parts of streams that originate outside of the study area.  Those streams 
will be referred to as main streams.  All other streams, including the 
default reaches deleted in the culling process, drain areas contained within 
the polygon. 
For example, Figure 4.18 shows the various default reaches involved.  The 
area depicted is in the vicinity of Clackamas County, Oregon.  The major 
stream in Clackamas County is the Willamette River, which flows into the 
Columbia River at Portland.  The Clackamas River flows from the southeast 
corner of the county to the Willamette River in the northwest corner.  It is 
the shown in red on Figure 4.19. The reach on the Tualatin River and the 
upstream reach of the Willamette River shown in dark blue and the reaches 
shown in yellow are potential default source reaches that affect Clackamas 
County.  Only the yellow reaches are  true  source reaches. 

After the yellow reaches are deleted, the reach on the Tualatin River and the 
upstream reach of the Willamette River shown in dark blue and the upstream 
reaches shown in red are the potential default sources among the remaining 
reaches that affect Clackamas County.  Those shown in dark blue were flagged 
and, so, only the potential sources shown in red are deleted.  After the 
culling process is completed, only reaches shown in dark blue (Tualatin and 
Willamette Rivers) remain. 
In addition to the reaches meeting the threshold drainage area that have been 
selected, there are, at this stage, three other categories of reaches: 
1. 
Reaches meeting the threshold drainage area that flow to the selected reaches 

2. 
Reaches meeting the threshold drainage area to which the selected reaches 
flow 

3. 
Default reaches that are on main streams 


Each category is used differently with the selected reaches to compute 
discharge values for the selected reaches. Upstream reaches are used to 
determine drainage areas and identify upstream gages. Downstream reaches are 
used to identify downstream gages.  The default flood frequency information 
must be used for reaches on the main streams. 
Cell values in the watershed grid created with the threshold reaches that are 
not associated with the selected or upstream reaches are set to null.  Thus, 
the watershed grid only contains watersheds that are within the drainage 
areas associated with the selected reaches. 
The selected reaches that are on main streams must be identified.  As 
described earlier, collection of default reaches comprising the main stream 
differs from the collection of selected reaches on the main stream both in 
number of reaches and configuration or detail.  The difference is evident for 
the North Fork Shenandoah River, the main stream shown in Figure 4.19.  The 
red stream in Figure 4.19 is comprises four default reaches; the blue 
meandering main stream comprises 15 of the selected reaches. 

A transition matrix is created for the remaining default reaches.  The 
 sources  of the networks defined by the remaining default reaches are 
identified using the transition matrix.  The watersheds remaining in the 
watershed grid are converted to polygons. The union of those polygons is a 
polygon covering the area draining to the selected reaches.  Note that 
because the DEM is not the same DEM as used to create the default reaches, 
the watershed boundaries may not coincide where expected. The covering 
polygon is buffered inward one cell size.  The boundary of the resulting 
polygon intersects the source reaches in the remaining default reaches. If 
the  source  reach and boundary intersect at more than one point, the most 
downstream of the intersections is identified and stored in a list. 
Using the flow direction grid, the flow path is determined from each of the 
points in that  source  points list. That path will intersect the selected 
reaches and coincide with selected reaches from that intersection downstream 
(to the outlet of the selected reaches).  Each path is buffered one-half a 
cell size.  Lists are made of the selected reaches contained in each of the 
buffered paths. Thus, any selected reach that is on a main stream is 
contained in one or more of those main stream lists.  The default flood 
frequency information is used on the reaches in the main stream lists. 
4.2.2.2 Define Drainage Area 
The next step is to define the drainage area associated with each node in the 
networks of selected reaches and the reaches up- and downstream of the 
selected reaches.  The step begins with defining the transition matrix for 
the selected reaches.  Recall that a watershed grid is created when the 
threshold is chosen and the threshold reaches created.  The cells in that 
grid are attributed with the nearest (in flow direction) reach.  The groups 
of cells having (associated with) the same attributes (reaches) are converted 
to polygons.  The conversion process may create more than one polygon for a 
given watershed. 
Each polygon in the resulting collection of polygons is compared to every 
other polygon in the collection. Whenever the two polygons have the same 
attribute (are associated with the same reach), one is replaced with their 
union the other is placed in a list for removal.  After each polygon has 
passed through the process, the polygons in the removal list are removed from 
the polygon feature table.  The resulting feature table contains one polygon 
for each reach.  Those polygons are the drainage areas associated with the 
corresponding reaches. 
Note that the polygons associated with the source nodes have not been found 
at this point in the algorithm.  That is, the watersheds associated with 
source reaches include the watersheds draining to the upstream points of the 
source reaches.  They are not watersheds but are, instead, the drainage areas 
of the downstream nodes of the source reaches. 
The source reaches are identified using the transition matrix.  For each 
source reach, the upstream point (actually a point 0.1 percent of the reach 
length along the reach) is found.  A watershed grid is created by identifying 
the groups of cells that flow to each of the source points and associating 
each cell with the corresponding point.  Those groups are converted to 
polygons the same way as were the watersheds associated with the reaches.  
The number of cells in each group is divided by the number of cells in one 
square mile, yielding the watershed area.  The polygon and area for each 
source point is stored as the upstream watershed and drainage area for the 
corresponding reach. The same process is applied to the watersheds associated 
with the reaches. The resulting polygons and areas are the watersheds and 
watershed areas associated with each of the reaches. 
The drainage area of the downstream node of each reach is the sum of the 
watersheds upstream of the node. The reaches that flow through each 
downstream node are identified using the transition matrix.  The sum of the 
watershed areas associated with those reaches plus the watershed area 
associated with the reach being analyzed is the drainage area of the 
downstream node. The union of the polygons associated with those watersheds 
is the polygon associated with the drainage area of the node. The resulting 
polygons and areas are stored as the downstream drainage area polygon and 
value for the reach. 
Samples of the polygons described in the process are shown on Figure 4.20.  
The drainage areas associated with the upstream nodes of a source reach and 
an interior reach are shown in yellow. The watershed areas are shown in light 
blue. 

At this point in the algorithm, the drainage areas associated with the 
upstream nodes of the source reaches and those associated with the downstream 
nodes of all of the reaches have been determined.  Note that the drainage 
areas associated with the downstream nodes are the union of the blue and 
yellow polygons shown in Figure 4.20. 
The drainage areas of the upstream nodes of the interior (non-source) reaches 
are determined by subtracting the area associated with the watershed of 
corresponding reach from the drainage area of the corresponding downstream 
node.  Similarly, if a reach is an interior reach, the polygon associated 
with the drainage area of its upstream node is the difference between the 
polygons associated with drainage area and watershed at the downstream node. 
4.2.2.3 Default Gage Identification 
Before moving to the algorithms that perform the hydrologic analyses, the 
default gage dataset is reduced to the gages that may be used those analyses.  
Using the transition matrix of the reaches meeting the threshold requirement 
(i.e., selected reaches and those up- and downstream), the outlet reaches are 
identified.  The union of the drainage area polygons associated with the 
downstream nodes of the outlet reaches contains all gages that will be used 
in the hydrologic analyses.  Note that gages affecting the analyses of the 
main streams were accounted for in developing the default reaches and 
watersheds files. 
The union of drainage areas covering the study reaches and the gages within 
and near that area are shown in Figure 4.21. Each gage within the identified 
area is associated with the nearest reach (the reach feature table is 
spatially joined to the gage feature table).  That association is checked 
using drainage areas. 

The points representing the gage locations are attributed with total and 
contributing drainage areas and the mean, standard deviation, and coefficient 
of skew of the flood frequency curve at the gage. If the total drainage area 
of the gage is less than the drainage area of the upstream node and greater 
than the drainage area of the downstream node of the reach associated with 
the gage, the gage is assumed to be on that reach.  If not, the gage is 
removed from the gage feature table. That is, the gage is not used in 
subsequent analyses.  For example, no gages with less than the threshold 
drainage are used in subsequent analyses. 
4.2.2.4 Automated Adjustments 
Note there are two categories of reaches for which the algorithms have or may 
have underestimated the drainage areas.  Drainage areas for reaches on the 
main streams do not include the area beyond the study region that drain to 
the reaches.  Drainage areas associated with those reaches are underestimated 
by at least 100 square miles (the default drainage area threshold).  
Therefore, gages located on main streams will not be used in subsequent 
hydrologic analyses.  Recall that the default flood frequency data 
incorporates the gage information and will be used for reaches on the main 
streams.  
Drainage areas associated with reaches downstream of the selected reaches may 
be underestimated.  Tributaries that are not selected but flow into reaches 
that are downstream of the selected reaches do not survive the culling 
algorithm.  Drainage area calculations on down stream reaches do not include 
areas draining to those tributaries.  Before comparing the gage drainage 
areas with the reach drainage areas, the drainage areas associated with 
downstream reaches identified as containing a gage are adjusted. 
4-36 
The adjustment uses the watershed grid and the accumulation grid.  A new grid 
is created.  Cells in the new grid corresponding to cells contained in the 
group in the watershed grid that are associated with the reach are given the 
value of the corresponding cell in the accumulation grid. All other cells in 
the new grid are given a value of zero.  The maximum cell value of the new 
grid is the number of cells draining to the downstream node of the reach.  
That number minus the number of cells in the group identified in the 
watershed grid is the number of cells draining to the upstream node.  
Dividing each of those numbers by the number of cells in one square mile 
yields the respective drainage areas. 
Those drainage areas are compared to the drainage areas at the gages 
associated with the reach. Gages not satisfying the drainage area test are 
deleted from the feature table, leaving only those gages that will be used in 
the hydrologic analyses. 
4.2.3 Hydraulic Analyses 
Hydraulic analyses are performed to determine the flood depths along the 
reach.  Flood depths are determined by defining a flood surface grid and 
subtracting the ground elevations at corresponding cells in the DEM.  The 
resulting grid defines the spatial distribution of flood depths. Cells with 
positive values form the floodplain.  The idea is illustrated in Figure 4.22. 

The description of the hydraulic analyses begins with a description of the 
flood surface mapping algorithms.  Those algorithms create a flood surface 
grid using a DEM, a polygon representing a floodplain, two line segments 
representing the up- and downstream limits of the study reach, and a set of 
line segments representing cross sections attributed with flood elevations.  
Those data can be user supplied or determined by HAZUS using the default 
databases and the DEM. 
4.2.3.1 Mapping the Flood Surface 
There are default (level 1) options in HAZUS that approximate the floodplain 
associated with a stream reach, find the up- and downstream limits of that 
approximation, generate a set of cross sections within the reach, and 
attribute those cross sections with flood elevations and discharge values. 
There are also options to import the results of applying the Flood 
Information Tool (FIT) to more detailed, user supplied floodplains, limits, 
cross sections, and flood elevation data. Creating the flood surface for the 
Model requires a means to interpolate flood elevations between the cross 
sections. 
Consider the sample reach and set of cross sections shown in Figure 4.23. 

In many cases connecting the end points of the cross sections would suffice 
for enveloping the lateral extent of the floodplain and interpolating flood 
elevations.  In some cases, such as that illustrated in Figure 4.24, simply 
connecting the end points of the cross sections creates obvious errors. A 
large portion of the floodplain between the middle two cross sections was not 
captured by the interpolation scheme.  The two middle cross sections need to 
be extended or, alternatively, one or more intermediate cross sections need 
to be added and attributed with the appropriate elevation information.  Note 
also that the interpolation scheme missed a portion of the floodplain between 
the left two cross sections. 

To avoid those problems, the algorithm in HAZUS uses an estimate of the 
floodplain to define lines along which elevations are interpolated.  In 
particular, for any polygon representing a floodplain, HAZUS uses up- and 
downstream limits of the polygon to define right and left boundaries and a 
 centerline.   The bounding polygon is defined by buffering the centerline. 
Flood elevations are interpolated between cross sections along the 
centerline. 
The algorithms that create flood surface data in FIT and HAZUS are, 
essentially, the same. Defining the centerline of the floodplain is 
fundamental to those algorithms. 
The following example is from an application of FIT.  Note that the  reach  
in the example is defined by up- and downstream limits not necessarily 
located at nodes in the stream network. Users of FIT may define reaches of 
any length. 
Figure 4.25 shows a portion of the floodplain contained on the Q3 map for 
Travis County, Texas.  The source of flooding is Williamson Creek.  The study 
reach is within the City of Austin.  The flow direction is from west to east.  
Figure 4.25 also shows the base flood elevation (BFE) lines digitized from 
the Flood Insurance Rate Map (FIRM).  The base flood is the flood magnitude 
having a 1-percent annual chance of being exceeded. 

The tributary entering from the west is labeled Williamson Creek Tributary 4 
on the FIRM.  The floodplain associated with the tributary is part of the 
same polygon as Williamson Creek on the Q3 map.  The BFE lines associated 
with the tributary are contained in the list of cross sections for Williamson 
Creek.  That is, the input data does not distinguish between Williamson Creek 
and Williamson Creek Tributary 4.  The distinction is accomplished by 
defining a centerline through the study reach. 
Defining the up- and downstream study limits establishes study reach, its 
flow direction and, consequently, the right and left sides of the floodplain.  
The study reach shown in Figure 4.26 is approximately 2 miles long. 

The boundary of the polygon containing the study reach is defined by 
converting the polygon to a polyline. In that case the resulting polyline is 
a continuous line that starts and ends at the same point. The process is 
illustrated in Figure 4.27.  Note that the polygon includes its interior.  
The polyline is the boundary of the polygon.  Even though the polyline starts 
and ends at the same point, it is not recognized as a closed line. 

The algorithms for finding the left and right boundaries and the centerline 
manipulate polygons with no holes, or islands, in their interiors.  Before 
applying the algorithms to polygons with islands (doughnut-shaped, for 
example) the islands must be removed.  To accomplish that, the polygon is 
converted to polylines, the polylines are converted to lists of points, and 
polygons are made from those lists.  The union of those polygons forms the 
floodplain polygon without islands. 
The polyline is split, or clipped, into four or five polylines by the up- and 
downstream limits.  If the starting point and end point fall on one of the 
limits the split results in four polylines, otherwise it results in five 
polylines. The clipping is illustrated in Figure 4.28. 

The floodplain boundaries within the reach are found by assessing the 
intersections between the limits and the polylines from the polygon.  Each 
polyline that intersects both the up- and downstream limits is a floodplain 
boundary.  There is at least one such polyline.  If two such polylines are 
identified, the starting and end point is located either on a limit (four 
polylines) or outside (up- or downstream) of the reach.  In either case, the 
two polylines intersecting both limits are the floodplain boundaries. 
If only one polyline intersects both limits, there are five polylines.  Two 
of those polylines intersect one of the limits twice.  Those two polylines 
are outside of the reach.  The remaining two polylines are merged to form the 
other floodplain boundary. 
Polylines have direction. Both boundaries are checked to ensure that the 
direction is from upstream to downstream.  If the direction of a boundary 
does not meet that convention, the boundary is  flipped.  One boundary is 
compared to other to set a convention for right and left. Is the second 
boundary is right of the first boundary, the first boundary is the left 
boundary; otherwise it is the right boundary. 
Just as the polyline that is the boundary of the floodplain polygon was split 
into several polylines, the up- and downstream limits are split into three 
polylines.  For each limit, that polyline segment whose midpoint is contained 
in the polygon is identified.  The segments are checked and, if necessary, 
flipped to ensure that the direction of each is from the left boundary to the 
right boundary.  The segments are stored as the clipped limits. 
Once the right and left boundaries are defined, a  centerline  of the study 
reach can be defined. The idea is to define a path that captures the overall 
flow direction within the floodplain.  The portion of the floodplain that 
conveys floodwater can be estimated by tracing the boundaries of the 
floodplain  smoothing  out the areas of backwater and omitting the 
tributaries.  Here,  backwater  areas mean areas where water is not conveyed 
but, rather, pond at the elevation of the main stream.  The center of those 
smoothed boundaries defines a path that preserves a sense of flow direction 
and (relative) distance within the floodplain.  
Imagine a circle centered somewhere on the upstream limit and with a radius 
such that it just touches the right and left floodplain boundaries.  That is, 
the circle is inscribed in the floodplain. As the circle moves downstream, 
adjusting its size and position so that it is always inscribed in the 
floodplain, its center follows the path we seek. 
A property of that path is every point on the left side of the path is closer 
to the left floodplain boundary than the right floodplain boundary.  Every 
point on the right side of the path is closer to the right floodplain 
boundary than the left floodplain boundary.  The path is, in some sense, a 
centerline.  The algorithm to define the centerline exploits that property. 
The algorithm begins by defining a polygon using the up- and downstream 
limits and the right and left boundaries. To do that, the up limit and right 
boundary are flipped, making the direction continuous. The vertices of the 
polylines are added to a list in order (left, down, right, up) and that list 
is used to define the clipped polygon. The smallest rectangle that contains 
the clipped polygon is found and expanded five cell sizes. A cell size is the 
posting of the DEM. 
A grid is created with a posting equal to the DEM posting and bounded by the 
rectangle. Each cell in the grid is closer to one floodplain boundary or the 
other.  Attributing each cell with the closest floodplain boundary partitions 
the rectangle into two regions.  The boundary between those regions is the 
centerline. 
One of the regions is converted to a polygon which, subsequently, is 
converted to a polyline. That polyline is divided by the clipped polygon.  
The resulting polyline segments that are contained in the clipped polygon are 
identified and merged.  The resulting polyline is the centerline.  It is 
checked and, if necessary, flipped to ensure that its direction is upstream 
to downstream.  Applying that algorithm to the Williamson Creek reach results 
in the centerline shown in Figure 4.29. 

As shown in Figure 4.30, the BFE lines associated with the study reach are 
those lines that intersect the centerline.  The BFE lines are ordered from 
up- to downstream by measuring the distance to their intersections with the 
centerline along the centerline.  The centerline is also used to establish 
the right side-left side convention (looking downstream). 

A buffer around the centerline is created.  A buffer is a polygon that is 
everywhere the same distance from the object it buffers.  Moving a circle of 
radius r along the centerline from one end point to the other traces a buffer 
of distance or size. 
The buffer is used to define the bounding polygon. In Level 1 analyses, the 
buffer size is ten times the square root of the largest discharge value 
determined for the reach.  The user selects the buffer size in FIT. The flood 
surface computations will be confined to the bounding polygon. The flood 
surfaces around tributaries and backwater areas are added later. 
If necessary, the up- and downstream study limits are extended to the edges 
of the buffer.  In FIT, the limits are extended with the orientation at their 
endpoints. That is, the extension is a straight line passing through the end 
point being extended and the closest point to that end point. The 
ramifications of extending the limits in a straight line should be considered 
when using FIT. The limits derived for Level 1 analyses are the most upstream 
and downstream cross sections (derived by HAZUS).  They are extended as cross 
sections.  
As with the floodplain, the buffer polygon is converted to a polyline and 
clipped using the extended limits.  The clipped buffer and extended limits 
form the bounding polygon.  The results of the process for the Williamson 
Creek example are shown in Figure 4.31. 

A series of buffers is created at regularly spaced intervals between the 
centerline and the bounding polygon. Each buffer is converted to a polyline 
and clipped at the up- and downstream study limits.  The result is a 
partitioning of the bounding polygon into semi-parallel flow corridors 
similar to dividing the conveyance area of the floodplain into flow tubes. 
As each buffer is created, the end points of each BFE line are checked to 
determine whether they are contained within the buffer.  If not, the cross 
section is extended to the point on the edge of the buffer closest to end 
point of the BFE line.  Extending the BFE lines that way aligns the extended 
portions perpendicular to the flow corridors. 
The flow corridors and extended BFE lines for Williamson Creek are shown in 
Figure 4.32. Note that, on the  inside  portions of the meanders, several 
extensions can coincide. 
The width of each  corridor  in the figure is exaggerated for illustrative 
purposes.  The actual spacing for computations is the cell size chosen for 
the flood surface grid. 

Between BFE lines, elevations are interpolated.  Points are identified at 
regular intervals, equal to the flood surface grid spacing, along the 
centerline.  At each point, the ratio of the distance, along the centerline, 
to the upstream BFE line and the distance between the up- and downstream BFE 
lines is determined.  The difference in elevations associated with the up- 
and downstream BFE lines is determined and multiplied by that ratio.  
Subtracting the result from the elevation associated with the upstream BFE 
line yields the interpolated elevation at the point on the centerline. 
The process applied to the centerline is repeated at each flow corridor edge.  
That is, rather than interpolating BFE lines to determine the location of an 
interpolated elevation, the interpolated elevations are evenly spaced along 
each flow corridor edge.  In general, the number of interpolated points 
between cross sections varies from corridor to corridor and from left side to 
right side of the centerline. 
The result is an irregularly spaced grid of points formed along the flow 
corridors.  The elevation information is stored in the feature table for the 
collection of points.  In addition to the flood elevation, each point is 
attributed with the interpolation ratio and the identification (record 
number) of the upstream cross section (BFE line in the example). 
Figure 4.33 shows the grid for Williamson Creek.  Consistent with Figure 
4.32, the point spacing shown in the figure is exaggerated.  The actual 
spacing for computations is the cell size chosen for the flood surface grid. 

A surface is created from the points in Figure 4.33.  The surface is stored 
in a regularly spaced grid. The elevation at each cell in the grid is a 
weighted average of the elevations of all points in the irregular grid 
(Figure 4.33) within a distance equal to 1.5 times the cell size chosen for 
the surface grid.  The average is weighted by the inverse of the distance to 
each such point. 
The flood surface cell size used to develop the Williamson Creek example is 
10 feet.  There are actually over 97,000 points in the irregular grid 
represented by Figure 4.33. The flood surface developed using the process is 
shown in Figure 4.34.  Flood elevation contours are shown on Figure 4.34 to 
help visualize the surface. 
The flood depth grid is created by subtracting (cell-by-cell) the ground 
elevation, contained in the DEM grid, from the flood elevation.  The flood 
depth grid for Williamson Creek is shown on Figure 4.35.  Note that a few 
backwater areas and, in particular, Tributary 4 are not entirely covered by 
the grid. Algorithms for analyzing such areas are described in Section 
4.2.3.2.9, Nonconveyance Areas. 



4.2.3.2 Default Hydraulic Analyses 
The default or Level 1 hydraulic analyses are performed through a series of 
estimates of the floodplain. Floodplain limits are estimated and, then, used 
to define a set of cross sections that are, subsequently, used to refine the 
floodplain estimate.  Flood elevations are estimated using Manning s equation 
with a friction slope equal to the slope of the reach (i.e., normal depth 
calculations). The flood elevations are used to redefine the floodplain 
limits; the new limits are used to adjust the cross section alignments; and 
so on. 
Manning s equation relates the velocity of a unit mass of floodwater to the 
friction slope, sf, the roughness, expressed as a coefficient, n, and the 
hydraulic radius, R, of the floodplain. The hydraulic radius is the area 
divided by the perimeter of the submerged portion of the cross section. 
Multiplying that equation by the submerged area, A, yields the discharge 
value, 
1.486
Q = AR . (4-6)
n 
The discharge values used in Manning s equation are interpolated as power 
functions of drainage area, A, between the up- and downstream nodes of each 
study reach: 
Q =aA_ . (4-7) 
The two parameters, a and _, that define the power function are stored in two 
tables: the alpha and beta tables. In the flood frequency table created by 
the hydrologic analysis algorithms, pairs of records, n and n+1, where n is 
even starting with zero, represent the up- and downstream nodes of a reach 
selected for study. Each field other than the first (the reach identification 
field) and second (the drainage area field) represents a specific frequency. 
For each pair of records in the flood frequency table, a record is created in 
the alpha and beta tables. The integer part of the reach identification in 
the flood frequency table is put in the first field of the alpha and beta 
tables. The remaining (frequency) fields are populated as follows. 
The difference between the base 10 logarithms of the values in the drainage 
area fields is computed (downstream area minus upstream area).  For each 
frequency, if the discharge values at both the up- and downstream nodes are 
not greater than zero, zeros are put in the corresponding fields in the alpha 
and beta tables.  Otherwise, the difference (downstream minus upstream) 
between the base 10 logarithms of those values is computed.  If the 
difference between the base 10 logarithms of the areas is zero, a zero is put 
in the frequency field of the beta table. Otherwise, the ratio of the two 
differences, _, (the difference between the base 10 logarithms of the 
discharges divided the difference of the base 10 logarithms of the drainage 
areas) is put in the frequency field of the beta table. 
At each node, the base 10 logarithm of a is computed as the base 10 logarithm 
of the corresponding discharge value minus beta times the base 10 logarithm 
of the corresponding drainage area. The final value of a is 10 raised to the 
average of the two a. That value is put in the frequency field of the alpha 
table. 
Using the alpha and beta tables, the discharge value of a given frequency can 
be readily calculated for cross sections placed arbitrarily along the chosen 
reaches. The point at which the cross section intersects the reach is 
identified and the drainage area is computed using the value of the 
corresponding cell in the accumulation grid.  The drainage area is raised to 
the value in the beta table corresponding to the reach and frequency.  
Resulting value is multiplied by the value in the alpha table corresponding 
to the reach and frequency.  That product is the discharge value at the point 
in question corresponding to the frequency being investigated. 
The hydraulic analyses are performed on individual reaches, one at a time.  
One of the selected reaches is chosen. The chosen reach is identified in the 
feature table representing the reaches selected from those meeting the 
drainage area threshold (see Section 4.2.1 Identifying Stream Reaches) and 
the reaches draining to the selected reaches.  The downstream reach is 
identified. It is the reach attributed with an upstream node identifier that 
equals the downstream node identifier of the study reach.  If a downstream 
reach exists, the study reach is extended into the downstream reach to create 
an  overlap  with the downstream reach. 
The reach is extended downstream.  The point on the downstream reach 450 feet 
or one-half of its length from the upstream node, which ever is less, is 
identified.  The portion of the downstream reach upstream of that point is 
added to the downstream end of the study reach. That is, the  extension  
follows the same path as the downstream reach.  If no downstream reach 
exists, the study reach is an outlet reach and the  extended  reach is the 
same as the study reach. 
The first flood depth grid created is associated with the frequency having 
the largest discharge value. The point 90 percent of the length of the study 
reach from its upstream node is identified. The value from the flow 
accumulation grid at that point is determined and drainage area there 
computed.  A list of discharge values is made using the alpha and beta 
tables.  The maximum discharge value is determined and inserted at the 
beginning of the list.  That value is the reference discharge value used to 
estimate the extent of a polygon, the bounding polygon that will contain the 
conveyance portions of the floodplains associated with all frequencies 
analyzed in the hydrologic analysis. As mentioned in the previous section, 
the default  buffering distance  of the bounding polygon is set at ten times 
the square root of the reference discharge value. 
4.2.3.2.1 Initial Cross Sections 
A buffer is created around the extended reach.  The buffering distance is two 
times the cell size (posting) of the DEM. Up- and downstream limits of the 
buffer are created using the extended reach. An example of a buffered 
extended reach is shown in Figure 4.36.  For illustrative purposes, the 
buffer distance in Figure 4.36 is much greater than two cell sizes. 

A line is projected from the upstream node through the buffer.  The 
projection is along the same bearing as the most-upstream section of the 
extended reach.  A circle is created centered where the projection intersects 
the buffer with a radius equal to the buffer distance.  A polyline is created 
using the points where that circle intersects the buffer and the upstream 
point of the extended reach.  That polyline is the upstream limit. The 
process is shown in Figure 4.36. The downstream limit is defined the same 
way. 
Islands are removed from the buffer, it is converted to a polyline, clipped, 
and the centerline of the buffer is determined.  That centerline is not the 
extended reach.  In general, the centerline of a buffer about a polyline is 
not that polyline. 
Cross sections are equally spaced along the centerline at intervals no 
greater than 1000 feet.  The number of cross sections is, therefore, two plus 
the integer value of the length of the centerline divided by 1000. The first 
cross section is located at the upstream end of the centerline; the remaining 
cross sections are located at intervals, measured along the centerline, equal 
to the length of the centerline divided by one less than the number of cross 
sections.  The last cross section is the located at the downstream end of the 
centerline.  Thus, if the centerline is less 2000 feet long, the number of 
cross sections is three: the up- and downstream ends and one more at the 
midpoint of the extended reach.  The cross sections are, at this point, 
stored in a list. 
Each cross section is oriented by connecting the centerline point to the 
points on the right and left boundaries, closest to the centerline point.  
Specifically, the points that define the polyline representing the cross 
section are arranged form the left boundary to the centerline to the right 
boundary, assuring a left-to-right direction.  The first and last cross 
sections become the up- and downstream limits, respectively. 
The cross sections are extended by buffering the centerline a number of times 
at equally spaced buffering distances ranging from three cell sizes to the 
buffering distance of the bounding polygon (ten times the square root of the 
reference discharge value).  The buffering distance is increased one cell 
size until the bounding polygon is exceeded. 
For each buffer created, the algorithm removes the islands and, then, 
connects the end points of the up- and downstream limits to the nearest 
points on the (polyline) boundary of the buffer.  The polyline is clipped and 
the end points of each cross section are extended to the closest points on 
the respective boundaries (of the clipped polyline).  That is, the left end 
point is extended to the left boundary; the right end point is extended to 
the right boundary.  The points defining the cross section polylines are 
arranged to ensure a left-to-right direction.  The first and last cross 
sections become the up- and downstream limits and the process is repeated at 
the next buffering distance. 
The buffering and extending algorithms create limits that tend away from the 
floodplain.  The initial upstream limit shown in Figure 4.36, for example, is 
 v-shape  tending  away  from the buffer. After the cross sections have been 
extended, the end points of the downstream limit are check to ensure they are 
within the coverage of the DEM.  The downstream cross section (limit) of an 
outlet reach oriented somewhat perpendicular to the edge of the DEM may not 
be covered by the DEM. If both end points of the downstream cross section are 
not within the DEM coverage, that cross section is deleted from the list 
making the next cross section upstream the most downstream cross section and, 
therefore, the downstream limit. 
4.2.3.2.2 Initial Flood Elevation Estimates 
A feature table is made to store the cross sections from the cross section 
list. 
Flood elevations are computed using Manning s equation.  In addition to cross 
section geometry, two parameters are needed for Manning s equation: a 
roughness coefficient, or n-value, and a friction slope. The default n-value 
is 0.08. The friction slope is calculated using the DEM.  It is determined by 
fitting a straight line through data obtained at 101 points, evenly spaced at 
1 percent intervals along the reach (not centerline and not the extended 
reach). 
The data are stored in two lists.  One list stores the distances to the 
points along the reach from upstream to downstream.  The first entry in that 
list is zero (upstream).  The other list stores the values (elevations) of 
the cells in the DEM that contain the points.  The line is fit to that data 
by the method of least squares.  The slope of that line is used as the 
friction slope for all cross sections in the reach. 
Recall Manning s equation times area: 
1.486
Q =AR . (4-8)
n 

Given the discharge value, Q, N-value, n, and friction slope, sf, the only 
unknown parameters are functions of the flood depth or elevation.  Because 
the geometry of a natural floodplain cross section is not regular, those 
functions are unknown and, therefore, the algorithm to solve Manning s 
equation is iterative. That is, one starts with an elevation, computes the 
area, A, and hydraulic radius, R, below that elevation, and calculates the 
discharge value.  The elevation is adjusted by comparing the calculated 
discharge value with the known value. 
In HAZUS, the first elevation is computed by adding a depth to the elevation 
at the point on the cross section where it intersects the extended reach.  
That depth is estimated by assuming the floodplain is an isosceles triangle.  
Assuming that geometry the functions relating area, hydraulic radius, and 
depth are known. Given the other parameters, Manning s equation can be solved 
directly. 
The average elevation of the right and left boundaries is computed.  Similar 
to the slope calculations, 101 points are sampled at intervals equal to one-
percent of the length of each side of the bounding polygon. The  depth  of 
the triangle is the average of the elevations at those 202 points minus the 
average elevation of the least-square-fit line used to define the slope. 
The points at the intersections of each cross section and the reaches (study 
reach or downstream reach) are found.  At each point the value of the flow 
accumulation grid is determined and the drainage area associated with that 
value is calculated (i.e., the value times the number of cells per square 
mile).  The reference discharge value is computed using the drainage area and 
the interpolation coefficients in the record associated with reach in the 
alpha and beta tables.  Those discharge values are stored in a list. 
The values of the DEM at those points are also determined and stored in a 
list.  Those values are the  streambed  elevations. 
The length of each cross section is determined and the average of those 
lengths is computed. That average is the  topwidth  of the triangle.  Twice 
the depth of the triangle divided by its topwidth is the side-slope, Ss, of 
the triangle.  Assuming the topwidth is much greater than the depth (small 
side-slope) the perimeter is only slightly greater than the topwidth.  In 
that case the hydraulic radius is approximately equal to the average depth or 
one-half the triangle depth.  To summarize, the depth, d, in an isosceles 
triangle cross section can be approximated as, 

3 
d. ...1.07nQS
s ... 8 . (4-9) . sf . 
A reference depth is computed by adding a (arbitrary)  channel  depth of 
three feet to the depth estimate from applying the above equation using the 
maximum value in the list of reference discharge values. The flood elevation 
estimate is the  streambed  elevation of the cross section plus the reference 
depth. 
Each cross section is attributed with the elevation estimate, the n-value, 
the slope, and the reference discharge value. 
4.2.3.2.3 Revised Flood Elevation Estimates 
The elevation estimate is revised by defining the  true  geometry of the 
cross section using the DEM. For each cross section, points are added to the 
list of vertices that define the polyline.  In particular, points are added 
between vertices so that there is a point every cell size along the polyline. 
Two lists are made from that augmented list of points.  One list stores the 
distance from the left most (starting) point of the polyline (i.e., the 
station).  The other list, the ground elevation list, stores the elevation 
from the DEM. 
The algorithm maintains a list of polygons representing areas below the flood 
elevation that are isolated from the extended reach by expanses of DEM cells 
with elevations greater than the flood elevation. Those polygons represent 
pools of floodwater not  hydraulically  connected to the conveyance portion 
of the floodplain. For purposes of defining cross section geometry, the 
elevations of points contained in one of those pool polygons are stored in 
the ground elevation list as very high (over 1,000,000 feet). The list is 
empty at this point in the analysis. 
Maximum and minimum flood elevations are set by adding and subtracting 50 
feet, respectively, from the initial estimate.  The stations at which the 
flood elevation estimate equals the ground elevation on the DEM are found and 
stored with the other elevation-station data.  For each point on the cross 
section, the station is stored in another list, an expanded station list.  
The difference between the flood elevation estimate and the ground elevation 
is stored in a list, the difference list. If the difference has a different 
sign (plus or minus) than the previous point, the station at which the 
difference is zero is calculated.  That station is added to the expanded 
station list and a zero is added to the difference list. The station and 
difference at the point being investigated are then added to their respective 
lists. 
Values from the difference list are read sequentially.  If two adjacent 
values (depths) are greater than or equal to zero the area under water 
between the two points is average of the values times the difference in the 
corresponding stations from the expanded station list.  The wetted perimeter 
between the two points is the square root of the sum of the squares of the 
depth difference and the station difference. The sum of those pair-wise areas 
and wetted perimeters are kept.  After reaching the ends of the lists, the 
total area and wetted perimeter are used to calculate the hydraulic radius 
and the discharge value associated with estimated flood elevation. 
If the calculated discharge value is less than the reference discharge value 
at the cross section, the minimum flood elevation is replaced by the 
estimated flood elevation.  Otherwise the maximum flood elevation is replaced 
by the estimated flood elevation.  The mean of the minimum and maximum flood 
elevations is used as the revised flood elevation estimate.  If the 
difference between the minimum and maximum flood elevations is greater than 
0.01 feet, the process is repeated up to 20 times.  If the process has been 
repeated 20 times or the difference between the maximum and minimum flood 
elevations is less than 0.01, cross section is attributed with the last flood 
elevation estimate.  The estimate is the reference flood elevation.  The 
initial elevation estimates in the cross section feature table are replaced 
with the reference flood elevation. 
4.2.3.2.4 Remove Pools 
With a centerline, bounding polygon, and set of cross sections identified, 
the mapping algorithms create the irregular spaced grid of points and the 
regularly spaced flood elevation grid. Subtracting the DEM from the flood 
elevation grid yields a difference (depth) grid.  The difference values 
(depths) less than zero are set to  no value.   That is accomplished by 
taking the square root of the difference grid and squaring the result.  A 
function operating on a grid returns  no value  for cells attributed with 
values for which the function is not defined, square root of a negative 
number, for example. 
Setting the cells in the depth greater than zero to one and the remaining 
cells to zero creates a floodplain grid. The floodplain grid is converted to 
a polygon feature table.  The polygons in that table may be a collection 
several disconnected polygons.  If a bridge crossing a stream is reflected in 
the DEM, the floodplain could be divided into an upstream portion 
disconnected from the downstream portion.  Because the flood elevation grid 
covers the entire bounding polygon, any depressions in the ground surface 
lower than the flood elevation and reflected in the DEM will be included in 
the floodplain polygon.  Areas of ponds or  pools  that do not convey 
floodwater must be removed from the cross section geometry used in the flood 
elevation calculations. 
Each polygon in the feature table is tested.  If it does not intersect the 
centerline, the polygon is added to a list of pools and removed from the 
feature table.  As mentioned earlier, as the each point is selected for 
defining cross section geometry, the algorithm checks if it is contained in 
any of the polygons in the list of pools. The elevation of any point 
contained in a pool is set very high, thereby omitting it from the area (and, 
therefore, conveyance) calculations. 
Figure 4.37 shows the initial cross sections assigned to a tributary of Irwin 
Creek in Charlotte, North Carolina. Note the deep gravel mining operation 
north and east of the upstream portion of the study reach. The gravel pit 
affects the three most upstream cross sections.  The first estimate of the 
flood depth grid from that data is shown on Figure 4.38.  Without removing 
the area of the gravel pit from the calculations, the submerged area and 
wetted perimeter are at the bottom of the pit. The flood elevation 
interpolation between the most upstream cross section not in the pit and most 
downstream of the cross sections affected by the pit results in a flood 
surface 50 feet and more below the ground surface at the stream channel.  The 
latter cross section is shown in Figure 4.39. 


Bridge 
Cross Section with Pool 

Station (feet) 
Figure 4.39 Cross Sections Affected by Gravel Pit 
The true channel in Figure 4.39 is shown in red.  Because the centerline does 
not intersect the polygon representing the pool in the gravel pit, that 
polygon is added to the pool list.  The flood elevation estimates are 
recalculated with all points on the cross section shown on Figure 4.39 
between the blue dashed vertical lines assigned very high elevation values.  
The cross sections are attributed with those new estimates. 
Each point in the irregular grid (see Figure 4.33, for example) is attributed 
with a new elevation. Because each point was attributed with the 
interpolation ratio and the identification of the upstream cross section, the 
grid need not be reconstructed.  The new elevation at each point is the 
elevation of the downstream cross section plus the difference between the up- 
and downstream cross sections times the interpolation ratio. 
Removing the pools tends to increase the flood elevation estimates and may 
create more pools. Additional, the polygons associated with deep pools may 
not cover enough of the pool to eliminate its affects.  Note that as in 
Figure 4.39 only that portion of the pool at elevations less than the flood 
elevations are contained in the polygon.  There may be portions of the pool 
with ground elevations greater than the first flood elevation estimate but 
less than the revised flood elevation estimate.  The portion of the cross 
section just left of the left dashed line in Figure 4.39 is such an area. 
Those portions should be omitted from the flood elevation calculations. 
The difference between the last flood elevation calculation and the previous 
estimate is determined at each cross section.  If the sum of the squares of 
those differences is less than one, the pools have been successfully removed.  
Otherwise, the latest depth grid is converted to floodplain grid and, then, a 
polygon, increasing the list of pools.  The flood elevation estimates are 
recalculated, first at the cross sections, then at each of the points in the 
grid of irregularly spaced points.  A new depth grid is created from the 
points and the sum of the squares of the differences in the flood elevation 
estimates is computed.  The process continues until that sum is less than 
one, signaling that all pools have been removed (from the calculations). 
The result of the algorithm applied to the tributary of Irwin Creek is shown 
in Figure 4.40.  Note that the floodplain now includes the stream channel on 
the upper portion of the study reach. 

Bridge 
Because Figure 4.40 shows the flood depth grid, the pool is included.  It has 
depths greater than zero. The grid is converted into floodplain grid and, 
then, a polygon.  The pool is not included in the polygon feature table 
representing the floodplain. Recall that as pools are added to the list of 
pools, the corresponding polygons are removed from the feature table. 
The collection of polygons, without pools, defines the floodplain associated 
with the flood elevations calculated as described. There are at least two 
polygons representing the floodplain calculated for the tributary. Note the 
break in the floodplain at the location labeled  bridge  in Figure 4.40.  The 
placement and orientation of the cross sections used to calculate the flood 
elevations are not necessarily compatible with the floodplain.  A new set of 
cross sections is defined. 
4.2.3.2.5 New Limits and Centerlines 
Centerlines are defined for each polygon.  Up- and downstream limits are 
determined for each polygon and, then, the centerline of each polygon is 
determined.  The new centerlines are used to define the new set of cross 
sections.  Figure 4.41 shows a possible configuration of floodplain polygons, 
centerline, and cross sections. 

Because of the apparent road crossing between Polygons 1 and 2, the algorithm 
that created the direction grid (see Section 4.2.1 Identifying Stream 
Reaches) increased the DEM cells in the region upstream of the crossing until 
all cells had a downstream neighbor.  Doing so misaligns the reach and, 
consequently the centerline, in the vicinity of that region.  Note that the 
floodplain follows the  true  path.  The situation was not mentioned earlier 
but can be seen in Figure 4.38 upstream of the bridge. 
For each polygon, a list is made of all cross sections that intersect the 
polygon.  The list is ordered from upstream (first entry) to downstream (last 
entry).  If the first cross section in the list is the most upstream cross 
section for the reach, it is the upstream limit of the polygon. Likewise, if 
the last cross section in the list is the most downstream cross section for 
the reach, it is the downstream limit of the polygon.  Note that each cross 
section intersects at least one polygon and can intersect more than one 
polygon (i.e., appear in more than one list).  Also, flood plain polygons may 
exist that do not intersect any cross section. 
If the first cross section in the list is not the most upstream cross section 
for the reach, the cross section in the reach just upstream of the first 
cross section in the list is identified.  It is the closest (in terms of 
centerline distance) cross section to the upstream limit of the polygon that 
does not intersect (is outside of) the polygon. The points where those cross 
sections intersect the centerline are identified as the  in  point and  out  
point, respectively.  The point along the centerline midway between those 
points is identified. 
The points on the left and right bounding polygon boundaries that are nearest 
that centerline point are identified and a new cross section is created with 
the three points.  If that new cross section does not intersect the polygon, 
its intersection with the centerline becomes the out point. If it does 
intersect the polygon, that intersection becomes the in point.  Ten such 
cross sections are created so that the distance between the in point and out 
point is less than one foot. 
After ten iterations, a cross section is made from the three points defined 
by the in point and the closest points on the bounding polygon.  If that 
cross section intersects the polygon, it is the upstream limit of the 
polygon.  If that cross section does not intersect the polygon, the in point 
is the original in point from the first cross section in the list (the most 
upstream original cross section that intersects the polygon).  The situation 
occurs when that in point is not contained in the polygon. The cross section 
made from three points may not intersect the polygon.  The first cross 
section in the list does.  That cross section is the upstream limit. 
If the last cross section in the list is not the most downstream cross 
section in the reach, the downstream limit is determined the same way as just 
described for the upstream limit. 
If no cross sections intersect a polygon, the center point of the section of 
centerline contained in the polygon is identified. A cross section is created 
at that point as just described, and that cross section becomes both the 
first and last cross section in the list.  The limits are determined using 
the same algorithm. 
Lists are made to assign a sense of order or direction among the polygons.  
Consider a polygon that intersects cross sections 3 through 10; and another 
polygon that intersects cross sections 4 through 8. Which polygon is the 
 upstream  polygon? 
A list of the floodplain polygons is made from the feature table.  As the up- 
and downstream limits are determined for each polygon, the points (up point 
and down point) at which those limits cross the centerline are added to one 
of two lists corresponding to the polygon list.  The information in the up 
point and down point lists records the extent or  length  of the polygons as 
projections onto the centerline, thus recording a sense of direction.  The 
projection of a polygon onto the centerline is the portion of the centerline 
between the up point and the down point. 
To ensure that each limit intersects the polygon exactly twice, the polygon 
is modified slightly. A buffer at a distance of one cell size is created 
around each limit.  A new polygon is formed from the union of those buffers 
and the polygon.  The islands are removed from the polygon resulting in a 
slightly modified polygon that the limits intersect exactly twice each. 
Centerlines are created between the limits as described in Section 4.2.3.1 
Mapping the Flood Surface. Some of the polygons within a reach may be quite 
narrow (in terms of number of DEM cells). In those situations the centerline 
may not be continuous and/or it may not intersect both limits.  The 
centerline is checked.  If it is not continuous or it does not traverse the 
entire polygon, the polygon is modified slightly. 
The modified polygon is the union of the polygon and a one-cell-size buffer 
around the centerline. The centerline of the new polygon is created and 
checked.  The algorithm is repeated until the centerline is continuous and it 
intersects both limits.  Note that the only difference from the original 
polygon is that the new polygon is two cells  wider  at locations where the 
original polygon was originally one or two cells wide. 
4.2.3.2.6 Conveyance Limits 
Associated with each polygon in the reach is a left boundary, right boundary 
and centerline. Those lines are used to define left and right  conveyance  
limits within the floodplain limits. The conveyance limits are  smooth  
boundaries within which floodwater is conveyed.  The limits are used to 
determine whether the bounding polygon (buffer) is large enough. 
Points are identified along the centerline spaced at a distance of one cell 
size.  Two lists of points, one for each side of the floodplain, are created.  
Each list consists of the points on the floodplain boundary closest to the 
points on the centerline.  Because the centerline points are ordered from 
upstream to downstream, the lists created along the boundaries are ordered 
from upstream to downstream.  A polyline is created from each list. 
Connecting the upstream end points of the left conveyance limit polyline 
(point 1), the polygon centerline (point 2), and the right conveyance limit 
polyline (point 3) creates an upstream limit. Similarly, a down stream limit 
is created by connecting the downstream end points.  The conveyance limit 
polylines are combined with the up- and downstream limits to make a polygon. 
Circles are created, centered at each of the points on the centerline and 
having a radius equal to the average minimum distance to the left and right 
boundaries (i.e., to the points contained in the aforementioned lists).  The 
union of those circles and the polygon discussed in the preceding paragraph 
defines the conveyance polygon. Two parts of the boundary of the conveyance 
polygon are the conveyance limits. 
Before clipping the boundary with the up- and downstream limits, a couple of 
details must be checked. 
The limits are checked to ensure that both  sides  of each limit intersect 
the boundary of the polygon. A side in this context is the portion of the 
limit polyline connecting two points: points 2 and 1 or points 2 and 3.  If 
any side does not intersect the polygon, the point on the polygon boundary 
closest to end point of the side is identified.  In that situation, the limit 
is revised by replacing the end point (either point 1 or 3) with that closest 
point. 
If the up- and downstream limits share an end point, either the right or left 
conveyance limit (or both) does not exist.  In that situation, a circle with 
a radius of one-half a cell size is centered on the common end point.  The 
up- and downstream limits are redefined by replacing the common end point 
with the closest point on the intersection of that circle and the conveyance 
polygon. 
The boundary of the conveyance polygon is clipped with the revised up- and 
downstream limits resulting in a left conveyance limit and a right conveyance 
limit.  Those limits are essentially, the boundaries traced by a circle, 
inscribed in the floodplain, traversing the (extended) reach.  The conveyance 
limits for a typical floodplain are shown in Figure 4.42. 

If the bounding polygon is everywhere  wider  than the conveyance polygon, 
the bounding polygon is wide enough. Because the polygons will probably 
intersect at the up- and/or downstream limits, the  wide enough  test 
compares left and right boundaries.  Specifically, the left and right 
boundaries of the bounding polygon are buffered one cell size.  If neither 
the left nor the right conveyance limit intersects those polygons (the 
buffered boundaries) the bounding polygon is wide enough. Failing that test, 
the bounding polygon is increased. 
Increasing the bounding polygon is, in effect, adding more points to the 
irregularly spaced grid of points. The Minimum distance between the 
centerline and the right or left boundary defined by the outermost clipped 
buffer used to create the irregularly spaced points is the bounding polygon 
buffer distance. If the bounding polygon is not wide enough, more buffers 
(around the centerline) are added, one at a time, at buffering distances 
increasing by one cell size.  Buffers are added until the buffering distance 
is 15 percent greater than the bounding polygon buffering distance. 
The mapping algorithms are applied (see Section 4.2.3.1 Mapping the Flood 
Surface). As each buffer is added, any islands are removed and the boundary 
is defined as a polyline.  The up- and down stream limits (first and last 
cross sections) are extended to the boundary; the boundary is clipped by the 
extended limits; and each cross section is extended to the right and left 
(clipped) boundaries. Points are located along both boundaries at one-cell- 
size intervals. The reference flood elevations are interpolated and each 
point is attributed with the reference flood elevation, the upstream cross 
section identifier, and the interpolation ratio. 
The points are converted to a flood surface grid. The DEM is subtracted 
creating a flood depth grid that is converted to a floodplain grid.  The 
floodplain grid is converted to floodplain polygons (only polygons that 
intersect the centerline).  The boundaries and the up- and downstream limits 
of each polygon are found; the boundaries are clipped and the conveyance 
limits are defined.  The conveyance limits are compared with the outermost 
buffer to check if the new bounding polygon is wide enough. The process is 
repeated until the bounding polygon is wide enough or its width is double its 
original width (five iterations). 
The flood elevations are not recalculated while testing the width of the 
bounding polygon.  As the area available (bounding polygon) for conveyance is 
increased the conveyance will, if anything, increase, thereby decreasing the 
flood elevation estimate.  Decreased flood elevation estimates will not 
increase the estimated floodplain width and, consequently, the width between 
the conveyance limits.  Thus, if the bounding polygon is wide enough for the 
current flood elevation estimates, it is wide enough for recalculated 
estimates based on a wider floodplain. 
4.2.3.2.7 Final Estimates 
The centerline is redefined using the centerlines of the floodplain polygons.  
Recall that three lists were made storing the polygons and the up- and 
downstream points of projections of the polygons onto the centerline. The 
lists are associated with each other through their order.  For example, the 
location of the upstream point of the second polygon in the polygon list is 
the second number in the up point list. The centerlines of the polygons in 
shown in Figure 4.41 are shown in Figure 4.43. 
A fourth list is created to store a reordering sequence, upstream to 
downstream, of records of the other three lists.  The record of the smallest 
value in the upstream point list is the first entry in the reordering list; 
the record next smallest value is the second entry; and so on.  In Figure 
4.43 the order of polygons is 4,2,3,1. 

The projection of each polygon centerline onto the centerline is compared 
with the other projections. The record numbers of polygons whose projections 
are contained in another s projection are identified. Those polygons and 
their associated data are removed from the four lists. Polygon 3 in Figure 
4.43 meets that criterion.  That is, the location (on the centerline) of the 
up point of Polygon 3 is greater than the location of the up point of Polygon 
2; and the location of the down point of Polygon 3 is less than the location 
of the down point of Polygon 2. 
An additional consideration in ordering the polygons is the location of the 
most upstream cross section that (if any) intersects the polygon.  For each 
polygon, the next downstream polygon as defined by the up points is unique 
(after the culling process in the proceeding paragraph has been applied). 
However, there may two or more polygons that intersect the next downstream 
cross section. In that situation, the polygon with the most downstream point 
(the longest one, in some sense) is  chosen.  Other polygons intersecting the 
cross section and their associated data are removed from the four lists. 
Each of the remaining polygons, except the most downstream polygon, share one 
of two possible configurations with a unique (in both up point and next cross 
section) downstream polygon. Either there is a gap between their projections 
(i.e., the location of the down point of the upstream polygon is less than 
that of the up point of the downstream polygon); or their projections 
overlap. Polygons 4 and 2 have a  gap , Polygons 2 and 1  overlap.  
The algorithm  connects  the polygon centerlines by building a list of the 
points (vertices) that defines the connected centerline. The overall list 
begins with the vertices in the most upstream polygon centerline. If there is 
a gap before the next downstream polygon, the list of vertices of the 
downstream polygon centerline is added to the overall list. 
If there is an overlap with the next downstream polygon, some of the points 
in the list of vertices of the downstream polygon centerline must are 
removed.  The intersection of the most upstream cross section and the polygon 
centerline is determined and all points located upstream of that point are 
remove from the list.  The intersection point is inserted at the beginning of 
the list.  The list is then added to the end of the overall list. 
The process continues until the list of vertices associated with the 
centerline of the most downstream polygon is added to the overall list. The 
resulting overall list of points is converted to a polyline, the center of 
the polygons. 
Cross sections are realigned using the center of the polygons and the 
conveyance limits.  Starting at the upstream end, the points where each cross 
section intersects the center of the polygons are determined.  Because the 
intersection may be at more than one point, the algorithm eliminates 
intersections not contained in the conveyance polygons and finds the most 
upstream intersection that is not upstream of the next upstream cross 
section.  That point is the middle, or second, of three points used to define 
the cross section. 
The remaining two points are determined using the conveyance limits 
associated with cross section. The conveyance limits are those associated 
with the conveyance polygon that contains the middle point. The left, or 
first, point is the closest point on the left conveyance limit.  The center 
of polygons may intersect the left conveyance limit of a polygon (Polygon 1 
in Figure 4.43, for example) that  overlaps  the upstream polygon.  The 
algorithm checks for that situation and, if it exists, moves the left point 
downstream along the left conveyance limit one cell size. The right, or 
third, point is identified similarly. 
The cross section in the feature table is replaced by the polyline defined by 
the three (left, middle, and right) points.  Reference flood elevations are 
calculated for the new set of cross sections using the algorithm for solving 
Manning s equation.  The cross sections are attributed with those elevations 
and a new grid of irregularly spaced points (see Figure 4.33) is created. 
The distance between each vertex in the bounding polygon and the center of 
polygons is determined and the maximum of those distances is identified.  The 
center of polygons is buffered that maximum distance and any islands are 
removed.  The resulting polygon is the new bounding polygon. 
A polygon is created from the union of the conveyance polygons and the center 
of polygons buffered one cell size. The islands are removed from that polygon 
and it is converted to a polyline. That polyline is clipped, using the 
upstream most and downstream most cross sections as the up- and downstream 
limits.  The process results in two (left and right) continuous conveyance 
limits.  The cross sections are extended to the bounding polygon and the 
irregularly spaced points are defined and attributed as described in Section 
4.2.3.1 Mapping the Flood Surface. Those points are used to define the flood 
surface and, subsequently, the flood depth grids. 
4.2.3.3 Non-conveyance Areas 
To complete the development of a flood depth grid, areas where flood waters 
are not conveyed but, rather, pond at the elevation on the main stream need 
to be identified, attributed with the main stream flood elevation, and added 
to the flood depth grid.  The approach uses the technique, discussed in 
Section 4.2.1 Identifying Stream Reaches, to define the direction grid. 
Recall that the value of each cell in a direction grid denotes the 
neighboring cell which defines the steepest descending path. If, however, the 
elevation at a cell is lower than that of each of its neighbors, there are no 
descending paths. Such cells are referred to as sinks.  For purposes of 
defining flow networks on DEMs, sinks are typically removed or, more 
accurately described, filled-in. Filling-in sinks may create larger sinks 
composed of several neighboring cells that flow to the original sink.  The 
 filling  process is iterative and continues until all sinks are filled and 
the DEM is  hydrologically  correct. 
A sink could be the result of a DEM including the top of a bridge crossing a 
stream.  The filling routine would continue until all portions of the 
floodplain upstream of the bridge are  filled  to the elevation of the lowest 
point on the bridge or surrounding ground.  The result can be an artificial 
large flat area covering the floodplain and making the determination of flow 
directions difficult. To avoid having to find flow directions within large 
filled areas, the cells associated with certain segments of streams can be 
assigned artificially low elevations, or  burned  into the DEM. 
By adding that portion of the flood depth grid within the conveyance 
boundaries to the DEM, we created sinks at places where the flood depths are 
greater than zero but flows are not conveyed. That is, the non-conveyance 
areas are treated as sinks. 
Consider, for example, the reach shown in Figure 4.44.  It is the most 
downstream reach of the North Fork of the Shenandoah River. 

The centerline of the conveyance polygon and the reach through the downstream 
watershed are burned into the DEM.  By burning the reaches, no  filling  will 
occur within the reach at elevations greater than non-conveyance areas.  That 
is, bridges, for example, will not  overshadow  the floodplain. 
Figure 4.45 shows the portion of the flood depth grid contained in the 
conveyance polygon for the example reach.  Depth values at cells from that 
portion of the flood depth grid are added to the elevation values of the 
corresponding cells in the portion of the DEM covering the watersheds. The 
sinks in the resulting grid are filled.  Call that filled grid the  wet  
grid. 
The sinks in the DEM covering the watersheds are filled, forming the  dry  
grid.  Outside of the conveyance polygon, the nonzero differences between the 
wet and dry grids are the flood depths in the non-conveyance areas. Those 
areas are shown in Figure 4.46.  Merging those cells with the flood depth 
grid contained in the conveyance polygon yields the final flood depth grid. 


4.2.3.4 Calculating Flood Depths for Other Return Frequencies 
A depth frequency relation at a given point provides a means to calculate the 
probability that the point is subject to flooding at least as deep as a given 
depth. For example, such a relation can be used to compare the probabilities 
of a point being flooded by 2 or more feet and being flooded by 5 or more 
feet.  Depth frequency relations are required to estimate expected annual 
losses.  Once the bounding polygon and nonconveyance zones are established, 
depth grids can be created for other floods by attributing the cross sections 
(or equivalent) with the appropriate elevations. 
Depth grids are created for each frequency analyzed on each reach.  For each 
reach, the cross section feature table is edited to include the discharge 
values and flood elevations associated with the 2-, 5-, 10-, 25- 50-, 100-, 
200-, and 500-year floods.  The elevations are subsequently added to the 
feature table representing the grid of irregularly spaced points.  Those 
points are used to interpolate a flood elevation surface grid.  The DEM is 
subtracted from that grid resulting in a flood depth grid for each frequency. 
As described earlier (see Section 4.2.3.2, Default Hydraulic Analyses), 
discharge values are interpolated as power functions of drainage areas.  The 
values are interpolated between the corresponding values at the up- and 
downstream nodes of the reach.  A list of discharge values corresponding to 
the list of flood frequencies is made at each cross section.  If no discharge 
value was determined for a given frequency, the list is given a value of 
zero.  Recall that the largest value in the list is inserted at the beginning 
of the list and used as the reference discharge value in the hydraulic 
analyses. That value is deleted from the list and the calculations for all 
frequencies begin in order of increasing frequency.  As each discharge or 
elevation value is computed, it becomes the reference value for the next 
frequency. 
The cross section geometry is determined as discussed earlier (see Section 
4.2.3.2.3 Revised Flood Elevation Estimates). The list of pools (see section 
4.2.3.2.4, Remove Pools) contains one polygon. That polygon is defined by 
removing the conveyance polygon from the bounding polygon. It is  doughnut-
shaped  with the conveyance are being the  doughnut hole.   Recall that 
elevations at points along a cross section and within a pool are set very 
high.  Thus only those portions of the cross sections within the conveyance 
area are used in the flood elevation computations. 
The elevations are computed twice.  The first computation uses the conveyance 
area from the previous frequency (using the reference values) to estimate the 
flood elevation.  The feature table of irregularly spaced points is updated 
using those elevations and the interpolating values stored in the table (see 
Section 4.2.3.1 Mapping the Flood Surface). Those points are used to develop 
a flood surface grid from which the DEM is subtracted resulting in a flood 
depth grid.  The flood depth grid is converted to a polygon and the 
conveyance area within that polygon is determined. That new conveyance area 
is used to define a new (doughnut-shaped) pool and the process is repeated. 
The second flood depth grid is developed using the conveyance area associated 
with the frequency under investigation. The cells from that grid that are 
contained in the conveyance polygon are extracted to make the conveyance 
flood depth grid.  One such grid is made for each (non-zero) frequency for 
each reach. 
4.2.3.4.1 Depth Frequency 
Creating several flood depth grids and recording the frequencies associated 
with respective flood events allows an estimate of the depth frequency 
relation.  Similarly, given the depth frequency relation at a point, 
determining the depth associated with any frequency flood is a relatively 
easy computation. 
Flood frequency is usually defined as a relation between the flood flow or 
discharge value on a stream reach and the probability that value is exceeded 
in a given year.  Those values are generally taken to be log-Pearson Type III 
distributed.  If Q is a log-Pearson Type III distributed random variable 
denoting the maximum annual discharge value at some location on a stream and 
y0 = log10(q0) then the probability that the Q exceeds q0 in any given year 
is 
8 kk -1 
P[Q > q0] =.. (y G- (km )) e-. ( y-m) dy , (4-10) 
y0 
where m, ., and k are the so-called location, scale and shape parameters, 
respectively, and G(k) is the gamma function of k. In practice, the discharge 
value associated with a certain frequency flood is found using a table of 
normalized random variables.  Each entry in the table represents the number 
of standard deviations between the mean of the distribution and the value, 
y0, yielding the probability P[Q>q0]. If 5Q is the mean and sQ is the 
standard deviation of the Pearson Type III distribution the discharge value, 
q0, with an annual probability, P, of being exceeded is 
q0 = 105+KP ,GQs , (4-11) 
where KP,G denotes the normalized random variable which, as indicated, 
depends on the probability, P, and the coefficient of skew, GQ. In terms of 
the distribution parameters,  
5Q = m + k ; s Q = 
k ; and GQ = 2 (4-12)
.. k 
To assign frequencies to flood depths, the relation between discharge and 
depth must be known. That relation is referred to as a rating curve.  
Typically, rating curves are approximated by a series of power functions.  
That is, rating curves are piecewise described by power functions. For 
successive ranges, the depth is approximately proportional to the discharge 
raised to some power: 
D =aQ_ (4-13) 
If z0 = log10(d0) and y0 = log10(q0), then 
4-70 
y0 = z0 - log10 (a) (4-14)
__ 
and 
8 kk -1 
P[D > d0] =. . ( y - m) e-. ( y-m) dy . (4-15) 
z0 log10 (a ) G(k)
-
__ 
Equivalently, 

, (4-16) 

or 
8 kk -1
z
P[D > d0] =..D ( G- (km ) D ) e-.D (z-mD )dz , (4-17) 
z0 
again, Pearson Type III with .D = ./_ and mD = log10(a) + _m. The mean, 
standard deviation and coefficient of skew for the distribution describing 
depth frequency are 
5D = log10 (a) + _5Q ; s D = _s Q ; and GD = GQ . (4-18) 
In summary, the depth frequency relation at any point can be defined given 
the discharge frequency relation for the reach and the rating curve at the 
point. The user must supply the discharge frequency information and attribute 
the cross sections required in the basic algorithm with three or more pairs 
of elevations and associated frequencies. Note that the algorithm must either 
restrict  reach  to contain only one discharge frequency relationship or the 
user must supply  sub-reach  limits where discharge frequency relationships 
change. 
4.2.3.4.2 Discharge Frequency 
Almost every floodplain study meeting the requirements for use in the Preview 
Model has discharge and frequency values associated with the flood 
elevations. The flood hazard information presented in a Flood Insurance 
Study, for example, will include a description of the hydrologic analysis 
performed and a summary of discharges and associated recurrence intervals 
(i.e., frequencies). The hydraulic models used to develop floodplain studies 
necessarily contain the discharge values associated with the floods being 
modeled. The frequencies of those floods are known. Indeed, most modeling 
efforts begin with a determination of the level of risk to be investigated. 
That level is usually documented. 
Some floodplain studies document the discharge frequency relation in terms of 
the mean, standard deviation, and coefficient of skew of the (base 10) 
logarithms of the discharges. Most studies, however, provide pairs of 
discharge values and associated recurrence intervals.  For example, the 100-
year flood discharge is 26,680 cubic feet per second (cfs) and the 10-year 
flood discharge is 14,110 cfs. 
Because the Pearson Type III distribution has three parameters, three pairs 
of discharge values and recurrence intervals are required to define the 
distribution.  The user supplies at least three such pairs and the algorithm 
finds the mean, standard deviation and coefficient of skew that fits the data 
best. The fitting procedure is equivalent to plotting the data on sheets of 
log-Pearson Type III probability paper, each sheet associated with a 
different coefficient of skew.  A least-squares fit of the data is performed 
on each sheet and the fit yielding the highest correlation coefficient is 
deemed the best fit. 
The algorithm calls on tables of normalized random variables associated with 
a range of skews (-9 to 9 by steps of 0.1) and finds the values associated 
with the recurrence intervals.  A line is fit through pairs of those values 
and the corresponding logarithms of the discharge values.  The fit is by the 
method of least-squares regression.  The coefficient of skew that results in 
the fit with the highest correlation coefficient is chosen.  The slope of the 
line is the standard deviation of the distribution and the log(Q) intercept 
of the line is its mean. 
A plot of correlation coefficient from those fits as a function of 
coefficient of skew has, at most, one local maximum.  If it has no local 
maximum, then the maximum is where the coefficient of skew is either  9 or 9.  
The algorithm exploits that characteristic to converge to the best fit.  The 
algorithm samples the mean of the maximum and minimum values (starting with 
+/- 9) and the next highest value.  If the correlation coefficient associated 
with the next highest value is (not) greater than that associated with the 
mean, the trend is increasing (decreasing) and the mean becomes the minimum 
(maximum).  The process converges to the local maximum. 
4.2.3.4.3 Rating Curves 
Depth grids are created for each elevation in the attribute table for the 
cross sections.  Because the rating curves are determined using the 
logarithms of depths, 1.0 foot is added to each depth in each grid.  The 
recurrence interval of the depth grid is used with the discharge frequency 
relation to determine the discharge associated with the elevation.  In 
increasing order of recurrence interval, the rating curves are computed for 
each successive pair of flood depths for each cell with both depths greater 
than zero. The difference in the logarithms of the depths is divided by the 
difference in the logarithms of the discharge.  The resulting ratio is the 
exponent,_, of the rating curve within the range of discharge values.  The 
proportionality constant, a, is computed by dividing one of the depths by the 
corresponding discharge value raised to the power _. 
The mean, 5D, and standard deviation, sD, are computed as discussed and 
stored in grids associated with the recurrence intervals.  The result is a 
collection of grids that, piecewise, define the flood depth frequency at each 
cell. That information can be used to determine expected annual losses at 
specific sites or over arbitrary regions.  The information can also be used 
to determine depth grids and associated floodplains for frequencies other 
than those supplied by the 
4-72 
user. Algebraic operations on grids are accomplished almost as quickly as the 
same operations on numbers. 
Suppose a user wishes to create the depth grid associated with the 40-year 
flood for a particular reach with a coefficient of skew of G. The probability 
of exceeding that event is 1/40 = 0.025. The normalized random variable 
K0.025,G is found from the table of  normalized random variables. The list of 
user supplied recurrence intervals is searched to identify the 5D and sD 
grids associated with the 40-year flood. The depth grid is 1.0 subtracted 
from10 raised to the power 5D + K0.025,G sD, where, here, 5D and sD represent 
grids. 
4.2.4 Exploring Scenarios 
The Flood Model Oversight Committee identified specific items that they 
believed would enhance the user community s acceptance of the flood model.  
These capabilities provided a level of  What-if  functionality to the user 
allowing them to utilize the flood model as a planning tool. Identified as 
additional capabilities, the Flood Committee established assessing the 
impacts of a levee, flow regulations and velocity as additional capabilities 
necessary for user acceptance. 
The following sections continue the discussion of the hazard development as 
related to the capability of performing this analysis. 
4.3 Coastal Flood Hazard 
4.3.1 Introduction 
Coastal flood hazards in HAZUS are calculated using a general approach and 
methods that are similar to those presently used by FEMA to produce coastal 
Flood Insurance Rate Maps (FIRMs): 
 	
Transects are drawn perpendicular to the shoreline 

 	
Three FEMA-type models (dune/bluff erosion, wave height, wave runup) are 
available to calculate water surface elevations (including wave effects), 
flood depths and flood hazard zones 

 	
Shoreline characteristics and wave conditions at the shoreline determine 
which models are run along each transect 

However, HAZUS flood hazard results may differ from those shown on a coastal 
FIRM for several reasons: 

 	
HAZUS can compute coastal flood hazards for flood return periods between 10 
years and 500 years (FIRMs show the 100-yr flood hazard, and sometimes the 
500-yr flood hazard) 

 	
The topography used by HAZUS will most likely be different than that used in 
the Flood Insurance Study (FIS) to produce a community s FIRMs 

 	
Coastal flood hazard computations made by HAZUS contain simplifications to 
FEMA s erosion, WHAFIS and RUNUP models   this allows users to estimate flood 
hazards with less input and knowledge than that required by FEMA s models 

 	
Coastal flood hazard computations made by HAZUS extend and improve some 
aspects of FEMA s models, by incorporating more recent scientific 
developments   

 	
HAZUS allows the user to examine the effects of long-term erosion and shore 
protection through  what-if  analyses 


4.3.2 User Inputs and Overview of Coastal Flood Hazard Model 
The HAZUS user is required to supply certain information for the coastal 
flood model to run -- without this information no coastal hazard results can 
be produced.  Needless to say, more accurate user-supplied information will 
result in more accurate coastal hazard and economic loss results. The 
following user inputs are required: 
 	
Study region, over which coastal flood hazards will be computed.  The study 
region boundaries must be specified by the user at the beginning of the 
analysis.  Given the region, the model will determine which coastal counties 
are part of the study region and will locate default data (in look-up tables) 
associated with each coastal county. 

 	
Shoreline(s),  The user must identify the start and end of shorelines across 
which coastal flooding will propagate.  Except for small islands, the user 
may choose to divide each shoreline into two or more segments.  Segmentation 
of shorelines is not required (if a shoreline is not segmented, the entire 
shoreline will be attributed with the same shore characterization and 100-yr 
flood conditions). 

 	
Coastal flood return period(s). The user may specify one or more return 
periods between 10 years and 500 years.  A separate coastal hazard analysis 
will be performed for each return period specified. 


Two inputs are required to characterize each segment of a shoreline: 
 	Wave exposure, used to determine whether coastal wave analyses will be 
run, and if so, to determine the peak wave period Tp at the shoreline from a 
look-up table (default wave period data are listed for 364 coastal counties 
in the table): 
-	open coast (full exposure), with over-water fetches > 50 miles 
-	moderate, with fetches between 10 miles and 50 miles 
-	minimal, with fetches between 1 and 10 miles 
-	sheltered, with fetches less than 1 mile (Note that if a shoreline or 
segment is classified as sheltered, the model assumes that no damaging waves 
will affect that shoreline or segment, and no dune/bluff erosion or wave 
analyses will be computed along transects associated with that shoreline or 
segment   a stillwater flood surface will be computed by the model.) 
 	Shore type, categorized using FEMA categories, and used to determine 1) 
which coastal models are run, and 2) average slope and roughness for wave 
runup modeling: -sandy beach, small dune -sandy beach, large dune -
sand/sediment bluff -rocky bluff -rigid shore protection structure 
-open wetland Two inputs are required to characterize 100-yr flood conditions 
at each shoreline segment: 
 	
100-yr Flood Stillwater Elevation (SWEL), obtained from the FIS report or 
from another source 

  
100-yr wave setup, obtained from the FIS report or from another source In 
addition, the user may edit certain parameters calculated by the model: 

 	
10-yr, 50-yr and 500-yr flood stillwater elevations (the model calculates 
values for these based on the 100-yr value and default flood elevation 
ratios; the user may specify site-specific values from the FIS or another 
source) 

 	
significant wave height Hs at the shoreline (the model assumes depth-limited 
wave heights at all shorelines and exposures   except sheltered; the user may 
specify a value less than the depth-limited height) 

 	
peak wave period Tp at the shoreline (the model uses values from look-up 
tables; the user may specify site-specific values less than the default 
values) 


The coastal flood model will take the user inputs and default data, then 
create a stillwater flood surface throughout the entire study region (i.e., 
the coastal model will: 1) overlay a stillwater flood surface over the DEM, 
and 2) identify and remove any isolated pools without a hydraulic connection 
to the flood source).  Subsequent erosion and wave analyses will be used to 
determine where the flood surface will lie above the stillwater surface.  
Figure 4.47 illustrates the overall coastal hazard modeling process; Table 
4.1 compares the coastal flood model process with the Flood Information Tool 
(FIT). 
User Inputs 
  
Study region 

  
Shoreline(s), shoreline segments 

  
Return period(s), n, to be analyzed 

  
Wave exposure  

  
Shore type 

  
100-yr SWEL 

  
100-yr wave setup 




Table 4.1 Coastal Flood Hazard Modeling Process 
(U = User action; R = Required; O = Optional; P = Program completes activity; 
N/A = Activity not supported or undertaken) 


Activity  Flood Model  FIT  
ShorelineCharacterization  1. Limit Study Area  U-R  U-R1  
2(a). Identify Shoreline(s) for analysis  U-R  N/A  
2(b). Draw Shoreline(s) for analysis  N/A  U-R  
3. Segment and characterize shoreline(s): 100-yr swl, wave setup, shoreline 
type, level of protection, wave exposure  U-R2  U-R  
4. Smooth shoreline for transect construction  P  P  
5. Draw transects  P 3  P 3  
6. Edit transects (spacing, location, orientation, length)  N/A  U-O  
ErodedGround 7. Select dune/bluff peak and toe for erosion analysis  P  U-O4  
8. Calculate eroded ground elevations along transects  P  P  
9. Interpolate to determine eroded ground surface between transects  P  P  
100-Yr Flood Hazard  10. Supply 100-yr flood surface polygons (flood zone and 
elevations)  N/A  U-R  
11. Select model type (wave height, wave runup) and constraints  P  N/A  
12. Calculate wave height and/or wave runup elevations along transect  P  N/A  
13. Test for flooding from adjacent transects  P  N/A  
14. Interpolate between transects to develop 100-year flood surface  P  N/A  
15. Repeat steps 2-14 for analysis of other flood sources  U, P  U, P  
16. Merge 100-year flood surfaces to determine highest 100-year flood 
elevation and most hazardous zone at every grid cell  P  N/A  
17. Calculate 100-year depth grid and vertical erosion grid  P  P  
n-Yr FloodHazard  18. Flood elevation ratios for other return period analyses  
U-O5  U-O5  
19. Calculate n-year return period flood surfaces  P  P  
20. Calculate n-year return period depth grids and vertical erosion grids  P  
P  
What-ifs 21. Long-term erosion  U-O  Flood Model  
22. Shore protection  U-O  Flood Model  

1 	User  selects  study area by supplying terrain and flood surfaces over 
a common area. 
2 	The only required user inputs for model are 100-yr swl, wave setup, 
wave exposure and shore type -model will assume regional default wave 
conditions unless user overrides. 
3 	Shore-perpendicular transects are used by model for eroded ground, 
flood hazard and what-if 
calculations; transects are used by FIT for eroded ground and what-if 
calculations. 
4 	Model will select a dune/bluff peak and toe; user can edit locations in 
FIT, but not in model. 
5 	Model will have default values, editable by users in both model and 
FIT. 
4.3.3 Terms and Definitions 
4.3.3.1 Coast 
The coastal flood model distinguishes between four coasts:  Atlantic, Gulf of 
Mexico, Pacific, and Great Lakes. Certain attributes for each coast (e.g., 
reference elevation, default flood elevation ratios, wave regeneration 
factors, dune reservoir, etc.) are contained in look-up tables used by the 
coastal flood model. 
4.3.3.2 Depth-Limited Wave 
Wave characteristics (height and period) are limited by water depth.  When a 
wave travels from deep water into shallow water, the wave  feels  the bottom 
and changes shape, ultimately breaking when the water depth gets too shallow 
to support the wave.  A portion of the wave s energy is dissipated during 
breaking, and smaller waves reform and continue propagating inland. 
The coastal flood model   in keeping with FEMA s flood mapping procedures -- 
assumes that the limitation on significant wave height, Hs, is given by: Hs < 
0.49 ds, where ds = the local stillwater depth (note that this is equivalent 
to a controlling wave height limit of 0.78 times the 
local stillwater depth). 
The coastal flood model also assumes the peak wave period, Tp, will be 
limited by depth along interior fetches (flooded areas along a transect, 
inland of the first dune).  The limitation on interior fetch wave periods is 
given by:  Tp < 1.7 (average ds)0.5, where average ds is the average 
stillwater depth (in feet) along the fetch. 
4.3.3.3 Dune Peak 
The top of a dune, or the highest ground elevation close to the shoreline. 
The coastal flood model takes the highest ground elevation within 500 ft of 
the shoreline as the first peak along a transect; other peaks may exist 
farther landward. 
4.3.3.4 Dune Reservoir 
For a single dune case, the cross-section above the stillwater level and 
seaward of the dune peak (see Figure 4.54).  In the case of multiple dune 
peaks, a different procedure is used to calculate the size of the dune 
reservoir (see Section 4.3.7.2). 
4.3.3.5 Dune Toe 
The seaward base of the dune, where the dune face ends and the beach begins.  
The coastal flood model takes the landward-most intersection of the 10-year 
stillwater elevation and the ground seaward of the dune peak as the dune toe 
(see Figure 4.54).  The dune toe is used by the coastal flood model to 
generate the eroded dune profile.  
4.3.3.6 Fetch 
The overwater distance across which winds blow and waves develop or grow.  
The coastal flood model uses the fetch concept in two ways:  1) the fetch at 
the initial shoreline will determine the transect s wave exposure; and 2) the 
length of interior fetches along a transect will be used in wave regeneration 
calculations. 
4.3.3.7 Flood Elevation Ratio 
The ratio between the n-year stillwater elevation and the 100-year stillwater 
elevation at a shoreline. The coastal flood model uses default flood 
elevation ratios (which the user can override) to calculate n-year stillwater 
elevations from the 100-year stillwater elevation.  The default flood 
elevation ratios used by the coastal model for tidally influenced coasts 
represent average values from 46 Atlantic, Gulf of Mexico and Pacific 
locations.  The coastal model uses different default flood elevation ratios 
for each of the Great Lakes, based on data at 53 locations along the Great 
Lakes. 
4.3.3.8 Freeboard 
For wave runup and overtopping calculations, the vertical distance between 
the stillwater elevation and the top of the barrier upon which waves run up 
(see Figure 4.61). 
4.3.3.9 Reference Elevation 
A water level used in the computation of n-year stillwater elevations.  For 
Atlantic, Gulf of Mexico and Pacific shorelines, the reference elevation is 
taken to be 0.0 NGVD or NAVD (approximately mean sea level).  For the Great 
Lakes shorelines, the reference elevation is taken as the local chart datum 
(varies from 244.0 ft NGVD for Lake Ontario to 601.0 ft NGVD for Lake 
Superior). 
4.3.3.10 Roughness 
For wave runup and overtopping calculations, roughness of the slope upon 
which waves run up. Roughness is characterized by a coefficient that equals 
1.0 for smooth slopes, and that reduces as roughness increases. The coastal 
flood model uses default roughness values for each of the five shore types 
subject to wave runup. 
4.3.3.11 Shore Type 
For coastal flood modeling purposes, all shorelines are classified as one of 
six types specified by FEMA (1995, 2002). See Section 4.3.6.2.  The shore 
type determines which coastal models are used, and which slope and roughness 
factors are used in wave runup and overtopping calculations.  Note that all 
shorelines crossed by a transect will be attributed with the same shore type. 
Users may wish to modify their study region if different shore types exist 
along a transect that crosses multiple shorelines. 
4.3.3.12 Shoreline 
The intersection of the land with the sea, bay or lake under normal 
conditions.  The coastal flood model uses two shorelines: 1) a smoothed 
shoreline, based on TIGER shorelines, and from which transects are drawn 
(this is the shoreline displayed to the user); and 2) a DEM shoreline which 
is the intersection of the terrain along a transect with the reference 
elevation. 
4.3.3.13 Slope 
For wave runup and overtopping calculations, an assumed average slope upon 
which waves run up. Slope is measured as an angle from the horizontal (see 
Figure 4.61).  The coastal flood model uses default slope values for each of 
the five shore types subject to wave runup. 
4.3.3.14 Stillwater Depth 
The vertical distance between the stillwater flood surface and the bottom.  
For wave height calculation purposes, stillwater depth includes the effects 
of wave setup.  For dune erosion and wave runup calculation purposes, the 
stillwater depth excludes the effects of wave setup. 
4.3.3.15 SWEL 
The stillwater elevation at a given location.  Different SWELs occur for 
different return periods at a given location. Flood Insurance Study reports 
typically list the 10-year, 50-year, 100-year and 500-year SWELs along a 
shoreline reach. 
4.3.3.16 Transect 
An imaginary line drawn perpendicular to the shoreline, across which dune 
erosion and wave effects calculations are made.  Transects are drawn 
automatically by the coastal flood model at approximately 1,000-foot 
intervals along the shoreline, and the user cannot add, delete or edit 
transects. Transect lengths vary by coastal county from 2 to 30 miles, based 
on upland elevations and Storm Surge Inundation Maps (2003) reviewed during 
model development.  Any coastal flooding beyond the transects is assumed to 
be stillwater flooding (no wave effects). Transects can be divided into two 
sections: 1) the portion between the transect start and the first dune, bluff 
or barrier encountered; and 2) flooded areas inland of the first 
dune/bluff/barrier (interior fetches). The terrain along a transect will be 
used as input for the erosion, wave height and wave runup models. 
4.3.3.17 Wave Exposure 
The exposure of a shoreline to wave attack.  For the purposes of the coastal 
flood model, shoreline segments will be classified by the user according to 
one of four exposures, from open coast (full exposure   the most severe wave 
conditions) to sheltered (waves can be ignored and flooding will be 
approximated by stillwater flooding).  See Section 4.3.6.1 for wave exposure 
classification. The coastal flood model scales initial wave periods (which 
are used in wave runup calculations) according to wave exposure and coast. 
4-80 
4.3.3.18 Wave Height 
The vertical distance between the trough and crest of a wave.  Since most 
waves are irregular 
(height, length and period are not constant at a given location and point in 
time), the coastal flood model adopts the  significant  wave height concept   
the significant wave height Hs is approximately the average of the highest 
one-third of wave heights at a site.  The significant wave height is 
approximately 63% of FEMA s controlling wave height. 
4.3.3.19 Wave Overtopping 
In some cases, wave runup heights will exceed the freeboard, and waves or 
water will pass over the top of a barrier   this is referred to as wave 
overtopping.  Wave overtopping can lead to the mapping of AO zones. 
4.3.3.20 Wave Period 
The time between passage of two successive wave crests past a fixed point. As 
in the case of wave height, wave periods vary. The  peak  wave period Tp (the 
period associated with the peak of the wave spectrum) is used by the coastal 
flood model for wave runup calculations, in 
keeping with FEMA (1995, 2002) methods. 
4.3.3.21 Wave Regeneration 
The coastal flood model accounts for the growth of waves over interior 
fetches   this process is referred to as wave regeneration. Regeneration is 
governed by average stillwater depth in the interior fetch, length of fetch, 
and coast. 
4.3.3.22 Wave Runup 
The height above the stillwater level that waves rush up a slope after 
breaking (see Figure 4.61). Since incoming waves vary, wave runup also varies 
at a given site.  The coastal flood model uses the average wave runup height 
Rave to determine flood hazards associated with wave runup.  
4.3.3.23 Wave Setup 
Wave setup is a local increase in the stillwater level due to the presence of 
breaking waves (see Figure 4.60).  The coastal flood model assumes the wave 
setup component is a maximum near the shoreline (at the dune toe), and decays 
in the inland direction at a rate of 10% per 100 feet. Wave setup is not 
calculated for interior fetches. 
4.3.4 Shorelines and Transects 
Shorelines used by the coastal flood model are based on state TIGER 
shorelines.  The coastal flood model pre-processes and divides shoreline 
features into three categories: 
 	
mainland 

 	
large island (perimeter > 5,000 ft) 

  
small island (perimeter < 5,000 ft) 
State TIGER shorelines have been pre-processed and smoothed for several 
reasons: 



1.	
a smoothed shoreline follows general shoreline trends and results in better 
transect layout 

2.	
the smoothing process (which uses a < mile buffer out and buffer in, with 
interior rings removed) smoothes shoreline irregularities, eliminates many 
small tributaries and inlets that intersect the shoreline (see Figure 4.48), 
and clusters groups of islands that are separated by narrow waterways 

3.	
smoothing reduces the number of transect crossings, and improves ground and 
flood surface interpolation between transects 



TIGER shoreline 
buffer 

In general, transects are drawn perpendicular to the smoothed shoreline. 
Transects along mainland shorelines begin 500 feet seaward of the smoothed 
shoreline and extend inland to the county default transect length, or to the 
study region boundary, whichever occurs first. Transects on large islands are 
drawn within the convex hull of the smoothed shoreline (see Figure 4.49). The 
number and orientation of transects on the mainland and large islands will 
vary, depending on the shoreline length and orientation. 

Transects on small islands are drawn differently (see Figure 4.50). Eight 
transects are drawn for all small islands, four from each corner of the 
bounding rectangle, and four from the midpoint of each side of the bounding 
rectangle. Note that four transects are reversals of the other four 
transects. 

4.3.5 Shoreline Segmentation 
The coastal flood model allows, but does not require, the user to segment 
mainland and large island shorelines (but does not allow small island 
shorelines to be segmented).  Segmentation permits the user to characterize 
different portions of a shoreline differently (different wave exposures, 
different shore types, different 100-year and n-year SWELs, etc.).  In the 
case of large islands, users should segment the shoreline into two or more 
segments, even if the shore type is uniform along the entire shoreline (one 
for each flood source affecting the island). 
4.3.6 Shoreline Characterization 
Each shoreline segment must be characterized by the user.  This involves two 
basic steps: 
1.	
characterizing the wave exposure and shore type for each segment (see Figure 
4.51) 

2.	
characterizing the 100-year flood and wave conditions for each segment (see 
Figure 4.52). 





4.3.6.1 Wave Exposure 
Wave exposure is used to classify the severity of wave conditions that will 
accompany the 100-year coastal flood event. The coastal flood model divides 
wave exposure into four categories: 
  open coast (full exposure), with over-water fetches > 50 miles 
  
moderate, with fetches between 10 miles and 50 miles 

  
minimal, with fetches between 1 and 10 miles 

  
sheltered, with fetches less than 1 mile 


Users should select a wave exposure for each segment corresponding to its 
fetch under 100-year flood conditions, not under normal conditions.  For 
example, a mainland shoreline segment landward of a low-lying island may have 
a short fetch under normal conditions, but the island may be flooded and the 
fetch may increase under more severe conditions. 
Typical shoreline characteristics associated with wave exposures is 
summarized in Table 4.2, along with the influence of wave exposure on wave 
conditions at the shoreline. 
Table 4.2 Shoreline Wave Exposure Classification for Coastal Flood Model1 

Wave Exposure at Shoreline  Typical Location  Wave Height at shoreline (ft)  
Typical Peak Wave Period at shoreline (sec)  
Exposed, Open Coast, (maximum possible wave conditions -- fully developed 
waves)  shorelines directly fronting Atlantic, Gulf of Mexico, Pacific, Great 
Lakes (deepwater with fetches > 50 miles)  Hs = 0.49 times local stillwater 
depth  Tp   2-20 sec (varies by coast and flood return period)  
Moderate Exposure (wave conditions somewhat reduced from maximum by fetch)  
large bays and water bodies, with fetches between 10 miles and 50 miles  Hs = 
Hs open coast  Tp   0.45 to 0.70 Tp open coast (varies by coast)  
Minimal Exposure (wave conditions significantly reduced from maximum)  small 
bays and water bodies, with fetches between 1 mile and 10 miles  Hs = Hs open 
coast  Tp   0.25 to 0.40 Tp open coast (varies by coast)  
Sheltered (no appreciable waves capable of causing erosion or building damage 
-- essentially stillwater flood conditions)  water bodies, with fetches < 1 
mile   H   0  T   0  

1. Wave heights and periods will vary by region, degree of exposure and flood 
return period.   
4.3.6.2 Shore Type 
Shore type is categorized as one of six types using FEMA (1995, 2002) 
categories: 
  
sandy beach, small dune 

  
sandy beach, large dune 

  
sand/sediment bluff 

  
rocky bluff 

  
rigid shore protection structure 


  open wetland 
Note that when the user classifies a segment as a rigid shore protection 
structure, the user must also input the level of protection provided by the 
structure. For the purposes of the coastal flood model, level of protection 
refers to the return period coastal flood event for which the structure will 
prevent erosion and wave damage.  Stillwater flooding may still occur 
landward of the rigid shore protection structure, depending on the terrain 
elevation. 
Users should note that the shore type selected for a shoreline segment will 
be applied to all transects that cross that segment, and more importantly, 
will be applied to all interior shorelines (shorelines landward of the 
characterized shoreline segment) crossed by those transects.  If a user 
chooses a large study region (e.g., containing islands, bays and mainland), 
transects may cross many shorelines, which will all be attributed with the 
same shore type as the characterized segment.  If all shorelines crossed by a 
transect are similar, this doesn t matter, but if the shorelines crossed by 
the transect are of different shore types, then coastal flood hazard errors 
will result on the mischaracterized shorelines.  The user can avoid this 
problem by selecting smaller study regions such that transects cross fewer 
shorelines. 
The shore type is used by the coastal flood model to determine which coastal 
models (i.e., erosion, wave height, wave runup) are run (see Table 4.3). 
Table 4.3 Coastal Flood Model Selection by Shoreline Type 

Shoreline Type  Model Applied  
Erosion Assessment1  Wave Runup2  Wave Height3  
Sandy Beach, Small Dune  X  X4  X  
Sandy Beach, Large Dune  X  X  X  
Sand/Sediment Bluff  X  X  X  
Rocky Bluff  X  X  
Rigid Erosion Protection Structure  5  X  X  
Open Wetland  X  

1. 
Erosion assessment used	by HAZUS accounts for multiple dunes, unlike normal 
FEMA erosion assessment procedure. 

2. 	
Wave runup model applied by HAZUS is a modified version of FEMA s RUNUP 2.0 
model, using procedures outlined in the USACE (2003) Coastal Engineering 
Manual.  HAZUS wave overtopping results are estimated using wave runup height 
and freeboard, and do not require overtopping rates to be calculated. 

3. 
Wave height model applied by HAZUS will be simplified version of FEMA s 
WHAFIS (Wave Height Analysis for Flood Insurance Studies) model, with 
improved wave regeneration coefficients. 

4. 
HAZUS wave runup model is also applied to sandy beach, small dune shore type.  
	RUNUP 2.0 is not normally applied to this shore type. 

5. 
The erosion model will be applied to a rigid shore protection structure where 
the level of protection provided by the structure (user-defined) is less than 
the return period of the coastal flood being analyzed. 


Table 4.3 shows that the coastal flood model treats the following shore types 
as non-erodible during the n-year coastal flood event:  rocky bluffs, rigid 
erosion protection structure (where level of protection > n), and open 
wetland. The latter type is assumed to be submerged by stillwater flooding 
and not subject to erosion. 
Table 4.3 also shows that the wave height model will be run for all shore 
types, and that the wave runup model will be run for all shore types except 
open wetland.  There is one variation   the wave runup model will not be run 
when a sandy beach (large or small) dune profile is eroded and completely 
submerged by stillwater flooding. 
4.3.6.3 100-Year Stillwater Elevation 
The 100-year stillwater elevation is a required user input.  The parameter 
represents the water surface elevation due to tides and/or storm surge, and 
does not include the effects of wave heights, wave runup or wave setup. The 
value for this parameter can be obtained from the  Summary of Stillwater 
Elevations  table contained in the FIS report. 
Note that some FIS reports do not contain a  Summary of Stillwater 
Elevations  table, but contain a  Summary of Elevations  table instead.  In 
this case, the elevations contained in the table are likely to be BFEs (Base 
Flood Elevations) and not stillwater elevations.  The user should review the 
accompanying text carefully, and if necessary, contact FEMA for stillwater 
elevations. 
4.3.6.4 Wave Setup 
Wave setup is a local rise in the stillwater level due to the presence of 
breaking waves in the nearshore region (see Figure 4.53).  The coastal flood 
model assumes the wave setup reaches a maximum near the dune toe (profile 
intersection with 10-year SWEL), and decays 10% for each 100 ft inland from 
that point.  If the terrain rises above the stillwater flood level and wave 
effects diminish to zero, the coastal flood model terminates the wave setup 
at the same point. 
The 100-year flood conditions tab of the Shoreline Characteristics dialog 
(see Figure 4.52) asks the user whether or not wave setup is included in the 
100-year SWEL.  A careful reading of the Summary of Stillwater Elevations 
table and accompanying text should answer this question.  If the wave setup 
component is included in the 100-year SWEL listed in the table, the user 
should select  yes  and input the wave setup height (in feet). 
The coastal flood model follows FEMA (1995, 2002) procedures, and adds the 
wave setup component to the SWEL (without wave setup) for wave height 
calculation purposes only. Erosion assessment and runup calculations are made 
with the SWEL (without wave setup) values. 
If the FIS is unclear as to whether or not wave setup is included in the 100-
year SWEL, the 10-year, 50-year, 100-year and 500-year stillwater values 
given in the table can be graphed on semi-log paper.  If the 100-year SWEL 
does not contain wave setup, it should fall on a line drawn through the other 
SWELs.  If the 100-year SWEL contains wave setup, it should fall above a line 
drawn through the other SWELs (and the 100-year wave setup value can be 
estimated as the vertical difference between the 100-year SWEL and the line).   

Figure 4.53 Wave Setup Sketch 
4.3.6.5 Other Stillwater Elevations 
The coastal flood model estimates 10-year, 50-year and 500-year stillwater 
elevations from the 100-year stillwater elevation input by the user.  The 
model does this using flood elevation ratios derived from FIS data (see 
Tables 4.4 and 4.5). Given the 100-year stillwater elevation, n and the 
flood elevation ratio corresponding to return period n, Ration, the n-year 
stillwater elevation is calculated using Eq. 4-19: 
n-year stillwater elevation = Ration (100-yr stillwater elevation - RE) + RE 
(4-19) 
Where:  
n = coastal flood return period 
RE = reference elevation (= 0 for Atlantic, Gulf of Mexico and Pacific 
coasts; = chart datum elevation for the Great Lakes, as given in Table 4.6) 
For the Atlantic, Gulf of Mexico and Pacific coasts, flood elevation ratios 
are given in Table 4.4. These ratios (for 10-yr, 50-yr and 500-yr flood 
events) were calculated using data contained in FIS reports for 46 coastal 
locations in 11 states (MA, DE, NC, SC, GA, FL, AL, TX, CA, OR, WA).  The FIS 
reports were published between 1977 and 2000. 
Table 4.4 Coastal Flood Elevation Ratios for Atlantic, Gulf of Mexico and 
Pacific Coastal 
Areas (based on 46 locations: 27 Atlantic, 11 Gulf of Mexico, and 8 Pacific) 


Return Period  Flood Elevation Ratio  
Average  Minimum  Maximum  Std. Dev.  
Overall Sample (n=46)  
10-yr  .64  .44  .89  .12  
50-yr  .88  .51  .96  .07  
500-yr  1.23  1.07  1.80  .13  
Open Coast (n=31)  
10-yr  .65  .48  .79  .10  
50-yr  .89  .76  .94  .04  
500-yr  1.21  1.07  1.38  .08  
Bay Shoreline (n=15)  
10-yr  .64  .44  .89  .15  
50-yr  .87  .51  .96  .11  
500-yr  1.29  1.13  1.80  .19  
Wave Crest Dominant (n=38)  
10-yr  .61  .44  .89  .13  
50-yr  .87  .51  .96  .08  
500-yr  1.25  1.07  1.80  .14  
Wave Runup Dominant (n=8)  
10-yr  .74  .69  .80  .04  
50-yr  .92  .90  .94  .02  
500-yr  1.18  1.12  1.24  .04  

The average flood elevation ratios are listed in Table 4.4, along with 
minimum values, maximum values and standard deviations. Ratios are listed for 
the overall sample (46 locations), for sub-samples broken down by type of 
flood source (open coast or bay), and for sub-samples broken down by the 
controlling factor in the establishment of the cross section (wave crest 
elevation or wave runup elevation). Given the relatively small variation in 
average ratios across flood sources and dominant wave effects, the coastal 
flood model uses the overall sample average ratios as the default 10-year, 
50-year and 500-year ratios for Atlantic, Gulf of Mexico and Pacific coastal 
counties. Ratios for other return periods are interpolated by the coastal 
flood model. 
For the Great Lakes, flood elevation ratios are given in Table 4.5.  These 
ratios (for 10-yr, 50-yr and 500-yr flood events) were calculated using USACE 
data contained in Appendix A of FEMA (1996). Given the relatively small 
variation in average ratios across the Lakes, the coastal flood model uses 
the overall sample average ratios as the default 10-year, 50-year and 500-
year ratios for all Great Lakes coastal counties.  Ratios for other return 
periods are interpolated by the coastal flood model. 
Table 4.5 Flood Elevation Ratios for Great Lakes 
(based on 53 locations: 5 Superior, 10 Michigan, 8 Huron, 25 Erie and 5 
Ontario) 


Return Period  Flood Elevation Ratio  
Average  Minimum  Maximum  Std. Dev.  
Overall Sample (n=53)  
10-yr  .79  .84  .73  .03  
50-yr  .95  .98  .93  .01  
500-yr  1.13  1.16  1.09  .02  
Lake Superior (n=5)  
10-yr  .80  .81  .79  NA  
50-yr  .94  .94  .94  NA  
500-yr  1.11  1.12  1.11  NA  
Lake Michigan (n=10)  
10-yr  .77  .78  .76  NA  
50-yr  .94  .94  .94  NA  
500-yr  1.14  1.15  1.13  NA  
Lake Huron (n=8)  
10-yr  .75  .77  .73  NA  
50-yr  .93  .94  .93  NA  
500-yr  1.15  1.16  1.13  NA  
Lake Erie (n=25)  
10-yr  .83  .84  .80  NA  
50-yr  .95  .98  .94  NA  
500-yr  1.11  1.14  1.09  NA  
Lake Ontario (n=5)  
10-yr  .82  .82  .81  NA  
50-yr  .96  .96  .96  NA  
500-yr  1.13  1.13  1.12  NA  

Calculation of n-year stillwater elevations for the Great Lakes using Eq. 4-
19 requires knowledge of reference elevations (chart datums) for the Great 
Lakes.  These are given in Table 4.6.  
Table 4.6 Reference Elevation (Chart Datum) for Great Lakes 

Great Lake  Chart Datum (ft NGVD)  
Superior  601.0  
Michigan  578.1  
Huron  578.1  
St. Clair  573.1  
Erie  570.0  
Ontario  244.0  

The coastal flood model uses the flood elevation ratios and procedures 
described above to populate the 10-yr, 50-yr and 500-yr stillwater elevations 
in the 100-year Flood Conditions tab of the Shoreline Characteristics dialog.  
The user can edit these model-calculated values using site-specific data from 
the FIS. 
4.3.6.6 Significant Wave Height at Shore 
The coastal flood model calculates the significant height Hs at the shoreline 
during 100-year flood conditions as the depth-limited value given by Eq. 4-
20. 
Hs = 0.49 (100-yr SWEL + 100-year wave setup   reference elevation) (4-20) 
The model assumes depth-limited waves (given by Eq. 4-20) are present for all 
wave exposures (except sheltered) on all coasts, which is a conservative 
assumption for some situations.  The model calculated value is displayed in 
the model-estimated significant wave height at shore window of the 100-year 
Flood Conditions tab of the Shoreline Characteristics dialog. The user can 
override the model-calculated value by checking the user-defined radio button 
and inserting 
a smaller Hs value (the user cannot input a larger value).  Hs units are in 
feet. 
The model-calculated or user-defined Hs will be used by the model for wave 
height and wave runup calculations. 
4.3.6.7 Peak Wave Period 
The coastal flood model uses default peak wave period values from a look-up 
table, where values vary by coast, county and wave exposure.  These are 
displayed in the model-estimated peak wave period window of the 100-year 
Flood Conditions tab of the Shoreline Characteristics dialog. The values in 
the table were derived from USACE (2002) Wave Information Study wave data and 
the USACE (2003) Coastal Engineering Manual (for Atlantic, Gulf of Mexico, 
Pacific and Great Lakes coasts), and from Basco and Shin (1993) (for the 
Chesapeake Bay).  The user can 
override the model-calculated value by checking the user-defined radio button 
and inserting a smaller Tp value (the user cannot input a larger value). Tp 
units are in seconds. 
The following assumptions were made in the calculation of the default peak 
wave periods accompanying the 100-year coastal flood event: 
 	
The peak wave period associated with the 100-year coastal flood event on the 
Atlantic and Gulf of Mexico coasts is the 100-year peak wave period 

 	
The peak wave period associated with the 100-year coastal flood event on the 
Pacific coast is the 5-year peak wave period 

 	
The peak wave period associated with the 100-year coastal flood event on the 
Great Lakes coast is the 3-year peak wave period (Lakes Superior, Michigan, 
Huron and Erie) and the =-year peak wave period (Lake Ontario) 


The coastal flood model assumes Pacific and Great Lakes wave periods are 
weakly correlated with the stillwater flood elevation, unlike the Atlantic 
and Gulf of Mexico coasts where the wave periods and stillwater elevations 
are strongly correlated.  This approach is consistent with that recommended 
by the National Academy of Sciences (1977) and FEMA (1996). 
Peak wave periods (for all coasts) also vary with wave exposure at the 
shoreline.  In general, for a given flood return period, wave periods at the 
open coast are much greater than those associated with moderate or minimal 
exposures.  The coastal flood model uses ratios of minimal-to-open coast and 
moderate-to-open coast peak wave periods to estimate wave periods for lesser 
exposures.  These are summarized below. 
 	
Tp minimal exposure = 0.25 * open coast Tp for Atlantic, Gulf of Mexico and 
Pacific coasts 

 	
Tp minimal exposure = 0.40 * open coast Tp for Great Lakes coast 

 	
Tp moderate exposure = 0.45 * open coast Tp for Atlantic and Gulf of Mexico 
coasts 

 	
Tp moderate exposure = 0.40 * open coast Tp for Pacific coast 

 	
Tp moderate exposure = 0.70 * open coast Tp for Great Lakes coast 

The coastal flood model also scales wave periods for flood return periods 
other than 100-year flood. Holding wave exposure constant, wave periods for 
n-yr flood events were scaled from 100-yr flood event wave conditions based 
on average results for WIS data: 

 	
10-yr flood Tp = 0.84 * 100-yr flood Tp 

 	
50-yr flood Tp = 0.94 * 100-yr flood Tp 

 	
500-yr flood Tp = 1.10 * 100-yr flood Tp 


Note that the n-year scaling factors used for Tp are approximately equal to 
the square root of the scaling factors for Hs given in Table 4.4 (overall 
sample, average values), in keeping with wave height and wave period scaling 
observed in WIS wave data for the Gulf of Mexico. 
4.3.7 Erosion Assessment 
The coastal flood model uses an erosion assessment procedure that is similar 
to the FEMA (1995, 2002) procedure, with a few modifications.  The standard 
FEMA procedure allows for a dune/bluff erosion assessment to be made at a 
single dune (or bluff) as a function of the dune reservoir above the SWEL, 
where the dune reservoir is the cross-section above the stillwater elevation 
and seaward of the dune peak (see Figure 4-54). 

If the dune reservoir is less than a critical size (which varies by coast and 
flood return period), then the dune will be removed during the flood event.  
The eroded profile is shown in Figure 4.55. If the dune reservoir is greater 
than or equal to the critical size, the dune will retreat (see Figure 4.56). 
4.3.7.1 Dune Reservoir Size and Erosion (Retreat or Removal) 
The critical dune reservoir sizes used by the coastal flood model to 
distinguish between removal and retreat are given in Table 4.7.  The Atlantic 
and Gulf of Mexico critical dune reservoir sizes for were taken from FEMA 
(1989).  The Great Lakes values for the 100-year flood event were taken from 
FEMA (1996).  In the absence of better data, Great Lakes values for 10-year, 
50-year and 500-year flood return periods were scaled from the 100-year value 
using the same scaling as evident in the Atlantic and Gulf of Mexico data.  
In the absence of better data, values for the Pacific coast were assumed to 
be equal to the Atlantic and Gulf of Mexico values.  The coastal flood model 
interpolates between the values given in Table 4.7. 

        Original profile 100-yr SWEL 
 10-yr SWEL (toe) 
1       50


     Eroded profile 

Figure 4.55 Eroded Dune Profile 
  Original profile 1 

100-yr SWEL 
1 
1 10-yr SWEL (toe) 
40 


     Retreat profile 
Figure 4.56 Dune Retreat Profile 
Table 4.7 Required Dune Reservoir Sizes (sq ft above SWEL, seaward of peak) 
to Prevent 
Dune Removal 


Flood Return Period  Atlantic, Gulf of Mexico  Pacific1  Great Lakes2 (Erie, 
Huron, Superior, Michigan)  Great Lakes2 (Ontario)  
10  215  215  105  75  
20  280  280  140  100  
50  405  405  205  145  
100  540  540  270  190  
500  1,050  1,050  525  370  

1. 	
No guidance from FEMA for Pacific; values assumed equal to Atlantic/Gulf of 
Mexico 

2. 	
No guidance from FEMA for Great Lakes except 100-yr; values for other return 
periods scaled using Atlantic/Gulf of Mexico trends 


4.3.7.2 Erosion Model Treatment of Multiple Dunes 
FEMA (1995, 1996, 2002) guidelines and specifications for coastal flood 
hazard mapping give no guidance for conducting erosion assessments for 
multiple dunes.  Inasmuch as transects are long in the coastal flood model, 
the likelihood of encountering multiple dunes is great. The procedures 
described herein were developed specifically for the coastal flood model. 
One of the first checks made by the coastal flood model is to determine the 
number and locations of profile crossings of the n-year stillwater elevation 
  this tells the model the location and number of potential reservoir areas 
along the transect.  The next check is to determine the distance between 
crossings   if a downward crossing (landward side of dune) is followed by an 
upward crossing (dune face) within 200 feet, then the coastal model groups 
the dunes together for dune reservoir calculation purposes (see Figure 4.57); 
otherwise, the coastal model treats the dunes spaced more than 200 feet apart 
as individual dunes, each with a separate dune reservoir computation.  The 
dune reservoir for closely spaced dunes is calculated according to Eq. 4-21: 
Reservoir Area = (0.7 *(A1 + A2 + A3 + . . .A n-1) )+ An 	    (4-21) 
Where n = number of dune reservoirs grouped together.  The coefficient 0.7 is 
taken as an average value for ridge-type dunes (with a single, pronounced 
peak, and with the dune reservoir approximately equal to 50% of the total 
dune cross-section above the SWEL   see Figure 4.54) and mound-type dunes 
(with a broad, flat top, where the peak is placed at the landward shoulder, 
and where the dune reservoir is approximately 90% the total dune cross-
section above the SWEL). 
The erosion model uses the same critical reservoir value to prevent removal 
of multiple dunes as it does single dunes. In other words, if Table 4.7 
indicates a dune reservoir of 540 square feet is required to prevent removal 
during a 100-year flood, the erosion model will accumulate dune reservoirs 
(using Eq. 4-21) for closely spaced multiple dunes until such time as the 
total reservoir exceeds the critical value. For example, if the accumulated 
reservoir surpasses the critical value on the third dune, the erosion model 
will remove the first two dunes and retreat the third dune (see Figure 4.58). 
If the total accumulated reservoir for all three dunes is less than the 
critical value, the model will remove all the dunes in the group (see Figure 
4.59). Note that the model fills troughs between eroded/retreated dunes, and 
can handle many profile geometries (not just those shown in Figures 4.58 and 
4.59). 
PEAK 


Reservoir = 0.7A1 + 0.7A2+  +An 

Figure 4.57 Computation of Dune Reservoir for Closely Spaced Multiple Dunes 

Figure 4.58 Closely-Spaced Multiple Dunes with Dune Reservoir Greater than 
the Critical 
Value Required to Prevent Removal 


Figure 4.59 Closely-Spaced Multiple Dunes with Dune Reservoir Less than the 
Critical 
Value Required to Prevent Removal 

4.3.7.3 Erosion Treatment on Interior Fetches 
The coastal model will calculate erosion of dunes and bluffs (using the same 
procedures described above) on interior fetches when the peak wave period at 
the base of the dune/bluff is greater than or equal to the 10-year open coast 
wave period for the coastal county (the coastal model look-up tables contain 
this wave period or allow it to be calculated for each coastal county. No 
interior dune/bluff erosion will be calculated for cases where the peak wave 
period is less than the 10-year open coast wave period for the county. 
4.3.8 Wave Height Model 
The wave height model is predicated on the same basic principles as FEMA s 
WHAFIS model. However, in the case of the wave height model, certain 
simplifications have been made to reduce required user inputs (the level of 
user input required by WHAFIS is substantial; the level of user input 
required by the wave model is minimal). 
Both the wave height model and WHAFIS use similar depth-limitations for wave 
heights (see 
Section 4.3.3.2), and both set wave height = 0 when the stillwater + wave 
setup depth = 0. Both establish the wave crest elevation using the 
controlling wave height (Hc = 1.6 Hs). Both assume 70% of the controlling 
wave height lies above the stillwater + wave setup elevation; therefore, a 
vertical difference of 2.1 feet or more between the wave crest elevation and 
the stillwater + wave setup elevation produces a V zone (0.7 * 3.0 ft = 2.1 
ft). See Figure 4.60. The limiting equation for significant wave height and 
the governing equation for wave crest elevation are given by Eqs. 4-22 and 4-
23: 
Hs = 0.63 Hc < 0.49 ds (4-22) 
Wave crest elevation = stillwater elevation + wave setup + 0.7 Hc (4-23) 
Differences arise between the wave height model and WHAFIS in the delineation 
of fetches along a transect, the incorporation of vegetation effects, and in 
the computation of wave regeneration across flooded fetches. 
 	
WHAFIS requires a user to classify all portions of a transect as a  fetch  or 
an  obstruction . The latter allows wave heights to grow, the former causes 
wave heights to diminish.  The coastal model wave height procedure does not 
require the user to differentiate between fetches and obstructions   it 
treats any flooded section as a fetch where wind energy can be added to waves 
and wave heights can grow. 

 	
The wave height model does not allow for wave height reductions due to the 
presence of marsh grass or other vegetation, or due to other obstructions 
(e.g., buildings)   only water depth limits wave heights and wave crest 
elevations. 


The wave height model uses an improved wave regeneration algorithm.  The 
WHAFIS wave height regeneration algorithm is based on a typical case (fixed 
water depth and wind speed) analyzed by the National Academy of Sciences 
(1977), and its fetch factors are a function of fetch length only. The wave 
height model fetch factors are a function of fetch length, coast and return 
period (the latter two parameters introduce a dependence on wind speed). 

4.3.9 Wave Runup Model 
Wave runup, R, (see Figure 4.61) represents the average height that waves 
rush up a slope. 

F = freeboard (barrier height above SWEL) 
Figure 4.61 Wave Runup Model Definition Sketch 
4.3.9.1 Description of Wave Runup Model 
Wave runup using a minimum of user inputs (only shoreline type and wave 
exposure characterization are required), but with recent procedures contained 
in the USACE (2003) 
Coastal Engineering Manual. 
Wave runup will not be calculated for open wetland shore types, or for any 
store type profile which fails to rise above the flood stillwater elevation 
(submerged profile). 
Wave runup will be calculated using the following variables*: 
  
local peak wave period, Tp 

  
local significant wave height, Hs 

  
average local slope angle, a 


The model will make use of known or default information (coast and county, 
peak wave period and significant wave height at toe) to calculate average 
wave runup, Rave, but can accept user-supplied information (actual peak wave 
period and significant wave height at toe, if known).   
The wave runup model will not use the terrain along a transect to calculate 
the local slope, a. Instead, the model will assign a slope based on the user-
supplied shore type: 
 Shore Type	 tan a 
sandy beach, large dune 	0.5 
rocky bluff 	1.0 
sediment bluff 	0.5 
erosion protection 	1.0 
sandy beach, small dune 	0.1 
Shore type will also be used as a proxy for roughness, which reduces the 
runup height: 
 Shore Type	 roughness factor .r 
sandy beach, large dune	 1.0 
sandy beach, small dune	 1.0 
rocky bluff 	0.85 
sediment bluff 	1.0 
erosion protection 	0.85 
The wave runup calculation procedure used by the coastal model is as follows:  
Step 1. Calculate R2% for smooth slopes based on USACE (2003) Coastal 
Engineering Manual Figure VI-5-3 with: 
R2%/Hs = 1.6 .  for . < 2.5 	(4-24.1) 
R2%/Hs = (-0.2 .) + 4.50 for 2.5 < . < 10.0 	(4-24.2) 
R2%/Hs   2.5 for . > 10.0 	(4-24.3) 
* 	Wave runup is a complex phenomenon, and difficult to predict with a 
high degree of confidence, even with full knowledge of wave and site 
conditions.  Wave runup depends on a variety of factors: incident wave 
characteristics (deepwater wave height and period); local bathymetry and 
topography; local stillwater depth at the toe of the slope; slope angle and 
roughness.  Given the nature of the coastal model (relatively few user 
inputs, and reliance on default data), calculation of wave runup will result 
in approximate wave runup elevations, at best.  Users, who desire a more 
accurate flood surface in areas where wave runup controls cross sections, are 
advised to calculate a flood surface outside of HAZUS and bring that surface 
into HAZUS through the Flood Information Tool. 
where: 
R2% = height of the highest 2 % of wave runups, measured from SWEL . = surf-
similarity parameter = tan a / [(2p Hs)/(gTp2)]1/2 (4-25) Tp = peak wave 
period Hs = local significant wave height at toe of slope 
a = average local slope angle 
Step 2. Take R2% as an approximation of Rmax and assume Rave   0.5 R2% [per 
FEMA (1995, 2002) Guidelines and Specifications]. Account for roughness of 
slope, .r. Thus: 
Rave/Hs = 0.8 .r . for . < 2.5 (4-26.1) Rave/Hs = [(-0.1 .) + 2.25] .r for 
2.5 < . < 10.0 (4-26.2) Rave/Hs = 1.25 for . > 10.0 (4-26.3) 
where: 
Rave = average height of wave runup, measured from SWEL 
The average runup calculation at the first dune/bluff/barrier encountered on 
a transect will use the default values of Tp and Hs built into HAZUS, but 
will allow the user to substitute other values (as long as those values do 
not exceed certain maximum values established for each 
coastal county and wave exposure combination). 
The average runup for dunes/bluffs/barriers on interior fetches will be 
computed using Tp and Hs values resulting from the coastal model s wave 
regeneration procedure (see Sec. 4.3.10). 
Step 3. All computed values of Rave will be constrained by the following 
conditions: 
 	
Rave must be greater than 1.0 ft to be included in mapping, otherwise ignore 

 	
Rave/Hs must be < 2.0 (maximum reasonable value) 

 	
Rave must be < a predetermined value which will vary by county and wave 
exposure (see Table 4.8) 

 	
an upper limit will be placed on the runup elevation [SWEL + Rave], such that 
it cannot exceed the local terrain (local transect peak) by more than 3 feet.  
This corresponds to the case where waves overtop a barrier and an AO zone is 
mapped on the landward side (see Section 4.3.9.2) 


Table 4.8 Upper Limit for Rave Permitted by Coastal Model 

Coast  Wave exposure  Maximum Rave (ft)  
Atlantic/Gulf  Open coast  15  
Moderate  10  
Minimal  5  
Pacific  Open coast  20  
Moderate  10  
Minimal  5  
Alaska, north shore  Open coast  10  
Moderate  5  
Minimal  2  
Alaska, other  Open coast  20  
Moderate  10  
Minimal  5  
Hawaii  Open coast  20  
Moderate  10  
Minimal  5  
Great Lakes1  All exposures  5  

1. maximum runup limits for Great Lakes approximated from Keillor (1998) 
Step 4. Map flood hazard zones associated with wave runup.  V zones will 
occur where runup depths (Rave   ground) are equal to or greater than 3.0 ft.  
A zones will occur where runup depths are less than 3 ft. 
4.3.9.2 Wave Overtopping 
Wave overtopping occurs when runup heights exceed the local terrain 
elevation, and floodwaters flow over and down the backside of a dune remnant, 
structure or barrier.  Unfortunately, 
overtopping is a more complex phenomenon than wave runup, and the overtopping 
rate Qave (cfs/ft-sec) is not well-correlated with runup height Rave (ft). 
Separate overtopping calculations must be made using incident wave 
conditions, structure slope and roughness, runup height and freeboard F. 
FEMA (1995, 2002) Guidelines and Specifications use AO zones (and sometimes, 
zone V) to depict landward flooding from wave runup and overtopping.  The 
Guidelines and Specifications use two tests to evaluate overtopping: 
 	
if the average runup elevation [Rave + SWEL] exceeds the local terrain peak, 
AO zones are mapped (map as AO-2  where runup elevation > 3.0 ft above peak; 
map as AO-1  where runup elevation < 3.0 ft above peak.  See 2002 G&S figure 
D-15). 

 	
If freeboard F is less than approximately 2.0 Rave, compute overtopping rate 
Qave and assign landward flood hazard zones based on rate (map as Zone X if 
Qave < 0.0001 cfs/ft; map as AO-1  if 0.01 cfs/ft < Qave < 0.0001 cfs/ft; map 
as AO-2  if 0.1 cfs/ft < Qave < 0.01 cfs/ft; map as AO-3  if 0.1 cfs/ft < 
Qave < 1.0 cfs/ft; map as Zone V if Qave > 1.0 cfs/ft. See 2002 


G&S Table D-7). 
Because of the complexity of site-specific conditions and overtopping 
calculations, the coastal model will use a methodology that combines both 
tests and simplifies the process, as shown in the Table 4.9. 
Table 4.9 Coastal Flood Model Wave Overtopping as a Function of Wave Runup 

condition F/Rave  Rave/F  (SWEL + Rave) -Peak  Zone/Cross section seaward of 
peak mapped as:  Zone, Cross section landward of peak mapped as:  
ave runup elevbelow peak > 2.00  < 0.50  < 0 ft  (SWEL + Rave)  No flooding  
1.00 - 1.99  0.51 - 1.00  (SWEL + Rave)  AO-1   
ave runup elevabove peak < 1.00  > 1.00  0.00 ft - 1.99 ft  (SWEL + Rave)  
AO-2   
2.00 ft - 2.99 ft  (SWEL + Rave)  AO-3   
> 3 ft  (SWEL + Rave)1  V2, (SWEL + Rave)1  

1 (SWEL + Rave) limited to 3.0 ft above peak elevation 
2 V zone 30-ft wide with cross section at (SWEL + Rave)1, then AO-3   

4-104 
4.3.10 Wave Regeneration 
Wave heights, H, are calculated at any point along an interior fetch using 
Eq. 4-27, subject to the local depth limitation Hs < 0.49 ds. The results are 
used by the wave height model and the wave generation model. 
H = [(Gds-ave)2 + Hi2]0.5 (4-27) 
Where: 
Hi = initial wave height at the beginning of the fetch,  
G = wave height fetch factor, between 0.0 and 1.0 
ds-ave = average stillwater depth over the fetch  
The coastal model computes values for G by coast and return period, using the 
general relationship given by Eq. 4-28 and the coefficients contained in 
Table 4.10 (the model interpolates for other return periods. The general 
relationship is a curve-fit through values computed using standard wave 
growth equations (USACE, 1984) and wind speeds approximated from NBS (1979). 
G = a ln (fetch)  b (4-28) 
Where: 
Ln = the natural logarithm 
a and b = dimensionless coefficients (see Table 4.10) 
fetch = fetch length (in feet) between the start of the fetch and the point 
of 
interest Wave periods, T, are also calculated at any point along an interior 
fetch using Eq. 4-29, subject to the depth limitation Tp < 1.7 (ds-ave)0.5. 
The results are used by the wave runup model. 
Table 4.10 Wave Height Fetch Factor Coefficients for Use in Eq. 4-28 

Return period (yrs)  Atlantic/Gulf of Mexico  Pacific  Great Lakes  
a  
10  0.0928  0.0790  0.0668  
50  0.1015  0.0870  0.0731  
100  0.1065  0.0934  0.0807  
500  0.1149  0.1040  0.0934  
b  
10  0.5374  0.4843  0.4274  
50  0.5637  0.5152  0.4514  
100  0.5779  0.5416  0.4893  
500  0.5926  0.5758  0.5416  

T = [(G ds-ave)2 + Ti2]0.5 (4-29) 
Where: 
Ti = initial wave period at the beginning of the fetch,  
G  = wave period fetch factor, between 0.0 and 1.0 
ds-ave = average stillwater depth over the fetch  
The coastal model computes values for G  by coast and return period, using 
the general relationship given by Eq. 4-30 and the coefficients contained in 
Table 4.11 (the model interpolates for other return periods. The general 
relationship is a curve-fit through values computed using standard wave 
growth equations (USACE, 1984) and wind speeds approximated from NBS (1979). 
G  = c ln (fetch)  d (4-30) 
Where: 
Ln = the natural logarithm 
c and d = dimensionless coefficients (see Table 4.11) 
fetch = fetch length (in feet) between the start of the fetch and the point 
of 
interest 
Table 4.11 Wave Period Fetch Factor Coefficients for Use in Eq. 4-30 

Return period (yrs)  Atlantic/Gulf of Mexico  Pacific  Great Lakes  
c  
10  0.0623  0.0563  0.0507  
50  0.0661  0.0598  0.0539  
100  0.0684  0.0625  0.0571  
500  0.0724  0.0671  0.0625  
d  
10  0.3365  0.3055  0.2756  
50  0.3556  0.3236  0.2927  
100  0.3669  0.3374  0.3099  
500  0.3862  0.3609  0.3374  

4.3.11 What-If Scenarios 
The coastal flood model permits users to investigate the effects of long-term 
erosion and shore protection. 
4.3.11.1 Long-term Erosion 
Long-term erosion can be a consequence of a number of factors at a site 
(storms, interruptions in longshore sediment transport, sea level rise, human 
activities).  However, the purpose of this what-if scenario is not to 
determine causation, but to estimate the effects of long-term erosion on 
coastal flood hazards.  The what-if requires the user to input two quantities 
for each segment considered: 
  
the average annual long-term erosion rate (ft/yr) 

  
a time period (years), over which long-term erosion acts 


Note that erosion rates can be different for different segments but the time 
period must be the same for all segments analyzed.  The product of these two 
quantities is a recession distance, X. Figure 4.62 shows how the coastal 
model will approximate the profile following long-term erosion. The basic 
process calls for copying the original profile and shifting the copy landward 
a distance = X. The original profile is trimmed seaward of the intersection, 
and the copy is trimmed landward of the intersection.  The resulting profile 
is the shifted profile landward of the intersection and the original profile 
landward of the intersection.  Note that this process is slightly different 
from a common assumption that the profile maintains its shape as it recedes 
(Heinz Center, 2000). 
The erosion, wave height and wave runup models can be run on the composite 
profile, and the results can be compared against those computed using the 
original profile. 

4.3.11.2 Shore Protection 
Another what-if permitted by the coastal model is to modify the shore type 
for one or more segments. The user can specify a level of protection for a 
hypothetical shore protection structure along one or more segments, and rerun 
the coastal model. The results can be compared against those associated with 
the original shore types. Note that this what-if assumes the shore protection 
structure will prevent erosion and wave effects from propagating landward of 
the structure (up through return periods associated with the structure s 
level of protection), but will not preclude potential stillwater flooding 
landward of the structure. 
4.4 Other Types of Flooding: 
4.4.1 Sheet Flooding 
Sheet flooding is currently not handled in the Flood Model. This could 
possibly be an enhancement in the future. 
4.4.2 Great Lakes Flooding 
The flooding in the Great Lakes is captured in the Coastal part of the Flood 
Model and follows the same methodology with the coastal flooding on the 
Pacific, Gulf and Atlantic coasts. However, there are a few differences in 
the parameters that are used, such as the reference elevations, required dune 
reservoir to withstand erosion and transect lengths. The reference elevations 
used by HAZUS are as follows: 
Lake Erie  570.0 feet  
Lake Huron  578.1 feet  
Lake Ontario  244.0 feet  
Lake Michigan  578.1 feet  

4-108 
Lake St. Clair 573.1 feet Lake Superior 601.0 feet 
4.5 References 
Riverine 
1.	
Anderson, J.R., Hardy, E.E., Roach, J.T., and Witmer, R.E., 1976, A land use 
and land cover classification system for use with remote sensor data:  U.S. 
Geological Survey Profession Paper 964, 28 p. 

2.	
Buntz, Rob, 1998, Preparing for El Nino: Integrating the HEC-RAS hydraulic 
model with ArcView GIS: ArcUser, 1(2), p. 14-17. 

3.	
Burkham, D.E., 1978, Accuracy of flood mapping:  Journal Research of U.S. 
Geological Survey, Vol. 6, No. 4, July-August 1978, p. 515-527. 

4.	
Dempster, G.R., Jr., 1983, Streamflow/basin characteristics retrieval 
(program E796):  U.S. Geological Survey WATSTORE User s Guide, vol. 4, 
chapter II, section B, 31 p. 

5.	
Federal Emergency Management Agency, 1995, Flood insurance study guidelines 
and specifications for study contractors:  FEMA 37, Washington, DC. 

6.	
Harris, D.D., Hubbard, L.L., and L.E. Hubbard, 1979, Magnitude and frequency 
of floods in Western Oregon:  U.S. Geological Survey Open-File Report 79-553, 
35 p. 

7.	
Hershfield, D.M., 1961, Rainfall frequency atlas of the United States for 
durations from 30 minutes to 24 hours and return periods from 1 to 100 years: 
Technical Paper 40, U.S. Weather Bureau, U.S. Department of Commerce, 
Washington, DC, 71 p. 

8.	
Inman, E.J., 1987, Simulation of flood hydrographs for Georgia streams:  U.S. 
Geological Survey 2317, 25 p. 

9.	
Interagency Advisory Committee on Water Data, 1982, Guidelines for 
determining flood flow frequency: Bulletin 17B of the Hydrology Subcommittee, 
Office of Water Data Coordination, U.S. Geological Survey, Reston, Virginia, 
183 p. 

10. 
Jennings, M.E., Thomas, W.O., Jr., and Riggs, H.C., 1994, Nationwide summary 
of U.S. Geological Survey regional regression equations for estimating 
magnitude and frequency of floods for ungaged sites, 1993: U.S. Geological 
Survey Water-Resources Investigations Report 94-4002, 196 p. 

11. 
Jones, J.L., Haluska, T.L., Williamson, A.K., and Erwin, M.L., 1998, Updating 
flood maps efficiently: Building on existing hydraulic information and modern 
elevation data with a GIS: 

U.S. Geological Survey Open-File 98-200, 9 p. 

12. 
Lepkin, W.D. and DeLapp, M.M., 1979, Peak flow file retrieval (program J980):  
	U.S. Geological Survey Open-File Report 79-1336-I, WATSTORE User s 
Guide, vol. 4, chapter I, section B, 64 p. 

13. 
Miller, J.F., Frederick, R.H., and Tracey, R.J., 1973, 
Precipitation frequency atlas of the western United States, 13 volumes:  NOAA 
Atlas 2, National Weather Service, National Oceanic and Atmospheric 
Administration, U.S. Department of Commerce, Silver Spring, Maryland 

14. 
Sauer, V.B, 1974, Flood characteristics of Oklahoma streams:  	U.S. 
Geological Survey Water-Resources Investigation 52-73, 301 p. 

15. 
Sauer, V.B., 1989, Dimensionless hydrograph method of simulating flood 
hydrographs: Transportation Research Record, National Research Council, 
Washington, DC, ppl 67-78. 

16. 
Sauer, V.B., Thomas, W.O., Jr., Stricker, V.A., and Wilson, K.V., 1983, Flood 
characteristics of Urban Watersheds in the United States:  U.S. Geological 
Survey Water-Supply Paper 2207, 63 p. 

17. 
U.S. Water Resources Council, 1981, Estimating peak flow frequencies for 
natural ungaged watersheds, A proposed nationwide test: U.S. Water Resources 
Council, Hydrology Committee, Washington, DC. 

18. 
Wahl, K.L., Thomas, W.O., Jr., and Hirsch, R.M., 1995, The stream-gaging 
program of the 


U.S. Geological Survey:  U.S. Geological Survey Circular 1123, 22 p. 
Coastal 
19. 
Basco, D.R. and C.S. Shin. 1993. Design Wave Information for Chesapeake Bay 
and Major Tributaries in Virginia.  Department of Civil Engineering, Old 
Dominion University. 

20. 
FEMA. 	1989. Basis of Assessment Procedures for Dune Erosion in Coastal 
Flood Insurance Studies. 

21. 
FEMA. 	1995. Guidelines and Specifications for Wave Elevation 
Determination and V Zone Mapping -- Final Draft. 

22. 
FEMA. 	1996. Guidelines and Specifications for Wave Elevation 
Determination and V Zone Mapping, Great Lakes -- Draft. 

23. 
FEMA. 2002. Guidelines and Specifications for Flood Hazard Mapping Partners, 
Appendix 

D: Guidance for Coastal Flooding Analysis and Mapping. 

24. 
Heinz Center. 2000. Evaluation of Erosion Hazards. 

25. 
Keillor, J.P.  	1998. Coastal Processes Manual   How to Estimate the 
Conditions of Risk to Coastal Property from Extreme Lake Levels, Storms, and 
Erosion in the Great Lakes Basin.  Second Edition. University of Wisconsin 
Sea Grant Institute, Report WISCU-H-98-001. 

26. 
National Academy of Sciences.  	1977. Methodology for Calculating Wave 
Action Effects Associated with Storm Surges. 

27. 
National Bureau of Standards. 	1979. Extreme Wind Speeds at 129 Stations 
in the Contiguous United States. NBS Building Science Series 118. 

28. 
Storm Surge Inundation Maps.  2003. Retrieved from various web sites. 

29. 
U.S. Army Corps of Engineers. 1984. Shore Protection Manual. 

30. 
U.S. Army Corps of Engineers.	  2002. Wave Information Studies Data.  
Retrieved from USACE, Waterways Experiment Station, Coastal Hydraulics 
Laboratory, web site. 

31. 
U.S. Army Corps of Engineers.  2003. Coastal Engineering Manual. 


Chapter 5.  Direct Physical Damage - General Building Stock 
5.1 Introduction 
This chapter describes methods for determining building damage to the general 
building stock associated with riverine and coastal flooding, as well as 
methods for estimating damage related to floodwater velocity. The flowchart 
of the overall methodology, highlighting the building damage component and 
showing its relationship to other components, is shown in Figure 5.1. 

5-2 
5.1.1 Scope 
This chapter focuses on the loss estimation process as defined by HAZUS for 
the flood model. The scope of this chapter includes development of methods 
for estimation of flood damage to buildings and contents given knowledge of 
the occupancy and its typical configuration (e.g., foundation type and 
assumed first floor elevation), and an estimate of the depth of flooding 
throughout the study area. The extent of damage to the building and its 
contents is estimated directly from the depth of flooding by the application 
of a depth-damage curve associated with each occupancy class. Building 
parameters related most directly to flood damage are discussed in Section 5.2 
Section 5.3 discusses the various sources of residential and non-residential 
depth-damage curves from which the HAZUS damage function library was 
developed.  This section also identifies the default damage curve associated 
with each occupancy class.  Section 5.4 provides flood damage models for 
velocity, Section 5.5 describes models for damage reduction resulting from 
warning, and Section 5.6 addresses the flood models treatment of uncertainty.  
Finally, Section 5.7 provides guidance for expert users, including a 
discussion of estimation of benefits associated with flooding and natural 
floodplains. 
5.1.2 Input Requirements and Output Information 
Input required to estimate building damage using depth damage curves includes 
the following two items: 
 	
Occupancy class, foundation type, and assumed first floor elevation, 
typically related to the development era (e.g., pre-FIRM or post-FIRM) 

 	
Depth of flooding throughout the census block 


The  output  from a depth-damage curve is an estimate of the damage to the 
building(s) at a given depth, expressed as a percentage of the replacement 
cost of the structure(s), and later translated into dollars using the 
Valuation module (described in Chapter 14). 
For the analysis of the general building stock in a given census block, the 
Flood Model assumes that the inventory is evenly distributed throughout the 
census block, and area-weighted estimates of damage (rather than depth) are 
utilized to reflect the variation in flood depth throughout the block. (Area 
weighted depths are not used, as many of the depth-damage curves are non-
linear). While the depth damage curves may be applied to a single building as 
well as to all buildings of given type, they are more reliable as predictors 
of damage for large, rather than small, population groups. The user is 
advised to use and report the results with the appropriate amount of caution. 
5.1.3 Form of Damage Functions 
As noted, flood damage functions are in the form of depth-damage curves, 
relating depth of flooding (in feet), as measured from the top of the first 
finished floor, to damage expressed as a percent of replacement cost. 
Depth-damage functions are provided separately for buildings and for 
contents.  For flood loss analyses, buildings are defined to include both the 
structural (load-bearing) system, as well as architectural, mechanical and 
electrical components, and building finishes.  (This varies from the 
earthquake loss analysis definition wherein the structural components are 
limited to the load-bearing system, and the non-load-bearing systems, such as 
architectural, mechanical, electrical, and finishes are defined as  non-
structural .)   
5.2 Building Parameters Related to Flood Damage 
Unlike the earthquake model where the model building type, design level and 
quality of construction all play a critical role in the structure s ability 
to resist earthquake damage, these features do not play a major role in 
damage resistance to flooding.  Unless the floodwaters flow at a high 
velocity and the structure and the foundation become separated, or the 
structure is impacted by flood-borne debris, it is unlikely that a building 
will suffer structural failure in a flood. (Structural failure should be 
distinguished, however, from suffering substantial damage, wherein the damage 
due to inundation exceeds 50% of the structure s total replacement cost and 
the building is considered a total loss.)  In general, it is expected that 
the major structural components of a building will survive a flood, but that 
the structural finishes and contents/inventory may be severely damaged due to 
inundation. 
5.2.1 Building Age 
Building age is a key parameter for estimating expected flood damage.  Age is 
an issue because building codes (and expected building performance) change 
over time, and because development regulations change when a community enters 
the National Flood Insurance Program (NFIP).  For example, if half of the 
total building floor area of a census block was developed prior to entrance 
in the NFIP, then it can be assumed that this half of the exposure will be 
more susceptible to damage resulting from a 100-year flood event. 
To address the issue of age, both sources of the HAZUS Flood Model s 
inventory data (U.S. Census and D&B, see Chapter 3) were reviewed for content 
of this data.  The Census data does provide a range of year of construction 
at the Block Group level.  The ranges are in decades starting with pre-1939 
structures and including every decade up to 1990, as seen in Table 3.8.  It 
can be assumed that typical development practices will result in the 
homogenous development of all blocks within a single block group.  In other 
words, the commercial/industrial development and the residential development 
throughout the block group are assumed to occur concurrently. It is therefore 
possible to distribute the census block group age distribution throughout the 
constituent census blocks, as well as to assume that this distribution is 
applicable to non-residential development. 
5.2.2 Foundation Type and First Floor Elevation 
Because first floor elevation (as determined from foundation type) is another 
key parameter for the estimation of flood damage, information on foundation 
types for the general building stock is required. Within the HAZUS Flood 
Model, all census blocks have been assigned a code identifying the primary 
local flood hazard type as well as a foundation mapping scheme.  The rules 
for the census block mapping schemes are as follows:   
 	
The default value for all census blocks is  R  (riverine).   

 	
Those census blocks that are immediately adjacent to the Great Lakes have 
been coded as  L  for Great Lakes. 

 	
Those census blocks that are within the FEMA Q3 s for coastal regions will be 
coded as  C  (coastal). 

 	
For those blocks with both riverine and coastal hazards, it is assumed that 
the coastal foundation practices will dominate, since the building codes for 
coastal are more stringent.   


5.2.3 Model Building Types 
Although most flood depth-damage functions are independent of structural 
system or construction material, the HAZUS inventory database includes Model 
Building Type as a basic parameter because of the importance of structure 
type to the estimation of earthquake and hurricane damage.  Within the Flood 
Model, the Model Building Types are a simplified version of the ones used by 
the HAZUS Earthquake Model, and are listed in Table 5.1. 
Table 5.1 Model Building Types 

No.  Label  Description  Height  
Range  Typical  
Name  Stories  Stories  Feet  
1  Wood  Wood (light frame and commercial and industrial)  All  1 to 2  14 to 
24  
2  Steel  Steel frame structures including those with infill walls or 
concrete shear walls  Low-rise Mid-rise High-rise  1-3 4-7 8+  2 5 13  24 60 
156  
3  Concrete  Concrete frame or shear wall structures including tilt-up, 
precast, and infill walls  Low-rise Mid-rise High-rise  1-3 4-7 8+  2 5 12  
20 50 120  
4  Masonry  All structures with masonry bearing walls  Low-rise Mid-rise 
High-rise  1-3 4-7 8+  2 5 12  20 50 120  
5  MH  Mobile Homes  All  1  10  

A general discussion of the five (5) structural systems is provided in the 
following sections. 
Wood (W) 
Within the HAZUS model, there are two general types of wood structures:  1) 
small, multi-family or single family dwellings of not more than 5,000 square 
feet of floor area; and 2) large multi-family, commercial, or industrial 
buildings of more than 5,000 square feet of floor area. The essential 
structural feature of the smaller (5,000 square feet or less) buildings is 
repetitive framing by wood rafters or joists on wood stud walls.  These 
buildings may have masonry chimneys and may be partially or fully covered 
with masonry veneer.  Most of these buildings, especially the single-family 
residences, are not engineered but are constructed in accordance with 
 conventional construction  provisions of building codes.  The floors and 
roofs may be sheathed with sawn lumber, plywood or fiberboard sheathing.  
Walls are covered with boards, stucco, plaster, plywood, gypsum board, 
particleboard, or fiberboard, or a combination of several materials.  
Interior partition walls are usually covered with plaster or gypsum board. 
The larger buildings (floor areas greater than 5,000 square feet) have 
framing systems consisting of beams or major horizontal members spanning 
between columns supporting lighter floor joists or rafters. These horizontal 
members may be glue-laminated wood, solid-sawn wood beams, wood or steel 
trusses, or steel beams.  The exterior walls are covered with plywood, 
stucco, plaster, other types of paneling, or a combination of materials.  The 
interior surfaces of the walls and interior partitions usually are covered 
with gypsum board or plaster. 
Steel (S) 
Steel buildings are usually framed with a series of steel girders spanning 
between steel columns supporting beams and various forms of wood or concrete 
floors and roof.  Exterior walls are constructed of steel siding, window 
walls, or cladding panels, but may include cast-in-place concrete shear walls 
or unreinforced masonry infill walls.  If ceilings are used in these 
buildings they are usually suspended acoustical tiles.  These buildings most 
commonly house offices, warehouses, commercial, or industrial occupancies. 
Concrete (C) 
Concrete buildings are those where the structural frames and/or exterior 
walls are made of reinforced concrete, either cast-in-place, pre-cast tilt-
up, or pre-cast elements.  Interior framing can be steel, wood, concrete, 
pre-cast, or any combination.  These buildings are most commonly used for 
office, warehouse, commercial, or industrial occupancies.  Interior finishes 
are usually concrete, gypsum board, or plaster. 
Masonry (M) 
Masonry buildings are those where the exterior walls are masonry, either 
reinforced or unreinforced. These buildings are most commonly used for 
office, warehouse, commercial, industrial, or multi-family occupancies.  
Interior finishes are usually, concrete, gypsum board, or plaster. 
5-6 
Mobile Homes (MH) 
These are prefabricated housing units that are trucked to the site and then 
placed on isolated piers, jack stands, or masonry block foundations (usually 
without any positive anchorage). Floors and roofs of mobile homes usually are 
constructed with plywood and outside surfaces are covered with sheet metal. 
5.3 Building and Contents Damage Due to Flooding 
The HAZUS Flood Model methodology for estimating direct physical damage 
(e.g., repair costs) to the general building stock is fairly simple and 
straightforward.  For a given census block, each occupancy class (and 
foundation type) has an appropriate damage function assigned to it (i.e., 1-
story, no basement), and computed water depths are used to determine the 
associated percent damage.  This percent damage is multiplied by the full 
(and depreciated) replacement value of the occupancy class in question to 
produce an estimate of total full (and depreciated) dollar loss. The  damage 
states  are derived from the percent damage (e.g., 1-10% damage is considered 
slight, 11-50% damage is considered moderate, and 51-100% is considered 
substantial damage. In addition to the library of damage functions (including 
FIA  credibility-weighted  damage functions, as well as various USACE 
District functions), inventory data on foundation type and first floor 
elevation, the presence of basements and estimates of the number of stories 
are required (see Chapter 3 for more discussion of inventory parameters). 
5.3.1 Compilation of Depth-Damage Functions  
Estimation of direct damage to the general building stock (percent damage to 
structures and their contents) is accomplished through the use of readily-
available depth-damage curves, compiled from a variety of sources including 
the Federal Insurance and Mitigation Administration (FIMA) FIA1  credibility 
weighted  depth-damage curves, and selected curves developed by the U.S. Army 
Corps of Engineers (USACE), and the USACE Institute for Water Resources 
(USACE IWR). Functions have been compiled for the USACE Chicago, Galveston, 
New Orleans, New York, Philadelphia, St. Paul, and Wilmington Districts.  
While default damage functions have been selected for each occupancy class, 
all referenced damage functions have been incorporated into the damage 
function library housed within the software and are available for user review 
and selection. 
5.3.1.1 FIMA (FIA) Residential Depth-Damage Curves - Riverine  
FIMA (formerly known as the FIA) is responsible for administering the 
National Flood Insurance Program (NFIP).  FIA has created national depth-
damage curves that are used in the actuarial rate setting process. The 
original depth-damage functions, developed in 1970 and 1973, 
1 
During recent re-organizations at the FEMA, the Federal Insurance 
Administration (FIA) and the Mitigation Directorate were combined into a 
single entity called the Federal Insurance and Mitigation Administration 
(FIMA).  However, because the damage functions were published as the FIA 
credibility-weighted functions, they will continue to be referred as FIA 
depth damage functions. 
are referred to as  theoretical base tables.  Some of the information used to 
develop the initial curves came from post-flood surveys conducted by the 
Corps of Engineers.   
With time, a wealth of damage and loss data has been collected as part of the 
flood insurance claims process.  Losses include both structure and contents 
losses, and are determined relative to actual cash value (depreciated 
replacement cost).  The majority of claims are for residential structures. 
The FIA damage functions are updated annually based on this damage data, as 
part of the flood insurance rate review process. A statistical  credibility  
analysis is used to combine the  theoretical base tables  with the  rate 
review  results.  When sufficient claims exist to provide statistical 
confidence in the results, the depth-damage relationship is based exclusively 
on the claims data.  When claims data are insufficient, the claims data and 
base tables are combined using a weighting process.  The result is two sets 
of curves: pure summaries of claims data, and credibility analyses combining 
available claims data into weighted curves. 
The  Depth Damage  report prepared by the NFIP Actuarial Information System 
(1998)2 indicates that actual depth-damage claims data are available for 10 
categories of structures.  Each category, along with the historic number of 
claims for the period of 1978   1998 are given below. 
1. 
One floor, no basement (255,717 claims) 

2. 
One floor, with basement  (3,310 claims) 

3. 
Two floors, no basement (65,623 claims) 

4. 
Two floors, with basement (86,236 claims) 

5. 
Three of more floors, no basement (28,434 claims) 

6. 
Three of more floors, with basement (28,989 claims) 

7. 
Split-level, no basement (4,278 claims) 

8. 
Split-level, with basement (10,280 claims) 

9. 
Mobile home, no basement (8,182 claims) 

10. 
Mobile home, with basement (285 claims) 


According to the NFIP Actuarial Information System  Credibility and 
Weighting  report (1998), credibility analyses and the resulting weighted 
curves are available for six structure categories. These categories represent 
aggregations of the original ten categories: 
1. 
One floor, no basement  

2. 
Two or more floors, no basement  

3. 
Two or more floors, with basement  

4. 
Split-level, no basement  

5. 
Split-level, with basement  

6. 
Mobile home  


2 While the current discussion references FIA data through 1997, the final 
damage functions incorporated into the current version of the HAZUS Flood 
Model software are based on FIA data through 2001. 
Categories with fewer documented claims will rely more heavily on the 
theoretical base tables. Figure 5.2 presents the six FIA credibility-weighted 
damage functions.  It should be noted that several of the curves are not 
continuous.  That is, because claims data are often sparse, damage values are 
not provided for all depths.  For use in the HAZUS software, missing damage 
values (e.g., damage at 6.0 feet for structures with two floors, no basement) 
have been interpolated between known water depths to facilitate damage 
function application. 
5.3.1.1.1 	Modification of FIA Single Family Residential Depth-Damage Curve 
to Reflect Basement Exclusions 
As noted, the FIA claims data and  credibility-weighted  depth-damage curves 
reflect the limitations of FIA insurance coverage.  That is, damage to items 
not covered by FIA policies (e.g., basement flooring and other finishes) will 
not be represented in the FIA damage functions. Because the intent of HAZUS 
is to estimate total flood damages regardless of insurance  
-4 -3 -2 -1 0 1 2 3 4 5 6 7 8 910111213141516 1718 
Water Depth (ft) 
coverage, the damage functions for structures with basements (two-floor, with 
basement, and split-level with basement) were modified to estimate likely 
basement losses for use as a default damage function. 

As outlined in the NFIP  Dwelling Policy,  a number of coverage exclusions 
apply to basements, as well as to enclosures under elevated structures3. 
Basement exclusions include: 
 	
Personal property (contents) 

 	
Building equipment, machinery, fixtures and components, including finished 
walls, floors, ceilings and other improvements, except for required utility 
connections, fiberglass insulation, drywalls and sheetrock walls and ceilings 
but only to the extent of replacing drywalls and sheetrock walls in an 
unfinished manner (i.e., nailed to framing but not taped, painted or 
covered). 

 	
Enclosure exclusions for elevated Post-FIRM buildings include: 

 	
Personal property (contents) 

 	
Building enclosures, equipment, machinery fixtures and components (except for 
required utility connections and the footings, foundation, posts, pilings, 
piers or other foundation walls and anchorage system as required for support 
of the buildings). 


To estimate likely basement damage relative to FIA policy exclusions in 
basements, a distribution of basement component replacement cost, relative to 
the total structure replacement cost was required.  Residential replacement 
cost models taken from  Means Square Foot Costs  (Means, 2000) were used to 
develop the component cost distribution given in Table 5.2. Table 5.2 also 
indicates the extent of the policy exclusion as it applies to each component.  
As shown, two-thirds of the cost of wall finishes are covered, while one-
third is excluded (typically the cost to tape and finish, and paint the 
walls).  Costs for floor finishes, finished ceilings, light fixtures and 
additional heating ductwork and also assumed to be excluded from coverage. 
3 It should be noted that the FIA V-Zone depth-damage functions reflect full 
coverage in any enclosures, and therefore do not require adjustment. 

o	Electrical plugs, receptacles and switches would need to be replaced 
(33% loss, 100% exclusion) 

o	Ductwork for heating would also require replacement (100% loss, 100% 
exclusion) 


 	-1 foot: 
o	Ceiling suspension system requires replacement (remaining 67% of cost, 
100% exclusion) 

o	Light fixtures require replacement (remaining 67% of cost, 100% 
excluded). 


To complete the damage curve modification, the excluded damage cost for each 
damaged component (expressed as a percent of total building replacement cost) 
was added to the tabulated FIA damage curve.  A total of 7% damage was added 
in at  4 feet, with an additional 4% added in at  1 foot, for a total of 11% 
added damage.  (This equates to the net basement value of 20% minus 8% for 
undamaged walls, and an additional 1% for items already covered by FIA, 
including 2/3 of the cost of wall finishes, and in some cases, part of 
ceiling costs.)  The resulting structure damage curve for  two or more 
floors, with basement  is given in Figure 5.3. Figure 5.4 shows the curves 
for  split level, with basement.    
The resulting curves may be compared to the limited claims data available for 
basement structures with water depths below 0 feet.  While basement coverage 
was discontinued in 1983, it is assumed some of the claims data for basement 
buildings with damage below the first floor reflects claims made prior to the 
implementation of the exclusions.  Review of the claims database for  two-
floor with basement  structures (where 13 percent of the 86,236 claims were 
for structures with water depths less than 0 feet), indicates average damages 
(FPAVG   average damage amount divided by property value) on the order of 7-
15 percent for water depths between  10 and -1 feet, roughly consistent with 
the proposed curve. 
Similarly, contents claims data for a variety of basement structures ranged 
from about 15 - 40 percent for water depths between  10 and -1 feet.  The 
 contents-residential, first floor only  CWDD curve reaches its maximum of 
about 60 percent damage at 10 feet of water, as does the  contents-
residential, first floor and above  CWDD curve.  The difference between these 
two curves is small, and both are based on a limited number of claims; 
approximately 57,000 claims contribute to the credibility weighting for the 
first curve, while only 17,400 claims are available for the second. The 
majority of claims are for depths of five feet and less, and full credibility 
(i.e., resulting curve based entirely on claims history) is available only 
for a depth of 1 foot for the first curve. 
For comparison, detailed contents damage functions developed by the USACE New 
Orleans District (for structures with no basements) were reviewed.  These 
expert opinion-based functions were developed on a component basis for one 
and 2-story structures.  The resulting contents damage function for 1-story 
residence reaches its maximum damage of about 91 percent damage at 5 feet of 
water. At 5 feet, damage to the 2-story structure is 55 percent, and it 
reaches its maximum of 92.5 percent at 14 feet (approximately 5 feet above 
the finished second floor).  This implies an approximate 60/40 split of 
contents on the first and second floor of a 2-story structure. 
This information was used to adjust the FIA  credibility-weighted  depth 
damage functions for contents. Based on the limited claims data, it is 
assumed that approximately one-third of a building s contents will be in the 
basement.  For 2-story structures, the 60/40 first/second-story split was 
used, resulting in a contents distribution of 33 percent in the basement, 40 
percent on the first floor, and 27 percent on the second. 
The adjusted contents curve for  two-floor with basement  (resulting from the 
modification of the  first floor only  FIA curve) is given in Figure 5.5.  At 
 4 feet (the lowest point on the FIA curve), it is assumed that basement 
contents (33 percent of total contents) are a total loss.  The remainder of 
the curve simply reflects the addition of the basement losses.  As shown, the 
curve reaches its maximum of about 93 percent at 10 feet.  To adjust the 
multi-story curve, the slope of the original curve was applied, using the 33 
percent basement damage as the y-intercept.  The resulting curve, also shown 
on Figure 5.5, reaches about 75 percent at 8 feet (total loss of basement and 
first floor contents), and 100 percent at 13 feet (total loss of all contents 
when water is about 4 feet above the second floor. 
Damage (% of RC) 
80 
70 
60 
50 
40 
30 
20 
10 
0 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 91011 1213141516 1718 


Water depth (feet) 

-4 -3 -2 -1 0 1 2 3 4 5 6 7 8 910 11 12 13 14 15 16 17 18 
Water depth (feet) 
HAZUS-MH MR3 Technical Manual 
5.3.1.2 FIMA (FIA) Residential Depth-Damage Curves   Coastal 
In addition to the riverine (non-velocity zone) depth-damage functions, the 
FIA has developed depth-damage curves appropriate to velocity zones, 
designated as V-zone curves by the FIA. These curves are applicable to areas 
subject to three-foot wave action associated with 100-year flood events. 
Three curves each are available for estimating structure and contents 
damage; no obstruction,   with obstruction,  and  combined.   The obstruction 
designation refers to the  possible presence of machinery, equipment or 
enclosures below the elevated floor  (H. Leikin, 1987). 
5.3.1.3 USACE Depth-Damage Curves (Residential and Non-Residential) 
5.3.1.3.1 Chicago District 
The Chicago District developed seven sets of generic structure and content 
damage functions to represent commercial, industrial and public occupancies 
in conjunction with the 1996 Feasibility Study on the Upper Des Plaines River 
in northeast Illinois.  These damage functions, based on models developed by 
the Baltimore and Galveston Districts, classify structures as low, mid and 
high structure vulnerability, and low, mid and high contents vulnerability, 
resulting in seven curves representing the various ranking combinations.  In 
addition, seven residential damage functions (1-story, 2-story, split-level 
with and without basement, and mobile home) were also provided. 
5.3.1.3.2 Galveston District 
The Galveston District has numerous damage functions, including residential, 
and more than 145 different non-residential flood damage functions (IWR 85-R-
5).  These non-residential damage functions include damage to the structure, 
as well as to its inventory and equipment.  The damage functions are based on 
flood damage records, as well as post-event surveys, including surveys 
following Hurricane Claudette in 1979.  The damage curves are currently used 
by Galveston and other Districts, including Tulsa and Fort Worth, and are 
applicable to fresh-water flooding, under slow-rise, slow-recession 
conditions, with little velocity.  In addition, the functions are based on 
damage to structures without basements, as structures along the Texas Coastal 
Plain are built without basements because of the high water table. 
5.3.1.3.3 New Orleans District 
The New Orleans District has developed expert opinion damage functions for 
the flood control feasibility study in Jefferson and Orleans Parishes (GEC, 
1996), and for the Lower Atchafalaya Re-evaluation (GEC, 1997). Depth-damage 
functions include residential and non-residential structure and contents 
damage for four types of flooding: 
  
Hurricane flooding, long duration (one week), salt water 

  
Hurricane flooding, short duration (one day), salt water 

 	
Riverine or rainfall flooding, long duration (two or three days), freshwater 

 	
Riverine or rainfall flooding, short duration (one day or less), freshwater 

Residential structures are classified by number of stories (one, two, or 
mobile home), and by foundation (piers or slab).  Commercial structures are 
classified according to material and typical configuration (metal frame, 
masonry bearing, wood or steel frame).  In addition, non-residential contents 
damage functions are provided for a variety of occupancies: 

 	
Eating/recreation   restaurants, bars, bowling alleys, theatres, etc. 

 	
Groceries/gas stations   grocery stores, bakeries, liquor stores, gas 
stations, convenience stores, etc. 

 	
Multi-family residences   garden apartments, high-rise apartments, condos, 
etc. 

 	
Professional businesses   banks, offices, medical offices, funeral homes, 
etc.  

 	
Public/semi-public   schools, government facilities, utility companies, etc. 

 	
Repairs & home use   auto repair, watch repair, reupholstery, home repair, 
etc. 

 	
Retail & personal   department stores, furniture stores, clothing stores, 
barbershops, laundromats, etc. 

 	
Warehouse & contractor services   warehouses, manufacturers, etc. 


Structures are assumed to be  no basement  structures, as damage curves 
typically begin at  1 foot of water, and the reference point for water depth 
appears to be the top of the finished floor, based on review of detailed 
component loss tables. 
5.3.1.3.4 New York District 
As part of the Passaic River Basin studies, the New York District developed a 
variety of residential and non-residential structure and contents damage 
functions, for structures with and without basements.  Also included in the 
damage functions are models for 10 utility facilities, such as electric power 
substations, pump houses, and water treatment plants. 
Residential damage functions include bi-level, cape, colonial, mobile home, 
split, two-family and other types. Commercial structures are handled with one 
damage function, while for contents assessment; the occupancies are organized 
into 10 different groups.  For commercial facilities, both structure and 
contents damage functions consider the presence of a basement.  In addition, 
there are 35 different industrial damage functions. 
5.3.1.3.5 Philadelphia District 
The U.S. Army Corps of Engineers Philadelphia District published coastal 
depth-damage curves as part of a 1991 study entitled  Delaware Coast From 
Cape Henlopen to Fenwick Island, Delaware; Reconnaissance Study Report.   The 
depth-damage curves consider various structural characteristics, including 
location (A-zone vs. V-zone), height (1-, 1.5-, and 2-story), foundation 
(structures on piles and not on piles), and construction material for 
structure not on piles (wood frame, concrete block, or masonry).  The curves 
were based on  previous studies of similar areas and FIA curves  and predict 
damage to both structures and contents.  However, these studies are not 
documented and the Corps no longer uses the approach laid out in the Delaware 
Coast report.  As such, the Delaware curves are included herein solely as an 
example data set, and are not relied upon for the development of HAZUS damage 
evaluation methodology. 
5.3.1.3.6 St. Paul District 
The St. Paul District has estimated damage to the Grand Forks area as part of 
a flood control project, as documented in  General Reevaluation Report and 
Environmental Impact Statement  (1998). The depth-damage functions used in 
that report include residential and non-residential functions, whose source 
is the Vicksburg District.  All non-residential uses, including commercial, 
professional (e.g., offices), industrial, public, semi-public (e.g., 
churches), recreation, and warehouses are represented by one single damage 
function. 
It should be noted that these damage functions are identified as  no 
basement.   For the Corps  Grand Forks application, it appears that the Corps 
essentially shifted the damage curve to the left for structures with 
basements, allowing damage to occur at lower water depths. 
5.3.1.3.7 Wilmington District 
The Wilmington District provided 13 residential structure and contents damage 
functions, and 49 non-residential structure functions which may be applied to 
contents using a contents-to-structure value ratio, as well as a number of 
damage functions reflecting erosion.  The residential damage functions 
consider structure size (1-, 1.5-, and 2-story, split-level, and mobile 
home), and configuration (basement, no basement, high-raised, high-raised 
with = living area below). Non-residential classes include: apartments, 
appliances, auto dealership, auto junk yard, auto parts, bait stand, bank, 
barber shop, beauty shop, boat stalls, book store, bowling alley, business, 
church, cleaners, clinic (medical), clothing, dentist office, department 
store, doctor s office, drug/super, funeral home, furniture, garage, halls, 
hardware, hotel, jewelry, laundry, liquor, lumber, market/super, 
market/drive, motel, newspaper, office building, post office, private club, 
restaurant, rest home, school, service station, theater, theater (drive-in), 
TV station, tavern, variety store, wash-a-teria, and warehouse. 
5.3.1.3.8 USACE Institute for Water Resources (IWR) 
The USACE Institute for Water Resources (IWR) is working on a compilation 
project of "past flood damage surveys" with the identified objective of 
compiling residential and business damage functions and content valuation 
functions.  The outcome of this study will be a set of recommended depth-
damage functions for use throughout the Corps (e.g., a national standard). To 
date, the only model that has been finalized is for single family residential 
structures, without basements (IWR concluded that available data on basement 
damage are insufficient to develop statistical functions at this time.).  The 
IWR recommended structure and contents model for single family residential 
structures (no basement) is included in the HAZUS damage function library. 
5.3.1.4 Other Coastal Depth-Damage Functions 
The Tampa Bay Regional Planning Council as part of a 1988 contingency 
planning study developed additional coastal depth-damage functions.  Depth-
damage curves (or  loss coefficients ) based on historic property damage were 
presented.  These relationships consider structure location (A-zone vs. V-
zone) and use (single family, multi-family, mobile home, commercial, 
industrial, and non-residential). No contents damage functions were 
presented. 
5.3.2 Default Structure and Contents Damage Curves 
Default curves to estimate structure and contents damage for Level 1 analyses 
have been selected for each HAZUS occupancy class, for conditions of riverine 
and coastal flooding.  These curves are identified in Tables 5.3 and 5.4.  It 
should be noted that the default riverine damage functions for residential 
structures with basements have been modified from the original FIA 
relationship (which reflects FIMA (formerly FIA) policy exclusions) to 
reflect total damage.  The modification is documented in Section 5.3.1.1.1. 
Table 5.3 Default Damage Functions for Estimation of Structure Damage 

HAZUS Occ. Class  Flooding Type/Zone  Curve Source  Curve Description  
Riverine/ A- Zone  FIA  credibility-weighted  depth-damage curves (CWDD)  1 
floor, no basement 2 floor no basement 2 floor, split level, no basement  
RES1  Riverine/ A- Zone  Modified FIA CWDD:  EQE-modified versions of FIA 
CWDD: 2 floor, w/ basement 2 floor, split level, w/ basement  
Coastal/ V- Zone  FIA V-Zone Damage function  Combined curve (average of with 
and without obstruction)  
Coastal/ A- Zone  FIA V-Zone Damage function  Combined curve (average of with 
and without obstruction)  
RES2  All Zones  FIA CWDD  Mobile home  
RES3  All Zones  USACE   Galveston*  Apartment  

Notes: 
* 	
All depth-damage curves developed by the USACE Galveston District are assumed 
to represent structures with no basement. 

Notes: 

* 	
All depth-damage curves developed by the USACE Galveston District are assumed 
to represent structures with no basement. 


Table 5.3 Default Damage Functions for Estimation of Structure Damage 
(Continued) 

HAZUS Occ. Class  Flooding Type/Zone  Curve Source  Curve Description  
RES4  All Zones  USACE   Galveston  Average of  Hotel  and  Motel Unit   
RES5  All Zones  No RES5 curves available   use RES6  
RES6  All Zones  USACE   Galveston  Nursing Home  
COM1  All Zones  USACE   Galveston  Average of 47 retail classes  
COM2  All Zones  USACE   Galveston  Average of 22 wholesale/warehouse classes  
COM3  All Zones  USACE   Galveston  Average of 16 personal and repair 
services classes  
COM4  All Zones  USACE   Galveston   Average of  Business  and  Office    
COM5  All Zones  USACE   Galveston  Bank  
COM6  All Zones  USACE   Galveston  Hospital  
COM7  All Zones  USACE   Galveston   Average of 4 medical office/clinic 
classes  
COM8  All Zones  USACE   Galveston   Average of 15 entertainment & recreation 
classes  
COM9  All Zones  USACE   Galveston   Average of 3 theatre classes  
COM10  All Zones  USACE   Galveston  Garage  
IND1  All Zones  USACE   Galveston   Average of 16 heavy industrial classes  
IND2  All Zones  USACE   Galveston   Average of 14 light industrial classes  
IND3  All Zones  USACE   Galveston   Average of 10 food/drug/chemical classes  
IND4  All Zones  USACE   Galveston   Average of 4 metals/mineral processing 
classes  
IND5  All Zones  No IND5 curves available   use IND3  
IND6  All Zones  USACE   Galveston   Average of 8 construction classes  
AGR1  All Zones  USACE   Galveston   Average of 3 agricultural classes   
REL1  All Zones  USACE   Galveston  Church  
GOV1  All Zones  USACE   Galveston   Average of  City Hall  and  Post Office   
GOV2  All Zones  USACE   Galveston   Average of  Police Station  and  Fire 
Station   
EDU1  All Zones  USACE   Galveston  Average of  School  and  Library   
EDU2  All Zones  USACE   Galveston  Average of  School  and  Library   

Table 5.4 Default Damage Functions for Estimation of Contents Damage 

HAZUS Occ. Class  Flooding Type/Zone  Curve Source  Curve Description  
Riverine/ A- Zone & Coastal/ A- Zone  FIA  credibility-weighted  depth-damage 
curves (CWDD)  Residential contents   1st floor only (for 1 floor, no 
basement) Residential contents   1st floor and above( for 2 floor no 
basement, and 2 floor, split level, no basement)  
RES1  Riverine/ A- Zone  Modified FIA CWDD:  EQE-modified versions of FIA 
CWDD: Residential contents   1st floor and above (for 2 floor, w/ basement, 
and 2 floor, split level, w/ basement)  
Coastal/ V- Zone  FIA V-Zone Damage function  Combined curve (average of with 
and without obstruction)  
RES2  All Zones  FIA CWDD  Contents   Residential   Mobile Home  
RES3  All Zones  USACE   Galveston *  Apartment contents  
RES4  All Zones  USACE   Galveston   Average of  Hotel   Equipment  and 
 Motel Unit - Inventory   
RES5  All Zones  No RES5 curves available   use RES6  
RES6  All Zones  USACE   Galveston  Nursing Home  Equipment  
COM1  All Zones  USACE   Galveston   Average of 47 retail classes   equipment 
and inventory, when available  
COM2  All Zones  USACE   Galveston   Average of 22 wholesale/warehouse 
classes   equipment and inventory, when available  
COM3  All Zones  USACE   Galveston   Average of 16 personal and repair 
services classes   equipment and inventory, when available  
COM4  All Zones  USACE   Galveston   Average of  Business   inventory  and 
 Office, equipment   
COM5  All Zones  USACE   Galveston  Average of Bank inventory and equipment  
COM6  All Zones  USACE   Galveston  Average of Hospital inventory and 
equipment  
COM7  All Zones  USACE   Galveston   Average of 4 medical office/clinic 
classes, inventory and equipment, when available  
COM8  All Zones  USACE   Galveston   Average of 13 entertainment & recreation 
classes, inventory and equipment, when available  
COM9  All Zones  USACE   Galveston   Average of 3 theatre classes, equipment  
COM10  All Zones  USACE   Galveston  Garage, inventory  
IND1  All Zones  USACE   Galveston   Average of 16 heavy industrial classes, 
inventory & equipment, when available  

Notes: 
* 	All depth-damage curves developed by the USACE Galveston District are 
assumed to represent structures with no basement. 
Table 5.4 Default Damage Functions for Estimation of Contents Damage 
(Continued) 

HAZUS Occ. Class  Flooding Type/Zone  Curve Source  Curve Description  
IND2  All Zones  USACE   Galveston   Average of 14 light industrial classes, 
inventory & equipment, when available  
IND3  All Zones  USACE   Galveston   Average of 10 food/drug/chemical 
classes, inventory & equipment, when available  
IND4  All Zones  USACE   Galveston   Average of 4 metals/mineral processing 
classes, inventory & equipment, when available  
IND5  All Zones  No IND5 curves available   use IND3  
IND6  All Zones  USACE   Galveston   Average of 8 construction classes, 
inventory & equipment, when available  
AGR1  All Zones  USACE   Galveston   Average of 3 agricultural classes, 
inventory & equipment, when available   
REL1  All Zones  USACE   Galveston  Average of  Church  inventory and 
equipment  
GOV1  All Zones  USACE   Galveston   Average of  City Hall  and  Post Office  
equipment  
GOV2  All Zones  USACE   Galveston   Average of  Police Station  equipment 
and  Fire Station  inventory  
EDU1  All Zones  USACE   Galveston   Average of  School,  Equipment and 
 Library,  Inventory  
EDU2  All Zones  USACE   Galveston   Average of  School,  Equipment and 
 Library,  Inventory  

Notes: 
 	All depth-damage curves developed by the USACE Galveston District are 
assumed to represent structures with no basement. 
5.3.2.1 Commentary on the Assignment and Implementation of Coastal Damage 
Functions 
Several recent studies point to the need for distinguishing between coastal 
A-zones and riverine A-zones. While the dominant form of damage to buildings 
in the latter is inundation, buildings in coastal A-zones are often subject 
to more severe flood forces.  Recent post-disaster building damage 
assessments in coastal areas have shown buildings in coastal A-zones are 
often damaged by waves, high velocity flows, scour and erosion, and floating 
debris.  Conditions in coastal A-zones are probably closer to those in V-
zones than non-coastal A-zones.  This observation is supported by FEMA-
sponsored laboratory tests of breakaway wall failures.  The tests found 
typical wood frame wall panels fail under wave conditions much less severe 
than the 3-foot wave that presently divides V-zones and coastal A-zones.  
Finally, FEMA's newly revised Coastal Construction Manual introduces the 
coastal A-zone as a flood hazard zone distinct from the non-coastal A-zone. 
Design and construction recommendations for coastal A-zones are similar to 
those required for V-zones. Accordingly, FIA V-Zone damage functions have 
been selected as the default damage function for single-family residential 
structures in coastal A- zones as well as coastal V-zones.  
While the importance of reflecting the differences between coastal and 
riverine flooding damage is recognized, well-documented coastal damage 
functions are available only for the RES1 occupancy category (single family 
homes).  However, since single-family dwellings make up the majority of the 
coastal exposure in the default database, this assumption is deemed adequate. 
The USACE Galveston non-residential damage functions have been selected as 
defaults in both riverine and coastal areas, until more detailed non-
residential coastal damage functions become available. 
In general, A-zone and V-zone depth-damage curves define water depth 
differently. 
 	
In A-zones (non-velocity zones), the water depth is relative to the top of 
the finished flooring of the lowest floor, excluding the basement. 

 	
In V-zones, the water depth is relative to the bottom of the floor beam of 
the lowest floor. 


This variation in reference depth requires that particular attention be paid 
to both default distributions of foundations and their associated height 
above grade in Level 1, and individual building elevations being analyzed in 
Level 2 analyses. 
The HAZUS Flood Model addresses direct damage to buildings and their 
contents.  Readers are referred to the recent Heinz Center (2000) report   
The Hidden Costs of Coastal Hazards: Implications for Risk Assessment and 
Mitigation   for an expanded discussion of direct costs and other costs 
associated with coastal flood disasters. 
5.4 Building Damage Due to Velocity 
Flooding with significant velocity can result in structure and content damage 
in addition to the damage caused by simple inundation.  The USACE notes that 
velocity is a    major factor aggravating structure and content damage   and 
that the    additional force creates greater danger of foundation collapse 
and forceful destruction on contents  (USACE, 1996a).  
The relationship between velocity and damage has been addressed in a number 
of models and methods.  For example, the Ontario Ministry of Natural 
Resources provides the following guidelines (OMNR, 1997) for structures: 
 Structural Integrity (structures above ground) -A depth of 0.8m is the safe 
upper 
limit for the above ground/super structure of conventional brick veneer, and 
certain types of concrete block buildings.  The structural integrity of 
elevated 
structures is more a function of flood velocities (e.g., erosion of 
foundations, 
footings or fill) than depth. The maximum permissible velocity depends on 
soil 
type, vegetation cover and slope but ranges between 0.8-1.5m/s (2.62 ft/sec   
4.92 
ft/sec) . 
Within the HAZUS flood model, velocity-based building collapse curves 
developed by the Portland District of the U.S. Army Corps of Engineers have 
been utilized (except for manufactured housing).  These curves (as provided 
in IWR 85-R-5, 1985) relate collapse potential (e.g., collapse or no 
collapse) to overbank velocity (in feet per second) and water depth (in feet) 
for three building material classes (wood frame, steel frame, and masonry or 
concrete bearing wall structures). The Portland collapse curves for wood 
frame, masonry and steel frame are given in Figures 5.6, 5.7 and 5.8, 
respectively.  
For application within the flood model, it has been assumed that below 
velocities of 2 feet per second, collapse potential is extremely low and 
damage is due to inundation only.  Further, the  masonry and concrete bearing 
wall  model is applied to both the concrete and masonry HAZUS building types. 
For manufactured housing (MH), velocity damage curves are based on 
information developed by FEMA (FEMA, 1985), relating velocity and depth to 
drag forces.  Based on information provided within that document, it is 
assumed that drag forces exceed MH design capacity at around 13 pounds per 
linear foot of home length, and it is possible to determine the relationship 
between depth and velocity for this threshold level of drag force.  This 
results in a simpler velocity damage function for MH than for the other 
material types; for a given depth, if the velocity equals or exceeds the 
collapse velocity, the structure is assumed to collapse. 
Velocity-depth damage functions as implemented within HAZUS are provided in 
Tables 5.5 through 5.8 for building types wood, masonry and concrete, steel, 
and manufactured homes, respectively.  These functions relate velocity and 
depth to collapse potential.  If it is determined that a given building or 
building type collapses, the building is assumed to be a total loss, and the 
percent damage is reset to 100. If the model indicates that the velocity does 
not lead to collapse, damage is estimated based on inundation levels only. 



Chapter 5.  Direct Physical Damage - General Building Stock 
Table 5.5 Velocity-Depth Damage Relationship for Wood Buildings 

Material  # Stories (hgt)  Depth Threshold in feet DT(hgt)  Velocity 
Threshold in feet/sec VT(hgt)  Collapse Potential  
V < 2 fps any Depth  V < VT(hgt) D < DT(hgt)  V < VT(hgt) D >= DT(hgt)  V >= 
VT(hgt) any Depth  
Wood  1 story  10  5.34  no collapse  no collapse  collapse  collapse if D > 
268.38V-1.9642  
Wood  2 story  15  4.34  no collapse  no collapse  collapse  collapse if D > 
268.38V-1.9642  
Wood  3 story  20  3.75  no collapse  no collapse  collapse  collapse if D > 
268.38V-1.9642  
Wood  4+ stories  no collapse  no collapse  no collapse  no collapse  

Table 5.6 Velocity-Depth Damage Relationship for Masonry and Concrete 
Buildings 

Material  # Stories (hgt)  Velocity Threshold in feet/sec VT(hgt)  Collapse 
Potential  
V < 2 fps  V < VT(hgt)  V >= VT(hgt)  
Masonry & Concrete  1 story  6.31  no collapse  no collapse  collapse if D > 
525.09V-2.0406  
Masonry & Concrete  2 story  7.47  no collapse  no collapse  collapse if D > 
1210.6V-1.9511  
Masonry & Concrete  3 story  9.02  no collapse  no collapse  collapse if D > 
-4.8864V+69.086  
Masonry & Concrete  4+ stories  no collapse  no collapse  no collapse  

5-27 

Table 5.7 Velocity-Depth Damage Relationship for Steel Buildings 

Material  # Stories (hgt)  Velocity Threshold in feet/sec VT(hgt)  Collapse 
Potential  
V < 2 fps  V < VT(hgt)  V >= VT(hgt)  
Steel  1 story  5.40  no collapse  no collapse  collapse if D > 0.3125V2 - 
6.6875V + 39.125  
Steel  2 story  5.40  no collapse  no collapse  collapse if D > 0.5808V2 - 
12.595V + 74.859  
Steel  3 story  5.40  no collapse  no collapse  collapse if D > 0.7737V2 - 
17.112V + 104.89  
Steel  4+ stories  no collapse  no collapse  no collapse  

HAZUS-MH MR3 Technical Manual 
Table 5.8 Velocity-Depth Damage Relationship for Manufactured Housing  
Flood Depth (ft) relative to top of finished floor  Collapse velocity (fps)  
-0.9  11.08  
-0.5  4.52  
0.0  3.20  
0.5  2.61  
1.0  2.26  
1.5  2.02  
2.0  1.85  
3.0  1.60  
4.0  1.43  
5.0  1.31  
6.0  1.21  
7.0  1.13  
8.0  1.07  
9.0  1.01  
10.0  0.96  
11.0  0.92  
12.0  0.89  

5.5 Consideration of Warning and Associated Damage Reduction 
Information detailing the implementation of damage reduction was obtained 
from the IWR and the USACE New York District. The following publications were 
received and reviewed to identify applications within HAZUS for damage 
reduction based on flood warning: 
 	
URS Consultants, Inc. (1992a),  Updated Flood Damage Evaluation Guidelines 
for the Passaic River Basin Project,  prepared by URS Consultants for the 
USACE New York District. 

 	
URS Consultants, Inc. (1992b),  Passaic River Basin Economic Updates   Sample 
Selection Requirements,  prepared by URS Consultants for the USACE New York 
District. 

 	
USACE (1994),  Framework for Estimating National Economic Development 
Benefits and Other Beneficial Effects of Flood Warning and Preparedness 
Systems,  IWR Report 94-R-3, March 1994. 

 	
USACE (1984),  Flood Emergency Preparedness System: Passaic River Basin, New 
Jersey and New York, Detailed Project Report and Environmental Assessment,  
USACE New York District. 


This material was reviewed to identify applications within HAZUS for damage 
reduction related to flood warning. The work done by the New York District 
models the effectiveness of a flood warning system through the modification 
of the Day curve, for conditions specific to the Passaic River basin. Harold 
Day, in a series of publications in the late 1960 s, developed a method that 
introduced the consideration of warning time to the depth-damage 
relationship.  Application of the methodology resulted in several curves that 
relate damage reduction to forecast lead time, defined as the time required 
for warning dissemination and effective public response.  The Day curve based 
on a scenario of riverine flooding in residential areas is presented as 
Figure 5.9. 

The original Day curve indicates a maximum loss reduction of 35% of total 
damage (e.g., structure and contents), and assumes a public response rate of 
100%.  A response rate of 100% is not likely in all circumstances, and as 
such, the New York district modified this and some other of the major lead 
time assumptions inherent in the Day curve: 
1.	
Building location   Forecast lead-time will vary at each building, based on 
water velocity, storm type (riverine or flash), basin time of concentration, 
and structure elevation. These variables were considered to develop a mean 
forecast lead-time for the Passaic River basin, defined as the average time 
available for public response. 

2.	
Warning dissemination   The speed of warning dissemination is affected by 
several factors, including the dissemination medium (TV, radio, siren, etc.), 
time of day, source, and content. As such, the public will receive the flood 
warning at varying times.  The New York District used this understanding to 
develop distributions of warning dissemination. 

3.	
Public response   Once the warning is disseminated, all residents will not 
respond with damage reduction activity at the same rate.  Research has shown 
that the public response rate is conditioned upon demographic factors, such 
as age, income, ethnicity, and past experience with floods.  The District 
used the results of a literature review to develop a public response time 
distribution, which was capped at a rate of 85%. 


5-30 
The work performed by the New York District improved upon the original Day 
curve producing a modified curve that was tailored to conditions in the 
Passaic River basin.  While a few select sophisticated users could modify the 
Day curves in a fashion similar to the New York District, most will not have 
the expertise.  Accordingly, the implementation of damage reduction from 
warning within HAZUS will be based on the generalized Day curve shown in 
Figure 5.9, and will allow the user to make a few simple modifications, as 
follows: 
 	
The user must enter warning time in hours (default is no warning, and 
accordingly, no damage reduction). 

 	
The default assumption for the maximum reduction in damage to contents (and 
inventory) will be set at 35 percent, varying as shown on the Day Curve.  The 
user will have the option to adjust the maximum damage reduction, and the 
software will automatically scale the damage reduction function accordingly. 

 	
The user may opt to apply the damage reduction factor to structure damage (in 
addition to contents damage), if flood-fighting efforts (e.g., sandbagging, 
etc.) are considered significant.  As with contents and inventory, the user 
will have the option to adjust the maximum damage reduction up to a maximum 
of 35%. 

 	
The user will have the option of applying a damage reduction factor to 
vehicles.  The user must specify the percent of vehicles (0   100%) removed 
from the floodplain as a result of the warning. 


It should be noted that the use of the original Day curve as the basis for 
modeling the effect of warning time on the depth-damage relationship, is not 
the most accurate method available.  IWR Report 94-R-3 expands upon this 
point: 
The Day curve methodology is perfectly applicable today.  The actual Day 
curves, however, should not be used. The Day curve methodology was surely a 
pathfinding work at the time, but continued use of curves based on the 
contents of a typical house in the early 1960s likely do not apply to current 
floodplain situations. 
Even given this deficiency, the Day curves appear to be the best currently 
available source for use as a nationally applicable default data set. 
5.6 Consideration of Uncertainty 
The HAZUS flood model, like the earthquake model, does not address 
uncertainty.  While the importance of the consideration of uncertainty is 
widely acknowledged, it has not been addressed in the current version of 
HAZUS. Therefore, model results should not be considered exact figures, and 
should be used accordingly.  Nevertheless, it is the belief of the Flood 
Committee that planning decisions made with the benefit of model results will 
be better than decisions made without any consideration of science. 
5.7 Guidance for Expert Users 
5.7.1 Selection of Alternate Depth-Damage Functions 
The flood model provides the user with the opportunity to compare and select 
alternative depth damage functions from the extensive library of functions 
within the model.  The user can identify and select the damage function they 
would prefer to use in the estimation of damage to buildings of any occupancy 
class. 
5.7.2 Sources of Additional Depth-Damage Functions 
Additional depth-damage functions may be available from local USACE Districts 
or floodplain managers and may include depth-damage relationships developed 
from post-flood surveys. Users can also develop custom depth-damage functions 
reflecting the unique characteristics of their community. 
5.7.3 Development of Custom Depth-Damage Functions 
The user can develop a new damage function using features within the model.  
The user would create the damage function in the Building Damage Function 
menu and save the function under a name provided by the user.  The user would 
then assign the newly-developed damage function to the occupancy class of 
interest. 
5.8 References 
1.	
FEMA (1985), Manufactured Home Installation in Flood Hazard Areas , Federal 
Emergency management Agency Publication FEMA-85. 

2.	
GEC (1996),  Depth  Damage Relationships for Structures, Contents, and 
Vehicles and Content-to-Structure Value Ratios (CSVRs) in Support of the 
Jefferson and Orleans Flood Control Feasibility Studies , Final Report, 
prepared by Gulf Engineers & Consultants for the USACE New Orleans District. 

3.	
GEC (1997),  Depth  Damage Relationships for Structures, Contents, and 
Vehicles and Content-to-Structure Value Ratios (CSVR) in Support of the Lower 
Atchafalaya Reevaluation and Mroganza to the Gulf, Louisiana Feasibility 
Studies  Final Report, Volume I, prepared by Gulf Engineers & Consultants for 
the USACE New Orleans District. 

4.	
The H. John Heinz III Center for Science, Economics and the Environment 
(2000).   The Hidden Costs of Coastal Hazards   Implications for Risk 
Assessment and Mitigation. 

5.	
OMNR (1997),  Natural Hazards Training Manual , Ontario Ministry of Natural 
Resources. 

6.	
URS Consultants, Inc. (1992a),  Updated Flood Damage Evaluation Guidelines 
for the Passaic River Basin Project,  prepared by URS Consultants for the 
USACE New York District. 

7.	
URS Consultants, Inc. (1992b),  Passaic River Basin Economic Updates   Sample 
Selection Requirements,  prepared by URS Consultants for the USACE New York 
District. 

8.	
USACE (1984),  Flood Emergency Preparedness System: Passaic River Basin, New 
Jersey and New York, Detailed Project Report and Environmental Assessment,  
USACE New York District. 

9.	
USACE (1985),  Business Depth-Damage Analysis Procedures , U.S. Army Corps of 
Engineers, Institute for Water Resources. 

10. 
USACE (1994),  Framework for Estimating National Economic Development 
Benefits and Other Beneficial Effects of Flood Warning and Preparedness 
Systems,  IWR Report 94-R-3, March 1994. 

11. 
USACE (1996a),  Risk-Based Analysis for Flood Damage Reduction Studies , 
Engineering Manual EM 1110-2-1619, U.S. Army Corps of Engineers, CECW-EH-Y, 
Washington D.C. 


6-1 
Chapter 6.  Direct Physical Damage - Essential and High Potential Loss 
Facilities 
6.1 Introduction 
This chapter describes the methods for determining direct physical damage to 
essential facilities. In general, these methods are identical to those 
presented in Chapter 5 for determination of damage to the general building 
stock (the HAZUS Flood Model applies pre-selected default damage functions to 
estimate damage to essential facility structures), except that essential 
facilities are handled as point facilities in the determination of their 
hazard exposure.  The flowchart of the methodology highlighting the essential 
and high potential loss facility damage components and showing its 
relationship to other components is shown in Figure 6.1. 
6.1.1 Scope 
The scope of this chapter includes: (1) classification of essential 
facilities, (2) building damage functions for essential facilities, (3) 
methods for estimation of flood damage to essential facilities, given 
knowledge of the model building type and occupancy classification, and an 
estimate of first floor elevation and basement, and (3) guidance for expert 
users, including estimation of damage to high potential loss (HPL) 
facilities. 
Essential facility buildings and their damage functions are described in 
Sections 6.2 and evaluation of damage to essential facilities is given in 
Section 6.3.  Section 6.4 provides guidance for expert users. Typically, 
sections of Chapter 6 reference (rather than repeat) material of the 
corresponding section of Chapter 5. 
6.1.2 Essential Facilities Classification 
Facilities that provide services to the community and those that should be 
functional following a flood are considered to be essential facilities.  
Examples of essential facilities include hospitals, police stations, fire 
stations, emergency operations centers (EOC s) and schools. 
Essential facilities are classified on the basis of facility function and, in 
the case of hospitals, size. Table 6.1 lists the classes of essential 
facilities used in the methodology.  Hospitals are classified on the basis of 
number of beds (assumed to reflect hospital size) to ensure consistency with 
the previously developed earthquake model. 

Table 6.1 Essential Facilities Occupancy Classes 

No.  Label  Occupancy Class  Description  
Medical Care Facilities  
1  EFHS  Small Hospital  Hospital with less than 50 Beds  
2  EFHM  Medium Hospital  Hospital with beds between 50 & 150  
3  EFHL  Large Hospital  Hospital with greater than 150 Beds  
4  EFMC  Medical Clinics  Clinics, Labs, Blood Banks  
Emergency Response  
5  EFFS  Fire Station  
6  EFPS  Police Station  
7  EFEO  Emergency Operation Centers  
Schools  
8  EFS1  Schools  Primary/ Secondary Schools (K-12)  
9  EFS2  Colleges/Universities  Community and State Colleges, State and 
Private Universities  

6.1.3 Input Requirements and Output Information 
Input required to estimate essential facility damage using depth-damage 
curves includes the following: 
 	
Model building type including height, basement and first floor elevation for 
the essential facility (or type of essential facilities) of interest, 

 	
Damage curve assignment or creation and assignment of the user s own damage 
function, and 

 	
Site-specific water depth determined by the hazard module 


The  output  of the depth-damage curves is an estimate of the expected damage 
as a percentage of replacement cost of the structure and/or contents.  Depth 
damage curves, their sources and applicability are discussed in detail in 
Chapter 5. 
Typically, the model building type (including height, basement, and first 
floor elevation) is not known for each essential facility and must be 
inferred from information available in the inventory of essential facilities 
using the occupancy relationships described in Chapter 3. 
6.1.4 Form of Damage Functions 
The form of the damage functions used for essential facilities is the same as 
those for the general building stock, described in detail in Chapter 5 of 
this report.  Since vulnerability to flooding damage is less dependent on 
building type than earthquake damage, application of depth damage functions 
developed for the general building stock to other structures is not 
unreasonable.  Flood 
6-4 
proofing of essential facilities can be accounted for by modifying the depth 
damage function to reflect the level of expected protection from the flood 
proofing. 
6.2 Description of Occupancies and Model Building Types 
The model building types and associated building parameters (foundation, 
first floor elevation, etc.) used for essential facilities are identical to 
those used for general building stock.  Building parameters related to flood 
damage are described in Section 5.2.  For each class of essential facility, a 
default building type, configuration, and first floor elevation have been 
assumed. These default assumptions are documented in Table 6.2.  It should be 
noted that facility age will be based on the median age of structures within 
the essential facility s census block. 
Essential facilities also include certain special equipment, such as 
emergency generators, and certain special contents, such as those used to 
operate a hospital.  Special equipment and contents of essential facilities 
are considered to be sensitive to flooding and can potentially impact the 
functionality of the hospital. Table 6.2 also provides depth thresholds 
beyond which the essential facilities are considered non-operational.  That 
is, the depth of flooding at which point the facility may close.   
Table 6.2 Essential Facilities Classification and Model Building Types 

Occupancy Class  Description  Age  Model Building Type  Basement  First Floor 
Elev.  (ft)  Building Height  Damage Function  Depth Threshold for 
Functionality (feet)  
EFHS  Small Hospital  Median  Concrete  Yes  3  Low  COM6  0.5  
EFHM  Medium Hospital  Median  Concrete  Yes  3  Mid  COM6  0.5  
EFHL  Large Hospital  Median  Concrete  Yes  3  Mid  COM6  0.5  
EFMC  Medical Center  Median  Concrete  Yes  3  Low  COM7  0.5  
EFFS  Fire Station  Median  Concrete  No  0  Low  GOV2  2  
EFPS  Police Station  Median  Concrete  Yes  0  Low  GOV2  1  
EFEO  Emergency Operations  Median  Concrete  Yes  0  Low  GOV2  1  
EFS1  School  Median  Brick  No  0  Low  EDU1  0.5  
EFS2  University  Median  Concrete  No  0  Low  EDU2  0.5  

6.3 Building Damage Due to Flooding 
Damage to essential facilities is estimated in a manner similar to damage to 
the general building stock, except that essential facility hazard exposure is 
based on site-specific data.  That is, depth is determined at the latitude 
and longitude location of the essential facility.   
For each class of essential facility, a default damage function has been 
identified.  These defaults, noted in Table 6.2, are identified as the 
default for a selected occupancy class.  For example, the default damage 
function for small, medium and large hospitals classified as essential 
facilities is the same as the default used for hospitals in the general 
building stock (COM6). For a detailed discussion of building damage 
functions, see Chapter 5 of this Technical Manual. 
6.4 Guidance for Expert Users 
6.4.1 Selection of Alternate Depth-Damage Functions 
The flood model provides the user the opportunity to compare and select 
alternative depth damage functions from the extensive library of functions 
within the model.  The user can identify the damage function they would 
prefer to use in the estimation of damage to essential facilities and add the 
identification into the inventory field labeled Damage function.  Within any 
scenario that impacts that facility, the selected damage function will be 
accessed. 
6.4.2 Development of New Depth-Damage functions 
The user can develop a new damage function using features within the model.  
The user would create the damage function in the Building Damage Function 
menu and save the function under a name provided by the user.  The user would 
then use the process discussed above to assign the function to the selected 
facility classification.   
6.4.3 Velocity-Damage Functions 
While velocity-damage functions have been included in the methodology for the 
general building stock (see Section 5.4), the current versions of HAZUS Flood 
does not allow the application of these damage functions to essential 
facilities.  It is expected that this functionality will be added to 
subsequent versions of the model. 
6.4.4 High Potential Loss Facilities 
6.4.4.1 Introduction 
This section describes damage evaluation of high potential loss (HPL) 
facilities.  HPL facilities could result in heavy flood losses, if 
significantly damaged.  Examples of such facilities include nuclear power 
plants, certain military and industrial facilities, dams, etc. 
6.4.4.2 Input Requirements and Output Information 
The importance of these facilities (in terms of potential flood losses) 
suggests that damage assessment be done in a special way as compared to 
ordinary buildings. Each HPL facility should be treated on an individual 
basis by users who have sufficient expertise to evaluate damage to such 
facilities.  Required input for damage evaluation includes depth-damage 
6-6 
functions developed specifically for each individual HPL facility, reflecting 
the facilities configuration and specific vulnerabilities. 
The direct output (damage estimate) from implementation of the depth-damage 
curves is an estimate of percent damage (relative to replacement cost). This 
output is used directly as an input to other damage or loss estimation 
methods or combined with inventory information to predict the distribution of 
damage as a function of facility type, and geographical location.  In the 
latter case, the number and geographical location of facilities of interest 
would be a required input to the damage estimation method. 
6.4.4.3 Form of Damage Functions and Damage Evaluation 
The form of user-supplied HPL facility damage functions should be the same as 
that of buildings (Chapter 5) and their use in the methodology would be 
similar to that of essential facilities. 
6.5 Essential Facility and HPL Damage References 
Refer to Section 5.8 for building damage references. 

Chapter 7.  Lifelines: Transportation and Utilities 
7.1 Introduction 
The treatment of lifelines is discussed in this section of the report.  
Lifelines are defined as the transportation and utility infrastructure that 
provides the United States with communications, water, power, mobility and 
other necessities for both continuity of governance and economic health. 
HAZUS provides some default data, but due to the sensitive nature of these 
facilities, national datasets are typically unavailable.  Therefore, it is 
usually necessary for local communities to provide the data for analysis. The 
flood model has developed damage and loss functions for the infrastructure 
that is most vulnerable to the impact of inundation.   
The flood model approaches the damage to lifeline facilities by identifying 
those components are either particularly expensive to replace, or when 
damaged by floodwaters force an extended closure, thereby removing critical 
infrastructure from the community and the emergency responders attempting to 
restore the community.  Table 7.1 provided the basis for this effort and the 
determination of those facilities and components that would require damage 
functions. Within each of these facilities, there are components that could 
be identified that drove the overall facilities vulnerability to flooding. 
Table 7.1 further identifies sub-hazards that may affect the various lifeline 
components and the expected maximum level of vulnerability.  The flood sub-
hazards that are being considered include: 
  
Inundation   a function of water elevation 

  
Scour/erosion   a function of floodwater velocity and duration. 

  
Debris Impact/Hydraulic Loading   a function of water elevation and velocity 


Most of the components are vulnerable to inundation except 
bridges/foundations and buried pipeline crossings that are vulnerable to 
scour, and bridge decks that are vulnerable to hydraulic pressure. 
The overall maximum vulnerability (the highest of any of the three sub-
hazards) is listed in Table 7.1. Therefore, the lifeline components selected 
for fragility modeling are identified in Table 7.1, including: 
  
Bridges 

  
Water system components with medium or high exposure,  

  
Wastewater system components with medium or high exposure. 

 	
Electrical power, communications, natural gas, and petroleum lifeline system 
components with fragilities similar to water and wastewater facilities. 


Evaluation of  special  components such as dams and power plants is beyond 
the scope of this project. 
Along with the determination of vulnerability, the impact of damage on the 
systems functionality, whether or not damage to the component is a high 
dollar loss item, and the overall recovery time for the component are 
identified on Table 7.1. 
HAZUS-MH MR3 Technical Manual Chapter 7. Lifelines: Transportation and 
Utilities HAZUS-MH MR3 Technical Manual 
Table 7.1 Lifeline System Components, Vulnerability to Flood Sub-hazards, 
Criticality and Potential Dollar Loss  and Outage Time 

Lifeline  Selected for Evaluation (X)  Special  (S)  Overall Vulnerability  
Flood Sub-hazard Vulnerability  Criticality  Dollar Loss and Outage Time  
Inundation  Scour/ Erosion  Debris Impact/ Hydraulic Pressure  
Bridges  X  High  Low  Medium  High  Medium  High  
Water Systems  
Water Treatment Plants  X  High  High  Low  Low  High  High  
Tanks  Low  Low  None  None  Medium  Medium  
Reservoirs (Impoundment Controlled Channels/Pipelines)  Low  None  None  None  
Low  Medium  
Dams/Impoundments (free flow)  S  High  None  High  Low  High  High  
Pump Stations  X  Medium  High  None  None  Medium  Medium  
Pipelines   Bridge Crossings  X  High  Low  None  Medium  Medium  Low  
Pipelines   Buried River Crossings  X  High  None  High  Low  Medium  Medium  
Pipelines   Distribution/Transmission  Low  None  Low  None  Low  Low  
Control Vaults (Air release valves, meter pits, control valves)  X  High  
High  Low  Low  Medium  Low  
Wastewater Systems  
Treatment Plants  X  High  High  Low  Low  High  High  
Pump/Lift Stations  X  High  High  None  None  Medium  Medium  
Pipelines   Bridge Crossings  X  High  Low  None  Medium  Medium  Low  
Pipelines   Buried River Crossings  X  High  None  High  Low  Medium  Medium  
Collection Systems  X  Medium  High  None  None  Low  Low  
Control Vaults (meter pits, control valves)  X  High  High  Low  Low  Low  
Low  

Table 7.1 Lifeline System Components, Vulnerability to Flood Sub-hazards, 
Criticality and Potential Dollar Loss  and Outage Time (Continued)

Lifeline  Selected for Evaluation (X)  Special  (S)  Overall Vulnerability  
Flood Sub-hazard Vulnerability  Criticality  Dollar Loss and Outage Time  
Inundation  Scour/ Erosion  Debris Impact/ Hydraulic Pressure  
Power  
Generation Plants  S  High  High  None  None  Low  High  
Substations  X  High  High  None  None  Low  Medium  
Transmission/Distribution (above)  Low  None  Medium  Low  Low  Low  
Distribution (below)  Low  Low  None  None  Low  Medium  
Access Vaults  Low  High  Low  Low  Low  Low  
Telecommunications  
Switching Station  X  High  High  Low  Low  High  High  
Transmission/Distribution Bridge Crossing  X  High  Low  None  Medium  Medium  
Low  
Transmission/Distribution Buried River Crossing  X  High  Low  High  Low  
Medium  Medium  
Transmission/Distribution Buried  Low  Low  Low  None  Medium  Low  
Access Vaults  Low  High  Low  Low  Low  Low  
Natural Gas  
Compressor Station  Low  Medium  None  None  Medium  Medium  
Pipelines   Bridge Crossings  X  High  None  None  Medium  Medium  Medium  
Pipelines   Buried River Crossings  X  High  None  High  Low  Medium  Medium  
Pipelines   Distribution/Transmission  Low  None  Low  None  Low  Low  
Control Stations (regulator stations, meter pits, control valves)  Low  
Medium  None  None  Low  Low  

Table 7.1 Lifeline System Components, Vulnerability to Flood Sub-hazards, 
Criticality and Potential Dollar Loss  and Outage Time (Continued) 

Lifeline  Selected for Evaluation (X)  Special  (S)  Overall Vulnerability  
Flood Sub-hazard Vulnerability  Criticality  Dollar Loss and Outage Time  
Inundation  Scour/ Erosion  Debris Impact/ Hydraulic Pressure  
Petroleum  
Refineries  S  Medium  High  None  None  Low  High  
Pumping Plants  X  Medium  High  None  None  Low  Medium  
Tank Farms  S  High  Medium  None  None  Low  Medium  
Pipelines   Bridge Crossings  X  High  None  None  Medium  Medium  Medium  
Pipelines   Buried River Crossings  X  High  None  High  Low  Medium  Medium  
Pipelines -- Transmission  Low  None  Low  None  Low  Low  

7-6 
7.2 Scope 
The scope of this chapter includes development of methods for estimation of 
flood damage to the selected lifeline infrastructure given knowledge of 
facility and an estimate of the depth of flooding throughout the study area. 
Model facility types are defined at the end of this section. The extent and 
severity of damage to structural contents of these facilities are estimated 
directly from the depth of flooding and the application of the assigned depth 
damage curve.  This chapter focuses on the loss estimation process as defined 
by HAZUS for the flood model.  
The interaction between the estimation of direct damage to the other 
components of the flood loss estimation model can be seen in Figure 7.1 
below.  While this should appear familiar to the reader of the earthquake 
model technical manual, the figure has been modified to accurately reflect 
those features unique to the flood model and remove those features unique to 
the earthquake model. 

7-8 
Analyzing losses for lifeline facilities is for the most part similar to that 
discussed in the general building stock chapter, but the damage functions for 
lifelines really define the potential damage to components of the lifeline 
system that are either uniquely vulnerable to inundation (such as electrical 
components) or are expensive or difficult to repair/replace (such as motors, 
controllers, etc.). 
7.2.1 Input Requirements and Output Format 
7.2.2 Form of the Damage Functions 
With the obvious exception of bridges (discussed below) the damage functions 
for lifelines are very similar to the depth to damage curves used to the 
general building stock.  The depth of flooding within the facility is 
compared to the height of critical components and the amount of damage can be 
estimated.  In most cases, the elevation of the equipment also provides for a 
depth of flooding at which point the functionality of the facility starts to 
become questionable. 
7.2.3 Transportation Bridges 
In discussions with the FHWA, no comprehensive database of bridge damage 
could be identified. As a result, the proposed damage relationships are 
estimates that should be calibrated once the overall flood module is 
operable. 
HAZUS comes with the national bridge inventory database as part of the 
default data and the objective is to use as much of the information in that 
database as possible.  This database includes all bridges in the U.S. with a 
span of 20-feet or greater. In discussions with an FHWA representative it was 
suggested that possible fields within the bridge inventory database include: 
scour potential, waterway adequacy, and span type (simple versus continuous). 
The FHWA also provided other references for further information. 
The database field discussed, the scour potential rating, has values that 
range from 0 to 9, where 0 indicates the bridge is closed as a result of 
scour damage, and 9 indicates that the bridge is not over water. Scour 
ratings 4-9 are not used in HAZUS.  Ratings of significance to the flood 
model are: 
  
1   closed 

  
2   existing problem 

  
3   100-year flood could damage. 


There appears to be a very low probability of failure due to flooding for 
bridges with a scour potential greater than 3. As bridges are designed for 
500-year floods, for single span bridges over water (i.e.   scour potential < 
9), the HAZUS Flood Model assumes a 1% probability of failure for floods with 
a return period of 100 years, and 1.5% probability for floods with a return 
period of 1000 years. For continuous bridges, the HAZUS Flood Model uses 25% 
of the values for single span bridges. 
The waterway adequacy gives some indication how much the bridge is 
restricting the channel, but there is no known correlation to bridge damage 
due to flooding.  Most bridge failures are simple spans. Scour of bridge 
piers on continuous spans does not usually result in collapse. Expected 
damage for continuous span bridges is taken to be 25% of that for single span 
bridges. Similarly, bridges with multiple piers provide redundancy that 
reduces their vulnerability. The span type is included in the National Bridge 
Database, but the number of piers is not. 
The relationships for single-span and continuous-span bridge damage due to 
flooding is shown below. 
Table 7.2 Highway Single-span Bridge Damage Relationship 

Flood Return Period  Scour Potential(1)/Probability of Failure (percent)  
1  2  3  4-8  9  
100-year  5  2  1  0  N/A  
500-year (2x 100-year probability)  10  4  2  0  N/A  
1000-year (1.5x 500-year probability)  15  6  3  0  N/A  

The Scour Potential is a field in the HAZUS Bridge database and is from the 
FHWA inventory of bridges 
Table 7.3 Highway Continuous-Span Bridge Damage Relationship 

Flood Return Period  Scour Potential(1)/Probability of Failure (percent)  
1  2  3  4-8  9  
100-year  1.25  0.5  0.25  0  N/A  
500-year (2x 100-year probability)  2.5  1  0.5  0  N/A  
1000-year (1.5x 500-year probability)  3.75  1.5  0.75  0  N/A  

(1) The Scour Potential is a field in the HAZUS Bridge database and is from 
the FHWA inventory of bridges 
In the future, it may be possible to develop damage relationships for 
different bridge span materials (concrete, steel, wood), but no data exists, 
and the focus is on the bridge foundation vulnerability rather than the span. 
 Failure  is defined to be loss of function due to flood/scour damage. As 
there is very limited data, the preliminary recommendation is that  failure  
represents a damage value of 25% of replacement cost. This is a mean value 
taking into account damage that could be scour/undercutting of a single pier, 
to collapse of a span.  This same relationship is applied to rail and light 
rail bridges. The damage relationships are applicable to pipelines supported 
on highway bridges. 
7-10 
7.2.4 	Treatment of Plants, Pump Stations, Vaults, Substations, and 
Telecommunication Facilities - Inundation 
Fragility relationships based on inundation have been developed for treatment 
plants, pump stations, vaults, substations, and telecommunications 
facilities.  The flood model currently provides damage and loss estimates for 
select potable water, wastewater, oil, and natural gas facilities only.  
Electric power and telecommunications was deferred to a later date.  The 
operational vulnerabilities, damage, and restoration times are primarily a 
function of the fragility of: 
1. 
Electrical equipment, 

2. 
Mechanical equipment, and 

3. 
Building damage (minor impact on operation).  


However, in many situations, the operation of these facilities will be 
terminated based on a management decision to shut down the facility at some 
threshold floodwater elevation. 
There are two general scenarios for inundation damage, diked/protected and 
unprotected/ undiked. For unprotected facilities, the damage and recovery 
time will increase to a maximum as the water depth increases to a defined 
level (assumed to be one-half a story height (i.e. damage is 100% when flood 
level is 4 feet above the floor level).  
For protected facilities, there will be no damage until the protection 
elevation is exceeded (dike overtops). At this point the entire facility 
would be expected to flood.  This same approach may also be used for 
facilities with below-grade components.  For example, for a wet-well/dry-well 
sewage pump station, there would be no damage until the water elevation rose 
above the ground floor slab elevation. Once that elevation was exceeded, the 
dry well and the electrical components located in the dry well would be 
submerged.  The user will be required to input this information as part of 
the site data.  It should be noted that flood protection can be automatically 
or manually implemented.   Automatic  implementation could be inherent in the 
design (i.e. dikes are always in place), or may require intervention (closing 
floodgates, etc.). 
For some facilities such as treatment plants, there may be multiple 
structures with different floor slab elevations. In this situation, the user 
is required to select the elevation that best represents the vulnerability of 
the overall facility.  One approach might be to select the floor elevation of 
the facility with key process electrical equipment.  When addressing 
treatment plants (and sewer treatment facilities), the Flood Model includes 
the value of the control buildings and support buildings in the total value 
of the treatment plant, but generally, the damage associated with the 
structure is minimal when compared to the damages associated with the 
electrical components and systems within the structure.  In other words, 
damage to buildings at a water treatment plant do not play a role in the 
functionality of the plant.  Damage to the electrical controls and components 
within the plant are the critical path for functionality. 
7.2.5 Utility Systems 
The flood model performs loss estimation analysis on  
 	
potable water system facilities including: treatment plants, control vaults 
and control stations, tanks, wells, 

 	
wastewater system facilities including treatments plants, control vaults and 
control stations, lift stations, 

 	oil facilities including refineries, control vaults and control 
stations, and tanks, 

 	
natural gas facilities including: compressor stations and control vaults and 
control stations, 

 	
electric power plants and substations. 


7.2.6 Lifeline Classifications, Functionality Thresholds and Damage Functions 
Tables 7.4 through 7.9 provide the user with the classifications for the 
various facility types available within HAZUS. For the convenience of HAZUS-
MH Earthquake Model users, the new classifications are aligned with older 
classifications.  The flood model project team developed several damage 
functions for various classifications of lifelines and these functions are 
available for the user to access in the model.  The user can assign a 
different damage function to each facility class within their study region.  
Therefore a user whose study region has a closed pressure system can use 
functions related to that system.  
Table 7.4 Potable Water Classifications, Functionality Thresholds and Damage 
Function 

Label  HAZUS-99 Earthquake Classification  Specific Occupancy  Functionality 
Threshold Depth  Percent Damage by depth of flooding in feet2  Comments  
0  1  2  3  4  5  6  7  8  9  10  
PWP1 PWP2  PWP1, PWP2  Exposed Transmission Pipeline Crossing  N/A  0  0  0  
0  0  0  0  0  0  0  0  No damage expected from submergence  
PWP1 PWP2.  PWP1, PWP2  Buried Transmission Pipeline Crossing  N/A  0  0  0  
0  0  0  0  0  0  0  0  No damage expected from submergence  
PWP1 PWP2  PWP1, PWP2  Pipelines (noncrossing)  N/A  0  0  0  0  0  0  0  0  
0  0  0  No damage expected from submergence  
PWTS  PWT1, PWT2  Small Water Treatment Plants Open/Gravity  0  0  5  8  10  
17  24  30  30  30  30  40  Cleanup, repair of small motors, buried conduits, 
and transformers required when flood level exceeds ground level. Clean and 
repair of major electrical equipment initiated when flood level exceeds 3 
feet.  
PWTM  PWT3, PWT4  Medium Water Treatment Plants Open/Gravity  0  0  5  8  10  
17  24  30  30  30  30  40  
PWTL  PWT5, PWT6  Large Water Treatment Plants Open/Gravity  0  0  5  8  10  
17  24  30  30  30  30  40  
PWT_  0  0  3  4  5  9  12  15  15  15  15  15  PW_O Less than average damage  
PWT_  0  0  8  12  15  26  36  45  45  45  45  45  PW_O More than average 
damage  
PWTS  PWT1, PWT2  Small Water Treatment Plants Closed/Pressure  4  0  1  2  5  
15  30  40  40  40  40  40  Assumes all equipment raised 3 feet above ground 
level. Mechanical/electrical equipment represents a greater percentage of the 
plant value that for "open" design  
PWTM  PWT3, PWT4  Medium Water Treatment Plants Closed/Pressure  4  0  1  2  
5  15  30  40  40  40  40  40  
PWTL  PWT5, PWT6  Large Water Treatment Plants Closed/Pressure  4  0  1  2  5  
15  30  40  40  40  40  40  
PWT_  4  0  1  1  3  8  15  20  20  20  20  20  PW_C Less than average damage  

Chapter 7. Lifelines: Transportation and Utilities HAZUS-MH MR3 Technical 
Manual 
Table 7.4 Potable Water Classifications, Functionality Thresholds and Damage 
Functions (Continued) 

Label  Earthquake Classification  Specific Occupancy  Functionality Threshold 
Depth  Percent Damage by depth of flooding in feet2  Comments  
0  1  2  3  4  5  6  7  8  9  10  
PWT_  4  0  2  3  8  23  45  60  60  60  60  60  PW_C More than average 
damage  
PPPS  PPP1, PPP2  Pumping Plants (Small) Below Grade  4  0  0  0  0  40  40  
40  40  40  40  40  Assumes entrance is 3 feet above ground level and is not 
sealed. Assumes all electrical equipment is below grade. Once entrance level 
exceeded, entire pump station floods.  
PPPM / PPPL  PPP3, PPP4  Pumping Plants (Med/Large) Below Grade  4  0  0  0  
0  40  40  40  40  40  40  40  
PPPS  PPP1, PPP2  Pumping Plants (Small) Above Grade  4  0  1  2  5  15  30  
40  40  40  40  40  Assumes all equipment raised 3 feet above ground level.  
PPPM / PPPL  PPP3, PPP4  Pumping Plants (Med/Large) Above Grade  4  0  1  2  
5  15  30  40  40  40  40  40  
PCVS  N/A  Control Vaults and Stations  1  0  40  40  40  40  40  40  40  40  
40  40  Assumes entrance is at grade, and is not sealed.  
PSTGC  PST1, PST2  Water Storage Tanks At Grade Concrete  24  0  0  0  0  0  
0  0  0  0  0  0  Assumes that the tank bottom is at grade, and that the 
water level in the tank exceeds the flood depth (so the tank will not float).  

7-14

Chapter 7. Lifelines: Transportation and Utilities 
Table 7.4 Potable Water Classifications, Functionality Thresholds and Damage 
Functions (Continued) 

Label  Earthquake Classification  Specific Occupancy  Functionality Threshold 
Depth  Percent Damage by depth of flooding in feet2  Comments  
0  1  2  3  4  5  6  7  8  9  10  
PSTGS  PST3, PST4  Water Storage Tanks At Grade Steel  24  0  0  0  0  0  0  
0  0  0  0  0  
PSTGW  PST6  Water Storage Tanks At Grade Wood  24  0  0  0  0  0  0  0  0  0  
0  0  
PSTAS  PST5  Water Storage Tanks Elevated  80  0  0  0  0  0  0  0  0  0  0  
0  Assumes that the tank foundations are not damaged.  
PSTBC  N/A  Water Storage Tanks Below Grade (all)  4  0  0  0  0  5  5  5  5  
5  5  5  Assumes that the tank vent is 3 feet above grade, and that cleanup 
will be required.  
PWE  PWE1  Wells  4  0  1  2  5  20  25  30  30  30  30  30  Assumes that the 
electrical equipment and well vent/openings are s 3 feet above grade. Assumes 
that the well is not permanently contaminated.  

2Assumes electrical switch gear located 3 feet above grade 
Table 7.5 Wastewater classifications, Functionality Thresholds and Damage 
Functions 

Label  Earthquake Classification  Specific Occupancy  Functionality Threshold 
Depth  Percent Damage by depth of flooding in feet2  Comments  
0  1  2  3  4  5  6  7  8  9  10  
WWP1 WWP2  WWP1, WWP2  Exposed Collector River Crossings  N/A  0  0  0  0  0  
0  0  0  0  0  0  No damage expected from submergence  
WWP1 WWP2  WWP1, WWP2  Buried Collector River Crossings  N/A  0  0  0  0  0  
0  0  0  0  0  0  No damage expected from submergence  
WWP1 WWP2  WWP1, WWP2  Pipes (noncrossings)  N/A  0  5  5  5  5  5  5  5  5  
5  5  No damage expected from submergence  
WWTS  WWT1, WWT2  Small Wastewater Treatment Plants  0  0  5  8  10  17  24  
30  30  30  30  40  Cleanup, repair of small motors, buried conduits, and 
transformers required when flood level exceeds ground level. Clean and repair 
of major electrical equipment initiated when flood level exceeds 3 feet  
WWTM  WWT3, WWT4  Medium Wastewater Treatment Plants  0  0  5  8  10  17  24  
30  30  30  30  40  
WWTL  WWT5, WWT6  Large Wastewater Treatment Plants  0  0  5  8  10  17  24  
30  30  30  30  40  
WWT_  0  3  4  5  9  12  15  15  15  15  20  WWT_ Less than average damage  
WWT_  0  8  12  15  26  36  45  45  45  45  60  WWT_ More than average damage  
WWCV  N/A  Control Vaults and Control Stations  1  0  40  40  40  40  40  40  
40  40  40  40  Assumes entrance is at grade, and is not sealed. 

HAZUS-MH MR3 Technical Manual 
7-16

Chapter 7. Lifelines: Transportation and Utilities 
Table 7.5 Wastewater classifications, Functionality Thresholds and Damage 
Functions (Continued) 

Label  Earthquake Classification  Specific Occupancy  Functionality Threshold 
Depth  Percent Damage by depth of flooding in feet2  Comments  
0  1  2  3  4  5  6  7  8  9  10  
WLSS  WLS1, WLS2  Lift Station (Small) Wet Well/Dry Well  4  0  0  0  0  40  
40  40  40  40  40  40  Assumes entrance is 3 feet above ground level and is 
not sealed. Assumes all electrical equipment is below grade. Once entrance 
level exceeded, entire pump station floods  
WLSM / WLSL  WLS3, WLS4  Lift Station (Med/Large) Wet Well/Dry Well  4  0  0  
0  0  40  40  40  40  40  40  40  
WLSS  WLS1, WLS2  Lift Station (Small) Submersible  N/A  0  0  0  0  10  10  
10  10  10  10  10  Same as WLSW and WLMW except flood water does not harm 
submersible pumps, only electrical equipment.  
WLSM / WLSL  WLS3, WLS4  Lift Station (Med/Large) Submersible  N/A  0  0  0  
0  10  10  10  10  10  10  10  

2Assumes electrical switch gear is located 3-feet above grade. 
Table 7.6 Crude and Refined Oil Classifications, Functionality Thresholds and 
Damage Functions 

Label  Earthquake Classification  Specific Occupancy  Functionality Threshold 
Depth  Percent Damage by depth of flooding in feet(2)  Comments  
0  1  2  3  4  5  6  7  8  9  10  
OIP1 OIP2  OIP1, OIP2  Exposed Transmission Pipelines River Crossings  N/A  0  
0  0  0  0  0  0  0  0  0  0  No damage expected from submergence  
OIP1 OIP2  OIP1, OIP2  Buried Transmission Pipelines River Crossings  N/A  0  
0  0  0  0  0  0  0  0  0  0  No damage expected from submergence  
OIP1 OIP2  OIP1, OIP2  Pipelines (noncrossing)  N/A  0  0  0  0  0  0  0  0  
0  0  0  No damage expected from submergence  
OPP  OPP1, OPP2  Pumping Plant  0  0  1  2  5  15  30  40  40  40  40  40  
Assumes all equipment raised 3 feet above ground level.  
OTF  OTF1, OTF2  Tank Farm  0  0  0  0  0  0  0  0  0  0  0  0  Assumes that 
the tank bottom is at grade, and that the water level in the tank exceeds the 
flood depth (so the tank will not float). Assumes pump stations and control 
stations are considered separately.  
OCV  N/A  Oil Control Vault & Control Station  1  0  40  40  40  40  40  40  
40  40  40  40  Assumes entrance is at grade, and is not sealed. 

HAZUS-MH MR3 Technical Manual Chapter 7. Lifelines: Transportation and 
Utilities 
Table 7.7 Crude and Refined Oil Classifications, Functionality Thresholds and 
Damage Functions 

Label  Earthquake Classification  Specific Occupancy  Functionality Threshold 
Depth  Percent Damage by depth of flooding in feet(2)  Comments  
0  1  2  3  4  5  6  7  8  9  10  
ORFS  ORF1, ORF2  Small Oil Refinery  4  0  1  2  5  15  30  40  40  40  40  
40  Assumes all equipment raised 3 feet above ground level. 
Mechanical/electrical equipment represents a greater percentage of the plant 
value that for "open" design  
ORFM  ORF3, ORF4  Medium Oil Refinery  4  0  1  2  5  15  30  40  40  40  40  
40  
ORFL  ORF3, ORF4  Large Oil Refinery  4  0  1  2  5  15  30  40  40  40  40  
40  

2Assumes electrical switch gear is located 3-feet above grade. 
7-18 

Table 7.8 Natural Gas Classifications, Functionality Thresholds and Damage 
Functions 

Label  Earthquake Classification  Specific Occupancy  Functionality Threshold 
Depth  Percent Damage by depth of flooding in feet2  Comments  
0  1  2  3  4  5  6  7  8  9  10  
NGP1 NGP2  NGP1, NGP2  Exposed Transmission Pipelines River Crossings  N/A  0  
0  0  0  0  0  0  0  0  0  0  No damage expected from submergence  
NGP1 NGP2  NGP1, NGP2  Buried Transmission Pipelines River Crossings  N/A  0  
0  0  0  0  0  0  0  0  0  0  No damage expected from submergence  
NGP1 NGP2  NGP1, NGP2  Pipelines (Noncrossing)  N/A  0  0  0  0  0  0  0  0  
0  0  0  No damage expected from submergence  
NGCV  N/A  Control Valves and Control Stations  1  0  40  40  40  40  40  40  
40  40  40  40  Assumes entrance is at grade, and is not sealed.  
NGC  NGC1, NGC2  Compressor Stations  4  0  1  2  5  15  30  40  40  40  40  
40  Assumes all equipment raised 3 feet above ground level.  

2Assumes electrical switch gear is located 3-feet above grade. 
HAZUS-MH MR3 Technical Manual 
Chapter 7. Lifelines: Transportation and Utilities 
Table 7.9 Electric Power Classifications, Functionality Thresholds and Damage 
Functions 

Label  Earthquake Classification  Specific Occupancy  Functionality Threshold 
Depth  Percent Damage by depth of flooding in feet2  Comments  
0  1  2  3  4  5  6  7  8  9  10  
ESSL  ESS1, ESS2  Low Voltage Substation  4  0  2  4  6  7  8  9  10  12  14  
15  Control room damaged starting at 0 feet, and maximized at 7' depth. 
Additional damage to cabling and incidental damage to transformers and 
switchgear.  
ESSM  ESS3, ESS4  Medium Voltage Substation  4  0  2  4  6  7  8  9  10  12  
14  15  
ESSH  ESS5, ESS6  High Voltage Substation  4  0  2  4  6  7  8  9  10  12  14  
15  
EDC  EDC1, EDC2  Distribution Circuits Elevated Crossings  N/A  0  0  0  1  1  
1  1  2  2  2  2  Low vulnerability due to flooding of ends of buried cables 
and possible barge traffic impacting transmission towers  
EDC  EDC1, EDC2  Distribution Circuits Buried Crossings  N/A  0  0  0  0  0  
0  0  0  0  0  0  No damage due to submergence.  
EDC  EDC1, EDC2  Distribution Circuits (noncrossing)  N/A  0  0  0  0  0  0  
0  0  0  0  0  No damage due to submergence.  
EPPS  EPP1, EPP2  Small Power Plants  4  0  2.5  5  7.5  10  12.5  15  17.5  
20  25  30  Support facilities damaged on ground level. Control and 
generation facilities damaged when water elevation reaches 2nd level.  
EPPM  EPP3, EPP4  Medium Power Plants  4  0  2.5  5  7.5  10  12.5  15  17.5  
20  25  30  
EPPL  EPP3, EPP4  Large Power Plants  4  0  2.5  5  7.5  10  12.5  15  17.5  
20  25  30  

2Assumes electrical switch gear is located 3-feet above grade. 
7.2.7 References 
US Department of Transportation, Federal Highway Administration,  Recording 
and Coding Guide for the Structure Inventory and Appraisal of the Nations 
Bridges , Federal Highway Administration Report No. FHWA-96-001, December 
1995. 
Rhodes, J., Trent R,  Economics of Floods Scour and Bridge Failures,  
Hydraulic Engineering Conference Proceedings (1993), Rhodes, J., Trent R,  An 
Evaluation of Highway Flood Damage Statistics,  Hydraulic Engineering 
Conference Proceedings (1993), Pan American Health Organization, Natural 
Disaster Mitigation in Drinking Water and Sewerage Systems - Guidelines for 
Vulnerability Analysis, 1998. 
Chapter 8.  Direct Damage to Vehicles 
8.1 Introduction 
Motor vehicles of all types and sizes are damaged during flood events.  It is 
known that these damages can be significant, particularly for events with 
limited warning.  The HAZUS Flood Model is capable of estimating the dollar 
cost of flood related damages to motor vehicles for flood events of various 
size. The user has the option of estimating these damages in default mode or 
of applying local information on the vehicle fleet, location, dealerships, 
and other information available to planners. 

8.1.1 Motor Vehicle Damage Estimation 
Unlike many other assets, motor vehicles can be moved out of harms way 
provided that ample time is available before the event and there is a place 
to deposit the vehicles out of the expected inundation area. The available 
time for relocating vehicles is important, in part, due to safety issues for 
drivers who may be operating vehicles in flooded or partially flooded areas.  
Vehicles are found in flood areas for several reasons including: 
  
They may be parked at residences, in structures, or on the street; 

  
They may be in parking facilities at transportation facilities; 

  
They may be in parking facilities at business locations; 

  
They may be in use at a business facility or site; or 

  
They may be parked at motor vehicle sales and repair facilities. 


For each location and vehicle profile there is a different likelihood that 
the vehicle will be damaged and/or that it can be relocated.  For example, 
vehicles at residences can be differentiated by the availability of someone 
to move the vehicle.  This may also be a function of the time available 
between the warning and the event.  If an operator is in the residence and 
there is an appropriate alternative location for the vehicle, out of the 
flood risk area, the vehicle can be protected. However, no one may be 
available at the location and/or they may not be able to reach the location 
in a suitable time. 
Transportation facilities are a collection point for vehicles.  Travelers to 
airports, train stations, and mass transit facilities often park a car at the 
facility.  Depending on their travel plans, they may or may not be available 
to relocate the vehicle.  Multi-story parking facilities may place only those 
vehicles at or below ground at risk.  Vehicles parked at a work location may 
be moveable provided that the employee works at the vehicle site.  Many 
workers leave their vehicles at a central work location and actually perform 
their work at a different location.  Here again, the distance from the 
vehicle and the available warning time will determine if the vehicle can be 
moved. 
Other categories of vehicles potentially at risk include those parked at 
retail facilities and those located in dealer inventories or at vehicle 
repair facilities.  Often employees are concerned about their own family and 
belongings and are not available to the employer.  Note that vehicles may 
need to be stored for several days after the initial event. 
These risks extend to all types of vehicles including cars, small trucks, 
heavy-duty trucks, and construction equipment.  Automobiles are more 
susceptible to water damage then larger vehicles, but even heavy duty 
construction equipment can be damaged or destroyed if the water is high 
enough, debris laden, or sediment filled.  Damage to privately owned vehicles 
and business owned vehicles could have economic and social impacts beyond the 
direct cost of the damage. 
8-4 
Employment may be reduced as a result of the loss of capital equipment and 
social structures may be impacted by reduced mobility. 
8.1.2 Classification 
Vehicles were classified into three major classifications including passenger 
cars, light trucks, and heavy trucks. These classifications were chosen to 
identify the general height of the vehicle above the ground and therefore the 
depth of flooding necessary to start causing damage to the vehicle. 
8.1.3 Input Requirements and Output Information 
There are no special input or output information necessary for the flood 
model to assess damage to vehicles. 
8.1.4 Form of Damage Functions 
The vehicle flood damage functions take into consideration the  step-wise  
nature of flood damage to vehicles.  That is, depths of less than a foot or 
two are likely to cause no damage, but when the engine compartment is 
submerged, the vehicle is likely to be considered a total loss as the 
electronic/computer components and electrical systems are destroyed.  In 
addition, warning is a significant component of vehicle loss modeling, as 100 
percent of the loss can easily be avoided by moving the vehicle out of the 
inundation area.  It is anticipate that damage functions will be developed 
using an expert opinion-based approach for each class of vehicle included in 
the vehicle inventory. 
8.2 Damage Due to Inundation 
8.2.1 Overview 
For each vehicle type (car, light truck, heavy truck), percentage of damage 
with regard to the flood level is assigned depending on whether the flood is 
below carpet, between carpet and dashboard, or above dashboard, as given in 
Table 8.1. 
Table 8.1 Vehicle Depth Damage Relationships 

Flood Level (feet)  Car  Light Trk  Heavy Trk  % of Damage  
Below Carpet  <1.5  <2.7  <5  15%  
Between Carpet & Dashboard  1.5-2.4  2.7-3.7  5-7.5  60%  
Above Dashboard  >2.4  >3.7  >7.5  100%  

After flood information is provided, these tables of figures will allow one 
to use HAZUS to estimate the value of vehicle damage in an area. All the 
numbers presented here are suggested default values and users should be able 
to modify them according to local characteristics. 
Generally, one will expect the number of vehicles parked in an area to more 
or less match the number of registered vehicles in that area, especially 
during nighttime.  
8.2.2 Depth-Damage Functions 
The vehicular depth damage functions are step functions since flood damage 
occurs when critical components are immersed.  The depth damage functions can 
be seen in Figure 8.2 below. 
Vehicle Damage Functions 
100
90
80
70
60
50
40
30
20
10

0 

Estimated Damage (%) 
8-6 
8.2.3 Consideration of Warning and Associated Damage Reduction 
The flood model provides an input parameter to allow the user to account for 
the potential reduction of vehicle losses due to warning. The approach is a 
simple reduction of the net damage and loss based on the users input.  As 
there is very little research information regarding the impact of flood 
warning on vehicle damage it was determined that the best approach would be 
to allow the user to anticipate the complete removal of all vehicles from the 
flooded area with sufficient warning. The default value currently assumes 
that all vehicles are in the census block at the time of the flood. 
8.3 Guidance for Expert Users 
There is little additional analysis an expert user can perform over the 
default provisions, the advanced user can modify the default data, or use 
their local transportation planning square footage formula to develop a 
revised inventory that can be imported into the flood model.  The advanced 
user can develop new depth damage functions for various vehicle types based 
on the existing functions. 

Chapter 9.  Direct Damage to Agriculture (Crops) 
9.1 Introduction 
This chapter describes the methods for determining the direct physical damage 
to agriculture products, specifically crops. Unique to the flood model within 
HAZUS, the treatment of agriculture products is based in part on the 
methodology developed by the USACE in their Agriculture Damage Assessment 
Model (AGDAM).  The methodology required the project team to gather 
information from multiple sources and compile the data into a format usable 
within the model. The flood model collected and provides default data as a 
starting point for the user. 
The user should use care as the agriculture industry is in a continual state 
of flux with farmers continually changing their planting efforts to 
anticipate or meet the needs of the marketplace. The value of agriculture 
products varies widely as the condition of the farming community varies due 
to weather, insects, and market trends.  Every effort has been made to allow 
the user to modify the inventory and valuation of the agriculture product 
default data. 
HAZUS-MH MR3 Technical Manual Chapter 9.  Direct Damage to Agriculture 
(Crops) 

9.1.1 Scope 
The scope of this chapter includes (1) classification of the agriculture 
products, (2) crop damage functions based on the Julian calendar, function 
modifiers for duration, (3) methods for estimation of flood damage to 
agriculture products, given knowledge of the crop type and relative location 
in the floodplain, and (4) guidance for expert users. 
9.1.2 Classification of Agriculture Products 
There are a wide variety of agriculture products in production at any given 
moment within the United States.  This, combined with a relative limited 
number of damage functions related to agriculture products, forced the flood 
model team to limit the quantity of crops defined within each state. The 
project team collected the top 20 agriculture products within each state and 
captured the current valuation (sale price) of the products to give the user 
a strong basis for moving forward.  The resulting list of 40 crops included 
within the HAZUS Flood Model (subject to the limit of 20 per state) is given 
in Table 9.1. 
Table 9.1 Crop Types Currently Available Within the HAZUS Flood Model 

Crop Type  Crop Type  Crop Type  
Alfalfa Hay  Apples  Bahiagrass  
Barley  Bromegrass-Alfalfa Hay  Common Bermudagrass  
Corn  Corn Silage  Corn, Sweet  
Cotton Lint  Crested Wheatgrass-Alfalfa Hay  Flax  
Grain Sorghum  Grapes, Wine  Grass Hay  
Grass-Clover  Grass-Legume Hay  Improved Bermudagrass  
Kentucky Bluegrass  Oats  Oranges  
Orchard Grass  Orchardgrass-Alfalfa Hay  Peanuts  
Pears  Potatoes, Irish  Reed Canarygrass  
Rice  Smooth Bromegrass  Soybeans  
Sugar Beets  Tall Fescue  Tall Fescue-Ladino  
Timothy-Red Clover Hay  Tobacco  Tomatoes  
Trefoil-Grass Hay  Watermelons  Wheat  
Wheat, Winter  

The crop data was gathered using the National Resources Inventory (NRI) 
dataset.  The NRI consists of sample points throughout the county.  These 
data points are associated with soils data and given expansion factors that 
identify what the data point represents in terms of a sampling in terms of 
acres.  For example, on point could represent 25,000 acres of crop types and 
soils.  The NRI data is also associated with polygons that have been 
developed by intersecting the 8-digit hydraulic unit codes (HUC) developed by 
the USGS with the county boundary.  The data provides the crop type and 
units. The NRI is captured approximately every 5-years.  To smooth the data 
out, the project team averaged five years of data to develop the default crop 
inventory. 
HAZUS-MH MR3 Technical Manual 
9-4 
To identify the crop yield, the sample points within a given polygon are 
multiplied by the expansion factor and then summed over the polygon.  The 
total yields are then averaged over the collection intervals of the NRI data 
to produce the average yield seen by the user when viewing the inventory 
data. 
To capture the annual fluctuations in pricing for each crop type, the 
National Agriculture Statistical Survey (NASS) was obtained.  The NASS covers 
nearly every aspect of the agriculture industry and provides the link between 
the crop types and the price per unit.  Since the NASS data represents a 
snapshot of the current agriculture crop market price, the most recent survey 
is used. 
To provide the user with some concept of the costs invested by the farmer in 
the preparation of their crops, the National Resources Inventory and Analysis 
Institute (NRIAI) compiles data related to the crop care budgets and from 
that the harvest cost, or the amount of money invested by the farmer to bring 
the crop to market. 
9.1.3 Input Requirements and Output Information 
The flood model provides a default agriculture product base, which includes 
the key input requirements for the analysis.  These inputs include the crop 
type, its association with a geographic dataset that locates the crop within 
non-developed areas within the study region, the current market value of the 
product, and the planting season of the crop as it relates to the time of 
flooding. 
9.1.4 Form of Damage Functions 
The damage functions for crops do not depend on the depth of flooding.  
Damage to crops depends on when the flood occurs and the duration of 
flooding. The user is provided with damage functions based on calendar date 
and the duration modifiers based on Julian date.  The flood model 
automatically converts calendar date to the Julian calendar system.  The user 
is able to modify the damage functions to reflect local crop planting cycles 
and the user has the capability of modifying the functions. 
The user is required to provide a date of flooding (calendar) and the flood 
model estimates the losses based on standard durations provided by the USACE 
of 3-days, 7-days and 14-days. Losses are estimated based on the area of 
inundation versus total area of crop land and the subsequent reduction in 
output, investment, and income. 
The loss model used is based on the USACE s AGDAM methodology and program.1 
Only two viable methodologies for agricultural flood loss estimation were 
identified in the literature search: AGDAM and the USACE Vicksburg District s 
CACFDAS (Computerized Agricultural Crop Flood Damage Assessment System) 
model.2  The principal difference between them is that AGDAM considers 
probabilistic flood hazard, while CACFDAS is deterministic and uses 
1 The Hydrologic Engineering Center, U.S. Army Corps of Engineers. 1985. 
AGDAM Users Manual (provisional).  Davis, California. 
2 The USDA s Risk Management Agency does not have any model specifically for 
flood. 
Chapter 9.  Direct Damage to Agriculture (Crops) 
historic flood records as inputs; otherwise, for any specific event, the core 
methodologies are very similar.  The core of the AGDAM model can be 
represented as follows for each crop and elevation range that is flooded: 
L =A(pY0 - H )7 D(t) 7 R(t) (9-1) 
where: 
L = loss ($), 
A = cultivated area (acres),  
P = price ($/bushel), 
Y0 = normal annual yield (bushels/acre),  
H I = harvest cost ($/acre),  
D(t) = crop loss at day t of the year (% of maximum net revenue), and  
R(t) = the crop loss modifier for flood duration (percent of maximum 
potential loss). 
During the proof of concept analysis, the vulnerability of crops to standing 
water became evident as comparisons to the 1993 floods in Story County showed 
that much of the crop loss was due to standing water that pooled in 
 potholes  (local term) from accumulated rainfall.  The County is very flat 
with poorly defined drainage. While the  potholes  generally drain within 36 
to 72 hours, in 1993, frequent and continuous rainfall caused the potholes to 
stay filled, leading to crop loss.  Potholes  are a regional topographic 
feature defined by the extent of recent glaciation. They tend to pose a 
flooding problem in central and northern Iowa, southern Minnesota, Wisconsin, 
and northern Illinois.  In southern Iowa, southern Illinois, and Indiana, 
they are generally not a problem and crop loss would primarily be from river 
flooding.3  Other reasons for the underestimation of losses include the 
neglect of weather-related factors such as a wet, cool growing season, 
delayed planting, and early frost that were all important in reducing yields 
in 1993. 
It is critical to note, therefore, that the losses modeled with the AGDAM 
approach are limited to crops lost due to riverine flooding and different in 
scope than the estimates of  actual  1993 flood loss.  Since modeling the 
behavior of standing water would be exceedingly difficult, the flood model 
will estimate crop losses using an AGDAM-type approach and the user must 
modify the results as appropriate based on local knowledge of factors such as 
 potholes,  assumed weather impacts, etc.  This modification would allow a 
more reasonable estimate of direct loss in the agriculture sector that could 
be input into the model for indirect economic losses. 
9.2 Damage Due to Inundation 
The flood model directly addresses the damage associated with inundation, and 
as discussed in the previous section, the damage functions developed by the 
USACE include modifiers to account for the duration of inundation.  In most 
cases, crops will not suffer significant losses for 
3 Communication with Dr. A. Austin, Iowa State University Center for Disaster 
Related Research, 2/19/99. 
HAZUS-MH MR3 Technical Manual 
9-6 
a very short-term flood.  As the duration of the flood approaches 14-days, 
the likelihood of significant damage greatly increases. 
9.3 Damage due to Collateral Hazards (duration) 
The flood model is not developing a specific duration factor.  However, the 
flood model will estimate agriculture damage for a range of durations.  The 
USACE has a set of duration functions with factors for 0, 3, 7, and 14 days 
of duration.  The flood model will provide a single table that lists the 
losses by crop type for each duration period. 
9.4 Benefits of Flooding 
In recent years, there has been growing interest in more detailed and 
accurate assessment of the beneficial functions of floodplains to support 
floodplain management decision-making (Kusler, 1997). Undeveloped and 
evacuated floodplains provide a number of valuable functions to society, 
which have historically been overlooked.  The major functions include 
attenuation of flood flows, maintenance of high soil and water quality, water 
supply, wildlife habitat, and recreational opportunities. 
9.4.1 Flood Attenuation 
Undeveloped floodplains help attenuate flooding through the absorption and 
storage of floodwaters.  Floodplains also help reduce flood velocity because 
friction factors are much higher in the floodplain than in the main channel.  
The velocity reduction reduces erosion and allows for the deposition of 
sediment.  The construction of flood control projects such as levees, 
floodwalls, and channel modifications result in decreased floodway width and 
increased hydraulic conveyance. Such structural remedies are designed to 
protect areas previously located in the historic floodway and quickly pass 
floods downstream.  However, they also serve to separate the river from its 
floodplain, eliminating the flow management services provided by floodplain 
wetlands. The resulting increases in flow rates, flood depths, and sediment 
loads often increase the costs of flood damages for downstream communities 
and property owners.  In coastal areas, wetlands can form a buffer between 
development infrastructure and hurricanes and other storm surges. Coastal 
wetlands absorb enormous amounts of water and dissipate wave energy that 
would otherwise allow storms to do severe damage inland (LCWCRTF, 1997).  The 
flood attenuation value that natural floodplains provide to society is 
manifested in the following ways: 
  
Loss reductions in structure, contents, infrastructure, and income 

  
Cost reductions in emergency response, administration, and health care 

  
Retarded rate of sediment deposition into lakes, reservoirs, and estuaries 


Chapter 9.  Direct Damage to Agriculture (Crops) 
9.4.2 Soil Quality 
As flood flows spread out over a floodplain, nutrient rich sediments are 
deposited.  This deposition can improve soil quality for agricultural and 
environmental purposes. 
9.4.3 Water Quality 
Undeveloped floodplains provide water treatment value by improving water 
quality.  Floodplain plants and soils provide natural water filtering, 
nutrient uptake, and detoxification of pollutants that would otherwise flow 
into watercourses (USACE, 1996).  Trees growing along the riverbank and in 
within wetlands provide shade that reduces water temperatures. Studies of 
polluted waters flowing through wetlands have shown significant reductions in 
biochemical oxygen demand (BOD), phosphorous, and nitrogen. 
9.4.4 Water Supply 
Floodplains are an important setting for groundwater recharge.  Riverine 
floodplains reduce the frequency and duration of low surface flows (maintain 
base flows) by slowly releasing water stored during flooding (Cowdin, 1999).  
Water stored through floodplains can be used for agricultural, municipal, 
industrial purposes. 
9.4.5 Wildlife Habitat 
It has been estimated that nearly 70% of all vertebrate species rely on the 
floodplain during their life cycle (American Rivers, 2000) for food, shelter, 
migration, and reproduction.  Natural floodplains have a high degree of 
biological diversity and productivity.  River corridors are frequently used 
as migration avenues for birds; aquatic and wetland areas provide habitats 
for fish; floodplain trees serve as important nesting habitats. 
9.4.6 Recreational Setting 
Floodplains provide the setting for a host of recreational activities, such 
as swimming, boating, fishing, hunting, hiking, camping, and viewing 
wildlife. 
9.4.7 Others 
There are several other important functions provided by natural floodplains 
that are not considered further in this paper. Floodplains improve air 
quality through removal of atmospheric carbon, can moderate temperatures in 
urban areas, and provide a setting for educational and scientific research 
activity. 
HAZUS-MH MR3 Technical Manual 
9-8 
9.5 Ecological Assessment of Natural Floodplain Functions 
The term floodplain is defined by FEMA as any land area susceptible to being 
inundated by floodwater from any source (FEMA, 2000).  Within this broad 
definition are aquatic, riparian, and wetland sections (Figure 9.2).  Aquatic 
areas are characterized by having standing or moving water at some time 
during the year, such as streams and lakes, whereas riparian areas border 
rivers, streams and creeks and typically include the channel banks and the 
greater floodplains. Wetlands are special aquatic areas that often develop in 
transitional zones between aquatic and upland habitats, and can occur in 
riverine, lacustrine, and coastal settings.  Wetlands are either permanently 
or seasonally wet and support specially adapted vegetation and wildlife 
(Cowdin, 1999). 

The majority of methods used for the functional assessment of natural 
floodplains are focused on the evaluation of wetlands. Not all wetlands 
perform all functions, nor do they perform all functions equally well. 
Numerous factors, such as the size of a wetland and its location within a 
watershed, may determine what functions it will perform (Novitski, et al., 
1996).  Several wetland assessment methods have been developed that are used 
by wetland managers and planners to assign floodplain functions to specific 
wetlands.  Three of these methods are summarized in the following paragraphs. 
Chapter 9.  Direct Damage to Agriculture (Crops) 
9.5.1 Wetland Evaluation Technique 
First developed in 1983, the wetland evaluation technique (WET) is designed 
to evaluate individual wetlands based on their functions.  Characteristics 
such as vegetation, topography, and watershed characteristics are used to 
estimate whether a wetland has a high, medium, or low probability of 
performing various functions (Novitski, et al., 1996): 
  
groundwater recharge   production export 

  
groundwater discharge   wildlife diversity/abundance 

  
flood flow alteration   aquatic diversity/abundance 

  
sediment stabilization   recreation 

  
sediment/toxicant retention   uniqueness/heritage 

  
nutrient removal/transformation 


The evaluation is based on the capability and potential of a wetland to 
perform each function, as well as its societal significance (ecologic and 
economic).  The resulting probability ratings estimate the likelihood of a 
wetland to perform each function. 
9.5.2 Environmental Monitoring Assessment Program 
The environmental monitoring assessment program (EMAP) was created by the 
U.S. EPA in 1988, with the goal of measuring the condition and trends of many 
types of ecological resources, such as forests, wetlands, deserts, 
agricultural systems, and surface waters (EPA, 1997).  The wetlands component 
of the EMAP is designed to identify indicators of wetland condition, 
standardize measurement protocols, develop indices of condition, and 
establish a national network for monitoring wetland condition (Novitski, et 
al., 1996).  The categories used to assess wetland condition are biological 
integrity, harvestable productivity, flood reduction and shoreline 
protection, groundwater conservation, and water quality improvement.  Indices 
of wetland condition relate to one or more of these categories, and are 
compared to those of the least impacted wetlands in the region, so called 
reference wetlands. 
9.5.3 Hydrogeomorphic Approach 
Hydrogeomorphic (HGM) classification was originally developed in the early 
1990s, and is somewhat of a hybrid of the WET and EMAP methodologies.  The US 
Army Corps of Engineers has adopted the approach in order to satisfy 
requirements of Section 404 of the Clean Water Act. The approach is intended 
to be regionally-applicable, with the ability both to assess a variety of 
wetland types and functions, and to assess functions accurately and 
efficiently within time and resource constraints (Smith, et al., 1995).  To 
apply HGM classification to a given region, the functions performed by 
wetlands in a specific hydrogeomorphic setting are first identified.  The 
characteristics of a specific wetland are then compared to the 
characteristics of reference wetlands (Novitski, et al., 1996). This 
comparison is used to assign a value to each beneficial function. The 
particular characteristics evaluated are limited to those important to the 
region and hydrogeomorphic setting. 
HAZUS-MH MR3 Technical Manual 
9-10 
The WET, EMAP, and HGM methodologies are the primary approaches used for 
wetland assessment, but are by no means the only ones.  Unfortunately, many 
existing techniques have been plagued by a variety of problems and 
limitations including high costs, technical expertise needs, and margins of 
error.  Moreover, the methods may provide only a portion of the assessment 
information needed for specific floodplain management purposes.  Overall, 
these techniques can be used to help assess the natural functions of wetland 
and broader floodplain areas, but should be approached with care (Kusler, 
1997). 
9.6 Economic Valuation of Natural Floodplain Functions 
Floodplains perform a multitude of complex and interrelated functions that 
provide valuable goods and services to society (Cowdin, 1999).  But because 
of the non-market nature of most floodplain benefits, they are often 
difficult to quantify.  However, several techniques have been developed to 
measure the economic value of non-market goods and services; they can be 
grouped into four primary categories: market approaches, indirect market 
methods, expressed preference models, and benefit transfer. 
9.6.1 Market Approaches 
These methods rely on market-determined prices to determine the value of 
ecosystem goods that are sold in organized markets. 
 	
Direct Market Price Method   current or past market prices are used to assign 
value to goods or services. Some examples of floodplain goods sold in the 
open market include water supply, commercially harvested fish, and wood 
products. 

 	
Factor Income/Productivity Method   the value of a marketed good is measured 
relative to the change in value of a non-market ecosystem service that serves 
as a factor of production for the marketed good.  The factor 
income/productivity method relies on estimating and using this production 
relationship to estimate how changes in an ecosystem will affect the 
production costs or profits of the marketed good.  For example, an increase 
in soil quality could lead to lower crop production expenses.  The resulting 
increase in agricultural profits could be used as a measure of the soil 
quality function of an undeveloped floodplain. Weakness: applicable only if 
the production unit in question is small relative to the overall production 
of the marketed good, or if the improvement in the ecosystem service 
represents only a small marginal change (USACE, 1996). 


9.6.2 Indirect Market Methods 
These methods infer values for goods and services based on prices observed 
for other related goods and services (Cowdin, 1999): 
 	Avoided Costs   Avoided costs can be estimated in two ways.  In the 
least cost alternative method, the value of a good or service is measured by 
assuming that its benefit cannot have a value higher than the alternative 
costs avoided.  For example, the water quality benefit of a floodplain could 
be measured as the cost of building and operating a water treatment facility. 
Chapter 9.  Direct Damage to Agriculture (Crops) 
In the property damages avoided method, the benefit of the ecosystem service 
is estimated based on the dollar value of property damages expected to result 
from not having the service (assumes no alternative).  As an example, 
consider the justification of a floodplain development regulation.  The 
benefits of flow and velocity reduction could be estimated by the value of 
avoided property damages. 
 	
Replacement Costs   measures the value of a good or service assuming that its 
benefit cannot have a value higher than the cost of producing the same good 
or service in another way.  For example, the value of preserving habitat in 
one particular location can be measured by the cost of replacing that habitat 
(with similar structural and functional characteristics) in another (Cowdin, 
1999). 

 	
Hedonic Pricing   measures the contributions of various characteristics to 
the price of a good. For example, the hedonic property value model asserts 
that the price paid for a property directly reflects environmental attributes 
such as clean air, beauty, and proximity to wetlands, fishing, and hiking 
(Farnam, 1999).  As such, this method is useful for the value estimation of 
ecosystem amenities and aesthetics.  Strength:  actual market prices are 
used. Weakness: the environmental characteristics must be shown to affect 
price. 

 	
Travel Cost   measures the value for a good or service based upon the costs 
(time and money) incurred by consumers to obtain that good.  This method is 
typically applied to the measurement of recreational benefits.  Strength:  
allows the use of observed values. Weakness: region-wide modeling is required 
to estimate impacts of changes in site quality. 


9.6.3 Expressed Preference Models 
In this approach, values are determined for ecosystem services directly 
through expressed preferences in money bids, hypothetical markets, policy 
referenda, and surveys (USACE 1996). 
 	Contingent Valuation Method   sophisticated surveys are used to 
estimate willingness to pay for environmental improvement.  Strength:  can be 
applied to estimate values for a multitude of ecosystem benefits. Weakness:  
questions can be misinterpreted and responses can be biased. In short, the 
results may not be reliable. 
9.6.4 Benefit Transfer 
The benefit transfer approach is not really an evaluation model, but rather a 
way to apply results between studies. Existing non-market values are 
transferred to a new study which is different from the study for which the 
values were originally estimated (Farnam, 1999).  Strength: estimates can be 
quickly and cheaply developed. Weakness: the quality of the results depends 
heavily on the quality of the original study. 
Table 9.2 indicates which economic methods are applicable to various 
functions of natural floodplains. Table 9.3 summarizes the results of 
previous studies in which monetary values were derived for certain floodplain 
functions through the application of economic valuation methods. 
HAZUS-MH MR3 Technical Manual 
9-12

Chapter 9.  Direct Damage to Agriculture (Crops) 
Table 9.2 Economic Methods for Valuing Natural Floodplain Functions 

Natural Floodplain Functions  Valuation Method  
Market Price Analysis  Factor Income/ Productivity  Avoided Costs  
Replacement Costs  Travel Costs*  Hedonic Property Pricing*  Contingent 
Valuation*  
Attenuate Flood Flows  X  X  X  X  X  
Maintain Soil Quality  X  X  X  X  X  
Maintain Water Quality  X  X  X  X  X  
Maintain Water Supply  X  X  X  X  X  
Maintain Wildlife Habitats  X  X  X  X  X  X  X  
Maintain Air Quality  X  X  X  X  X  X  

* 
Original studies or transfers from other studies. (Adapted from Cowdin, 1999) 

*
 Motorized and non-motorized boating. (Adapted from Farnam, 1999)


Table 9.3 Summary of Non-market Values ($ 1998) 

Activity  Number of Studies  Methodologies  Range  Mean  Units  
Camping  24  Travel cost; Contingent valuation  9.10 - 32.5  23.50  $/Day  
Picnicking  12  Travel cost; Contingent valuation  6.5 - 52  20.80  $/Day  
Biking  2  Travel cost; Contingent valuation  60.20 - 61.38  60.81  $/Day  
Boating *  21  Travel cost; Contingent valuation  7.70 - 216.55  51.35  $/Day  
Recreational Fishing  4  Travel cost; Contingent valuation  15 - 95.30  55.00  
$/Day  
Waterfowl Hunting  21  Travel cost; Contingent valuation  27.60 - 113.16  
51.51  $/Day  
Flood Prevention  3  Hedonic Pricing  5 - 10  7  % of property value  
Value of Wetlands  2  Contingent valuation  19.57-251  $/respondent  

HAZUS-MH MR3 Technical Manual 
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9-14 
9.7 Guidance for Expert Users 
Agriculture products is a simplified analysis intended to provide the user 
with a broad range of potential losses to account for the variation of 
duration that may occur.  The flood model provides the expert user with the 
capability to add or set existing crops to zero.  The crop inventory must be 
placed in an existing sub-county polygon.  The user should not modify the GIS 
layer as the browser viewer is joined to the flAG.mdb file in order to 
display those polygon areas where the NASS shows with no crops within the 
sub-county regions. 
The advanced user can adjust the duration factors and the damage curves to 
meet their needs. There is currently no damage function library for 
agriculture crops at this time. 
9.8 References 
1.	
USACE Hydraulic Engineering Center, AGDAM (Agriculture Flood Damage Analysis) 
User Manual (provisional), April, 1985 

2.	
T.C. Hannan, I.C. Goulter, Model for Crop Allocation in Rural Floodplains, 
Journal of Water Resources Planning and Management Vol 114, No. 1, January 
1988. 

3.	
USACE Institute for Water Resources, National Economic Development Procedures 
Manual 

  Agriculture Flood Damage, IWR Report87-R-10, October 1987 

4.	
American Rivers.  2000. Flood Plains. Internet site, 
http://www.amrivers.org/flood.html. 

5.	
Cowdin, S.W. 1999. Multi-Objective Approaches to Floodplain Management on a 
Watershed Basis: Natural Floodplain Functions and Societal Values.  
California Department of Water Resources, Division of Planning and Local 
Assistance. 

6.	
EPA (U.S. Environmental Protection Agency).  1997. Environmental Monitoring 
and Assessment Program (EMAP):  Research Strategy.  Office of Research and 
Development EPA/620/R-98/001. 

7.	
Farnam, F.  1999. Multi-Objective Approaches to Floodplain Management on a 
Watershed Basis: Nonmarket Evaluation Techniques.  California Department of 
Water Resources, Division of Planning and Local Assistance. 

8.	
FEMA (Federal Emergency Management Agency).  2000. FEMA: NFIP.  Internet 
site, http://www.fema.gov/nfip/19def2.htm. 

9.	
Kusler, J. 1997. Assessing the Natural and Beneficial Functions of 
Floodplains:  Issues and Approaches; Future Directions. Association of State 
Wetland Managers. 

10. 
LCWCRTF (Louisiana Coastal Wetlands Conservation and Restoration Task Force).  
	1997. The 1997 Evaluation Report to the U.S. Congress on the 
Effectiveness of Louisiana Coastal Wetland Restoration Projects. Internet 
site, http://www.lacoast.gov/Programs/CWPPRA/ 

11. 
Reports/EvaluationReport1997/Title.htm 

12. 
Novitski, R.P., R.D. Smith, and J.D. Fretwell.  	1996. Wetland 
Functions, Values, and Assessment.  National Water Summary on Wetland 
Resources.  U.S. Geological Survey Water Supply Paper 2425. 

13. 
Philippi, N.S.  	1996. Floodplain Management   Ecologic and Economic 
Perspectives.  R.G. Landes Company, Austin, TX. 

14. 
Smith, R.D., A. Ammann, C. Bartoldus, and M. Brinson.  	1995. An Approach 
for Assessing Wetland Functions Using Hydrogeomorphic Classification, 
Reference Wetlands, and Functional Indices. USACE (U.S. Army Corps of 
Engineers), Waterways Experiment Station. Wetlands Research Program Technical 
Report WRP-DE-9. 

15. 
USACE. 	1996. Monetary Measurement of Environmental Goods and Services:  
Framework and Summary of Techniques for Corps Planners.  IWR Report 96-R-24. 


Chapter 9.  Direct Damage to Agriculture (Crops) 
HAZUS-MH MR3 Technical Manual 
Chapter 10.  Induced Damage Models - Hazardous Materials Release 
10.1 Introduction 
Hazardous materials are those chemicals, reagents or substances that exhibit 
physical or health hazards, whether the materials are in a usable or waste 
state. The scale, and hence the consequences, of hazardous materials releases 
can vary from very small, such as a gallon of paint falling off of shelves, 
to regional, such as release of toxic chemicals from a processing plant. Most 
hazardous materials incidents have immediately led to human casualties only 
in cases where explosions have occurred.  Non-explosive hazardous materials 
incidents, which comprise the vast majority, typically have led to 
contamination of the environment and temporary health consequences to human 
beings.  Hazardous materials releases can also lead to fires.  With specific 
reference to flood caused hazardous materials incidents, the data thus far 
indicate that there have been no human identified casualties.  The 
consequences of these incidents have been fires and contamination of the 
environment, and have led to economic impacts because of the response and 
clean-up requirements.  The methodology highlighting the Hazardous Materials 
Release component is shown in Figure 10.1. 
While the flood model does not directly estimate damage caused by the release 
of hazardous materials, nor does the model estimate the probabilities of such 
a release occurring. The user can, however, place the locations of the 
hazardous materials inventory onto the hazard data (the depth grid) and 
identify those areas where the hazardous materials sites had been exposed to 
significant flooding. 

10.1.1 Scope 
This loss estimation methodology has been restricted to identifying the 
location of facilities that contain hazardous material which could lead to a 
significant immediate demand on health care and emergency response 
facilities.  These types of incidents would include large toxic releases, 
fires or explosions.  Thus, the default database of hazardous material 
facilities is limited to facilities where large quantities of chemicals that 
are considered highly toxic, flammable or highly explosive are stored. 
Estimates of releases that could cause pollution of the environment and the 
need for long-term clean-up effects are beyond the scope of this methodology. 
An exhaustive search of the existing literature for models that can be 
utilized to predict the likelihood of occurrence of hazardous materials 
releases during flooding was conducted at the beginning of this study.  
Unfortunately, no directly usable models were found. 
Due to the limitations of state-of-the-art hazardous materials release 
models, this module is restricted to establishing a standardized approach for 
classifying materials and developing a good database that can be used by 
local planners to identify those facilities that may be most likely to have 
significant releases in the event of flooding.  A default database of 
potential sites is provided from an EPA database of hazardous materials 
sites.  This database can be supplemented by the user with local information.  
A more detailed vulnerability assessment would involve going to individual 
facilities to determine how chemicals are stored, the vulnerability of 
buildings and storage tanks and other relevant information. 
10.1.2 Classification of Hazardous Materials 
The most widely used detailed classification scheme is the one that has been 
developed by the National Fire Protection Association, and is presented in 
the 1991 Uniform Fire Code, among other documents.  This classification 
scheme is shown in Table 10.1.  The hazards posed by the various materials 
are divided into two major categories: Physical Hazards and Health Hazards. 
Depending upon the exact nature of the hazard, these two major categories are 
divided into subcategories.  These subcategories of hazards, with their 
definitions, and examples of materials that fall within each category, are 
contained in Appendix 10A and 10B.  A more detailed description of these 
categories, with more extensive examples can be found in Appendix VI-A of the 
1991 Uniform Fire Code.  Table 10.1 also contains minimum quantities of the 
materials that must be on site to require permitting according to the Uniform 
Fire Code.  It should be noted that the minimum permit quantities might vary 
depending upon whether the chemical is stored inside or outside of a 
building. 
10.1.3 Input Requirements and Output Information 
The input to this module is essentially a listing of the locations of 
facilities storing hazardous materials and the types/amounts of the materials 
stored at the facility.  Facilities need only be identified if they use, 
store or handle quantities of hazardous materials in excess of the quantities 
listed in Table 10.1.  Other facilities that may have hazardous materials, 
but in quantities less than those listed in Table 10.1 should not be included 
in the database because it is anticipated that releases of these small 
quantities will not put significant immediate demands on health and emergency 
services.  However, the user may choose to modify threshold amounts in 
building the database. 
Table 10.1 Classification of Hazardous Materials and Permit Amounts 

Label  Material Type  Permit Amount  Hazard Type & Remarks  
Inside Building  Outside Building  
HM01  Carcinogens  10 lbs  10 lbs  Health  
HM02  Cellulose nitrate  25 lbs  25 lbs  Physical  
HM03  Combustible fibers  100 cubic ft  100 cubic ft  Physical  
HM04 HM05 HM06  Combustible liquids Class I Class II Class III-A  5 gallons 
25 gallons 25 gallons  10 gallons  60 gallons 60 gallons  Physical  
HM07  Corrosive gases  Any amount  Any amount  Health [1]  
HM08  Corrosive liquids  55 gallons  55 gallons  Physical; Health  
HM09 HM10 HM11 HM12 HM13  Cryogens Corrosive Flammable Highly toxic 
Nonflammable Oxidizer (including oxygen)  1 gallon 1 gallon 1 gallon 60 
gallons 50 gallons  1 gallon 60 gallons 1 gallon 500 gallons 50 gallons  
Health Physical Health Physical Physical  
HM14  Highly toxic gases  Any amount  Any amount  Health; [1]  
HM15  Highly toxic liquids & solids  Any amount  Any amount  Health  
HM16  Inert  6,000 cubic ft  6,000 cubic ft  Physical; [1]  
HM17  Irritant liquids  55 gallons  55 gallons  Health  
HM18  Irritant solids  500 lbs  500 lbs  Health  
HM19  Liquefied petroleum gases  > 125 gallons  > 125 gallons  Physical  
HM20  Magnesium  10 lbs  10 lbs  Physical  
HM21  Nitrate film  (Unclear)  (Unclear)  Health  
HM22  Oxidizing gases (including oxygen)  500 cubic feet  500 cubic feet  
Physical [1]  
HM23 HM24 HM25 HM26  Oxidizing liquids Class 4 Class 3 Class 2 Class 1  Any 
amount 1 gallon 10 gallons 55 gallons  Any amount 1 gallon 10 gallons 55 
gallons  Physical  
HM27 HM28 HM29 HM30  Oxidizing solids Class 4 Class 3 Class 2 Class 1  Any 
amount 10 lbs 100 lbs 500 lbs  Any amount 10 lbs 100 lbs 500 lbs  Physical  

Table 10.1 Classification of Hazardous Materials and Permit Amounts 
(Continued) 

Label  Material Type  Permit Amount  Hazard Type & Remarks  
Inside Building  Outside Building  
HM31 HM32 HM33 HM34  Organic peroxide liquids and solids Class I Class II 
Class III Class IV  Any amount Any amount 10 lbs 20 lbs  Any amount Any 
amount 10 lbs 20 lbs  Physical  
HM35 HM36  Other health hazards Liquids Solids  55 gallons 500 lbs  55 
gallons 500 lbs  Health  
HM37  Pyrophoric gases  Any amount  Any amount  Physical [1]  
HM38  Pyrophoric liquids  Any amount  Any amount  Physical  
HM39  Pyrophoric solids  Any amount  Any amount  Physical  
HM40  Radioactive materials  1 m Curie in unsealed source  1 m Curie in 
sealed source  Health [1]  
HM41  Sensitizer, liquids  55 gallons  55 gallons  Health  
HM42  Sensitizer, solids  500 lbs  500 lbs  Health  
HM43  Toxic gases  Any amount  Any amount  Health [1]  
HM44  Toxic liquids  50 gallons  50 gallons  Health  
HM45  Toxic solids  500 lbs  500 lbs  Health  
HM46  Unstable gases (reactive)  Any amount  Any amount  Physical [1]  
HM47 HM48 HM49 HM50  Unstable liquids (reactive) Class 4 Class 3 Class 2 
Class 1  Any amount Any amount 5 gallons 10 gallons  Any amount Any amount 5 
gallons 10 gallons  Physical  
HM51 HM52 HM53 HM54  Unstable solids (reactive) Class 4 Class 3 Class 2 Class 
1  Any amount Any amount 50 lbs 100 lbs  Any amount Any amount 50 lbs 100 lbs  
Physical  
HM55 HM56 HM57  Water -reactive liquids Class 3 Class 2 Class 1  Any amount 5 
gallons 10 gallons  Any amount 5 gallons 10 gallons  Physical  
HM58 HM59 HM60  Water-reactive solids Class 3 Class 2 Class 1  Any amount 50 
pounds 100 pounds  Any amount 50 pounds 100 pounds  Physical  

[1] Includes compressed gases 
To build the hazardous materials database for a selected region, the user 
should attempt to gather the following information: 
  
Name of Facility or Name of Company 

  
Street Address 

  
City 

  
County 

  
State 

  
Zip Code 

  
Name of Contact in Company 

  
Phone Number of Contact in Company 

  
Standard Industrial Classification (SIC) Code 

  
Chemical Abstracts Service (CAS) Registry Number 

  
Chemical Name 

  
Chemical Quantity 

  
HAZUS Hazardous Material Class (From Table 10.1) 

  
Latitude and Longitude of Facility 


The Chemical Abstracts Service (CAS) registry number is a numeric designation 
assigned by the American Chemical Society s Chemical Abstracts Service and 
uniquely identifies a specific chemical compound.  This entry allows one to 
conclusively identify a material regardless of the name or naming system 
used.  To obtain this data the user must identify the local agency with which 
users of hazardous materials must file for permits.  Based upon current 
understanding of the process, this local agency would be the Fire Department 
for incorporated areas, and the County Health Department for unincorporated 
areas. The user may opt to use only the information contained in a modified 
version of the EPA-TRI Database that is provided in the methodology.  This 
database, however, is limited and the user is urged to collect additional 
inventory. 
The output of this module is essentially a database that can be sorted 
according to any of the fields listed above. It can be displayed on a map and 
overlaid with other maps. 
10.2 Description of Methodology 
The analysis here is divided into three levels, as described below: 
 	
Default Analysis: Listing of all facilities housing hazardous materials that 
are contained in the default hazardous materials database. 

 	
User-Supplied Data Analysis: Listing of all facilities housing hazardous 
materials that are contained in the default hazardous materials database and 
refined by the user with locally available information. 

 	
Advanced Data and Models Analysis: Detailed risk assessment for individual 
facilities, including expert-generated estimates. 


10.3 Guidance for Expert-Generated Estimates 
The flood model is not configured to perform an expert generated estimate for 
hazardous materials.  This is because flood related release of hazardous 
materials creates the added requirement of identifying the amount of dilution 
of the material in the flood waters (depending on the material), reactivity 
with water and other issues that are beyond the current scope of the HAZUS 
Flood Model. 
Should the user want to pursue further analysis and estimate of material 
release, it is recommended that the user identify the site of the hazardous 
materials using GPS units and determine the depth of flooding at that 
location.  The user can then determine if the materials are in fact released.  
The user will also be able to determine the general velocity (low, medium, 
high) and the flood water discharge at the release site.  Using this 
information, the user may be able to use existing plume models to determine 
the spread and dilution of the materials. 
The most elementary form of detailed analysis would consist of a hazardous 
materials expert doing a walk through to identify target hazard areas.  In 
most jurisdictions, the fire department personnel are the best trained in 
issues pertaining to hazardous materials.  Many fire departments are also 
willing to meet with major users of hazardous materials to do what is termed 
 pre-planning . In this effort, fire departments visit the facilities of 
users, identify areas that they think are particularly vulnerable, and 
suggest improvements.  If there were code violations, the fire department 
personnel would point this out.  In highly industrialized areas, there are 
consulting firms that are capable of conducting this assessment.  The smaller 
consulting firms tend to be comprised only of individuals with expertise in 
hazardous materials issues. 
It must be borne in mind that when assessing the potential for hazardous 
materials releases during floods, the depth of flooding, the performance of 
the storage facility/container with respect to inundation are important.  
Another very important factor is the level of preparedness, especially where 
it pertains to the ability to contain an incident and prevent it from 
spreading or enlarging. 
10-8 
The structural vulnerability of a hazardous materials facility are assessed 
by a qualified structural engineer. For example, the integrity of a storage 
tank, containing 100,000 gallons of petroleum, should be evaluated by a 
structural engineer. 
In conducting a detailed analysis, it is important not only to assess the 
potential for occurrence of incidents, but it is also important to assess the 
capability of containing incidents and preventing them from spreading or 
becoming enlarged.  The level of preparedness of the individual facilities 
generally determines this.  There have been a number of cases where the 
incidents would have been smaller than they actually were, had the 
organization/facility had the capability to respond in a timely manner.  The 
type of expert needed here is an  Emergency Planner .  Unfortunately, it is 
not easy to find an emergency planner who specializes in assessing individual 
facilities. Here again, perhaps the most qualified and educated personnel are 
fire department personnel.  In most cases, hazardous materials consultants 
also address issues pertaining to response.  In the case when an expert is 
not available, the document by the U.S. Environmental Protection Agency (EPA, 
1987), which provides technical guidance for hazards analysis and emergency 
planning for extremely hazardous substances is an excellent guide.  Another 
useful guide is the  Hazardous Materials Emergency Response Guide  published 
by the National Response Team (1987). The user should keep in mind that both 
of these documents are quite general in nature, and do not address flood 
concerns specifically.  Nevertheless, in the absence of more specific 
information, these guides are definitely useful in getting the user started 
towards assessing the risks. 
10.4 References 
International Conference of Building Officials, Uniform Fire Code, 1991. 
U.S. Environmental Protection Agency 1987, FEMA, U.S. Department of 
Transportation, Technical Guidance for Hazards Analysis   Emergency Planning 
for Extremely Hazardous Substances, U.S. Environmental Protection Agency 
December. 
National Response Team 1987, Hazardous Materials Emergency Planning Guide, 
March. 

Appendix 10A. Listing of Chemicals contained in SARA Title III, including 
their CAS Numbers, Hazards and Threshold Planning Quantities 
CAS Number  Chemical Name  Hazard  Threshold Planning Quantity (pounds)  
00075-86-5 01752-30-3 00107-02-8 00079-06-1 00107-13-1 00814-68-6 00111-69-3 
00116-06-3 00309-00-2 00107-18-6 00107-11-9 20859-73-8 00054-62-6 00078-53-5 
03734-97-2 07664-41-7 00300-62-9 00062-53-3 00088-05-1 07783-70-2 01397-94-0 
00086-88-4 01303-28-2 01327-53-3 07784-34-1 07784-42-1 02642-71-9 00086-50-0 
00098-87-3 00098-16-8 00100-14-1 00098-05-5 03615-21-2 00098-07-7 00100-44-7 
00140-29-4 15271-41-7 00111-44-4 00542-88-1 00534-07-6 04044-65-9 10294-34-5 
07637-07-2 00353-42-4 28772-56-7 07726-95-6 01306-19-0 02223-93-0 07778-44-1 
00056-25-7 00051-83-2  Acetone cyanohydrin Acetone thiosemicarbazide Acrolein 
Acrylamide Acrylonitrile Acrylyl chloride Adiponitrile Aldicarb Aldrin Allyl 
alcohol1 Allylamine Aluminum phosphide Aminopterin Amiton Amiton oxalate 
Ammonia, anhydrous Amphetamine Aniline Aniline, 2,4,6-trimethyl Antimony 
pentafluoride Antimycin A Antu Arsenic pentoxide Arsenous oxide Arsenous 
trichloride Arsine Azinphos-ethyl Azinphos-methyl Benzal chloride 
Benzehamine,3-(trifluoromethyl)Benzene, 1-(chloromethyl)-4-nitro-
Benzenearsonic acid Benzimidazole, 4,5-dichloro-2-(trifluoromethyl) 
Benzotrichloride (benzoic trichloride) Benzyl chloride Benzyl cynaide Bicyclo 
[2,2,1]heptane-2-carbonitrile,5-chloro6((((methylamino)carbonyl)oxy)imino)-
(1S-(1alpha,2-beta,4-alpha,5-alpha,6E))-Bis(2chloroethyl)ether 
Bis(chloromethyl)ether Bis(chloromethyl)ketone Bitoscanate Boron trichloride 
Boron trifluoride Borontrifluoride compound with methyl ether (1:1) 
Bromadiolone Bromine Cadmium oxide Cadmium stearate Calcium arsenate 
Cantharidin Carbachol chloride  Poison Poison Flammable liquid & poison 
Poison Flammable liquid & poison Poison Poison Deadly poison Poison Flammable 
liquid & poison Flammable liquid & poison Flammable solid & poison Poison 
Deadly poison Deadly poison Poison Deadly poison Poison Poison Corrosive to 
skin, eyes, mucuous membranes Poison Poison Poison Poison Poison Poison gas & 
flammable gas Poison Poison Moderately toxic Poison Poison Deadly poison 
Poison Corrosive & poison Corrosive & poison Poison Poison Poison Poison & 
carcinogen Poison Poison Corrosive, poison, irritant & reactive with water 
Poison & strong irritant Flammable, corrosive & poison Deadly poison 
Corrosive & poison Poison Poison Poison & carcinogen Deadly poison Deadly 
poison  1,000 1,000 + 500 1,000 + 10,000 100 1,000 100 + 500 + 1,000 500 500 
500 + 500 100 + 500 1,000 1,000 500 500 1,000 + 500 + 100 + 100 500 100 100 + 
10 + 500 500 500 + 10 + 500 + 100 500 500 500 + 10,000 100 10 + 500 + 500 500 
1,000 100 + 500 100 + 1,000 + 500 + 100 + 500 +  

10A-2 

CAS Number  Chemical Name  Hazard  Threshold Planning Quantity (pounds)  
26419-73-8 01563-66-2 00075-15-0 000786-19-6 00057-74-9 00470-90-6 07782-50-5 
24934-91-6 00999-81-5 00079-11-8 00107-07-3 00627-11-2 00555-77-1 00067-66-3 
00107-30-2 03691-35-8 01982-47-4 21923-23-9 10025-73-7 10210-68-1 62207-76-5 
00064-86-6 00056-72-4 05836-29-3 00095-48-7 00535-89-7 00123-73-9 04170-30-3 
00506-68-3 00506-78-5 02636-26-2 00675-14-9 00066-81-9 000108-91-8 17702-41-9 
08065-48-3 00919-86-8 10311-84-9 19287-45-7 00110-57-6 00149-74-6 00062-73-7 
00141-66-2 01464-53-5 00814-49-3 01642-54-2 00071-63-6 02238-07-5 20830-75-5 
00115-26-4 00060-51-5 06923-22-4 00075-78-5 00057-14-7 00099-98-9 02524-03-0 
00077-78-1 00644-64-4 00534-52-1  Carbamic acid, methyl-O-(((2,4-dimethyl-
1,3dithiolan-2-yl)methylene)amino)-Carbofuran Carbon disulfide 
Carbophenothion Chlordane Chlorfenvinfos Chlorine (not muratic acid or 
bleach) Chlormephos Chlormequat chloride Chloroacetic acid Chloroethanol 
Chloroethyl chloroformate Tris(2-chloroethyl)amine Chloroform Chloromethyl 
methyl ether Chlorophacinone Chloroxuron Chlorthiophos Chromic chloride 
Cobalt carbonyl Cobalt,((2,2 -
(1,2ethanediylbis(nitrilomethylidyne))bis(6fluorophenolato))(2-)-
N,N ,O,O )Colchicine Coumaphos Coumatetralyl Othro-cresol Crimidine 
Crotonaldehyde E-crotonaldehyde Cyanogen bromide Cyanogen iodide Cyanophos 
Cyanuric fluoride Cycloheximide Cyclohexylamine Decaborane (14) Demeton 
Demeton-s-methyl Dialifor Diborane Trans-1,4-dichlorobutene 
Dichloromethylphenylsilane Dichlorvos Dicrotophos Diepoxybutane Diethyl 
chlorophosphate Diethylcarbamazine citrate Digitoxin Diglycidyl ether Digoxin 
Dimefox Dimethiate 3-(Dimethoxy phosphinyloxy)-N-methyl-cis croton-
amide(monocrotophos) Dimethyldichlorosilane Dimethylhydrazine Dimethyl-p-
phenylenediamine Dimethyl phosphochloridothioate Dimethyl sulfate Dimetilan 
4,6-Dinitro-o-cresol  Poison Poison Flammable liquid & poison Poison 
Flammable liquid & poison Poison Poison gas Poison Corrosive & poison 
Flammable liquid & poison Poison Moderately toxic Poison Flammable liquid & 
poison Poison Poison Poison Poison Poison Poison Poison Poison Poison Poison 
Deadly poison Poison Flammable liquid & poison Poison Poison Poison Poison 
Poison Flammable liquid & poison Deadly poison Poison Poison Flammable gas & 
poison Poison Flammable liquid & poison Poison Poison Poison Deadly poison 
Poison Deadly poison Poison Deadly poison Poison Poison Poison Poison & 
irritant Flammable liquid & poison Poison Corrosive & poison Corrosive & 
poison Poison Poison  100 + 10 + 10,000 500 1,000 500 100 500 100 + 100 + 500 
1,000 100 10,000 100 100 + 500 + 500 1 + 10 + 100+ 10 + 100 + 500 + 1,000 + 
100 + 1,000 1,000 500 + 1,000 + 1,000 1000 100 + 10,000 500 + 500 500 100 + 
100 500 1,000 1,000 100 500 500 100+ 100+ 1,000 10+ 500 500+ 10 500 1,000 10+ 
500 500 500+ 10+  

CAS Number  Chemical Name  Hazard  Threshold Planning Quantity (pounds)  
26419-73-8 01563-66-2 00075-15-0 000786-19-6 00057-74-9 00470-90-6 07782-50-5 
24934-91-6 00999-81-5 00079-11-8 00107-07-3 00627-11-2 00555-77-1 00067-66-3 
00107-30-2 03691-35-8 01982-47-4 21923-23-9 10025-73-7 10210-68-1 62207-76-5 
00064-86-6 00056-72-4 05836-29-3 00095-48-7 00535-89-7 00123-73-9 04170-30-3 
00506-68-3 00506-78-5 02636-26-2 00675-14-9 00066-81-9 000108-91-8 17702-41-9 
08065-48-3 00919-86-8 10311-84-9 19287-45-7 00110-57-6 00149-74-6 00062-73-7 
00141-66-2 01464-53-5 00814-49-3 01642-54-2 00071-63-6 02238-07-5 20830-75-5 
00115-26-4 00060-51-5 06923-22-4 00075-78-5 00057-14-7 00099-98-9 02524-03-0 
00077-78-1 00644-64-4 00534-52-1  Carbamic acid, methyl-O-(((2,4-dimethyl-
1,3dithiolan-2-yl)methylene)amino)-Carbofuran Carbon disulfide 
Carbophenothion Chlordane Chlorfenvinfos Chlorine (not muratic acid or 
bleach) Chlormephos Chlormequat chloride Chloroacetic acid Chloroethanol 
Chloroethyl chloroformate Tris(2-chloroethyl)amine Chloroform Chloromethyl 
methyl ether Chlorophacinone Chloroxuron Chlorthiophos Chromic chloride 
Cobalt carbonyl Cobalt,((2,2 -
(1,2ethanediylbis(nitrilomethylidyne))bis(6fluorophenolato))(2-)-
N,N ,O,O )Colchicine Coumaphos Coumatetralyl Othro-cresol Crimidine 
Crotonaldehyde E-crotonaldehyde Cyanogen bromide Cyanogen iodide Cyanophos 
Cyanuric fluoride Cycloheximide Cyclohexylamine Decaborane (14) Demeton 
Demeton-s-methyl Dialifor Diborane Trans-1,4-dichlorobutene 
Dichloromethylphenylsilane Dichlorvos Dicrotophos Diepoxybutane Diethyl 
chlorophosphate Diethylcarbamazine citrate Digitoxin Diglycidyl ether Digoxin 
Dimefox Dimethiate 3-(Dimethoxy phosphinyloxy)-N-methyl-cis croton-
amide(monocrotophos) Dimethyldichlorosilane Dimethylhydrazine Dimethyl-p-
phenylenediamine Dimethyl phosphochloridothioate Dimethyl sulfate Dimetilan 
4,6-Dinitro-o-cresol  Poison Poison Flammable liquid & poison Poison 
Flammable liquid & poison Poison Poison gas Poison Corrosive & poison 
Flammable liquid & poison Poison Moderately toxic Poison Flammable liquid & 
poison Poison Poison Poison Poison Poison Poison Poison Poison Poison Poison 
Deadly poison Poison Flammable liquid & poison Poison Poison Poison Poison 
Poison Flammable liquid & poison Deadly poison Poison Poison Flammable gas & 
poison Poison Flammable liquid & poison Poison Poison Poison Deadly poison 
Poison Deadly poison Poison Deadly poison Poison Poison Poison Poison & 
irritant Flammable liquid & poison Poison Corrosive & poison Corrosive & 
poison Poison Poison  100 + 10 + 10,000 500 1,000 500 100 500 100 + 100 + 500 
1,000 100 10,000 100 100 + 500 + 500 1 + 10 + 100+ 10 + 100 + 500 + 1,000 + 
100 + 1,000 1,000 500 + 1,000 + 1,000 1000 100 + 10,000 500 + 500 500 100 + 
100 500 1,000 1,000 100 500 500 100+ 100+ 1,000 10+ 500 500+ 10 500 1,000 10+ 
500 500 500+ 10+  

10A-4 

CAS Number  Chemical Name  Hazard  Threshold Planning Quantity (pounds)  
00088-85-7 01420-07-1 00078-34-2 00082-66-6 00152-16-9 00298-04-4 00514-73-8 
00541-53-7 00316-42-7 00115-29-7 02778-04-3 00072-20-8 00106-89-8 02104-64-5 
00050-14-6 00379-79-3 01622-32-8 10140-87-1 00563-12-2 13194-48-4 00538-07-8 
00107-15-3 00371-62-0 00151-56-4 00075-21-8 00542-90-5 22224-92-6 00122-14-5 
00115-90-2 04301-50-2 07782-41-4 00640-19-7 00144-49-0 00359-06-8 00051-21-8 
00944-22-9 00050-00-0 00107-16-4 23422-53-9 02540-82-1 17702-57-7 21548-32-3 
03878-19-1 00110-00-9 13450-90-3 00077-47-4 04835-11-4 00302-01-2 00074-90-8 
07647-01-0 07664-39-3 07722-84-1 07783-07-5 07783-06-4 00123-31-9 13463-40-6 
00297-78-9 00078-82-0 00102-36-3 00465-73-6  Dinoseb Dinoterb Dioxathion 
Diphacinone Diphosphoramide, octamethyl Disulfoton Dithiazamine iodide 
Dithiobiuret Emetine, dihydrochloride Endosulfan Endothion Endrin 
Epichlorohydrin EPN Ergocalciferol Ergotamine tartate Ethanesulfonyl 
chloride,2-chloro Ethanol,1,2-dichloroacetate Ethion Ethoprophos Ethylbis(2-
chloroethyl)amine Ethylenediamine Ethylene fluorohydrin Ethyleneimine 
Ethylene oxide Ethylthiocyanate Fenamiphos Fenitrothion Fensulfothion 
Fluenetil Fluorine Fluoroacetamide (1061) Fluoroacetic acid Fluoroacetyl 
chloride Fluorouracil Fonofos Formaldehyde Formaldehyde cyanohydrin 
Formetanate hydrochloride Formothion Formparanate Fosthientan Fuberidazole 
Furan Gallium trichloride Hexachlorocyclopentadiene Hexamethylenediamine,N,N-
dibutyl Hydrazine Hydrocyanic acid Hydrogen chloride (gas only) Hydrogen 
fluoride Hydrogen peroxide (conc. >52%)Hydrogen selenide Hydrogen sulfide 
Hydroquinone Iron pentacarbonylIsobenzan Isobutyronitrile Isocyanic aicd,3,4-
dichlorophenyl ester Isodrin  Poison Poison Poison Poison Poison Poison 
Poison Poison Poison Poison Poison Poison Flammable liquid & poison Poison 
Poison Poison Poison Combustible & poison Poison Poison Deadly poison 
Corrosive, flammable liquid, irritant Poison Flammable liquid & poison 
Flammable gas & poison Poison Poison Poison Poison Poison Oxidizer & poison 
Poison Poison Poison Poison Poison Combustible liquid & poison Poison Poison 
Poison Poison Poison Poison Flammable liquid & poison Poison Corrosive & 
deadly poison Poison Flammable liquid, corrosive & poison Deadly poison 
Highly corrosive irritant Corrosive & poison  Oxidizer, moderately toxic 
Flammable gas & deadly poison Flammable gas & poison Poison Poison Poison 
Flammable liquid & poison Poison Poison  100+ 500+ 500 10+ 100 500 500+ 100+ 
1+ 10+ 500+ 500+ 1,000 100+ 1,000+ 500+ 500 1,000 1,000 1,000 500 10,000 10 
500 1,000 10,000 10+ 500 500 100+ 500 100+ 10+ 10 500+ 500 500 1,000 500+ 100 
100+ 500 100+ 500 500+ 100 500 1,000 100 500 100 1,000 10 500 500+ 100 100+ 
1,000 500+ 100+  


CAS Number  Chemical Name  Hazard  Threshold Planning Quantity (pounds)  
00055-91-4 04098-71-9 00108-23-6 00119-38-0 00078-97-7 21609-90-5 00541-25-3 
00058-89-9 07580-67-8 00109-77-3 12108-13-3 00950-10-7 01600-27-7 07487-94-7 
21908-53-2 10476-95-6 00760-93-0 00126-98-7 00920-46-7 30674-80-7 10265-92-6 
00558-25-8 00950-37-8 02032-65-7 16752-77-5 00151-38-2 00074-83-9 00080-63-7 
00079-22-1 00060-34-4 00624-83-9 00556-61-6 00074-93-1 00502-39-6 03735-23-7 
00676-97-1 00556-64-9 00075-79-6 00079-84-4 01129-41-5 07786-34-7 00315-18-4 
00050-07-7 06923-22-4 02763-96-4 00505-60-2 13463-39-3 00054-11-5 00065-30-5 
07697-37-2 10102-43-9 00098-95-3 01122-60-7 10102-44-0 00051-75-2 00062-75-9 
00991-42-4 PMN-82-147 00630-60-4 23135-22-0  Isofluorphate Isophorone 
diisocyanate Isopropyl chloroformate Isopropylmethylpyrazolyl 
dimethylcarbamate Lactonitrile Leptophos Lewisite Lindane Lithium hydride 
Malononitrile Mangenese tricarbonyl methylcyclopentadienyl Mephosfolan 
Mercuric acetate Mercuric chloride Mercuric oxide Methacrolein diacetate 
Methacrylic anhydride Methylacrylonitrile Methacryloyl chloride 
Methacryloyloxyethylisocyanate Methamidophos Methanesulfonyl fluoride 
Methidathion Methiocarb Methomyl Methoxyethylmercuric acetate Methyl bromide 
Methyl 2-chloroacrylate Methyl chloroformate Methyl hydrazine Methyl 
isocyanate Methyl isothiocyanate Methyl mercaptan Methylmercuric dicyanamide 
Methyl phenkapton Methyl phosphonic dichloride Methyl thiocyanate Methyl 
trichlorosilane Methyl vinyl ketone Metolcarb Mevinphos Mexacarbate Mitomycin 
C Monocrotophos Muscinol Mustard gas Nickel carbonyl Nicotine Nicotine 
sulfate Nitric acid (.40% pure) Nitric oxide Nitrobenzene Nitrocyclohexane 
Nitrogen dioxide Nitrogen mostard N-Nitrosodimethylamine Norbormide 
Organorhodium complex Ouabain Oxamyl  Poison Poison Flammable liquid & poison 
Poison Poison Poison Poison Poison Flammable solid & poison Poison Poison 
Poison Poison Poison Powerful oxidant Poison Poison Poison Poison Poison 
Poison Poison Poison Poison Poison Poison Poison gas Moderately toxic 
Flammable liquid, corrosive & poison Flammable liquid, corrosive, poison 
Flammable liquid & poison Flammable liquid & poison Flammable gas & poison 
Poison Poison Corrosive & poison Poison Flammable liquid, corrosive & poison 
Poison Poison Poison Poison Poison Poison Poison Flammable liquid & poison 
Poison Poison Corrosive, oxidizer & poison Poison gas Poison Poison Oxidizer 
& moderately toxic Deadly poison Poison Poison Flammable & toxic Poison 
Poison  100 100 1,000 500 1,000 500+ 10 1,000+ 100 500+ 100 500 500+ 500+ 
500+ 1,000 500 500 100 100 100+ 1,000 500+ 500+ 500+ 500+ 1,000 500 500 500 
500 500 500 500+ 500 100 10,000 500 10 100+ 500 500+ 500+ 10+ 10,000 500 1 
100 100+ 1,000 100 10,000 500 100 10 1,000 100+ 10+ 100+ 100+  

10A-6 

CAS Number  Chemical Name  Hazard  Threshold Planning Quantity (pounds)  
00078-71-7 02497-07-6 10028-15-6 01910-42-5 02074-50-2 00056-38-2 00298-00-0 
13002-03-8 19624-22-7 02570-26-5 00079-21-0 00594-42-3 00108-95-2 04418-66-0 
00064-00-6 00058-36-6 00696-28-6 00059-88-1 00062-38-4 02097-19-0 00103-85-5 
00298-02-2 04104-14-7 00947-02-4 00075-44-5 00732-11-6 13171-21-6 07803-51-2 
02665-30-7 50782-69-9 02703-13-1 03254-63-5 02587-90-8 07723-14-0 10025-87-3 
10026-13-8 01314-56-3 07719-12-2 00057-47-6 00057-64-7 00124-87-8 00110-89-4 
23505-41-1 10124-50-2 00151-50-8 00506-61-6 02631-37-0 00106-96-7 00057-57-8 
00107-12-0 00542-76-7 00070-69-9 00109-61-5 00075-56-9 00075-55-8 02275-18-5 
00129-00-0  Oxetane,3,3,-bis(chloromethyl)-Oxydisulfoton Ozone Paraquat 
Paraquat methosulfate Parathion Parathion-methyl Paris green Pentaborane 
Pentadecylamine Peracetic acid Perchloromethylmercaptan Phenol Phenol,2,2-
thiobis(4-chloro-6-methyl) Phenol,3-(1-methylethyl)-methylcarbamate 
Phenoarsazine 10,10-oxydi-Phenyl dichloroarsine Phenylhydrazine hydrochloride 
Phenylmercury acetate Phenylsilatrane Phenylthiourea Phorate Phosacetim 
Phosfolan Phosgene Phosmet Phosphamidon Phosphine Phosphonothioic acid, 
methyl-o-(4-nitrophenol)ophenyl ester Phosphonothioic acid, methyl-s-(2-
(bis(1methylethyl)amino)o-ethyl ester` Phosphonothioic acid methyl,-o-ethyl-
o-4(methylthio)phenyl ester Phosphoric acid, dimethyl,4-(mehtylthio)phenyl 
ester Phosphorothioic aicd,o,o-dimethyl-s-(2-methyl-thioethyl ester 
Phosphorus Phosphorus oxychloride Phosphorus pentachloride Phosphorus 
pentoxide Phosphorus trichloride Physostigmine Physostigmine, salicylate 
(1:1) Picrotoxin Piperidine Pirimifos-ethyl Potassium arsenite Potassium 
cyanide Potassium silver cyanide Promecarb Propagyl bromide beta-
Propiolactone Propionitrile Propionitrile, 3-chloro Propiophenone,4-amino 
Propyl chloroformate Propylene oxide Propyleneimene Prothoate Pyrene  Poison 
Poison Poison Poison Poison Poison Poison Poison Flammable liquid & poison 
Poison Corrosive & poison Poison Poison Poison Poison Poison Poison Poison 
Poison Poison Poison Poison Poison Poison Poison gas Poison Poison Flammable 
& poison gas Poison Poison Deadly poison Poison Poison Flammable solid & 
poison Corrosive, irritant & poison Corrosive & poison Corrosive & poison 
Corrosive & poison Poison Poison Poison Poison Poison Poison Deadly poison 
Poison & irritant Poison Flammable liquid & deadly poison Poison Flammable 
liquid & poison Poison Poison Flammable liquid, corrosive & poison Flammable 
liquid & poison Flammable liquid & poison Poison Poison  500 500 100 10+ 10+ 
100 100+ 500+ 500 100+ 500 500 500+ 100+ 500+ 500+ 500 1,000+ 500+ 100+ 100+ 
10 100+ 100+ 10 10+ 100 500 500 100 500 500 500 100 500 500 10 1,000 100+ 
100+ 500+ 1,000 1,000 500+ 100 500 500+ 10 500 500 1,000 100+ 500 10,000 
10,000 100+ 1,000+  

CAS Number  Chemical Name  Hazard  Threshold Planning Quantity (pounds)  
00140-76-1 00504-24-5 01124-33-0 53558-25-1 14167-18-1 00107-44-8 07783-00-8 
07791-23-3 00563-41-7 03037-72-7 07631-89-2 07784-46-5 26628-22-8 00124-65-2 
00143-33-9 00062-74-8 13410-01-0 10102-18-8 10102-20-2 00900-95-8 00057-24-9 
00060-41-3 03689-24-5 03569-57-1 07446-09-5 07783-60-0 07446-11-9 07664-93-9 
00077-81-6 13494-80-9 07783-80-4 00107-49-3 13071-79-9 00078-00-2 00597-64-8 
00075-74-1 00509-14-8 10031-59-1 06533-73-9 07791-12-0 02757-18-8 07446-18-6 
02231-57-4 39196-18-4 00297-97-2 00108-98-5 00079-19-6 05344-82-1 00614-78-8 
07550-45-0 00584-84-9 00091-08-7 08001-35-2 01031-47-6 24017-47-8 00076-02-8 
01558-25-4 27137-85-5 00115-21-9 00327-98-0 00098-13-5 00998-30-1  
Pyridine,2-methyl-5-vinyl Pyridine,4-amino Pyridine,4-nitro-,1-oxide 
Pyriminil Salcomine Sarin Selenous acid Selenium oxychloride Semicarbazide 
hydrochloride Silane, (4-aminobutyl)diethoxymethyl Sodium arsenate Sodium 
arsenite Sodium azide  Sodium cacodylate Sodium cyanide Sodium fluoroacetate 
Sodium selenate Sodium selenite Sodium tellurite Stannane, acetoxytriphenyl 
Strychnine Strychnine, sulfate Sulfotep Sulfoxide,3-chloropropyloctyl Sulfur 
dioxide Sulfur tetrafluoride Sulfur trioxide Sulfuric acid (>93%) Tabun 
Tellurium Tellarium hexafluoride TEPP Terbufos Teraethyllead Tetraethyltin 
Tetramethyllead Tetranitromethane Thallium sulfate Thallous carbonate 
Thallous chloride Thallous malonate Thallous sulfate Thiocarbazide Thiofanox 
Thioazin Thiophenol Thiosemicarbazide Thiourea, (2-chlorophenyl) Thiourea (2-
methylphenyl) Titanium tetrachloride Toluene 2,4-diisocyanate Toluene 2,6-
diisocyanate Toxaphene Triamiphos Triazofos Trichloroacetyl chloride 
Trichloro(chloromethyl)silane Trichloro(chlorophenyl)silane 
Trichloroethylsilane Trichloronate Trichlorophenylsilane Triethoxysilane  
Poison Poison Poison Poison Poison Deadly poison Poison Poison Poison Poison 
Poison Deadly poison Poison Poison Deadly poison Deadly poison Poison Poison 
Poison Poison Poison Poison Poison Poison Poison gas Poison gas Corrosive & 
poison Corrosive & poison Poison Poison Poison gas Poison Deadly poison 
Flammable liquid & poison Poison Poison Oxidizer & poison Poison Poison 
Poison Poison Poison Poison Poison Poison Flammable liquid & poison Poison 
Poison Poison Corrosive & poison Poison Poison Poison Poison Poison Corrosive 
& moderately toxic Poison Corrosive & poison Flammable liquid & poison Poison 
Corrosive & poison Poison  500 500+ 500+ 100+ 500+ 10 1,000+ 500 1,000+ 1,000 
1,000+ 500+ 500 100+ 100 10+ 100+ 100+ 500+ 500+ 100+ 100+ 500 500 500 100 
100 1,000 10 500+ 100 100 100 100 100 100 500 100+ 100+ 100+ 100+ 100+ 1,000+ 
100+ 500 500 100+ 100+ 500+ 100 500 100 500+ 500+ 500 500 100 500 500 500 500 
500  

10A-8 

CAS Number  Chemical Name  Hazard  Threshold Planning Quantity (pounds)  
00075-77-4 00824-11-3 01066-45-1 00639-58-7 02001-95-8 01314-62-1 00108-05-4 
00081-81-2 00129-06-6 28347-13-9 58270-08-9 01314-84-7  Trimethylchlorosilane 
Trimethylolpropane phosphate Trimethyltin chloride Triphenyltin chloride 
Valinomycin Vanadium pentoxide Vinyl acetate monomer Warfarin Warfarin sodium 
Xylene dichloride Zinc, dichloro(4,4-
dimethyl5((((methylamino)carbonyl)oxino)pentanenitrile)-,(T4) Zinc phosphide  
Flammable liquid, corrosive & moderately toxic Poison Deadly poison Poison 
Poison Poison Flammable liquid & moderately toxic Poison Poison Poison Poison 
Flammable solid & poison  1,000 100+ 500+ 500+ 1,000+ 100+ 1,000 500+ 100+ 
100+ 100+ 500  

Note:  For the Treshold Planning Quantities marked with a  + , the quantity 
listed applies only if in powdered form and with a particle size of less than 
100 microns, or is handled in solution or molten form, or has a NFPA rating 
for reactivity of 2, 3 or 4.  Otherwise the Treshold Planning Quantity is 
10,000 lbs.  The material is still required to be reported on an annual 
inventory at the Treshold Planning Quantity or 500 lbs, whichever is less. 
Source of hazard information:  N. Irving San and Richard J. Lews, Sr., 
Dangerous Properties of Industrial Materials, Seventh Edition, Volumes I - 
III, Van Nostrand Reinhold, New York, (1989). 

Appendix 10B. Listing of Chemicals contained in the TRI Database, including 
their CAS Numbers and Hazards 
CAS NUMBER  CHEMICAL NAME  HAZARDS  
75-07-0 60-35-5 67-64-1 75-05-8 53-96-3 107-02-8 79-06-1 79-10-7 107-13-1 
309-00-2 107-05-1 7429-90-5 1344-28-1 117-79-3 60-09-3 92-67-1 82-28-0 7664-
41-76484-52-2 7783-20-2 62-53-3 90-04-0 109-94-9 134-29-2 120-12-7 7440-36-
07440-38-21332-21-47440-39-398-87-3 55-21-0 71-43-2 92-87-5 98-07-7 98-88-4 
94-36-0 100-44-7 7440-41-7 92-52-4 111-44-4 542-88-1 108-60-1 103-23-1 75-25-
2 74-83-9 106-99-0 141-32-2 71-36-3 78-92-2 75-65-0 85-68-7 106-88-7 123-72-8 
2650-18-2 3844-45-9 4680-78-8 569-64-2 989-38-8 1937-37-7 2602-46-2  
Acetaldehyde Acetamide Acetone Acetonitrile 2-Acetylaminofluorene Acrolein 
Acrylamide Acrylic acid Acrylonitrile Aldrin Allyl chloride Aluminum (fume or 
dust) Aluminum oxide 2-Aminoanthraquinone4-Aminoazobenzene 4-Aminobiphenyl 1-
Amino-2-methylanthraquinone  Ammonia Ammonium nitrate (solution) Ammonium 
sulfate (solution) Aniline o-Anisidine p-Anisidine o-Anisidine hydrochloride 
Anthracene  Antimony  Arsenic  Asbestos (friable)  Barium Benzal chloride 
Benzamide Benzene Benzidine Benzoic trichloride (Benzotrichloride) Benzoyl 
chloride Benzoyl peroxide Benzyl chloride Beryllium Biphenyl Bis(2-
chloroethyl) ether Bis(chloromethyl) ether Bis(2-chloro-1-methyulethyl) ether 
Bis(2-ethylhexyl) adipate Bromoform (Tribromomethane) Bromomethane (methyl 
bromide) 1,3-Butadiene Butyl acrylate n-Butyl alcohol sec-Butyl alcohol tert-
Butyl alcohol Butyl benzyl phthalate 1,2-Butylene oxide Butyraldehyde C.I. 
Acid Blue 9, diammonium salt C.I. Acid Blue, disodium salt C.I. Acid Green 3 
C.I. Basic Green 4 C.I. Basic Red 1 C.I. Direct black 38 C.I. Direct Blue 6  
Poison Experimental carcinogen Moderately toxic Poison Moderately toxic 
Poison Poison Poison Poison Poison Poison Not considered a industrial poison 
Experimental tumorigen  Experimental carcinogen Poison Poison Experimental 
neoplastigen Poison Powerful oxidizer & an allergen Moderately toxic Poison 
Moderately toxic Moderately toxic Experimental carcinogen Experimental 
tumorigen Poison Carcinogen Carcinogen Poison Poison Moderately toxic Poison 
Poison Poison Carcinogen Poison Poison Deadly poison Poison Poison Poison 
Poison Experimental carcinogen Poison Poison Experimental carcinogen 
Moderately toxic Poison Poison Moderately toxic Moderately toxic Moderately 
toxic Moderately toxic Poison Experimental neoplastigen Experimental 
tumorigen  Poison Poison Experimental tumorigen Experimental carcinogen  

10B-2 

CAS NUMBER  CHEMICAL NAME  HAZARDS  
16071-86-6 2832-40-8 3761-53-3 81-88-9 3118-97-6 97-56-3 842-07-9 492-80-8 
128-66-5 7440-43-9156-62-7 133-06-2 63-25-2 75-15-0 56-23-5 463-58-1 120-80-9 
133-90-4 57-74-9 7782-50-510049-04-4 79-11-8 532-27-4 108-90-7 510-15-6 75-
00-3 67-66-3 74-87-3 107-30-2 126-99-8 1897-45-67740-47-37440-48-47440-50-
8120-71-8 1319-77-3 108-39-4 95-48-7 106-44-5 98-82-8 80-15-9 135-20-6 110-
82-7 94-75-7 1163-19-5 2303-16-4615-05-4 39156-41-7 101-80-4 25376-45-8 95-
80-7 334-80-3 132-64-9 96-12-8 106-93-4 84-74-2 25321-22-6 95-50-1 541-73-1 
106-46-7 91-94-1 75-27-4 107-06-2  C.I. Direct Brown 95 C.I. Disperse Yellow 
3 C.I. Food Red 5 C.I. Food Red 15 C.I. Solvent Orange 7 C.I. Solvent Yellow 
3 C.I. Solvent Yellow 14 C.I. Solvent Yellow 34 (Auramine) C.I. Vat Yellow 4  
Cadmiun Calcium cyanamide Captan Carbaryl Carbon disulfide Carbon 
tetrachloride Carbonyl sulfide Catechol Chloramben Chlordane  Chlorine 
Chlorine dioxide Chloroacetic acid 2-Chloroacetophenone Chlorobenzene 
Chlorobenzilate Chloroethane Chloroform Chloromethane (Methyl chloride) 
Chloromethyl methyl ether Chloroprene  Chlorothalonil  Chromium Cobalt Copper 
p-Cresidine Cresol (mixed isomers) m-Cresol o-Cresol p-Cresol Cumene Cumene 
hydroperoxide Cupferron Cyclohexane 2,4-D (Acetic acid,(2,4-dichlore-
phenoxy)) Decabromodiphenyl oxide Diallate 2,4-Diaminoanisole 2,4-
Diaminoanisole sulfate 4,4-Diaminophenyl ether Diaminotoluane (mixed isomers) 
2,4-Diaminotoluene Diazomethane Dibenzofuran 1,2-Dibromo-3-chloropropane 
(DBCP) 1,2-Dibromoethane (Ethylene dibromide) Dibutyl 
phthalateDichlorobenzene (mixed isomers) 1,2-Dichlorobenzene 1,3-
Dichlorobenzene 1,4-Dichlorobenzene 3,3-Dichlorobenzidine 
Dichlorobromomethane 1,2-Dichloroethane  Experimental carcinogen Experimental 
tumorigen Poison Experimental carcinogen  Experimental carcinogen 
Experimental carcinogen Poison Experimental carcinogen Poison Poison 
Moderately toxic Poison Poison Poison Poison Moderately toxic Experimental 
carcinogen Poison Moderately toxic Moderately toxic Poison Poison Poison 
Experimental carcinogen Mildly toxic  Poison Mildly toxic Poison Poison 
Moderately toxic Poison Poison Experimental tumorigen Moderately toxic 
Moderately toxic Poison Poison Poison Moderately toxic Moderately toxic 
Poison Poison Poison Experimental neoplastigen Poison Poison Poison Poison 
Poison Poison Experimental tumorigen Poison Poison  Moderately toxic Poison 
Poison Poison Poison Experimental carcinogen Moderately toxic Poison  

CAS NUMBER  CHEMICAL NAME  HAZARDS  
540-59-0 75-09-2 120-83-2 78-87-5 542-75-6 62-73-7 115-32-2 1464-53-5111-42-2 
117-81-7 84-66-2 64-67-5 119-90-4 60-11-7 119-93-7 79-44-7 57-14-7 105-67-9 
131-11-3 77-78-1 534-52-1        51-28-5 121-14-2 606-20-2 117-84-0 123-91-1 
122-66-7 106-89-8 110-80-5 140-88-5 100-41-4 541-41-3 74-85-1 107-21-1 151-
56-4 75-21-8 96-45-7 2164-17-250-00-0 76-13-1 76-44-8 118-74-1 87-68-3 77-47-
4 67-72-1 13355-87-1 680-31-9 302-01-2 10034-93-2 7647-01-074-90-8 7664-39-
3123-31-9 78-84-2 67-63-0 80-05-7 7439-92-158-89-9 108-31-6 12427-38-2 7439-
96-5108-78-1  1,2-Dichloroethylene Dichloromethane (Methylene chloride) 2,4-
Dichlorophenol 1,2-Dichloropropane 1,3-Dichloropropylene Dichlorvos Dicofol  
Diepoxybutane Diethanolamine di-(2-ethylhexyl) phthalate (DEHP)  Diethyl 
phthalate Diethyl sulfate 3,3-Dimethoxybenzidine 4-Dimethylaminoazobenzene 
3,3-Dimethylbenzidine (o-Tolidine) Dimethylcarbamyl chloride 1,1-Dimethyl 
hydrazine 2,4-Dimethylphenol Dimethyl phthalate Dimethyl sulfate 4,6-Dinitro-
o-cresol 2,4-Dinitrophenol 2,4-Dinitrotoluene 2,5-Dinitrotoluene n-Dioctyl 
phthalate 1,4-Dioxane 1,2-Diphenylhydrazine (Hydrazobenzene) Epichlorohydrin 
2-Ethoxyethanol Ethyl acrylate Ethylbenzene Ethyl chloroformate Ethylene 
Ethylene glycol Ethyleneimine (Aziridine) Ethylene oxide Ethylene thiourea  
Fluometuron Formaldehyde Freon 113 Heptachlor (1,4,5,6,7,8,8,-Heptachloro-
3a,4,7,7a-tetrahydro-4,7methano-1H-indene) Hexachlorobenzene Hexachloro-1,3-
butadiene Hexachlorocyclopentadiene Hexachloroethane Hexachloronaphthalene 
Hexamethylphosphoramide Hydrazine Hydrazine sulfate  Hydrochloric acid 
Hydrogen cyanide  Hydrogen fluoride Hydroquinone IsobutyraldehydeIsopropyl 
alcohol 4,4-Isopropylidenediphenol  Lead Lindene Maleic acid Maneb  Manganese 
Melamine  Poison Poison Poison Moderately toxic Poison Poison Poison Poison 
Moderately toxic Poison Poison Poison Moderately toxic Poison Poison Poison 
Poison Poison Moderately toxic Poison Poison Deadly poison Poison Moderately 
toxic Mildly toxic Poison Poison Poison Moderately toxic Poison Moderately 
toxic Poison Simple asphyxiant Poison Poison Poison Poison Poison Poison 
Mildly toxic Poison Poison Poison Deadly poison Poison Poison Experimental 
carcinogen Poison Poison Poison Deadly poison Poison Poison  Moderately toxic 
Poison Poison Poison Poison Poison Experimental carcinogen Experimental 
tumorigen Experimental carcinogen  

10B-4 

CAS NUMBER  CHEMICAL NAME  HAZARDS  
7439-97-667-56-1 72-43-5 109-86-4 96-33-3 1634-04-4101-14-4 101-61-1 101-68-8 
74-95-3 101-77-9 78-93-3 60-34-4 74-88-4 108-10-1 624-83-9 80-62-6 90-94-8 
1313-27-5505-60-2 91-20-3 134-32-7 91-59-8 7440-02-07697-37-2139-13-9 99-59-2 
98-95-3 92-93-3 1836-75-551-75-2 55-63-0 88-75-5 100-02-7 79-46-9 156-10-5 
121-69-7 924-16-3 55-18-5 62-75-9 86-30-6 621-64-7 4549-40-059-89-2 759-73-9 
684-93-5 16543-55-8 100-75-4 2234-13-120816-12-0 56-38-2 87-86-5 79-21-0 108-
95-2 106-50-3 90-43-7 75-44-5 7664-38-27723-14-085-44-9 88-89-1   Mercury 
Methanol Methoxychlor (Benzene-1,1-(2,2,2,-trichloroethylidene)bis(4methoxy) 
2-MethoxyethanolMethyl acrylate  Methyl tert-butyl ether 4,4-Methylenebis(2-
chloro aniline) 4,4-Methylenebis (N,N-dimethyl)benzenamine 
Methylenebis(phenylisocyanate) Methylene bromide 4,4-Methylenedianiline 
Methyl ethyl ketone Methyl hydrazine Methyl iodide Methyl isobutyl ketone 
Methyl isocyanate Mehtyl methacrylate Michler s ketone  Molybdenum trioxide 
Mustard gas Naphthalene alpha-Naphthylamine beta-Naphthylamine Nickel  Nitric 
acid Nitrilotriacetic acid 5-Nitro-o-anisidine Nitrobenzene 4-Nitrobephenyl  
Nitrofen Nitrogen mustard Nitroglycerin 2-Nitrophenol 4-Nitrophenol 2-
Nitropropane p-Nitrosodiphenylamine N,N,-Dimethylaniline N-Nitrosodi-n-
butylamine N-Nitrosodiethylamine N-Nitrosodimethylamine N-
Nitrosodiohenylamine N-Nitrosodi-n-propylamine  N-Nitrosomethylvinylamine N-
Nitrosomorpholine N-Nitroso-N-ethylurea N-Nitroso-N-methylurea N-
Nitrosonorrnicotine N-Nitrosopiperidine  Octachloronaphthlene Osmiun 
tetroxide Parathion Pentachlorophenol Peracetic acid Phenol p-
Phenylenediamine 2-Phenylphenol Phosgene  Phosphoric acid  Phosphorus 
Phthalic anhydride Picric acid  Poison Poison Moderately toxic  Moderately 
toxic Poison Flammable Poison Moderately toxic Poison Poison Poison 
Moderately toxic Poison Poison Poison Poison Moderately toxic Poison Poison 
Poison Poison Poison Poison Poison Poison Poison Moderately toxic Poison 
Poison Poison Deadly poison Poison Poison Poison Poison Poison Poison 
Moderately toxic Poison Poison Moderately toxic Moderately toxic Poison 
Poison Poison Poison Experimental carcinogen Poison Poison Poison Deadly 
poison Poison Poison Poison Poison Poison Poison Poison Poison Poison Poison  

CAS NUMBER  CHEMICAL NAME  HAZARDS  
1336-36-3 1120-71-457-57-8 123-38-6 114-26-1 115-07-1 75-55-8 75-56-9 110-86-
1 91-22-5 106-51-4 82-68-8 81-07-2 94-59-7 7782-49-27440-22-41310-73-2 7757-
82-6 100-42-5 96-09-3 7664-93-9100-21-0 79-34-5 127-18-4 961-11-5 7440-28-0 
62-55-5 139-65-1 62-56-6 1314-20-17550-45-0108-88-3 584-84-9 91-08-7 95-53-4 
636-21-5 8001-35-268-76-8 52-68-6 120-82-1 71-55-6 79-00-5 79-01-6 95-95-4 
88-06-2 1582-09-895-63-6 126-72-7 51-79-6 7440-62-2 108-05-4 593-60-2 75-01-4 
75-35-4 1330-20-7 108-38-3 95-47-6 106-42-3 87-62-7 7440-66-6 12122-67-7  
Polychlorinated biphenyls (PCBs)  Propane sultone beta-Propiolactone 
Propionaldehyde Propoxur Propylene (propene) Propyleneimine Propylene oxide 
Pyridine Quinoline Quinone Quintozene (Pentachloronitrobenzene)Saccharin 
Safrole  Selenium Silver Sodium hydroxide (solution) Sodium sulfate 
(solution) Styrene Styrene oxide  Sulfuric acid Terephthalic acid 1,1,2,2,-
Tetrachloroethane Tetrachloroethylene Tetrachlorovinphos Thallium 
Thioacetamide 4,4-Thiodianiline Thiourea  Thorium dioxide  Titanium 
tetrachloride Toluene Toulene-2,4-diisocyanate Toluene-2,6-diisocyanate o-
Toluidine o-Toluidine hydrochloride  Toxaphene Triaziquone Trichlorfon 
(Phosphoric acid (2,2,2-trichloro-1-hydroxyethyl)dimethyl ester 1,2,4-
Trichlorobenzene 1,1,1-Trichloroethane (methyl chloroform) 1,1,2-
Trichloroethane Trichloroethylene 2,4,5-Trichlorophenol 2,4,6-Trichlorophenol  
Trifluralin 1,2,4-Trimethylbenzene Tris(2,3-dibromopropyl) phosphate Urethane 
(Ethyl carbamate)Vanadium (fume or dust) Vinyl acetate Vinyl bromide Vinyl 
chloride Vinylidene chloride Xylene (mixed isomers) m-Xylene o-Xylene p-
Xylene 2,6-Xylidine Zinc (fume or dust) Zineb  Moderately toxic Poison Poison 
Moderately toxic Poison Simple asphyxiant Poison Poison Poison Poison Poison  
Experimental carcinogen Moderately toxic Poison Poison Experimental tumorigen 
Poison Moderately toxic Experimental poison Moderately toxic Poison 
Moderately toxic Poison Experimental poison Poison Poison Poison Poison 
Poison Carcinogen Poison Poison Poison Poison Poison Poison Poison Poison 
Poison Poison Poison Poison Experimental poison Poison Poison Moderately 
toxic Moderately toxic Poison  Moderately toxic Poison Moderately toxic 
Moderately toxic Poison Poison Moderately toxic Moderately toxic Moderately 
toxic Moderately toxic Moderately toxic Skin & systemic irritant Moderately 
toxic  

Chapter 11.  Induced Damage Methods   Debris 
11.1 Introduction 
Debris disposal can be a significant issue following most natural disasters, 
including floods. Flood debris can include flood-damaged building finishes 
(e.g., carpeting, drywall, etc.) and contents (e.g., furniture, appliances, 
etc.), and in extreme cases, materials from buildings requiring major 
structural repair or demolition.  Flood-fighting efforts and the floodwaters 
themselves add additional debris, such as sandbags, mud and sediment.  The 
HAZUS flood debris model focuses on building-related debris, and does not 
address contents removal or additional debris loads such as vegetation and 
sediment.  For additional information on debris management, the reader is 
referred to material published by the EPA (EPA, 1995) and FEMA (FEMA, 1999). 
The HAZUS flood model debris estimation methodology is similar to that 
developed for the HAZUS earthquake model, in that it evaluates building-
related debris by major component (finishes, structural components, and 
foundation materials), yet it recognizes a fundamental difference in the type 
of debris generated by the two types of events.  For earthquakes, the debris 
from damaged buildings includes both structural and nonstructural components, 
while most flood-related debris is contents and finishes.  The methodology 
highlighting the debris component is shown in the flowchart in Figure 11.1. 

11.1.1 Scope 
The HAZUS flood model will estimate debris from building damage during 
floods, including building finishes, and structural components.  No debris 
estimates are made for building contents, or for bridges or other lifelines. 
11.1.2 Form of Damage Estimate 
The debris module will determine the expected amounts of debris generated 
within each census block. Output from this module is the debris weight (in 
tons). The classes of debris are defined as follows: 
 	
Building finishes 

 	
Structural components 

 	
Foundation materials 


11.1.3 Input Requirements and Output Information 
Input to this module includes the following items: 
 	
Depth of flooding throughout the census block 

 	
Building square footage by occupancy class for each census block from the 
inventory 

 	
Foundation distribution information, classified into  Footings  and  Slab on 
grade , and RES1 foundation information regarding the presence of basements 


11.2 Description of Methodology 
The HAZUS Flood Model debris estimation methodology was developed using a 
simplified engineering-based component analysis to identify building 
components requiring replacement (i.e., wood sub-floor, carpet, wall 
finishes, etc) at various depths of water, and to estimate their weight. For 
each occupancy class, structural component weights have been assumed as an 
average over typical model building types, based on the original the HAZUS 
Earthquake debris model ( Unit Weight (tons per 1000 ft2) for Structural and 
Nonstructural Elements for the Model Building Types , Table 11.1, HAZUS 
Earthquake Loss Estimation Methodology Technical Manual, FEMA/NIBS, 2002). 
For each occupancy type, a determination has been made as to the typical 
depth associated with substantial damage (i.e., more than 50% damage), 
assuming a typical building configuration. For buildings suffering 
substantial damage, the debris model assumes that the building will be 
demolished with no salvage, including the foundation.   
11.2.1 Debris Generated From Damaged Structures 
Debris generated from damaged buildings (in tons) is based on the following 
factors: 
1.	
Water depth in ranges relevant to debris generation; typically 0 to 4 feet, 4 
to 8 feet, 8 to 12 feet, etc. 

2. 
Occupancy type 

3. 
Foundation type ( Footing  vs.  Slab on grade ), and presence of basement 
(RES1 only) The recommended debris model of debris weights by occupancy class 
is given in Table 11.1. 


Table 11.1 Debris Weight by Occupancy Class 

Occupancy  Depth of Flooding  Debris Weight (Tons/1000 sq. ft.)  
Finishes  Structure  Foundations  
Footing  Slab on Grade  
RES1 (without basement)  0 -4   4.1  
4  to 8   6.8  
8 +  6.8  6.5  12.0  25.0  
-8  to -4   1.9  
RES1  -4  to 0   4.7  
(with basement)  0  to 6   8.8  
6 +  10.2  32.0  12.0  25.0  
RES2  0  to 1   4.1  
1 +  6.5  10.0  12.0  25.0  
RES3 (small 1 to 4 units)  0  to 4   4.1  
4  to 8   6.8  
8 +  10.9  6.5  12.0  25.0  

Table 11.1 Debris Weight by Occupancy Category (Continued) 

Occupancy  Depth of Flooding  Debris Weight (Tons/1000 sq. ft.)  
Finishes  Structure  Foundations  
Footing  Slab on Grade  
RES3 (medium 5 to 19 units)  0  to 4   4.1  
4  to 8   6.8  
8  to 12   10.9  
12 +  13.6  6.5  13.8  25.0  
0  to 4   4.1  
RES3  4  to 8   6.8  
(large 20+ units)  8  to 12   10.9  
12 +  13.6  47.8  19.0  25.0  
0  to 4   4.1  
RES4  4  to 8   6.8  
8  to 12   10.9  
12 +  13.6  58.6  9.9  25.0  
0  to 4   4.1  
RES5  4  to 8   6.8  
8  to 12   10.9  
12 +  13.6  68.7  18.3  25.0  
0  to 4   4.1  
RES6  4  to 8   6.8  
8  to 12   10.9  
12 +  13.6  6.5  12.0  25.0  
0  to 4   1.3  
EDU1  4  to 8   2.0  
8  to 12   3.3  
12 +  5.3  30.4  21.3  25.0  
0  to 4   1.3  
EDU2  4  to 8   2.0  
8  to 12   3.3  
12 +  5.3  30.4  21.3  25.0  
0  to 4   1.8  
COM1  4  to 8   2.5  
8  to 12   4.3  
12 +  5.0  47.1  2.1  25.0  
0  to 4   0.5  
COM2  4  to 8   1.0  
8  to 12   1.5  
12 +  2.0  50.4  5.2  37.5  

Table 11.1 Debris Weight by Occupancy Category (Continued) 

Occupancy  Depth of Flooding  Debris Weight (Tons/1000 sq. ft.)  
Finishes  Structure  Foundations  
Footing  Slab on Grade  
0  to 4   0.5  
COM3  4  to 8   1.0  
8  to 12   1.5  
12 +  2.0  43.7  8.7  25.0  
0  to 4   1.8  
COM4  4  to 8   2.5  
8  to 12   4.3  
12 +  5.0  30.3  7.5  25.0  
0  to 4   1.8  
COM5  4  to 8   2.5  
8  to 12   4.3  
12 +  5.0  23.5  8.7  25.0  
-8  to -4   1.6  
COM6 (with  -4  to 0   2.5  
basement  0  to 4   5.9  
4  to 8   8.3  
0  to 4   1.8  
COM7  4  to 8   2.5  
8  to 12   4.3  
12 +  5.0  31.3  8.7  25.0  
0  to 4   1.0  
COM8  4  to 8   1.3  
8  to 12   2.8  
12 +  3.1  26.9  8.7  25.0  
0  to 4   1.0  
COM9  4  to 8   1.3  
8  to 12   2.8  
12 +  3.1  44.9  8.7  25.0  
COM10  Any > 0   0.0  69.3  5.2  37.5  
0  to 4   1.8  
GOV1  4  to 8   2.5  
8  to 12   4.3  
12 +  5.0  31.3  8.7  25.0  
0  to 4   0.5  
GOV2  4  to 8   0.7  
8  to 12   1.5  
12 +  1.7  18.0  8.7  25.0  

Table 11.1 Debris Weight by Occupancy Category (Continued) 

Occupancy  Depth of Flooding  Debris Weight (Tons/1000 sq. ft.)  
Finishes  Structure  Foundations  
Footing  Slab on Grade  
0  to 4   0.5  
IND1  4  to 8   0.7  
8  to 12   1.5  
12 +  1.7  50.4  5.2  37.5  
0  to 4   0.5  
IND2  4  to 8   0.7  
8  to 12   1.5  
12 +  1.7  40.2  5.2  37.5  
0  to 4   0.5  
IND3  4  to 8   0.7  
8  to 12   1.5  
12 +  1.7  49.8  5.2  37.5  
0  to 4   0.5  
IND4  4  to 8   0.7  
8  to 12   1.5  
12 +  1.7  34.8  5.2  25.0  
0  to 4   0.5  
IND5  4  to 8   0.7  
8  to 12   1.5  
12 +  1.7  58.3  5.2  25.0  
0  to 4   1.8  
IND6  4  to 8   2.5  
8  to 12   4.3  
12 +  5.0  34.0  5.2  37.5  
0  to 4   1.0  
REL1  4  to 8   1.3  
8  to 12   2.8  
12 +  3.1  32.5  8.7  25  
AGR1  Any > 0   0.0  12.3  5.2  25.0  

To implement the debris estimation model, the default foundation distribution 
must be reclassified into two foundation types;  slab-on-grade  and 
 footings .  Table 11-2 provides the association of the detailed foundation 
types into the more general foundation types for debris estimation.  As shown 
in the table, structures with basements are treated as slab foundations to 
reflect the typical presence of a concrete floor slab. 
Table 11.2 Mapping Of Detailed HAZUS Foundation Types Into General Foundation 
Types For Debris Estimation 
Foundation types considered  Slab-on-grade   Foundation types considered 
 Footings   
Basement/Garden Level  Pile  
Slab-on-Grade  Solid wall  
 Pier/Post  
Crawlspace  
Fill  

The input provided from hazard module is the depth distribution throughout 
the census block, which may be applied to the square footage exposure for 
each occupancy type to determine square footage exposure by depth. 
The expected debris amount (in tons) for occupancy type i in the current 
census block is given by: 
EDW(i, j) =Depth% j W Fai W(DWFi, j + DWSi, j +(DWFoFi, j W FoF%)+ (DWFoSi, j 
W FoS%)) 
(11-1) 
where: 
EDW(i,j)  = Expected debris weight of occupancy i, for depth j 
Depth%j = the percent of the current census block subjected to the given 

depth, j 
Fai = floor area of occupancy i (in 1000 square feet) 
DWFi,j = debris weight (in tons per 1000 square foot) of building 

finishes for occupancy i, and depth j, taken from Table 11-1. 
DWSi,j = 	debris weight (in tons per 1000 square foot) of building 
structural components for occupancy i, and depth j, taken 
from Table 11-1. 

DWFoFi,j = 	debris weight (in tons per 1000 square foot) of foundation 
materials for buildings with footing foundations for 
occupancy i, and depth j, taken from Table 11-1. 

FoF% = 	percent of building area with footing foundations, aggregated 
from default foundation type distribution according to Table 
11-2. (Note: FoF% and FoS% must sum to 100%) 

DWFoSi,j = 	debris weight (in tons per 1000 square foot) of foundation 
materials for buildings with slab-on-grade foundations for 
occupancy i, and depth j, taken from Table 11-1. 

FoS% = 	percent of building area with slab-on-grade foundations, 
aggregated from default foundation type distribution 
according to Table 11-2. (Note: FoF% and FoS% must sum
to 100%) 

For RES1 structures, a factor is added to incorporate the basement/no-
basement distribution, in conjunction with the basement and no basement 
models presented in Table 11-1.  
11.2.2 Natural Debris Carried by Floodwaters 
The HAZUS Flood module currently does not estimate debris loads associated 
with vegetation, sediment and other natural debris carried by floodwaters. 
11.3 Guidance for Expert-Generated Estimates 
There is no difference in the debris estimation methodology for Advanced Data 
and Models Analysis. Users seeking more accurate debris estimates are 
encouraged to input more detailed inventory data, or to use alternative 
methods to estimate debris loads associated with building contents, and 
natural debris. 
11.4 References 
1.	
EPA (1995), Planning for Disaster Debris, U.S. Environmental Protection 
Agency, Solid Waste and Emergency Response, EPA530-K-95-010. 

2.	
FEMA (1999), Debris Management Guide, Federal Emergency Management Agency, 
Publication FEMA-325. 

3.	
FEMA/NIBS (2002), HAZUS-99 Service Release 2 (SR-2) Technical Manual, 
developed by the Federal Emergency Management Agency, through agreements with 
the National Institute of Building Sciences, Washington, D.C. 


Chapter 12.  Direct Social Losses   Casualties 
12.1 Introduction 
During the Flood Model methodology development, casualty data was collected 
and analyzed to assess the feasibility of incorporating a flood casualty 
model.  The data collection and review are discussed in Section 12.2. 
Available casualty data for flood events was essentially limited to 
fatalities.  That is, data on flood-related injuries was not widely 
available.  Further, review of the fatality data indicated that drowning 
dominated the data for cause of death.  Accordingly, a fatality model for 
drowning deaths in floods was proposed, but its implementation was deferred 
by the Flood Model Oversight Committee. The model development work done to 
date and documented here will be used as a basis for future methodology 
development for eventual inclusion in the HAZUS Flood Model. 
The Oversight Committee deferred the implementation of a flood casualty model 
because the committee felt that the methodology, while valid, was based on 
only a few storm events and was therefore not validated with the same level 
of scientific vigor as the rest of the Flood Model. Furthermore, the 
Oversight Committee believed that casualties related to flooding do not 
create the same significant impact on the medical infrastructure as those 
associated with earthquakes. Additional data collection following future 
flood events would facilitate development of more rigorous models, and is 
strongly recommended.  In anticipation of future casualty model 
implementation, Section 12.3 provides suggestions for the potential form of 
casualty models for the Flood Model.  Finally, the Flood Model Oversight 
Committee recommended that the Flood Model software include a PDF reference 
file that contains a discussion of flood-related casualties (provided here as 
Section 12.4) for the Flood Model users. 

12.2 Summary of Collected Casualty Data 
Available casualty data from U.S. floods, including the well-documented 
casualties in Houston, Texas associated with Tropical Storm Allison (June 
2001), were reviewed to assess the types and causes of casualties in 
flooding. Primary data sources include the Morbidity and Mortality Weekly 
Report (MMWR), published by the Center for Disease Control (CDC), and various 
publications of the Texas Department of Health, Bureau of Epidemiology. 
Data reviewed include both pure flood events (e.g., 1993 Mississippi floods) 
and hurricane events, which may be accompanied by heavy rainfall and 
significant inland flooding.  For hurricane events, wind-related casualties 
are distinguished from flood-related casualties by cause. For example, deaths 
caused by fallen trees are considered wind-related, while drowning is 
generally flood-related. The eighteen events for which casualty data were 
reviewed are listed in Table 12.1.  For each of these events, the total 
number of fatalities is provided in Table 12.2, and Table 12.3 provides a 
breakdown of all deaths by their cause.  As shown, heavy death tolls occurred 
both in hurricane events (Floyd, Hugo, and Andrew) and flood events 
(Midwestern floods, Central Texas storms and Tropical Storm Alberto).  
Drowning was the cause of more than half of the deaths in all events (54%). 
Table 12.1 Flood Events with Available Data Casualty 

Event Name/Type  Date  Area Impacted  Notes  
Tropical Storm Allison  June, 2001  Harris County, TX  Rapid rise flooding  
Hurricane Floyd  September 16, 1999  North Carolina  Hurricane with 
significant inland flooding  
Storm-related flooding  October 17, 1998  Central Texas  Rapid rise flooding  
Hurricane Georges  September 21, 1998  Puerto Rico  
Hurricane Marilyn  September 15, 1996  US Virgin Islands & Puerto Rico  
Hurricane Opal  October 4, 1996  Florida, Alabama, Georgia, North Carolina  
Storm  May 5, 1995  Dallas Co., TX  Rapid rise flooding  
Flood  October, 1994  Texas  Rapid rise flooding  
Tropical Storm Alberto  July 4, 1994  Georgia  Tropical storm with 
significant inland flooding  
Midwestern Floods  Summer/Fall, 1993  Missouri  
Midwestern Floods  Spring/ Summer 1993  Iowa  
Hurricane Andrew  August 24-26, 1992  Florida, Louisiana  
Nor'easter  December, 1992  CT, DE, MD, MA, NJ, RI, NY (Suffolk, Westchester 
and Nassau Counties, and 5 cos. In NY City), and Philadelphia Co PA.  
Hurricane Hugo  September 21, 1989  South Carolina  
Hurricane Hugo  September 18, 1989  Puerto Rico  
Hurricane Gloria  September 27, 1986  Rhode Island, Connecticut  
Hurricane Elena  September 2,1986  Mississippi  

Table 12.2 Fatalities, By Event 

Event Type  Total Number of Deaths  
Hurricane Floyd  52  
Midwestern Flood - Missouri  43  
Hurricane Hugo   South Carolina  35  
Hurricane Andrew- Florida  33  
Storm   Central Texas, 1998  31  
Tropical Storm Alberto  30  
Hurricane Opal  27  
Tropical Storm Allison  24  
Storm   Dallas, Texas  20  
Flood   Texas, 1994  19  
Hurricane   Louisiana  17  
Hurricane Marilyn  10  
Hurricane Hugo   Puerto Rico  9  
Hurricane Georges  8  
Hurricane Gloria  5  
Nor'easter  4  
Hurricane Elena  3  
Midwestern Flood - Iowa  1  
Total Number of Deaths  371  

Table 12.3 Deaths, By Cause   All Events 

Cause category  Total Number of Deaths  
Drowning (in MV)  119  
Drowning (other)  38  
Drowning (on boat)  20  
Drowning (as pedestrian)  15  
Drowning (in home)  9  
Sub-total: All drownings  201 (54%)  
Trauma (inc. crush)  55  
Cardiac  26  
Electrocution  25  
MVA  15  
Fire/Burns/Smoke Inhalation  20  
Asphyxiation  9  
Carbon Monoxide poisoning  4  
Other  12  
Unknown  4  
Sub-total: Non-Drowning  170 (46%)  
TOTAL: All Causes  371  

Of the eighteen events, only six events were  flooding  events for which both 
exposed population estimates and fatality data were available.  These events, 
which may all be categorized as  rapid rise  or  very rapid rise  flooding, 
account for 173 of the fatalities within the overall database (47%).  Only 
these events were included in subsequent analysis:  
  
Tropical Storm Allison (24 deaths) 

  
Hurricane Floyd (52 deaths) 

  
Storm-related flooding in Central Texas in 1998 (31 deaths) 

  
Storm-related flooding in Dallas County, Texas in 1995 (20 deaths) 

  
Flooding in Texas in 1994 (19 deaths) 

  
Midwestern Floods (27 deaths) 


Table 12.4 provides a breakdown of deaths by their cause for the final six 
flooding events included in the analysis. As shown, drowning was the cause of 
more than 77% of deaths in these events. Because drowning is the primary 
cause of death in floods, it was proposed that the HAZUS Flood casualty model 
focus on drowning deaths in  rapid rise  or  very rapid rise  flooding. 
Table 12.4 Deaths, By Cause   Flood Events Only 

Cause Category  Total Number of Deaths  
Drowning (in MV)  86  
Drowning  21  
Drowning (as pedestrian)  15  
Drowning (on boat)*  7  
Drowning (in home)  5  
Sub-total: All drownings  134 (77.5%)  
MVA  9  
Cardiac  9  
Trauma  7  
Electrocution  7  
Hypothermia  2  
Fire  2  
Fall  1  
Carbon Monoxide poisoning  1  
Asphyxiation  1  
Sub-total: Non-Drowning  39 (22.5%)  
TOTAL: All Causes  173  

* These drownings occurred during Hurricane Floyd and are wind/rain-related 
drownings, rather than flood-related drownings 
It should be noted that data on non-fatal injuries was available for just six 
events total, and included data for just one flood event. Accordingly, the 
available data are insufficient to develop a non-fatal injury model for 
flood.   
12.3 Proposed Form of Casualty Models for Eventual Inclusion into HAZUS Flood 
It is suggested that the NIBS Flood Module consider three types of flood 
casualties, as follows: 
 	
Casualties that occur in the floodwaters -- these casualties would be 
evaluated in aggregate at the community level.  That is, injury and death 
rates per 100,000 population would be applied to the "exposed community." 
These casualty rates would depend on the rate of inundation (tentatively 
characterized into 3 classes: rapid/very rapid rise, moderate speed rise, 
slow rise), as well as selected demographic characteristics (Male/female, 
age, etc).  The rates would include a reduction factor if the community has a 
swift-water rescue capability.  

 	
Casualties that occur within buildings -- these casualties would be evaluated 
for two time frames:  during the flooding, and during flood clean up.  
Building casualties during flooding depend on the amount of warning, flood 
depth or damage, and occupancy type.  Building casualties during clean up 
depend on flood depth or damage, occupancy type, and electric power service 
interruption. 

 	
Rain-related motor vehicle casualties -- in addition to the casualties that 
occur in the floodwaters, rain-related motor vehicle accident injuries and 
deaths are quantifiable. Previous research conducted by the UCLA School of 
Public Health, Center for Public Health and Disasters focused on the El Nino 
phenomenon provided source data for the development of casualty rates (per 
100,000 population) based on low, medium and high rainfall rates. These 
casualty estimates would be optional, and the user would be expected to 
select the appropriate rainfall category. 


12-8 
12.4 Documentation Displayed in the HAZUS Flood Model 
Flooding of all types (riverine, flash flooding, coastal, fluctuating lake 
levels and other sources) is a major hazard in the U.S., accounting for the 
single largest total property losses, and major life loss, of any one hazard. 
Flooding has a long history in the U.S., including the infamous Johnstown 
flood of 18891, and the Mississippi floods of 1927.  Recent floods have 
included the Mississippi Flood of 1993, the Northwest floods of 1996, and the 
North Dakota Red River flood of 1997. Figure 12.2 and Figure 12.3 show U.S. 
fatalities due to flooding, with an increasing trend that, if normalized for 
population growth, appears to be relatively steady (FEMA, 1997). 
An effort has been made to develop methodology to estimate casualties due to 
flooding.  Because there is limited data related to casualties beyond 
fatalities (i.e., injuries requiring hospitalization, minor injuries), the 
Flood Model Oversight Committee and FEMA decided to defer the estimation of 
casualties while further data collection and methodology development could 
continue.  Below are two charts that can help the user asses the likelihood 
of incurring casualties during a given flood event. It should be noted that 
the United States averages approximately 100 deaths per year due to flooding, 
although this has been increasing over the last few years. 
1	Johnstown PA, the victim of a disastrous flood in 1889, is one of the 
greatest natural disasters in U.S. history.   At 3:10 PM on May 31, the South 
Fork Dam, a poorly maintained earthfill dam holding a major upstream 
reservoir, collapsed after heavy rains, sending a great wall of water rushing 
down the Conemaugh Valley at speeds of 20 to 40 miles per hour (32 to 64 
km/h). At 4:07 PM, the 30-foot high wall of water smashed into Johnstown, 
which lay on the floodplain of the Conemaugh River. The flood swept away most 
of the northern half of the city, killing 2,209 people and destroying 1,600 
homes.  After another disastrous flood in 1936, a flood-control program was 
completed (1943), but this did not prevent heavy flooding in July 1977 in 
which 68 people were killed. 


12-10 
12.5 References 
1.	
CDC (1986), "Epidemiologic Notes and Reports Hurricanes and Hospital 
Emergency-Room Visits -Mississippi, Rhode Island, Connecticut", MMWR 1986; 
34(51-52); 765-770. 

2.	
CDC (1989a), "Deaths Associated with Hurricane Hugo - Puerto Rico", MMWR 
1989; 38(39); 680-682. 

3.	
CDC (1989b), "Update: Work-Related Electrocutions Associated with Hurricane 
Hugo Puerto Rico", MMWR 1989; 38(42); 718-725. 

4.	
CDC (1989c), "Medical Examiner/Coroner Reports of Deaths Associated with 
Hurricane Hugo - South Carolina", MMWR 1989; 38(44); 754,759-762. 

5.	
CDC (1992), "Preliminary Report: Medical Examiner Reports of Deaths 
Associated with Hurricane Andrew - Florida, August, 1992", MMWR 1992; 41(35); 
641-644.  

6.	
CDC (1993a), "Surveillance of Deaths Attributed to a Nor'easter - December, 
1992", MMWR 1993; 42(01);4-5 

7.	
CDC (1993b), "Public Health Consequences of a Flood Disaster - Iowa, 1993", 
MMWR 1993; 42(34); 653-656. 

8.	
CDC (1993c), "Flood-Related Mortality - Missouri, 1993", MMWR 1993; 42(48); 
941-943 

9.	
CDC (1993d), "Injuries and Illnesses Related to Hurricane Andrew - Louisiana, 
1992", MMWR 1993; 43(13); 242-243, 250-251 

10. 
CDC (1993e), "Morbidity Surveillance Following the Midwest Flood - Missouri, 
1993", MMWR 1993; 43(41); 797-798. 

11. 
CDC (1994), "Flood-Related Mortality - Georgia, July 4 - 14, 1994", MMWR 
1994; 43(29); 526-530. 

12. 
CDC (1996a), "Deaths Associated with Hurricanes Marilyn and Opal - United 
States, September-October 1995", MMWR 1996; 45(2); 32-38. 

13. 
CDC (1996b), "Surveillance for Injuries and Illnesses and Rapid Health-Needs 
Assessment Following Hurricanes Marilyn and Opal, September-October 1995", 
MMWR 1996; 45(4); 81-85. 

14. 
CDC (1998), "Deaths Associated with Hurricane Georges - Puerto Rico, 
September, 1998", MMWR 1998; 47(42); 897-898 

15. 
CDC (2000a), "Storm-Related Mortality - Central Texas, October 17-31, 1998", 
MMWR 2000; 49(07); 133-135 

16. 
CDC (2000b), "Morbidity and Mortality Associated With Hurricane Floyd - North 
Carolina, September-October 1999", MMWR 2000; 49(17); 369-372. 

17. 
FEMA (1997), Multihazard Identification and Risk Assessment, Federal 
Emergency Management Agency, Washington DC: U.S. Government Printing Office. 

18. 
NOAA (2001), "Service Assessment - Tropical Storm Allison Heavy Rains and 
Floods, Texas and Louisiana, June, 2001", U.S. Department of Commerce, 
National Oceanic and Atmospheric Administration, National Weather Service, 
Silver Spring, Maryland. 

19. 
Texas Department of Health (1999), "Storm-Related Mortality in Central Texas, 
October 1731, 1998: An Epidemiologic Investigation by the Injury Epidemiology 
And Surveillance Program". 

20. 
Texas Department of Health (1995), "Storm-Related Mortality, Dallas County, 
Texas, May 5-12, 1995". 

21. 
Texas Department of Health (1994), "Flood-Related Mortality, - Southeast 
Texas October 16-25, 1994". 


13-1 
Chapter 13.  Direct Social Losses - Displaced Households Due to Loss of 
Housing Habitability and Short-term Shelter Needs 
13.1 Introduction 
A significant part of any planning scenario is to estimate the number of 
individuals who will need to be sheltered in the short-term.  Modifications 
have been made to the algorithm developed for HAZUS Earthquake to reflect the 
difference in sheltering needs between earthquakes and floods. Flood 
sheltering needs are based on the displaced population, not the Damage State 
of the structure. The HAZUS Flood Model determines the number of individuals 
likely to use government-provided short-term shelters through determining the 
number of displaced households as a result of the flooding. To determine how 
many of those households and the corresponding number of individuals will 
seek shelter in government-provided shelters the number is modified by 
factors accounting for income and again by factors accounting for age. The 
flowchart highlighting the Shelters component is shown in Figure 13.1. 

13.2 Displaced Households - Form of Loss Estimate 
The displaced population is based on the inundation area.  Individuals and 
households will be displaced from their homes when the home has suffered 
little or no damage either because they were evacuated (i.e., a warning was 
issued) or there is no physical access to the property because of flooded 
roadways.  Those displaced persons using shelters will most likely be 
individuals with lower incomes and those who do not have family and friends 
within the immediate area. Consequently, modification factors for flood are 
based primarily on income.  Age plays a secondary role in that there are some 
individuals who will seek shelter even though they have the financial means 
of finding their own shelter.  These will usually be younger, less 
established families and elderly families. 
13.2.1 Input Requirements 
The algorithm uses information from the census database in the following 
areas: 
  
Total number of households in the community;  

  
Total number of individuals in the community;   

  
Distribution of households by income; and 

  
Distribution of individuals by age. 

The user can use either the census information or any local database that 
contains the same information.  Should local information be used, the income 
data needs to be formatted into the following categories: 

  
Household income up to $10,000 per year; 

  
Household income greater than $10,000 but less than $15,000 per year; 

  
Household income from $15,000 to less than $25,000 per year; 

  
Household income from $25,000 to less than $35,000 per year; and 

  
Household income of $35,000 or greater per year. 
The age distribution data needs to be formatted into the following 
categories: 


  
Individuals less than 16 years of age; 

  
Individuals from 16 to 65 years of age; and 

  
Individuals greater than 65 years of age. 


13-4 
13.2.2 Description of Methodology 
The controlling factor is physical access into the area where the property is 
located.  This is a function of the depth of water and the ability to travel 
into the area either on foot or by vehicle. For short-term sheltering 
estimations the user will need to determine at what depth of flooding will 
access to the area be obstructed.   This depth typically would vary somewhere 
between 6  (typical curb height) and 12  (where vehicles will begin to 
float).  Any residential unit located in the area where flood depth, defined 
as di, equals or exceeds that depth will be displaced from their home. 
The determination of the number of displaced individuals is represented by: 
n #DIIN = S POPIN (13-1) j=1 
where: 
#DIIN  =  The number of displaced individuals as a result of inundation where 
d = i  
POPIN  =  The population of a census block located within the area of 
inundation defined by d = i  
J  =  the number of census blocks within the flooded area defined by d = i  
D  =  depth of flooding  
I  =  the depth of flooding at which travel into the area is restricted.  

Under some planning scenarios it might be important to know how many 
households have been displaced. Using the census data this calculation is as 
follows: 
n #DHIN = S HIN (13-2) j=1 
where: 
#DHIN = 	The number of displaced households as a result of inundation 
where d = i 
HIN = 	The number of households in a census block located within the 
area of inundation defined by d = i 
J  =  the number of census blocks within the flooded area defined by  
d = i  
D  =  depth of flooding  
I  =  the depth of flooding at which travel into the area is restricted.  
13.2.2.1  Displaced Persons As A Result Of Utility Damage  

The utilities required for a structure to be occupied are water and sewer.  
During colder weather loss of utilities that provide heat such as gas or 
electricity may result in increased population within the shelters, certainly 
it would increase the number of displaced persons. 
It is recommended that the number of displaced persons as a result of utility 
losses be calculated in a similar manner to that of the Earthquake Model.   
The equation would be: #DIUTIL = %WAG [POP   #DIIN] (13-3) where: #DIUTIL = 
Number of displaced persons as a result of lost utility services %WAG = 
Probability of a dwelling unit being without utilities and vacated (user 
supplied) default value = 0 Similarly, when considering displaced households 
due to utility loss, the equation would be: #DHUTIL = %WAG [H   #DHIN] (13-4) 
where: #DHUTIL = Number of displaced households as a result of lost utility 
services %WAG = Probability of a dwelling unit being without utilities and 
vacated (user supplied) default value = 0 H = Total number of households 
When determining the probability that a dwelling unit will be without 
utilities, the user is encouraged to review past flooding events within the 
jurisdiction to see if utilities have been adversely affected.  This would 
give an indication as to the probability of loss of utilities. Additionally, 
looking at the utility distribution within the study area would also provide 
some guidance. 
The number of displaced persons needs to be modified by factors that reflect 
the likelihood that an individual would use publicly provided shelters.  The 
key factors in this determination will be income and age as follows: 
  
Low income families as they lack the means to find other shelter on their 
own; and 

  
Young and elderly families who may have the means of finding temporary 
shelter on their 


own, but prefer to use publicly provided shelters. Factors provided are 
 Shelter Category Weights  and  Shelter Relative Modification Factors  
similar to that of the Earthquake model.  These factors are used in the 
calculation of a constant defined as: 
akm = (IW x IMk) + (AW x AMm) (13-5) 
where: IW = Shelter Category Weight for Income. AW = Shelter Category Weight 
for Age. IMk = Relative Modification Factor for income. AMm = Relative 
Modification Factor for Age. 
The number of persons using publicly provided shelters is then represented 
by: 5 3 #STP = .. {akm x DP x HIk x HAm} (13-6) k=1 m=1 
Where: #STP = Number of people using established shelters akm = a constant DP 
= Displaced population = #DIIN + DIUTIL HIk = Percentage of population in the 
kth income class HAm = Percentage of population in mth age class 
13.2.2.2 Shelter Category Weight 
These factors (see Table 13.1) represent the importance of the particular 
category in the determination of how many will seek publicly provided 
shelters.  During flood events most displaced individuals will obtain their 
own sheltering in hotels or with friends and family.  The ability to find 
shelter on their own is primarily a function of income.  Age is also a factor 
in that young families and older families (65 years or older) will tend to 
use community provided shelters. In many respects, this is also a function of 
income since many young families tend to fall in the lower income brackets 
and the older (65 years or older) are living on fixed incomes potentially in 
the lower brackets. 
Table 13.1 The Shelter Category Weights 

CLASS  DESCRIPTION  DEFAULT  
IW  Income Weighting Factor  0.80  
AW  Age Weighting Factor  0.20  

Recognizing the importance of income in the equation, the default-weighing 
factor was set at 
0.80. This automatically establishes age weighting factor as 0.20 as the sum 
of these factors must total 1.  Shelter statistics from previous flood 
disasters usually do not include information on household income and age.  
Typically, the Red Cross does not record this information citing privacy as 
the reason.  Therefore, there is little guidance available to the user in 
modifying the default values.  In sample calculations that were performed 
using a displaced population in excess of 49,000 and using the default 
Relative Modification Factors, changing the weight factors from .8 to .75 and 
from 0.2 to 0.25 decreased the shelter estimation by approximately 3.5 
percent.  It is therefore recommended that the default Shelter Category 
Weights be used unless the user has good statistical data on the individuals 
whom have used shelters in the past. 
13.2.2.3 Shelter Relative Modification Factors 
These factors (see Table 13.2) estimate the percentage of each category that 
will seek publicly provided shelters. As described in the Earthquake Model 
Technical Manual, these factors were originally developed by George 
Washington University1 And modified for the flood model. 
The factors for income are the estimated percentage of that particular group 
that will most likely seek public shelter. As the household annual income 
increases, the likelihood of the household using public shelter decreases. 
Since income is the most significant component, the factors have been set to 
total 1 or 100%. If the user wishes to modify the default values, care should 
be taken to make sure the revised factors total 1.  It should be noted that 
this is a departure from the George Washington University study and is based 
on the proof of concept evaluation. 
Table 13.2 Relative Modification Factors 
Class  Description  Default  Default for Communities with 60% or More of 
Households with Income >$35,000  
Income  
IM1  Household Income < $10,000  0.40  0.46  
IM2  $10,000 < Household Income < $15,000  0.30  0.36  
IM3  $15,000 < Household Income < $25,000  0.15  0.12  
IM4  $25,000 < Household Income < $35,000  0.10  0.05  
IM5  $35,000 < Household Income  0.05  0.01  
Age  
AM1  Population under 16 Years Old  0.05  
AM2  Population Between 16 and 65 Years Old  0.20  
AM3  Population Over 65 Years Old  0.50  

13.2.3 User-defined Changes to Weight and Modification Factors 
Sensitivity calculations where performed on the income factors to ascertain 
the impacts of changing default values. Eight random communities were 
selected of varying sizes.  When the default values for IM1 through IM3 were 
increased by 10 percent and the factors IM4 and IM5 were reduced to maintain 
the same ratio as the default, the resulting change in the shelter estimation 
decreased in 5 of the communities by between 1 percent and 3.7 percent.  This 
indicates that small modifications in the factors have relatively little 
affect on the estimated shelter population. 
In three of the communities, the population change was much more significant 
in that population decreased between 10.5 percent and 12 percent. Review of 
the census data for these communities showed that more than 60% of the 
households in these communities had incomes greater than $35,000. In the 
other five communities, the percentages ranged from a low of 23.45 percent to 
a high of 40.23 percent.  To compensate for the large segment of the 
population within the higher income bracket, default values for IM1 through 
IM3 were increased, and values for IM4 and IM5 were decreased. Therefore, a 
second set of default values has been developed for use in communities where 
60 percent or more of the households have an income greater than $35,000. 
As with the Shelter Category Weights it is recommended that default values be 
used unless the user has good statistical data available on who uses 
shelters. 
The factors for age are intended to estimate those individuals who will use 
public shelter without regard to their income.  These tend to be primarily 
younger families and those over the age of 65.   
In the first grouping of age, those under 16 years, we can establish the 
default value as nearly 0 because the income categories are households, while 
the age category is individuals.  If a household uses a shelter, this will 
usually include the children.  It would be very rare that this age group 
would use the shelters while the family went elsewhere.  The second category, 
those between 16 and 65 is also established as a relatively small percentage 
since it is in this age bracket that most working people fall.  If their 
income is low, they will be accounted for with the income factor.  Few of 
these individuals will use publicly provided shelter if their income allows 
them to find other means.  Additionally, those in this category are most 
likely to have friends and/or family near the area that could provide them 
shelter. 
The largest of the factor is the final category and those are the individuals 
over 65.  These individuals will tend to be on fixed incomes and will be 
estimated with the income factor. However, these individuals are less likely 
to have family and friends to help them or, because of their age are less 
likely to try and find shelter elsewhere.   
13.3 References 
1.	HAZUS Earthquake Loss Estimation Methodology   Technical Manual Volume 
III, pages 14-7 and 14-8.  These constants where originally developed by 
George Washington University under contract with the Red Cross and are based 
on  expert  opinion (Harrald, Fouladi, and Al-Hajj, 1992)  The modification 
factors provided [default for earthquake model] are the mean of the George 
Washington University modification factors   
Chapter 14.  Direct Economic Losses 
14.1 Introduction 
This chapter describes the conversion of percent damage, developed in 
previous modules, into estimates of dollar loss. In the past, loss estimation 
studies have generally limited the consideration of loss to estimates of the 
repair and replacement costs of the building stock. 
The methodology provides estimates of the building repair costs and the 
associated loss of building contents and business inventory. Building damage 
can also cause additional losses by restricting the building s ability to 
function properly.  To account for this, business interruption and rental 
income losses are estimated.  These losses are calculated from the building 
damage estimates by use of methods described later.  The methodology 
highlighting the Direct Economic Loss component is shown in Figure 14.1. 
This expression of losses provides an estimate of the costs of building 
repair and replacement that is a frequently required output of a loss 
estimation study. The additional estimates of consequential losses give an 
indication of the immediate impact of such building damage on the community: 
the financial consequences to the community s businesses due to businesses 
interruption, the financial resources that will be needed to make good the 
damage, and an indication of job and housing losses.  In strict economic 
terms, buildings, inventories, and public facilities represent capital 
investments that produce income, and the value of the building and inventory 
will be the capitalized value of the income produced by the investment that 
created the building or inventory. Hence, if we estimate the dollar value of 
the buildings damaged or destroyed, and add the income lost from the absence 
of the functioning facilities we may be overestimating the indirect economic 
loss (see Chapter 15). However, for the assessment of direct economic loss, 
the losses can be estimated and evaluated independently. 
Since a significant use for loss estimation studies is expected to be that of 
providing input into future benefit-cost studies used to evaluate mitigation 
strategies and budgets, the list of these consequential losses is similar to 
that developed for the FEMA benefit-cost procedure described in FEMA 
publications 227 and 228, and 255 and 256. This procedure is, however, 
limited to conventional real-estate parameters similar to those used in 
evaluating the feasibility of a development project and does not attempt to 
evaluate the full range of socio/economic impacts that might follow specific 
mitigation strategies. 
Thus, for this loss estimation methodology, even though the derivation of 
these consequential losses represents a considerable expansion of the normal 
consideration of building damage/loss, this module is still limited in its 
consideration of economic loss to those losses that can be directly derived 
from building and infrastructure damage, and that lend themselves to ready 
conversion from damage to dollars. The real socio/economic picture is much 
more complex: economic impacts may have major societal effects on individuals 
or discrete population groups, and there may be social impacts that 
ultimately manifest themselves in economic consequences. In many cases the 
linkages are hard to trace with accuracy and the effects, while easy to 
discern, are difficult to quantify because definite systematic data is 
lacking. 

Given the complexity of the problem and the present paucity of data, the 
methodology focuses on a few key issues that are of critical importance to 
government and the community, that can be quantified with reasonable 
assurance, and that provide a picture of the cost consequences of building 
and infrastructure damage that are understandable and would be of major 
concern to a municipality or region. In addition, application of the 
methodology will provide information that would be useful in a more detailed 
study of a particular economic or social sector, such as impact on housing 
stock or on a significant local industry. Finally, the structure of the 
methodology should be of assistance in future data gathering efforts. 
While the links between this module and the previous modules dealing with 
damage are very direct and the derivations are very transparent, the links 
between this module and that of Chapter 15, Indirect Economic Losses, are 
less so. While some of the estimates derived in this module, such as income 
loss by sector, building repair costs, and the loss of contents and 
inventories, may be imported directly into the Indirect Loss Module, some 
interpretation of the direct economic loss estimates would be necessary for a 
more detailed indirect economic loss study. It would be necessary, for 
example, to translate the repair and replacement times and costs derived in 
this module to monthly reconstruction investment estimates for use in a 
longer-term indirect loss estimate. 
14.1.1 Scope 
This chapter provides descriptions of the methodologies, the derivation of 
default data, and explanatory tables for a number of direct economic loss 
items, derived from estimates of building (Section 14.2), lifeline (Section 
14.3), vehicle (Section 14.4) and agriculture (Section 14.5) damage.   
Within the HAZUS methodology, direct economic losses to buildings include: 
  
Building repair and replacement costs (structural and non-structural damage) 

  
Building contents losses 

  
Building inventory losses 

  
Relocation expenses 

  
Capital related income losses (previously loss of proprietor s income) 

  
Wage losses 

  
Rental income losses 


The first three categories are building-related losses, termed  Capital Stock 
Losses,  while the last four are time-dependent income losses, requiring an 
estimation of building restoration or outage time. 
14-4 
Direct economic losses for the HAZUS Flood Module are similar to those 
currently implemented in the HAZUS99 Earthquake Module as listed above, 
except that earthquake losses depend on damage state probability, while flood 
losses depend on depth-related percent damage.  It should be noted that the 
earthquake module estimates damage and losses to structure and non-structural 
components separately, while the flood module estimates one aggregate 
 building  loss. 
In addition to the existing income losses, the Flood Module incorporates 
output losses, and employment losses (in terms of the number of jobs). 
Direct economic losses for utility and transportation systems (Section 14.3) 
are limited to the cost of repairing damage to selected lifeline systems, as 
follows: 
 	
Transportation - bridges 

 	
Water - water treatment plants, pump stations, control vaults, and pipeline 
river crossings.  

 	
Wastewater - wastewater treatment plants, lift stations, control vaults, 
pipeline river crossings, and collection systems. 

 	
Petroleum   pump stations and pipeline river crossings. 

 	
Natural gas   control vaults/metering stations and pipeline river crossings. 

 	
Telecommunications   switching stations, control vaults/stations and cable 
river crossings. 

 	
Electrical power   substations. 


Default full replacement values for transportation and utility lifeline 
facilities, developed originally for the HAZUS Earthquake Model, are provided 
as a guide.  It is recommended that the user input more accurate replacement 
values based on knowledge of lifeline facility values in the region. 
Direct economic losses to vehicles (discussed in Section 14.4) include dollar 
losses associated with damage to cars, light trucks and heavy trucks.  And 
finally, direct economic losses to agriculture (Section 14.5) include 
estimates of losses associated with flood durations of 1 day, 3 days, 7 days 
and 14 days. 
14.1.2 Form of Direct Economic Loss Estimates 
Direct economic loss estimates are provided in 2005 dollars except for 
employment losses, which are presented in terms of number of jobs. 
14.1.3 Input Requirements 
The input data for calculation of the direct economic losses consists 
primarily of building and transportation and utility system damage estimates 
from the direct physical damage module. These damage estimates are in the 
form of percent damage relative to full replacement cost, and associated 
dollar estimates of damage for each occupancy class.  The dollar estimates of 
damage are based on default building and lifeline replacement cost 
(valuation) models available for each occupancy or facility class. 
The types of economic data that the user can supply or modify from default 
include; contents value (percent of building replacement cost) for different 
occupancies, annual gross sales by occupancy, typical rental costs, 
relocation expenses and income by occupancy, as well as replacement values 
for the various transportation and utility facilities.  While default values 
are provided for these data, the user may wish to provide more accurate local 
values or update default values to current dollars. 
14.2 Description of Methodology: Buildings 
The Flood Loss Estimation Module of HAZUS includes two basic building 
valuation models; a full replacement cost model, and a depreciated cost 
model.  Both models are based on industry standard cost models published by 
R.S. Means Company ( Means Square Foot Costs , 2005). The Means-based 
approach is consistent with previous cost modeling within the earthquake 
module of HAZUS, although the incorporation of depreciation is a new feature 
added for the Flood Model. 
14.2.1 Full Building Replacement Costs 
14.2.1.1 Default Values for Building Replacement Cost 
Building replacement cost models within HAZUS are based on industry-standard 
cost-estimation models published in Means Square Foot Costs (R.S. Means, 
2005). Replacement cost data are stored within HAZUS at the census block 
level for each occupancy class.  For each HAZUS occupancy class, a basic 
default structure full replacement cost model (cost per square foot) has been 
determined, and is provided in Table 14.1. Commercial and industrial 
occupancies have a typical building replacement cost model associated with 
each occupancy class (e.g., COM4, Professional/Technical/Business Services, 
is represented by a typical, 80,000 square foot, 5 to 10 story office 
building). In most cases, the typical building chosen to represent the 
occupancy class is the same as was used in the original HAZUS earthquake 
model (based on Means, 1994), except for single family residential, multi-
family residential, and industrial uses.  Square foot costs presented in the 
table have been averaged over the various alternatives for exterior wall 
construction (e.g., wood siding over wood frame, brick veneer over wood 
frame, stucco on wood frame or precast concrete, concrete block over wood 
joists or precast concrete, etc.).  For nonresidential structures, the 
default configuration assumes structures without basements.   
The RES1 (single family residential) replacement cost model is the most 
complex, utilizing socio-economic data from the census to determine an 
appropriate mix of construction classes (Economy, Average, Custom and Luxury) 
and associated replacement cost models.  The algorithm is described in 
Section 14.2.1.1.1.  Within Means, basements are not considered in the base 
cost of the structure and are handled as an additive adjustment (additional 
cost per square foot of main structure).  Table 14.2 provides Means (2005) 
replacement costs for the various single family dwelling configurations 
available in the default building inventory (1, 2, and 3 story and split-
level), assuming a typical size of 1,800 square feet.  Costs have been 
averaged for the various alternatives for exterior wall construction.   
Because the default single family residential (SFR) damage model is based on 
the FIA credibility-weighted depth damage functions, whose coverage extends 
to garages, the replacement cost of garages will also be included in the 
basic replacement cost.  Relevant Means models for SFR garages include costs 
by construction class (economy, average, custom, and luxury), for detached 
and attached 1-car, 2-car and 3-car garages, constructed of wood or masonry.  
For incorporation into HAZUS, costs by size and construction class have been 
averaged for attached/detached and various materials.  Average costs 
associated with garage types included in the default inventory for single 
family residential structures (1-car, 2-car and 3-car) are provided in Table 
14.3. 
14.2.1.1.1 Single-Family Residential Valuation Algorithm 
The algorithm defined below will be used to develop the valuation for single-
family residential buildings at the census block level.  This algorithm 
utilizes socio-economic data from the census to derive an appropriate Means-
based cost for each census block.  The earthquake and wind models shall use a 
 roll-up  of the results from the flood model calculations.  Some round-off 
error will occur, but this cannot be avoided. 
The valuation algorithm can be summarized mathematically in equation (1) 
below: 
4 4 	4 4 
VRES1, k = (ARES1, k)*[ SS wi,k*wj,k*Ci,j] + (ARES1, k)*wl,k*[ SS 
w.i,k*wj,k*Ci,j,l]i=1 j=1 i=1 j=1 
44
 +(RES1Cnt k)*[ S . S wi,k*wm,k*Ci, m] 	(14-1)
i=1 j=1 
Where: 
VRES1, k	is the total estimated valuation for single-family residences 
(RES1) 
for a given census block (k). VRES1, k is editable when viewing the 
dollar exposure by specific occupancy table. 

ARES1,k	is the total single-family residential (RES1) floor area (square 
feet) 
for a given census block (k) found in the square foot by specific 
occupancy table. ARES1,k is editable when viewing the square foot 
by specific occupancy table. 

14-7  
i  the Means construction class (1 = Economy, 2 = Average,  3 = Custom, 4 = 
Luxury).  
wi,k  is the weighting factor for the Means construction class (i) for the 
given census block (k) and is determined from the income ratio range as shown 
in Table 14.4 below. Values are displayed in percent to the user and are 
editable when viewing the dollar exposure parameters tables.  
j  the number of stories class for single-family (RES1) structures  (1 = 1-
story, 2 = 2-story, 3 = 3-story, and 4 = split level)  
wj,k  is the weighting factor for the Number of Stories class (j) for the 
given census block (k) depending on the census region of that block (by state 
FIPS). Weighting factors were developed from regional construction type 
distributions as discussed in Section 3.   Values are displayed in percent to 
the user and are editable when viewing the dollar exposure parameters tables.  
Ci,j  is the single-family (RES1) cost per square foot for the given Means 
construction class (i) and number of stories class (j).  RES1 replacement 
costs are seen in the third column of Table 14.2.  Values are editable when 
viewing the dollar exposure parameters tables.  
l  the basement status available for single-family residences  (1 = yes, 2 = 
no).  
wl,k  is the weighting factor for basements for the given census block (k) 
depending on the census region of that block (by state FIPS).  Weighting 
factors were developed from regional foundation type distributions as 
discussed in Section 3.  Values are displayed in percent to the user and are 
editable when viewing the dollar exposure parameters tables.  Default will be 
established based on whether the block is a coastal or non-coastal block.  
Ci,j,l m  the additional cost, per square foot of the main structure, for a 
finished basement for the given Means construction class (i) and number of 
stories class (j), as shown in Table 14.2, Column 4.  Note: Ci,j,l = 0 when l 
= 2. Values are editable when viewing the dollar exposure parameters tables. 
the garage combinations available for single-family residences (1 = 1-car, 2 
= 2-car, 3 = 3-car, 4 = carport, and 5 = none).  
wm,k  is the weighting factor for the garage type (m) for the given census 
block (k) depending on the census region of that block (by state  

FIPS). Weighting factors were developed from regional construction type 
distributions as discussed in Section 3.  Values are displayed in percent to 
the user and are editable when viewing the dollar exposure parameters tables.  
Ci,m   the additional replacement cost for a given garage type (m), for the 
given Means construction class (i) as shown in Table 14.3.  Note: Ci,m = 0 
when m = 4 (covered carport) or m = 5 (none).  Values are editable when 
viewing the dollar exposure parameters table.  
RES1Cnt  the count of RES1 structures within the given census block (k) taken 
directly from the Building Count by occupancy table.  

As the algorithm shows, the basic replacement cost per square foot is a 
function of the Means construction class, the number of stories and an 
additional cost per square foot of the main structure for the existence of a 
finished or unfinished basement.  Finally, there is an additional cost per 
housing unit based on the garage associated with the structure.  The 
valuation parameters are presented in a series of tables in Section 
14.2.1.1.4 of this document. 
14.2.1.1.2 Manufactured Housing Valuation Algorithm 
It is necessary to clarify that RES2 within HAZUS99 and HAZUS-MH, while 
designated Manufactured Housing, represents Mobile Homes and not single-
family pre-manufactured housing. The US Census provides a detailed count of 
the mobile homes within each census block and this quantity is used to 
develop the total floor area (square foot) of the RES2 occupancy 
classification. The total floor area was developed assuming a typical floor 
area and average distribution of singlewide to doublewide mobile homes.  
Unlike other occupancy classifications, there are no allowances for variation 
of floor heights (number of stories) or other valuation parameters.  The 
valuation of manufactured housing is the straight multiplication of the total 
floor area by the baseline replacement cost per square foot.  The cost per 
square foot (CRES2) is defined in Table 14.1. 
The algorithm for manufactured housing is defined in equation (2) below: 
VRES2,k = ARES2,k *CRES2	 (14-2) 
Where: 
VRES2,k	is the total estimated valuation for Manufactured Housing (RES2) 
for a given census block (k). VRES2, k is editable when viewing the dollar 
exposure by specific occupancy table. 
ARES2,k	is the total Manufactured Housing (RES2) floor area (square feet) 
for a given census block (k) found in the square foot by specific occupancy 
table. ARES2,k is editable when viewing the square foot by specific occupancy 
table. 
CRES2	is the Manufactured Housing (RES2) cost per square foot.  RES2 
replacement costs are given in Table 14.1 ($30.90/SqFt).  The 
value is editable when viewing the dollar exposure parameters 
tables. 

The flood model has accounted for differential areas between singlewide and 
doublewide manufactured housing in the total floor area, it is assumed that 
the cost per square foot does not vary greatly between the two structure 
types. 
14.2.1.1.3 Other Residential and Non-Residential Occupancies 
The algorithm for the remaining residential occupancies (RES3-RES6) and all 
non-residential (COM, IND, EDU, REL, GOV, and AGR) occupancies is not as 
complex as the single family model but allows for the potential incorporation 
of a distribution for number of stories.  It should be noted that the 
replacement costs seen in Table 14.1 are an average replacement cost by 
occupancy. In other words, the replacement cost is averaged across structure 
types, stories and construction classes to produce the values in Table 14.1. 
The algorithm for the remaining residential occupancies and non-residential 
occupancies can be seen in equation (3) below: 
Vx,k = .Ax,k*Cx	 (14-3) 
Where: 
defines the remaining occupancy classifications (x ranges from 3 to 33 for 
the remaining occupancies, i.e., RES5, COM1, REL1, etc.) for which the cost 
is being calculated. 
Vx,k	is the total estimated valuation for the specific occupancy (x) (such 
as RES4, COM3, or IND6) for a given census block (k). Vx, k is 
editable when viewing the dollar exposure by specific occupancy 
table. 

Ax,k	is the total floor area (square feet) for a specific occupancy (x) 
(such as RES3, COM8, IND4, GOV1, etc.) for a given census 
block (k) found in the square foot by specific occupancy table. 
Ax,k is editable when viewing the square foot by specific 
occupancy table. 

Cx	is the cost per square foot for the specific occupancy (x). The 
replacement costs are seen in Table 14.1 below by specific 
occupancy. Values are editable when viewing the dollar exposure 
parameters tables. 

At this time, the flood model depreciation models for non-single-family 
residential structures will not depend on features such as the number of 
stories.  A distribution of number of stories will still be developed in the 
dollar exposure parameters table since the creation of such depreciation 
models are seen as a potential enhancement in future versions of the HAZUS 
Flood Model. 
14.2.1.1.4 Valuation Tables 
The following tables present the baseline valuation parameters for the 
variables discussed in Section 14.2 of this document.  Each of these 
parameters is editable by the user. 
Table 14.1 Default Full Replacement Cost Models (Means, 2006) 

HAZUS Occupancy Class Description  Sub-category  Means Model Description 
(Means Model Number)  Means Typ Size  Means Cost/SF (2006) 
OCC Code  OCC Description  OCC sub-class  
RES1  Single Family Dwelling  See Table 14-2  
RES2  Manufactured Housing  Manufactured Housing  Manufactured Housing 
Institute, 2004 average sales price and size data for new manufactured home 
(latest data available)  1,625  $35.75  
RES3A  Multi Family Dwelling   small  Duplex  SFR Avg 2 St., MF adj, 3000 SF  
3,000  $79.48  
RES3B  Triplex/Quads  SFR Avg 2 St., MF adj, 3000 SF  3,000  $86.60  
RES3C  Multi Family Dwelling   medium  5-9 units  Apt, 1-3 st, 8,000 SF 
(M.010)  8,000  $154.31  
RES3D  10-19 units  Apt., 1-3 st., 12,000 SF (M.010)  12,000  $137.67  
RES3E  Multi Family Dwelling    20-49 units  Apt., 4-7 st., 40,000 SF (M.020)  
40,000  $135.39  
RES3F  large  50+ units  Apt., 4-7 st., 60,000 SF (M.020)  60,000  $131.93  
RES4  Temp. Lodging  Hotel, medium  Hotel, 4-7 st., 135,000 SF (M.350)  
135,000  $132.52  
RES5  Institutional Dormitory  Dorm, medium  College Dorm, 2-3 st, 25,000 SF 
(M.130)  25,000  $150.96  
RES6  Nursing Home  Nursing home  Nursing Home, 2 st., 25,000 SF (M.450)  
25,000  $126.95  
COM1  Retail Trade  Dept Store, 1 st  Store, Dept., 1 st., 110,000 SF (M.610)  
110,000  $82.63  
COM2  Wholesale Trade  Warehouse, medium  Warehouse, 30,000 SF (M.690)  
30,000  $75.95  
COM3  Personal and Repair Services  Garage, Repair  Garage, Repair, 10,000 SF 
(M.290)  10,000  $102.34  
COM4  Prof./ Tech./Business Services  Office, medium  Office, 5-10 st., 
80,000 SF (M.470)  80,000  $133.43  
COM5  Banks  Bank  Bank, 1 st., 4100 SF (M.050)  4,100  $191.53  
COM6  Hospital  Hospital, medium  Hospital, 2-3 st., 55,000 SF (M.330)  
55,000  $224.29  

14-12 
Table 14.2 Default Full Replacement Cost Models (Means, 2006) (Continued) 

HAZUS Occupancy Class Description  Sub-category  Means Model Description 
(Means Model Number)  Means Typ Size  Means Cost/SF (2006) 
OCC Code  OCC Description  OCC sub-class  
COM7  Medical Office/Clinic  Med. Office, medium  Medical office, 2 st., 
7,000 SF (M.410)  7,000  $164.18  
COM8  Entertainment & Recreation  Restaurant  Restaurant, 1 st., 5,000 SF 
(M.530)  5,000  $170.51  
COM9  Theaters  Movie Theatre  Movie Theatre, 12,000 SF (M.440)  12,000  
$122.05  
COM10  Parking  Parking garage  Garage, Pkg, 5 st., 145,000 SF (M.270)  
145,000  $43.72  
IND1  Heavy  Factory, small  Factory, 1 st., 30,000 SF (M.200)  30,000  
$88.28  
IND2  Light  Warehouse, medium  Warehouse, 30,000 SF (M.690)  30,000  $75.95  
IND3  Food/Drugs/Chemicals  College Laboratory  College Lab, 1 st., 45,000 SF 
(M.150)  45,000  $145.07  
IND4  Metals/Minerals Processing  College Laboratory  College Lab, 1 st., 
45,000 SF (M.150)  45,000  $145.07  
IND5  High Technology  College Laboratory  College Lab, 1 st., 45,000 SF 
(M.150)  45,000  $145.07  
IND6  Construction  Warehouse, medium  Warehouse, 30,000 SF (M.690)  30,000  
$75.95  
AGR1  Agriculture  Warehouse, medium  Warehouse, 30,000 SF (M.690)  30,000  
$75.95  
REL1  Church  Church  Church, 1 st., 17,000 SF (M.090)  17,000  $138.57  
GOV1  General Services  Town Hall, small  Town Hall, 1 st., 11,000 SF (M.670)  
11,000  $107.28  
GOV2  Emergency Response  Police Station  Police Station, 2 st., 11,000 SF 
(M.490)  11,000  $166.59  
EDU1  Schools/Libraries  High School  School, High, 130,000 SF (M.570)  
130,000  $115.31  
EDU2  Colleges/Universities  College Classroom  College Class. 2-3 st, 50,000 
SF (M.120)  50,000  $144.73  

Table 14.3 Replacement Costs (and Basement Adjustment) for RES1 Structures by 
Means 
Constructions Class (Means, 2006) 

Means Construction Class  Height Class  Average Base cost per square foot  
Adjustment for Finished Basement (cost per SF of main str.)  Adjustment for 
Unfinished Basement (cost per SF of main str.)  
Economy  1 story  $    65.91  $ 19.30  $  7.10  
2 story  $ 70.13  $ 11.10  $ 4.65  
3-story  N/A   use 2 st  N/A   use 2 st  N/A   use 2 st  
Split level  $ 64.46  $ 13.90  $ 5.50  
Average  1 story  $ 92.84  $ 24.05  $ 8.45  
2 story  $ 90.15  $ 15.55  $ 5.45  
3-story  $ 94.49  $ 12.35  $ 4.25  
Split level  $ 84.96  $ 18.45  $ 6.50  
Custom  1 story  $  114.91  $ 39.55  $ 5.45  
2 story  $ 112.91  $ 22.90  $ 9.20  
3-story  $ 116.99  $ 16.80  $ 6.85  
Split level  $ 105.25  $ 28.55  $ 11.35  
Luxury  1 story  $  139.76  $ 43.75  $ 16.75  
2 story  $  133.09  $ 25.75  $ 10.10  
3-story  $ 137.08  $ 19.00  $ 7.60  
Split level  $ 124.81  $ 31.90  $ 12.45  

Note: Assumes main living area is 1800 square feet. 
Table 14.4 Single Family Residential Garage Adjustment (Means, 2006) 

Means Construction Class  Garage Type  Average Additional Garage Cost per 
Residence  
Economy  1 car  $12,600   
2 car  $19,780   
3 car  $26,750   
Average  1 car  $13,120   
2 car  $20,460   
3 car  $27,580   
Custom  1 car  $15,030   
2 car  $23,850   
3 car  $32,380   
Luxury  1 car  $17,320   
2 car  $27,700   
3 car  $37,630   

Table 14.5 Weights (percent) for Means Construction/Condition Models 

Income  Weights (w) for:  
cLg  cCg  cAa  cEp  
Ik < 0.5  - - - 100  
0.5 = Ik < 0.85  - - 25  75  
0.85 = Ik < 1.25  - 25  75  - 
1.25 = Ik < 2.0  - 100  - - 
Ik = 2.0  100  - - - 

14.2.2 Contents Replacement Cost 
Contents replacement value is estimated as a percent of structure replacement 
value.  The NIBS Flood Module will utilize the same contents to structure 
value ratios as are employed in the HAZUS99 and HAZUS-MH Earthquake Module 
(Table 15.5 in the HAZUS 1999 Technical Manual), provided in Table 14.5. 
Table 14.6 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/ Business Services  100  
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  

Table 14.6 Default HAZUS Contents Value Percent of Structure Value 
(Continued) 
No.  Label  Occupancy Class  Contents Value (%)  
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  

14.2.3 Default Values for Regional Cost Variation 
All costs provided in the tables are average national costs.  Within the 
HAZUS flood module, the national costs will be localized by application of a 
residential and non-residential Means location factor, provided with HAZUS-MH 
by state and county throughout the U.S. 
14.2.4 Procedure for Updating Building Cost Estimates 
All calculations associated with estimating building replacement values by 
occupancy for each census block have been performed, and complete data for 
any county within the U.S. are provided as default data within HAZUS.  Users 
have the ability to modify replacement cost values for individual occupancy 
classes at the census block level.  These costs may be edited directly within 
the table browsers of the HAZUS Flood Model. 
14.2.5 Depreciated Building Replacement Cost 
The depreciation models utilized in the HAZUS Flood Model are based on 
industry-standard depreciation methods presented in Means Square Foot Costs 
(R.S. Means, 2002).  Within Means, two depreciation cost models are 
available; one for single family residential structures, and one for 
commercial/industrial/institutional structures. 
14.2.5.1 Single Family Residential 
Means (2002) includes three tabular depreciation models for residential 
structures, based on actual structure age and general condition (Good, 
Average, and Poor).  These models are shown graphically in Figure 14.2. 
The underlying assumption in the methodology used in the HAZUS Flood Model is 
that for any community, some combination of the full replacement cost models 
(economy, average, custom or luxury) and depreciation models (good, average, 
or poor) will best represent the true depreciated value. This basic premise 
was tested on more than 8000 homes in Grand Forks, North Dakota, more than 
160,000 homes in Mecklenburg County, North Carolina, and more than 60,000 
homes in Fort Collins in Colorado.  Results indicated that good agreement 
with assessed 
(depreciated) value could be attained from the models.  A socio-economic 
analysis was performed to determine the optimal means for selecting 
combinations of models, based on 
available census data.  The result of that analysis  is a selection 
algorithm, documented in  
Section 3.7.5.  
Means Residential Dep reciation  


0  5  10  15  20  25 Age in Years  30  35  40  45  50  
Figure 14.2 Single Family Residential Dep reciat ion Mod els (Means, 2002)  
14.2.5.2  Other Residential and Non-Residential  

Unlike the residential depreciation model, the Means 
commercial/industrial/institutional depreciation is determined from "observed 
age" and building framing material (frame, masonry on wood, and masonry on 
masonry or steel), although there is little variation between the models for 
the different framing types.  Accordingly, an average depreciation model has 
been developed and tested, and selected for implementation with the default 
inventory.  A non-residential structure's "observed age" is assumed to 
reflect the structure's condition (e.g., the observed age should reflect any 
remodeling or renovation that would reduce deterioration, and therefore 
decrease the observed age). 
During testing, it was assumed that chronological age is approximately 
equivalent to observed age for the non-residential structures, primarily 
because these structures are less likely to be used far beyond their typical 
life expectancy. (For example, in Grand Forks many homes are significantly 
older than the typical life expectancy of about 50 - 60 years, whereas 
commercial and industrial structures did not demonstrate the same widespread 
longevity.)  Based on the results of the testing, it appears that the 
methodology will produce reasonable approximations of current (depreciated) 
value employing this assumption.  Accordingly, for the default inventory, age 
of non-residential structures will be assumed to be distributed in a manner 
similar to the residential structures in the same Census Block Group.  It 
should be noted, however, that when the user inputs more detailed building 
inventory data at Level 2, entry of actual or "observed" age data for these 
structures is expected to supersede the default age data, and to enhance 
their results. 

0 10 20 30 40 50 60 
Observed Age (Years) 
14.2.6 Building Contents Losses 
Building contents are defined as furniture, equipment that is not integral 
with the structure, computers and other supplies.  Contents do not include 
inventory or nonstructural components such as lighting, ceilings, mechanical 
and electrical equipment and other fixtures.  Contents damage functions are 
applied in the same manner as building damage functions and are discussed in 
Section 5. 
14.2.7 Business Inventory Losses 
Inventory losses in the flood module are determined in a manner consistent 
with the other building losses, as well as the methodology currently utilized 
in the HAZUS earthquake module. For occupancies with inventory considerations 
(COM1, COM2, IND1 - IND6 and AGR1, as defined in the HAZUS99 Earthquake 
Technical Manual), inventory losses are estimated using USACE-based depth-
damage functions, in conjunction with HAZUS default inventory values 
determined as a percentage of annual sales per square foot (see Earthquake 
Loss Estimation Methodology HAZUS Technical Manual, Section 15.2.3). 
The damage function library assembled includes 144 Inventory depth-damage 
curves provided by the U.S. Army Corps of Engineers, Galveston District.  
These functions include: 
 	
44 depth-damage functions for facilities classified according to their SIC 
code and description as COM1 Retail Trade (e.g., grocery store, furniture 
store) 

 	
23 functions for COM2 Wholesale Trade (e.g., food warehouse, paper products 
warehouse) 

 	
10 functions for IND1 Heavy Industrial (e.g., fabrication shop, machine shop-
heavy) 

 	
11 functions for IND2 Light Industrial (e.g., furniture manufacturing, 
commercial printing) 

 	
12 functions for IND3 Foods/Drugs/Chemicals (e.g., chemical plant, feed mill) 

 	
4 functions for IND4 Metals/Mineral Processing (e.g., foundry) 

 	
3 functions for IND6 Construction (e.g., roofing contractor, plumbing 
company) 


For each occupancy class, applicable functions will be averaged to develop a 
default depth-damage function. 
To estimate inventory losses, percent damage (determined from the depth-
damage function) will be multiplied by the total inventory value (determined 
according to HAZUS Earthquake Methodology - floor area times the percent of 
gross sales or production per square foot), as follows: 
INVi = . %DAM-INVi,j * Fai,j * SALESi * Bii (10) 	(14-4) 
j 
23 
INV = INV7 + INV8 + . INVi (11) 	(14-5) 
i=17 
Where: INVi = value of inventory losses for occupancy I INV = total value of 
inventory losses %DAM-INVi,j = percent inventory damage for occupancy i and 
depth j (from 
depth-damage function) Fai,j = floor area of occupancy group i and depth j 
(in square feet)  SALESi = annual gross sales or production (per square foot) 
for occupancy i (see Table 14.7) 
Bii = 	business inventory as a percentage of annual gross sales for 
occupancy i (i = 7, 8, 17-23, see Table 14.8) 

Tabulated monetary direct economic parameter values from the original HAZUS 
Earthquake Technical Manual have been updated to current (2005) costs for use 
in HAZUS using a ratio of the annual Consumer Price Index (CPI).  That is, 
the original dollar value was multiplied by the ratio of the CPI value for 
the current year to the CPI value for the year the data was developed. Annual 
CPIs for the years 1990 through 2005 are given in Table 14.6.  For example, 
to scale 1990 annual sales data to current year (2005), the 1990 value 
($30/sf) was multiplied by 1.4836 (the ratio of 193.9 to 130.7). 
Table 14.7 Consumer Price Index 1990 - 2006 (Source: 
http://www.bls.gov/cpi/home.htm) 

Year  Annual  
CPI  
1990  130.70  
1991  136.20  
1992  140.30  
1993  144.50  
1994  148.20  
1995  152.40  
1996  156.90  
1997  160.50  
1998  163.00  
1999  166.60  
2000  172.20  
2001  177.10  
2002  179.90  
2003  184.00  
2004  188.90  
2005  195.30  
2006  201.39*  

Note: Based on data for January   August, 2006 
Table 14.8 Annual Gross Sales or Production (Dollars per Square Foot) 

No.  Label  Occupancy Class  Output/ Employment  Sq. ft. floor 
Space/Employee3  Annual Sales (2006 $/sf)  
Commercial  
7  COM1  Retail Trade1  $43,171  825  46  
8  COM2  Wholesale Trade1  $64,414  900  66  
Industrial  
17  IND1  Heavy2  $333,501  550  616  
18  IND2  Light2  $98,333  590  196  
19  IND3  Food/Drugs/Chemicals2  $247,695  540  602  
20  IND4  Metals/Minerals Processing2  $495,655  730  567  
21  IND5  High Technology2  $135,772  300  378  
22  IND6  Construction3  $167,259  250  664  
Agriculture  
23  AGR1  Agriculture3  $98,286  250  128  

2005 values (latest available) of output/employment estimated using ratios 
from BLS  Industry Productivity & Cost Survey  data on Output per Person by 
Industry 
2 2006 values (based on 1st 2 quarters) of output/employment estimated using 
ratios from BLS  Industry Productivity & Cost Survey  data on Output per 
Person by Industry 
3 2004 values (latest available) estimated from BEA commodity output data and 
BLS employment data, because Industry Productivity data not available 
4 ATC-13, Table 4.7, pages 94-97 (ATC, 1985) 
Table 14.9 Business Inventory (% of Gross Annual Sales) (ref: NIBS/FEMA HAZUS 
Technical Manual, Table 15.8) 

No.  Label  Occupancy Class  Business Inventory (%)  
Commercial  
7  COM1  Retail Trade  13  
8  COM2  Wholesale Trade  10  
Industrial  
17  IND1  Heavy  5  
18  IND2  Light  4  
19  IND3  Food/Drugs/Chemicals  5  
20  IND4  Metals/Minerals Processing  3  
21  IND5  High Technology  4  
22  IND6  Construction  2  
Agriculture  
23  AGR1  Agriculture  8  

14-22 
14.2.8 Relocation Expenses 
Relocation expenses in the HAZUS Flood Model are estimated in a manner 
consistent with the current earthquake model.  In the HAZUS99 & HAZUS-MH 
earthquake model, relocation expenses represent disruption costs to building 
owners for selected occupancies.  These include all occupancies except 
entertainment (COM8), theatres (COM9), parking facilities (COM10) and heavy 
industry (IND1). Expenses include    disruption costs that include the cost 
of shifting and transferring, and the rental of temporary space .  These 
costs are assumed to be incurred once the building reaches a damage threshold 
of 10% (beyond damage state  slight  in the earthquake model).  Below that 
threshold, it is assumed unlikely that the occupants will not need to 
relocate.  Relocation losses will be estimated as follows: 
.(1 - %OOi )*(DCi ) +.RELi = . j If %DAM-BLi,j >10%: Fai,j* ..%OOi *(DCi + 
RENTi * RTi, j ).. (14-6) 
where: 
RELi = 	relocation costs for occupancy class i (i = 1-13 and 18-28) 
Fai,j = 	floor area of occupancy group i and depth j (in square feet)  
%DAM-BLi,j = 	percent building damage for occupancy i and water depth j 
(from depth-damage function), if greater than 10%.

Dci = 	disruption costs for occupancy i ($/ft2, column 6 in Table 
14.9) 

RTi,j = 	recovery time (in days) for occupancy i and water depth j  
(See Table 14.11 for preliminary flood restoration time 
estimates) 

%OOi = 	percent owner occupied for occupancy i (HAZUS99 
Technical Manual Table 15.14, reprinted here as Table 
14.10) 

RENTi = 	rental cost ($/ft2/day) for occupancy i (column 5 in Table 
14.9) 

It should be noted that the default values for rental costs and disruption 
costs provided in Table 14.9, have been updated from the original development 
year of 1994 to the year 2005 baseline using CPI scaling, as discussed in 
Section 14.3.7. 
Colleges/Universities 
Table 14.10 Rental Costs and Disruption Costs 

No.  Label  Occupancy Class  Rental Cost (2006)  Disruption Costs (2006)  
($/ft2/month)  ($/ft2/day)  ($/ft2)  
Residential  
1  RES1  Single-family Dwelling  0.68  0.02  0.82  
2  RES2  Mobile Home  0.48  0.02  0.82  
3  RES3A  Multi-family Dwelling; Duplex  0.61  0.02  0.82  
4  RES3B  Multi-family Dwelling; il /Q d  0.61  0.02  0.82  
5  RES3C  Multi-family Dwelling; 5 - 9 units  0.61  0.02  0.82  
6  RES3D  Multi-family Dwelling; 10 - 19 units  0.61  0.02  0.82  
7  RES3E  Multi-family Dwelling; 20 - 49 units  0.61  0.02  0.82  
8  RES3F  Multi-family Dwelling; 50+ units  0.61  0.02  0.82  
9  RES4  Temporary Lodging  2.04  0.07  0.82  
10  RES5  Institutional Dormitory  0.41  0.01  0.82  
11  RES6  Nursing Home  0.75  0.03  0.82  
Commercial  
12  COM1  Retail Trade  1.16  0.04  1.09  
13  COM2  Wholesale Trade  0.48  0.02  0.95  
14  COM3  Personal and Repair Services  1.36  0.05  0.95  
15  COM4  Professional/Technical/ Business S i  1.36  0.05  0.95  
16  COM5  Banks  1.70  0.06  0.95  
17  COM6  Hospital  1.36  0.05  1.36  
18  COM7  Medial Office/Clinic  1.36  0.05  1.36  
19  COM8  Entertainment & Recreation  1.70  0.06  0.00  
20  COM9  Theaters  1.70  0.06  0.00  
21  COM10  Parking  0.34  0.01  0.00  
Industrial  
22  IND1  Heavy  0.20  0.01  0.00  
23  IND2  Light  0.27  0.01  0.95  
24  IND3  Food/Drugs/Chemicals  0.27  0.01  0.95  
25  IND4  Metals/Minerals Processing  0.20  0.01  0.95  
26  IND5  High Technology  0.34  0.01  0.95  
27  IND6  Construction  0.14  0.00  0.95  
Agriculture  
28  AGR1  Agriculture  0.68  0.02  0.68  
Religion/Non-Profit  
29  REL1  Church/Membership Organization  1.02  0.03  0.95  
Government  
30  GOV1  General Services  1.36  0.05  0.95  
31  GOV2  Emergency Response  1.36  0.05  0.95  
Education  
32  EDU1  Schools/Libraries  1.02  0.03  0.95  
33  EDU2  

1.36 0.05 
Table 14.11 Percent Owned Occupied (ref: NIBS/FEMA HAZUS Technical Manual, 
Table 15.14) 

No.  Label  Occupancy Class  Percent Owner Occupied  
Residential  
1  RES1  Single-family Dwelling  75  
2  RES2  Mobile Home  85  
3  RES3  Multi-family Dwelling  35  
4  RES4  Temporary Lodging  0  
5  RES5  Institutional Dormitory  0  
6  RES6  Nursing Home  0  
Commercial  
7  COM1  Retail Trade  55  
8  COM2  Wholesale Trade  55  
9  COM3  Personal and Repair Services  55  
10  COM4  Professional/Technical/ Business Services  55  
11  COM5  Banks  75  
12  COM6  Hospital  95  
13  COM7  Medial Office/Clinic  65  
14  COM8  Entertainment & Recreation  55  
15  COM9  Theaters  45  
16  COM10  Parking  25  
Industrial  
17  IND1  Heavy  75  
18  IND2  Light  75  
19  IND3  Food/Drugs/Chemicals  75  
20  IND4  Metals/Minerals Processing  75  
21  IND5  High Technology  55  
22  IND6  Construction  85  
Agriculture  
23  AGR1  Agriculture  95  
Religion/Non-Profit  
24  REL1  Church/Membership Organization  90  
Government  
25  GOV1  General Services  70  
26  GOV2  Emergency Response  95  
Education  
27  EDU1  Schools/Libraries  95  
28  EDU2  Colleges/Universities  90  

14.2.9 Loss of Income 
Income-related losses are time-dependent; the losses will depend on the 
amount of time required to restore business operations. Restoration times 
include time for physical restoration of the damage to the building, as well 
as time for clean-up, time required for inspections, permits and the approval 
process, as well as delays due to contractor availability.   
Earthquake damage restoration and flood damage restoration differ in a 
variety of ways, including: 
 	
Damage due to flooding is likely to be widespread throughout the inundated 
area; earthquakes will cause differing degrees of damage to structures 
located within the same area. 

 	
In an earthquake, inventory that does not break can be picked up and sold.  
Flooded-damaged inventory is usually a total loss. 

 	
An earthquake-damaged business may be able to re-open quickly with undamaged 
inventory in a new location (e.g., alternate space, parking lot) in parallel 
with clean up.  A flood-damaged business is less likely to re-open during 
clean up, in particular, re-opening may depend on resupply of inventory. 


Because flood damage is fundamentally different than earthquake damage, a 
flood-specific restoration time model has been developed.  The project team 
has developed draft estimates of required restoration time by occupancy, 
assumed to vary with flood depth.  Here, flood depths are generally examined 
in increments of four feet, to coincide with likely physical repair 
strategies. For example, once inundation has exceeded the finished floor and 
damaged the lower portion of the wall, a sheet of 4x8 dry wall will be laid 
horizontally to replace the damaged wallboard. The proposed restoration model 
is provided in Table 14.11 on the following page, and includes restoration 
time required for physical building restoration, as well as additional time 
required for clean-up, permitting, contractor availability, and potential 
hazardous materials issues. (This table corresponds to the existing HAZUS 
earthquake Table 15.11, Building Recovery Time). 
It should be noted that restoration times increase with depth, until the 
building has reached the 50% damage threshold, beyond which the building is 
considered a total loss.  Once a building reaches 50% damage, it is assumed 
that the building will be demolished and re-built.  For structures, outside 
the 100-year floodplain, reconstruction can be accomplished at the same site, 
and will require 18 months; 12 months for physical construction, plus 6 
months for damage determination, permits, approvals, etc.  If the structure 
is located within the 100-year floodplain, reconstruction to the original 
configuration at the same location will not be allowed, and the building is a 
potential buy-out candidate. Associated political considerations are assumed 
to add an additional 6-month delay to the reconstruction process, bringing 
the total time estimate to 24 months. 
Future model development will include an assessment as to whether 
Interruption time multipliers (reduction factors), similar to those used in 
the earthquake model (Table 15.12   Building and Service Interruption Time 
Modifiers), are applicable to flood.  For consideration in this process, the 
project team has reviewed the list of occupancies to determine the dominant 
restoration element, provided in Table 14.12.  
Table 14.12 Flood Restoration Time by Occupancy 

Occupancy  Depth  Location  Physical Restoration Time (Months)  Add-ons  Max 
Total Time  Notes  
Dry-out & Clean up  Insp., permits, Ord., approval  Contr. Avail.  Hazmat 
Delay  
0    4   3 to 6  1  2  3  12  
4    8   6 to 9  1  2  3  15  
RES1 (No Base)  8 +  Outside 100-yr  12  1  2  3  18  Total loss, requires 
replacement  
8 +  Inside 100-yr  18  1  2  3  24  Total loss, subject to buy-out 
review/political process  
(-8 )   (4 )  3 to 6  1  2  3  9  No sub-floor repair required  
(-4 )   0   6 to 9  1  2  3  15  
RES1 (W/Base)  0    6   9 to 12  1  2  3  18  
6 +  Outside 100-yr  12  1  2  3  18  Total loss, requires replacement  
6 +  Inside 100-yr  18  1  2  3  24  Total loss, subject to buy-out 
review/political process  
0  TO 1   3 to 6  1  2  3  12  
RES2  1 +  Outside 100-yr  12  1  2  3  18  Total loss, requires replacement  
1 +  Inside 100-yr  18  1  2  3  24  Total loss, subject to buy-out 
review/political process  
0    4   3 to 6  1  2  3  12  Same as RES1  
4    8   6 to 9  1  2  3  15  
RES3 (SM)  8 +  Outside 100-yr  12  1  2  3  18  
8 +  Inside 100-yr  18  1  2  3  24 

HAZUS-MH MR3 Technical Manual Chapter 14.  Direct Economic Losses HAZUS-MH 
MR3 Technical Manual Chapter 14.  Direct Economic Losses HAZUS-MH MR3 
Technical Manual 
Table 14.12 Flood Restoration Time by Occupancy (Continued) 

Occupancy  Depth  Location  Physical Restoration Time (Months)  Add-ons  Max 
Total Time  Notes  
Dry-out & Clean up  Insp., permits, Ord., approval  Contr. Avail.  Hazmat 
Delay  
0    4   5 to 8  1  2  3  14  (RES1*1.2) + 1 Month based on 3-5 units per 
floor  
4    8   8 to 12  1  2  3  18  
RES3 (MED)  5-9 & 1019 units  8    12   12  1  2  3  18  Note:  available apt 
models reach 5-% damage ~ 12   
12 +  Outside 100-yr  12  1  2  3  18  Total loss, requires replacement  
12 +  Inside 100-yr  18  1  2  3  24  Total loss, subject to buy-out 
review/political process  
0    4   5 to 8  1  2  3  14  (RES1*1.2) + 1 Month based on 3-5 units per 
floor  
RES3  4    8   8 to 12  1  2  3  18  (RES1*1.2) + 1 Month based on 3-5 units 
per floor  
(LRG)  20-49 & 50+ units  8 +  12  1  2  3  18  Note:  available apt models 
reach 5-% damage ~ 12   
12 +  Outside 100-yr  12  1  2  3  18  Total loss, requires replacement  
12 +  Inside 100 yr  18  1  2  3  24  Total loss, subject to buy-out 
review/political process  
0    4   5 to 8  1  2  3  14  Use RES3 (LRG)  
4    8   8 to 12  1  2  3  18  
RES4  8 +  12  1  2  3  18  
12 +  Outside 100-yr  12  1  2  3  18  Total loss, requires replacement  
12 +  Inside 100 yr  18  1  2  3  24  Total loss, subject to buy-out 
review/political process  

Table 14.12 Flood Restoration Time by Occupancy (Continued) 

Occupancy  Depth  Location  Physical Restoration Time (Months)  Add-ons  Max 
Total Time  Notes  
Dry-out & Clean up  Insp., permits, Ord., approval  Contr. Avail.  Hazmat 
Delay  
0    4   6 to 10  1  2  3  16  Repairs may require less work (fewer 
partitions & finishes), but have more politics or funding issues.  Use RES3 
(LRG) but increase 1.2 factor to 1.5  
RES5 RES6  4    8   10 to 15  1  2  3  21  
8    12   19  1  2  3  25  
EDU1 EDU2  12 +  Outside 100-yr  12  1  2  3  18  Total loss, requires 
replacement  
12 +  Inside 100-yr  18  1  2  3  24  Total loss, subject to buy-out 
review/political process  
0    4   7 to 13  1  2  3  19  Use RES3*2.0   Longer clean up, but no wood 
sub-floor, perimeter wall, linoleum. Inventory damaged/destroyed, restoration 
depends on resupply, damage widespread in inundation area, insurance is a 
factor.  
COM1 COM2 COM8  4    8   13 to 19  1  2  3  25  
8 +  25  1  2  3  31  
COM9 REL1  12 +  Outside 100-yr  12  1  2  3  18  Total loss, requires 
replacement  
12 +  Inside 100 yr  18  1  2  3  24  Total loss, subject to buy-out 
review/political process  
0    4   3 to 6  1  2  3  12  On average, same as RES1 without a basement.  
4    8   6 to 9  1  2  3  15  
COM3  8 +  Outside 100-yr  12  1  2  3  18  Total loss, requires replacement  
8 +  Inside 100 yr  18  1  2  3  24  Total loss, subject to buy-out 
review/political process  

Table 14.12 Flood Restoration Time by Occupancy (Continued) 

Occupancy  Depth  Location  Physical Restoration Time (Months)  Add-ons  Max 
Total Time  Notes  
Dry-out & Clean up  Insp., permits, Ord., approval  Contr. Avail.  Hazmat 
Delay  
0    4   6 to 10  1  2  3  16  Use RES3 (LRG)*1.5 (same as RES5 & RES6) 
COM4  4    8   10 to 15  1  2  3  21  
COM5 COM7 GOV1 GOV2  8    12   19  1  2  3  25  
12 +  Outside 100-yr  12  1  2  3  18  Total loss, requires replacement  
12 +  Inside 100-yr  18  1  2  3  24  Total loss, subject to buy-out 
review/political process  
COM6  (-8 ) - (4 )  6  1  2  3  16  Hospitals are highly regulated, have 
equipment issues.  This model represents full repair/restoration, but certain 
repairs will be prioritized to allow selected operations to begin sooner.  
(assume  (-4 )   0   12  1  2  3  21  
w/base)  0    4   18  1  2  3  18  
4    8   24  1  2  3  24  
COM10  Any > 0   1  1  Parking lot restoration is not dependent on flood 
depth, only clean up.  
IND1  Any > 0   1 to 3  1  2  1  7  For heavy industrial, clean up is the 
primary issue, especially for equipment.  Relocation is unlikely.  Hazmat is 
a potential for this occupancy class.  
IND2 IND6  Any > 0   1 to 2  1  2  5  Like heavy industrial except no 
equipment issues.  Totally content issues.  
0    4   6 to 10  1  2  3  1  17  Like laboratories, perimeter walls.  Hazmat 
a potential issue.  Use RES3*1.5 + Hazmat delay. Similar to RES5, RES6, COM4, 
COM5, COM7.  
4    8   10 to 15  1  2  3  1  22  
IND3  8    12   19  1  2  3  1  26  
12 +  Outside 100-yr  12  1  2  3  18  Total loss, requires replacement  
12 +  Inside 100-yr  18  1  2  3  24  Total loss, subject to buy-out 
review/political process  

Table 14.12 Flood Restoration Time by Occupancy (Continued) 

Occupancy  Depth  Location  Physical Restoration Time (Months)  Add-ons  Max 
Total Time  Notes  
Dry-out & Clean up  Insp., permits, Ord., approval  Contr. Avail.  Hazmat 
Delay  
0    4   6 to 10  1  2  3  2  18  Like IND3, but use a 2-month delay for 
hazmat.  
4    8   10 to 15  1  2  3  2  27  
8    12   19  1  2  3  2  26  
IND4  12 +  Outside 100-yr  12  1  2  3  18  Total loss, requires replacement  
12 +  Inside 100-yr  18  1  2  3  24  Total loss, subject to buy-out 
review/political process  
0    4   7 to 13  1  2  3  2  21  Use RES3*2 + 2-month Hazmat delay. (Similar 
to COM1, COM2, COM8, COM9.  
4    8   13 to 19  1  2  3  2  27  
8    12   25  1  2  3  2  33  
IND5  12 +  Outside 100-yr  12  1  2  3  2  20  Total loss, requires 
replacement  
12 +  Inside 100-yr  18  1  2  3  2  26  Total loss, subject to buy-out 
review/political process  
AGR1  Any > 0   1 to 2  1  2  2  7  Like IND2 with 2-month hazmat delay,  

Table 14.13 Elements Dominating Building and Service Interruption for Floods 

Label  Occupancy Class  Element Dominating Restoration  
Residential  
RES1  Single Family Dwelling  Building (+ Utilities)  
RES2  Mobile Home  Building (+ Utilities)  
RES3     Multi Family Dwelling  Building (+ Utilities)  
RES4     Temporary Lodging  Building (+ Utilities)  
RES5     Institutional Dormitory  Building (+ Utilities)  
RES6  Nursing Home  Building (+ Utilities)  
Commercial  
COM1  Retail Trade  Inventory  
COM2  Wholesale Trade  Inventory  
COM3     Personal and Repair Services  Inventory/Equipment  
COM4  Professional/Technical/    Business Services  Building (+ Utilities)  
COM5     Banks/Financial Institutions  Building (+ Utilities)  
COM6  Hospital  Building (+ Utilities)/Equipment  
COM7  Medical Office/Clinic  Building (+ Utilities)  
COM8     Entertainment & Recreation   Building (+ Utilities)/Contents  
COM9  Theaters  Building (+ Utilities)/Contents  
COM10  Parking  
Industrial  
IND1  Heavy  Equipment  
IND2  Light  Inventory  
IND3     Food/Drugs/Chemicals  Inventory/Equipment  
IND4     Metals/Minerals Processing  Equipment  
IND5     High Technology  Inventory/Equipment  
IND6  Construction  Building (+ Utilities)  
Agriculture  
AGR1  Agriculture  Inventory/Equipment  
Religion/Non-Profit  
REL1     Church/Membership Organization  Building (+ Utilities)  
Government  
GOV1     General Services  Building (+ Utilities)  
GOV2     Emergency Response  Building (+ Utilities)  
Education  
EDU1     Schools/Libraries  Building (+ Utilities)  
EDU2  Colleges/Universities  Building (+ Utilities)  

14.2.9.1 Capital Related, Wage, Output and Employment Losses 
Capital related loss (previously referred to as proprietor s income loss and 
described in more detail in the HAZUS99 & HAZUS-MH Technical Manual) is 
estimated as follows in the HAZUS Flood Model: 
YLOSi = .	(1-IRFi)*FAI,j*INCi* LOFi, j (14-7) 
j 
where: 
YLOSi = 	capital related losses for occupancy class I 
FAi,j = 	floor area of occupancy class i (in square feet) at depth j 
INCi = 	income per day (per square foot) for occupancy class i (column 5 
in Table 14.13) 

LOFi,j = 	business loss of function time (in days) for occupancy i and 
water 
depth j. (See Table 14.11 for preliminary restoration time 
estimates) 

IRFi = 	Income recapture factor for occupancy class i (see Table 14.14) 
It should be noted that capital related loss uses  loss of function  rather 
than  recovery time  as the time-dependent variable, and therefore is 
evaluated over the entire damage range, rather than just above the 10% damage 
threshold. 
In addition to capital related losses, several other income-related losses 
may be calculated in the same manner, as follows: 
 	
Wage losses, currently calculated within HAZUS, are estimated by substituting 
Wages (per square foot per day, column 6 from Table 14.13) for Income, and 
replacing the income recapture factor with the wage recapture factor (Table 
14.14). 

 	
Sales or output losses can be estimated by substituting Output (per square 
foot per day, column 8 from Table 14.13) for Income, and replacing the income 
recapture factor with the output recapture factor (Table 14.14).  The 
resulting output losses may then be used to derive employment losses 
( equivalent jobs  lost), by multiplying the output losses by 
employment/output ratios for each industry.  The employment/output ratios can 
be obtained from published sources and IMPLAN tables, and default values will 
be provided with the flood methodology. 


14-34 
Table 14.14 Proprietor s Income 

No.  Label  Occupancy Class  Income (2006)  Wages (2006) per Sq. Ft. per Day  
Employees per Sq. Ft.  Output  (2006) per Sq. Ft. per Day  
Residential  
1  RES1  Single-family Dwelling  0.00  0.00  0.00  0.00  0.00  
2  RES2  Mobile Home  0.00  0.00  0.00  0.00  0.00  
3  RES3A  Multi-family Dwelling; Duplex  0.00  0.00  0.00  0.00  0.00  
4  RES3B  Multi-family Dwelling; il /Q d  0.00  0.00  0.00  0.00  0.00  
5  RES3C  Multi-family Dwelling; 5 - 9 units  0.00  0.00  0.00  0.00  0.00  
6  RES3D  Multi-family Dwelling; 10 - 19 units  0.00  0.00  0.00  0.00  0.00  
7  RES3E  Multi-family Dwelling; 20 - 49 units  0.00  0.00  0.00  0.00  0.00  
8  RES3F  Multi-family Dwelling; 50+ units  0.00  0.00  0.00  0.00  0.00  
9  RES4  Temporary Lodging  35.90  0.10  0.23  0.00  0.52  
10  RES5  Institutional Dormitory  0.00  0.00  0.00  0.00  0.00  
11  RES6  Nursing Home  59.83  0.16  0.39  0.01  0.86  
Commercial  
12  COM1  Retail Trade  22.15  0.06  0.21  0.00  0.45  
13  COM2  Wholesale Trade  36.32  0.10  0.26  0.00  0.58  
14  COM3  Personal and Repair Services  47.86  0.13  0.31  0.00  0.69  
15  COM4  Professional/Technical/ Business S i  377.12  1.03  0.37  0.00  
1.00  
16  COM5  Banks  430.34  1.18  0.60  0.01  3.26  
17  COM6  Hospital  59.83  0.16  0.39  0.01  0.86  
18  COM7  Medial Office/Clinic  119.65  0.33  0.77  0.01  1.72  
19  COM8  Entertainment & Recreation  219.43  0.60  0.48  0.01  1.08  
20  COM9  Theaters  71.79  0.20  0.46  0.01  1.03  
21  COM10  Parking  0.00  0.00  0.00  0.00  0.00  
Industrial  
22  IND1  Heavy  90.79  0.25  0.41  0.00  1.74  
23  IND2  Light  90.79  0.25  0.41  0.00  1.74  
24  IND3  Food/Drugs/Chemicals  121.05  0.33  0.55  0.00  2.32  
25  IND4  Metals/Minerals Processing  275.03  0.75  0.43  0.00  1.84  
26  IND5  High Technology  181.57  0.50  0.83  0.01  3.48  
27  IND6  Construction  88.51  0.24  0.45  0.01  1.72  
Agriculture  
28  AGR1  Agriculture  83.99  0.23  0.09  0.00  0.86  
Religion/Non-Profit  
29  REL1  Church/Membership Organization  47.86  0.13  0.31  0.00  1.72  
Government  
30  GOV1  General Services  39.31  0.11  2.96  0.03  0.69  
31  GOV2  Emergency Response  0.00  0.00  4.50  0.04  0.79  

Table 14.15 Proprietor s Income (Continued) 

No.  Label  Occupancy Class  Income (2006)  Wages (2006) per Sq. Ft. per Day  
Employees per Sq. Ft.  Output  (2006) per Sq. Ft. per Day  
Education  
32  EDU1  Schools/Libraries  59.83  0.16  0.39  0.01  3.33  
33  EDU2  Colleges/Universities  119.65  0.33  0.77  0.01  5.06  

Table 14.16 HAZUS99 Earthquake Table of Recapture Factors 

Occ.  Wage Recapture (%)  Employment Recapture (%)  Income Recapture (%)  
Output Recapture (%)  
RES1  0  0  0  0  
RES2  0  0  0  0  
RES3  0  0  0  0  
RES4  0.60  0.60  0.60  0.60  
RES5  0.60  0.60  0.60  0.60  
RES6  0.60  0.60  0.60  0.60  
COM1  0.87  0.87  0.87  0.87  
COM2  0.87  0.87  0.87  0.87  
COM3  0.51  0.51  0.51  0.51  
COM4  0.90  0.90  0.90  0.90  
COM5  0.90  0.90  0.90  0.90  
COM6  0.60  0.60  0.60  0.60  
COM7  0.60  0.60  0.60  0.60  
COM8  0.60  0.60  0.60  0.60  
COM9  0.60  0.60  0.60  0.60  
COM10  0.60  0.60  0.60  0.60  
IND1  0.98  0.98  0.98  0.98  
IND2  0.98  0.98  0.98  0.98  
IND3  0.98  0.98  0.98  0.98  
IND4  0.98  0.98  0.98  0.98  
IND5  0.98  0.98  0.98  0.98  
IND6  0.95  0.95  0.95  0.95  
AGR1  0.75  0.75  0.75  0.75  
REL1  0.60  0.60  0.60  0.60  
GOV1  0.80  0.80  0.80  0.80  
GOV2  0  0  0  0  
EDU1  0.60  0.60  0.60  0.60  
EDU2  0.60  0.60  0.60  0.60  

14.2.10 Rental Income Losses 
Rental income losses will be estimated as follows: 
RYi = . If %DAM-BLi,j >10%: (1-%OOi) * FAi,j * RENTi * RTi,j 	(14-8) 
j 
where: 
Ryi = 	rental income losses for occupancy I 
%DAM-BLi,j = 	percent building damage for occupancy i and water depth j 
(from depth-damage function), if greater than 10%.

%Ooi = 	percent owner occupied for occupancy i (HAZUS99 
Technical Manual Table 15.14, reprinted here as Table 
14.10) 

FAi,j = 	floor area of occupancy group i (in square feet) at depth j 
RENTi = 	rental cost ($/ft2/day) for occupancy i (column 5 in Table 
14.9) 

RTi,j = 	recovery time (in days) for occupancy i and water depth j 
(See Table 14.11 for preliminary flood restoration time 
estimates) 

14.2.11 Guidance for Estimates Using Advanced Data and Models Analysis 
The default data provided with the HAZUS model are sufficient for a Level 1 
analysis.  However, depending on the type of analysis required, much more 
detailed economic cost information can be obtained from various public data 
sources or from private consultants.  For example, more accurate rental costs 
may be obtained from local realtors or from the local Chamber of Commerce.  
For replacement costs, professional building cost estimators maintain 
detailed records of costs and trends, and have knowledge of local building 
practices that might affect a loss estimate.  
Certain kinds of estimates, for example one focused on the implications of 
hospital or specific industry losses, would require individual building cost 
estimates (together with similar individual building damage estimates) that 
might result in costs considerably different than the typical aggregated 
costs provided as part of the default database provided with this 
methodology. 
14-38 
14.3 Description of Methodology:  Lifelines 
14.3.1 Bridges 
The flood model uses the following methodology to develop losses for highway 
bridges, railway bridges, and light rail bridges.  The flood model uses data 
provided with the default bridge inventory within HAZUS, including the 
following fields: 
 	
BridgeId: (this is the unique identifier for each bridge).  Remember that 
bridges are point facilities and are independent of each other. 

 	
BridgeClass:  This field is the first part of the bridge specific occupancy 
defined in the bridge damage function tables 

 	
ScourIndex: This field is key to the analysis and is the second part of the 
bridge specific occupancy in the bridge damage function tables. 

 	
Cost: This field is necessary for the estimation of loss 

The flood model will examine all bridges within the inundated area by 
performing a point in grid analysis. Those bridges that are not in the 
inundated area are assumed to be undamaged and are skipped. For bridges 
within the flood depth grid, but where the flood depth is <= 0 feet, the 
bridge is close to the floodplain but not inundated and the bridge is 
skipped.  Therefore, only those bridges where the flood depth is greater than 
0-feet are analyzed.  If the bridge is considered inundated then, the scour 
index is checked.  If the scour index is in (4, 5, 6, 7, 8, 9, T, N) then no 
analysis is performed as the engineering study has determined that the bridge 
will not be subjected to scour. If the scour index is in (U, 1, 2, 3) then an 
analysis must be performed. As with other models, the user can define their 
own damage function and the model will check to see if the user is using the 
default functions or one of their own. 

 	
Damage ($) is calculated as follows: 


(%) = Prob of failure * 0.25 
($) = Prob of failure * 0.25 * Cost 
Function = (1-Prob of Failure) 
Note = Prob of Failure is interpolated above 
0.25 is a hard value based on expert opinion (failure represents 25% damage, 
see Section 7.2.3) 
Cost is from the Cost field in the hzBridge tables (provided in the Technical 
Manual in the classification tables in Chapter 3, see for example, Table 
3.19) 
14.3.2 Utility Systems 
14.3.2.1 Potable Water Facilities 
The flood model uses the depth of flooding and its impact on critical 
components of the water system to determine the percentage of damage expected 
for those facilities.  The damage functions were discussed and presented in 
Section 7.0 of this document.  Once the expected amount of damage is know in 
percent (%), it is necessary to multiply this with the replacement value (see 
Table 3.26) to determine the amount of loss.  The equations for this analysis 
are shown below. 
(% damage) = damage at (depth of water   equipment height) and is read 
directly from the table of depth damage values 
($ Loss) = (% Damage) * (Inventory $ value) 
The flood model performs this analysis for control vaults and control 
stations, wells, tanks, pumps and potable water treatment plants.  In the 
case of the potable water treatment plants, the flood model is essentially 
examining the components most critical to functionality and loss and ignoring 
other features such as buildings that are likely to be included in the 
General Building Stock. If the user needs to have the analysis include 
damages to the buildings within the treatment plant, it is recommended that 
they create User Defined facilities to analyze the structures. 
14.3.2.2 Wastewater Systems 
The flood model uses the depth of flooding and its impact on critical 
components of the wastewater system to determine the percentage of damage 
expected for those facilities.  The damage functions were discussed and 
presented in Section 7.0 of this document.  Once the expected amount of 
damage is know in percent (%), it is necessary to multiply this with the 
replacement value (see Table 3.27) to determine the amount of loss.  The 
equations for this analysis are shown below. 
(% damage) = damage at (depth of water   equipment height) and is read 
directly from the table of depth damage values 
($ Loss) = (% Damage) * (Inventory $ value) 
The flood model performs this analysis for control vaults and control 
stations, lift stations, and wastewater treatment plants.  In the case of the 
wastewater treatment plants, the flood model is essentially examining the 
components most critical to functionality and loss and ignoring other 
features such as buildings that are likely to be included in the General 
Building Stock.  If the user needs to have the analysis include damages to 
the buildings within the treatment plant, it is recommended that they create 
User Defined facilities to analyze the structures. 
14.3.2.3 Petroleum Systems 
The flood model uses the depth of flooding and its impact on critical 
components of the petroleum (oil) transmission system to determine the 
percentage of damage expected for those facilities. The damage functions were 
discussed and presented in Section 7.0 of this document. Once the expected 
amount of damage is know in percent (%), it is necessary to multiply this 
with the replacement value (see Table 3.28) to determine the amount of loss.  
The equations for this analysis are shown below. 
(% damage) = damage at (depth of water   equipment height) and is read 
directly from the table of depth damage values 
($ Loss) = (% Damage) * (Inventory $ value) 
The flood model performs this analysis for control vaults and control 
stations, tanks, and refineries. In the case of the refineries, the flood 
model is essentially examining the components most critical to functionality 
and loss and ignoring other features such as buildings that are likely to be 
included in the General Building Stock.  If the user needs to have the 
analysis include damages to the buildings within the refinery, it is 
recommended that they create User Defined facilities to analyze the 
structures. 
14.3.2.4 Natural Gas Systems 
The flood model uses the depth of flooding and its impact on critical 
components of the natural gas transmission system to determine the percentage 
of damage expected for those facilities. The damage functions were discussed 
and presented in Section 7.0 of this document.  Once the expected amount of 
damage is know in percent (%), it is necessary to multiply this with the 
replacement value (see Table 3.29) to determine the amount of loss.  The 
equations for this analysis are shown below. 
(% damage) = damage at (depth of water   equipment height) and is read 
directly from the table of depth damage values 
($ Loss) = (% Damage) * (Inventory $ value) 
The flood model performs this analysis for control vaults and control 
stations, compressor plants, and tanks. 
14.3.2.5 Electric Power Systems 
The flood model uses the depth of flooding and its impact on critical 
components of the electric power generation and transmission system to 
determine the percentage of damage expected for those facilities. The damage 
functions were discussed and presented in Section 7.0 of this document.  Once 
the expected amount of damage is know in percent (%), it is necessary to 
multiply this with the replacement value (see Table 3.30) to determine the 
amount of loss.  The equations for this analysis are shown below. 
(% damage) = damage at (depth of water   equipment height) and is read 
directly from the table of depth damage values 
($ Loss) = (% Damage) * (Inventory $ value) 
The flood model performs this analysis for control vaults and control 
stations, substations, and power plants. In the case of the power plants, the 
flood model is essentially examining the components most critical to 
functionality and loss and ignoring other features such as buildings that are 
likely to be included in the General Building Stock.  If the user needs to 
have the analysis include damages to the buildings within the power plant, it 
is recommended that they create User Defined facilities and analyze the 
structures. 
14.3.2.6 Communication Systems 
The determination of losses for communications facilities has been deferred 
to future versions of the flood model. It is anticipated that the methodology 
would be very similar to that described above, although the facilities have 
not been completely defined. 
14.4 Description of Methodology: Vehicles 
The vehicle loss methodology is very similar to that developed for General 
Building Stock, in that the area weighted depth damage will be utilized to 
estimate the total damage and total loss by vehicle type. 
The methodology takes the inventory data and manipulates the data in order to 
perform the analysis. The vehicle count is identified as Car (Car), Light 
Truck (LtTrk), and Heavy Truck (HvyTrk). These are the key occupancies and 
the occupancies by which results will be reported. Vehicles are distributed 
evenly over each census block.  As noted, the inventory is split between used 
vehicles and new vehicles. Results do not make this distinction and are a 
summation over the two classes.  There are no parameters for elevation of the 
vehicle.  These are implicit in the damage functions. 
The percentage of the census block at a given flood depth is determined in 
the same fashion as the GBS analysis. The damage function for each of the 
three occupancies can either be the default functions, or those created and 
selected by the User.  The Flood Model uses piece-wise linear interpolation 
to identify the actual percentage of damage at the given flood depth.  The 
damage will be the Percentage Damage (%) multiplied by the Percentage of 
Census Block at the flood depth multiplied by the day and night vehicle 
count.  The estimated dollar loss will be the above percentages multiplied by 
the dollar exposures, as follows: 
DmgByOccup(Count)= FP*DP*OccupCount (14-9) 
and 
LossByOccup($)= FP*DP*OccupExp($) (14-10) 
14-42 
Where 
FP = 	the percentage of the census block at the given flood 
depth 

DP = 	the damage percent at the given flood depth for the given 
occupancy 

OccupCount = 	the total count of Cars, LtTrk, or HvyTrk for the given 
census block 

OccupExp = 	the total exposure of NewCars, UsedCars, NewLtTrk,  
UsedLttrk, NewHvyTrk, UsedHvyTrk. 

14.5 Description of Methodology: Agriculture (Crops) 
The flood model performs an assessment of the amount of flooding that has 
occurred within the given sub-county polygons generated from the intersection 
of the 8-digit HUCS, the US Census County boundary, and the USGS Land Use, 
Land Cover dataset where the land use is an agricultural classification.  The 
depth of flooding is irrelevant in the current methodology, but the user is 
required to define a date of flooding in order to determine where in the crop 
cycle the user is interested in assessing losses.  The  date  of the flood is 
input by the user in the format of day and month (01-Jan or 11 July), by 
selecting values in a combo box, and the model converts this into the Julian 
Calendar day (day 1 or day 192 respectively in this example).   
Since the flood model hazard does not directly handle duration, the duration 
will be handled through the production of results for four duration intervals 
0-day, 3-day, 7-day and 14-day.  The methodology can be described as follows: 
 	
Determine affected area as the intersection of the floodplain polygon with 
the agriculture polygon (acres) 

 	
Identify the quantity of crops in the polygon and the affected area 
(Yield/acre * area) 

 	
Identify damage (read from the damage function for Julian day) 

 	
Initial Loss = Yield in flooded area * $ * % damaged crop  

  
Duration Loss = Initial Loss * duration modifier This methodology will 
produce a value for a single day flood (0-day), a 3-day flood, 7-day flood 
and 14-day flood. Review of the damage functions indicates that most crops 
will be receiving the maximum impact at 14-days, which therefore defines an 
upper bound for the user. 


14.6 References 
1.	
1991. A Benefit-Cost Model for the Seismic Rehabilitation of Hazardous 
Buildings, FEMA Publications 227, 228, 255, and 256. 

2.	
American Rivers.  2000. Flood Plains. Internet site, 
http://www.amrivers.org/flood.html. 

3.	
T.C. Hannan, I.C. Goulter, Model for Crop Allocation in Rural Floodplains, 
Journal of Water Resources Planning and Management Vol 114, No. 1, January 
1988. 


4.	
R.S. Means (2002), Means Square Foot Costs 

5.	
R.S. Means (2005), Means Square Foot Costs 

6.	
Pan American Health Organization, Natural Disaster Mitigation in Drinking 
Water and Sewerage Systems - Guidelines for Vulnerability Analysis, 1998 


7.	
Rhodes, J., Trent R,  Economics of Floods Scour and Bridge Failures,  
Hydraulic Engineering Conference Proceedings (1993). 

8.	
Rhodes, J., Trent R,  An Evaluation of Highway Flood Damage Statistics,  
Hydraulic Engineering Conference Proceedings (1993). 


9.	
USACE Hydraulic Engineering Center, AGDAM (Agriculture Flood Damage Analysis) 
User Manual (provisional), April, 1985 

10. 
USACE Institute for Water Resources, National Economic Development Procedures 
Manual 


  Agriculture Flood Damage, IWR Report87-R-10, October 1987 
11. US Department of Transportation, Federal Highway Administration, 
 Recording and Coding Guide for the Structure Inventory and Appraisal of the 
Nations Bridges , Federal Highway Administration Report No. FHWA-96-001, 
December 1995. 
Chapter 15.  Indirect Economic Losses 
15.1 Introduction 
The HAZUS Indirect Economic Loss Module evaluates the economic disruption or 
ripple effects that follow from direct losses.  The relationship between the 
Indirect Economic Loss Module and other HAZUS modules is shown in Figure 
15.1, the flowchart of the overall methodology. 
This chapter provides background, explanation, and discussion of the Indirect 
Economic Loss Module. Section 15.2 provides background on the concept of 
indirect losses.  Section 15.3 briefly presents the traditional modeling 
approach for tracing indirect losses, known as input-output modeling. 
Following this background, Section 15.4 describes how indirect losses are 
modeled within HAZUS. Because traditional approaches do not account for many 
of the important features of natural disaster impacts, the Indirect Economic 
Loss Module utilizes an innovative approach for addressing the supply and 
demand shocks that occur in such events.  The core of the module is a 
computational algorithm that rebalances a region s interindustry trade flows 
based on discrepancies between sector supplies and demands. 
Section 15.5 provides guidance on running the HAZUS Indirect Economic Loss 
Module.  It discusses data requirements, module operation, and the types of 
results produced.  In running the module, the user can choose between two 
levels of analysis:  a Default Data Analysis ( Level 1 ) that runs on data 
included with HAZUS, or a User-Supplied Data Analysis ( Level 2 ) that 
requires the user to obtain and input certain economic data on the study 
region.   
Section 15.6 offers discussion and presents examples to assist the user in 
interpreting the Indirect Economic Loss Module s results.  It outlines the 
types of situations and questions for which the module is intended to be 
used. It also discusses how the module is not intended to be used. Numerical 
examples are provided to educate users about the principles of indirect loss, 
to caution against common misunderstandings, and to demonstrate how to 
properly account for losses. Guidance is provided on how to compare module 
results with observed economic consequences in actual disasters. 

15.2 Background: What are Indirect Losses? 
This section provides a conceptual overview of indirect economic losses in 
natural disasters. Section 15.2.1 briefly defines and discusses some key 
terms and principles in natural hazard loss measurement.  Section 15.2.2 
describes the concept of indirect losses more fully, while section 
15.2.3 discusses the significance of whether a regional or national 
accounting stance is adopted in the measurement of this loss. 
15.2.1 Principles of Natural Hazard Loss Estimation 
This section briefly sets forth some basic economic principles of loss 
estimation, including clarifying the confusion between direct and indirect 
losses, property damage and business interruption losses, and real resource 
costs and tradeoffs.  (For a detailed discussion, see Rose and Lim (2002) and 
Rose (2002).)   
Welfare economics, the scientific basis for economic policy-making (see, 
e.g., Boadway and Bruce, 1984), provides a starting point for an analysis of 
economic loss from natural hazards. A major theme is that cost should be 
measured in terms of the value of resources used (or destroyed) and at prices 
that represent their efficient allocation.  This provides a guide for 
avoiding double-counting and being inclusive of all resources, including non-
market ones. Business interruption losses represent a proxy for the ideal 
resource valuation because of measurement problems and because businesses, 
insurers, and governments typically make decisions on the basis of associated 
metrics such as lost sales or profits. 
Lost sales, however, represent a "gross" measure of production.  A more 
appropriate measure is "value-added," which is a "net" measure that includes 
only the contributions of primary factors of production (labor, capital, 
natural resources), and omits the cost of "intermediate" goods (goods used by 
businesses to produce other goods and that hence do not yield direct utility 
to final consumers).  These intermediate goods thus represent a form of 
double-counting if included in impact estimates.  Sales (revenues) losses are 
often the most important consideration to businesses, so they are prevalent 
in the literature.  An alternative that is sometimes used is loss of profits, 
but this represents only one component of value-added (returns to capital) 
because it ignores returns to labor (wages and salaries) and natural 
resources (rents and royalties).  
One of the fundamental distinctions in economics is between stocks and flows.  
Stocks refer to a quantity at a single point in time, while flows refer to 
the services or outputs of stocks over time. Property damage represents a 
decline in stock value and usually leads to a decrease in service flows. 
Business interruption losses are a flow measure, but emanate only in part 
from a company's own property damage. 
One reason flow measures are superior to stock measures is that the former 
include a time dimension.  Stock measures pertain simply to the value of an 
asset at a single point in time.  The typical measure of damage (purchase or 
replacement cost) is thus invariant to how long the asset is out of service. 
For example, if a factory is damaged in a flood, there is a tendency to 
specify the loss in fixed terms, irrespective of whether production is shut 
down for a week or a year awaiting repairs.  Attention to flow losses 
represents a major shift in the focus of hazard loss estimation that losses 
are not a definite or set amount but are highly variable depending on the 
length of the  economic disruption,  typically synonymous with the recovery 
plus reconstruction periods. This also brings home the point that disaster 
losses are not simply determined by the strength of the stimulus (coupled 
with initial vulnerability), but also highly dependent on human ingenuity, 
will, and resources.   
Care should be exercised to avoid double-counting of hazard losses.  Many 
goods and services have quite diverse attributes, and all of those 
damaged/interrupted should be counted (e.g., a hydroelectric dam provides 
electricity, recreational opportunities in the reservoir behind it, and flood 
control). It is important, however, to remember that some goods and services 
cannot yield all of these attributes to their maximum simultaneously, and 
that only one or the other, or some balance of the two, should only be 
counted (e.g., a river can provide services to swimmers or it can be a 
repository for waste but not both at the same time).  Another way to avoid 
double-counting is to avoid attributing losses to more than one entity in the 
case of private goods, as in the case of avoiding counting retail store sales 
as a loss to both the storeowner and its customers. Just as important, 
however, is the inclusion of all relevant losing entities or stakeholders. 
Caution must be exercised here because of the regional character of most 
hazards and the inclination just to consider those living within its 
boundaries.  Tourism associated with natural environments is an excellent 
case in point.  Loss of environmental value should not just be gauged by 
local residents but by all potential users.   
A closely related consideration pertains to the distinction between costs and 
transfers.  Tax expenditures, in particular, do not reflect the use of 
resources and are not real costs to society. In general, they simply 
represent a shifting of dollars from one entity to another.  The complication 
that arises here, however, pertains to the spatial delineation of the 
affected group.  Local property or sales taxes within a region are transfers, 
but payments of federal income tax do represent an outflow and can be 
legitimately included in the regional cost estimates. 
While total business interruption losses are the bottom line, distinguishing 
between its direct and indirect components helps ensure that everything is 
counted and provides more precise information for decision-making.  
Unfortunately, this has been the subject of great confusion from the outset.  
Clarification is best made in terms of flow measures.  Some analysts have 
characterized direct loss as pertaining to property damage and indirect loss 
as pertaining to business interruption (see, e.g., ATC, 1991; Heinz Center, 
2000); however, this is not helpful because both have direct and indirect 
counterparts.   
Direct flow losses pertain to production in businesses damaged by the hazard 
itself.  A business that shuts down because its office building has been 
flooded would suffer such direct flow losses.  The term also includes lost 
production stemming from direct loss of public utility and infrastructure 
services.  For example, a factory may have to shut down because the bridge 
that its suppliers and employers use to reach it is damaged. 
The extent of business interruption does not stop here, but sets off a chain 
reaction of indirect flow losses (also sometimes referred to as  second-
order  or  higher-order  effects). A factory shutdown will reduce supplies to 
its customers, who may be forced to curtail their production for lack of 
critical inputs. In turn, their customers may be forced to do the same, as 
will the customers of these customers, and so on.  The factory shutdown will 
also reduce orders for its inputs. Its suppliers will then have to reduce 
their production and hence cancel orders for their inputs. The suppliers of 
the suppliers will follow suit, and so forth.  The sum total of all of these 
indirect effects is a multiple of the direct effects; hence, the concept of a 
 multiplier  is often applied to their estimation. 
Many analysts are hesitant to measure indirect losses for various reasons.  
First, they cannot be as readily verified as direct losses.  Second, modeling 
them requires utilizing simple economic models carefully, or, more recently, 
utilizing quite sophisticated economic models.  Third, the size of indirect 
effects can be quite variable depending on the resiliency of the economy and 
the pace of recovery. Fourth is the danger of manipulating indirect effects 
for political purposes (e.g., it is not unusual in the context of economic 
development for promoters to inflate multipliers).  However, none of these 
reasons undercut the importance of indirect effects, especially if one 
considers their likely size (see, e.g., Cochrane, 1997). 
In the Indirect Loss Module, the term  indirect effects  will be used to 
cover all flow losses beyond those associated with the direct curtailment of 
output as a result of hazard-induced property damage or loss of utility and 
infrastructure services in the producing facility itself. This term covers 
all of the higher-order Input-Output effects associated with quantity 
interdependence effects, as well as general equilibrium price interdependence 
effects. 
15.2.2 How Indirect Losses Occur 
As noted earlier, floods and other natural disasters may produce dislocations 
in economic sectors not sustaining direct damage.  All businesses are 
forward-linked (rely on regional customers to purchase their output) or 
backward-linked (rely on regional suppliers to provide their inputs) and are 
thus potentially vulnerable to interruptions in their operation.  Such 
interruptions are called indirect economic losses.  Note that these losses 
are not confined to immediate customers or suppliers of damaged enterprises.  
All of the successive rounds of customers of customers and suppliers of 
suppliers are impacted.  In this way, even limited physical damage causes a 
chain reaction, or ripple effect, that is transmitted throughout the regional 
economy. 
The extent of indirect losses depends upon such factors as the availability 
of alternative sources of supply and markets for products, the length of the 
production disturbance, and deferability of production. Figure 15.2 provides 
a highly-simplified depiction of how direct damages induce indirect losses. 
In this economy firm A ships its output to one of the factories that produce 
B, and that factory ships to C. Firm C supplies households with a final 
product (an example of a final demand, FD) and could also be a supplier of 
intermediate input demand to A and B.  There are two factories producing 
output B, one of which is destroyed in the flood.  The first round of 
indirect losses occurs because:  1) direct damage to production facilities 
and to inventories cause shortages of inputs for firms needing these supplies 
(forward-linked indirect loss); 2) damaged production facilities reduce their 
demand for inputs from other producers (backward-linked indirect loss); or 3) 
reduced availability of goods and services stunt household, government, 
investment, and export demands (all part of final demand). 
INVENTORIES 

INVENTORIES 
The supply shortages caused as a result of reduced availability of input B 
could cripple factory C, if C is unable to locate alternative sources. Three 
options are possible: 1) secure additional supplies from outside the region 
(imports); 2) obtain additional supplies from the undamaged factory (excess 
capacity); and 3) draw from B's unsold stock of output (inventories).  The 
net effect of diminished supplies are referred to as forward-linked losses, 
the term forward (often referred to as downstream) implying that the impact 
of direct damages is shifted to the next stage or stages of the production 
process. 
Disasters can also produce indirect losses if producer and consumer demands 
for goods and services are reduced. If, in the example provided in Figure 
15.2, firm B has a reduced demand for inputs from A, then A may be forced to 
scale back operations.  As in the case of forward-linked losses, the affected 
firms may be able to circumvent a weakened market, in this case by either 
finding alternative outlets such as exports or building up inventory. 
(Building up inventory is not a permanent solution, since eventually the 
inventories have to be sold.  Firms may be willing to do so on a temporary 
basis, hoping that market conditions will improve at a later date.) 
The higher rate of unemployment caused by direct damages and subsequent 
indirect factory slowdowns or closures would reduce personal income payments 
and could cause normal household demands to erode.  However, it is more 
likely that the receipt of disaster assistance, unemployment compensation, or 
borrowing, would buoy household spending throughout the reconstruction 
period. Evidence from recent events (Hurricanes Andrew and Hugo, the Loma 
Prieta Earthquake and the Northridge Earthquake) confirms that normal 
household demands are only slightly altered by disaster in the short-run.  As 
a result of this observation, the Indirect Loss Module discussed below 
delinks household incomes and demands.  
15.2.3 Regional vs. National Losses 
It has sometimes appeared that natural disasters tend to stimulate employment 
and revitalize a region. Clearly, the generous federal disaster relief 
policies in place after the 1964 Alaskan earthquake, the 1971 San Fernando 
earthquake, and Hurricane Agnes in 1972, served to buoy the affected 
economies, thereby preventing the measurement of significant indirect losses.  
From a regional accounting stance, it appeared that the net losses were 
inconsequential.  However, this viewpoint fails to take into account the cost 
of disasters on both household and federal budgets. 
Some, if not most, public and private post-disaster spending is unfunded; 
that is, it is not paid for out of current tax revenues and incomes.  In the 
case of households this amounts to additional indebtedness which shifts the 
burden or repayment to some future time period.  Federal expenditures are not 
budget neutral either. As in the case of households, governments cannot 
escape the financial implications of increased spending for disaster relief.  
Either lower priority programs must be cut, taxes raised, or the federal debt 
increased.  The first two options simply shift the reduction in demand and 
associated indirect damages to other regions.  Projects elsewhere may be 
canceled, services curtailed, and/or household spending diminished as 
aftertax incomes shrink.  The debt option provides no escape either, since 
it, too, places the burden on others, e.g., a future generation of taxpayers. 
From a national accounting stance, indirect losses can be measured by 
deriving regional indirect impacts, adjusted for the liability the Federal 
government incurs in providing disaster relief, and for offsetting increases 
in outputs elsewhere. The positive effects outside aid produces for the 
region are to some degree offset by negative effects produced by the three 
federal budget options. Since it is impossible to know a priori which option 
the federal government will utilize, it is safest to assume that the two 
effects cancel; i.e., that the positive outcomes from federal aid are offset 
by the negative national consequences caused by the budget shortfall.   
Since the primary user of the HAZUS.MH Loss Estimation Methodology is likely 
to be the local entity involved in disaster planning and response decisions, 
the Indirect Loss Module is designed accordingly.  That is, it adopts a local 
accounting stance.  One simplistic approach to obtaining a national measure 
of net loss would be to exercise the Loss Module excluding outside federal 
assistance. 
15.3 Background: How are Indirect Losses Modeled? 
The most widely used tool of regional economic impact analysis is known as 
Input-Output analysis (Miller and Blair, 1985; Rose and Miernyk, 1989).  
Input-output techniques are commonly utilized to assess the total (direct 
plus indirect) economic gains and losses caused by sudden changes in the 
demand for a region's products.  Higher demand for rebuilding and a lower 
demand for tourism, for example, lend themselves to traditional input-output 
I-O methods. This technique is relatively simple to apply and is already in 
widespread use in state and local agencies, though not necessarily those 
associated with emergency management.   
The HAZUS Indirect Loss Module is based on the Input-Output framework but 
incorporates important methodological innovations that adapt it to natural 
hazard analysis.  Section 15.3.1 
15-8 
therefore provides a brief introduction to the principles of Input-Output 
analysis, which is accompanied in Section 15.3.2 by a simple numerical 
example of how it can be applied to evaluate indirect losses.  Disaster 
losses are to some extent offset by the gains associated with reconstruction 
activities; modeling this stimulus in the Input-Output context is discussed 
in Section 15.3.3. Finally, Section 15.3.4 identifies some limitations of 
Input-Output analysis and notes alternative modeling methodologies that have 
been developed in the disaster context. These discussions provide background 
for the HAZUS Indirect Loss Module methodology. 
15.3.1 A Primer on Input-Output Loss Modeling Techniques 
Input-output analysis was first formulated by Nobel laureate Wassily Leontief 
and has gone through several decades of refinement by Leontief and many other 
economists.  At its core is a static, linear model of all purchases and sales 
between sectors of an economy, based on the technological relationships of 
production.  Input-output (I-O) modeling traces the flows of goods and 
services among industries and from industries to household, governments, 
investment, and exports. These trade flows indicate how much of each 
industry's output is comprised of its regional suppliers' products, as well 
as inputs of labor, capital, imported goods, and the services of government.  
The resultant matrix can be manipulated in several ways to reveal the 
economy's interconnectedness, not only in the obvious manner of direct 
transactions but also in terms of dependencies several steps removed (e.g., 
the construction of a bridge generates not only a direct demand for steel but 
also indirect demands via steel used in machines for its fabrication and in 
railroad cars for its transportation). 
Input-Output techniques have been applied to estimate natural hazard impacts 
since the seminal work of Cochrane (1974).  More recently, a number of 
researchers have refined and applied Input-Output methods in the disaster 
context, addressing such issues as flexible treatment of imports, business 
resiliency, transportation and utility lifeline impacts, and optimal recovery 
strategies. For reviews of this literature, see Jones and Chang (1995) and 
Rose (2002). 
A very brief technical review of the basic Input-Output methodology is 
provided here for those users who may be unfamiliar with interindustry 
modeling.1  The presentation is restricted to a simple three industry 
economy.  The shipments depicted as arrows in Figure 15.2 are represented as 
annual flows in Table 15.1. The X's represent the dollar value of the good or 
service shipped from the industry listed in the left-hand heading to the 
industry listed in the top heading.  The Y's are shipments to consumers 
(goods and services), businesses (investment in plant and equipment and 
retained inventories), government (goods, services and equipment), to other 
regions (exported goods and services). The V's are the values-added in each 
sector, representing 
1 
Input-output and  interindustry  are often used synonymously because of the 
emphasis in I-O on the sectoral unit of analysis, mainly comprised of 
producing industries.  Strictly speaking, however, interindustry refers to a 
broad set of modeling approaches that focus on industry interactions, 
including activity analysis, linear programming, social accounting matrices, 
and even computable general equilibrium models.  Most of these have an input-
output table at their core.  The reader interested in a more complete 
understanding of I-O analysis is referred to Rose and Miernyk (1989) for a 
brief survey; Miller and Blair (1985) for an extensive textbook treatment; 
and Boisvert (1992) for a discussion of its application to earthquake 
impacts.  For other types of interindustry models applied to natural disaster 
impact analysis, the reader is referred to the work of Rose and Benavides 
(1998) for a discussion of mathematical programming and to Brookshire and 
McKee (1992) for a discussion of computable general equilibrium analysis. 
payments to labor (wages and salaries), capital (dividends, rents, and 
interest), natural resources (royalties and farm rents), and government 
(indirect business taxes).  The M's represent imports to each producing 
sector from other regions.    
Table 15.1 Intersectoral Flows of a Hypothetical Regional Economy (dollars) 

To: From:  A  B  C  Final Demand  Gross Output  
A  XaA  XaB  XaC  Ya  Xa  
B  XBA  XBB  XBC  YB  XB  
C  XCA  XCB  XCC  YC  XC  
V  Va  VB  VC  
M  Ma  MB  MC  
Gross Outlay  Xa  XB  XC  Y  X  

A basic accounting balance holds:  total output of any good is equal to that 
sold as an intermediate input to all sectors and that sold as final goods and 
services: 
XA = XAA + XAB + XAC + YA (15-1) 
Rearranging terms, the amount of output available from any industry for final 
demand is simply the amount produced less the amount shipped to other 
industries. 
To transform the I-O accounts into an analytical model, it is then assumed 
that the purchases by each of the industries have some regularity and thus 
represent technological requirements. Technical coefficients that comprise 
the structural I-O matrix are derived by dividing each input value by its 
corresponding total output. That is: 
aAA = X AA ; aAB = X AB ; a AC = X AC ; (15-2)XAXB XC 
The a's are simply the ratios of inputs to outputs.  An aAB of 0.2 means that 
20 percent of industry B's total output is comprised of product A.  
Equation (15-1) can then be written as: 
XA =aAAX A +aABX B +aAC X C +YA (15-3) 
In matrix form Equation (15-3) is: 
X = AX + Y (15-4) 
To solve for the gross output of each sector, given a set of final demand 
requirements, we proceed through the following steps: 
15-10 
(I - A)X = Y (15-5) (I - A)-1Y = X (15-6) 
The term (I - A)-1 is known as the Leontief Inverse.  It indicates how much 
each sector s output must increase as a result of (direct and indirect) 
demands to deliver an additional unit of final goods and services of each 
type. It might seem that a $1 increase in the final demand for product A 
would result in the production of just an additional $1 worth of A.  However, 
this ignores the interdependent nature of the industries.  The production of 
A requires ingredients from a combination of industries, A, B, and/or C.  
Production of B, requires output from A, B, and/or C, and so on. Thus, the 
one dollar increase in demand for A will stimulate A's production to change 
by more than one dollar.  The result is a multiple of the original stimulus, 
hence, the term "multiplier effect  (a technical synonym for ripple effect).  
Given the assumed regularity in each industry's production requirements, the 
Leontief Inverse need only be computed once for any region (at a given point 
in time) and can then be used for various policy simulations reflected in 
changes in final demand (e.g., the impact of public sector investment) as 
follows: 
(I - A)-1.Y = .X (15-7) 
More simply, the column sums of the Leontief Inverse are sectoral 
multipliers, M, specifying the total gross output of the economy directly and 
indirectly stimulated by a one unit change in final demand for each sector.  
This allows for a simplification of Equation (15-7) for cases where only one 
sector is affected (or where one wishes to isolate the impacts due to changes 
in one sector) as follows:2 
MA.YA = .X (15-8) 
Under normal circumstances final demand changes will alter household incomes 
and subsequently consumer spending.  Thus, under some uses of input-output 
techniques, households 
2 
Note that the previous discussion pertains to demand-side (backward-linked) 
multipliers.  A different set of calculations is required to compute supply-
side (forward-linked) multipliers.  (Computationally, the structural 
coefficients of the supply-side model are computed by dividing each element 
in a given row by the row sum.)  Though mathematically symmetric, the two 
versions of the model are not held in equal regard.  There is near universal 
consensus that demand-side multipliers have merit because there is no 
question that material input requirements are needed directly and indirectly 
in the production. However, the supply-side multipliers have a different 
connotation that the availability of an input stimulates its very use.  To 
many, this implies the fallacy of  supply creates its own demand.   Thus, 
supply-side multipliers must be used with great caution, if at all, and are 
not explored at length here.  For further discussion of the conceptual and 
computational weaknesses of the supply-side model, see Oosterhaven (1988) and 
Rose and Allison (1988). 
Note also that the multipliers discussed thus far pertain to output 
relationships.  Multipliers can also be calculated for employment, income, 
and income distribution effects in analogous ways.  Also note that sectoral 
output multipliers usually have values of between 2.0 and 4.0 at the national 
level and are lower for regions, progressively shrinking as these entities 
become less self-sufficient and hence the endogenous cycle of spending is 
short-circuited by import leakages.  For example, sectoral output multipliers 
for Suffolk County, the core of the Boston Metropolitan Statistical Area, are 
for the most part in the range of 1.5 to 2.0. 
(broadly defined as the recipients of all income payments) are "endogenized" 
(included within the A matrix) by treating it as any other sector, i.e., a 
user (consumer) of outputs and as a supplier of services. An augmented 
Leontief inverse is computed and yields a set of coefficients, or 
multipliers, that capture both  indirect  (interindustry) and subsequent 
 induced  (household income) effects.  Multipliers are computed from a matrix 
with respect to households.  These are referred to as Type II multipliers in 
contrast to the Type I multipliers derived from the  open  I-O table, which 
excludes households. Of course, since they incorporate an additional set of 
spending linkages, Type II multipliers are larger than Type I, typically by 
around 25%. 
15.3.2 An Illustration of Input-Output Techniques 
Conventional input-output models provide a starting point for measuring 
indirect losses that are backward-linked, providing that the disaster does 
not significantly alter the region s input patterns and trade flows. (Section 
15.4 discusses how the HAZUS Indirect Loss Module modifies the methodology in 
cases where such changes are significant.) The calculation of indirect losses 
for a simple case is illustrated in the following example beginning with the 
input-output transactions matrix presented in Table 15.2. 
Table 15.2 Interindustry Transactions 

To: From:  A  B  Households  Other Final Demand  Gross Output  
A  20  45  30  5  100  
B  40  15  30  65  150  
Households  20  60  10  10  100  
Imports  20  30  30  0  80  
Gross Outlay  100  150  100  80  430  

This simplified transactions table is read as follows:  $20 of industry A s 
output is used by itself (e.g., a refinery uses fuel to transform crude oil 
into gasoline and heating oil).  $45 of output A is shipped to industry B. 
$30 is marketed to the household sector and $5 is sold to government, used in 
investment, or exported to another region.  $20 worth of household services 
is required to produce $100 of output A, and $60 is needed for $150 of B.  
According to the table, 30 percent of the consumer s gross outlay is 
allocated to the purchase of A, 30 percent to B, 10 percent to household 
services, and 30 percent to imports. 
Assume that the input-output table shown above represents a tourist-based 
economy. Industry A represents construction while B represents tourism.  What 
would happen to this economy if a flood destroyed half the region s hotels?  
Direct economic losses are comprised of manmade assets destroyed in the 
disaster plus the reductions in economic activity3 in the tourist sector. 
Assume that the damage to hotels influences some tourists to vacation 
elsewhere the year of the disaster, reducing the annual $95 million demand 
for hotel accommodations by $45 million. For the purposes of this 
illustration, household spending and demands are linked.  Therefore, a Type 
II multiplier would be utilized to assess the income and output changes 
anticipated.  The effect of declining tourism on the region s economy is 
easily derived from the initial change in demand and the Type II multipliers 
presented in Figure 15.3.  Each tourist dollar not spent results in a loss of 
$1.20 and $2.03 worth of production from A and B, respectively. 
The resultant total (direct plus indirect) decline in regional household 
income is $1.17 per tourist dollar lost (row 3 column 2 of the closed 
Leontief Inverse).  If nothing else changed (including no pick up in 
construction activity), the regional income lost for the year is $52.65 
million ($45 million times 1.17).  Of this total, $18 million (40 cents of 
lost income for each tourist dollar lost, or .4 times $45 million) is 
directly traceable to the disaster, while the other $34.65 million in 
regional income loss represents indirect income losses cause by reduced 
demands for intermediate goods and consumer items via backward interindustry 
linkages and normal household spending. 
Total Coefficients (Type II Multiplier)  
Construction  Tourism  Household   
2.12  1.2  1.11  
(I-A)1=  1.29  2.03  1.11  
1.04  1.17  1.85  
x $45 Million  
= $52.65 Million  

Direct Coefficients  
Construction  Tourism  Household  
0.2  0.3  0.3  
A =  0.4  0.1  0.3  
0.2  0.4  0.1  
x $45 Million  
= $18 Million  

Direct, Indirect, Induced Income Losses  
Secondary Income  = $52.65 Million  
Loss  = $34.65 Million  

Direct Income Losses  
Minus  $18 Million  


Figure 15.3 Illustrative Computation 
15.3.3 The Stimulative Impact of Reconstruction Aid 
The preceding example was an illustration of how Input-Output techniques can 
be used to simply estimate the negative impacts, or losses, caused by a 
disaster.  These negative effects would be countered by the stimulative 
impact of state and federal disaster aid and insurance settlements. Whether 
these positive forces completely offset the negatives produced by the 
reduction in tourist trade in the preceding example hinges on the magnitude 
of the direct effects and the 
Economic activity can be gauged by several indicators.  One is Gross Output 
(sales volume).  Another is Value-Added, or Gross National Product (GNP), 
which measures the contribution to the economy over and above the value of 
intermediate inputs already produced, thereby avoiding double-counting (note 
the  Gross  in GNP simply refers to the inclusion of depreciation and differs 
from double-counting meaning of the term in Gross Output.)  Specifically, 
Value-Added refers to returns to primary factors of production:  labor, 
capital, and natural resources.  The concept is identical to the oft used 
term National Income, which is numerically equal to GNP. 
associated multipliers for these two activities.  Assume, for example, that 
$50 million of outside reconstruction funds pour into the community in the 
first year.  The Type II income multiplier for the construction industry is 
1.04. The net regional income loss the year of the disaster is, therefore: 
($50 million x 1.04) - ($45 million x 1.17), or a net loss of $0.65 million. 
Indirect income changes in this case are very significant and can be computed 
as the difference of total income impacts and direct income impacts.  We know 
from the direct coefficients matrix that household income changes directly by 
20 and 40 cents, respectively, for each dollar change in construction and 
tourist expenditures. The net indirect regional impact from the reduction in 
tourism, and the aid program are therefore: ($50 x 1.04 - $50 x .2) - ($45 x 
1.17 - $45 x .4), or a net gain of $7.35 million. 
This is what the region loses or gains; however, national impacts are quite 
different.  The $50 million of federal assistance injected into the region 
must be paid for either by cutting federal programs elsewhere, raising taxes, 
or borrowing.  Each option impacts demand and outputs negatively. Although it 
is unlikely that they will precisely offset the gains the region enjoys, it 
is safe to assume that they will be similar in magnitude.  If so, indirect 
losses from a national perspective is the net regional loss with the positive 
effects from federal aid omitted. The national net income loss will then 
remain $52.65 million. 
The foregoing analysis was limited to the year of the disaster and 
presupposed that unemployed households did not dip into savings or receive 
outside assistance in the form of unemployment compensation, both of which 
are often the case.  In terms of the summation of impacts over an extended 
time horizon, results do not significantly change if alternative 
possibilities are introduced. For example, if households choose to borrow or 
utilize savings while unemployed or to self-finance rebuilding, future 
spending is sacrificed.  Therefore, even though an unemployed household may 
be able to continue to meet expenses throughout the reconstruction period, 
long-term levels of expenditure and hence product demand, must decline. 
In the preceding analysis, indirect losses were derived from demand changes 
only.  This approach lends itself to events in which supply disruptions are 
minimal, or where sufficient excess capacity exists. A different method is 
required when direct damage causes supply shortages, as is often the case in 
floods and other disasters.  The HAZUS Indirect Loss Module, Section 15.4 
below, modifies the basic I-O methodology to accommodate both supply and 
demand disruptions. 
15.3.4 Alternative Modeling Techniques 
This section briefly discusses the strengths and limitations of Input-Output 
methods and makes comparisons with two other major modeling approaches, 
computable general equilibrium (CGE) and econometric models.  For a more 
detailed discussion, including issues of validation and modeling uncertainty, 
see Rose (2002). 
Input-Output techniques provide a valuable guide for the estimation of 
indirect losses, but they are also subject to a number of limitations.  Among 
their advantages is the focus on production interdependencies, which makes 
the method especially well suited to examining how damage in some sectors can 
ripple through the economy.  Input-Output analysis also provides an excellent 
organizational framework for data collection and display, a transparent view 
of the structure of an economy, and a ready capacity to accommodate 
engineering data. It is well-suited to performing distributional analysis and 
providing insight into the inherent unevenness of direct and indirect impacts 
across industries and between industries, households, government, and other 
institutions. However, care must be exercised in applying these models to a 
given context, depending on resource availabilities, the timing of recovery, 
etc.    
Disadvantages of the basic Input-Output model include its linearity, lack of 
behavioral context, lack of interdependence between price and output, lack of 
explicit resource constraints, and lack of input and import substitution 
possibilities.  The linearity assumption permeates every facet of the 
workings of the model and implies no economies or diseconomies of scale and 
no input substitution.  The lack of input substitution will lead to upper-
bound results, e.g., a twenty percent decrease in electricity available in 
any sector would lead to a twenty percent reduction in that sector s output. 
The inflexibility of anything other than constant returns to scale, however, 
might lead to understating losses (as scale decreases due to capital stock 
damage or input curtailment, unit cost increases would not be reflected).  
Infinite supply elasticities overlook real world capacity limitations.  Thus, 
several refinements are needed to apply the basic Input-Output model to 
properly estimate higher-order losses from natural hazards.   
As an alternative, the use of computable general equilibrium (CGE) modeling 
for impact analysis in general is rapidly increasing, especially at the 
regional level (see Partridge and Rickman, 1998). CGE is a multi-market 
simulation model based on the simultaneous optimizing behavior of individual 
consumers and firms in response to price signals, subject to economic account 
balances and resource constraints (see, e.g., Shoven and Whalley, 1992).   
This approach is not so much a replacement for I-O as a more mature cousin or 
extension, and it retains many of the latter s advantages and overcomes most 
of its disadvantages (Rose, 1995). For example, CGE retains the multi-sector 
characteristics and emphasis on interdependence, but also incorporates 
input/import substitution, increasing or decreasing-returns-to-scale, 
behavioral content (in response to prices and changes in taste or 
preferences), workings of markets (both factor and product) and non-infinite 
supply elasticities (including explicit resource constraints). Moreover, the 
empirical core of most CGE models is an I-O table extended to include 
disaggregated institutional accounts, thus becoming a social accounting 
matrix (SAM).   
At the same time, CGE models do have shortcomings, the major ones being the 
assumption that all decision-makers optimize and that the economy is always 
in equilibrium.  The latter is not a problem when the period of analysis is 
more than one year and the external shock is small, but natural hazards have 
the opposite characteristics.  As with Input-Output techniques, CGE models 
are typically developed from non-survey or data-reduction techniques due to 
cost considerations, and their accuracy is difficult to assess. Moreover, 
while Input-Output models are overly rigid and exaggerate hazard impacts, the 
typical CGE model is overly flexible and understates them. 
Until recently, all applications of CGE models to natural hazards have been 
experiments with synthetic models (see Boisvert, 1992; Brookshire and McKee, 
1992).  More realistic applications have been undertaken by Rose and Guha 
(1999), and Rose and Liao (2002) to impacts of utility lifeline disruptions 
in the aftermath of earthquakes.  Potential application to other aspects of 
hazard loss estimation, however, is unlimited given the capabilities of this 
approach. Currently, several refinements are needed of the standard CGE model 
for estimating economic losses from natural hazards, most notably to 
incorporate elasticities with low numerical values, reflecting the short-run 
or very short-run nature of hazard recovery. 
Again care must be exercised in applying CGE models properly.  I-O models, 
with an adjustment for inventories, are probably better suited to recovery 
periods of less than one week, but CGE models are better suited to all other 
cases, except possibility where martial law is declared and resource 
reallocation is undertaken by centralized administration and not through 
market signals. CGE models can be adjusted for a greater range of resiliency 
options than I-O, though the adjustment process is more complex. 
A third alternative is econometric models.  These models have only rarely 
been used in indirect loss estimation because of their expense, lack of 
sectoral detail, and difficulty in distinguishing direct and indirect effects 
(the major exception being the work of Guimaraes et al., 1993; Ellson et al., 
1984). Second, econometric models have their own, well-established set of 
criteria for model validation.  Still, the potential application of these 
models to indirect loss estimation is great, since neither I-O nor CGE models 
have forecasting capabilities, which are especially useful in examining 
potential impacts of a future disaster or in distinguishing the actual 
activity of an economy from what it would have been like in the absence of 
the shock (i.e., establishing a baseline). Furthermore, for longer timeframes 
of impact (e.g., two or more years) where the timepath of the economy is 
important, econometric models may be superior to either Input-Output or CGE 
techniques. 
15.4 Methodology of the Indirect Loss Module 
This section discusses the functionality of the Indirect Loss Module. It 
begins with an overview of the purpose and structure of the module (Section 
15.4.1), including a summary of differences between the module in the HAZUS 
Flood and Earthquake methodologies.  This is followed by detailed discussion 
of how the underlying model works:  the data inputs (Sections 15.4.2~4) and 
the core model algorithms (15.4.5).  Special attention is paid to the 
treatment of changes over time, the effects of rebuilding and borrowing, 
estimating tax revenue impacts, and modeling distributional impacts.  Section 
15.4.6 focuses on two special sectors, which are treated uniquely in the 
analysis:  agriculture and tourism. Results are discussed in Section 15.4.7, 
and a cautionary note is provided in Section 15.4.8 regarding the appropriate 
application of the module in the context of small study regions. 
15.4.1 Overview 
This module estimates the indirect economic impacts that result from damage 
caused by flood disasters. In the first instance, physical damage disrupts 
economic activity and thus causes direct economic losses to various sectors 
in the regional economy.  These direct economic losses are estimated in the 
Direct Economic Loss Module (see earlier chapter).  Subsequently, because 
businesses are interdependent, the direct economic losses cause  upstream  
and  downstream  ripple effects to other businesses and business sectors 
(e.g., losses to customers or suppliers of flood-damaged businesses).  
Indirect economic losses are defined as this additional disruption to 
economic activity.  It is important to recognize, however, that damage-
induced losses are to some extent balanced by economic gains from repair and 
reconstruction activity.  The Indirect Loss Module first estimates total 
economic disruption, including the effects of both losses and gains. 
Disruptions to the economy can be measured in terms of impacts on regional 
income, employment, or production.  The module then subtracts the direct loss 
component to arrive at an estimate of indirect economic impacts.  
Currently, there exists no standard methodology for evaluating the economic 
disruption effects of flood disasters. The Indirect Loss Module of the HAZUS 
Flood methodology therefore builds on the approach developed in the HAZUS 
Earthquake methodology and makes several modifications to address differences 
between the two types of hazards.  In particular, it recognizes that the 
types of economies at risk from floods typically differ from those exposed to 
earthquake. The Flood Indirect Loss Module therefore includes new 
capabilities for handling agricultural losses and tourism impacts.  In 
addition, in comparison with the Earthquake Indirect Loss Module, new 
capabilities have been added to estimate impacts on local tax revenues and to 
look at how total impacts are distributed among social groups.  The latter 
provides a sense of who are the  winners  and  losers  from disasters.   
The structure of the Indirect Loss Module can be described as consisting of 
the following major elements, which are discussed more fully below: 
 	
Data describing the economy of the study region   This consists of two main 
types of information:  (1) data on regional economic size and structure; (2) 
data on factors influencing regional economic response to external shocks, 
such as the unemployment rate.   

 	
Inputs representing direct economic losses (damage-related)   This represents 
the linkage with flood damages and the Direct Economic Loss module. 

 	
Inputs representing direct economic gains (reconstruction-related)   This 
drives the indirect gains related to reconstruction stimulus effects. 

 	
Algorithms for estimating how the economy responds to these inputs   This is 
the core of the Indirect Loss Module. The algorithms handle both demand 
shocks (along the tradition of Input-Output methods) and supply shocks. 

 	
Results   The module generates a series of results that provide a multi-
faceted view of flood impacts on the regional economy.   


The Indirect Loss Module can be run at two levels of analysis.  The Default 
Data Analysis ( Level 1 ) utilizes primarily default data and requires 
minimal user input.  In User-Supplied Data Analysis ( Level 2 ), the user 
provides information specific to the economy of the study region and the 
disaster being modeled.  The model algorithms and types of required data are 
the same in each case; the two levels differ only in the degree to which they 
use region-specific data. 
15.4.2 Required Economic Data 
The Indirect Loss Module requires data on the size and structure of the 
regional economy.  Size is indicated by the current regional employment and 
income levels.  Note that employment refers to the number of persons who work 
within the study region, rather than the number of employed persons who 
reside there. Employment by place of work is appropriate in this type of 
analysis because the model will estimate job loss within the study region due 
to physical damage there from the disaster.   
Regional economic structure is represented by a regional Input-Output 
transactions table that shows inter-industry purchases for a base year.  A 
10-industry disaggregation scheme is used. The user may obtain Input-Output 
data for the region from IMPLAN, a standard source (this represents a Level 2 
analysis; see section 15.5.2 below).  If the user does not provide this data, 
the module will select and apply synthetic data for a default economy (i.e., 
a Level 1 analysis). Synthetic Input-Output data for 21 default economies are 
provided, defined by their economic type (e.g., primarily manufacturing), 
size (e.g., large), and, for agricultural economies, the region of the U.S. 
in which they are located. The Input-Output data for each synthetic economy 
was developed by averaging data for a series of actual county economies of 
that description.  For details on the synthetic data for agricultural 
economies, see section 15.4.6.1 below.  For details on other economic types, 
see Appendix 15A. 
Data are also required on how the regional economy may respond to external 
shocks.  The unemployment rate provides a general indicator of the available 
slack or unused capacity in the economy.  Estimates are also needed of the 
degree to which excess supply can be absorbed by new export markets or 
inventory accumulation and the degree to which excess demand can be satisfied 
by new imports or drawing from inventories.  Default values are provided. 
15.4.3 Damage-Related Inputs 
The Indirect Loss Module is linked to preceding modules through several 
channels that indicate either damage or rebuilding.  In terms of damage, 
first, flooded buildings lead to various degrees of loss of function in the 
10 industries or sectors of the economy, forcing them to cut output.  A 
vector of loss of function by industry in the first year of the disaster 
provides a set of constraints to the Indirect Loss module that is related to 
the general building stock damage levels.  Loss of function is based upon the 
time needed to clean up and repair a facility or to rent an alternative 
facility to resume business functions (see preceding chapter).  Loss of 
function is calculated for each occupancy class. Table 15.3 links the 
occupancy classes in the Direct Loss Module to the economic sectors in the 
Indirect Loss Module.  Loss of function associated with lifeline disruption 
is not evaluated. 
Table 15.3 Correspondence between Building Occupancy Classes and Economic 
Sectors 

Building Occupancy Class (Direct Loss Module)  Economic Sector  (Indirect 
Loss Module)  
IND3  Agriculture (Ag)  
NONE  Mining (Mine)  
IND6  Construction (Cnst)  
IND 1,2,3,4,5 (AVG.)  Manufacturing (Mfg)  
COM3  Transportation (Trans)  
COM 1,2 (AVG.)  Trade (Trde)  
COM 5,4 (AVG.)  Finance, Insurance and Real Estate (FIRE)  
(COM 2,4,6,7,8,9; RES 4,6; REL; ED 1,2) (AVG.)  Service (Serv)  
GOV1  Government (Govt)  
NONE  Miscellaneous (Misc)  

In addition to buildings, the Indirect Loss module also requires input on 
flood damage to agricultural production. As described in an earlier chapter, 
the HAZUS methodology for estimating direct flood damage to agriculture 
considers only crop losses; livestock losses are not included in this 
analysis. It does, however, consider the time-of-year that flooding takes 
place. The user will be required to provide a date of flooding, and the model 
will estimate the losses based on 3-day, 7-day, and 14-day flood durations. 
If flooding occurs before planting, no losses are assumed.  Outputs of the 
direct loss module for agriculture will include the dollar value of lost 
crops for the inundated area, by crop type. The Indirect Loss Module uses the 
direct loss estimates for 7-day flooding, but the user may override this 
default and select one of the other durations. The module estimates regional 
economic impacts due to agricultural damage in a similar manner to how 
economic impacts due to building-related damage are evaluated. Specifically, 
the link between agricultural damage and the Indirect Loss Module consists of 
a direct, percent reduction in production in the agriculture sector.  This 
percent reduction accounts for the relative shares of crop versus livestock 
production in the study area. 
15.4.4 Reconstruction-Related Inputs 
The stimulus effect of reconstruction activities will partially offset the 
loss effect deriving from physical damage.  The module requires two main 
types of reconstruction-related inputs:  post-disaster spending on 
reconstruction, repair and replacement of damaged buildings and their 
contents, and of transportation and utility lifelines.  This spending 
stimulus is based on the total dollar damage caused by the flood in these 
categories and modified by the percent of damage that is replaced or rebuilt. 
Several modifications to the dollar damage estimates are needed before they 
can be used in the analysis of reconstruction stimulus.  The first adjustment 
accounts for the fact that not all damage may be repaired or replaced.  A 
percentage rebuilding factor is applied to the dollar damage estimates.  The 
second modification is the timing of the reconstruction in terms of weeks, 
months, or years after the disaster.  The distribution of reconstruction 
expenditures over time is discussed further below. 
The third modification is the itemization of expenditures by type (plant, 
equipment, etc.) so that this spending injection is compatible with the 
economic model used to determine indirect effects. The input-output (I-O) 
model at the core of the module disaggregates the economy into 10 sectors 
according to one-digit Standard Industrial Classification (SIC) codes.  The 
brunt of the reconstruction expenditures will be assigned to Manufacturing 
and Construction sectors.   
One idiosyncrasy of the I-O model is the role of Wholesale and Retail Trade 
and of Transportation. These sectors are based on the concept of a "margin," 
i.e., the cost of doing business (labor, insurance, electricity, gasoline, 
office supplies) plus profits, but does not include the items sold or shipped 
(which are merely a pass-through in any case).4  Those expenditures assigned 
to Construction require no adjustment, but when spending on manufactured 
goods is inserted into the model, portions of the total should be assigned to 
the Wholesale/Retail Trade sector and to the Transportation sector.  For very 
large items bought directly from the factory, there is no Trade sector 
activity, but for smaller items (e.g., office equipment, trucks), the 
adjustment is necessary.  Generally, the Wholesale margin is 80%.  Whether 
purchased from the factory or from the Trade sector, the Transportation 
margin is always applicable and is typically equal to 20%. 
A similar adjustment is necessary in nearly all cases for consumer spending 
for replacement of contents. In this case, it is more appropriate to use the 
Retail Trade margin of 80%.  Again, the Transportation margin of 20% would be 
applicable to purchases of larger items. 
In cases where the margin adjustment is required, the module simply applies 
the following formulas: 
.L 
=.YM	(15-9)
1+ tm 
.L -.YM	=.T (15-10) 
where: 
.L = 	Portion of loss estimate (reconstruction/replacement) to which margin 
adjustment applies. 

.YM = 	Manufacturing expenditures after margin adjustment. 
.T = 	Retail/wholesale, trade or transportation expenditures. 
tm = 	Retail/wholesale, trade or transportation margin. 
4 
The reason for this device is that many items are sold through wholesale and 
retail outlets and transported commercially, and, if included as "inputs" to 
these sectors, the linkage between buyers and sellers would be lost, i.e., it 
would appear that most purchases were from Wholesale/Retail Trade or 
Transportation, as if these sectors produced most items in the economy. 
15-20 
15.4.5 Model Algorithms for Rebalancing the Economy 
15.4.5.1 Core Algorithms 
Traditional Input-Output analysis, as illustrated above, models how demand 
shocks filter through the economy to produce indirect losses. In contrast, 
the supply shocks that would be caused by a natural disaster require a 
different treatment.  The Indirect Loss Module begins with the same inter-
industry trading patterns that are represented by the A matrix in Input-
Output analysis. However, once damage to buildings and lifelines constrain 
the capacity of each economic sector to ship its output to other sectors, or 
receive shipments, the trading patterns have to be readjusted.   
To do this, the Indirect Loss Module estimates how much each sector's output 
will decline as a result of direct damage and then addresses how the 
resultant excess demands and/or supplies will be filled and/or disposed of. 
In the event that the sum of all interindustry demands and final demands 
exceed the post-disaster constraint on production, then available imports and 
inventory changes could temporarily help to rebalance the economy.  In some 
sectors excess supplies might exist.  If so, inventories may be allowed to 
accumulate or new markets might be found outside the affected region.  
Surviving production is reallocated according to the interindustry direct 
coefficients matrix until all sector excess supplies and demands are 
eliminated.  At this point, a new level of regional output, value added and 
employment is computed and contrasted with the levels observed prior to the 
disaster. The difference between these levels approximates indirect loss.5 
Thus, the Indirect Loss Module is a computational algorithm that utilizes 
input-output coefficients to reallocate surviving production.  The algorithm 
computes post-event excess demands and supplies.  It rebalances the economy 
by drawing from imports, inventories, and idle capacity when supplies are 
constrained.  It allows for inventory accumulation, production for export (to 
other regions) and sales to meet reconstruction needs in the event that 
normal demands are insufficient to absorb excess supplies The process of 
reallocation is governed by the amount of imbalance detected in each of the 
economy's sectors.  Rebalancing is accomplished iteratively by adjusting 
production proportionately until the discrepancy between supplies and demands 
is within a tolerable limit.6  A simple schematic of the process is provided 
in Figure 15.4. 
5 
This approach relies on both the existence of regional input-output tables 
and several assumptions regarding: inventory management, importability of 
shortages, exportability of surpluses and the amount of excess capacity 
existing in each sector. It does not accommodate the effects of relative 
price changes on final demands, nor does it entertain the degree to which 
labor and capital are substitutable in the underlying production functions.  
Treatment of these issues require a more sophisticated approach, one which is 
discussed in the literature under the topic heading Computable General 
Equilibrium (CGE) Systems.  
6 
The tolerable limit is the degree to which the solution values vary from one 
iteration to the next. 

As an illustration of the core algorithms, consider a simple economy with 
three industries: construction, manufacturing, and trade.  Table 15.4 shows 
the inter-industry transactions matrix for this economy before the disaster.  
There are also two rows for payments to households (HH) from those industries 
and imports which those industries require, plus two columns that represent 
household demands and exports.  Households make no purchases from other 
households.  All amounts in the table are in dollars.  In the economy s 
initial state, the row and column sums are equal. 
Table 15.4 Initial Transactions 

From/To  Constr  Mfg  Trade  HH  Export  Sum  
Constr  10  30  20  20  35  115  
Mfg  20  20  10  30  80  160  
Trade  15  20  5  40  5  85  
HH  30  40  20  90  
Import  40  50  30  120  
Sum  115  160  85  90  120  

Table 15.5 shows how the economy changes due to the direct impact from a 
disaster.  In this case, there is a 10% loss of manufacturing output as the 
result of damage to manufacturing facilities. Corresponding to this loss, 
both the purchases and sales of the manufacturing sector fall by 10%, as 
reflected in the row and column sums.  The transactions directly affected are 
highlighted in bold type in the table.  A new column, named  Lost HH,  has 
been added to this table to reflect manufacturing output that is unavailable 
to households because of the disaster. 
Table 15.5 10% Direct Loss in Manufacturing 

From/To  Constr  Mfg  Trade  HH  Export  Sum  Lost HH  
Constr  10  27  20  20  35  112  
Mfg  18  18  9  27  72  144  3  
Trade  15  18  5  40  5  83  
HH  30  36  20  86  
Import  40  45  30  115  
Sum  113  144  84  87  112  

Table 15.6 illustrates the first example of the indirect response to this 
situation.  This is a  fullyconstrained  economy, characterized by no more 
than 2% unemployment, 0% import replacement, 0% inventory availability or 
replacement, and 0% additional exports.  This means that there are no ways 
for manufacturers to replace inputs that were disrupted by the disaster. 
Under these circumstances, construction and trade firms must cut their 
previous manufacturing by 10%. There is full employment in the local economy, 
meaning that other firms in manufacturing cannot increase output to meet the 
desired purchases by construction and trade. Further imports are not allowed, 
and there are no inventories of manufacturing output to use. Construction and 
trade firms, faced with an irreplaceable 10% loss in manufactured goods have 
no choice but to reduce their production by 10%. The net result is that the 
10% direct loss in manufacturing translates into a 10% loss throughout the 
entire economy.  Portions of the table affected by indirect loss are 
highlighted in italics.  The row and column sums are once again in balance. 
Household consumption is decreased for all three sectors, and there is no way 
to make up for it. 
Table 15.6 Response to Loss with Fully Constrained Economy 

From/To  Constr  Mfg  Trade  HH  Export  Sum  Lost HH  
Constr  9  27  18  18  31.5  103.5  2  
Mfg  18  18  9  27  72  144  3  
Trade  13.5  18  4.5  36  4.5  76.5  4  
HH  27  36  18  81  
Import  36  45  27  108  
Sum  103.5  144  76.5  81  108  

The fully constrained economy is an extreme case, and most economies are 
characterized by some flexibility, or slack, so that inputs can be replaced 
and outputs can be sold.  We illustrate this by raising the potential level 
of additional imports by 10%, and the potential level of additional exports 
by 40%. This is insufficient to ensure that construction and trade can 
acquire the supplies they need to meet local demands and sell products that 
are no longer being bought by manufacturing.7  Sectors not suffering direct 
losses return to their pre-event levels of production.8  Manufacturing might 
import additional manufactured inputs where needed to replace its own direct 
losses, but labor is not available due to the low unemployment rate and the 
assumption that the temporarily unemployed labor in manufacturing will not be 
available to other firms in the sector.  Manufacturing losses will only be 
replaced as damaged manufacturing facilities return to production. 
In Table 15.7, the underlined values show where the important changes have 
occurred.  Both construction and trade were allowed to import the 
manufactured inputs they lost as a result of the disaster. Also, construction 
and trade exported that portion of their output that manufacturing no longer 
purchased.  Because of these two factors, there is no indirect loss in the 
case illustrated in Table 15.7. 
The same results may be obtained in other ways.  Instead of increasing 
imports, there might be some unemployment in the local economy.  In this 
case, other firms in the manufacturing sector could hire some of the 
unemployed resources to make up the shortfall.  Alternatively, there might be 
inventories of manufactured goods, either at the manufacturers or in storage 
at the construction and trade firms that require those goods.  On the output 
side, firms faced with a reduction in purchases from the manufacturing sector 
may decide to continue production and store the resulting product in 
inventory until the disrupted facilities are back in production or until they 
can find new export markets. 
Table 15.7 Response to Loss with Relaxed Import and Export Constraints 

From/To  Constr  Mfg  Trade  HH  Export  Sum  Lost HH  
Constr  10  27  20  20  38  115  
Mfg  18  18  9  27  72  144  3  
Trade  15  18  5  40  7  85  
HH  30  36  20  86  
Import  42  45  31  118  
Sum  115  144  85  87  117  

In Table 15.7, manufacturing remains at its immediate post-disaster level 
because the situation being illustrated is immediately after the event, 
before reconstruction can take place.  If the slack in the system came from 
unemployment instead of imports, the results would be different.  That 
portion of the manufacturing sector undamaged by the disaster could hire 
additional resources and make up the direct losses.  Overall production would 
regain its pre-disaster levels. Therefore, unlike the example illustrated 
which shows no net indirect change, there would be a 
7 
Construction only needs to increase its level of imports by 2, 5% of its 
initial imports of 40, and trade only requires an increase in imports of 1, 
or 3.3% of 30.  Construction requires additional exports of 3, or 8.6% of 
original exports.  The limiting sector is trade, required to find export 
markets for 2 units, 40% of the 5 units it originally exported. 
8 
Even if the slack assumptions are set higher, the algorithm limits sectoral 
production to be no higher than prior to the earthquake (unless there is a 
positive counter-stimulus from, say, reconstruction activity). 
net indirect increase in sales that would be equal to the direct loss, making 
for a net economic change of zero. 
Tables 15.6 and 15.7 show an important way in which this algorithm departs 
from traditional Input-Output (I-O) analysis.  The technical coefficients for 
both tables are different from those of the original economy.  This is 
because imports and exports have been allowed to replace lost supplies and 
sales in the system.  The usual technical coefficients in an I-O table assume 
that the relationships between imports and intermediate inputs are fixed, as 
well as assuming that the relationships between exports and intermediate 
outputs are fixed.  Though these assumptions are convenient for the purposes 
of I-O analysis, they are a departure from reality in general, and especially 
so in emergency situations.  Also note, from Table 15.7, that the household 
and import/export sectors are no longer balanced in terms of row and column 
sums.  This is due to the short-run nature of the problems being solved in 
the model. In the longer run, households must repay their borrowing, and 
exports must rise to repay the short-run imports, unless government disaster 
aid or some other form of external financing is used to pay for the short-run 
consumption and imports. 
Tables 15.6 and 15.7 illustrate the two extremes that the model can reflect 
in responding to pure supply-side disruptions. In its fully functional 
implementation, the model adjusts simultaneously for multiple shocks of 
varying amplitude in any number of sectors, while also accounting for demand-
side (final demand) increases that typically accompany disasters. 
15.4.5.2 The Time Dimension 
The model is evaluated at various levels of temporal resolution for the 15-
year period following the disaster. For the first 2 months after the 
disaster, weekly time intervals are used.  Between 2 months and 24 months, 
the economy is evaluated on a monthly basis.  From 2 years to 15 years, the 
economy is evaluated annually.  It is made dynamic by considering how 
industry loss of function is restored and reconstruction expenditures are 
made over the time windows.  Thus while the inputs to the Indirect Economic 
Loss module differ with each time interval, the rebalancing algorithm for the 
economy and adjustment factors (e.g., availability of supplemental imports to 
make up for lost production) do not change.  The time patterns of functional 
restoration and reconstruction are user inputs and are discussed in Section 
15.5. 
15.4.5.3 The Effect of Borrowing for Rebuilding 
Borrowing impacts the model in that future demands are reduced in proportion 
to the temporal payments for rebuilding.  In the case of the Northridge 
earthquake, for example, this amounted to less than 50 percent.  Federal 
assistance and insurance settlements provided the bulk of the financial 
resources for reconstruction. The importance of refinancing lies in longer-
term effects of repayment.  If the affected region receives no assistance, 
then the stimulative effects of rebuilding are only temporary.  The region 
will eventually have to repay loans and future spending will suffer. This is 
accounted for in the model as follows.   
1.	
It is assumed that all loans mature 15 years from the time of the disaster. 
Therefore, the first year's loans are for 15 years. The second year's loans 
are for 14 years, and so on.  

2.	
Tax implications are ignored.  Interest is not tax deductible.  

3.	
Borrowing costs are assumed to be 6 percent.  This is a real interest rate 
(inflation free).  The discount rate is assumed to be 3 percent. It too is 
inflation free. 


The loan payments are computed as follows (Table 15.8). 
Table 15.8 Annual Borrowing Costs 

Year  1  2 through 15  
Annual Payment  r r loan( ( )( )) 11 115 1 -+ . .. . .-+ .  r r loan Payt t 
t( ( )( )) 1 1 16 1 1-+ . .. . .. +-++ -  
Explanation  loan 1 times the annual payment factor (r is real interest)  
payment from t-1 plus loan t times the annual payment factor  

Future demands are reduced by the annual payments times the percentage 
households spend on each sector s output. For example, if households are 
paying back $50 million in year 1 then spending from all categories declines 
as shown in the following table.  The second column in Table 15.9 is the pre-
disaster spending pattern.  For example, 0.2 percent of household income was 
spent on agricultural products; 24.6 percent was spent on services.  This 
percentage times the $50 million loan repayment cost yields the reduction in 
household spending by sector in year 1. 
Table 15.9 The Effect of Loan Repayment on Household Demands 

Sector  Household Spending (% spent on each sector)  Reduced Demand in $ 
millions (% times loan payment)  
Ag  0.2%  0.08  
Mine  0.0%  0  
Cnst  11.2%  5.59  
Mfg  7.5%  3.75  
Trns  6.2%  3.08  
Trde  21.6%  10.82  
FIRE  23.2%  11.59  
Serv  24.6%  12.3  
Govt  5.3%  2.63  
Misc  0.3%  0.15  

Exercising the module sequentially using average values over the 
reconstruction period derives time dependent indirect losses. 
15.4.5.4 Tax Revenue Impacts 
In addition to estimates of losses in terms of regional output, income, and 
employment, the Indirect Loss Module evaluates the potential impacts of the 
disaster on local government tax revenues. The application and relative 
importance of different types of taxes varies across levels of government. 
For example, sales and income taxes are the major revenue sources at the 
state level but are rarely imposed by either cities or counties.  On the 
other hand, property taxes are rarely imposed at the state level and are the 
major source of funding for local government entities, including towns, 
cities, and some special purpose districts such as schools.   
Taxes also differ according to whether they are imposed periodically or on an 
on-going basis, a feature that makes a significant difference for the timing 
of hazard impacts.  For example, property taxes are typically collected 
annually, and revenues will not be affected by destruction of assets until 
the next tax period.  Additionally, those property owners who suffer sizeable 
damage may be granted a waiver for all or part of their property tax for a 
temporary period.   
The Indirect Loss Module estimates tax revenue losses only for local 
government.  Note that most floods affect only sub-state areas, and state-
level tax impacts are not directly applicable. However, state-level tax 
collections (and federal collections as well mainly in the form of personal 
and corporation income taxes) are returned to local jurisdictions or spent by 
higher-government entities in or elsewhere on behalf of local areas.  Of 
course, expenditures hardly ever match revenues because of national level 
purchases (e.g., aircraft carriers) or because of redistribution objectives, 
but calculation of tax revenue reductions to all levels of government in the 
aftermath of disaster is justified.  However, these calculations should be 
done separately for each level. Note that state level tax impacts (e.g., 
corporate and personal income) are not likely to have much affect on spending 
in the region impacted by flood. The region is likely to be a small portion 
of the state and considerations of need for recovery and reconstruction are 
likely to stave off any reduction in government spending.  Thus, there is 
little need to calculate lost state personal or corporate income tax payment 
decreases except for the largest events. 
To evaluate local tax impacts, the module makes use of the Indirect Business 
Tax (IBT) feature of IMPLAN Input-Output data. The IBT component is composed 
of sales taxes, property taxes, licenses, and fees (although not 
disaggregated into these components).  These components accrue variously to 
local, state, and federal governments.  The tax impact methodology depends 
upon whether the analysis is using default, synthetic Input-Output data 
(Level 1 analysis) or the user has provided region-specific Input-Output data 
(Level 2 analysis) (see section 15.4.2 Required Economic Data, above).   
If the analysis is using default data, the module applies default IBT 
coefficients.  To derive the default IBT coefficients, IMPLAN data were used 
to compute ratios of indirect business tax to sector output for one percent 
of the nation s counties and states.  The resultant averages and associated 
standard deviations are provided in Table 15.10.  It appears that, except for 
mining, the averages vary within a narrow range.  Furthermore, taxes 
collected in the aggregated sectors of transportation/utilities (433), trade 
(447), and finance, insurance and real estate (456) make up the predominance 
of the IBT revenue.  These findings suggest that IBT will change in 
proportion to output loss. The average IBT ratios shown in Table 15.10 are 
applied to the estimates of total output loss, or the difference between pre- 
and post-flood sectoral outputs, caused by the disaster.  The resulting 
estimate is reported as a percent reduction in IBT tax collection. The 
percent reduction in IBT tax is reported, rather than the dollar amount, 
since this is an approximation.  The user could then apply the percent 
reduction to an accurate measure of IBT for their particular county. This 
would give them the reduction in IBT tax revenue.   
Table 15.10 Ratio of IBT to Sector Output for 1 Percent of the  
Nation s Counties and States 

Sector  Average  Standard deviation  
Agriculture  1.38%  0.88%  
Mining  11.63%  8.87%  
Construction  0.43%  0.16%  
Manufacturing  1.11%  0.55%  
TCPU  4.34%  1.04%  
Trade  14.11%  2.61%  
FIRE  12.91%  2.06%  
Services  1.43%  0.83%  
Government  0.00%  0.00%  
Other  0.00%  0.00%  

If the user has provided IMPLAN Input-Output data for the study region (Level 
2), a more refined treatment of tax revenue loss is possible, emphasizing 
impacts on local government. Here, IBT coefficients are extracted from the 
IMPLAN dataset.  The user supplies information on the local property tax rate 
and local sales tax rate, if any (i.e., excluding any state sales tax), and 
the categories of sales that are taxed.  Since few jurisdictions impose local 
income tax, this category is not evaluated. Loss of property tax, a major 
source of local government revenue, is calculated from HAZUS direct damage 
estimates to buildings.  The value of structural damage, excluding tax-exempt 
categories of buildings, is multiplied by the local property tax rate.  As 
for local sales tax, this loss is evaluated by first identifying the sub-
sectors whose sales are taxable (e.g., hotels) and their shares of the major 
sectors (e.g., services).  The model s results on sectoral loss of output are 
then multiplied by taxable sub-sector shares to derive an implicit output 
loss to these sub-sectors.  This is in turn multiplied by the tax rate to 
derive the loss of local sales tax revenue. To avoid double-counting of 
various IBTs, sales tax and property tax revenue decreases are subtracted 
from the estimated IBT total revenue decrease.  This will yield an IBT 
residual loss category comprised of licenses, fees, severance taxes, etc., 
that can be considered separately or added back to sales and property tax 
revenue losses to obtain an appropriate total. 
The loss of local property and sales tax revenues, in addition to simply 
being reported, will have an impact in the model on local government sector 
spending.  Government consumption in each year will be reduced by the amount 
of tax revenue loss in the previous year. 
15-28 
15.4.5.5 Distributional Impacts 
Income distribution impacts of natural hazards are especially important for 
both normative and positive reasons.  Several analysts have found that the 
poor are especially vulnerable to hazards in particular, and that hazards 
exacerbate inequalities in income distribution in general.  Thus, the 
evaluation of such impacts is important from the perspective of equity, 
including new approaches to the topic, such as  Environmental Justice. 9 
Distributional impacts are also important from the vantage point of 
predicting and improving public policy through increased public 
participation.  Impact analyses often focus on how the community as a whole 
is affected. However, this imparts an inordinate amount of altruism on the 
part of the citizenry, since individuals primarily want to know how they 
themselves will be affected.  Only when citizens are well-informed will 
public participation be worthwhile. Thus, income distribution impacts provide 
stakeholders with useful information that can help promote the best interests 
of society as a whole.  
Data on the size distribution of personal income are tedious to compile at 
the small area level. Moreover, some types of data compatible with an input-
output model are non-existent at any level. This pertains primarily to 
capital-related income payments such as dividends, interest, rents, and 
royalties. The reason is that most individuals receive these payments from 
more than one sector and often from sources outside the given region.  Thus, 
care must be taken to link sectors and individuals (or households) and to 
adjust for  transboundary flows  of income, pertaining to leakages of 
capital-related income and the wages and salaries of commuting workers. 
Tracing sectoral specific linkages is important because hazards have 
differential effects across the economy.  Adjusting for transboundary flows 
is desirable to avoid inflating the impact, e.g., dividends not distinguished 
by geographic origin will mistakenly show up as a reduction in the impacted 
area, despite the fact that a majority of income flows to areas that are 
unscathed by the hazard.10 
The type of income distribution data best suited to the Input-Output 
framework of the IELM of HAZUS is a multi-sector income distribution matrix 
(see Rose et al., 1988; Rose and Beaumont, 1988). This matrix contains the 
income payments from each economic sector to each separate income bracket.  
Dividing each of the income payment flows for a given sector by the gross 
output or total direct income of that sector yields a coefficient, or 
structural, matrix of income distribution that can be adapted to economies of 
various scales or sectoral mixes.  To calculate the distribution of impacts 
across income brackets, the module multiplies the structural matrix of income 
distribution by the estimated changes in sectoral gross output due to the 
disaster.   
9 
The focus here is on income, but the unevenness of hazard impacts also 
relates to socio-economic groups such as minorities and the elderly. 
10 Dividends represent business profits paid out to share-holders (in 
contrast to retained earnings).  Of course, non-residents of 
the region impacted by the hazard are affected because ownership is spread 
throughout the U.S., thus broadening the 
geographic coverage of impacts.  Ideally, these impacts on other regions 
would be calculated separately, but this is probably 
impractical given cost and data limitations. 
At present, the most up-to-date multi-sector income distribution matrix (IDM) 
for the U.S. is benchmarked to 1987 and is contained in Li et al. (1999).  A 
matrix for 1992 has been developed by the U.S Department of Agriculture 
Economic Research Service and is available for public use. These two matrices 
are deemed adequate for a Level 1 analysis.11 Although not region specific 
per se, a good portion of the difference in such matrices between regions is 
captured by the differences in the relevant prominence of economic sectors, a 
feature that is picked up by the various synthetic economies at a Level 1.   
A more accurate approach is adopted in a Level 2 analysis where the user has 
supplied IMPLAN Input-Output data for the region. From the IMPLAN database, 
the Indirect Loss Module accesses data on sectoral factor payments (i.e., 
payments by economic sectors to labor and other factors of production) and on 
income distribution to households.   
The income distribution features of IMPLAN are contained in the Social 
Accounting Matrix (SAM) component, which is now well integrated into that 
system (this was not the case a few years ago).12  This disaggregation is 
both on the income and consumption sides13 for nine household income brackets 
(the upper bracket is $70,000 and above). The soundness of data for the 
income distribution component are solid for wages and salaries (usually about 
80% of the total), but somewhat weaker for capital-type income (usually about 
10-15% of the total).  For the latter, IMPLAN typically uses national 
averages for each region, which aren t even sectorally specific, though the 
SAM implicitly makes a reasonable net transboundary flow adjustment.14 
To calculate distribution impacts across income groups, the module first 
computes sectoral output changes due to the disaster. It next computes 
sectoral factor15-output ratios (total direct income payments per dollar of 
gross output) based on the IMPLAN data.  The sectoral output changes are 
multiplied by the corresponding factor-output ratios to derive a vector of 
sectoral total income payment changes.  This vector of income changes is then 
multiplied by the IMPLAN SAM data on income distribution across the 9 income 
brackets. 
11 Both of these matrices can be updated to the current year by a methodology 
developed by Hanson and Rose (1997) and adapted by Oladosu (2000). 
12 Many of the income component definitions and impact analysis procedures in 
the current version of IMPLAN are consistent with the elaborated discussion 
in Rose et al. (1988). 
13 Income groups differ significantly in terms of the mix of goods and 
services they purchase, which has a significant effect not 
only on sectoral impacts, but also overall impacts.  Differentials in savings 
rates also greatly affect the latter, and are important 
in assessing the ability to withstand a disaster and the pattern of 
replenishing the savings in the years following it. The 
disaggregation of consumption by income bracket, however, is still too 
complicated for even a Level 2 analysis at this point.  
14 Ideally, the adjustment would be at the gross level, counting both all 
inflows and all outflows. Inflows would not meet the 
endogeneity requirement to be included in a Type II multiplier.  The gross 
flow and adjustment implicitly assumes that any 
region that has a positive net outflow of dividends does not have any inflow 
of dividends. This  no cross-payments  
assumption is analogous to be  no cross-hauling  assumption for goods and 
services, and with the same implications  
overstating impacts (see Rose and Stevens, 1991).  Again data and cost 
limitations preclude any major adjustments.  
15 Factor payments refers to the fact that the entries are:  employee 
compensation  (wages and salaries) and  proprietary and other property 
income  (capital-related income). 
15.4.6 Special Sectors 
The modeling of impacts in two sectors, agriculture and tourism, requires 
separate discussion. Losses in these sectors can be particularly significant 
in flood disasters, and the modeling of the loss mechanisms also differs from 
that of the general algorithm described above.  Indirect loss modeling in the 
HAZUS Earthquake methodology did not specifically consider these two sectors. 
15.4.6.1 Agriculture 
As indicated above, the treatment of agricultural losses in the Indirect Loss 
Module differs somewhat from that of other sectors.  The direct loss input 
includes not only building-related loss but also damage to agricultural 
production.  In addition, for default data analyses (Level 1), HAZUS provides 
synthetic Input-Output tables for agriculture-based economies.  Both of these 
elements of the methodology are discussed in this section. 
Estimates of direct agricultural damage include crop loss; however, HAZUS 
does not evaluate livestock loss, which is assumed to be zero.  To account 
for this, the total dollar value of crop loss for the inundated area is 
divided by the dollar value of expected crop production for the study region 
to yield a percentage crop loss figure.  This figure is then adjusted for the 
ratio of crop versus livestock production to derive a percent output loss 
estimate for the agriculture sector. This then serves as one of the inputs 
driving the core rebalancing algorithm of the Indirect Loss Module. 
If the user has supplied IMPLAN Input-Output data for the region (Level 2 
analysis), the ratio of crop versus livestock production is obtained from the 
actual data on the composition of the agriculture sector in the study region.  
Direct loss estimates for various crop types (aggregated to match the IMPLAN 
sub-sectoring scheme for agriculture) are divided by their respective 
expected production levels for the study region to yield crop-specific loss 
percentages.  These estimates are then aggregated to develop an overall 
agriculture sector loss percentage by weighting them with their actual shares 
of regional agricultural production.   
If a default data analysis is being run (Level 1 analysis), the user will 
have selected the synthetic economic type that best represents the study 
area.  Default data have been developed for the crop share of total 
agriculture based on the USDA-NASS, 1997 Census of Agriculture.  Table 15.11 
shows census data on livestock sales as a percent of total agricultural sales 
by state. The default crop share of total agricultural production is the 
difference between total and livestock sales for the applicable state. 
Table 15.11 Livestock Sales as a Percent of Total Agricultural Sales, by 
State 

State  Sheep, Lambs, & Wool  Cattle & Calves  Dairy Products  Hogs & Pigs  
Other Livestock & Livestock Products  Poultry & Poultry Products  Total  
Alabama  (Z)  9.2  1.7  1.1  2.1  65.5  79.6  
Alaska  0.4  6.6  11.3  1.3  15.5  0.1  35.2  
Arizona  0.3  18.7  14.8  1.1  0.6  0.2  35.7  
Arkansas  (Z)  6.9  1.4  4  1.7  46.1  60.1  
California  0.2  6.1  13.8  0.2  0.6  5.1  26  
Colorado  3  56  4.2  3.8  0.6  3.1  70.7  
Connecticut  0.1  1.5  15.7  0.2  2.8  17.1  37.4  
Delaware  (Z)  1.4  2.8  0.9  0.2  69.3  74.6  
Florida  (Z)  5  6.4  0.1  2.4  5.8  19.7  
Georgia  (Z)  4.5  4.3  2.1  0.5  50.2  61.6  
Hawaii  (Z)  5.6  5.8  1.3  2.8  3.6  19.1  
Idaho  0.9  27.2  16.7  0.1  1.6  0.4  46.9  
Illinois  0.1  6.4  3  12.4  0.3  1.1  23.3  
Indiana  0.1  6.3  4.9  16.1  0.7  9.9  38  
Iowa  0.3  15.5  3.4  25.4  0.2  3.5  48.3  
Kansas  0.1  59.3  1.7  3.3  0.1  0.5  65  
Kentucky  (Z)  18.1  7.8  3.7  11  7.9  48.5  
Louisiana  (Z)  7  5.1  0.2  4.1  14.1  30.5  
Maine  0.1  2.3  21.8  0.3  10.4  16.7  51.6  
Maryland  0.1  4.4  13.3  1.1  2.8  43.4  65.1  
Massachusetts  0.1  1.4  13.2  0.5  2.7  3.5  21.4  
Michigan  0.2  7.8  18.1  6.4  1.2  4.7  38.4  
Minnesota  0.2  9  13.2  17.5  0.5  9  49.4  

Table 15.11 Livestock Sales as a Percent of Total Agricultural Sales, by 
State (Continued) 
State  Sheep, Lambs, & Wool  Cattle & Calves  Dairy Products  Hogs & Pigs  
Other Livestock & Livestock Products  Poultry & Poultry Products  Total  
Mississippi  (Z)  6.9  2.7  2.6  8.6  38  58.8  
Missouri  0.1  21.1  5.5  15.7  0.6  14  57  
Montana  1.7  44.6  1.9  1.7  1.5  0.3  51.7  
Nebraska  0.1  50.4  1.2  8  0.2  1.5  61.4  
Nevada  2.8  38  15.5  0.2  0.8  (Z)  57.3  
New Hampshire  0.3  3.2  31.4  0.8  2.1  12.9  50.7  
New Jersey  0.1  1.3  5.4  0.6  2.5  5.1  15  
New Mexico  1.1  40  28.6  0.1  0.6  1  71.4  
New York  0.1  7  51.5  0.5  2.5  3  64.6  
North Carolina  (Z)  2.2  2.3  33.5  0.6  27.6  66.2  
North Dakota  0.3  17.4  2.8  1.2  0.9  1  23.6  
Ohio  0.2  7.4  10.9  7.8  1.1  12.2  39.6  
Oklahoma  0.1  55.7  3.6  8.2  0.7  9.7  78  
Oregon  0.9  16.2  7  0.2  1.2  3.3  28.8  
Pennsylvania  0.1  8.9  33  5.9  1.9  18  67.8  
Rhode Island  0.1  1.4  9.8  1  1.8  4.2  18.3  
South Carolina  (Z)  4.8  3.4  4.1  0.9  37  50.2  
South Dakota  1  37.3  4.7  7.9  0.7  2.1  53.7  
Tennessee  0.1  19.6  9.6  3.3  1.5  13.5  47.6  
Texas  0.7  52.7  5.4  0.8  0.9  8.3  68.8  
Utah  3.4  29.6  22.3  4.6  4.1  7.8  71.8  
Vermont  0.2  7.5  74  0.1  4.4  1.2  87.4  
Virginia  0.2  16.6  11.6  3.3  2.5  32.4  66.6  
Washington  0.1  13.6  13.1  0.2  1.4  3.6  32  
West Virginia  0.5  25.6  8  0.5  1  49.9  85.5  
Wisconsin  0.1  12  49.2  2.7  2.2  4.3  70.5  
Wyoming  7.9  67.6  1.1  2.7  1.5  (Z)  80.8  

The selection of the synthetic economic type in default data analysis (Level 
1) also differs for agricultural versus other types of economies such as 
manufacturing-based economies.  For the latter, the module considers the size 
of the study area economy when selecting the most appropriate synthetic 
economy to use as a proxy.  For agricultural economies, on the other hand, 
size is less important a distinguishing factor than the type of region in 
which the study area is located. 
A variety of experiments were conducted to establish a set of agricultural 
regions that lay users could easily apply. One such experiment derived a set 
of tables from counties dependent on agriculture, where dependence was 
defined as the county s prominence in terms of government payments and 
agricultural products sold normalized by county income.  The counties were 
then grouped as: low dependence, moderate dependence, and high dependence.  A 
subsequent evaluation of the procedure concluded that unique counties could 
dominate the results, rendering the resulting synthetic tables too specific.  
As a result the approach was abandoned in favor of a more accepted means of 
categorizing agricultural regions, one developed by the Department of 
Agriculture s Economic Research Service (ERS). 
The task of the developing a set of homogeneous agricultural regions had been 
undertaken by the ERS; using a form of cluster analysis, the ERS observed 
that few commodities tended to dominate farm production in geographic areas 
that transcend State boundaries (and watersheds). Cropping and livestock 
patterns in such areas clustered according to a region s climate, soil, 
water, and topography. The ERS identified those counties with similar 
agricultural practices and environmental characteristics (as reflected in 
USDA's Land Resource Regions) and produced a crude map of farm resource 
regions.  See Figure 15.5.  These nine Farm Resource Regions served as the 
basis for deriving the Level 1 default agriculture-based economies. 

Inter-industry or Input-Output tables were derived for each of the nine 
regions as follows. Budget limitations dictated that tables be derived from a 
random sample of counties.  45 counties (five from each of the nine ERS 
regions) were drawn from the population (3,111 U.S. counties). The sample was 
then slightly16 altered to reflect a wider geographic area, and to include 
counties nearer to rivers or streams.  The final set of counties used to 
establish the nine synthetic agricultural economies is provided in Appendix 
15A.  IMPLAN tables were obtained for all 45 counties and aggregated by 
region.  The resulting regional IMPLAN files are shown in Table 15.12. 
Table 15.12 Level 1 Synthetic IMPLAN Tables 

ERS Region  Region Name  IMPLAN File Name  ERS Description  
1  Heartland  Region1.iap  Most farms (22%), highest value of production 
(23%), and most people (27%).  Cash grain and cattle farms  
2  Northern Crescent  Region2.iap  Most populous region. 15% of farms, 15% of 
value of production, 9% of cropland. Dairy, general crops, and cash grain 
farms.  
3  Northern Great Plains  Region3.iap  Largest farms and smallest population.  
5% of farms, 6% of production value, 17% of cropland.  Wheat, cattle and 
sheep farms.  
4  Prairie Gateway  Region4.iap  Second in wheat, oat, barley and cotton 
production.  13% of farms, 12% of production value, and 17% of cropland. 
Cattle, wheat, sorghum, cotton, and rice farms.  
5  Eastern Uplands  Region5.iap  Most small farms of any region.  15% of 
farms, 5% of production, and 6% of cropland.  Part-time cattle, tobacco, and 
poultry farms.  
6  Southern Seaboard  Region6.iap  Mix of small and larger farms.  11% of 
farms, 9% of production value, and 6% of cropland.  Part-time cattle, general 
field crop, and poultry farms.  
7  Fruitful Rim  Region7.iap  Largest share of large and very large family 
farms and non-family farms.  10% of farms, 22% of production value, 8% of 
cropland.  Fruit, vegetables, nursery, and cotton farms.  
8  Basin and Range  Region8.iap  Largest share of non-family farms, smallest 
share of U.S. cropland.  4% of farms, 4% of value of production, 4% of 
cropland. Cattle, wheat, and sorghum farms.  
9  Mississippi Portal  Region9.iap  Higher proportion of both small and large 
farms than elsewhere. 5% of farms, 4% of value, 5% of cropland.   Cotton, 
rice, poultry, and hog farms.    

16 Five counties out of randomly drawn 45 were replaced.   
15.4.6.2 Tourism 
Some economic sectors are more prone to flood damage than others.  A good 
example is tourism, which is especially prevalent in coastal areas that are 
relatively more vulnerable to wave surges from hurricanes and severe storms.  
This section discusses two issues related to tourism in the Indirect Loss 
Module: the representation of tourism-dominated economies in the Level 1 
synthetic economic tables, and the modeling of tourism-related impacts. 
In Level 1, with regard to synthetic economic tables, analysis suggested that 
tourism-based economies can be adequately represented by the category of 
service-dominated economies.  This treatment was governed by several 
considerations.  First,  tourism  does not have a universally accepted 
definition.  Moreover, IMPLAN, as well as every other major packaged regional 
economic model, lacks a specific tourist industry.  Underlying this is the 
absence of a single Standard Industrial Classification (SIC) code on which 
government data collection can be based. This situation is further 
complicated by the fact that not all of the expenditures in these sectors are 
for what is typically considered tourism (e.g., use of hotels by business 
travelers).   
Furthermore, the direct losses sustained by a region s tourism sector is 
likely to produce demand driven (and not supply constrained) dislocations.  
In this case, the choice of a synthetic economic table as well as the degree 
of sectoral disaggregation adopted in the Indirect Loss Module hinges on the 
accuracy of the model s multipliers (output, income, value added and 
employment).   
Multiplier analysis was performed to assess the sensitivity of IMPLAN 
multipliers to the makeup of the sectors used to reflect the tourism 
industry.  A number of aggregation schemes were developed, the most elaborate 
of which grouped all recreation and tourism related sectors.  This single 
aggregate was comprised of IMPLAN sectors 454 (eating and drinking), 463 
(hotels and lodging places), 483 (motion pictures), 484 (theatrical 
productions), 485 (bowling), 486 (commercial sports), 487 (racing and track), 
488 (amusement and recreation ) and 489 (membership sports).  Type I 
multipliers were estimated for five counties whose economies are dominated by 
tourism or services.   The results suggest that that the multipliers for this 
level of aggregation were comparable (varying from 1.24 to 1.33).  An 
alternative simpler aggregation scheme was also tested.  The output 
multipliers for the IMPLAN service sector (a 1-digit level of aggregation) 
resulted in a comparably narrow range of results (Type I multipliers of 1.28 
to 1.34). The value added, employment and personal income multipliers 
exhibited a slightly greater variance.  The multiplier analysis thus showed 
that the service sector serves as a reasonably accurate proxy for recreation 
and tourism. 
Tourism-related losses are treated somewhat differently from impacts deriving 
from damage to other sectors. The methodology here estimates losses in 
regional economic output, employment, and income due to a reduction of 
tourism activity in flood disasters.  It accounts for both the loss of direct 
tourist expenditures and its indirect or ripple effect throughout the 
regional economy. The decrease in tourism, which largely arises from 
perceptions of the disaster, may not correlate well with the actual amount of 
damage suffered.  Indeed, tourist activity may essentially cease for a time 
after the disaster. Loss of tourism is therefore treated as a demand shock to 
the economy.  Note that the Indirect Loss Module core algorithm is able to 
handle both supply and demand losses simultaneously without double-counting. 
The methodology does not distinguish between purchases by tourists and other 
types of visitors such as businessmen.  Rather, it simply refers to them all 
as  non-resident  expenditures, all of which are likely to be decreased by a 
major flood.  The methodology does not consider the stimulus effect of 
expenditures by relief workers coming into the disaster region.  It also does 
not consider the stimulus effect to hotel and other tourism sectors from the 
expenditures of local residents who are displaced from their homes. 
To evaluate the initial, direct loss of tourism activity, the user must 
provide estimates of the number of visitor-days lost and the timeframe over 
which activity returns to normal.  Default data are provided to translate 
this into the dollar and percent direct demand loss that is suffered by the 
service sector. The percent direct loss is then input to the IELM along with 
direct losses from other sources.  The user can elect not to evaluate 
tourism-related losses.  If tourism impacts are evaluated, the model will 
automatically constrain import and export parameters for the service sector. 
This is to ensure that the model does not try to import or export tourism 
services (e.g., hotels) as it tries to rebalance the economy.  Further detail 
on these steps is provided below. 
To translate visitor-days into percent demand loss for the tourist-related 
sectors, default data were developed. First, visitor-days must be converted 
to dollars.  Data on dollars per visitor-day were developed based on data 
from a variety of sources (see Tyrrell, 2001; Strauss and Lord, 1997). The 
U.S. Input-Output table is the basis for the sectoral components of tourists 
expenditures (Table 15.13). The standard trade and transport margins (see 
Table 15.14) were then applied to account for expenditures from these 
sectors.  Although most of the expenditures are for goods produced in the 
region in which the flood takes place (e.g., hotels) some goods are produced 
elsewhere, and regional distinctions are based on their importation.  Thus, 
default regional purchase coefficients (RPCs) (see final column of Table 
15.15) were applied to the trade/transportation-adjusted expenditure vector.  
The RPC values were developed from analysis of IMPLAN data for several 
tourism-based economies.17  To model impacts, the RPC-adjusted data are 
entered as they change, primarily in the service sector but also in trade and 
transportation sectors of the synthetic economy chosen for analysis. 
17 To develop these default values, regional purchase coefficients (RPCs) 
were calculated for several counties from IMPLAN data.  The analysis revealed 
that the RPCs embedded in IMPLAN are identical across counties for sectors 
484, 486, 487, 488, and 489. Table 15.15 also suggests that RPCs vary for the 
other sectors, but not by much.  The final column of Table 15.15 consists of 
educated guesses for RPCs that would be appropriate as default values in the 
Indirect Economic Loss Module.  Note that the RPC for sector 463 could 
arguably be set to 1. 
Table 15.13 National Personal Consumption for Tourism Sectors, 1997  
(millions of dollars) 

IMPLAN Sector  Industry Descriptor  Personal Consumption  
397  Travel Trailers and Camper  $1,396   
421  Sporting and Athletic Goods- N.E.C.  $5,379   
451  Automotive Dealers & Service Stations  $142,794  
454  Eating & Drinking  $253,683  
463  Hotels and Lodging Places  $37,536  
477  Automobile Rental and Leasing  $8,374  
484  Theatrical Producers- Bands Etc.  $7,707   
486  Commercial Sports Except Racing  $2,420   
487  Racing and Track Operation  $5,058   
488  Amusement and Recreation Services- N.E.C.  $52,466  
489  Membership Sports and Recreation Clubs  $14,606   
Total  $531,417 

 Source: U. S. Input-Output Table.  
Table 15.14 Margins for Tourism Commodities 
Margined Sector  Tourism Sector  
Petroleum Products  Travel Trailers and Campers  Sporting & Athletic Goods  
Transportation  0.023  0.004  0.004  
Wholesale Trade  0.147  0.006  0.129  
Retail Trade  0.200  0.241  0.450  
Total  0.370  0.251  0.583  

Source: MIG (2001).   
Table 15.15 RPCs for a Sample of Counties that Rely on Tourism 

IMPLA N Sector  Industry Descriptor  RPC Larimer, CO  RPC NY, NY  RPC 
Atlantic, NJ  RPC Center, PA  RPC Dare, NC  Default Values  
397  Travel Trailers & Campers  0.0008  1.0000  1.0000  1.0000  1.0000  1.00  
421  Sporting & Athletic Equipment  0.0956  0.0000  0.0060  0.3995  0.0000  
0.10  
451  Automotive Dealers & Service Stations  0.9500  0.9500  0.9473  0.9498  
0.9481  0.95  
454  Eating & Drinking Establishments  0.9000  0.8145  0.8084  0.8759  0.9000  
0.85  
463  Hotels and Lodging Places  0.8001  0.5230  0.5205  0.5855  0.7641  0.60  
477  Automobile Rental and Leasing  0.9000  0.7277  0.8263  0.8918  0.9000  
0.85  
484  Theatrical Productions  0.8355  0.85  0.85  0.8283  0.8501  0.85  
486  Commercial Sports Except Racing  0.8355  0.85  0.85  0.8283  0.8501  
0.85  
487  Racing & Track Operation  0.8355  0.85  0.85  0.8283  0.8501  0.85  
488  Membership Sports & Recreation Clubs  0.8355  0.85  0.85  0.8283  0.8501  
0.85  
489  Amusement & Recreation Services  0.8355  0.85  0.85  0.8283  0.8501  
0.85  

Secondly, dollars lost in tourism are converted to a percentage figure for 
Services, Trade, and Transportation. This involves dividing the loss into 
base levels of these sectors  production.  For example, the base level is 
obtained by scaling the Service sector in the synthetic economy to the 
employment level that the user has specified for the study region.  The 
initial percent loss in Services is linearly interpolated over the recovery 
duration specified. 
The tourist-related demand loss is then input to the Indirect Loss Module 
along with other direct losses from agricultural damage, building damage, 
consumption demand loss due to loan repayment, etc.  The core algorithm 
rebalances the economy and derives the associated indirect impacts. 
If the user elects to evaluate tourism impacts, the module will automatically 
constrain import and export parameters for the services sector.  The 
rationale is as follows:  A flood will either reduce demand for tourism or 
reduce the supply of hotel/motel units.  As noted above, the model should be 
able to accommodate either or both, without double counting.  The model 
normally adjusts for excesses supply or demand by 1) cutting demand to 
supplying sectors (if alternative supplies cannot be secured); or 2) cutting 
shipments to demanding sectors (if alternative markets cannot be found). In 
the case of tourism, the availability of hotels and eating establishments 
will shape the supply. Tourism can be thought of as being produced from a 
fixed coefficient production function. It is doubtful that people will visit 
amusement parks if lodging is unavailable.  It therefore seems reasonable to 
constrain tourism so that hotels are not imported (in the case where the 
flood damages units) or exported (in the case where tourists cancel trips to 
the flood prone area). One way to constrain the Service sector is to assume 
that services are not exportable or importable.  This assumption seems 
reasonable since a bulk of this sector is likely to be tied to the region. By 
clamping down on exports, the model will be able to handle a reduction in 
tourist spending the same way that a conventional, demand-driven Input-Output 
model would.  In the case of supply shortages, a buffer could be built in to 
reflect normal excess capacity such as normal hotel vacancy rates.  This can 
be reflected in the  inventory  or  excess capacity  parameter of the 
algorithm. 
The Level 2 analysis is similar to that in Level 1, except that instead of 
using default values, IMPLAN data for the specific study region will be used 
to calculate the direct loss of tourism activity.  As in Level 1, HAZUS will 
ask the user to specify (or accept default values for) the reduction in 
visitor-days and the recovery timeframe for tourism activity. 
15.4.7 Results 
Results of the analysis are reported in terms of changes in regional income, 
employment, and output. Direct, indirect, and total impacts are reported over 
a timeframe of 15 years following the disaster. Results are reported for two 
scenarios:  with and without reconstruction aid from outside the region. The 
comparison of the two scenarios may be of interest from a policy analysis 
standpoint. The case with outside aid represents the scenario specified by 
the user (default values are provided), in which some impacts may be positive 
due to economic gains deriving from an inflow of reconstruction funds to the 
region.  Results for the case without outside aid are obtained by 
automatically re-running the module without any external assistance, in which 
case reconstruction is assumed to be financed completely from regional 
resources. Detailed results are disaggregated by industry and, in the case of 
distributional impacts, by household income group.  Results on government tax 
revenue impacts are provided separately. 
15.4.8 A Note Regarding Small Study Areas 
Study regions may consist of single counties, higher levels of aggregation 
such as several counties comprising a metropolitan area, or lower levels of 
aggregation such as a group of contiguous census tracts. In principal, the 
methodology underlying the Indirect Economic Loss module is applicable 
regardless of the level of aggregation.  However, its accuracy is likely to 
be greater for study regions that represent cohesive economic regions, often 
called  trading areas  (e.g., cities or metropolitan areas) than for those at 
lower levels of aggregation because of the ability of the core Input-Output 
model to meaningfully represent the region s economic structure.  
Furthermore, in evaluating regional employment impacts, the module requires 
input data on the number of jobs located within the study region -- that is, 
data on employment by place of work rather than by place of residence.  While 
this information can be obtained at the county level, its availability and 
reliability at lower levels of aggregation are much more problematic.  
Similar problems are associated with other input data such as unemployment 
rates.  More generally, the user should also be aware that some of the input 
assumptions to the model (such as the availability of alternate markets) are 
related to the study region s level of aggregation.  By adjusting the nature 
of the economy and the linkage to surrounding regions, the analyst can get a 
 ball park  estimate of what the real indirect losses and gains might be.  
Section 15.5 below provides some discussion of appropriate input data and 
assumptions to the module. 
The Indirect Loss Module was developed on the assumption that the study area 
for analysis would consist of a single county or possibly multiple counties.  
The Input-Output tables that form the core of the methodology are based on 
county-level data.  As a study area is decreased in 
15-40 
size, it becomes more  open  and an increasing share of inter-industry 
transactions are conducted with entities outside the region. Thus, indirect 
effects are much less significant for small regions than for large ones. For 
flood analysis, a user could define a study region consisting of, for 
example, a single river reach that made up a small fraction of a county.  The 
evaluation of indirect economic impacts for such a small study area could 
very well be meaningless.   
If the user has defined a study area that is smaller than a single county, 
the Indirect Loss Module will be run at the county level. Damage and related 
inputs that pertain to the sub-county study area will be scaled to the county 
level.  This approach assumes that areas of the county outside the study area 
have not suffered any flood damage. 
15.5 Running the Indirect Loss Module 
This section provides guidance to the user on steps for running the Indirect 
Loss Module.  As noted earlier, the module can be run with default data 
(Level 1) or with user-provided, region-specific data (Level 2). 
15.5.1 Default Data Analysis (Level 1) 
15.5.1.1 User Inputs and Default Data 
Running the Indirect Economic Loss module requires a number of user inputs.  
While default values are provided for all of these inputs, as discussed 
below, it is advisable even in a Default Data Analysis to override certain of 
them with data for the study region where available. Table 15.16 describes 
the inputs required and their default values. 
HAZUS provides default values for the current employment based on Dun & 
Bradstreet data and income levels for the region based on County Business 
Pattern data.  It is recommended that the Default Data Analysis (Level 1) 
user review the default values provided and replace them if more accurate or 
recent data is available.  Note that in User-Supplied Data Analysis (Level 
2), where a user-provided IMPLAN Input-Output table is used instead of a 
synthetic table, the current employment and income levels are read in from 
the IMPLAN files and override the default values. 
The type or composition of the economy, together with the employment level, 
is used by the module to automatically select a synthetic Input-Output 
transactions table to represent the study region economy.  Default Data 
Analysis utilizes a synthetic transactions table aggregated from four basic 
classes of economies:  1) primarily manufacturing, 2) primarily service, 
secondarily manufacturing, 3) primarily service, secondarily trade, and 4) 
primarily agricultural.  Each of the first three types is broken into four 
size classifications: super (greater than 2 million in employment), large 
(greater than 0.6 million but less than 2 million), mid range (greater than 
30 thousand but less than .6 million) and  low (less than 30 thousand).  
Appendix 15A provides examples of regions in each type and size class.  While 
type 1 (manufacturing) is the default, the user should revise this as 
appropriate. Appendix Tables 15A.2, 15A.3, and 15A.4 can be used as a guide. 
Agricultural economies are broken into nine regional classifications (see 
Section 15.4.6.1 above). The Indirect Loss Module automatically selects the 
appropriate region based on the county FIPS code of the study area. 
Supplemental imports, inventories (demands), inventories (supplies), and new 
export markets represent available channels for excess supply or demand that 
can help reduce the bottleneck effects in the post-disaster economy.  As 
mentioned above, appropriate values depend in part on the level of 
aggregation of the study region.  Default values are set at 0 for inventories 
supply and demand for all industries.  Default values for imports and exports 
are set at values considered appropriate for a  distinct  or self-contained 
study region such as a metropolitan area.  The default values are presented, 
together with discussion of how they can be modified in a User-Supplied Data 
Analysis (Level 2), in Section 16.5.2.2. 
The supplemental imports variable, due to limitations on available data, 
needs further explanation. Data on the amount of imports per sector are 
available only in the aggregate.  For any one sector in the economy, the 
total amount of intermediate products imported is known, but the amount of 
these imports that comes from any individual sector is not known.  The amount 
of new imports that may be allowed must be set to a very small level.  
Otherwise, the amount of products that may be imported will almost always 
replace any intermediate goods lost from local suppliers, and no indirect 
output losses will be observed.  The level of supplemental imports also needs 
to be kept low because of factor homogeneity problems.  There will be cases 
when there are no substitutes for locally obtained intermediate goods.  In 
such cases, allowing imports would unreasonably eliminate indirect losses.  
Being conservative in the amount of imports allowed helps avoid both of these 
problems.  The default values for imports have been tested in the model, and 
are felt to yield realistic results. 
Table 15.16 User Supplied Inputs for Indirect Economic Loss Module 

Variable  Definition  Units(a)  Default Value  
Current Level of Employment  The number of people gainfully employed, by 
place of work (not residence).  Employed persons  Region-specific  
Current Level of Income  Total personal income for the study region.  Million 
dollars  Region-specific  
Composition of the Economy (Default Data Analysis only)    Primarily 
manufacturing   Primarily service, secondarily manufacturing.   Primarily 
service, secondarily trade.   Primarily agricultural.  1, 2, 3, or 4  1  
Supplemental Imports  In the event of a shortage, the amount of an immediate 
product unavailable from local suppliers which may be obtained from new 
imports.  Percent of current total current annual imports (by industry)  
Defaults for  distinct region   
Inventories (Supplies)  In the event of a shortage, the amount of a good that 
was supplied from within a region that can be drawn from inventories within 
the region.  Percent of annual sales (by industry)  0 (for all industries)  
Inventories (Demand)  In the event of a surplus, the amount of a good placed 
in inventory for future sale.  Percent of current annual sales (by industry)  
0 (for all industries)  
New Export Markets  In the event of a surplus, the amount of a good which was 
once sold within the region that is now exported elsewhere.  Percent of 
current annual exports (by industry)  Defaults for  distinct region   

Table 15.16 User Supplied Inputs for Indirect Economic Loss Module 
(Continued) 

Variable  Definition  Units(a)  Default Value  
Percent Rebuilding  The percent of damaged structures that are repaired or 
replaced  Percent  95%  
Unemployment Rate  The pre-event unemployment rate as reported by the U.S. 
Bureau of Labor Statistics  Percent  6%  
Outside Aid/Insurance  The percentage of reconstruction expenditures that 
will be financed by Federal/State aid (grants) and insurance payouts.  
Percent  50%  
Interest Rate  Current market interest rate for commercial loans.  Percent  
5%  
Restoration of function  The percent of total annual production capacity that 
is lost due to direct physical damage, taking into account reconstruction 
progress.  Percent (by industry, by time interval for 5 years)  Defaults for 
moderate-major event  
Rebuilding (buildings)  The percent of total building repair and 
reconstruction that takes place in a specific year.  Percent (by time 
interval for 5 years)  70% (yr.1), 30% (yr.2)  
Rebuilding (lifelines)  The percent of total transportation and utility 
lifeline repair and reconstruction that takes place in a specific year.  
Percent (by time interval for 5 years)  90% (yr.1), 10% (yr.2)  
Stimulus  The amount of reconstruction stimulus anticipated in addition to 
buildings and lifelines repair and reconstruction.  Percent (by industry, by 
Time interval for 5 years)  0% (for all)  
Property tax rate  The local property tax rate.  Percent  0%  
Property tax exemptions  Categories of building use types that are exempt 
from property tax.  Checklist  None checked  
Sales tax rate  The local sales tax rate.  Percent  0%  
Sales tax exemptions  Categories of sales that are exempt from sales tax.  
Checklist  None checked  
Tourism loss  Loss of tourism activity due to disaster, in terms of number of 
visitor-days lost.  Number  0  
Tourism restoration  Timeframe over which loss of tourism activity returns to 
normal.  Number of years  Same as years of rebuilding to reach 100%  

Note: (a) Percent data should be entered as percentage points, e.g. 60 for 
60%. 
The variables for percent rebuilding, unemployment rate, percent outside aid, 
and interest rate all influence how the economy is expected to react to the 
disaster, in particular the reconstruction stimulus, the available slack or 
unused capacity in the economy, and the associated indebtedness that would be 
incurred from reconstruction financing.  The user is recommended to revise 
the unemployment and interest rates as appropriate.  However, all of these 
variables can be adjusted for purposes of  what-if  scenario modeling.  For 
example, how would regional indirect economic losses change if only 20 
percent of reconstruction was financed by sources outside the region such as 
insurance or federal disaster aid? 
Parameters for functional restoration, as well as rebuilding for both 
buildings and lifelines, are associated with the anticipated speed of 
reconstruction and recovery.   To specify functional restoration, user inputs 
are required for the percent of each industry s production capacity that is 
lost as a result of physical damage in each year for the first 5 years after 
the disaster.  Default parameters are provided that are designed to be 
consistent with a  moderate-to-major  scale of disaster. These parameter 
values and suggestions for modifying them in a User-Supplied Data Analysis 
are provided in the next section.   
In terms of rebuilding, the module requires user inputs as to the percent of 
total rebuilding expenditures for buildings and lifelines respectively that 
are expected to be made in each of the first 5 years following the disaster. 
Table 15.17 provides an example.  Note that the total dollar amount required 
to fully rebuild damaged and destroyed public and private capital is provided 
by the Direct Economic Loss module.  The percent of this total that is 
actually rebuilt is specified by the user input on  percent rebuilding  and 
may be less than 100 percent if not all of the damage is repaired or 
replaced.  The annual percents for rebuilding buildings and lifelines as 
shown in Table 15.17 provide the timeline over which the reconstruction 
expenditures are made and should therefore sum to 100 percent over the 5-year 
period. 
Table 15.17 Rebuilding Expenditures Example 

Year  1  2  3  4  5  Total  
% of Total Rebuilding Expenditures (Buildings)  70  30  0  0  0  100  
% of Total Rebuilding Expenditures (Lifelines)  90  10  0  0  0  100  

Reconstruction speed is also to a large extent related to the scale of the 
disaster.  In general, lifeline reconstruction is expected to proceed much 
more quickly than building reconstruction, as has been the experience in 
previous disasters.  For a Default Data Analysis, default parameters are 
provided that are designed to be consistent with a  moderate-to-major  scale 
of disaster. Modifying these parameters would be appropriate in a User-
Supplied Data Analysis, and guidelines are provided below. These parameters 
can also be adjusted in Default Data Analysis for purposes of  what-if  
scenario modeling for faster or slower paces of reconstruction. 
No default data are provided for evaluating tax impacts, and this analysis is 
not conducted in a Default Data Analysis (Level 1).  Similarly, no default 
data is provided for visitor-days lost due to the disaster, and tourism 
losses are not estimated in Level 1.  The default value for the timeframe of 
loss will be the same as the default value (or user-override value) for 
recovery in the overall economy, as indicated by completion of rebuilding of 
buildings. 
15.5.1.2 Calculation of Indirect Impacts 
A direct shock is introduced into the Indirect Loss Module by adjusting the 
outputs and purchases in proportion to a sector's loss of function.  
Restrictions on shipments (forward linkages) and purchases (backward 
linkages) are computed and the resultant excess demands or supplies are 
derived. See Figure 15.6.  A sample transactions table is used to illustrate.  
The first two rows above the table indicate the total direct shock and 
associated indirect losses, which are initially zero. The first round effects 
are simply the direct loss of function times the inputs to that sector 
(backward links) and shipments from that sector (forward links).  In the 
event of a 30 percent loss of function in the transportation sector, for 
example, demand for manufactured goods would fall by 15.6 (0.3 times 51.9).  
The remainder of the column effects is computed similarly. 
IMPACT OF THE INITIAL SHOCK 
Direct Shock 

Restricted purchases Backward links 


NET EXCESS 
Excess supply of inputs 
DEMAND 
Figure 15.6 Initial Effects of the Shock 
The same 30 percent shock would limit shipments to other sectors; finance, 
insurance, and real estate, for example, will initially receive 38.0 less 
(0.3 times 126.7) in services from transportation. 
These first round effects produce excess demands and supplies that trigger a 
search for markets and alternative supply sources. 
In building the model, several critical choices had to be made regarding 
post-event household spending patterns, labor mobility, elasticity of 
supplies from the construction industry, and the potential for product 
substitutions due to relative price changes.  Evidence from previous 
disasters suggests that: 1) normal spending patterns are not significantly 
altered; 2) the workforce is highly mobile, particularly in the construction 
sector; and 3) relative prices do not change appreciably. Therefore, labor 
and construction sales are not constrained, and normal household spending is 
fixed and independent of current income.  Given these conditions, the model 
assesses the net excess supplies (output less the sum of intermediate and 
final demands).  A positive net value implies an excess supply; a negative 
indicates excess demand. It then attempts to resolve sectoral imbalances 
through a series of adjustments.  If excess demand is detected, the algorithm 
checks to see if sufficient capacity exists in a sector.  Excess capacities 
are a function of user defined level of unemployment and is calculated within 
the model using the following equation.   
AC = 2.36 x (UR - .02) 	(15-11) 
Where:  
AC 	is available production capacity and expressed as a percentage 
(measured as a decimal) of the pre-event capacity  

UR 	is the unemployment rate (e.g., .05).   
If idle capacity is insufficient to meet excess demand then the model 
explores the potential of importing and/or drawing down inventories.  These 
options are also provided by the user and are expressed as a percent of pre-
event capacities. 
Disposal of excess supplies is logically similar.  Two options, inventory 
accumulation and exports, are explored. As in the case of the previous 
options, both are expressed as a percentage and are determined by the user.  
In most cases excess supplies are not critical to the model's, operation, 
particularly when reconstruction spending looms large.  Much of the excesses 
are drawn into the rebuilding process. 
After completing the first iteration of output adjustments, the algorithm 
recalculates the intermediate supplies and demands and then reinvestigates 
the adjustment options previously explored. Outputs are revised in proportion 
to the amount each sector is out of balance.  A moving average of previously 
attempted outputs is used to initialize each iteration's search. The search 
is terminated once the sum of the absolute sectoral output differences 
diminishes to a specified level; the default is set at .00001.    
Indirect income loss is calculated as using the following formula.  
Tj (tdit, - ddit,)Yi 
t .=1 i .=1 (1 + r)t (15-12) 
where: 
tdi,t is the total percent reduction in sector i income during period t. 
Yt is income of sector i. 
ddi,t is the direct percent reduction in sector i income during period t. 
r is the real interest rate to discount the indirect losses 
j is the number of sectors 
dd is computed in the model by multiplying the initial sectoral income by the 
respective loss of function.  The variable td is the total percentage 
reduction in income caused by the combination of direct loss and forward and 
backward linked losses.  The difference between the two is then the 
percentage reduction in income attributable to indirect effects.  The 
difference is pure indirect loss. This percentage when multiplied by sectoral 
incomes yields indirect income lost.  A similar formula to Equation 15-12, 
without discounting, is used to evaluate indirect employment loss. 
15.5.1.3 The Formats of the Outputs 
The module produces three summary reports on the results:  (1) Total economic 
impact, (2) Indirect economic impact with aid, (3) Indirect economic impact 
without aid. 
The summary report on Total Economic Impact provides a picture of direct, 
indirect, and total economic impacts.  Its format is shown in Table 15.18 
below.  Estimates are provided for the study region by year following the 
disaster. Results pertain to scenarios with and without outside aid. The 
former represents net effects including both losses and reconstruction gains, 
while the latter refers to the case of losses alone.  Note that impacts may 
be either losses (negative numbers) or gains (positive numbers).  Results are 
given by time interval for the first 5 years. Average figures are also 
provided for years 6 to 15.   
Impacts are presented in terms of income, employment, and production or 
output effects.  All incomes are discounted at the rate of 3 percent.  In the 
case of income, Year 6 to Year 15 losses or gains are discounted to the 
present. Employment loss or gains are shown as numbers of workers. 
Table 15.18 Format of  Total Economic Impact  Summary Report 

First Year  Second Year  Third Year  Fourth Year  Fifth Year  Years 6-15  
With outside aid Income   Direct  ($ mil.)  
Indirect     
Total     
Employment    Direct  (jobs)  
Indirect     
Total     
Production   Direct  ($ mil.)  
Indirect     
Total  
Without outside aid Income   Direct  ($ mil.)  
Indirect  
Total  

Table 15.18 Format of  Total Economic Impact  Summary Report (Continued) 

First Year  Second Year  Third Year  Fourth Year  Fifth Year  Years 6-15  
Employment   Direct  (jobs)  
Indirect  
Total  
Production   Direct  ($ mil.)  
Indirect  
Total  

Several additional reports are provided. Two summary reports that break down 
the results by the 10 major industries.  Differences in impacts and recovery 
trends typically are very significant between industries, in part because 
much of the gains from the reconstruction stimulus accrues to the 
construction industry (and to some extent the manufacturing and trade 
industries).  A report on distributional impacts breaks down income impacts 
according to 9 major household income groups. A report on tax revenue impacts 
is also provided.  In addition, the HAZUS Quick Assessment Report includes 
indirect income impacts for Year 1, along with direct income impacts. 
15.5.2 User-Supplied Data Analysis (Level 2) 
This level of Analysis differs from the Default Data (Level 1) level of 
analysis in three main respects: (1) interindustry trade flows, as 
represented in the Input-Output model of the economy, 
(2) specification of restoration and rebuilding parameters, and (3) 
specification of tax and tourism parameters.  Rather than selecting from 
built-in synthetic Input-Output transactions tables, the user should obtain 
specific tables for the study region from a standard source, the Minnesota 
IMPLAN Group. In terms of specifying restoration and rebuilding parameters, 
the user can replace the built-in data with suggested parameter  packages  
appropriate to the disaster being modeled.  In addition, other parameters 
such as the availability of supplementary imports can also be modified. 
15.5.2.1 IMPLAN Input-Output Data 
HAZUS requires three files from the IMPLAN input-output data set (the 
asterisk in each of the following file names refers to the IMPLAN model name.  
Therefore, a model for Jackson County would produce a file named 
JACKSON.402): 
--*.402 This is the transactions matrix. 
--*.403 This is a file of final demands information. 
--*.404 This is a file of final payments information. 
Details regarding the operation of the IMPLAN program and the construction of 
these files can be obtained from the technical documentation for the system.  
IMPLAN is currently sold and supported by the Minnesota IMPLAN Group; the 
Group can be reached at: 
Minnesota IMPLAN Group, Inc. (MIG) 
1940 S. Greeley, Suite 201 
Stillwater, MN 55082 
Voice 612-439-4421 FAX 612-439-4813 
e-mail    linda003@maroon.tc.umn.edu 
Software and data for any county in the United States can be obtained from 
the IMPLAN group. When requesting data, regions can also be defined by 
specifying a zip code aggregation. 
The user can either request the three data files for the study region from 
MIG or obtain the software and database to construct the files. In the former 
case, the user should specify that the required industry aggregation scheme 
is essentially a one-digit Standard Industrial Classification (SIC) grouping 
that maps detailed IMPLAN industries into the ten industry groups used in the 
methodology.  Table 15.19 describes the correspondence between IMPLAN and 
HAZUS industry classes. 
Table 15.19 Industry Classification Bridge Table 

IMPLAN  HAZUS  
1-27  AG (Agriculture)  
28-47  MINE (Mining)  
48-57  CNST (Construction)  
58-432  MFG (Manufacturing)  
433-446  TRNS (Transportation)  
447-455  TRDE (Trade)  
456-462  FIRE (Finance, Insurance and Real Estate)  
463-509  SERV (Service)  
510-523  GOVT (Government)  
524  MISC (Miscellaneous)  

If the user obtains the IMPLAN software, the three data files can be 
constructed by following the instructions and constructing an aggregated 
Input-Output account using an existing or built-in template for 1-digit SIC 
classification. 
15.5.2.2 Specifying Indirect Loss Factors 
In addition to applying IMPLAN Input-Output data for the study region, a 
User-Supplied Data Analysis can involve adjusting module parameters to more 
closely fit the study region and disaster being modeled.  Parameter sets and 
selection algorithms are suggested below for both the four indirect loss 
 factors  -- supplemental imports, new export markets, inventories supply, 
and inventories demand -- and industry restoration and rebuilding. 
As previously noted in the Default Data Analysis discussion, availability of 
supplemental imports and new export markets is related in part to the size or 
level of aggregation of the study region and its geographic situation.  A 
single county making up part of a large metropolitan area would have a much 
higher new import/export capacity (i.e., to neighboring counties) than would 
a single-county city that was geographically a distinct urban area and at 
some distance from other urban areas. Table 15.20 suggests two possible sets 
of factor values for geographically  distinct  and  component  study regions 
based on expert opinion.   
Table 15.20 Suggested Indirect Economic Loss Factors (Percentage Points) 

Industry  Distinct Region  Component Region  
Imports  Inv. Supply  Inv. Demand  Exports  Imports  Inv. Supply  Inv. Demand  
Exports  
AGR  5  0  0  20  6  0  0  35  
MINE  5  0  0  30  6  0  0  45  
CON  999  0  0  10  999  0  0  25  
MFG  4  1  1  30  6  1  1  45  
TRNS  2  0  0  0  4  0  0  0  
TRDE  3  1  1  0  5  1  1  0  
FIRE  3  0  0  0  5  0  0  0  
SVC  3  0  0  0  5  0  0  0  
GOVT  3  0  0  0  5  0  0  0  
OTHER  4  0  0  0  6  0  0  0  

Selection of appropriate restoration and rebuilding parameters presents a 
more complex problem because of the need to link these values to physical 
damage levels in the disaster.  Industry functional restoration and 
rebuilding will generally proceed more slowly with increasing severity of the 
disaster and extent of physical damage.  For this reason, it is recommended 
that to run a User-Supplied Data Analysis for Indirect Economic Loss, the 
user first run all of the preceding modules in HAZUS, examine the damage 
results, modify the restoration and rebuilding parameters as appropriate, and 
then finally run the Indirect Loss module.  Several example restoration and 
rebuilding parameter sets designed based on expert opinion to represent 
different scales of disaster are presented below, together with a suggested 
algorithm for the user to select the most appropriate one. 
The following suggested procedure attempts to provide a rough but simple and 
credible link between restoration and rebuilding parameters in the Indirect 
Loss module and HAZUS results on physical damage.  Lifeline rebuilding and 
transportation industry functional restoration are linked to highway bridge 
damage.  Manufacturing industry restoration is linked to industrial building 
damage.  Buildings rebuilding and restoration for all other industries is 
linked to commercial building damage.  The values of the industry functional 
restoration parameters are intended to reflect not only facility damage 
levels but also each industry s resiliency to damage to its facilities, such 
as for example its ability to relocate or utilize alternative facilities.  
These parameters were derived judgmentally with consideration of observations 
from previous disasters.  Note that values for  restoration  in HAZUS 
represent the percent loss of industry function averaged over the year. 
STEP 1. Calculate damage indices for highway bridges and commercial and 
industrial buildings, respectively.  The damage index consists of the percent 
of structures in the  extensive  or  complete  damage states.  For example, 
if results indicate that 5 percent of bridges will suffer  extensive  damage 
and 3 percent  complete  damage, the damage index is 8 percent. Damage 
results for bridges can be found in the HAZUS summary report on 
Transportation Highway Bridge Damage.  Damage results for commercial and 
industrial buildings can be found in the HAZUS summary report on Building 
Damage by General Occupancy. 
STEP 2. Select transportation industry restoration parameters and rebuilding 
parameters for lifelines. Use the highway bridge damage index from Step 1 to 
read off parameters from Table 15.21. 
STEP 3. Select manufacturing industry restoration parameters.  Use the 
industrial building damage index from Step 1 to read off parameters from 
Table 15.22. 
STEP 4. Select restoration parameters for all other industries and rebuilding 
parameters for buildings.  Use the commercial building damage index from Step 
1 to read off parameters from Table 15.23. 
Table 15.21 Transportation Restoration and Lifeline Rebuilding Parameters  
(Percentage Points) 

Highway Bridge Damage Index  Impact Description  Parameter Set  Year 1  Year 
2  Year 3  Year 4  Year 5  
0%  None/ minimal  Restoration function - TRNS Ind.  0  0  0  0  0  
Rebuilding expenditures - Lifelines  100  0  0  0  0  
0-1%  Minor  Restoration function - TRNS Ind.  2  0  0  0  0  
Rebuilding expenditures - Lifelines  100  0  0  0  0  
1-5%  Moderate  Restoration function - TRNS Ind.  5  0  0  0  0  
Rebuilding expenditures - Lifelines  95  5  0  0  0  
5-10%  Mod.-major  Restoration function - TRNS Ind.  10  2  0  0  0  
Rebuilding expenditures - Lifelines  90  10  0  0  0  
10-20%  Major  Restoration function - TRNS Ind.  15  3  0  0  0  
Rebuilding expenditures - Lifelines  85  15  0  0  0  
>20%  Catastrophic  Restoration function - TRNS Ind.  20  5  0  0  0  
Rebuilding expenditures - Lifelines  80  20  0  0  0  

Table 15.22 Manufacturing Restoration Parameters (percentage points) 

Industrial building damage index  Impact description  Parameter Set  Year 1  
Year 2  Year 3  Year 4  Year 5  
0%  None/minor  Restoration function - MFG Ind.  1  0  0  0  0  
0-1%  Moderate  Restoration function - MFG Ind.  2  0  0  0  0  
1-5%  Mod.-major  Restoration function - MFG Ind.  4  0  0  0  0  
5-10%  Major  Restoration function - MFG Ind.  8  2  0  0  0  
>10%  Catastrophic  Restoration function - MFG Ind.  20  10  5  0  0  

Table 15.23 All Other Industries Restoration and Buildings Rebuilding 
Parameters (percentage points) 
Commercial bldg. damage index  Impact description  Parameter Set  Year 1  
Year 2  Year 3  Year 4  Year 5  
0%  None/minor  Restoration function - AG Ind.  0  0  0  0  0  
Restoration function - MINE Ind.  0  0  0  0  0  
Restoration function - CNST Ind.  0  0  0  0  0  
Restoration function - TRDE Ind.  1  0  0  0  0  
Restoration function - FIRE Ind.  0  0  0  0  0  
Restoration function - SERV Ind.  1  0  0  0  0  
Restoration function - GOVT Ind.  1  0  0  0  0  
Restoration function - MISC Ind.  1  0  0  0  0  
Rebuilding expenditures - buildings  100  0  0  0  0  
0-1%  Moderate  Restoration function - AG Ind.  0  0  0  0  0  
Restoration function - MINE Ind.  0  0  0  0  0  
Restoration function - CNST Ind.  1  0  0  0  0  
Restoration function - TRDE Ind.  2  0  0  0  0  
Restoration function - FIRE Ind.  1  0  0  0  0  
Restoration function - SERV Ind.  2  0  0  0  0  
Restoration function - GOVT Ind.  2  0  0  0  0  
Restoration function - MISC Ind.  2  0  0  0  0  
Rebuilding expenditures - buildings  80  20  0  0  0  

Table 15.23 All Other Industries Restoration and Buildings Rebuilding 
Parameters (percentage points) (Continued) 

Commercial bldg. damage index  Impact description  Parameter Set  Year 1  
Year 2  Year 3  Year 4  Year 5  
1-5%  Mod.-major  Restoration function - AG Ind.  0  0  0  0  0  
Restoration function - MINE Ind.  0  0  0  0  0  
Restoration function - CNST Ind.  2  0  0  0  0  
Restoration function - TRDE Ind.  4  0  0  0  0  
Restoration function - FIRE Ind.  2  0  0  0  0  
Restoration function - SERV Ind.  4  0  0  0  0  
Restoration function - GOVT Ind.  4  0  0  0  0  
Restoration function - MISC Ind.  4  0  0  0  0  
Rebuilding expenditures - buildings  70  30  0  0  0  
5-10%  Major  Restoration function - AG Ind.  1  0  0  0  0  
Restoration function - MINE Ind.  1  0  0  0  0  
Restoration function - CNST Ind.  4  0  0  0  0  
Restoration function - TRDE Ind.  8  2  0  0  0  
Restoration function - FIRE Ind.  4  0  0  0  0  
Restoration function - SERV Ind.  8  2  0  0  0  
Restoration function - GOVT Ind.  8  2  0  0  0  
Restoration function - MISC Ind.  8  2  0  0  0  
Rebuilding expenditures - buildings  60  30  10  0  0  
>10%  Catastrophic  Restoration function - AG Ind.  2  0  0  0  0  
Restoration function - MINE Ind.  2  0  0  0  0  
Restoration function - CNST Ind.  10  5  0  0  0  
Restoration function - TRDE Ind.  20  10  5  0  0  
Restoration function - FIRE Ind.  10  5  0  0  0  
Restoration function - SERV Ind.  20  10  5  0  0  
Restoration function - GOVT Ind.  20  10  5  0  0  
Restoration function - MISC Ind.  20  10  5  0  0  
Rebuilding expenditures - buildings  50  30  15  5  0  

15.6 Example Solutions 
The intended use of the module is to provide a rapid and reasonable 
projection of the economic consequences of an actual disaster, or a set of 
hypothetical disasters.  These economic consequences include lost employment, 
income, and tax revenue, as well as the distributional (winners and losers) 
impacts of an event.  Since local planners/emergency managers are unlikely to 
have experienced the economic repercussions of disaster, the module is as 
much an educational tool as it is a forecasting tool. The module is best 
suited: 
1.	
to help provide information (loss estimates) to the news media; 

2.	
to help in the development of disaster declaration applications; 

3.	
to help evaluate the indirect benefits of mitigation efforts; 

4.	
to help in the preparation of post disaster fiscal projections. 


It is important to recognize the limitations of the module.  HAZUS is not a 
replacement for local economic/financial projections.  It should be exercised 
in conjunction with the community s existing financial planning framework.  
It is intended to supplement and inform normal and ongoing economic 
projections.  
The examples below are presented with several objectives in mind: 
1.	To educate users about the principles of indirect loss, e.g.,  
a. 	
indirect loss as opposed to total economic impact 

b. 	
the interaction of indirect stimulus and bottleneck effects 

c. 	
the time profile of economic consequences (stimulus possibly followed by the 
dampening effects of indebtedness) 


2.	To illustrate how terms are used and what they mean 
a.
 indirect vs. direct 

b. 	
indirect vs. total economic impact 

c. 	
indirect gains and losses 

d. 	
local vs. regional or national economic consequences 


3.	To provide examples of pitfalls (misunderstandings) 
a. 	
how to avoid double counting losses 

b. 	
how to account for postponed production vs. lost production 

c. 	
how to separate the effects of the disaster from the effects of other factors 
occurring after the disaster but unrelated to the disaster (e.g., the effect 
of the terrorist attack from corporate accounting scandals) 


15-54 
4.	To show how to account properly in order to arrive at a credible loss 
assessment 
a. 	
what to include (income losses that flow directly of indirectly from the 
event) and exclude (financial repercussions) 

b. 	
what to focus on (lost income rather than lost regional product) 


5.	To show how the results compare with observed economic consequences 
Examples were chosen with these objectives in mind.   The examples are 
intended to be realistic, highlighting the kinds of information needed by the 
media, FEMA and governor s office (the paper work and data needed for the 
declaration of disaster), and mitigation efforts that utilize cost benefit 
methods.   
It is unlikely that users will need to contrast actual economic impacts with 
those forecast by the IELM. However, examples of how closely the IELM 
projections track the actuals are included so that users may gain some 
confidence in the IELM. 
15.7 References 
1.	
Applied Technology Council. 1991.  ATC-25:  Seismic Vulnerability and Impact 
of Disruption on Lifelines in the Conterminous United States.  Federal 
Emergency Management Agency, Applied Technology Council, CA. 

2.	
Boadway, R. and N. Bruce. 1984. Welfare Economics, Oxford: Basil Blackwell. 

3.	
Boisvert, R. 1992.  Direct and Indirect Economic Losses from Lifeline 
Damage,  in Indirect Economics Consequences of a Catastrophic Earthquake, 
Final Report by Development Technologies to the Federal Emergency Management 
Agency. 

4.	
Brookshire, D. and M. McKee. 1992.  Other Indirect Costs and Losses from 
Earthquakes: Issues and Estimation,  in Indirect Consequences of a 
Catastrophic Earthquake, Final Report by Development Technologies to the 
Federal Emergency Management Agency. 

5.	
Cochrane, H. 1997.  Forecasting the Economic Impact of a Mid-West 
Earthquake,  in B. Jones (ed.), Economic Consequences of Earthquakes: 
Preparing for the Unexpected. Buffalo, NY: MCEER. 

6.	
Cochrane, H. 1974.  Predicting the Economic Impact of Earthquakes,  in H. 
Cochrane et al., eds., Social Science Perspectives on the Coming San 
Francisco Earthquake, Natural Hazards Research Paper No.25, NHRAIC, 
University of Colorado, Boulder, CO. 

7.	
Ellson, R., J. Milliman, and R. Roberts. 1984.  Measuring the Regional 
Effects of Earthquakes and Earthquake Predictions,  Journal of Regional 
Science 24: 561-79. 

8.	
Guimaraes, P., F. Hefner, and D. Woodward.  1993.   Wealth and Income Effects 
of Natural Disasters: An Econometric Analysis of Hurricane Hugo,  Review of 
Regional Studies 23: 97-114. 

9.	
Hanson, K. and A. Rose. 1997.  Factor Productivity and Income Inequality:  A 
General Equilibrium Analysis,  Applied Economics 29: 1061-71. 

10. 
Heinz Center for Science, Economics and the Environment.  	2000. The Hidden 
Costs of Coastal Hazards: Implications for Risk Assessment and Mitigation. 
Washington, DC: Island Press. 

11. 
Jones, B.G. and S.E. Chang. 1995.  Economic Aspects of Urban Vulnerability 
and Disaster Mitigation,  in Urban Disaster Mitigation: The Role of 
Engineering and Technology, eds. 

F.Y. Cheng and M.S. Sheu, Oxford: Elsevier Science Ltd., pp.311-320. 

12. 
Li, P.C., A. Rose, and B. Eduardo. 	1999.  "Construction of an Input-Output 
Income Distribution Matrix for the U.S.," in G. Hewings et al. (eds.) 
Understanding and Interpreting Economic Structure, Heidelberg:  Springer.   

13. 
Miller, R. E. and P. D. Blair.  	1985. Input-Output Analysis: Foundations 
and Extensions. Englewood Cliffs, NJ: Prentice-Hall. 

14. 
Oladosu, G. 	2000.  A Non-Market Computable General Equilibrium Model 
for Evaluating the Economic Impacts of Climate Change in the Susquehanna 
River Basin,  Ph.D. Thesis, The Pennsylvania State University, University 
Park, PA. 

15. 
Oosterhaven, J. 	1988.  On the Plausibility of the Supply-Driven Input-
Output Model,  Journal of Regional Science 28: 203-17. 

16. 
Partridge, M. and D. Rickman. 1998.  Regional Computable General Equilibrium 
Modeling: A Survey and Critical Appraisal,  International Regional Science 
Review 21: 205-48. 

17. 
Rose, A. 1995.  Input-Output Economics and Computable General Equilibrium 
Models,  Structural Change and Economic Dynamics 6: 295-304. 

18. 
Rose, A. 	2002.  Model Validation in Estimating Higher-Order Economic 
Losses from Natural Hazards,  in C. Taylor and E. VanMarcke, eds., Acceptable 
Risk to Lifeline Systems from Natural Hazard Threats, New York: American 
Society of Civil Engineers.  

19. 
Rose, A. and T. Allison.  	1988.  On the Plausibility of the Supply-Driven 
Input-Output Model: Empirical Evidence on Joint Stability,  Journal of 
Regional Science 29: 451-58. 

20. 
Rose, A. and P. Beaumont. 	1988.  Interrelational Income-Distribution 
Multipliers for the West Virginia Economy,  Journal of Regional Science 28: 
461-75. 

21. 
Rose, A. and J. Benavides. 	1998.  Regional Economic Impacts,  in M. 
Shinozuka et al. (eds.), Engineering and Socioeconomic Impacts of 
Earthquakes:  An Analysis of Electricity Lifeline Disruptions in the New 
Madrid Area.  Buffalo, NY: MCEER. 

22. 
Rose, A. and G. Guha. 	1999.  Computable General Equilibrium Modeling of 
Electric Utility Lifeline Losses from Earthquakes,  Paper presented at the 
North American Meetings of the Regional Science Association International, 
Montreal, Canada. 

23. 
Rose, A. and S. Liao. 	2002.  Modeling Regional Economic Resiliency to 
Earthquakes:  A Computable General Equilibrium Analysis of Water Service 
Disruptions,  in Proceedings of the 7th National Conference on Earthquake 
Engineering, Oakland, CA: EERI. 

24. 
Rose, A. and D. Lim.	 2002.  Business Interruption Losses from Natural 
Hazards: Conceptual and Methodological Issues in the Case of the Northridge 
Earthquake,  Global Environmental Change B: Environmental Hazards, July. 

25. 
Rose, A., B. Stevens, and G. Davis. 1998. Natural Resource Policy and Income 
Distribution. Baltimore, MD: Johns Hopkins University Press. 

26. 
Rose, A. and W. Miernyk.  	1989.  Input-Output Analysis:  The First Fifty 
Years,  Economic Systems Research 1: 229-71. 

27. 
Rose, A. and B. H. Stevens. 	1991.  Transboundary Income and Expenditure 
Flows in Regional Input-Output Models,  Journal of Regional Science 31: 253-
72. 

28. 
Shoven, J. and J. Whalley.  	1992. Applying General Equilibrium. New York: 
Cambridge University Press. 

29. 
Strauss, C and B. Lord. 	1997. Economic Impact of Travel and Tourism in 
Southwestern Pennsylvania during 1995. Report to the Southwestern 
Pennsylvania Heritage Preservation Commission, Hollidaysburg, PA.  

30. 
Tyrrell, T. 	2001. Rhode Island Travel and Tourism Research Report. 
Office of Travel, Tourism and Recreation, Dept. of Resource Economics, 
University of Rhode Island.  Vol. 18, No. 1. 



Appendix 15A. Default Data Analysis, Synthetic Economies 
Excluding agricultural counties, 113 state and county IMPLAN tables were 
analyzed to derive synthetic transactions matrices for the Default Data 
Analysis model.  Four size classes were created resulting in the 12 way 
classification shown below (Table 15A.1).  A frequency histogram of 
employment (See Tables 15A.2 through 15A.4) revealed that 90 percent of the 
tables could be classified as Manufacturing/Service, Service/Manufacturing, 
or Service/Trade. Since nearly two thirds of employment in these tables can 
be traced to these three sectors, it was decided that this means of 
classifying economies could be used as a basis for deriving Default Data 
Analysis interindustry trade flows. Further adjustments were made to reflect 
the size of the economy.  The particular states and counties which were 
utilized to create the 12 synthetic tables are shown in Tables 15A.5 through 
15A.6. 
Table 15A.1 Classification of Synthetic Economies 
Employment  Type  
Upper Bound  Lower Bound  Manufacturing/ Service  Service/ Manufacturing  
Service/Trade  
unlimited  2 million  SUP1  SUP2  SUP3  
2 million  .6 million  LAR1  LAR2  LAR3  
.6 million  30,000  MID1  MID2  MID3  
30,000  0  LOW1  LOW2  LOW3  

Table 15A.2 Manufacturing/Service 
Sector  0  10  20  30  40  50  60  70  80  90  100  AVG  
Manufacturing  0  0  0  9  25  10  4  1  0  0  0  37.5%  
Government  0  0  14  35  0  0  0  0  0  0  0  21.5%  
FIRE  0  3  44  2  0  0  0  0  0  0  0  13.6%  
Trade  0  42  7  0  0  0  0  0  0  0  0  7.5%  
Service  0  46  3  0  0  0  0  0  0  0  0  6.3%  
Construction  0  46  3  0  0  0  0  0  0  0  0  6.3%  
Transportation  0  48  1  0  0  0  0  0  0  0  0  6.1%  
Agriculture  0  49  0  0  0  0  0  0  0  0  0  0.6%  
Mining  0  49  0  0  0  0  0  0  0  0  0  0.6%  

15A-2 
Table 15A.3 Service/Manufacturing 
Sector  0  10  20  30  40  50  60  70  80  90  100  AVG  
Government  0  0  1  20  11  1  0  0  0  0  0  28.6%  
Manufacturing  0  0  12  18  2  0  1  0  0  0  0  23.4%  
FIRE  0  2  29  2  0  0  0  0  0  0  0  13.9%  
Trade  0  27  6  0  0  0  0  0  0  0  0  8.4%  
Transportation  0  25  8  0  0  0  0  0  0  0  0  8.3%  
Service  0  28  5  0  0  0  0  0  0  0  0  7.8%  
Construction  0  28  5  0  0  0  0  0  0  0  0  7.1%  
Mining  0  32  1  0  0  0  0  0  0  0  0  2.2%  
Agriculture  0  33  0  0  0  0  0  0  0  0  0  0.4%  

Table 15A.4 Service/Trade 
Sector  0  10  20  30  40  50  60  70  80  90  100  AVG  
Government  0  0  0  2  7  6  0  1  0  0  0  37.4%  
Service  0  1  8  7  0  0  0  0  0  0  0  18.2%  
Transportation  0  10  6  0  0  0  0  0  0  0  0  9.3%  
Manufacturing  0  9  7  0  0  0  0  0  0  0  0  9.2%  
Construction  0  13  3  0  0  0  0  0  0  0  0  7.8%  
FIRE  0  13  3  0  0  0  0  0  0  0  0  7.4%  
Trade  0  14  2  0  0  0  0  0  0  0  0  6.0%  
Mining  0  13  2  1  0  0  0  0  0  0  0  4.1%  
Agriculture  0  16  0  0  0  0  0  0  0  0  0  0.5%  

Table 15A.5 Manufacturing/Service Economy 

15A-4 
Table 15A.6 Service/Manufacturing Economy 
Super  Large  
Fips  State/Cnty.  Employ.  Fips  State/Cnty.  Employ.  
36,000  New York  9,747,535  19,000  Iowa  1,635,164  
6,037  Los Angeles, CA  5,108,213  40,000  Oklahoma  1,614,109  
48,000  Texas  8,900,073  4,013  Maricopa, AZ  1,212,392  
34,000  New Jersey  4,327,815  22,000  Louisiana  1,969,967  
25,000  Massachusetts  3,644,604  5,000  Arkansas  1,194,095  
6,000  California  16,532,145  31,000  Nebraska  987,260  
13,000  Georgia  3,673,183  54,000  West Virginia  769,662  
51,000  Virginia  3,695,334  4,000  Arizona  1,870,344  
24,000  Maryland  2,697,448  20,000  Kansas  1,485,215  
8,000  Colorado  2,017,818  49,000  Utah  895,454  
Mid  Low  
Fips  State/Cnty.  Employ.  Fips  State/Cnty.  Employ.  
35,001  Bernalillo, NM  306,176  35,041  Roosevelt, NM  7,593  
53,053  Pierce, WA  263,512  
41,051  Multnomah, OR  441,788  
53,063  Spokane, WA  192,662  
48,085  Collin, TX  103,086  
6,089  Shasta, CA  71,398  
48,485  Wichita, TX  74,491  
49,011  Davis, UT  78,170  
6,071  San Bernardino, CA  529,198   
49,035  Salt Lake, UT  436,832  
6,065  Riverside, CA  434,846  
6,111  Ventura, CA  313,911  

Table 15A.7 Service/Trade Economy 
Super  Large  
FIPS  STATE/CNTY.  EMPLOY.  FIPS  STATE/CNTY.  EMPLOY.  
None  11,000  District of Columbia  761,680  
32,000  Nevada  741,574  
15,000  Hawaii  696,759  
35,000  New Mexico  745,539  
Mid  Low  
FIPS  STATE/CNTY.  EMPLOY.  FIPS  STATE/CNTY.  EMPLOY.  
30,000  Montana  433,623  48,397  Rockwall, TX  9,140  
8,005  Arapahoe, CO  217,208  8,067  La Plata, CO  19,079  
4,003  Cochise, AZ  39,611  56,001  Albany, WY  16,959  
38,000  North Dakota  377,987  56,041  Uinta, WY  9,948  
6,029  Kern, CA  262,422  55,125  Vilas, WI  8,364  
56,021  Laramie, WY  44,438  35,061  Valencia, NM  11,787  

The development of the synthetic tables for agricultural economies, according 
to nine Farm Resource Regions, was discussed in Section 15.4.6.1 in the main 
text.  The counties from which the synthetic tables were developed for each 
region are shown in Table 15A.8. 
Table 15A.8 Agricultural Counties Used in Synthetic Tables by Region 
Fips Code  County  State  Population  Area  ERS Region  
19123  Mahaska County  IA  21522  8977  1  
29177  Ray County  MO  21971  8611  1  
29163  Pike County  MO  15969  7128  1  
18163  Vanderburgh County  IN  165058  72637  1  
17161  Rock Island County  IL  148723  63327  1  
55053  Jackson County  WI  16588  7627  2  
26037  Clinton County  MI  57883  20959  2  
50007  Chittenden County  VT  131761  52095  2  
27129  Renville County  MN  17673  7442  2  
42077  Lehigh County  PA  291130  118335  2  
38083  Sheridan County  ND  2148  1061  3  
31117  McPherson County  NE  546  257  3  
38009  Bottineau County  ND  8011  4661  3  
30091  Sheridan County  MT  4732  2417  3  

15A-6 
Table 15A.8 Agricultural Counties Used in Synthetic Tables by Region 
(Continued) 
Fips Code  County  State  Population  Area  ERS Region  
46005  Beadle County  SD  18253  8093  3  
48197  Hardeman County  TX  5283  2678  4  
48205  Hartley County  TX  3634  1541  4  
20057  Ford County  KS  27463  10842  4  
20079  Harvey County  KS  31028  12290  4  
48369  Parmer County  TX  9863  3685  4  
21001  Adair County  KY  15360  6434  5  
40089  McCurtain County  OK  33433  13828  5  
51105  Lee County  VA  24496  10263  5  
42019  Butler County  PA  152013  59061  5  
1115  St. Clair County  AL  50009  20382  5  
45071  Newberry County  SC  33172  14455  6  
13123  Gilmer County  GA  13368  6986  6  
24029  Kent County  MD  17842  8181  6  
51075  Goochland County  VA  14163  5203  6  
13131  Grady County  GA  20279  8129  6  
4027  Yuma County  AZ  106895  46541  7  
6107  Tulare County  CA  311921  105013  7  
12107  Putnam County  FL  65070  31840  7  
12117  Seminole County  FL  287529  117845  7  
53035  Kitsap County  WA  189731  74038  7  
32033  White Pine County  NV  9264  3982  8  
8109  Saguache County  CO  4619  2306  8  
56023  Lincoln County  WY  12625  5409  8  
32017  Lincoln County  NV  3775  1800  8  
41025  Harney County  OR  7060  3305  8  
47069  Hardeman County  TN  23377  9174  9  
22019  Calcasieu Parish  LA  168134  66426  9  
28021  Claiborne County  MS  11370  4099  9  
5017  Chicot County  AR  15713  6191  9  
22063  Livingston Parish  LA  70526  26848  9  

Chapter 16.  Additional Capabilities 
The Flood Model Oversight Committee identified specific items that they 
believed would enhance the user community s acceptance of the flood model.  
These capabilities provided a level of  What-if  functionality to the user 
allowing them to utilize the flood model as a planning tool. Identified as 
additional capabilities, the Flood Committee established assessing the 
impacts of a levee or upstream storage as additional capabilities necessary 
for user acceptance. 
The following sections continue the discussion of the hazard development as 
related to the capability of performing this analysis. 
16.1 Levees 
The levee  What-If  is primarily intended for Level 1 users since the base 
assumption is that a Level 2 user will perform a detailed hydrologic and 
hydraulic analysis that would include the levee and that would be brought in 
through the FIT.  In general, DEMs are not reliable for identifying a 
continuous embankment with relatively little width.  Because grid cells are 
connected at the corners as well as the sides, an embankment that is not a 
straight line, in the strictest sense, must be at least two cells wide to be 
treated as a barrier to flow.  A tool is available in HAZUS to add a levee 
alignment, attribute the levee with a level of protection and, for Level 1 
analyses determines (within the limits of a Level 1 analysis) the effects of 
a levee on flood depths within the unprotected portion of the floodplain. 
In areas identified as protected by a levee, flood depths are zero for 
frequencies up to the recurrence interval of the level of protection provided 
by the levee.  For recurrence intervals exceeding the level of protection, 
flood depths are those computed without consideration of the levee. 
Similarly, if the option to determine the ramifications of a levee is chosen, 
two sets of flood depth grids are created: one with the levee and one without 
the levee reflected in the DEM. 
The levee option is applied by drawing a polyline with the mouse.  Flood 
depth grids have been created for the reach and the user chooses a grid on 
which to draw the levee alignment.  The alignment should cross the floodplain 
twice.  The user is prompted to supply the recurrence interval, in years, 
corresponding to the level of protection provided by the levee. 
If a flood depth grid has been created corresponding to the level of 
protection or if enough grids have been created to interpolate that 
particular grid, the floodplain associated with that grid is determined.  The 
levee alignment and section of that floodplain between the points where the 
alignment crosses the floodplain are used to define a polygon.  If the 
floodplain associated with the recurrence interval cannot be determined, the 
floodplain associated with the flood depth grid chosen to draw the alignment 
is used to define the polygon. 
If the levee alignment does not cross the floodplain twice the user is 
notified and cautioned that the floodplain information and supplied levee 
alignment indicate that the levee does not provide the entered level of 
protection. 
If flood depth grids were developed with Level 1 analyses, the user must re-
create the depth grids if the user wants an added levee represented in the 
DEM.  Note that because the default hydraulic analyses are performed using 
normal depth calculations (i.e., no consideration of backwater effects), 
flood elevations and, consequently, flood depths and the extent of 
floodplains will change only at cross sections within the levied portion of 
the reach.  The effects of the levee on upstream cross sections will not be 
reflected. 
If the user chooses to investigate the local increases in flood depths 
resulting from a levee alignment, a buffer is created one cell size around 
the user-supplied polyline.  The resulting polygon is attributed with a high 
elevation value and a grid is created from the polygon.  Note that the grid, 
or levee, is everywhere at least two cells wide.  That grid is merged with 
the DEM creating a new DEM that reflects a continuous levee. The  new  DEM is 
only being created as a  provisional DEM  with the levee represented, but the 
original DEM is still the one that remains displayed on the map and not 
replaced with a  new  DEM.  The protected area is then treated as a  pool  
and, consequently not included in the water surface elevation computations.  
Figure 
16.2 shows a (buffered) levee alignment supplied by a user and upstream 
portion of the  without  levee flood depth grid shown in Figure 16.1. 


Figure 16-3 shows the affects of the levee on the flood depth grid.  Note, 
for example, the increase in the non-conveyance areas across the stream from 
the levee. 

HAZUS-MH MR3 Technical Manual 
16-4 
16.2 Flow Regulation 
The default hydrologic analyses apply to unregulated drainage areas.  
Regulation, through diversions and/or storage, changes the flood frequency 
curves downstream. HAZUS provides a tool for incorporating the downstream 
effects of flow regulation.  The tool allows users to modify the unregulated 
flood frequency curve at a specific location by entering one or more pairs of 
recurrence intervals and discharge values.  HAZUS identifies downstream 
reaches affected, and modifies the corresponding flood frequency curves as 
appropriate. 
Users identify, with the mouse, the location of a regulating structure, such 
as a flood control reservoir. The algorithm finds the drainage area upstream 
of that location and defines the unregulated flood frequency curve.  The 
curve is plotted and a table of recurrence intervals and associated discharge 
values is presented for the user to peruse and modify. 
As the user enters and/or modifies values in the table, both the curve and 
the table are revised to reflect the changes. The first modification results 
in revising all discharge values associated with recurrence intervals 
(frequencies) less (greater) than the user supplied recurrence interval to be 
no greater than the modified discharge value.  Graphically, the curve is 
revised by drawing a horizontal line from the modified point to the point 
where that line intersects the unregulated curve. The curve is not revised 
for recurrence intervals greater than recurrence interval of the user 
supplied point. Thus, graphically, a vertical line is drawn from the modified 
point to the point where that line intersects the unregulated curve. 
For example, Figure 16-4 shows the unregulated flood frequency curve 
associated with the most downstream reach of the North Fork of the Shenandoah 
River (solid line).  The drainage area there is approximately 1320 square 
miles. 
16-5 

Flood Frequency 

1,000,000 
100,000 
10,000 
1,000 

99.95 99.9 99.8 99.5 99 98 9695 90 80 70 60 50 40 30 20 10 5 4 2 1 0.5 0.2 
0.1 0.05 
Probability [Q>Discharge]  (percent) 
HAZUS-MH MR3 Technical Manual 
Consider the ramifications of placing a dam within the reach and controlling 
the outflow at 14,000 cubic feet per second (cfs).  The dam would be large 
enough to control up to a 50-year flood. The regulated flood frequency curve 
at the outflow point is shown on Figure 16.4.  The revised part is shown as 
the dashed line.  The modification was accomplished by entering a 50year 
discharge value of 14,000 cfs, shown as the triangle on the curve.  
Subsequent modifications are incorporated by assuming a log-normal 
distribution (straight line on the graph) between points.  Again, the point 
associated with the smallest modified recurrence interval is connected to the 
unregulated flood frequency curve with a line of constant discharge value 
(horizontal line). The point associated with the greatest modified recurrence 
interval is connected to the unregulated curve with a line of constant 
frequency (vertical line). 
The algorithm translates the effects downstream by assuming that the 
contribution to the unregulated flow at some point coming from any portion of 
the drainage area is proportional to the size of that portion.  That is, a 
132 square-mile area contributes 10 percent of the flow to our example reach.  
For a given recurrence interval, the reduction in flow at some point 
resulting from upstream regulation is determined as follows: 
 	
The unregulated flow value is determined at the point. 

 	
That value is multiplied by the ratio of the drainage areas of the regulated 
site and the point. The product is the unregulated contribution from the 
regulated site. 

 	
The frequency associated with that unregulated contribution is determined. 

 	
The regulated flow value associated with that frequency is determined and 
subtracted from the unregulated value. 


That difference is the reduction in flow at the point resulting from the 
upstream regulation. 
The South Fork of the Shenandoah River joins the North Fork to form the 
Shenandoah River at the downstream node of our example reach.  The drainage 
area there is approximately 3000 square miles.  The 100-year flood discharge 
is approximately 142,750 cfs.  In the algorithm, the contribution from the 
North Fork is 62,810 cfs, a little less than the 50-year flood discharge 
value. The regulated flow at the potential dam site is 14,000 cfs and, 
therefore the reduction is 48,810 cfs. The effects of the dam downstream at 
the upstream node of the Shenandoah River would be to reduce the 100-year 
flood discharge value from 142,750 to about 93,940 cfs. 
Such an analysis including the accompanying loss estimation in HAZUS can be 
used to justify a more detailed investigation into regulating the flow some 
upstream. 
16.3 Velocities 
The velocity of floodwater can contribute to the flood hazard by carrying 
large amounts of sediment and debris, impacting structures, and eroding soils 
from stream banks and under foundations. Although there are limited specific 
velocity-damage curves at this time for use in a level loss analysis, the 
spatial distribution of the flood water velocities is estimated for and 
offered as supplemental hazard information.  Like flood depths, the spatial 
distribution of velocities is presented as a grid. The Level 1 user can run a 
velocity grid for any reach that has ran the riverine hazard. The Level 2 
user will have the option of running a velocity grid for their FIT areas in 
addition to the Level 1 hazard.  Velocity grids are specific to a recurrence 
interval. Velocity-frequency relations are not developed. 
The average velocity within a cross section is the discharge value of the 
flood flow through the cross section divided by the under-water area of the 
part of the cross section conveying the flow. Within a cross section, the 
velocities are generally greater in the deeper areas.  The velocity in the 
channel, for example, is generally greater than the velocity in the shallow 
overbank areas. 
Velocities in HAZUS are calculated as the ratio of flood depth to the average 
depth within a cross section.  Between cross sections, velocities are 
interpolated using the velocities determined at the up- and downstream cross 
sections at the same relative position (distance from the right and left 
conveyance boundaries) in the floodplain. The velocity grids are created 
using the same irregularly spaced grid of points used to create the flood 
depth grids.   
Recall that points are placed for every cell size along buffers, also spaced 
one cell size apart, around the floodplain centerline. Those points are 
attributed with information needed to interpolate elevations. In particular 
each point is attributed with the upstream cross section number (and, so, 
implicitly, the downstream cross section number), and the weighting factor, 
based on the relative distance between cross sections, to be used for 
interpolating.  Thus, given up and downstream values, the value at each point 
is quickly determined.  Unlike interpolating water surfaces, the velocity 
along a cross section varies. 
The algorithm samples each cross section to find the  widest  cross section 
in the reach.  The widest cross section is the cross section with the 
greatest length within the conveyance area. That length is divided by the 
cell size to define a partitioning (into P segments) of the conveyance area. 
Each cross section is divided into P segments and the depth at the center of 
each segment is recorded and saved in a list. 
The average depth in each list multiplied by the width of the conveyance area 
at the corresponding cross section is the aforementioned under-water area.  
Dividing the discharge value at the cross section with that area gives the 
average velocity of floodwater within the cross section. The average velocity 
times the ratio of the depth at a given segment and the average depth is 
taken to be the velocity within that segment. 
Each point in the irregularly spaced grid that is outside of the conveyance 
polygon is attributed with a velocity of zero. For points within the 
conveyance polygon, a polyline connecting the closest (to the point) edges of 
the polygon through the centerline and point is created and the 
HAZUS-MH MR3 Technical Manual 
position, in percent of total length, of the point along that line is 
determined.  The integer part of that percent times P, the number of 
partitions, is the position (entry number) of the corresponding velocity 
values in the up- and downstream lists.  Using those list entries and the 
interpolating information associated with the point, the velocity is 
determined and assigned to the point for the recurrence interval studied. 
Figure 16.5 shows the spatial distribution of velocities associated with the 
depth grids shown in Figure 16.1. 

16.4 Policy Analysis 
An example of each case has been performed in the HAZUS software and a step-
by-step discussion to guide the user through the process. 
16.4.1 Floodplain Regulation   BFE+1 Foot 
This example demonstrates how the user can determine the impacts of the 
creation of modification of the floodplain regulatory requirements.  Base 
Flood Elevation (BFE) is the height of the base flood, usually in feet, in 
relation to the National Geodetic Vertical Datum of 1929, the North American 
Vertical Datum of 1988, or other datum referenced in the Flood Insurance 
Study Report, or average depth of the base flood, usually in feet, above the 
ground surface. Regulation sets BFE and development standards.  HAZUS will 
only show dollar loss associated with changes in first floor height to meet 
regulations.  The example analyzes the impact of requiring that every house 
within the floodplain be either built or retrofitted to BFE+1 foot. The 
example includes a Level 1 analysis using the baseline general building stock 
data, and a Level 2 analysis using site-specific user-defined building 
inventory data. 
In order to start the process, the user can run an analysis using the default 
mapping scheme to determine a baseline.  For example, a community that is 
trying to identify the losses avoided from joining the NFIP program in 1980, 
could run the analysis with the default pre-FIRM settings and develop losses. 
To then determine the losses avoided, the Level 1 user should adjust the 
foundation heights using the Flood Mapping Scheme dialogs to simulate the 
implementation of floodplain regulations. The Flood Mapping Scheme dialogs 
are as appears in Figure 16.6 below. 
HAZUS-MH MR3 Technical Manual 

Once the user defined occupancy mapping has been created, as seen in Figure 
16.7 below, the user should edit the foundation heights to meet the BFE to 
which they are interested in regulating, or are regulating to. 

For example in Figure 16.8 below the user has changed the Post-FIRM 
foundation heights to closely reflect a requirement to exceed a BFE of 4-feet 
by one foot.  The user should also be aware to ensure that foundation types 
that become restricted because of this requirement are set to zero. In other 
words, the requirement to have a first floor height of at least 5-feet 
indicates that slab on grade foundations are not likely to be used, and other 
foundation types such as fill are limited in their use. 
HAZUS-MH MR3 Technical Manual 

The user would then create or duplicate the previous scenario (where the user 
calculated all Pre-FIRM foundations) and re-run the analysis using the user 
defined Flood Mapping Occupancy Scheme and compare the results.  This can be 
done by exporting the results into Excel or MS Access. 
The Level 2 user has the advantage of using the User Defined data with the 
associated detailed information such as foundation height.  As with the Level 
1 user, the user would start by running a baseline analysis using the data as 
brought into HAZUS.  This could be done using the Inventory Collection and 
Survey Tool (InCAST) or by importing a dataset previously formatted with the 
necessary information into the User Defined facility tables. The reader 
should refer to the InCAST User Manual for directions on how to use that 
tool.  The baseline analysis will later be compared to results with using the 
modified inventory. 
The modification is simple.  The user can ether perform a query in the 
initial database and create a duplicate database with the foundations 
elevated to meet the BFE+1-foot requirement.  The revised data will then be 
analyzed and the results compared and the results can be defined as the 
losses avoided by the modification of the inventory. 
16.4.2 Flood Mapping Restudies 
This example demonstrates the use of HAZUS to analyze losses through the use 
of updated floodplain boundaries that result from floodplain mapping re-
studies.  The purpose is to demonstrate the value of re-mapping in land use 
planning and the resultant reduction in flood losses. The use of HAZUS to 
analyze potential losses under current and future land use scenarios is a 
valuable tool for policy makers. 
For this analysis, the user would utilize the Flood Information Tool (FIT) to 
prepare their new study for use in the flood model.  If possible, the user 
should also prepare the previous flood study for use in the flood model.  If 
the original floodplain is in hardcopy or paper format, the user will need to 
digitize the maps.  When digitizing, the user should ensure that the 
floodplain boundary is saved as an ArcGIS polygon and the Base Flood 
Elevation (BFE) lines are digitized as ArcGIS polylines attributed with the 
BFE elevation. 
The process would be as follows: 
1.	
Create one study region and duplicate the region, or create two duplicate 
regions where the user is interested in studying the differences between the 
two studies. 

2.	
Prepare the original flood study (if the data is available) or digitize the 
flood maps as noted above using the FIT. 

3.	
Prepare the restudy for use in the flood model.  If the data is available in 
digital format, the user should ensure that the data is registered and 
process it through the FIT in preparation for use in the flood model. 

4.	
Once the two datasets are prepared, the user can use the Hazard Menu, FIT 
Project Folders submenu, either Riverine FIT Project Folders or Coastal FIT 
Project Folders to point to the created FIT areas. It is suggested that the 
user assign one region to the original study and another region to the 
restudy. 

5.	
Analyze the original flood study, either with the default HAZUS data, or with 
local input data such as a county assessor data processed through the 
Building Import Tool (BIT).  This analysis should occur in one of the study 
regions created in the Step 1 above.  The user should make sure that the 
inventories analyzed within the two regions are the same in order to ensure 
that the results represents the difference in the studies. 

6.	
Analyze the restudy using study region 2 created in Step 1.  The inventories 
should be the same inventory used in Step 5 above. 

7.	
The user can use an ODBC connection through MS Access and link to the two 
databases and query the two results tables to draw a direct comparison.  The 
tables to be linked are identified as flFRGBSEcLossTotal.  The tables contain 
the total loss by census block for the two regions. Other tables provide the 
user with differences by occupancy and by building 


HAZUS-MH MR3 Technical Manual 
16-14 
type, but the initial assessment should be based on the total difference.  
Other tables can be found in the Data Dictionary of the Flood Model User 
Manual. 
There is no duplicate process for the Level 1 user.  For assistance in 
processing the flood study data through the Flood Information Tool, the user 
should refer to the Flood Information Tool User Manual. 
16.4.3 Building Acquisition and Removal 
In this example the effects of the acquisition and removal of a single 
structure or a small number of structures on flood losses will be analyzed.  
The example discusses how the user prepares the flood hazard data within the 
FIT, utilizes the InCAST (Inventory and Collection Tool) to prepare the 
inventory data, and imports the data into HAZUS.  The example also 
demonstrates estimating annualized losses in the study area within and 
without the targeted structures. Buyout and acquisition programs are one of 
the leading approaches for reducing a communities flood risk. The buyout 
program is most effective after a flood event, when people are more willing 
to relocate away from the floodplain, but communities should be mindful that 
a buyout program can occur at any time should the homeowner be willing to 
sell.  The flood model will provide the user with an opportunity to identify 
those structures that are the best candidates for acquisition based on their 
exposure to flooding of different return periods or repetitive loss nature. 
For example one home that has the possibility of flooding from a 50-year 
flood and higher is a better candidate for acquisition than a home that is 
not likely to flood until the 100year event. 
While the user can use the Level 1 hazard for this analysis (i.e. using the 
default DEM, hydrology and hydraulics), it is not recommended that this 
analysis be the basis for determine the best candidates for an acquisition 
program unless no other studies are available.  It is recommended that the 
user utilize the FIT tool and bring in a more accurate flood study to perform 
this analysis.  
Recommendations for this analysis include:  1) using a high quality DEM and 
not the default USGS 1 arc-second data, 2) use a high quality flood study, 
preferably one done with the same DEM noted previously and it is preferred to 
use an HEC analysis, and 3) use GPS technology to identify the locations of 
the structures. 
The user should use the Inventory Collection and Survey Tool (InCAST) to 
capture the location, structure types, and occupancies of all structures of 
interest.  In order to ensure the most accurate assessment of the losses and 
potential exposure to flooding, it is recommended that a Global Positioning 
Unit (GPS) be used to identify the location of the building within its 
parcel.  With this location, the flood model can determine the depth of 
flooding at the location.  It is also important to note the foundation type 
and subsequent first floor height. NOTE: The InCAST has a field for first 
floor elevation. The user will need to convert this to a height (either using 
a DEM or by also capturing the height at the time of the site visit).  With 
the buildings loaded into the User Defined facility tables, the user should 
create a study region and select the FIT areas of interest.   
The user then has two options that is really driven by the availability of 
data.  Assuming that the user has been able to process their FIT analysis has 
three return periods and the associated discharge values, the user can 
perform an annualized loss estimate and develop their  benefit/loss  based on 
the annualized loss. To do this, the user must first perform the hazard 
assessment with enough return periods for the annualized loss.  The flood 
model uses the suite (10, 50, 100, 200, 500) as the basis for extrapolating 
the Annual Losses.  Under the Hazard menu, Compute Hazard submenu, the user 
would select the Annualized Loss hazard assessment. Once this has been 
completed, the user can the select the Annualized Loss submenu on the 
Analysis menu.  The final results present to the user the probable annualized 
loss that might be exceeded in a given year. 
The user can return to the Inventory Menu, User Defined Facilities submenu, 
and either delete the structures targeted for the acquisition program, or 
reduce the square footage to 1-foot thereby leaving the facility in the 
database.  The user can then create a new scenario and reselect the FIT areas 
for a repeat of the previous analysis.  Comparison of the results will 
provide the user with the necessary information to determine if an 
acquisition program has a potential positive benefit. 
If the user does not have multiple return periods and/or discharges, it is 
recommended that the user perform a similar analysis for the available return 
period (most likely 100-year) and determine the loss for the structures.  The 
results of this analysis will become the baseline for the analysis of the 
benefits of acquisition.  They will not have a complete assessment, but 
something to work with. 
16.4.4 Flood Forecasting 
The current methodology allows the user to estimate the potential reduction 
in flood losses due to flood forecasting or warning. The current methodology 
uses the Day curves developed by the Chicago District of the US Army Corps of 
Engineers.  The user should review the Flood Model User Manual to see how the 
dialog is used to modify the damage associated with any given flood. The 
user, however, should review the Day curves in the Technical Manual to 
estimate the amount of damage reduction that might be afforded for their 
community.  For example, if the community has historically received only 15 
minutes of flood warning, the user can view the curve and estimate the total 
expected damage reduction based on effective warning and effective response. 
The effectiveness of the warning and the effectiveness of the response should 
drive the users selection of the expected warning reduction.  For example, if 
the user believes that they can effectively warn the population, either via 
radio, TV, and perhaps police/fire notification (such as reverse 911) and the 
user also believes the population understands the notification and 
effectively responds, then they should select the value provided by the Day 
Curve.  If the user believes there are limitations to this warning and 
response, then they should select a lower value. 
HAZUS-MH MR3 Technical Manual 
16-16 
16.5 References 
1.	
EQE International,  Flood Loss Estimation:  Methods and Data Proof of 
Concept.  Final Task 2 Report , Report Developed for the National Institute 
of Building Sciences (NIBS), Washington D.C., July, 1999 

2.	
EQE International, Inc.,  Phase 2, Year 1: Model Development Progress Report 
#2,  December 29, 1999, Prepared for the National Institute of Building 
Sciences (NIBS), Washington, D.C. 


Appendix A.  Limitations of Use for the Flood Model 
A.1 Introduction 
The user can expect the following limitations in using the Flood Model: 
1.	
SQL Server MSDE has a size limit of 4 GB per database, which affects the size 
of the region you can analyze. The data for the 3 hazards share the 4 GB 
limit.  To work-around the 4 GB database limit, the full version of Microsoft 
SQL Server 2005 must be used. 

2.	
Many functions take a long time to run.  The speed of study region 
aggregation can be increase by copying the database to the local hard disk. 
The process is described in Section 

A.6 of the Users Manual. 

3.	
Components of independently developed data sets might not line up on maps, 
for example, the placement of bridges and roads, and facilities. 

4.	
Inventory data and subsequently the Level 1 analysis functionality is 
unavailable for the US held territories. 

5.	
The BIT module only provides square footage, dollar exposure, and building 
count data to the Flood Model. 

6.	
Due to lack of default data, riverine users in the States of Hawaii and 
Alaska will be unable to perform hydrologic analyses.  These users may still 
compute riverine flood hazard; however, options of specific return period and 
suite of return periods will be unavailable. Instead specific discharge 
should be selected. 

7.	
Because of data limitations, the Flood Model Indirect Economic Loss Module 
(IELM) will not produce meaningful results if the economy is set to 
 Primarily Agricultural  for 33 counties within the United States. The 
distribution of these counties are as follows:  Alaska   27, Hawaii   5, 
Florida   1 (Miami-Dade).  Alaska and Hawaii users seeking primarily 
agricultural economies should contact technical support prior to performing 
the analysis. 

8.	
The replacement costs associated with schools and transportation facilities, 
such as runways, are based national data that might not reflect actual local 
conditions. 


A-2 
A.1.1 Freeing Memory Using SQL Server Manager 
SQL Server can often lock memory as a working set. Because memory is locked, 
HAZUS or other applications might receive out of memory errors or run slower. 
To work around this problem, do one of the following: 
1.	
Restart your computer by clicking Start, and then click Shut Down. In the 
 What do you want the computer to do?  list, click Restart. NOTE: Restarting 
will close all open applications, so be sure to save your work before 
choosing to re-start. 

2.	
Restart SQL Server using the SQL Service Manager. Use the following process 
to open SQL Server Service Manager (SQL SSM) and restart the service: 

a.	
Close HAZUS and related applications (BIT and InCAST), if they are running. 

b.	
Open windows explorer and browse to the  C:\Program Files\Microsoft SQL 
server\Tools\Binn\  folder (Figure A.1). 

c. 
Double click on the  sqlmangr.exe  application file to open the SQL Server 
Service Manager dialog. (Figure A.2) 

d.	
In the Server box, select the name of the server and the named instance of 
Microsoft. SQL Server  2000, or type the name of the remote server. The 
server instance for HAZUS is <computer name>\HAZUSPLUSSVR. 

e.	
Click on Stop and Click Yes to confirm that you wish to stop the SQL Server 
service. 

f.	
When the start button is activated (symbol on button turns green) and the 
stop and pause button are disabled, click on the Start/Continue button to 
restart service. (Figure A.3) 

g.	
Close the SQL Server Service Manager. 







HAZUS-MH MR3 Technical Manual 
A-4 
A.1.2 Increasing Virtual Memory to Run Large Study Regions 
An  out of memory  error might occur when running a hurricane analysis with 
the probabilistic option if the region size is more than 3000 census tracts 
or census blocks. This occurs if the current page file size is not enough to 
carry out updates to the SQL server database. To work around this problem 
increase the page file size. The process is as follows: 
1.	
Open the control panel folder and locate the system icon. To open the control 
panel, click on Start, point to Settings, and then click Control Panel. 

2.	
Double-click the system icon to open the System Properties dialog (shown in 
Figure A.4). 

3. 
On the Advanced tab, click Performance Options, and under Virtual memory, 
click Change. (Figure A.5 through Figure A.6) 

4.	
In the Drive list, click the drive that contains the paging file you want to 
change. (Figure A.7) 

5.	
Under Paging file size for selected drive, type a new paging file size in 
megabytes in the Initial size (MB) or Maximum size (MB) box, and then click 
Set. (Figure A.7) 





HAZUS-MH MR3 Technical Manual 

For best performance, set the initial size to not less than the recommended 
size under Total paging file size for all drives. The recommended size is 
equivalent to 1.5 times the amount of RAM on your system. If you cannot 
change the file size or cannot resolve the  out-of memory  error by 
increasing the page file size, consider creating smaller regions (each with 
less than 3000 census tracts or blocks).