![](media/image1.wmf)

Decision Support Tools Evaluation Report

For HAZUS-MH^®^ Hurricane Model, Version 1.0

Earth Science Applications Directorate HAZUS-MH^®^ DST Evaluation Team

John C. Stennis Space Center, Mississippi

Acknowledgments

This work was supported by the NASA Earth Science Applications
Directorate under contract number NAS 13‑650 at the John C. Stennis
Space Center, Mississippi.

# Table of Contents {#table-of-contents .Heading-NoTOC}

[**Abstract vi**](#abstract)

[1.0 Introduction 1](#introduction)

[1.1 Role of NASA in National Application DST Development
1](#role-of-nasa-in-national-application-dst-development)

[1.2 Introduction to HAZUS-MH Hurricane Model
3](#introduction-to-hazus-mh-hurricane-model)

[2.0 Overview of HAZUS-MH Loss Estimation Tool
3](#overview-of-hazus-mh-loss-estimation-tool)

[3.0 Detailed Description of HAZUS-MH Hurricane Model
6](#detailed-description-of-hazus-mh-hurricane-model)

[3.1 Capabilities of the Software 6](#capabilities-of-the-software)

[3.2 Functional Components of the Hurricane Model
7](#functional-components-of-the-hurricane-model)

[3.2.1 Modes of Analysis for Hurricane Wind Risk Assessment
8](#modes-of-analysis-for-hurricane-wind-risk-assessment)

[3.2.2 Geospatial Databases Used in Hurricane Model
9](#geospatial-databases-used-in-hurricane-model)

[3.3 Models Resident to HAZUS-MH Hurricane Model
9](#models-resident-to-hazus-mh-hurricane-model)

[3.3.1 Hurricane Hazard Model 10](#hurricane-hazard-model)

[3.3.2 Terrain Model 10](#terrain-model)

[3.3.3 Wind Load Model 13](#wind-load-model)

[3.3.4 Windborne Debris Models 14](#windborne-debris-models)

[3.3.5 Building Damage Model 14](#building-damage-model)

[3.3.6 Building Loss Model 15](#building-loss-model)

[4.0 Possible NASA Remote Sensing Inputs to DSTs
16](#possible-nasa-remote-sensing-inputs-to-dsts)

[5.0 Identified NASA Technology Gaps in Meeting Remote Sensing Needs of
DSTs
20](#identified-nasa-technology-gaps-in-meeting-remote-sensing-needs-of-dsts)

[6.0 Conclusions and Recommendations
22](#conclusions-and-recommendations)

[7.0 References 26](#references)

[Appendix A. Surface Roughness Length A-1](#surface-roughness-length)

[Appendix B. Glossary B-1](#glossary)

[Appendix C. Acronym and Abbreviation List
C-1](#acronym-and-abbreviation-list)

[Appendix D. Disaster Management Roadmap
D-1](#disaster-management-roadmap)

[Appendix E. HAZUS DSS One Page Summary
E-1](#hazus-dss-one-page-summary)

[Appendix F. Relevant Earth Observing Missions and Sensors
F-1](#relevant-earth-observing-missions-and-sensors)

[F.1. Aqua F-3](#aqua)

[F.2. Landsat 7 F-5](#landsat-7)

[F.3. Terra F-7](#terra)

[F.4. ASTER F-9](#aster)

[F.5. ETM+ F-11](#etm)

[F.6. MODIS F-13](#modis)

[F.7. SRTM F-15](#srtm)

[Appendix G. Additional Sources of HAZUS-Relevant Information
G-1](#additional-sources-of-hazus-relevant-information)

[Appendix H. HAZUS Inventory GIS Databases
H-1](#hazus-inventory-gis-databases)

# Tables {#tables .Heading-NoTOC}

[Table 1. Parameter/data versus data level.
7](#__RefHeading___Toc54595163)

[Table 2. NLCD Land Use/Land Cover versus look-up table of Z~0~.
13](#__RefHeading___Toc54595164)

[Table 3. NASA-related geospatial data available with potential for Z~0~
estimation studies. 17](#__RefHeading___Toc54595165)

[Table 4. NASA-related remote sensing models and assets with potential
for contributing to the HAZUS-MH Hurricane Model DST parameters other
than Z~0~. 20](#__RefHeading___Toc54595166)

[Table F-1. Partial list of the Earth observing missions and sensors
relevant to HAZUS. F-1](#__RefHeading___Toc54594804)

# Figures {#figures .Heading-NoTOC}

[Figure 1. Applications framework and the role of ESA systems
engineering. 2](#__RefHeading___Toc54595167)

[Figure 2. HAZUS-MH Hurricane Model DST output using data from Hurricane
Bertha over North Carolina. 5](#__RefHeading___Toc54595168)

[Figure 3. HAZUS-MH Hurricane Model flowchart.
8](#__RefHeading___Toc54595169)

[Figure 4. Model inputs for HAZUS-MH Hurricane Model.
10](#__RefHeading___Toc54595170)

[Figure 5. Peak gust velocity as a function of Z~0~.
11](#__RefHeading___Toc54595171)

[Figure 6. Total loss versus peak gust velocity as a function of
terrain-based Z~0~. The magenta line in the graph was included to
provide a point of reference. 12](#__RefHeading___Toc54595172)

[Figure 7. Examples of house models within wind tunnels.
14](#__RefHeading___Toc54595173)

[Figure 8. A flux tower with a sonic anemometer deployed at the top.
19](#__RefHeading___Toc54595174)

[Figure 9. Potential role of Stennis Space Center assets for improving
HAZUS-MH. 24](#__RefHeading___Toc54595175)

[Figure H-1. Summary of Inventory Databases listed in Appendix D of the
HAZUS99 SR2 User Manual (FEMA, 2000b). H-1](#__RefHeading___Toc54594814)

# Abstract {#abstract .Heading-TOC}

This report provides an evaluation of the Hazards United States -- Multi
Hazard (HAZUS-MH) Hurricane Model, due for Preview release in the last
quarter of FY2003. This decision support tool (DST) software provides
emergency managers with a new, computer-aided means to model and
mitigate for hurricane hazard risks, plus gives a method for estimating
damage and loss to buildings and infrastructure. The Preview Hurricane
Model contains an impressive level of functionality, though much more
capability will be developed between now and the final release scheduled
for 2006. The relatively nascent state of this DST provides a timely
opportunity for contributions of NASA remote sensing solutions towards
some of the needs of the envisioned full capability wind model for loss
estimation. This evaluation discusses apparent data needs for the DST,
assesses how NASA may be able to contribute remote sensing solutions,
plus reviews apparent technology and knowledge gaps for addressing needs
of the DST. It appears that NASA mission data has good potential for
providing necessary geospatial data for improving the HAZUS-MH Hurricane
Model. In particular, NASA remote sensing technologies have good
potential to provide crucial data for mapping land cover and for
modeling aerodynamic surface roughness of different targeted surface
cover types. Aerodynamic surface roughness is a critically important
parameter for estimating building damage and loss due to severe winds. A
research proposal submitted to NASA Headquarters in FY2003 is discussed
to quantify the ability of NASA remote sensing data for providing needed
data for improving the DST, using NASA ESE technologies and working with
the Federal Emergency Management Agency (FEMA), National Institute of
Building Sciences (NIBS), and Applied Research Associates (ARA).

# Introduction

This report discusses an evaluation of the potential use of NASA data
products and technologies for enhancing the Hazards U.S -- Multi-Hazard
(HAZUS-MH^®^) Hurricane Wind software for use in hurricane hazard risk
assessment. The report initially provides background information on
NASA's role in improving decision support tools (DSTs) of national
priority. The report then gives a general overview of HAZUS-MH software,
followed by a detailed description of this DST's Hurricane Model. The
subsequent sections identify possible NASA data inputs to the HAZUS-MH
Hurricane Model and NASA technology gaps in meeting HAZUS-MH Hurricane
Wind DST needs. The report follows with sections on conclusions and
recommendations.

## Role of NASA in National Application DST Development

In April 2002, the National Aeronautics and Space Administration (NASA)
redefined its mission as follows:

To understand and protect our home planet

To explore the universe and search for life

To inspire the next generation of explorers

...as only NASA can.

Earth system science, the first element of the NASA mission, includes
understanding the Earth's system and its response to natural and
human-induced changes, investing in technologies, and collaborating with
others to improve the quality of life and to create a more secure world.
Accordingly, the mission of NASA's Earth Science Enterprise (ESE) is as
follows:

To develop a scientific understanding of the Earth system and its
response to natural and human-induced changes to enable improved
prediction of climate, weather, and natural hazards for present and
future generations.

This mission is intended to increase our knowledge of the Earth as a
system of interactive processes. To accomplish this mission, the ESE has
established a science goal to "observe, understand, and model the Earth
system to learn how it is changing, and the consequences for life on
Earth" (NASA, 2001). ESE missions and research seek to answer questions
related to the Earth's variability, the forces acting on it, the Earth's
response, the resulting consequences, and improved predictions.

An important aspect of the Earth Science Enterprise is to ensure that
results of ESE research and technology produce positive impacts for the
citizens of the world. Consistent with the ESE mission, the Earth
Science Enterprise Applications Program mission is as follows:

Expand and accelerate the realization of societal and economic benefits
from Earth science, information, and technology.

The ESE Applications Program contributes to the NASA vision by enabling
individuals and organizations in the public and private sectors to
routinely deliver and use Earth science information that saves lives,
that improves the quality of life, and that saves resources through
improved decision making. The success of the ESE Applications Program is
based on the degree to which it has evaluated, verified and validated,
and benchmarked the capacity of ESE results to serve national
applications through improved decision-support solutions.

The Applications Division of NASA's Earth Science Enterprise has defined
12 National Applications that are the highest priority national needs
and opportunities (NASA, 2002a,b). The Applications Division at NASA
Headquarters (HQ) has also identified decision support systems (DSSs)
and DSTs for the National Applications. The goal of the program is to
enhance the National Application DSSs with NASA observations from remote
sensing systems and predictions from computational models.

The Earth Science Applications (ESA) Directorate at the NASA John C.
Stennis Space Center (SSC) in Mississippi provides cross-cutting systems
engineering support for the National Applications. The mission of ESA is

To optimize benefits from NASA's Earth science investments through
systems engineering to advance decision support tools that serve the
nation.

Through systems engineering, ESA systematically assimilates results of
ESE missions, models, and technologies into DSSs and DSTs to affect
policies and societal impacts. The Applications Division framework for
accomplishing its mission and the role of SSC/ESA are depicted in Figure
1.

+-----------------------------------------------------------------------+
| ![](media/image2.png){width="6.243055555555555in" height="4.0875in"}  |
|                                                                       |
| []{#__RefHeading___Toc54595167 .anchor}Figure 1. Applications         |
| framework and the role of ESA systems engineering.                    |
+-----------------------------------------------------------------------+

## Introduction to HAZUS-MH Hurricane Model

One of the 12 National Applications, Disaster Management, includes the
management of natural disasters due to severe winds, flooding,
earthquakes, and other factors. NASA recently selected the HAZUS-MH
software as a DST candidate for improvement using NASA remote sensing
assets and computer-aided modeling capabilities. HAZUS-MH software
enables use of geospatial data and geographic information system (GIS)
technology to assess the risk level of properties and communities to
damage and loss from multiple hazards, such as earthquakes, flooding,
and wind. Initially, NASA will work the Hurricane Wind Model aspect of
the HAZUS-MH software.

The HAZUS-MH Hurricane Wind Model has direct relevance to the Disaster
Management National Application. Ward and Main (1998) report that more
than half of the U.S. population resides in coastal areas, which compose
only about 1/4 of the land area. Boyd et al. (2002) indicate that
coastal areas contained nearly 3/4 of the Federally recognized natural
disasters occurring from 1992--1997. Applied Research Associates, Inc.,
(ARA) et al. (2003) report that on average, two hurricanes breach
portions of the U.S. coastline each year. Herbert et al. (1996) estimate
that, after adjusting for inflation, hurricanes in the United States
alone caused an annual average loss of \$1.6 billion for the period
1950--1989, \$2.2 billion over 1950--1995, and \$6.2 billion over
1989--1995. In fact, hurricanes signify the costliest natural disasters
in the United States (Pielke and Landsea, 1998). Coastal human
population and related infrastructure has quadrupled over the past 30
years, according to the National Science and Technology Council (NSTC)
(2003). Insured coastal property values have grown dramatically with
increases of 100 to 250% from 1980 through 1993 and more since then
(Ross and Lott, 2000; Insurance Institute for Property Loss Reduction
and Insurance Research Council, 1995). The development and utilization
of the HAZUS-MH Hurricane Model will help save lives, will help people
living in coastal areas to prepare for hurricane hazards, and will help
emergency managers and the planning community to mitigate hurricane risk
(Bausch, 2003; Lavelle et al., 2003; FEMA, 2003b).

The HAZUS-MH Hurricane Model could benefit many other National
Applications identified by NASA, most specifically coastal management,
ecological forecasting, homeland security, public health, and water
management. The U.S. Atlantic and Gulf Coasts contain large, growing
human populations. These low-lying regions are vulnerable to negative
impacts from hurricanes and severe storms. Hurricanes can radically
alter fisheries, cause significant coastal erosion, and cause flooding
and an influx of toxins into affected waterways. For example, in 1996
Hurricane Fran caused a tremendous amount of illegal sewage discharges
(Mallin et al., 1997), whereas Hurricane Andrew resulted in massive
erosion of barrier islands in southeastern Louisiana (Guntenspergen,
1998). Hurricanes also pose threats to homeland security, with respect
to both offshore and terrestrial security. The HAZUS-MH Hurricane Model
may help in regard to other National Applications, such as agricultural
efficiency, aviation, energy management, and carbon management.
Hurricanes can raise havoc with agricultural areas, disrupt electricity,
delay air travel, and cause extensive damage to vegetation.

# Overview of HAZUS-MH Loss Estimation Tool

The HAZUS-MH Loss Estimation Tool is a GIS-based decision support system
tool developed by the U.S. Federal Government for natural hazards loss
estimation. Several publications give general descriptions of the
HAZUS-MH software, including Schauer et al. (2002), Bausch (2003),
Lavelle et al. (2003), and Srinivasan (2003). The Federal Emergency
Management Agency (FEMA) oversees HAZUS-MH activities at large, whereas
the National Institute for Building Sciences (NIBS) manages this tool's
development and implementation for use by the Federal, State, and
Municipal emergency management community. FEMA initially released HAZUS
in 1999 as HAZUS-99 SR2, then primarily a tool for earthquake disaster
risk assessment.

More recently, HAZUS has been expanded to accommodate multi-hazard loss
estimation and consequently renamed to HAZUS-MH. The Federal Government
has developed HAZUS-MH as a GIS-based planning tool for emergency
managers to address a fundamental question: As earthquakes, hurricanes,
and floods continue to occur, how can we plan to minimize damage and
loss of life from these natural events to ensure that natural hazards do
not become large-scale, catastrophic natural disasters? This software
provides a means to convey risk to the public so that communities can be
informed and motivated to respond appropriately through better hazard
planning, preparation, mitigation, and response. The HAZUS-MH software
is designed to be easy and quick to use, given a familiarity with the
software.

The main modules of HAZUS-MH pertain to earthquakes, wind, and flood
hazard risk assessment. The literature on HAZUS-MH contains some
ambiguity regarding what is considered a module and what is considered a
model. In this report, "module" denotes only the major software
components of HAZUS-MH: the Wind, Flood, and Earthquake Modules. The
most recent version of HAZUS-MH includes the new module for assessment
of coastal flooding in addition to the preexisting earthquake module.
The third module on wind hazards to assess risk of property loss and
damage from hurricane winds will soon be available to HAZUS users.

Lavelle et al. (2003) refer to the HAZUS-MH Hurricane Module as the
"Preview" Hurricane Model, scheduled for release in fall of 2003. A
full-capability Hurricane model is tentatively scheduled for release in
2006. The additional capabilities will include ability to estimate risk
of property damage and loss from hurricane wind, storm surge, waves, and
flooding damage. HAZUS-MH is also being expanded to address other forms
of wind hazards, including tornados, extra-tropical storms, hail storms,
and thunderstorms. The accommodation of additional wind hazards is
scheduled to be available in 2006.

Each hazard-specific module includes a basic functionality common to all
HAZUS natural hazards loss estimation software, plus additional
functionality unique to the particular hazard (e.g., Hurricane Wind).
Each HAZUS-MH module (earthquake, wind, flood) allows the user to map,
assess, and display geospatial data pertaining to a specific natural
hazard to assess and mitigate hazard risk. HAZUS-MH also enables
estimation of physical damage to buildings, critical facilities, and
other infrastructure. In addition, each hazard-specific module gives
estimates of economic loss (e.g., lost jobs, business interruption,
repair costs, construction costs) and social impacts (e.g., identifying
requirements for shelters and medical aid). HAZUS-MH allows end users
(1) to assess hazard vulnerability to identify areas requiring
additional planning consideration; (2) to assess pre-disaster
preparedness and readiness; (3) to compute potential losses from
assorted hazard events, such as pre-event, near real-time, and
post-event reporting scenarios; (4) to determine resource allocation
needed for most effective response and recovery; and (5) to prioritize
implementation of mitigation measures required to reduce/mitigate future
losses.

The HAZUS-MH Hurricane Model in "Preview" form will allow users to model
hurricane winds and to assess impacts (Figure 2). The model will also
compute basic estimates of potential damage to residential, commercial,
and industrial buildings. In addition, it will estimate direct economic
losses. However, the Preview version of the model will not estimate
indirect economic losses and impacts to lifelines. These additional
capabilities will be available in the full-capability version of the
software.![](media/image3.jpeg){width="4.0in"
height="2.8020833333333335in"}

HAZUS-MH runs on PCs in conjunction with GIS software made by the
Environmental Systems Research Institute (ESRI). Under ESRI's ArcView
and ArcGIS, the HAZUS-MH Flood Module requires use of ESRI's Spatial
Analyst Extension software. According to FEMA, the earthquake and
hurricane (winds) HAZUS-MH modules do not require this extension (FEMA,
2003a, 2003c). However, running the hurricane wind and flood models
together will require the Spatial Analyst Extension.

HAZUS-MH makes use of nationwide geospatial databases and employs
national standardized loss estimation and risk assessment methodology.
The use of GIS enables geospatial relationships to be computed and
assessed within and between map layers and enables hazard risk scenarios
to be more effectively modeled and communicated. The geospatial
databases used by HAZUS-MH employ many GIS coverages: (1) demographics,
(2) building stock, (3) essential facilities, (4) transportation,
(5) utilities, and (6) high potential loss facilities. These databases
are non-proprietary and are easily customized. The use of national
standardized loss estimation and risk assessment methodology came about
through engineering analysis in conjunction with hazard-specific
oversight committees consisting of expert practitioners and academic
professionals. This methodology is also non-proprietary and has been
well documented by FEMA (2003b), Lavelle et al. (2003), and others.

HAZUS-MH can be applied to multiple scales of observation, including
regional, community, neighborhood, and individual site (i.e., building
lot) assessment. However, individual site assessment requires additional
data entry from the end user. The software is highly flexible with
respect to data entry and can accept three levels of input: Level 1 --
nationwide data provided with the software, Level 2 -- data developed
and input directly by end users, and Level 3 -- data developed by
experts for input into the system.

The HAZUS-MH Hurricane Model constitutes an improvement over existing
loss estimation models in that it employs a wind hazard-load-damage-loss
framework (Bausch, 2003; Lavelle et al., 2003; FEMA, 2003b). This DST
initially applied to areas along the Atlantic and Gulf coasts, where the
majority of United States hurricane threats tend to occur. The software
has been recently expanded to include the Hawaiian Islands (FEMA,
2003a). The model will address wind pressure, windborne debris, surge
and waves, atmospheric pressure change, duration/fatigue, and rain.
Upgrades to the HAZUS-MH Hurricane Model will be made to aid future
assessment of mitigation, benefit-cost, and building code issues.

HAZUS-MH has a large and varied group of potential end users:
(1) emergency managers, (2) community planners, (3) building officials,
(4) federal agencies, (5) policy makers, (6) public works/utilities,
(7) engineers and scientists, (8) insurers, (9) academic institutions,
(10) software developers, and (11) GIS consultants. ARA et al. (2003)
estimated the total HAZUS-MH user base to be over 1700 prior to the 2003
release of the Hurricane and Flood modules and expect this user base to
grow to over 9000 end users when these two modules come on line. In
addition, HAZUS User Groups now exist in several states and/or regions
of the country. These user groups hold meetings and maintain Web sites
to exchange information on applications of the HAZUS-MH software (see
<http://www.fema.gov/hazus/us_main.shtm> for additional information).

A sizeable and diverse team including the following members oversees
software development: (1) FEMA Mitigation Directorate; (2) NIBS, which
is in charge of project management; (3) expert committees that provide
technical and expert oversight; and (4) leading firms in loss estimation
(e.g., ARA, Inc.) that are in charge of technical and software
development, pilot studies, and model calibration/validation. The expert
oversight committees consist of several Federal agencies, many
universities, some state agencies, and multiple consulting firms. In
addition, FEMA is providing software training and education, plus
technical support in the use of the HAZUS-MH.

# Detailed Description of HAZUS-MH Hurricane Model

HAZUS technical information was found in several documents, including
conference papers by Lavelle et al. (2003) and Schauer et al. (2003), a
draft pre-release technical manual by FEMA (2003b), and journal papers
regarding components of the HAZUS-MH Hurricane Model. Vickery et al.
(2000a, 2000b) and Vickery and Twisdale (1995a, 1995b) address modeling
and prediction of hurricane wind fields and simulation of hurricane
risks. In addition, a preliminary course manual by FEMA (2003b) provided
up-to-date information on the software.

## Capabilities of the Software

Lavelle et al. (2003) report that the HAZUS-MH Hurricane Model employs a
unique wind hazard-load-damage-loss framework considered to be a
significant improvement over pre-existing loss estimation models.
According to Bausch (2003) and FEMA (2003b), the HAZUS-MH Hurricane
Model features the following:

-   A building classification system that depends on the characteristics
    of the building envelope and building frame.

-   The capability to compute damages based on building classes and the
    effects of progressive failure from wind-induced rain damage.

-   The capability to compute damage to building contents and interior.

-   The capability to estimate tree blow down and structure debris
    quantities.

-   Loss estimates that will include direct and indirect economic loss,
    shelter requirements, and casualties.

FEMA (2003b) summarizes additional information regarding the data used
in the Hurricane Model and the user data input requirements as shown in
Table 1. While all data parameters can be addressed with Level 1 data
resident to the software, improved results can be obtained by
user-supplied data input for the following parameters: (1) wind model,
(2) building inventory; (3) terrain, and (4) shelter requirements. Level
1 data is nationally available geospatial data, using national standards
for geoprocessing such data. For HAZUS99 software (a precursor to
HAZUS-MH), Level 1 data includes 43 map coverages organized under the
following headings: (1) General Building Stock (2 coverages),
(2) Essential Facilities (3 coverages), (3) High Potential Loss
Facilities (3 coverages), (4) Transportation System (16 coverages), and
(5) Utility System (19 coverages). The origin of Level 1 data depends on
the thematic map coverage as summarized in Appendix H (FEMA, 2002b).
Sources for Level 1 data include map coverages from the U.S. Census
Bureau, the American Heart Association Database, FEMA, National Dams
Inspection Program, U.S. Census TIGER files, National Bridge Inventory,
Applied Technology Council ATC-13 and ATC-25 databases, and the
Environmental Protection Agency. Level 1 data enables regional studies
(e.g., county and state levels), whereas the user-supplied Level 2 and
the expert-supplied Level 3 data regard more site-specific assessment
(e.g., census block and property levels).

+-----------------+----------------+----------------+----------------+
| []{#__RefHeadin |                |                |                |
| g___Toc54595163 |                |                |                |
| .               |                |                |                |
| anchor}Table 1. |                |                |                |
| Parameter/data  |                |                |                |
| versus data     |                |                |                |
| level.          |                |                |                |
+-----------------+----------------+----------------+----------------+
| Parameter/Data  | Level 1        | Level 2        | Level 3        |
|                 | (Default Data) | (User-Supplied | (Advanced      |
|                 |                | Data)          | Data)          |
+-----------------+----------------+----------------+----------------+
| Wind Model      | Default        | User-Defined   |                |
|                 | Probabilistic  | Scenario       |                |
+-----------------+----------------+----------------+----------------+
| Building        | Default        | User-Supplied  |                |
| Inventory       |                |                |                |
+-----------------+----------------+----------------+----------------+
| Facilities and  | Residential    |                |                |
| Building        | Commercial     |                |                |
| Classes         |                |                |                |
|                 | Industrial     |                |                |
|                 | Essential      |                |                |
|                 |                |                |                |
|                 | Facilities     |                |                |
+-----------------+----------------+----------------+----------------+
| Utility,        | Display/Edit   |                |                |
| Transportation, | Locations Only |                |                |
| and High        | -- No Damage   |                |                |
| Potential Loss  | or Loss        |                |                |
| Facilities      | Estimates      |                |                |
+-----------------+----------------+----------------+----------------+
| Terrain         | Default        |                | E              |
|                 |                |                | xpert-Supplied |
+-----------------+----------------+----------------+----------------+
| Loss Functions  | Default        |                |                |
+-----------------+----------------+----------------+----------------+
| Damage          | Default        |                |                |
| Functions       |                |                |                |
+-----------------+----------------+----------------+----------------+
| Shelter         | Default        | User-Supplied  |                |
| Requirements    |                | Parameters     |                |
+-----------------+----------------+----------------+----------------+
| Debris          | Default        |                |                |
+-----------------+----------------+----------------+----------------+
| Source: FEMA,   |                |                |                |
| 2003b           |                |                |                |
+-----------------+----------------+----------------+----------------+

The "Preview" Hurricane Model has recently been delivered to FEMA for
distribution.

## Functional Components of the Hurricane Model

The FEMA (2003b) flowchart of the HAZUS-MH Wind Model (Figure 3) depicts
current and future components of the Hurricane Model in terms of six
functional components: (1) Hazard Type, (2) Modes of Analysis,
(3) Reference Geospatial Databases, (4) Hazard Effects, (5) Physical
Damage Estimation, and (6) Loss Estimation.

As of 2003, hurricane winds will be the only wind hazard applicable to
HAZUS-MH, although future versions will also allow other wind hazards,
such as extratropical storms, thunderstorms, tornados, and hail storms.
Extratropical storms are former tropical storms traveling through
temperate latitudes. To begin using the HAZUS-MH software, the user must
select the region of interest prior to performing risk assessment
analysis with the software (FEMA, 2003c).

+-----------------------------------------------------------------------+
| ![](media/image4.png){width="6.25in" height="4.0625in"}**Bold text**  |
| indicates currently enabled features; regular text indicates future,  |
| non-enabled features                                                  |
|                                                                       |
| Source: FEMA, 2003b                                                   |
|                                                                       |
| []{#__RefHeading___Toc54595169 .anchor}Figure 3. HAZUS-MH Hurricane   |
| Model flowchart.                                                      |
+-----------------------------------------------------------------------+

### Modes of Analysis for Hurricane Wind Risk Assessment

The HAZUS-MH Hurricane Model will allow users to implement all three
modes of analysis: probabilistic, scenario, and historical storm. The
addition of the historical storm mode is subsequent to the release of
the FEMA (2003b) draft technical manual on the software.

The probabilistic mode of hurricane risk analysis employs a 100,000-year
storm simulation dataset to estimate annualized loss for 7 return
periods of 10, 20, 50, 100, 200, 500, and 1000 years. In doing so, the
software also enables users to view GIS-based predictive maps (i.e.,
geospatial models) of peak wind fields for each return period. In
addition, users can compute summary descriptive and tabular reports for
estimated damage and loss for each probabilistic run. The probabilistic
mode can be used to estimate how many years will pass before a hurricane
of magnitude X occurs for a given area based on the storm simulation
data. For example, this mode of analysis can be used to estimate how
many years will pass before a Hurricane Andrew returns to southern
Florida.

The scenario mode of hurricane risk analysis requires user inputs
regarding specific storm scenarios. For example, this mode can be used
to estimate damage and loss on a per-scenario basis, given user-supplied
historic or simulated information on storm track data points in regard
to geolocation, intensity, and time period between data points.

The historic mode of hurricane risk analysis allows end users to
estimate loss and damage in relation to historic storms frequenting a
specified region of interest. HAZUS software developers added the
historical storm mode subsequent to the release of the software's draft
technical manual (2003ba).

### Geospatial Databases Used in Hurricane Model

The software includes a large volume of resident GIS-readable databases,
including physiographic data relating to terrain (i.e., land cover),
topography, and inventory data pertaining to buildings and other
infrastructural features. These datasets include national data on
building structures and hazards as well as TIGER census data. The most
recent version of the Hurricane Model now also takes topography into
account.

Much of the geospatial data resident in the software concerns so-called
inventory data pertaining to buildings and other infrastructure (FEMA,
2003c). Inventory data is often common to all hazards, although some
such data is hazard specific. Generic inventory data is included with
the software for use as default Level 1 data (Table 1): (1) general
building types and occupancies, (2) lifelines, (3) replacement costs,
and (4) demographics. Hazard-specific inventory data pertains to
specific building types, elevation, and building configurations. For all
primary HAZUS-MH modules, buildings are categorized according to five
structural classes (wood, steel, concrete, masonry, and manufactured
housing) and seven major occupancy classes (residential, commercial,
industrial, agricultural, religion/nonprofit, government, and
education). Specific to the hurricane model, the building classification
scheme consists of 39 specific building types (e.g., wood, multi-unit
housing, one story) and up to 9 building characteristics per building
type (e.g., roof shape, roof cover type, and roof wall attachment type).
In all, the Hurricane Model contains a building classification
consisting of 1,884 building classes. General inventory data on
buildings is also referred to in the literature as building stock data
(FEMA, 2003b).

HAZUS-MH includes some utility software for user value-added geospatial
database development, especially with respect to building inventory
data. In particular, the software includes (1) the Inventory Collection
and Survey Tool (INCAST), and (2) the Building Inventory Tool (BIT).
These software utilities can be used for producing Level 2 datasets
(Table 1).

## Models Resident to HAZUS-MH Hurricane Model

Lavelle et al. (2003) discusses the HAZUS-MH Hurricane Model in terms of
six input models: (1) hurricane hazards, (2) terrain, (3) wind pressure,
(4) windborne debris, (5) physical damage estimation, and (6) building
loss estimation (Figure 4). These six input models collectively enable
the Hurricane Model software to estimate building damage and loss from
hurricanes occurring along the Atlantic and Gulf coasts and the Hawaiian
Islands. The remainder of this section provides an overview of these six
input models for the HAZUS-MH Hurricane Model based on Lavelle et al.
(2003) and FEMA (2003a,b).

+-----------------------------------------------------------------------+
| ![](media/image5.png){width="6.239583333333333in"                     |
| height="2.4479166666666665in"}Source: Lavelle et al., 2003            |
|                                                                       |
| []{#__RefHeading___Toc54595170 .anchor}Figure 4. Model inputs for     |
| HAZUS-MH Hurricane Model.                                             |
+-----------------------------------------------------------------------+

### Hurricane Hazard Model

The Hurricane Hazard Model consists of a hazard risk and track model as
well as a windfield model that enable hurricanes to be simulated by
modeling the full hurricane track. In doing so, the model estimates
central pressure as a function of sea surface temperature, allowing for
updates of storm heading, speed, etc. at a user-specified time interval
(e.g., 6 hours). The coupling of the hurricane track and windfield
models enables computational estimates of hurricane wind speed at any
location in the vicinity of coastlines. The model allows for statistical
analysis with respect to multiple landfalls, bypassing storms, and
hurricane storm characterization. It also enables modeling effects of
rainfall entering damaged buildings. In-depth documentation on the
hurricane track model can be found in Vickery et al. (2000b). Vickery et
al. (2000a) provides a detailed account of the wind field model. Powell
et al. (2003) have developed an approach to modeling the windfield at
landfall for the State of Florida, comparable to that described by
Vickery et al. (2000a,b).

### Terrain Model

Experts associated with HAZUS-MH software development regard terrain
models of surface roughness to be a crucial input for effective
estimation of wind effects and of damage and loss to buildings and
facilities. Ground surface roughness length is denoted as Z~0~ and is a
function of feature height and density distributions of features on the
ground. Figure 5 illustrates the impact of Z~0~ on the magnitude of peak
gust wind speed as a function of height above the ground. The x-axis in
this chart is actually dimensionless, showing the velocity profiles as
being normalized by the velocity measured at 10-meter height in open
terrain (Z~0~=0.03). Note that the second lowest curve (Z~0~=0.03)
passes through 1.0 at a height of 10 m. The higher Z~0~ values equate to
rougher surface length values (e.g., heterogeneous urban and uneven
canopy forest surfaces). The peak gust velocity is lowered for rough
surfaces (higher Z~0~ values). Rougher ground surfaces lower wind speeds
on the ground, even while the upper level wind speeds stay constant. See
Appendix A for additional description on equations used for estimating
Z~0~.

+-----------------------------------------------------------------------+
| ![](media/image6.jpeg){width="6.245833333333334in"                    |
| height="3.8694444444444445in"}                                        |
|                                                                       |
| Source: FEMA, 2003b                                                   |
|                                                                       |
| []{#__RefHeading___Toc54595171 .anchor}Figure 5. Peak gust velocity   |
| as a function of Z~0~.                                                |
+-----------------------------------------------------------------------+

Because of lowered surface roughness characteristics, hurricane winds
pose a greater risk of damage and loss to housing in open areas than to
housing in wooded areas (Figure 6). For example, Lavelle et al. (2003)
report that one- or two-storied buildings within forested areas receive
half the wind loads experienced by the same housing in open areas. By
default, the HAZUS-MH Hurricane software models the terrain for general
building stock as being a uniform value across a census tract.

The HAZUS-MH Hurricane model does currently not include a means for
directly estimating surface roughness over regions applicable to the
software. Instead, it employs an alternative approach: using land cover
maps and lookup tables for assigning an average roughness on a land
cover type basis. For all areas except Florida, the software uses land
cover map data from the National Land Cover Database (NLCD) that
originated from Landsat (Table 2). In doing so, wind engineering experts
on the HAZUS software development team assign land-cover-specific
surface roughness length based on *in-situ* measurements weighted by
expert consensus. Table 2 indicates that some *in-situ* measurements of
land cover types (e.g., low intensity residential) took place in many
more subregions of the country than others (evergreen forest). In some
cases, particularly over residential areas, aerial photograph analyses
were performed in the process of assigning surface roughness length to
land cover types. Validation of the NLCD data for use in HAZUS-MH
hurricane software applications revealed two types of errors:
(1) misclassification error in regard to certain land cover (e.g.,
residential areas), and (2) locational errors of as much as 120 meters
(FEMA, 2003b).

Areas in Florida make use of Florida land cover map data from the Land
Use Land Cover (LULC) databases maintained by the five Florida Water
Management Districts (FEMA, 2003a,b,c). The Florida LULC database is
used because it more discretely defines forested areas in terms of
average height and density than the NLCD data. The software allows
expert users to input terrain data needed (e.g., land cover/land use)
for computing surface roughness length.

+-----------------------------------------------------------------------+
| ![](media/image7.png)                                                 |
|                                                                       |
| Source: FEMA, 2003b                                                   |
|                                                                       |
| []{#__RefHeading___Toc54595172 .anchor}Figure 6. Total loss versus    |
| peak gust velocity as a function of terrain-based Z~0~. The magenta   |
| line in the graph was included to provide a point of reference.       |
+-----------------------------------------------------------------------+

+--------------------------+-----+-----+-----+-----+-----+-----------+
| []{#__                   |     |     |     |     |     |           |
| RefHeading___Toc54595164 |     |     |     |     |     |           |
| .anchor}Table 2. NLCD    |     |     |     |     |     |           |
| Land Use/Land Cover      |     |     |     |     |     |           |
| versus look-up table of  |     |     |     |     |     |           |
| Z~0~.                    |     |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| MRLC-NLCD Land Cover     | L   |     |     |     |     | Number of |
| Classification and       | and |     |     |     |     | Su        |
| Numerical Coding         | S   |     |     |     |     | b-Regions |
|                          | urf |     |     |     |     | Used      |
|                          | ace |     |     |     |     |           |
|                          | Rou |     |     |     |     |           |
|                          | ghn |     |     |     |     |           |
|                          | ess |     |     |     |     |           |
|                          | L   |     |     |     |     |           |
|                          | eng |     |     |     |     |           |
|                          | th, |     |     |     |     |           |
|                          |     |     |     |     |     |           |
|                          | *   |     |     |     |     |           |
|                          | z0* |     |     |     |     |           |
|                          | (m) |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
|                          | U   | A   | COV | M   | M   |           |
|                          | sed | vg. |     | ax. | in. |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| Water                    |     |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 11 Open Water            | **0 | 0.  | 0.  | 0.  | 0.  | 6         |
|                          | .01 | 013 | 612 | 030 | 010 |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 12 Perennial Ice/Snow    | **0 |     |     |     |     | 0         |
|                          | .01 |     |     |     |     |           |
|                          | 2** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| Developed                |     |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 21 Low Intensity         | **0 | 0.  | 0.  | 0.  | 0.  | 51        |
| Residential              | .35 | 307 | 496 | 600 | 010 |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 22 High Intensity        | **0 | 0.  | 0.  | 0.  | 0.  | 39        |
| Residential              | .60 | 401 | 498 | 800 | 080 |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 23                       | **0 | 0.  | 0.  | 0.  | 0.  | 28        |
| Commercial/Industrial/   | .35 | 265 | 605 | 600 | 030 |           |
| Transportation           | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| Barren                   |     |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 31 Bare Rock/Sand/Clay   | **0 | 0.  |     |     |     | 1         |
|                          | .20 | 300 |     |     |     |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 32 Quarries/Strip        | **0 |     |     |     |     | 0         |
| Mines/Gravel Pits        | .40 |     |     |     |     |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 33 Transitional          | **0 | 0.  | 0.  | 0.  | 0.  | 3         |
|                          | .40 | 400 | 661 | 600 | 100 |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| Forested Upland          |     |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 41 Deciduous Forest      | **0 |     |     |     |     | 0         |
|                          | .60 |     |     |     |     |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 42 Evergreen Forest      | **0 | 0.  |     |     |     | 1         |
|                          | .60 | 500 |     |     |     |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 43 Mixed Forest          | **0 |     |     |     |     | 0         |
|                          | .60 |     |     |     |     |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| Shrubland                |     |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 51 Shrubland             | **0 | 0.  |     |     |     | 1         |
|                          | .06 | 040 |     |     |     |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| Non-natural Woody        |     |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 61                       | **0 | 0.  | 0.  | 0.  | 0.  | 4         |
| Orchards/Vineyards/Other | .21 | 213 | 777 | 400 | 050 |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| Herbaceous Upland        |     |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 71 Grasslands/Herbaceous | **0 | 0.  | 0.  | 0.  | 0.  | 4         |
|                          | .15 | 193 | 662 | 300 | 050 |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| Herbaceous               |     |     |     |     |     |           |
| Planted/Cultivated       |     |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 81 Pasture/Hay           | **0 | 0.  | 1.  | 0.  | 0.  | 3         |
|                          | .15 | 160 | 301 | 400 | 030 |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 82 Row Crops             | **0 | 0.  | 0.  | 0.  | 0.  | 8         |
|                          | .10 | 100 | 913 | 300 | 030 |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 83 Small Grains          | **0 |     |     |     | 0   |           |
|                          | .03 |     |     |     |     |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 84 Fallow                | **0 |     |     |     | 0   |           |
|                          | .03 |     |     |     |     |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 85 Urban/Recreational    | **0 | 0.  | 0.  | 0.  | 0.  | 6         |
| Grasses                  | .15 | 155 | 514 | 250 | 030 |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| Wetlands                 |     |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 91 Woody Wetlands        | **0 | 0.  | 0.  | 1.  | 0.  | 10        |
|                          | .30 | 545 | 594 | 100 | 300 |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| 92 Emergent Herbaceous   | **0 | 0.  | 0.  | 0.  | 0.  | 10        |
| Wetlands                 | .03 | 188 | 959 | 600 | 050 |           |
|                          | 0** |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+
| Source: FEMA, 2003b      |     |     |     |     |     |           |
+--------------------------+-----+-----+-----+-----+-----+-----------+

### Wind Load Model

According to Lavelle et al. (2003), the wind-loading model is an
empirical model for estimating directionally dependent wind-induced
pressures subjected to building exteriors during windstorms. This model
employs pressure coefficient data from several boundary-layer wind
tunnel tests that measured wind pressures on model buildings (Figure 7).
The model also utilizes directional pressure coefficient information
from British and Australian wind codes. Pressure coefficient models have
been developed for several types of buildings as a function of number of
stories and roof type (FEMA 2003a,b). Windstorms exert both positive and
negative wind-induced pressure on buildings, which can lead to
structural damage and even failure. Positive wind-induced pressure
refers to pressure exerted externally onto a building, whereas negative
pressure denotes pressure exerted internally from the building outward
toward the building's exterior. The model also considers adjacency
effects regarding how the juxtaposition of buildings influences wind
loads. For example, adjacent buildings can reduce the negative pressures
on roofs and walls of low buildings by about 25% but do not affect
positive pressures acting on walls of such buildings (Lavelle et al.,
2003; FEMA, 2003b).

+-----------------------------------------------------------------------+
| ![](media/image8.jpeg){width="6.25in" height="2.9583333333333335in"}  |
|                                                                       |
| Source: FEMA, 2003b                                                   |
|                                                                       |
| []{#__RefHeading___Toc54595173 .anchor}Figure 7. Examples of house    |
| models within wind tunnels.                                           |
+-----------------------------------------------------------------------+

### Windborne Debris Models

Windborne debris can cause or lead to building structural breaches and
building failures during high wind events, such as hurricanes. The
physically based building damage model requires reasonable estimates of
windborne debris loads as an input. The HAZUS-MH Hurricane Model employs
two types of windborne debris models: one estimates debris missile
impacts from residential areas and one models debris impacts from
rooftop gravel, most commonly associated with commercial land use zones.
Lavelle et al. (2003) describes the residential debris model as a first
principles model for estimating hurricane debris impact probabilities,
impact momenta, and impact energy. The windborne debris model for
rooftop gravel was specifically developed for use in HAZUS-MH in
conjunction with commercial areas, where rooftop gravel missiles can
inflict significant damage to adjacent buildings. Researchers used wind
tunnels and residential model simulation to characterize the likelihood
of missile impacts from windborne debris due to shingles, wood, and
sheathing (FEMA, 2003a). Twisdale et al (2000a,b) and FEMA (2003b)
provide more detailed description on the development of the wind debris
models for use in the HAZUS-MH hurricane model.

### Building Damage Model

This damage model estimates damage to several building components,
including windows, doors, wall cladding, roof cladding, and roof cover.
This model is also used to predict failure of glazing from wind debris
impacts, wall failure from wind pressure for brick and wood frame walls,
wall/roof frame connection failure for wood and steel wood frames, and
foundation failure due to multiple earth movement processes for
manufactured housing. The Building Damage Model is important to the
building loss module, because loss estimates are based on estimations of
physical damage and repair costs.

The model inputs include nationally available regional building
inventory data and storm characteristics data. Calculation of storm
characteristics data depends on how the user sets up the region and
analysis mode (e.g., historic, probabilistic, or scenario). Damage
estimation is based on comparing building loads to resistances. The
software enables computation of loads by spatially integrating estimated
pressure coefficients at each point on a grid corresponding to the
building surface. The resistance for each failure mechanism per building
component is defined by a probability function used to sample
resistances for a given storm scenario. Damage is estimated in an
accumulative manner as the storm passes through the region of interest
by incorporating wind speed and direction at 15-minute time intervals
over the storm duration. The wind speed and direction can be user
specified in the scenario analysis mode via the software user interface.
As discussed earlier, the software also allows users to enhance the
building stock data loaded with the software. Each time interval
includes comparison of wind building loads and resistances to predict
physical damage. The software concurrently estimates the number of
missiles (e.g., flying 2x4s) impacting building walls to predict glazing
damage and damage to wall finish.

The Building Damage Model also estimates the number and size of wall
breaches due to failed windows, doors, and wall cladding. If additional
breaching occurs between time steps, the software re-computes internal
and external building pressure parameters to compare loads versus
resistances and to estimate additional building damage.

Building Damage Model validation compares of hurricane observations to
simulated damage test scenarios, involving full reproductions of wind
speed and direction time histories in conjunction with the hurricane
wind field model described earlier. According to Lavelle et al. (2003)
and FEMA (2003a), case studies include those on damage to roof cover,
roof sheathing, and windows. Simulations from HAZUS-MH Hurricane Model
analyses were compared to observed data from Hurricanes Andrew, Erin,
and Fran.

### Building Loss Model

The Building Loss Model provides estimates of economic losses to
buildings, content and/or inventory losses, and costs associated with
the loss of use of the building. As with the Building Damage Model,
inputs include nationally available regional building inventory data and
storm characteristics data. Calculation of storm characteristics data
depends on how the user sets up the region and analysis mode (e.g.,
historic, probabilistic, or scenario). The outputs of the model include
estimates of building loss, contents loss, loss of use, business
inventory loss, relocation expense, loss of income, rental income, wage
income, output loss, and economic loss (FEMA, 2003a). The software
computes building losses using a combination of explicit and implicit
costing techniques in conjunction with estimates of physical damage to
the building. Explicit costing includes estimated replacement costs to
exterior building components, such as windows, doors, sheathing, and
roof cover. The software employs replacement thresholds to determine if
the degree of physical damage and loss is sufficient to warrant complete
replacement. Implicit costing pertains to estimates of water volume
entering failed openings in the building and the resulting impacts to
interior housing components (e.g., electrical system) and contents.
Implicit costing employs empirical functions based on engineering
judgment and insurance company loss data. The software provides
different loss estimation capabilities and methodologies for different
building types (e.g., residential, manufactured homes, and commercial
buildings). The implicit functions relate the interior damage cost
estimation to building exterior damage estimation. The contents loss
model also employs an explicit and implicit costing approach. In
addition to exterior and interior loss estimation, the software also
enables prediction of the amount of time required to reconstruct a
house. Additional information on the Building Loss Model is available in
FEMA (2003a), the technical manual for the HAZUS-MH Hurricane Model.

# Possible NASA Remote Sensing Inputs to DSTs

The current version of the HAZUS-MH Hurricane Model focuses on
wind-related risk assessment. Eventually, however, this model will also
include risk assessment of hurricane damage due to storm surge, waves,
tides, and flooding. In effect, the eventual DST will enable a more
integrated, complete assessment of hurricane risk. NASA remote sensing
technologies may provide essential inputs needed to hasten and refine
the development of these additional capabilities. NASA remote sensing
technologies and related modeling software may also help improve certain
facets of the current hurricane wind risk assessment capability.

One of the main information needs of the current HAZUS-MH Hurricane
Model is for improved estimates of surface roughness length, Z~0~. In
fact, based on scenario analyses with HAZUS-MH Hurricane Model software,
the roughest surface terrain types (i.e., land cover/land use types) can
equate to a 50% reduction in total projected loss (see Figure 6 and
FEMA, 2003b). Developing residential areas with some forest cover can
greatly lower the risk of direct hurricane wind damage in coastal areas
and may therefore save a great deal of money (FEMA, 2003b). However, it
should be noted that coastal suburban areas with forest cover could be
at risk to indirect wind damage from tree blow down.

As discussed previously, the HAZUS-MH Hurricane Model currently employs
land cover maps with Z~0~ look-up tables. Such land cover maps are
derived from Landsat data. However, the land cover data used can be out
of date, as many United States coastal areas experience significant
growth and related land cover change (NSTC, 2003). Land cover maps are
not a direct measure of surface roughness length but are more an
approximation in which experts have used combined knowledge, data, and
simulations to assign an average roughness value to a given land cover.
The accuracy of the Z~0~ average values assigned to land cover types is
directly related to the accuracy of the land cover classification and to
the amount of within-class variation in surface roughness. A more direct
means of determining Z~0~ is to apply statistical regression techniques
to remote sensing data using anemometer data in a reference capacity.
This technique can be used alone or as supporting information to improve
Z~0~ values derived from another technique.

Predictive maps of surface roughness length may be computed using a
variety of data sources: (1) anemometer data, (2) land cover maps from
multispectral remote sensing imagery in conjunction with Z~0~ look-up
tables, (3) digital elevation models, (4) textural information from
remote sensing data (e.g., synthetic aperture radar (SAR) backscatter
data and high resolution electro-optical imagery), (5) geospatial
surface resistance models computed from multispectral reflectance
vegetation indices and thermal imagery (e.g., through NASA's Moderate
Resolution Imaging Spectroradiometer (MODIS) data), (6) stereoscopic
high-resolution remote sensing imagery (e.g., aerial photography), and
(7) thermal remote sensing data often included with multispectral
electro-optical data. NASA has a wealth of remote sensing technology
from a variety of platforms that may be of value in improving surface
roughness estimates of the HAZUS-MH Hurricane Model. Table 3 reflects an
initial investigation of remote sensing data that could address land
cover classification and digital elevation model (DEM) requirements for
the computation of surface roughness coefficients. These data range in
spatial and spectral resolution from coarse to fine, reflecting a
variety of inputs to be tested in a modeling environment. One aspect of
the partnership between NASA and FEMA will be to test these data and to
determine the model's sensitivity to changing spatial and spectral
resolutions.

  ----------------------------------- -----------------------------------
  []{#__RefHeading___Toc54595165      
  .anchor}Table 3. NASA-related       
  geospatial data available with      
  potential for Z~0~ estimation       
  studies.                            

  Remote Sensing Data/Model           

  NASA-Related Data Source            NASA-Related Data Products

  Flux Tower Anemometer Data          

  FLUXNET Ecological and Atmospheric  See <http://daac.ornl.gov/FLUXNET/>
  Monitoring Sites                    

  NASA Remote Sensing V&V Sites       e.g., NASA Stennis Space Center

  Multispectral Reflectance Data      

  MODIS Reflectance Data              MOD09A1 -- 8-Day Mean Surface
                                      Reflectance at 250 or 500 m

  MISR Reflectance Data               Level 1B2 -- Georectified Radiance

  ASTER Reflectance Data              AST_07 -- L2 Surface Reflectance
                                      VNIR/SWR

  Landsat Reflectance Data            SDP Landsat TM, ETM Products

  IKONOS Reflectance Data             SDP Products -- Select Sites

  QuickBird Reflectance Data          SDP Products -- Select Sites

  DEM Data                            

  SRTM DEM Data                       30 m SRTM DEM data -- see
                                      <http://srtm.usgs.gov/>

  Airborne LIDAR Altimetry DEM Data   Airborne Topographic Mapper (ATM)
  from NASA                           5 m DEM data

  Airborne LIDAR Altimetry Commercial SDP Products -- Select Sites
  DEM Data                            

  ASTER DEM Data                      AST14DEM ASTER DEM

  Airborne Interferometric Synthetic  SDP Products -- Select Sites
  Aperture Radar (InSAR) Commercial   
  DEM Data                            

  RS Textural Data                    

  MODIS Reflectance Data              MOD09A1 -- 8-Day Mean Surface
                                      Reflectance at 250 or 500 m

  ASTER Reflectance Data              AST_07 -- L2 Surface Reflectance
                                      VNIR/SWR

  Landsat Reflectance Data            SDP Landsat TM, ETM Products

  IKONOS Reflectance Data             SDP Products -- Select Sites

  QuickBird Reflectance Data          SDP Products -- Select Sites

  SRTM C-Band SAR Data                See <http://srtm.usgs.gov/>

  Evapotranspiration/Surface          
  Resistance                          

  MODIS Evapotranspiration/Surface    MYD16A2 -- 8-Day Mean L4 Global
  Resistance Data                     1 km

  Stereoscopic Reflectance Data       

  ASTER VNR Reflectance Data          AST_07 -- L2 Surface Reflectance
                                      VNIR/SWR

  NASA RS Data Archives               Aerial Photography

  Thermal Data                        

  MODIS Thermal Data                  MOD11A2 -- MODIS Land Surface
                                      Temperature/Emissivity 8‑Day L3
                                      Global 1 km

  ASTER Thermal Data                  AST_09T -- ASTER On-Demand L2
                                      Surface Radiance TIR

  Landsat Thermal Data                SDP Landsat TM, ETM Products
  ----------------------------------- -----------------------------------

As indicated in Table 3, anemometer data from FluxNet and from NASA
remote sensing verification and validation (V&V) sites could be useful
as reference data in evaluating and refining surface roughness
estimates. Using the land cover mapping approach, MODIS, Multi-angle
Imaging SpectroRadiometer (MISR), and Advanced Spaceborne Thermal
Emission and Reflection Radiometer (ASTER) data can be used to generate
land cover maps as input for estimating surface roughness. At this time,
utility of the land cover classification derived from such sensor data
is unknown in regard to HAZUS requirements for estimating property
damage and loss. Other approaches also may be useful to evaluate. For
example, ASTER data acquired in stereo mode can be used to develop
digital elevation models. NASA airborne LIDAR altimetry has already been
acquired along much of the U.S. Atlantic and Gulf coast. This data and
the data collection capability it represents could be used to improve
estimates of Z~0~ for the most critical areas. Other NASA DEM data could
be used as well, such as the 30 meter DEM data from the Shuttle Radar
Topography Mission (SRTM). The SRTM also produced C-band SAR backscatter
imagery that may also be used for characterizing surface roughness,
which may be worth evaluating for use in rain-prone regions where
electro-optical imagery can be difficult to collect. Textural data from
other remote sensing data types are also available for evaluation in
regards to Z~0~ estimation.

The relationships between evapotranspiration, thermal measurements, and
surface roughness are not immediately obvious. However, some
associations between heat flux, aerial resistance to the flow of
sensible heat, and surface roughness seem to exist and should be
explored (see Appendix A). Preliminary research shows (at least at the
equation level) that there may be some usable relationship between r~a~
and z~0~---but how can r~a~ be "measured" with NASA satellites? From a
phenomenological point of view, one can perceive that changes in leaf
structure in a crop or forest canopy will impact wind speed profiles
associated with z~0~. The thermal and evapotranspiration products
available from Aqua/MODIS and ASTER may, therefore, be useful in
inferring information about vegetation vigor and hence, surface
roughness. It should be noted, though, that this an area will require
research before it can be integrated into operational models such as
HAZUS.

ARA et al. (2003) discuss how NASA remote sensing technology and models
could address information needs for upcoming components of the HAZUS-MH
Hurricane Model. In particular, ARA et al. (2003) propose to integrate
the NOAA/NASA WAVEWATCH III deep-water wave model into the HAZUS-MH
Hurricane model to provide a risk estimation tool by integrating results
from models for storm surge, tides, wind, and wave activity. ARA et al.
(2003) also proposes to assess NASA remote sensing assets for improving
DEM information for low-lying coastal areas. The default DEM data for
the HAZUS-MH flood model is the 30-meter National Elevation Dataset.
This resolution is less than desirable for assessing hurricane-related
flooding risk of coastal communities and also poses limitations to
horizontal and positional accuracies. With its suite of satellites, NASA
may have the capability to develop improved DEMs. By demonstrating
relevant technologies, no matter what the resolution, NASA may provide
FEMA an alternative approach to the development of improved DEM data
products.

In addition to the need for improved Z~0~ estimation, other inputs to
the HAZUS-MH Hurricane DST wind model may be improved with NASA remote
sensing mission data (Table 4). For example, the HAZUS-MH Hurricane wind
model employs information on sea surface temperature, hurricane track,
and rainfall intensity (FEMA, 2003b) that can be derived from and
possibly improved with NASA remote sensing data. NOAA provides historic
storm data used in the HAZUS-MH software, and the NASA Aqua satellite
includes three sensors able to compute sea-surface temperature: MODIS
(Brown et al., 2002), the Advanced Microwave Scanning Radiometer
(AMSR-E), and the Tropical Rainfall Measuring Mission\'s (TRMM) sensors
(NASA, 2003). Tentatively available in 2007, the Global Precipitation
Mission sensors represent another means for estimating sea surface
temperature and a follow-up to AMSR and TRMM. Such data may provide
improved sea-surface temperature estimation inputs to HAZUS-MH during
hurricanes for forecasting or for comparison with historic storm data.
The Aqua satellite\'s AMSR-E and the TRMM, in addition to the Global
Precipitation Measurement (GPM) Microwave Imager, all offer the
advantage of detecting sea surface temperatures through clouds (NASA,
2003). Other NASA remote sensing assets available for sea surface
temperature studies include ASTER and Landsat thermal data, although
these data types may be problematic because atmospheric correction
and/or signal-to-noise resolution requirements.

The HAZUS-MH Hurricane Model also computes hurricane rainfall intensity
estimates, which may be improved and validated with the NASA TRMM and
GPM sensors, at least for areas within ground coverage of the sensor. In
addition, QuikSCAT and SeaWinds sensors are used to estimate wind
intensity and direction for hurricanes and may help to improve
hurricane-tracking information within the HAZUS-MH Hurricane Model.
NASA's Jason-1 satellite altimeter collects data on sea surface height,
a critical measure of ocean energy available to developing hurricanes.
The hurricane boundary layer model of the HAZUS-MH Hurricane Model
incorporates sea surface roughness and the sea-air temperature
difference at the estimated surface level (FEMA, 2003a), so Jason‑1
could be an effective contributor to HAZUS-MH as well.

NASA has maintained sites for remote sensing V&V that could be useful
for aiding surface roughness studies. An appropriate V&V site for the
HAZUS-MH study of surface roughness will include a highly developed
spatial and geodetic target range, a wealth of current remote sensing
datasets over the site, and a variety of weather instrumentation that is
frequently used in characterizing spaceborne and airborne remote sensing
assets (Pagnutti et al., 2002). Other required capabilities include flux
towers containing weather monitoring instrumentation, including
anemometers to measure wind speed (ARA et al., 2003). A typical flux
tower is shown in Figure 8.

+-----------------------------------------------------------------------+
| ![](media/image9.jpeg){width="5.3125in" height="3.502083333333333in"} |
|                                                                       |
| Photo Credit: Alessandro Carioca de Araújo                            |
|                                                                       |
| Source: CarboEurope Web site at                                       |
| <http://www.bgc-jena.mpg.de/public/carboeur/archive/foto.html>        |
|                                                                       |
| []{#__RefHeading___Toc54595174 .anchor}Figure 8. A flux tower with a  |
| sonic anemometer deployed at the top.                                 |
+-----------------------------------------------------------------------+

  -------------------------------- ---------------------------------------------------------------
  []{#__RefHeading___Toc54595166   
  .anchor}Table 4. NASA-related    
  remote sensing models and assets 
  with potential for contributing  
  to the HAZUS-MH Hurricane Model  
  DST parameters other than Z~0~.  

  HAZUS-MH Hurricane Model         
  Parameter                        

  NASA-Related Data Source         NASA-Related Data Products

  Elevation Data For Flood         
  Assessment                       

  Airborne NASA LIDAR Altimetry    Airborne Topographic Mapper (ATM) 5 m DEM data
  DEM Data                         

  Airborne Commercial LIDAR        NASA RS Data Archives - SDP Products -- Select Sites
  Altimetry DEM Data               

  Airborne Commercial and NASA     NASA RS Data Archives - Select Sites - Some SDP Data
  InSAR DEM Data                   

  Airborne Photogrammetric DEM     NASA RS Data Archives - Select Sites
  Data                             

  ASTER DEM Data                   AST14DEM ASTER DEM

  SRTM InSAR DEM Data              See <http://srtm.usgs.gov/>

  IKONOS DEM data                  NASA RS Data Archives - SDP Products -- Select Sites

  Sea Surface Temperature          

  MODIS data                       MYD28L2 - Sea Surface Temperature - 5-Min L2 Swath 1km

  AMSR-E Data                      AMSR-E/Aqua L2B Global Swath Ocean Products -- includes SST at
                                   56 km

  TRMM Sensor Data                 See <http://www.gsfc.nasa.gov/topstory/2003/0909hurricane.html>

  GPM Microwave Imager Data        Dual-Frequency Precipitation Radar products

                                   GMI Microwave Radiometer products

                                   See <http://gpm.gsfc.nasa.gov/index.html>

  ASTER Thermal Radiance Data      AST_09T -- ASTER On-Demand L2 Surface Radiance TIR -- 90 m

  Landsat Thermal Data             L7WRS2_L1 Level 1 WRS Scene -- 60 m

  Rainfall Amount/Intensity        

  TRMM Sensor Data                 See <http://trmm.gsfc.nasa.gov/data_dir/ProductStatus.html>

  GPM Microwave Imager Data        Dual-Frequency Precipitation Radar products

                                   GMI Microwave Radiometer products

                                   See <http://gpm.gsfc.nasa.gov/index.html>

  Wind                             

  QuikSCAT Data                    Level 3 Daily, Gridded Ocean Wind Vectors

  SeaWinds Data                    See <http://winds.jpl.nasa.gov/>

  Sea Surface Height               

  Jason-1 Altimeter Data           See
                                   <http://topex-www.jpl.nasa.gov/mission/jason-fact-sheet.html>
  -------------------------------- ---------------------------------------------------------------

# Identified NASA Technology Gaps in Meeting Remote Sensing Needs of DSTs

Assessing technology gaps is difficult, given the information gaps
regarding requirements for estimating Z~0~. Additional information is
needed to determine spatial, temporal, radiometric, and geometric
requirements of remote sensing data needed to compute estimates of Z~0~.
More research is needed to determine whether Landsat data is best suited
for computing surface roughness, albeit indirectly via land cover maps.
The current method for assigning terrestrial surface cover roughness is
rather arbitrary and could be refined with further study, perhaps using
a modified or completely different approach than the land cover method
currently used by the software. Alternative approaches will require
further study as suggested in the proposal by ARA et al. (2003). Land
cover maps from moderate spatial resolution sensors (e.g., Landsat ETM+)
may be sufficient for use with refined Z~0~ look-up tables to enable
improved HAZUS-MH hurricane risk assessment. Coarse spatial resolution
from MODIS may well be adequate or finer resolution ASTER imagery may be
best for residential areas and other sites of high concern. When going
from open to higher land cover, wind gusts may have to travel across
500--1000 meters of obstructing contiguous land cover to impose a
mitigating effect on the wind speed (personal communication, Frank
Lavelle, ARA). The spatial resolution requirements and the information
extraction approach for deriving Z~0~ are both in need of further study.
Another unresolved issue is how to validate Z~0.~ All of these questions
must be addressed before any integration of NASA remote sensing data can
occur. A systems engineering approach must be employed to determine
remote sensing data resolution requirements for mapping land covers of
interest to HAZUS-MH software developers and end users. Systematic
investigation of alternate approaches for deriving Z~0~ values must also
be performed. Once data and algorithm requirements are identified,
technology gaps in up-to-date data availability may need to be
addressed. For example, the current method of computing surface
roughness from Landsat 7 land cover maps may pose a technology gap given
the current degraded state of the sensor and the lack of scheduled
replacement.

NASA technology gaps may also exist for other HAZUS information needs
potentially addressable with NASA remote sensing assets (e.g., sea
surface temperature, storm rainfall intensity and amount, storm
direction, and sea surface roughness. These information needs regard
storm characterization data that include stringent temporal data
collection requirements compared to land cover mapping. Therefore, one
technology gap regards the lifespan of identified sensors and the need
for long-term timely data availability. Some sensors, such as SeaWinds
and MODIS/Aqua, will remain operational for several more years, whereas
others may soon be out of operation (e.g., TRMM). ADEOS SeaWinds,
launched in December 2002, is scheduled to operate for 5 years.
Aqua/MODIS, launched in May 2002, should be in operation for 6 years,
providing sea surface temperature as one of the products. On the other
hand, TRMM was launched in 1997 with an expected life span of 3 years.
TRMM still collects data, but will TRMM survive long enough for its
successor, GPM, to be launched as scheduled in fall 2007?

Difficulty integrating NASA data into HAZUS may also exist. To use NASA
remote sensing data directly for HAZUS information needs would require
significant programming resources, beyond what is being planned for
current HAZUS software development.

Another potential technology gap concerns the research nature of many
NASA remote sensing missions and related data products. For example,
much of the high-resolution airborne DEM data available in NASA data
archives was acquired for experimental purposes. This data is highly
suitable for use in technical feasibility tests of such data for a given
application (e.g., proof of concept studies) but is not usually
available in quantities that permit operational use. Data collected by
the NASA Airborne Topographic Mapper (ATM) LIDAR system presents another
example. While a fair amount of ATM DEM data has been collected along
the Atlantic and Gulf Coasts, these areas constitute thin strips of land
along the shoreline. More inland coverage might be required for
operational use.

Additional technology gaps may exist because of data quality issues. For
example, SRTM DEM data can have data gaps in the otherwise contiguous
DEM surfaces because of incoherence and interference effects. Such
errors can be land-cover specific, so additional testing may be needed
to assess the technical feasibility of SRTM data for use in HAZUS
applications and study area landscapes. SRTM data is an attractive
choice for providing DEM products operationally because of its broad
available coverage and because it provides information on the elevation
of the uppermost terrestrial surface.

# Conclusions and Recommendations

This report documents an evaluation of the HAZUS-MH Hurricane Model,
using relevant previously published papers, reports, research proposals,
technical manuals, presentations, course materials and some hands on
experience with the software. In addition, the evaluation included
attending a short course on using the software for hurricane risk
assessment.

FEMA, NIBS, and others, including multiple consulting firms, have
developed the HAZUS-MH Hurricane Model into a GIS-based DST for
hurricane risk assessment. The most recent version of the software
showed impressive capabilities for aiding emergency managers in
assessing risk, and in developing and implementing strategies for
mitigating risk from hurricanes.

A review of the literature indicated several publications with
information on the capability of this DST. Although limited, the
hands-on experience with the software aided the evaluation, although
additional software testing is recommended should NASA commit to aiding
development of this DST. The relatively nascent state of the Hurricane
Model for HAZUS-MH represents an opportunity for NASA to make a
significant contribution to the development and performance of this DST.

The evaluation revealed that use of NASA remote sensing and modeling
technologies have potential for improving the hurricane model and
especially for improving estimates of surface roughness, a critical
factor for estimating building damage and loss from hurricane winds.
Further research should be conducted into the feasibility of using NASA
remote sensing data for mapping surface roughness length to improve the
current capability of HAZUS-MH for hurricane risk assessment.

It appears to be technically feasible that NASA remote sensing assets
can aid improvement of surface roughness estimates. However, more
research is needed to assess more fully the potential of NASA remote
sensing for satisfying this DST information requirement. Fortunately,
NASA's remote sensing application V&V capability is well suited for
aiding such a study. In particular, NASA's remote sensing
instrumentation capability and preexisting data archive (e.g., at
Stennis Space Center) will be invaluable for assessing remote sensing
data resolution requirements for providing improved surface roughness
estimates.

The study identified several NASA sensors and sensor data products with
potential for improving surface roughness estimates and identified
multiple methods for computing surface roughness length with remotely
sensed data. Research is also needed into which method is most
appropriate and into whether alternative methods can be identified to
improve estimates of Z~0~. Even if the land cover method is the best
option, more research is needed to resolve spatial, spectral, temporal,
and radiometric resolution requirements for the input remote sensing
data. More research is needed to determine if coarse resolution remote
sensing (e.g., MODIS 250 to 1 km reflectance data) can be used or if a
finer spatial resolution (e.g., ASTER 15 meter reflectance data) than 30
meter Landsat data would be beneficial. Additional research is also
needed to determine the benefits of improving land cover classification
scheme specificity. For example, FEMA (2003b) reported that forest
height and density information is important for refining Z~0~ estimates
and for assessing risk of hurricane wind damage and loss. Other methods
and data types (e.g., LIDAR altimetry) offer high potential for
improving forest height and density estimates. A combination of surface
roughness measurement and land cover mapping may be needed to improve
risk estimates of hurricane wind damage and loss. Consequently, the DST
evaluation team recommends that NASA work with FEMA, NIBS, and ARA to
conduct system engineering studies, sensitivity analyses, and V&V on use
of remote sensing for improving Z~0~ estimates used in the HAZUS-MH
Hurricane Model DST.

A NASA verification and validation study related to surface roughness
should include an evaluation and verification NASA land cover and DEM
data sources through comparisons against actual surface roughness
measurements derived from in situ anemometers and against other remote
sensing data sources. Figure 9 illustrates how NASA ESA remote sensing
assets may be used in a systematic V&V study of Z~0~ estimation,
utilizing Stennis Space Center as a demonstration site.

The anemometer method offers the best means to collect *in situ* data
for characterizing Z~0~ of various land cover types common to the
coastal United States. However, employing this method in a weather
station scenario is expensive, and deploying this method over the vast
region of the United States coastline under consideration by HAZUS-MH
Hurricane Model is impractical. In addition, existing anemometers have
often been placed in ways that are problematic for extracting
land-cover-specific Z~0~ data without encountering adjacency affects
from other land cover. For example, these devices are sometimes deployed
on airport buildings adjacent to a mix of open areas and other
buildings, which makes it difficult to collect land-cover-specific
information on Z~0~. A more appropriate method would be to instrument a
flux tower with anemometers at various heights, starting at ground
level, and to locate the tower well within a homogeneous land cover,
thus enabling land-cover-specific characterization of the wind profile.
Because Z~0~ is typically measured at 10 meters above the ground for
land cover surface roughness studies, anemometers would also be deployed
above the 10-meter mark to characterize the wind profile more fully. The
atmospheric and ecological research community often utilizes flux towers
in this manner.

The Stennis Space Center includes a highly developed V&V target range, a
wealth of current remote sensing datasets over the site, and a variety
of weather instrumentation that is frequently used in characterizing
spaceborne and airborne remote sensing assets (Pagnutti et al., 2002).
This capability will soon include two flux towers containing weather
monitoring instrumentation (ARA et al., 2003). One of the towers will be
30 meters high and will include 6 separate cup anemometers arranged
vertically on the tower, similar to the flux tower shown in Figure 8.
The tower will be 10 meters high and mobile; it will include a cup and a
sonic anemometer, each located in a different spot vertically on the
tower. At least one anemometer per tower will be located at 10 meters
above the ground, the preferred distance for measuring Z~0~. Initially,
the towers will be deployed in open grassy land cover sufficiently large
to contain adequate fetch. The patch size of such sitings is still being
studied. The Environmental Protection Agency (2002) recommends that the
patch width be 10 times the height of the nearest obstruction.
Determination of a minimum patch size requirement is an issue in need of
further research and is still being actively pursued. It is also
conceivable that the tower locations may be moved to characterize
surface roughness of different land cover types.

  -----------------------------------------------------------------------
  []{#__RefHeading___Toc54595175 .anchor}Figure 9. Potential role of
  Stennis Space Center assets for improving HAZUS-MH.

  -----------------------------------------------------------------------

NASA also has laboratory facilities for remote sensing instrumentation
characterization, DST studies, and mapping application validation. For
example, Stennis Space Center includes a V&V laboratory for
characterizing field deployable remote sensing instruments and in-house
software, such as the Application Research Toolbox (ART) for
quantitative analysis of remote sensing data requirements for any of a
number of DST application evaluation needs (Zanoni et al., 2002).

NASA's remote sensing data archive can also be of great value in
developing improved estimates of Z~0~. This archive includes diverse
high-resolution DEM data ranging from LIDAR altimetry, InSAR, and
traditional aerial photogrammetry. The archive also comprises an equally
diverse assortment of high to moderate resolution electro-optical remote
sensing data acquired from spaceborne and airborne platforms. For
example, a variety of remote sensing and related DEM data depicts
Stennis Space Center, which includes estuarine coastal habitat adjacent
to the Pearl River. A great deal of the SDP commercial imagery was
acquired over Stennis Space Center, including Space Imaging IKONOS
multispectral data, DigitalGlobe QuickBird multispectral data, Intermap
STAR‑3*i* DEM and X-band SAR data, Positive Systems ADAR multispectral
data, and 3001 LIDAR altimetry data. Such data could be used with
anemometer and with other *in-situ* data to develop land cover maps for
use in developing and validating improved HAZUS‑MS surface roughness
estimation techniques.

In performing a V&V study of HAZUS-MH improved surface roughness, it is
further recommended that the following questions be addressed more
fully:

1.  Regarding wind models that enjoy the widest application:

    a.  What are the most significant criteria (i.e., parameters)?

    b.  Of these criteria, which ones can be derived from remote
        sensing?

    c.  Of the criteria that can be derived from remote sensing, can any
        be derived from ESE data? (This assumes knowledge of scale for
        the model and its application method.)

2.  How is the measure of surface roughness length, Z~0~, used in the
    DST?

    a.  How significant is this variable to the DST compared with other
        required variables?

    b.  How accurate can Z~0~ be calculated directly from remote sensing
        data? What is the validity of Z~0~ estimates using different
        methods and data sources? What is the sensitivity of Z~0~ as an
        input to models used by the DST?

    c.  What is the sensitivity of scale with respect to effective
        estimation of Z~0~ values? For potentially useful,
        cost-effective remote sensing techniques, what are the optimal
        spatial, spectral, radiometric, geometric, temporal, and
        signal-to-noise resolution requirements?

    d.  How do land cover classes affect variation in surface winds? How
        do different land cover types relate to a surface wind field? Do
        current standard land cover classification schemes permit
        complete and/or effective understanding of community risk from
        wind?

    e.  How does land cover height affect variation in surface winds?
        For a given land cover type, how does intra-patch heterogeneity
        affect surface wind variation? How does adjacent land cover
        affect surface wind variation? What is the benefit of improved
        elevation maps for improved estimates of Z~0~ values?

3.  How useful will high-resolution DEM data be compared to NED DEM data
    for other functions of the HAZUS-MH Hurricane Model? How useful will
    high resolution DEM data be for estimating hurricane risk from
    flooding, storm surge, and high tides?

Based on the proposed work by ARA et al. (2003), the DST owner, FEMA,
has considerable interest in working with NASA to improve surface
roughness estimates, thus improving the HAZUS-MH Hurricane Model's
capabilities. Improving these capabilities could increase the potential
of this powerful DST for helping emergency managers to mitigate
hurricane hazards effectively and to save human lives.

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Enterprise, <http://www.earth.nasa.gov/visions/appstrat2002.pdf>,
accessed August 6, 2003.

National Aeronautics and Space Administration, 2002b. *Earth Science
REASoN - Research, Education and Applications Solutions Network: A
Distributed Network of Data and Information Providers For Earth Science
Enterprise Science, Applications and Education*. NASA Cooperative
Agreement Notice, CAN-02-OES-01, Appendix F,
<http://research.hq.nasa.gov/code_y/nra/current/CAN-02-OES-01/can-02-oes-01.pdf>,
accessed August 6, 2003.

National Aeronautics and Space Administration, 2003. *NASA Satellites
Extract Ingredients in Hurricane Recipe to Improve Forecasts.* Top
Story, Goddard Space Flight Center, September 10, 2003,
<http://www.gsfc.nasa.gov/topstory/2003/0909hurricane.html>, accessed
September 29, 2003.

National Science and Technology Council, 2003. *Reducing Disaster
Vulnerability Through Science and Technology: Interim Report*. NSTC
Committee on the Environment and Natural Resources, Subcommittee on
Disaster Reduction, July 2003, 42 pp.,
<http://www.ostp.gov/NSTC/html/SDR_Report_ReducingDisasterVulnerability2003.pdf>,
accessed September 22, 2003.

Pagnutti, M., K. Holekamp, R. Ryan, S. Blonski, R. Sellers, B. Davis,
and V. Zanoni, 2002. Measurement sets and sites commonly used for
characterization. *Proceedings of the ISPRS Commission I Mid-Term
Symposium 2002: Integrated Remote Sensing at the Global, Regional and
Local Scale,* November 10--15, 2002, Denver, CO, IAPRS Vol. XXXIV, Part
1.

Pielke, R.A., and C. W. Landsea, 1998. Normalized hurricane damages in
the United States: 1925--95. *Weather and Forecasting,* 13:621--631.

Powell, M., G. Soukup, N. Morisseau-Leroy, C. Landsea, S. Cocke, L. Axe,
and S. Gulati, 2003. State of Florida hurricane loss projection model:
Atmospheric science component. *Proceedings of the Eleventh
International Conference on Wind Engineering,* June 2-5, 2003, Lubbock,
TX (Wind Science and Engineering Research Center, Texas Tech
University), <http://www.aoml.noaa.gov/hrd/project2003/powsof.pdf>,
accessed September 22, 2003.

Ross, T., and N. Lott, 2000. *A Climatology of Recent Extreme Weather
and Climate Events.* U.S. Department of Commerce, NOAA/NESDIS, National
Climatic Data Center Technical Report 2000-02, Asheville, NC,
<http://lwf.ncdc.noaa.gov/oa/reports/billion/new-paper2x.PDF>, accessed
September 22, 2003.

Schauer, B., E. Laatsch, J. Minor, P. Vickery, and F. Lavelle, 2002. The
HAZUS hurricane model and its use in mitigation. *Proceedings of 2002
National Hurricane Conference*, April 2002, Orlando, FL, 34 pp.,
<http://www.fema.gov/hazus/zip/dl_hurpres.zip>, accessed September 22,
2003.

Srinivasan, D., 2003. Battling hazards with a brand new tool. *Planning
Magazine,* February, pp. 10--13; also
<http://www.fema.gov/hazus/pdf/dl_plan.pdf>, accessed September 22,
2003.

Twisdale, L.A., P.J. Vickery, J.X. Lin, and A.C. Steckley, 2000a.
Analysis of hurricane windborne debris impact risk for residential
structures: Part I. Draft to be submitted to *ASCE Journal of Structural
Engineering*.

Twisdale, L.A., P.J. Vickery, J.X. Lin, and A.C. Steckley, 2000b.
Analysis of hurricane windborne debris impact risk for residential
structures: Part I. Draft to be submitted to *ASCE Journal of Structural
Engineering*.

Vickery P.J., P.F. Skerlj, A.C. Steckley, and L.A. Twisdale, 2000a. A
hurricane wind field and gust factor models for use in hurricane
simulations. *Journal of Structural Engineering,* 126(10):1203--1221.

Vickery, P.J., P.F. Skerlj, and L.A. Twisdale, 2000b. Simulation of
hurricane risk in the U.S. using an empirical storm track model
technique. *Journal of Structural Engineering,* 126(10):1222--1237.

Vickery, P.J., and Twisdale, L.A., 1995a. Prediction of hurricane wind
speeds in the United States. *Journal of Structural Engineering*,
121(11):1691--1699.

Vickery, P.J., and Twisdale, L.A., 1995b. Wind-field and filling models
for hurricane wind-speed predictions. *Journal of Structural
Engineering*, 121(11):1700--1709.

Ward, S., and C. Main, 1998. Population at risk from natural hazards.
*NOAA's State of the Coast Report,* National Oceanic and Atmospheric
Administration, Silver Spring, MD,
[http://oceanservice.noaa.gov/websites/retiredsites/sotc_pdf/PAR.PDF](http://state-of-coast.noaa.gov/bulletins/html/par_02/par.html),
accessed September 22, 2003.

Zanoni, V., B. Davis, R. Ryan, G. Gasser, and S. Blonski, 2002. Remote
sensing requirements development: A simulation-based approach.
*Proceedings of the ISPRS Commission I Mid-Term Symposium 2002:
Integrated Remote Sensing at the Global, Regional and Local Scale,*
November 10--15, 2002, Denver, CO, IAPRS Vol. XXXIV, Part 1.

####### Surface Roughness Length

One aspect of integrating NASA-developed technology into hurricane
modeling is the direct estimation of aerodynamic surface roughness
(Z~0~) using NASA remote sensing data. The surface of the Earth
interacts with the floor of the planetary boundary layer because of its
manmade and natural irregularities. These irregularities cause surface
roughness to exist on two levels: the microscopic level, dealing with
surface interaction of particles (e.g., airflow over different textures
of leaf surfaces), and the macroscopic level, dealing with surface areas
of larger objects (e.g., airflow around trees and buildings).

A sonic anemometer determines instantaneous wind speed and direction
(velocity) by measuring the degree to which wind affects the speed of
sound waves traveling between a pair of transducers. Anemometers located
on meteorological towers can obtain wind speed vertical profile
observations. Wind speed in the vertical profile tends to approach zero
close to the Earth's surface and to increase with height. The surface
height where wind speed comes closest to zero is known as the surface
roughness length, Z~0~. Where surface irregularities are small (smooth
surface), the wind speed will come closest to zero near the surface;
where surface irregularities are large (e.g., buildings, trees, terrain)
minimum wind speed will occur farther from the surface. Typical values
of Z~0~ range from several millimeters for bare soil to a few tens of
centimeters for growth-covered surfaces. Once Z~0~ is determined for a
wind direction, this parameter will not change with wind speed,
atmospheric stability, or surface stress.

$z_{0} = \frac{0\text{.}5\text{HS}}{A}$The most commonly accepted
formula for estimating surface roughness length is from Lettau (1969)

where *H* is average vertical extent, or average height (cm) within the
area; *S* is the silhouette area (cm^2^) of the average obstacle, or
total projected frontal area of the obstacles; and *A* is the surface
area (cm^2^). This equation has been demonstrated as valid for total
area sizes *A* up to and including the mesoscale (10^9^ m^2^). This
equation should be used only when the area of obstacles is of sufficient
distance in the upwind direction of the measuring anemometer tower.

Even when anemometer matching and calibration properly eliminate
instrumental errors, the estimation of surface roughness with the help
of measured wind-profile data is not without problems. In addition to
Z~0~, the determination of a so-called "zero-plane displacement" is
required. Ideally, Z~0~ is an unchanging surface characteristic of the
tower site, while the zero-plane displacement is "the corrective height
increment between the mathematically defined zero level of a numerical
model of the wind profile and the arbitrary datum level from which the
observer has measured anemometer heights" (Lettau, 1969). The zero-plane
displacement is the altitude above which the wind profile is again
described by a similarity equation. Z~0~ should be positive and
unchanging with respect to vertical tower positions, whereas the
zero-plane displacement should vary by exactly the height increment that
the tower assembly was moved, either up or down.

Another formula used for estimating Z~0~ using anemometer data takes
advantage of wind profile data.
$U_{H} = \frac{U}{k}\text{ln}\left( \frac{H}{z_{0}} \right)$The
adiabatic wind speed vertical profile is described in the following
logarithmic law:

where *U~H~* is the average wind speed at a height *H*, *k* is the von
Karman constant (*k* = 0.4), and *U* is the friction velocity. This
formula should be not used when obstacles are spaced very close together
(i.e., dense forests or urban buildings). In such cases, the zero-plane
displacement height is again required.

Past a definite lower threshold level of area density, the statistical
action of roughness elements breaks down and the individual airflow
around each obstacle must be considered separately. The statistical
notion of the roughness parameter, as well as visual range, would become
unacceptable if the cross sections of the obscuring elements are
significantly wider than that of the open air spaces between them.

The relationships between evapotranspiration, thermal measurements, and
surface roughness are not immediately obvious. However, some literature
indicates apparent associations between heat flux, aerial resistance
(a.k.a. aerodynamic resistance) to the flow of sensible heat, and
surface roughness that should be explored. For example, Rosenberg et al.
(1983) show a relationship between aerial resistance r~a~ and z~0~

$$r_{a} = \frac{\text{ln}\left\lbrack \frac{(z - d)}{z_{h}} \right\rbrack}{\text{ku}_{}} = \frac{\text{ln}\left\lbrack \frac{(z - d)}{z_{0}} \right\rbrack}{\text{ku}_{}} + \frac{\text{ln}(\frac{z_{0}}{z_{h}})}{\text{ku}_{}}$$

where *k* is the von Karman constant, *z~h~* is a parameter for heat
transfer that is analogous to the roughness parameter z~0~ for the
momentum transfer, *d* is the zero plane displacement (assumed to be the
same for heat as for momentum transfer), and *u~\*~* is the friction
velocity. Generally defined, surface resistance is a function of surface
roughness and is indirectly proportional to wind speed. Consequently,
zones of high surface roughness have a low aerodynamic surface
resistance. Surface resistance regards the resistance of surface to the
transfer of water vapor. Aerodynamic resistance is often used to model
the heat and water transfer near the surface and represents the effect
of surface roughness on the transfer. Within the context of the
Penman-Monteith model for evapotranspiration, surface resistance regards
the vegetative influence on the model (i.e., the transpiration).

Monteith and Unsworth (1990) report empirical relationships between r~a~
and z~0~ based on correlations using dimensionless groups derived from
engineering experiments discussed in the literature. These discussions
show, at least at the equation level, the occurrence of a potentially
usable mathematical relationship between r~a~ and z~0~. The question
remains as to how can r~a~ be "measured" with NASA satellites, although
one option to explore appears to be the Aqua/MODIS product, MOD 16. This
product contains 1-km resolution geospatial estimates of surface
resistance and evapotranspiration produced from 8-day average MODIS
thermal (MOD 11) and reflectance (MOD 13) data products (NASA, 2003).
From a phenomenological point of view, one can perceive leaf-structure
changes in a crop or forest canopy will impact wind speed profiles
associated with z~0~. The thermal and evapotranspiration products
available from Aqua/MODIS and from ASTER may therefore be useful in
inferring information about surface resistance and vegetation vigor;
hence, surface roughness. It should be noted, though, that this
technique will require more research compared to other approaches before
it could possibly be integrated into operational models such as HAZUS.

####### Glossary

**Adjacency effects** -- In terms of remote sensing, the change in the
spectral response (i.e., digital number) of a pixel caused by the
atmospheric scattering of radiance of adjacent pixels, which are located
outside the pixel in question's field of view. In terms of surface
roughness versus land cover type, the ability of adjacent land cover to
influence surface roughness length at a given location.

**Anemometer** -- instrument used to measure wind speed, usually either
from the rotation of wind-driven cups or from wind pressure through a
tube pointed into the wind.

**Central pressure** -- the estimated minimum barometric pressure in the
eye (approximate center) of a hurricane.

**Digital elevation model (DEM)** -- a representation of the topography
of the Earth in digital format; that is, by coordinates and numerical
descriptions of altitude.

**Electro-optical imagery** -- imagery derived from a device that
detects radiation by utilizing the influence of light in forming an
electrical signal.

**Explicit costs** -- costs entailing actual monetary transactions;
e.g., cost of repairing or replacing damaged structures or structural
elements.

**Geospatial data** -- information that identifies the geographic
location and characteristics of natural or constructed features and
boundaries on the earth. This information may be derived from, among
other things, remote sensing, mapping, and surveying technologies.
Statistical data may be included in this definition at the discretion of
the collecting agency.

**Geographic information system (GIS)** -- a system for archiving,
retrieving, and manipulating data that has been stored and indexed
according to the geographic coordinates of its elements. The system
generally can utilize a variety of data types, such as imagery, maps,
tables, etc.

**Historical storm mode** -- HAZUS mode that uses historical storm
records as a method of exploiting current and past motion and intensity
trends to project future storm positions and intensities.

**Hurricane** -- severe tropical storms whose winds exceed 74 mph.
Hurricanes originate over the tropical and subtropical North Atlantic
and North Pacific oceans, where there is high humidity and light wind.

**Impact energy** -- the amount of energy required to fracture a
material upon impact. The HAZUS-MH Hurricane Model uses impact energy
calculations in its two Windborne Debris Models.

**Impact momenta** -- the force with which objects collide. The HAZUS-MH
Hurricane Model uses impact momenta calculations in its two Windborne
Debris Models.

**Implicit costs** -- costs not entailing actual monetary transactions
but having negative financial impacts; e.g., loss of business while
repairing or replacing damaged structures or structural elements,
property value depreciation caused by flooding.

**Light detection and ranging (LIDAR)** -- LIDAR is in many ways
analogous to radar (radiation detection and ranging). LIDAR systems use
optical laser light (instead of microwave radiation) for atmospheric
monitoring, tracking, detection of speed, altitude, direction, and
range. Remote sensing personnel often use LIDAR interchangeably with
LADAR (laser detection and ranging). LIDAR is an active system that
requires both a transmitter (laser) to send a signal and a receiver to
measure the time delay, intensity, phase shift, and spectral change in
the return signal.

**Loss estimation** -- a projection of the damage likely to occur as the
result of various natural disasters, such as earthquakes, floods, and
hurricanes.

**Physiographic data** -- geographic data that deals with the exterior
physical features and changes of the Earth.

**Probabilistic mode** -- HAZUS mathematical mode of analysis in which
the behavior of one or more of the variables is either completely or
partially subject to probability laws.

**Synthetic aperture radar (SAR) backscatter** -- SAR is a coherent
radar system that generates high-resolution remote sensing imagery.
Signal processing uses magnitude and phase of the received signals over
successive pulses from elements of a synthetic aperture to create an
image. \"Backscatter\" is the portion of the outgoing radar signal that
the target redirects directly back towards the radar antenna.

**Scenario mode** -- HAZUS quantitative mode used to simulate a possible
disaster situation.

**Sea surface temperature** -- the temperature of the layer of seawater
(approximately 0.5 m deep) nearest the atmosphere.

**Stereoscopy** -- the science or art dealing with three-dimensional
effects and the methods by which these effects are produced.

**Surface roughness length** -- wind speed in the vertical profile tends
to approach zero close to the Earth's surface and to increase with
height. The surface height where wind speed comes closest to zero is
known as the surface roughness length, Z~0~.

**Topography** -- the technique of graphically representing the exact
physical features of a place or region on a map; the physical features
of a place or region.

**Wind loading** -- the amount of resistance to wind caused by a
structure or fitting.

####### Acronym and Abbreviation List

AMSR-E Advanced Microwave Scanning Radiometer

ARA Applied Research Associates

ART Application Research Toolbox

ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer

BIT Building Inventory Tool

DEM Digital Elevation Model

DSS Decision Support System

DST Decision Support Tool

EPEDAT Early Post-Earthquake Damage Assessment Tool

ESA Earth Science Applications

ESE Earth Science Enterprise

ETM+ Enhanced Thematic Mapper Plus

FEMA Federal Emergency Management Agency

GIS Geographic Information System

GPM Global Precipitation Measurement

GPS Global Positioning System

HAZUS-MH Hazards U.S. -- Multi-Hazard

HQ Headquarters

INCAST Inventory Collection and Survey Tool

InSAR Interferometric Synthetic Aperture Radar

LIDAR Light Detection And Ranging

LULC Land Use Land Cover

MISR Multi-angle Imaging SpectroRadiometer

MMI Modified Mercalli Intensity

MODIS Moderate Resolution Imaging Spectroradiometer

NASA National Aeronautics and Space Administration

NIBS National Institute for Building Sciences

NLCD National Land Cover Database

NSTC National Science and Technology Council

PC Personal Computer

QuikSCAT Quick Scatterometer

SAR Synthetic Aperture Radar

SCIGN Southern California Integrated GPS Network

SDP Scientific Data Purchase

SLOSH Sea, Lake, and Overland Surges from Hurricanes

SRTM Shuttle Radar Topography Mission

TRMM Tropical Rainfall Measuring Mission

V&V Verification and Validation

####### Disaster Management Roadmap![](media/image10.wmf){width="7.670138888888889in" height="5.7131944444444445in"}

####### HAZUS DSS One Page Summary

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####### Relevant Earth Observing Missions and Sensors

  -------------------------------- -------- ----------------------- ------------------------------
  []{#__RefHeading___Toc54594804                                    
  .anchor}Table F-. Partial list                                    
  of the Earth observing missions                                   
  and sensors relevant to HAZUS.                                    

  Platform/Mission                 Sensor   Product                 Comments

  Aqua                             MODIS    Land, ocean, and        Operational
                                            atmospheric processes   

  Terra                            MODIS    Land, ocean, and        Operational
                                            atmospheric processes,  

  Terra                            ASTER    Land surface            Operational
                                            temperature, elevation  

  Landsat 7                        ETM+     Land cover change,      Degraded Operation
                                            vegetation type         

  Shuttle                          SRTM     Digital Elevation       Data collected during 11-day
                                                                    mission in February 2000
  -------------------------------- -------- ----------------------- ------------------------------

Additional information about Aqua, Landsat 7, Terra, ASTER, ETM+, MODIS,
and SRTM are provided in the following pages.

######## Aqua

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########  Landsat 7

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  height="5.750694444444444in"}

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########  Terra

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  height="5.750694444444444in"}

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########  ASTER

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  ![](media/image15.png){width="7.670138888888889in"
  height="5.750694444444444in"}

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########  ETM+

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  ![](media/image16.png){width="7.670138888888889in"
  height="5.750694444444444in"}

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########  MODIS

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  height="5.750694444444444in"}

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########  SRTM

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  height="5.750694444444444in"}

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####### Additional Sources of HAZUS-Relevant Information

Arnfield, A.J., 2003. Two decades of urban climate research: A review of
turbulence, exchanges of energy and water, and the urban heat island.
*International Journal of Climatology*, 23:1--26; also
<http://www.environmental-center.com/magazine/wiley/0899-8418/pdf1.pdf>,
accessed September 22, 2003.

Bernard, J.C., L.L. Wendell, and V.R. Morris, 1998. Optimal filtering of
AC output anemometers. *Journal of Atmospheric and Oceanic Technology,*
15:1261--1271.

Carl, D.M., T.C. Tarbell, and H.A. Panofsky, 1973. Profiles of wind and
temperature from towers over homogeneous terrain. *Journal of
Atmospheric Sciences,* 30:788--794.

Grimmond, C.S.B., and T.R. Oke, 1999. Aerodynamic properties of urban
areas derived from analysis of surface form. *Journal of Applied
Meteorology,* 38:1262--1292.

Henjes, K., P.K. Taylor, and M.J. Yelland, 1999. Effect of pulse
averaging on sonic anemometer spectra. *Journal of Atmospheric and
Oceanic Technology,* 16(1):181--187.

Holtslag, A.A.M., 1984. Estimates of diabetic wind speed profiles from
near-surface weather observations. *Boundary-Layer Meteorology,*
29:225--250.

Kaplan, J., and M. DeMaria, 1995. A simple empirical model for
predicting the decay of tropical cyclone winds after landfall. *Journal
of Applied Meteorology,* 34(11):2499--2512; also
<http://shadi.aoml.noaa.gov/cgi-bin/w3-msql/refs/search_pubaoml1.html?author=kaplan&initial=j>
(abstract), accessed September 22, 2003.

Lavelle, F.M., P.J. Vickery, and L.A. Twisdale, 2003. Hurricane
Mitigation Cost/Benefit Analysis for Hawaii. *Proceedings of the
Eleventh International Conference on Wind Engineering,* June 2-5, 2003,
Lubbock, TX (Wind Science and Engineering Research Center, Texas Tech
University),
<http://129.118.105.4:9000/paperListing/viewDetails.aspx?ID=375>,
accessed October 16, 2003.

Lettau, H., 1969. Note on aerodynamic roughness-parameter estimation on
the basis of roughness-element description. *Journal of Applied
Meteorology,* 8:828--832.

Martano, P., 2000. Estimation of surface roughness length and
displacement height for single-level sonic anemometer data. *Journal of
Applied Meteorology,* 39:708--715.

Mason, P.J., 1988. The formation of areally-averaged roughness lengths.
*Quarterly Journal of the Royal Meteorological Society*, 114:399-420.

McVehil, G.E., 1964. Wind and temperature profiles near the ground in
stable stratification. *Quarterly Journal of the Royal Meteorological
Society,* 90:136--146.

Monteith, J.L., and M.H. Unsworth, 1990. *Principles of Environmental
Physics.* 2^nd^ ed., London: Edward Arnold, 291 p.

NASA, 2003. *EOS Data Products Handbook*. Volume 2 (C.L. Parkinson and
R.L. Greenstone, editors), NASA GSFC, Greenbelt, MD,
<http://eospso.gsfc.nasa.gov/ftp_docs/data_products_vol2.pdf>, accessed
October 22, 2003.

Opadeyi, J., 2002. Incorporating technology into disaster preparedness
and management operations: Mechanisms for the practical application of
scientific and technical solutions. *United Nations Development
Programme Caribbean Regional Seminar on the Use of Information
Technology in Comprehensive Disaster Management,* May 29-31, 2002, Ocho
Rios, Jamaica,
[http://www.cdera.org/pdfs/meetings/itconf2002/Opadeyi\'s%20paper.pdf](http://www.cdera.org/pdfs/meetings/itconf2002/Opadeyi's paper.pdf),
accessed September 22, 2003.

Penman, H.L., 1948. Natural evaporation from open water, bare soil and
grass. *Proceedings, Royal Society of London*, Series A, 193:120-145.

Pielke, R.A., R. Avissar, M. Raupach, A.J. Dolman, X. Zeng, A.S.
Denning, 1998. Interactions between the atmosphere and terrestrial
ecosystems: Influence on weather and climate. *Global Change Biology,*
4:461--475.

Rosenberg, N.J., B.L. Blad, and S.B. Verma, 1983. Microclimate: The
Biological Environment. 2^nd^ edition, New York: John Wiley & Sons 495
p.

Sadeh, W.Z., J.E. Cermak, and T. Kawatani, 1971. Flow over high
roughness elements. *Boundary-Layer Meteorology,* 1:321--344.

Simiu, E., 1973. Logarithmic profiles and design wind speeds. *ASCE
Journal of the Engineering Mechanics Division,* 99(5):1073--1083.

Simiu, E., and R.H. Scanlan, 1996. *Wind Effects on Structures:
Fundamental and Applications to Design.* 3rd Edition, New York: John
Wiley & Sons, 704 p.

Sozzi, R., M. Favaron, and T. Georgiadis, 1998. Method for estimation of
surface roughness and similarity function of wind speed vertical
profile. *Journal of Applied Meteorology,* 37:461--469.

Tennekes, H., 1973. The logarithmic wind profile. *Journal of
Atmospheric Sciences,* 30:234--238.

Thuillier, R.H., and U.O. Lappe, 1964. Wind and temperature profile
characteristics from observations on a 1400 ft tower. *Journal of
Applied Meteorology,* 3:299--306.

Tombrou, M., and D. Founda, 1998. Wind profile diagnosis from surface
routine meteorological data over a coastal area. *Boundary-Layer
Meteorology,* 87: 217--231.

####### HAZUS Inventory GIS Databases

+-----------------------------------------------------------------------+
| Source: FEMA, 2000b, Appendix D                                       |
|                                                                       |
| []{#__RefHeading___Toc54594814                                        |
| .anchor}![](media/image19.jpeg){width="6.25in"                        |
| height="7.65625in"}Figure H-. Summary of Inventory Databases listed   |
| in Appendix D of the HAZUS99 SR2 User Manual (FEMA, 2000b).           |
+-----------------------------------------------------------------------+

+-----------------------------------------------------------------------+
| Source: FEMA, 2000b, Appendix D                                       |
|                                                                       |
| ![](media/image20.jpeg){width="6.25in" height="5.46875in"}Figure H-,  |
| continued.                                                            |
+-----------------------------------------------------------------------+