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					             COMPARISON OF METHODS FOR
               VULNERABILITY ASSESSMENT
                    OF FLOODING HAZARDS

                                        by
                               Barbara Eberharter




                       1. BACHELOR THESIS
          submitted in partial fulfilment of the requirement of the degree
                               Bachelor of Science



                    Carinthia University of Applied Science
                          Department of Geoinformation




                   External Supervisor: Dr. Nancy Hultquist
  Geography Department, Central Washington University, Ellensburg, WA, USA


                    Internal Supervisor: Dr. Gernot Paulus
Department of Geoinformation, Carinthia University of Applied Science, Villach, AT




                             Villach, February 2009
Science Pledge



By my signature below, I certify that my thesis is entirely the result of my own work. I

have cited all sources I have used in my thesis and I have always indicated their

origin.




Villach, 11.02.2009

                                                                   Barbara Eberharter
Abstract



Disastrous Floods all over the world consistently demonstrate the power of nature.

Although protection systems are developed, complete protection against natural

disasters is impossible. So risk management is important to counteract damages of

flooding hazards. A first step is to analyze flood prone areas and to survey what is at

risk.

The main aspect of this study is to evaluate different methods for vulnerability

assessment. Literature was studied and several approaches are compared in the

literature section. In this part also a theoretical background on the relation of

urbanization and flooding is given followed by describing the components of risk.

In order to analyze the American standardized method of vulnerability assessment,

the flood hazard / flood loss estimation software HAZUS-MH was reviewed. HAZUS-

MH was developed by FEMA (the US Federal Emergency Management Agency) as

an easy-to-use tool that can effectively serve a large range of people with many

different needs and expertise. But because the modelling input is held very simple in

a Level 1 analysis, uncertainties can occur. So in this study the software model is

tested with comparing the estimated flood boundaries of HAZUS-MH with the real

inundation extent of a 100 year flood in Central Washington, USA. This was

accomplished with interpretation of aerial photographs, taken two days after the flood

and digitization of the flood extent in ArcGIS. This flood boundary was then overlaid

with the flood inundation map created by HAZUS-MH. The result of the analysis is an

underestimation of the airphoto interpretation flood extent and an overestimation of

the calculated inundation by HAZUS-MH. It might be noted that the results of both

the HAZUS-MH modelling as well as the airphoto interpretation are dependent on

accuracy provided by the available data.

                                                                                          3
Acknowledgments



I would like to thank André Egger for his love, support and patience throughout my

semester abroad and during my studies. Moreover his technical assistance allowed

me to install and use HAZUS-MH on my laptop which meant a substantial

improvement for my work flow.

I would also like to thank my family for their love and understanding throughout this

whole process.

I am especially thankful to my external supervisor Dr. Nancy Hultquist (Central

Washington University) and Dr. John Hultquist for their unresting encouragement

during my time at Central Washington University, USA and their constructive

comments and support.

Additionally I would like to thank my internal supervisor Dr. Gernot Paulus for

sparking my interest in the topic of natural hazards and for his assistance during my

studies.

I would also like to express thanks to Michael Wandler (Washington State

Department of Transportation), Daniel Church and Christopher Lynch (U.S.

Department of the Interior, Bureau of Reclamation, Upper Columbia Area Office),

Karen Hodges, Terry Keenhan and Michael Martian (Yakima County), Jerry Franklin

(Washington State Department of Ecology) for their kind assistance in data

acquisition.

I would like to thank Dr. Karl Lillquist (Central Washington University) for his

demanding lectures and the challenging project which motivated me to start all this.

Finally, I would like to thank my friends for their encouragement and constructive

discussions.
Table of Content


List of Abbreviations .......................................................................................... 7
1. Introduction ......................................................................................... 7
  1.1. Motivation ......................................................................................................... 8
  1.2. Organization of Work ....................................................................................... 9
2. Literature Review .............................................................................. 10
  2.1. Theoretical Background: Urban Flooding ....................................................... 10
  2.2. Components of risk ........................................................................................ 13
  2.3. Vulnerability Assessment ............................................................................... 15
      2.3.1. Uncertainty .........................................................................................................16
      2.3.2. Methods for vulnerability assessment .................................................................17
3. Methods ............................................................................................ 21
  3.1. HAZUS-MH Background ................................................................................ 21
  3.2. Application Design and Architecture .............................................................. 22
  3.3. Methodology .................................................................................................. 22
      3.3.1. Inventory Data ....................................................................................................24
      3.3.2. Levels of Analysis ...............................................................................................26
      3.3.3. Flood Loss Estimation Process ...........................................................................27
  3.4. Case Study .................................................................................................... 30
      3.4.1. Project Area ........................................................................................................31
      3.4.2. Data ....................................................................................................................34
4. Results and Discussion ..................................................................... 37
  4.1. Underestimation of digitized flood boundary on flood airphotos February 11,
  1996 ...................................................................................................................... 38
  4.2. Overestimation of HAZUS-MH flood boundary .............................................. 38
  4.3. Losses............................................................................................................ 39
      4.3.1. Flood Hazard Map ..............................................................................................40
      4.3.2. General Building Stock Damage by Occupancy / Building Type / Count .............41
      4.3.3. General Building Stock Economic Loss...............................................................42
      4.3.4. Essential Facilities ..............................................................................................42
      4.3.5. Debris .................................................................................................................43
      4.3.6. Shelter ................................................................................................................43
5. Summary and Further Perspectives .................................................. 45
6. References........................................................................................ 47
7. List of Figures ................................................................................... 50
8. List of Tables ..................................................................................... 51
9. Appendix ........................................................................................... 52




                                                                                                                                      5
List of Abbreviations




CFS ........................ cubic feet per second
CHP ........................ Center for Hazards Research and Policy Development
DEM ....................... Digital Elevation Model
DMA ....................... Disaster Mitigation Act
DOE ........................ Department of Energy
EROS ..................... Earth Resources Observation Systems
ESRI ....................... Environmental Systems Research Institute
FEMA ..................... Federal Emergency Management Agency
FIMA ....................... Federal Insurance and Mitigation Administration
FIRM ....................... Flood Insurance Rate Map
FIS .......................... Flood Insurance Studies
FIT .......................... Flood Information Tool
GIS ......................... geographic information system
HAZUS-MH............. HAZards United States Multi-Hazard
InCAST ................... Inventory Collection and Survey Tool
MMC ....................... Multihazard Mitigation Council
NAIP ....................... National Agriculture Imagery Program
NIBS ....................... National Institute of Building Sciences
NFIP ....................... National Flood Insurance Program
NRMC ..................... Natural Risk Management Carinthia
SQL ........................ Structured Query Language
UN/ISDR ................. United Nations International Strategy for Disaster Reduction
USACE ................... U.S. Army Corps of Engineers
USACE IWR ........... U.S. Army Corps of Engineers Institute for Water Resources
USGS ..................... United States Geological Survey
WSDOT .................. Washington State Department of Transportation




                                                                                         6
1. Introduction



Flooding is one of the most common and widespread of all natural disasters and

poses great danger to people, property and environment all over the world. It is

estimated that for the period of the last ten years flood damages worldwide caused a

loss of a trillion US$. (Herrmann, 2007)

While there is little that can be done to reduce the severity or frequency of natural

disasters it is important to understand what drives them to be able to forecast their

occurrence.   and reduce the losses therefrom.      ????

Loss estimation modelling is relatively new, particularly concerning natural hazard

assessment. Although some studies were produced in the 1960s, only in the 1990s

did loss estimation methodologies beca (o) me more common because of the advent

of geographic information systems (GIS) technology. With GIS it was possible to

overlay hazard data onto maps of various systems (e.g., building data, lifeline routes,

or population information). (MMC, 2005)

Since the 1990s the Federal Emergency Management Agency (FEMA) under

contract with the National Institute of Building Sciences (NIBS) and many technical

contractors has developed an American applicable standardized methodology and

software program for estimating potential losses from earthquakes, floods, and

hurricanes – HAZUS-MH (HAZards United States. Multi-Hazard). This software and

other studies are reviewed in this bachelor thesis in order to evaluate existing

methods in the field of “Vulnerability Assessment of Flooding Hazards”.




                                                                                        7
1.1. Motivation

As population growth forces more communities to expand into areas at risk from

natural threats, concern increases about the danger that geohazards pose to people,

property, and the environment. Natural disaster events such as floods (Province of

Lower Austria, 2002) in Austria and throughout the world caused significant damage

in recent years. In order to prevent such damages and to support sustainable

regional planning for authorities, the interdisciplinary project “Natural Risk

Management Carinthia (NRMC)” started in January 2004. The major goal of the

project was to capture, analyze, assess and visualize alpine geohazards, which are

torrents, avalanches, rock fall, landslides, and floods. The project partners are four

authorities of the provincial government (geology, water management, forestry and

regional planning), one state authority (Forest Technical Service for Torrent &

Avalanche Control Carinthia) and an educational institution (Carinthia University of

Applied Sciences – Department of Geoinformation). In the first phase of the project

(2004-2006) a digital geodata infrastructure for alpine geohazards has been created.

The second phase started in 2007 and focuses on developing and applying methods

for interdisciplinary risk assessment for flooding, avalanches and landslide hazards

based on an existing geodata infrastructure.

The research topic of this bachelor thesis is integrated in the NRMC working package

“Vulnerability Assessment of Flooding Hazards” and has the objective to assess and

evaluate existing methods in this field. The approach of the research project in

cooperation with Dr. Hultquist from Central Washington University is to test and

review the HAZUS-MH Methodology and Software.
1.2. Organization of Work

This thesis is organized in nine chapters. The introduction in chapter one

provides the motivation for this thesis and describes the interdisciplinary project

“Natural Risk Management Carinthia (NRMC)”. Chapter two gives a theoretical

background on how flooding can be related to urbanization, it addresses the

components of risk and finally it discusses different approaches of vulnerability

assessment. The methods section in chapter three presents the software HAZUS-

MH and delivers insight into the application design and architecture. The HAZUS-MH

methodology will be illustrated and then applied on a case study where a flood

hazard analysis of a 100 year flood generated with HAZUS-MH is tested. Flood

boundaries of the assessment by HAZUS-MH are compared with aerial photographs

of the inundated area. The results and discussion section in chapter four shows an

overestimation of the flood boundaries by HAZUS-MH and a flood boundary

underestimation of the airphoto analysis. In chapter five a summary is given and

further perspectives are shown. Finally References can be found in chapters six, List

of Figures in chapter seven, List of Tables in chapter eight and the Appendix in

chapter nine.
2. Literature Review



This chapter discusses different approaches of how to model and estimate potential

losses of from flooding. hazards. First the relation of urbanization and flooding is

described followed by the components of risk. In the section “vulnerability

assessment”, the factor uncertainty is considered and different approaches of

vulnerability assessment are presented.



2.1. Theoretical Background: Urban Flooding

As cities grow natural vegetation is replaced with concrete and asphalt which causes

a disruption of natural soil absorption systems. The surface gets more and more

impervious.




               Figure 2.1: Runoff and stream flow in undeveloped and developed
                                  watersheds (Greene, 2006)




                                                                                       10
As shown in Figure 2.1 precipitation in a natural environment tends to be absorbed

into the ground through the process of infiltration down through soil layers to the

water table. Some precipitation may evaporate or be intercepted by vegetation.

In urbanized areas natural infiltration is greatly reduced, and instead, the rainwater or

snowmelt runs along the surface directly into streams. A flood occurs more likely*.

Another consequence of the so called hardscape surfaces is less groundwater

resupply and there is less evapotranspiration in cities than there is in the natural

environment.                *suggestion:        A flood is more likely to occur.

Urban flooding is a consequence of manmade alterations. All the above mentioned

urbanization factors lead to greater overland flow and so more water reaches the

stream channels and it arrives far more quickly after a rainfall or storm. Natural

vegetation would slow run-off, concrete and asphalt speed it. Urbanization leads to

higher peak flows, more rapid raise and fall of flow, and lower base flows. These

flood situations can be tracked by a hydrograph which shows stream water level

versus elapsed time (Figure 2.2).




                 Figure 2.2: Hydrograph for the natural setting before and after
                          urban development (Hultquist lecture, 2008)



                                                                                       11
The flow of water is relatively predictable. While greatly affected by slope, soil type,

and vegetation, water always flows along the path of least resistance. When given

the opportunity, water will flow where water has flowed before. For this reason, the

areas along rivers and streams commonly experience flooding.

Heavy or continuous rainfall that exceeds the absorptive capacity of soil and the flow

capacity of rivers causes flooding along their banks onto adjacent lands. These

"flood-prone" areas, or floodplains, are most subject to recurring floods, making them

hazardous areas in which to live and work.

Floods are usually described in terms of their statistical frequency. A "100-year flood"

or "100-year floodplain," for example, describes an event or an area subject to a one

percent probability of a certain size flood occurring in any given year; it does not

mean such a flood will occur only once in one hundred years.

Floodplains can be mapped, with good accuracy, so the boundary of a 100-year flood

is commonly used in floodplain mitigation programs to identify areas where the risk of

flooding is significant. Any other statistical frequency of a flood event may be chosen

depending on the degree of risk that is selected for evaluation (e.g., 5-year, 20-year,

50-year, 500-year floodplain). Figure 2.3 shows the 100- and 500-year floodplains of

the Yakima River between Selah Gap and Union Gap.

As population grows there is more temptation for human settlement on floodplains.

If there is no or minimal land use control, people are often unaware that they are

moving onto a floodplain. Urban planning must be integrative: it cannot separate

environment, transportation, and neighbourhoods, but must adopt an integrative

multidimensional perspective.
           Figure 2.3: Floodplains of Yakima River between Selah Gap and Union Gap
                                      (Source: FEMA, 1996)



2.2. Components of risk

According to the Multihazard Mitigation Council a loss estimation model will consist of

three basic components (MMC, 2005):

     “1. A hazard model that characterizes the likelihood and severity of the hazard;

     2. An exposure model that quantifies the assets at risk in the area affected by

     the hazard; and




                                                                                       13
     3. A vulnerability model that relates the damage potential of various assets to

     varying hazard levels.”



This point of view corresponds also with the definition of risk by Crichton (2001). He

states that risk is only present if all of the three elements: Hazard, Exposure and

Vulnerability are present. Figure 2.4 displays Crichton‟s Risk Triangle where Hazard

is the event with given frequency and severity; Exposure is the value of life and

property in the affected area and Vulnerability is the extent to which life and property

are affected.




                     Figure 2.4: Crichton‟s Risk Triangle (Crichton, 2001)



The risk triangle has been widely applied to research on natural disasters. These

elements are further described by Fedeski (2007):

“RISK is the periodic cost of damage caused by the hazard being considered;

EXPOSURE is the extent and value of buildings affected to the hazard;

HAZARD is the extent, severity and probability of the type of hazard being

considered;

VULNERABILITY is the susceptibility of the buildings to the hazard.”



                                                                                       14
Figure 2.5 illustrates the simplified risk situation by Gilard and Givone (1997). Their

studies are based on two instead of three components, a socio-economic dimension

(vulnerability) and a hydrological-hydraulic dimension (hazard).




                  Figure 2.5: Risk conceptualization (Gilard and Givone, 1997)



But regardless of how many components are taken into account for a risk model, it is

difficult to directly measure vulnerability, as it depends on a number of location-

specific, social, economic, political and environmental factors (Gao et. al, 2007).



2.3. Vulnerability Assessment

There are a number of interpretations for vulnerability assessment. One of the best-

known definitions for vulnerability was formulated by the United Nations International

Strategy for Disaster Reduction (UN/ISDR), which defines it, as (UN/ISDR, 2004):

   “The conditions determined by physical, social, economic and environmental

   factors or processes, which increase the susceptibility of a community to the

   impact of hazards.”



The definition for vulnerability assessment delivered by FEMA in their Publication

“Using HAZUS-MH for Risk Assessment” is (FEMA, 2004):

   “Evaluation of the extent of injury and damage that may result from a hazard

   event of a given intensity in a given area. The vulnerability assessment should


                                                                                          15
   address impacts of hazard occurrences on the existing and future built

   environment.”



So vulnerability assessment is dependent on both,* the severity of a hazard as well

as the condition of the object at risk. To estimate vulnerability** approximations and

simplifications are necessary for a comprehensive calculation. These factors lead to

uncertainty which will be discussed in the next paragraph.       *? need for “ , ”

                                                                 ** better if “ , ” here

2.3.1. Uncertainty        2 lines down – is “scientific” necessary or appropriate

Vulnerability assessment has to deal with uncertainty which results from incomplete

scientific knowledge concerning natural hazards and their effects on the built

environment. A further aspect for uncertainty is the factor of the quality of data

available, particularly for analyses which are conducted on a national scale. Ideally

local data should be aggregated that would allow more accurate loss estimates and

vulnerability assessments, but generating “bottom-up” data seems to be an infeasible

challenge for state agencies. (Simpson and Human, 2008).



Hall et. al (2003) developed a methodology for national-scale flood risk assessment

in which they accounted for uncertainty by dealing with lower and upper bounds in

each stage of their analysis. There is no assumption about the distribution of the risk

between the boundaries, but the most suitable estimate will be at the midpoint. But

this point should be only quoted on a national scale. At a more local scale it is

advised to work with the wide bounds on risk estimates as long as there is no more

detailed data available to specify the outcome. That could also work as a motivation

to support the local data collection.



                                                                                           16
2.3.2. Methods for vulnerability assessment

In the following part of the thesis different approaches of how to assess vulnerability

for flooding hazards will be compared.



Herrmann et. al (2007) express vulnerability as a degree of damage in percent. After

determining the spatial distribution of elements at risk with analyzing land use data

and computing flood probability analysis, vulnerability functions of several studies

(IKSR, 2001; MURL, 2000; LfUG, 2005) are applied. These vulnerability functions are

summarized in Table 2.1. The degree of damage depends on the flood depth given in

cm.      “ cm. ” here is orphaned – maybe spell it out completely.

                   Table 2.1: Vulnerability Functions (Herrmann et. al, 2007)




Another methodology which is based on calculated flood depths is introduced by

Dutta et. al (2003). This model refers to the unit loss approach where a grid network

is used. It calculates unit damage percentage to any object for a given condition of

                                                                                        17
flood. Three types of damage are considered in that study: urban, rural and

infrastructure damage. To simplify this description only the mathematical models for

urban damage are presented here:

Urban damage is divided into four types: damage to building structure/property,

damage to building contents/stock, damage to outside property, and emergency and

clean up costs. The mathematical models use flood depth as the governing flood

parameter and are shown in Table 2.2.



       Table 2.2 Mathematical models for urban flood damage estimation (Dutta et. al, 2003)




The corresponding depth-damage curves are shown in Figure 2.6. These functions

relate flood damage to flood depth. Usually they are derived from damage data of

past floods or hypothetical analysis based on land cover/use patterns and other

information.


                                                                                              18
        Figure 2.6: Depth-damage curves for urban damage estimation (Dutta et. al, 2003)



As stated by Simpson and Human (2008) there is no single, accepted way to

determine hazard vulnerability. In their study they adopted the Center for Hazards

Research and Policy Development (CHP) model which implies (Simpson and

Human, 2008):                I think I would indent the following line.

“Hazard Vulnerability Score = [Exposure Score] * [Hazard Score]”

The Exposure Score is made up of six different variables/ranks (e.g., Population

Rank, Property Value Rank, etc.) which are calculated and then ranked separately
from 1-5 (1 = low, 5 = high). Next the ranks of every variable of every unit (in this

case census tract) are added to come to a generalised Exposure Score.

Together with the Hazard Score which needs information about the area affected or

the occurrence interval it is possible to generate the Hazard Vulnerability Score for

each unit.      the word „which‟ should be preceded with a comma, or use „that‟ in its place




Fedeski (2007) developed a GIS-based methodology for vulnerability and risk

assessment which uses the damage value as a function of hazard severity and

vulnerability, thus (Fedeski, 2007):                   ??? indent both lines below

“Damage = replacement ratio x damage value

Damage value = f {event severity, building vulnerability}”           ?? ƒ  for f

Fedeski mentions that the extent of the damage is not dependent on the hazard

severity alone, but also on the condition of the building stock. In his study the

vulnerability assessment of buildings is accomplished by visual inspection directly in

the field as well as with data sheets and digital photographs, and remotely from

maps, aerial photographs, and other records. A single vulnerability index is

calculated by explicit and indicator factors (property type, property age, structure

type, property design, openings, state of repair, context). Although this methodology

is generalised it is still a bottom-up methodology based on resource-consuming

fieldwork to collect vulnerability data for individual buildings. It can be seen as a first

step before developing standard damage functions like it is* adopted in the American

hazard- and loss-estimation software HAZUS-MH.                      *as   ???

did I do something to introduce this extra line ????




                                                                                               20
3. Methods



In this part of the thesis the software HAZUS-MH will be presented. First the

background of disaster management in the United States will be introduced. Then the

application design and the methodology of HAZUS-MH will be illustrated. The

inventory data will be explained as well as the flood loss estimation process. And

finally a selected case study of a 100 year flood of the Yakima River, Central

Washington will be presented to demonstrate the flood hazard analysis and the

prediction of losses on a real world project.




3.1. HAZUS-MH Background

For decades, in the United States the national response to flood disasters was

generally limited to building dams, levees or other forms of flood-control, and to

providing disaster relief to flood victims. These actions did little to minimize flood

losses or to prevent development in flood prone areas. To reduce the costs

associated with floods, the U.S. Congress passed the National Flood Insurance Act

of 1968 and established the National Flood Insurance Program (NFIP). It regulates

the mapping of flood prone areas and makes flood insurance available in

communities that meet floodplain management requirements.

To support better mitigation planning the U.S. Congress enacted the Disaster

Mitigation Act (DMA) 2000. FEMA is the lead agency encouraging the

implementation of the DMA 2000 requirements.

Since the 1990s the software HAZUS-MH is being developed by FEMA under

contract with the National Institute of Building Sciences (NIBS) and many technical

contractors.
HAZUS-MH is an American applicable methodology and software program for

estimating potential losses from natural hazards. It uses state-of-the-art Geographic

Information Systems (ArcGIS) software to produce detailed maps and analytical

reports that can describe direct physical damage to building stock, critical facilities,

transportation systems and utility systems. The damage reports can include direct

economic and social losses (casualties, shelter requirements, and economic impact)

and induced damage (inundation fire, threats posed by hazardous materials and

debris) depending on the hazard and available local data.



3.2. Application Design and Architecture

HAZUS-MH is built on the ArcGIS platform (ESRI) and it uses ArcGIS to map and

display hazard data and the results of damage and economic loss estimates. The

application has an internal data structure that implements “geodatabases” using SQL

Server technology. HAZUS-MH employs a three-tier architecture, including a

Presentation Layer with the display for user interface and overall control, an

Application Layer with the models for hazard-specific calculations and a Data Access

Layer with common and hazard-specific databases (Muthukumar, 2005; FEMA,

2007a).

Figure 3.1 shows the HAZUS-MH Software Architecture graphically.




3.3. Methodology

The HAZUS-MH Flood Model methodology consists of two basic analytical modules

as displayed in Figure 3.2: flood hazard analysis (Potential Earth Science Hazards),

and flood loss estimation analysis (Direct Physical Damage, Induced Physical

Damage, Direct Economic/Social Losses and Indirect Economic Losses).


                                                                                           22
       Figure 3.1 HAZUS-MH Software Architecture with HAZUS Data Tools (FEMA, 2007a)



In the hazard analysis phase, characteristics such as frequency, discharge, and

ground elevation are used to model the spatial variation of flood depth and velocity.

In the loss estimation phase, damage is calculated based on the results of the hazard

analysis through the use of depth-damage curves. In this thesis the emphasis will be




                                                                                        23
on the loss estimation phase although a coarse overview will be given about the

complete methodology.




    Figure 3.2: Overall Schematic of the HAZUS-MH Flood Model Methodology (FEMA, 2007a)



3.3.1. Inventory Data

The methodology development and software implementation of HAZUS-MH has

been performed by a team of flood loss experts composed of engineers, hydraulic

and hydrology modellers, emergency planners, economists, social scientists,

                                                                                          24
geographic information systems analysts, and software developers. FEMA has

collaborated with the following partners: U.S. Census Bureau, Dun & Bradstreet,

Department of Energy (DOE), Federal Insurance and Mitigation Administration

(FIMA), U.S. Army Corps of Engineers (USACE), and the USACE Institute for Water

Resources (USACE IWR) to develop a comprehensive inventory database for the

entire United States.

It consists of a proxy for the general building stock of the United States. HAZUS-MH

classifies the buildings into seven general occupancy classes (residential,

commercial, industrial, agriculture, religion/non-profit, government, and education).

These general occupancy classes are divided into 33 specific occupancy classes

(Table 3.1). Furthermore a sub classification into five general model building types

(Wood, Concrete, Masonry, Steel, and Manufactured Housing) is incorporated. Each

model building type is subdivided according to a number of stories or other

classifications. The total number of buildings is provided for each census block.

Additionally, the model contains national data for essential facilities (e.g., police

stations), high potential loss facilities (e.g., dams), selected transportation (e.g.,

highway bridges) and lifeline systems (e.g., potable water treatment plants),

demographics including population statistics, agriculture products (e.g., corn), and

vehicles. The general building stock is currently available at the census block level

resolution. A detailed table of the inventory data and sciences used for the loss

estimation is shown in Appendix 9.1.




                                                                                         25
Table 3.1: Specific occupancy classes of the general building stock within HAZUS-MH (FEMA, 2007b)




3.3.2. Levels of Analysis

HAZUS-MH is designed to support three levels of analysis depending on the

availability of local resources. These three levels are outlined in Figure 3.3.

A Level 1 Analysis is based on default hazard, inventory and damage data which is

provided by the software for the entire US to assess the losses.

A Level 2 Analysis combines user-supplied and default hazard, building, and damage

data for more accurate risk and loss estimates. Assistance from local emergency

management personnel, city planners, GIS professionals, and others may be

necessary for this level of analysis.

In A Level 3 Analysis user-supplied flood-specific detailed data and adjustment of

analysis parameters are necessary. This stage requires the involvement of technical
experts such as structural and geotechnical engineers who can modify loss

parameters based on the specific conditions of a community.




                Figure 3.3: Levels of Analysis within HAZUS-MH (FEMA, 2007b)


As with any modelling system, the accuracy of the vulnerability assessment results is

directly dependent on the quality of the input data. The Inventory Collection and

Survey Tool (InCAST) and the Flood Information Tool (FIT) are two software

programs developed by FEMA in cooperation with NIBS to help users collect and

import detailed data to improve the accuracy of risk assessment results.



3.3.3. Flood Loss Estimation Process

To be able to estimate flood losses the following steps have to be accomplished

within HAZUS-MH:



3.3.3.1. Study Region

First the user has to define a study region: the geographic limits of the spatial data,

the aggregation level (state, county, census tract, census block) and the hazard used

in the estimation. The aggregation process creates filters defined by the hazard type

                                                                                          27
and the study extent and is extracted from the datasets of HAZUS-MH. An inventory

database for the defined region is created:



After incorporating a DEM which covers both the study region and all the watersheds

that intersect the study region HAZUS-MH identifies default watersheds within the

assigned study area and imports the generated stream network as a feature table

into a database. The user has to set the threshold drainage area for the network of

possible study reaches. Selecting a small number for the drainage area such as one

square mile will result in a highly defined stream network.

After the stream network is generated the user can define a new scenario by

selecting specific stream reaches. To delineate the floodplain hydrologic analysis

based on return period or specific discharge values are performed. Reaches are

identified, the network navigation is calculated and reach drainage areas and

discharge values are computed. Finally after the hydraulic analysis the floodplain

boundary and the depth grid are produced.



HAZUS-MH ascertains the elevation difference between two surfaces, the flood

surface and the ground surface, based on the given DEM at each cell in the grid.

Cells where the flood surface elevation exceeds the ground surface elevation are

within the floodplain. The floodplain boundary is the collection of cells where the flood

elevation equals the ground elevation.



3.3.3.2. Overlay analysis for loss estimation

After HAZUS-MH calculated the flood-extent the so achieved depth grid raster file is

overlaid with the inventory data. Damage is estimated in percent and is weighted by




                                                                                       28
the area of inundation at a given depth for a given census block. The general building

stock is assumed to be evenly distributed throughout a census block.



3.3.3.3. Estimation of Damage

Estimation of direct damage to the general building stock (percent damage to

structures and their contents) is accomplished through the use of readily-available

depth-damage curves, compiled from a variety of sources including the Federal

Insurance and Mitigation Administration (FIMA) “credibility weighted” depth-damage

curves (Figure 3.4), and selected curves developed by the U.S. Army Corps of

Engineers (USACE), and the USACE Institute for Water Resources (USACE IWR).

HAZUS-MH includes over 700 depth-damage functions which relate water depth to

structure. With applying these functions percent damage is calculated. Losses are

developed for general building stock, essential facilities, vehicles, agricultural

products, selected transportation and utility features.




Figure 3.4: FIA Credibility-Weighted Building Depth-Damage Curves as of 12/31/1998 (FEMA, 2007a)


                                                                                              29
A graphical summary of the Flood Loss Estimation Process is illustrated in Figure

3.5: The basic HAZUS-MH flood loss estimation begins with (1) defining a study area

and adding a digital elevation model (DEM) or equivalent topographic information.

Combined with (2) stream discharge data, a flood surface elevation is derived which,

relative to the DEM data, provides the areas and depth of flooding. (3) The

population and properties at risk are overlaid on areas of flooding, to determine (4)

damage and (5) losses.




                   Figure 3.5: Flood Loss Estimation Process (FEMA, 2007c)



3.4. Case Study

To test the level 1 Analysis of HAZUS-MH the following case study was

accomplished. Vertical aerial photographs of the Yakima River Flood 1996, Central

Washington were compared with the calculated flooded zones by HAZUS-MH.

The 1996 Yakima River Flood had the extent of a 100 year flood.

The first step was to use the software to estimate the extent of the 1996 inundation

for the region between Selah Gap and Union Gap (Figure 3.6) by simulating a 100

year flood within HAZUS-MH. Having the predicted flooded area, the actual extent of

flooding based on interpretation of aerial photographs, taken on February 11, 1996

                                                                                        30
was examined. Finally these two findings were compared and the differences

between predicted scenario and actual incidence were shown.




        Figure 3.6: Project Area: Yakima River Reach between Selah Gap and Union Gap



Because HAZUS-MH uses ArcGIS to map and display hazard data, all available

datasets were integrated into this software. The different digital files (HAZUS-MH

estimates, airphotos of February 11, 1996 and the Flood Inundation Map, Yakima

County) needed to be registered in the same coordinate projection system to be able

to overlay and compare accurately. This allowed for an appropriate comparison of

the varying extents of the flood plain revealed by the different methods of

determination.




3.4.1. Project Area

The Yakima River flows 215 miles from the outlet of Keechelus Lake in the central

Washington Cascades southeasterly to the Columbia River, draining an area of


                                                                                       31
6,155 square miles. Agriculture is the principal economic activity in the basin,

followed by recreation and timber harvest and wood products (USGS, 2008).

The basin produces a mean annual unregulated runoff of about 5,600 cubic

feet/second (cfs) and a regulated runoff of about 3,600 cfs. There are eight major

tributary rivers and numerous smaller tributary streams. Most of the precipitation falls

during the winter, and occurs as snow in the mountains. Total mean annual

precipitation in the basin is about 27 inches. (USGS, 2008)

Elevations in the Yakima drainage range from 400 to nearly 8,000 feet. (USGS,

1998)                                 the line below: „ 3.7 ‟ should, I think be „ 3.6 ‟

The reach between Selah Gap and Union Gap (Figure 3.7) consists of

unconsolidated deposits, more precisely of Quarternary sediments and the two gaps

slice through anticlines of Columbia River Basalt. The length of the Yakima River

reach analyzed in this project is 10.2 miles and the average gradient for this region is

13.3 feet/mile. The average floodway width is 1,513 feet; the average velocity is six

feet per second (KCM, Inc., 2007).

Flooding is common in Yakima County. Since 1894, the flow in the Yakima River has

exceeded flood stage 48 times. Yakima County has been declared a federal disaster

area eight times since 1970, most recently in 1990, 1995, 1996 and 1997 (KCM, Inc.,

2007).

The largest historical floods are summarized in Table 3.2 as ranked by peak flow.



KCM, Inc. (2007) depicts that the February 9, 1996; flood was the second largest

flood of record. Flow crested on the Yakima River at Parker at 57,500 cfs, which

exceeded the predicted 100 year event of 56,300 cfs. This flood was a typical rain-

on-snow event caused by unseasonably warm weather and rainfall on a significant

snow pack. These weather conditions produced high discharge values because of

                                                                                           32
snowmelt combined with rainfall runoff. A station at Keechelus Reservoir (near

Snoqualmie Pass, 65 miles to the NW) reported over 11 inches of precipitation within

a three-day period; 5 inches of rain fell in 24 hours on Wednesday, February 7.


                  Table 3.2: Largest Yakima River Floods (KCM, Inc., 2007)




                                                                                  33
Flood damage was region-wide, occurring both along the Yakima mainstem and

tributary creeks. Several millions of dollars of flood damage occurred, primarily

associated with roads, Yakima Greenway facilities, levees, wastewater treatment

facilities, and residential property in Selah. More significant damage occurred outside

the project area. The flood required the rescue of 30 people in Selah stranded at the

Elks Golf Course lodge;* an airlift of 20 people in White Swan and residents near

Keys Road, because of threat of a levee failure.             * comma only, I think - above

          no comma after “Road” in the above line ??

3.4.2. Data

To run the Level 1 Analysis within HAZUS-MH it was necessary to enter a DEM.

Three DEMs were available (find data details in Appendix 9.2):

 o National Elevation Dataset, resolution 30 m (USGS, EROS Data Center, 1999)

 o National Elevation Dataset, resolution 10 m (USGS, EROS Data Center, 1999)

 o 10 Meter Digital Elevation Models for Washington State (USGS and the

     University of Washington, 2002)

Three different study regions had to be created within HAZUS-MH to be able to

compare the results of the stream network analysis where the program identifies the

watersheds within the study region. First it creates a flowgrid from the DEM, and then

it identifies stream reaches from the flowgrid and associates each reach with a

drainage area. The network is based on a threshold drainage area of 10 square

miles. This input is required by the software and indicates the density of the network.

After comparing the three generated stream networks by overlaying them with 2005

airphotos (NAIP, 2005) of the area it was decided to take the 10 Meter DEM for

Washington State because the calculated reaches matched best* with the actual

rivers.        * was this a visual match or computer assisted? Do you need to state which?




                                                                                             34
In a further step the software delineates the floodplain upon given user defined data

such as discharge data or recurrence interval (e.g., 100 years). It creates a set of

flood depth cross sections and interpolates the flood depth from the cross sections to

the grid cells. Because the 1996 Yakima River flood had the extent of a 100 year

flood, this recurrence interval was selected for a first analysis and historic discharge

values of the Yakima River, available from the U.S. Department of the Interior, 2008

were used for a second analysis. Figure 3.7 depicts the discharge values of the

Yakima River from February 1996 in form of a hydrograph.




             Figure 3.7: Flood Hydrograph for the Yakima River Flood, February 1996
                          (Source: U.S. Department of the Interior, 2008)



The two calculated flood boundaries were visually compared and it was decided to

work on with the 100 year flood boundary, because the flood boundary of the

analysis with the discharge value of 40,454 cfs seemed to be overestimated by

HAZUS-MH.




                                                                                       35
Airphoto Interpretation and Flood Extent Digitalization

On the aerial photographs of Flood 1996 (WSDOT, 1996) the flood extent was

analyzed and the inundated parts were digitized to make it possible to compare the

predicted and the factual extent of the flooding in

ArcGIS. In Figure 3.8 the digitized flood polygon

is shown in green.

First it was necessary to get an overview of the

whole area at a scale of about 1:70,000

   a) on the flood airphotos

   b) on the 2005 airphotos

Then the eight recognition elements (shape,

size, tone/color, association, texture, pattern,

shadow, site, and additionally presence/absence

as well as change in extent and position),

introduced by Avery et. al (1992) were

investigated on the airphotos at the relatively

small scale as well as at a larger scale of about

1:1,500 where a detailed observation was possible.

Finally the area which was classified as              Figure 3.8: Digitized Flood Extent on the
                                                      1996 Flood Airphoto of February 11, 1996
                                                      HEADS UP: This fig. is not in the List of..
flooded was digitized as a polygon.          
4. Results and Discussion



For a better inundation comparison the

two flood boundaries (Figure 4.1,

“HAZUS Flood Extent” shown as an

orange outline and “February 11, 1996

Flood Extent” shown as a solid green

area) can be overlaid with a third

boundary from the Flood Inundation

Map, February 1996, Yakima County

(find data details in Appendix 9.3). This

boundary line is shown in blue (Figure

4.1). The Flood Inundation Map was

established within the 1998 Yakima

County Flood Insurance Study by

FEMA.

With overlaying these three flood

boundaries it can be seen that the

digitized area in this project has the

smallest extent whereas the estimated

flood extent by HAZUS-MH has the

largest.                                          Figure 4.1: Overlay of the flood airphoto with the
                                                   digitized flood extent February 11 (green), the
These circumstances can be explained                HAZUS-MH flood boundary (orange) and the
                                                       Yakima County Inundation Map (blue).
as follows:

 Figure 4.1: Overlay of the flood airphoto with the digitized flood extent February 11 the HAZUS-MH
                       flood boundary and the Yakima County Inundation Map


                                                                                                       37
4.1. Underestimation of digitized flood boundary on flood airphotos

February 11, 1996

The digitizing of the flood took place on aerial photographs of February 11 so the

extent of the peakflow of February 9 could in some areas only be guessed according

to flood patterns (Figure 4.2). In some places flood water had already moved back

into the original channel and no impacts on the surface were detectable so these

places were classified as non-flooded. With ground truthing, for example interviews

with abutters or eyewitnesses or additional airphotos of February 9 this

underestimation could have been reduced.




                   Figure 4.2: Mark shows real flood extent on February, 9
                    although there is no water detectable on February 11


4.2. Overestimation of HAZUS-MH flood boundary

The 100 year flood analysis of HAZUS-MH basically overestimated the inundated

area. Although there were many locations where the flood boundary was similar to

the actual 1996 flood the software calculated a larger area as flooded whereas in

very few parts it missed the actual flood boundary by underestimating. Inaccuracies

may result because of inadequacies in the available data. For HAZUS-MH the 10-

meter resolution digital elevation model seemed to be not detailed enough in some

areas to allow precise flood extent estimation. In as much as HAZUS-MH can be
used to estimate losses at a state, county, census tract or census block level the

estimated flooded area may be adequate for a coarse assessment.

URS Group, Inc. (2007) mentions that comparing total losses from a study reach is

more accurate than comparing unit losses within individual census blocks. So with

enlarging the scale of estimation the results will get more appropriate.



HAZUS-MH was developed as an easy-to-use tool that can effectively serve a large

range of people with many different needs and expertise. But because the modelling

input is held very simple in a Level 1 analysis, uncertainties can occur. Meyer (2004)

highlights that a Level 1 analysis within HAZUS-MH uses only gauge data of the

USGS‟s gauge network for computing discharge values to put into the hydrologic

downhill elevation network creation of the stream network from the DEM. That factor

can increase the errors calculated in HAZUS. However, Meyer‟s study has shown

that, for many areas, a Level 1 analysis is sufficient.



4.3. Losses

Subsequent to the proofing of the inundation boundaries of the flood hazard analysis

in this section the results of the flood loss analysis will be examined. In HAZUS-MH

the losses can be viewed in tabular, map or report formats. In Figure 4.3 the

Transportation Facilities Loss of the project area is presented as tabular format. Of

course this mentioned Highway Bridge which experienced a loss of 6% in this

scenario can also be mapped. In section 4.3.1 the emphasis will be on the available

loss maps and to complete the available formats in Appendix 9.3 a list of summary

reports can be found.




                                                                                        39
                  Figure 4.3: Transportation Facilities Loss as tabular format

WAIT – HELP       You have two figures labeled 4.3               Holy Hannah Montana!

4.3.1. Flood Hazard Map            You used this in the List of Figures; not the caption above

The flood hazard map (Figure 4.3) is used as a basis for the loss estimation process.

On this map the flood depth grid and floodplain boundary for the 100-year-flood-

scenario are mapped. The flood depth grid has vertical units of feet and is displayed

in blue. The floodplain boundary is displayed in orange.




                                Figure 4.3: Flood Hazard Map




                                                                                                  40
4.3.2. General Building Stock Damage by Occupancy / Building Type /

Count

On the map in Figure 4.4 the general inventory damage results by occupancy,

building type, and building count can be demonstrated. In this case counts of

residential buildings which experienced substantial damage are shown by census

block level.




                      Figure 4.4: Residential Buildings with Substantial
                          Damage (Counts) by Census Block Level




                                                                                 41
4.3.3. General Building Stock Economic Loss

Figure 4.5 shows the economic losses for the general building stock by census block

by full replacement value in thousands of dollars. Another possibility would be to

display the losses by depreciated replacement value.




             Figure 4.5: General Building Stock Economic Loss by Full Replacement



4.3.4. Essential Facilities

It is further possible to view and map the damage and loss of hospitals, police

stations, fire stations, emergency operations centers, and schools.

In this scenario no entry for such infrastructure was available or affected by the flood.




                                                                                       42
4.3.5. Debris

The map in Figure 4.6 shows the estimated total of debris generated by the 100-

year-flood. Results are in tons and by census block.




                              Figure 4.6: Total Debris in tons



4.3.6. Shelter

The Shelter Map in Figure 4.7 represents the estimated number of households that

are expected to be displaced; and of those households, estimated number of people

to seek temporary shelter in public shelters due to the 100-year-flood.

                     change due to             because of




                                                                                   43
               Figure 4.7: Displaced Population (Counts) by Census Block Level



These loss analyses in tabular, map and report format give a comprehensive

overview on the losses which are expected to occur. The results are a coarse

assessment that could be improved with entering more detailed local data instead of

using the default inventory database of HAZUS-MH.




                                                                                  44
5. Summary and Further Perspectives



In this bachelor thesis the main focus was placed on analyzing the hazard and loss

estimation software HAZUS-MH to review the American standardized methodology

for assessing vulnerability and losses.

The proof of a Level 1 Analysis with comparing the real extent of the 1996 Yakima

River flood and the estimated flood boundary by HAZUS-MH delivered a slight

overestimation by HAZUS-MH. It was detected that accuracies of the flood

boundaries are highly dependent on the available data. Digitizing the flooded area on

an airphoto taken two days after the peak flow can mislead the analyst.

Underestimation of the inundated area is the consequence. For more appropriate

results ground truthing should be incorporated and/or more actual aerial photographs

should be taken for the analysis. Several photos from different angles would

presumably help eliminate trees, shadows, glare, and texture as confounding issues.

Flood estimation analyses within HAZUS-MH are highly dependent on the resolution

of the digital elevation model. In a further study one might use LIDAR technology,

which achieves finer resolution to get a more precise flood model output. In a Level 2

or 3 analysis a user-supplied water depth grid, achieved for example from a local

hydrologic analysis, could also be integrated, which would be a more precise basis

for loss estimation.



A further important aspect of the bachelor thesis was the evaluation of different

approaches to estimate and model vulnerability. It can be noticed that a highly

demanding part in this process is the task of generating a comprehensive inventory

database where different attributes, like occupancy, building material, property age,

etc. have to be considered. This preliminary procedure poses a challenge especially

                                                                                        45
for studies on a regional or national scale where the needed inventory data may (be)

based on many different data bases with different standards. In the United States

FEMA and NIBS have accomplished this challenge with the assistance of many

partners. However there are no standards for gathering inventory data in respect of

risk or vulnerability analysis for natural hazards on a national, European or worldwide

scale, so researches have to cope with the given circumstances in a given region.




                                                                                      46
6. References



Birkmann, J. (Editor), 2006, Measuring Vulnerability to Natural Hazards, Tokyo:
 United Nations University Press

Crichton, D., 2001, The Implications of Climate Change for the Insurance Industry,
 Building Research Establishment, Watford, England

Ding, A. 2008, “Evaluation of HAZUS-MH flood model with local data and other
 program”, Natural hazards review, Vol. 9, No. 1, pp. 20-28

Dutta, D., Herath, S., Musiake, K., 2003, “A mathematical model for flood loss
 estimation”, Journal of Hydrology, Vol. 277, No. 1-2, pp. 24-49

Federal Emergency Management Agency, 1996, Q3 Flood Data, YAKIMA, WA,
 Washington, D.C.

Federal Emergency Management Agency, 2004, Using HAZUS-MH for Risk
 Assessment, FEMA 433, Washington, D.C.

Federal Emergency Management Agency, 2007a, Hazus-MH Flood Technical
 Manual, Washington, D.C.

Federal Emergency Management Agency, 2007b, Hazus-MH Flood User Manual,
 U.S. Department of Homeland Security, FEMA Mitigation Division, Washington,
 D.C.

Federal Emergency Management Agency, 2007c, HAZUS-MH Overview and
 Installation Transcript, Washington, D.C.

Federal Emergency Management Agency, 2008, HAZUS-MH: The Science Inside:
 Flood Model Handout, Washington, D.C.

Fedeski, M., Gwilliam, J., 2007, “Urban sustainability in the presence of flood and
 geological hazards: The development of a GIS-based vulnerability and risk
 assessment methodology”, Landscape and urban planning, Vol. 83, No. 1, pp. 50-
 61

Forte, F., Strobl, R.O., Pennetta, L., 2006, “A methodology using GIS, aerial photos
 and remote sensing for loss estimation and flood vulnerability analysis in the
 Supersano-Ruffano-Nociglia Graben, southern Italy”, Environmental Geology, Vol.
 50, No. 4, pp. 581-594

Gao, J., Nickum, J.E., Pan, Y., 2007, “An assessment of flood hazard vulnerability in
 the Dongting Lake Region of China”, Lakes & Reservoirs: Research and
 Management, Vol. 12, No. 1, pp. 27-34

Gilard, O., Givone, P., 1997, “Flood Risk Management: New Concepts and Methods
 for objective Negotiations”, Destructive Water: Water Caused Natural Disasters,

                                                                                     47
 their Abatement and Control (Proceedings of the Conference held at Anaheim,
 California, June 1996). IAHS Publ. no. 239

Greene, R. P., Pick, J.B., 2006, Exploring the Urban Community: A GIS Approach,
 Upper Saddle River, NJ: Pearson Prentice Hall

Hall, J. W., Dawson, R. J., Sayers, P.B., Rosu, C., Chatterton, J.B., Deakin. R., 2003,
 „A methodology for national-scale flood risk assessment”, Proceedings of the
 Institution of Civil Engineers. Water and Maritime Engineering, Vol. 156, No. 3, pp.
 235-247

Herrmann, U., Thieken, A. H., Suhr, U., Lindenschmidt, K.-E., 2007,
 „Hochwasserrisikoanalysen an der Elbe – Methodenvergleich und Datenauflösung“,
 Österreichische Wasser- und Abfallwirtschaft, Vol. 59, No. 11-12, pp. 151-162

Hultquist, N., Urbanization Effects on Water.ppt, Lecture Urban Geography on
 November 12, 2008

IKSR (Internationale Kommission zum Schutz des Rheins), 2001, Übersichtskarten
  der Überschwemmungsgefährdung und der möglichen Vermögensschäden am
  Rhein – Abschlussbericht. Koblenz.

KCM, Inc., 2007, Upper Yakima River Comprehensive Flood Hazard Management
 Plan. A Report Prepared for the Yakima County Flood Control Zone District,
 Yakima, WA

LfUG (Sächsisches Landesamt für Umwelt und Geologie), 2005, Hochwasser in
  Sachsen – Gefahrenhinweiskarten. Dresden

Meyer, J., 2004, Comparative Analysis between different flood assessment
 technologies in HAZUS-MH. MA Thesis, Louisiana State University

Multihazard Mitigation Council (MMC), 2005, Natural Hazard Mitigation Saves: An
 Independent Study to Assess the Future Savings from Mitigation Activities.
 Washington D.C.: National Institute of Building Sciences

MURL (Ministerium für Umwelt, Raumordnung und Landwirtschaft des Landes
 Nordrhein-Westfalen), 2000, Potentielle Hochwasserschäden am Rhein in
 Nordrhein-Westfalen. Düsseldorf

Muthukumar, S., 2005, “Riverine Flood Hazard Modeling in HAZUS-MH: Overview of
 Model Implementation”, Georgia Water Resources Conference Proceedings,
 Athens, GA.

Scawthorn, C., Blais, N., Seligson, H., Tate, E., Mifflin, E., Thomas, W., Murphy, J.,
 Jones, C., 2006, “HAZUS-MH Flood Loss Estimation Methodology. I: Overview and
 Flood Hazard Characterization”, Natural hazards review, Vol. 7, No. 2, pp. 60-71

Scawthorn, C., Flores, P., Blais, N., Seligson, H., Tate, E., Chang, S., Mifflin, E.,
 Thomas, W., Murphy, J., Jones, C., Lawrence, M., 2006, “HAZUS-MH Flood Loss


                                                                                        48
 Estimation Methodology. II: Damage and Loss Assessment”, Natural hazards
 review, Vol. 7, No. 2, pp. 72-81

Simpson, D. M., Human, R. J., 2008, “Large-scale vulnerability assessments for
 natural hazards”, Natural Hazards, Vol. 47, No. 2, pp. 143-155

Tobin, G. A., Montz, B. E., 1997, Natural hazards: explanation and integration. New
 York: Guilford Press, pp. 281-319

UN/ISDR (United Nations International Strategy for Disaster Reduction), 2004, Living
 with Risk: A Global Review of Disaster Reduction Initiatives, Geneva: UN
 Publications

URS Group, Inc., 2007, HAZUS-MH Riverine Flood Model Validation Study,
 Washington County, Utah Vicinity, Flood Disaster of January 9-11, 2005. Prepared
 for the U.S. Department of Homeland Security, FEMA Mitigation Division,
 Washington, D.C.

U.S. Department of the Interior, 2008, Yakima Hydromet Archive Data Access,
 Retrieved November 16, 2008 from, Web site:
 http://www.usbr.gov/pn/hydromet/yakima/yakwebarcread.html

USGS, 2008, Yakima River Basin. Retrieved November 1, 2008 from, Web site:
 http://wa.water.usgs.gov/projects/yakimagw

USGS, 1998, Watershed and River Systems Management Program: Application to
 the Yakima River Basin, Washington, USGS Fact Sheet 037-98




                                                                                  49
7. List of Figures


Figure 2.1: Runoff and stream flow in undeveloped and developed watersheds
(Greene, 2006) ......................................................................................................... 10

Figure 2.2: Hydrograph for the natural setting before and after urban development
(Hultquist lecture, 2008) ........................................................................................... 11

Figure 2.3: Floodplains of Yakima River between Selah Gap and Union Gap (Source:
FEMA, 1996) ............................................................................................................ 13

Figure 2.4: Crichton‟s Risk Triangle (Crichton, 2001) ............................................... 14

Figure 2.5: Risk conceptualization (Gilard and Givone, 1997) .................................. 15

Figure 2.6: Depth-damage curves for urban damage estimation (Dutta et. al, 2003) 19

Figure 3.1 HAZUS-MH Software Architecture with HAZUS Data Tools (FEMA, 2007a)
................................................................................................................................. 23

Figure 3.2: Overall Schematic of the HAZUS-MH Flood Model Methodology (FEMA,
2007a) ...................................................................................................................... 24

Figure 3.3: Levels of Analysis within HAZUS-MH (FEMA, 2007b)............................ 27

Figure 3.4: FIA Credibility-Weighted Building Depth-Damage Curves as of
12/31/1998 (FEMA, 2007a) ...................................................................................... 29

Figure 3.5: Flood Loss Estimation Process (FEMA, 2007c) ..................................... 30

Figure 3.6: Project Area: Yakima River Reach between Selah Gap and Union Gap 31

Figure 3.7: Flood Hydrograph for the Yakima River Flood, February 1996 (Source:
U.S. Department of the Interior, 2008) ...................................................................... 35

Figure 4.1: Overlay of the flood airphoto with the digitized flood extent February 11
the HAZUS-MH flood boundary and the Yakima County Inundation Map ............... 37

Figure 4.2: Mark shows real flood extent on February, 9 although there is no water
detectable on February 11 ........................................................................................ 38

Figure 4.3: Flood Hazard Map .................................................................................. 40

Figure 4.4: Residential Buildings with Substantial Damage (Counts) by Census Block
Level ......................................................................................................................... 41

Figure 4.5: General Building Stock Economic Loss by Full Replacement ................ 42

Figure 4.6: Total Debris in tons................................................................................. 43

Figure 4.7: Displaced Population (Counts) by Census Block Level .......................... 44


                                                                                                                                50
8. List of Tables


Table 2.1: Vulnerability Functions (Herrmann et. al, 2007) ....................................... 17

Table 2.2 Mathematical models for urban flood damage estimation (Dutta et. al,
2003) ........................................................................................................................ 18

Table 3.1: Specific occupancy classes of the general building stock within HAZUS-
MH (FEMA, 2007b) ................................................................................................... 26

Table 3.2: Largest Yakima River Floods (KCM, Inc., 2007) ...................................... 33
9. Appendix


9.1 Science Inside: Flood Model




           Figure 10.1: The Science Inside: Flood Model, part 1 (FEMA, 2008)




           Figure 10.2: The Science Inside: Flood Model part 2 (FEMA, 2008)
9.2 Data used



10 Meter Digital Elevation Models for Washington State

Description: This data set is comprised of a set of mosaics depicting 10 meter

Digital Elevation Models of Washington State. Data are derived from contour lines at

40-foot or finer intervals on 7.5' maps. Hydrography was also used in creating some

of the files. Vertical units are integer US. Survey Feet.

Source: USGS and the University of Washington

Date of Publication: 11/2002

Spatial Information: State Plane Coordinate System 1983, South Zone

                       Planar Distance Units: survey feet

                       Horizontal Datum Name: North American Datum of 1983

                       Ellipsoid Name: Geodetic Reference System 80

                       Resolution: 10m



National Elevation Dataset (NED) 1/3 Arc Second (~10m resolution)

Source: U.S. Geological Survey, EROS Data Center

Date of Publication: 1999

Projection: Geographic

Horizontal Datum: NAD83

Vertical Datum: NAVD88

Vertical Units: Meters

Vertical Accuracy: +/- 7 meters (depends on source DEM)

Resolution: 1/3 arc-second (approx. 10m)
National Elevation Dataset (NED) 1 Arc Second (~30m resolution)

Source: U.S. Geological Survey, EROS Data Center

Date of Publication: 1999

Projection: Geographic

Horizontal Datum: NAD83

Vertical Datum: NAVD88

Vertical Units: Meters

Vertical Accuracy: +/- 7 to 15 meters (depends on the source DEM)

Resolution: 1 arc-second (approx. 30m)



Flood aerial photograph

Description: Color Orthorectified Photomosaic of the flooded Yakima River between

Selah Gap and Union Gap taken on February 11, 1996. The photo scale was

1:24,000.

Source: Washington State Department of Transportation (WSDOT)

Date of Exposure: February 11, 1996

Spatial Information: State Plane Coordinate System 1983, South Zone

                      Planar Distance Units: survey feet

                      Horizontal Datum Name: North American Datum of 1983/1991

                      HARN

                      Resolution: 1 foot



NAIP aerial photographs of 2005

Description: The National Agriculture Imagery Program (NAIP) acquires digital ortho

imagery during the agricultural growing seasons in the continental U.S. A primary

goal of the NAIP program is to enable availability of ortho imagery within one year of

                                                                                     54
acquisition. NAIP quarter quads are formatted to the UTM coordinate system using

NAD83. NAIP imagery may contain as much as 10% cloud cover per tile. The data

were later reprojected to Washington State Plane South.

Source: USDA-FSA Agriculture Photography Field Office; NAIP

Date of Exposure: 2005

Date of Publication: 09/2006

Spatial Information: State Plane Coordinate System 1983, South Zone

                     Planar Distance Units: survey feet

                     Horizontal Datum Name: North American Datum of 1983/1991

                     Ellipsoid Name: Geodetic Reference System 80

                     Resolution: 1m



Flood Inundation Map, February 1996, Yakima County

Description: Flood Polygon based on the revised Yakima County Flood Insurance

Study (FEMA 1998)

Source: Yakima County

Date of Publication: 2007

Spatial Information: State Plane Coordinate System 1983, South Zone

                     Planar Distance Units: survey feet

                     Horizontal Datum Name: North American Datum of 1983/1991

                     Ellipsoid Name: Geodetic Reference System 80

                     Resolution: 1m




                                                                                   55
9.3 List of Summary Reports within HAZUS-MH




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