ORI GIN AL PA PER

Estimating probable maximum loss from a Cascadiatsunami

Dale Dominey-Howes Æ Paula Dunbar Æ Jesse Varner ÆMaria Papathoma-Kohle

Received: 5 May 2008 / Accepted: 25 May 2009 / Published online: 17 June 2009� Springer Science+Business Media B.V. 2009

Abstract The Cascadia margin is capable of generating large magnitude seismic-tsu-

nami. We use a 1:500 year tsunami hazard flood layer produced during a probabilistictsunami hazard assessment as the input to a pilot study of the vulnerability of residential

and commercial buildings in Seaside, OR, USA. We map building exposure, apply the

Papathoma Tsunami Vulnerability Assessment Model to calculate building vulnerability

and estimate probable maximum loss (PML) associated with a 1:500 year tsunami flood.

Almost US$0.5 billion worth of buildings would be inundated, 95% of single story resi-

dential and 23% of commercial buildings would be destroyed with PML’s exceeding

US$116 million. These figures only represent a tiny fraction of the total values of exposed

assets and loss that would be associated with a Cascadia tsunami impacting the NW Pacific

coast. Not withstanding the various issues associated with our approach, this study

represents the first time that PML’s have ever been calculated for a Cascadia type tsunami,

and these results have serious implications for tsunami disaster risk management in

the region. This method has the potential to be rolled out across the United States and

elsewhere for estimating building vulnerability and loss to tsunami.

Keywords Tsunami � Cascadia � Building vulnerability assessment �Loss � PTVA model

D. Dominey-Howes (&)Australian Tsunami Research Center, School of Biological, Earth and Environmental Science,University of New South Wales, Sydney, NSW 2052, Australiae-mail: dale.dh@unsw.edu.au

P. DunbarNational Geophysical Data Center, National Oceanic and Atmospheric Administration (NOAA),Boulder, CO 80303, USA

J. VarnerCooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado,Boulder, CO 80309, USA

M. Papathoma-KohleDepartment of Geography and Regional Research, University of Vienna, 1010 Wien, Austria

123

Nat Hazards (2010) 53:43–61DOI 10.1007/s11069-009-9409-9

1 Introduction and aims

The Cascadia Subduction Zone (CSZ) (Fig. 1a) generates large (moment magnitude [ 8)

‘mega-thrust’ earthquakes similar to the 2004 Indian Ocean event (Satake and Atwater

2007). Coastal sediments in Oregon, Washington and British Columbia attest to the

occurrence of at least eight Holocene earthquake-tsunami (Kelsey et al. 2005; Losey 2005;

Nelson et al. 2006; Peters et al. 2007). Recurrence intervals along the CSZ range from 300

to 1,000 years with an average of 500–600 years (Peters et al. 2007).

Having recognized the hazard, efforts have begun to estimate future tsunami occurrence

using a probabilistic tsunami hazard assessment (PTHA) framework (The Tsunami Pilot

Working Group 2006). Once estimates are available, it is desirable to move to the next

stage of the risk management process by calculating likely probable maximum losses(PMLs) for particular events. This is important as PMLs are used to develop disaster

preparedness and response plans, to establish appropriate mitigation efforts such as land-

use zoning policies, to develop and apply building codes and regulations and to determine

where appropriate, insurance premiums. To estimate PMLs, information is needed about

the extent and severity of the hazard (in this case inundation distance and flow depth), asset

exposure (e.g., buildings located within the expected flood zone), the vulnerability of those

buildings and their market value (or replacement cost).

PACIFIC OCEAN

Seaside

Gearhart

Necanicum River

45.98 N

46.00 N

46.04 N

46.02 N

46.04 N

46.02 N

46.00 N

45.98 N

123.90 W123.92 W123.94 W123.96 W

123.90 W123.92 W123.96 W 123.94 W

0 200km

PACIFIC PLATE

JUAN DE FUCA PLATE

NORTH AMERICAN PLATE

California

Oregon

Washington

British Columbia

Vancouver

Portland

Seattle

44

48

122 W126

40 N

52

Vancouver Is

CA

SC

AD

IA S

UB

DU

CT

ION

ZO

NE

Juan

de

Fuca

Rid

ge

(a) (b)

130

Fig. 1 a Map of the Cascadia Subduction Zone (including crustal plates) and NW USA (together withplaces referred to in the text), b Seaside study location, c PTHA 1:500 tsunami map

44 Nat Hazards (2010) 53:43–61

123

Wood (2002), Wood et al. (2002a, b) and Wood and Good (2004) developed a

framework for identifying ‘relative vulnerability hotspots’—places that are exposed to

hazard processes. As valuable as the framework is, they acknowledge that their approach is

an ‘issues identification tool’ that does not provide a quantification of PML (Wood and

(c)

Fig. 1 continued

Nat Hazards (2010) 53:43–61 45

123

Good 2004; p. 265). They note communities interested in identifying and quantifying

vulnerability to [tsunami] damage and loss must apply an objective scientific weighting

scheme to the rankings of specific vulnerability attributes at the local level. Objective

analysis of, for instance, residential building vulnerability, should be carried out by

technical experts/engineers at high-resolution scales as assessment tools and data become

available. Recent reports state that there is still a need for credible fragility models and

laboratory data to understand the interaction of tsunami with the built environment

(Bernard et al. 2007; Grundy et al. 2005).

The Papathoma Tsunami Vulnerability Assessment (PTVA) model is a first generation

loss estimation tool designed to achieve the quantification of vulnerability advocated by

Wood and Good (2004) and Bernard et al. (2007). The PTVA model was developed using

information from historic tsunami, postevent surveys and damage assessments (Papathoma

2003; Papathoma and Dominey-Howes 2003; Papathoma et al. 2003). Papathoma (2003)

identified and ranked using expert judgement, a series of attributes (engineering, envi-

ronmental, social, etc.) responsible for controlling the type and severity of tsunami damage

to buildings. The 2004 Indian Ocean event enabled the model to be tested, validated and

evaluated and provides a robust framework to estimate tsunami vulnerability of buildings

(Dominey-Howes and Papathoma 2007).

The PTVA model is dynamic. The attribute data contained within the primary (GIS)

database may be continuously and quickly updated, allowing investigation of spatial and

temporal vulnerability. Table 1 outlines selected classes/attributes within the model. In the

absence of fully-developed and tested tsunami building fragility-damage assessment tools,

the PTVA model provides a framework capable of generating high-resolution first order

assessments of vulnerability and PML.

We apply the PTVA model to Seaside (46�0000000N, 123�9300000W on the northwest

coast of Oregon, 110 km northwest of Portland), OR (USA) (Fig. 1b), because (1) a

1:500 year PTHA tsunami flood map has recently been established (Fig. 1c) (The Tsunami

Pilot Working Group 2006), (2) it can provide the detail advocated by Wood and Good

(2004). The site for this study includes the city boundaries of Seaside and Gearhart

(hereafter referred to as ‘Seaside’) (Fig. 1b).

The aims of this study are to:

1. Map and quantify the ‘exposure’ of one-story residential and commercial buildings

within the 1:500 year tsunami flood hazard zone in Seaside

2. Use the PTVA model to quantify the vulnerability of these structures; and

3. To provide a preliminary estimate of PMLs in 2006 US$ for the buildings in the

1:500 year tsunami flood zone

2 Methods

In order to quantify the vulnerability of buildings in Seaside to the 1:500 year tsunami

flood, we undertook the following:

2.1 Step 1: Identification of tsunami inundation zone based on a PTHA

We selected the 1:500 year tsunami flood layer (Fig. 1c) generated by the PTHA assess-

ment described in the Tsunami Pilot Working Group (2006). We use the 1:500 year layer

46 Nat Hazards (2010) 53:43–61

123

rather than the 1:100 year layer they developed because no buildings are located within the

1:100 year flood layer (thus exposure, vulnerability and PML are negligible) and because

no other flood return periods (e.g., 1:1,000 year) were available. It should be noted that

since the 1:500 year tsunami flood layer is based upon a probabilistic calculation, it does

not exactly correlate with a specific event of a given magnitude. The Tsunami Pilot

Working Group (2006) was unable to determine a ‘credible worst case scenario’. However,

since the 1:500 year layer is dominated by a local CSZ tsunami (the Tsunami Pilot Study

Working Group 2006), we examine vulnerability deterministically based upon the prob-

abilistic 1:500 year flood layer.

Table 1 Selected class/attribute fields within the PTVA model relevant to this study (after Dominey-Howesand Papathoma, 2007)

Major class Attribute Attribute description Vulnerabilitydescriptor

The builtenvironment

Number of floorsin each building

Only one floor High

More than one floor Low

Description of groundfloor

Open plan with movable objects(e.g., tables and chairs)

High

Open plan or with big glass windowswithout movable objects

Medium

None of the above Low

Building surroundings No barrier Very high

Low/narrow earth embankment High

Low/narrow concrete wall Medium

High concrete wall Low

Building material, age,design

Buildings of field stone, unreinforced,crumbling and/or deserted, wood frameand wood construction

High

Ordinary masonry brick buildings, cementmortar, no reinforcement

Medium

Pre-cast concrete skeleton, reinforcedconcrete

Low

Shape and orientationof building

Square or oblong shaped structure High

Non cubic shaped building (e.g., hashexagonal or circular shaped floor plan)

Low

Walls parallel to shoreline High

Corners of building facing the shoreline Low

The environment Natural environment Narrow intertidal zone High

Intermediate intertidal zone Medium

Wide intertidal zone Low

Land cover (vegetation) No vegetation cover High

Scrub and low vegetation, small trees Medium

Trees and dense scrub Low

The ‘‘vulnerability descriptors’’ in the last column are defined as ‘relative’ to one another and are based onprevious field surveys

Nat Hazards (2010) 53:43–61 47

123

2.2 Step 2: Identification (and manipulation) of data sets for use in the tsunami

vulnerability assessment

To quantify building vulnerability, we needed data about Table 1 attributes at a high

resolution. We consulted the US multi-hazard loss estimation software (HAZUS-MH)

database for Oregon. HAZUS-MH includes attributes potentially useful for vulnerability

assessment and loss estimation and presents data in aggregate form at census block level.

We identified HAZUS-MH attributes similar to the PTVA Model attributes including

construction material, building condition, number of floors and building row number from

the coast. Although HAZUS-MH contains some data for these attributes, their coarse

aggregate nature meant that they are at a scale at odds with the very high resolution spatial

analysis we wished to undertake. We therefore obtained the Clatsop County Tax Assessor

GIS taxlot database, which includes Seaside (hereafter referred to as the ‘taxlot database’).

The taxlot database contains multiple attributes for every taxlot (corresponding to

individual buildings or land parcels). From this, attributes relevant to the PTVA model

included the number of floors, type and use of structure, year built and market value of the

improvement (building). The taxlot database contains 185 numerical classes (referred to as

‘‘Stat_Class’’) to classify building types and land use (Table 2). For example, there were 46

separate classes that identified 1- and 1.5-story residential structures (Table 2). In order to

simplify the taxlot database, we reclassified the building and land parcel classes (Table 2,

column 3).

2.3 Step 3: Combining the taxlot database with the 1:500 year inundation depth zones

and generation of water depth above ground surface

We obtained probabilistic tsunami water height raster layers produced by the Tsunami

Pilot Study Working Group (2006) from the USGS. The set of rasters contained annual

tsunami probability of exceedance for wave heights from 0.5 to 10.5 m above mean high

water (MHW) (see Tsunami Pilot Study Working Group (2006) for a description of how

this was achieved). The individual probabilistic wave height rasters were combined into

one, which represented the maximum tsunami water height with a 500-year recurrence

interval. This was achieved by summing the wave height for each grid where the annual

probability of exceedance was 0.002 or greater. We then determined the maximum 1:500

water depth above the land surface. To do this, we subtracted the DEM developed in the

Seaside Tsunami Pilot Study and calculated the water depth raster from the 500-year wave

height one. The resulting raster layer provided 1:500 water depths for each pixel (with

0.000558 degree cell size or about 60 m). A water depth was then assigned to each taxlot in

the taxlot database layer using the ArcGIS zonal statistics tool to compute the mean value

of the water depth raster contained within each taxlot polygon.

2.4 Step 4: Collecting high resolution data relating to the PTVA model attributes

and ground-truthing the HAZUS-MH and taxlot databases

Two key PTVA model attributes (building material, condition) were not available in the

taxlot database. However, the HAZUS-MH database provides summaries of the proportion

of buildings constructed from different materials. For example, 99% of residential struc-

tures in Oregon are constructed of wood. Although we used satellite imagery, we were

unable to determine the spatial distribution of these structures so we visited Seaside to

48 Nat Hazards (2010) 53:43–61

123

Table 2 Clatsop County Tax Assessor taxlot database classes, class descriptions and our simplified(revised) Stat_Class number

Original Clatsop County TaxAssessor taxlot Stat_Class

Description Our revisedStat_Class

110, 111, 112, 120, 121, 122, 123, 124, 125,130, 131, 132, 133, 134, 135, 140, 141,142, 143, 144, 145, 150, 151, 152, 153,154, 155, 160, 161, 162, 163, 164, 165,170, 171, 172, 173, 174, 175, 180, 181,182, 183, 184, 185, 630

Residential 1 story* 100

116, 126, 127, 128, 129, 136, 137, 138, 139,146, 147, 148, 149, 156, 157, 158, 159,166, 167, 168, 169, 177, 178, 186, 188,189, 656, 657, 646, 176, 179, 187, 637

Residential 2 ? story 120

420, 421 Residential apartments (low rise) 130

422, 423, 900 Residential apartments (medium andhigh rise, e.g., condos)

140

190, 181, 182, 183, 194, 199, 200, 300, 500 Residential single-widemanufactured homes (MFD),vacant MFD and miscellaneousoutbuildings

150

232, 233, 234, 242, 243, 244, 252, 253, 254 Residential all plexes (duplex,triplex, etc.)

155

410, 411, 412, 413, 414 Commercial—motels, hotels B&B 160

440, 441, 442, 443, 444, 446, 447, 448, 449,475, 490, 430, 431, 432, 433, 434, 435,540

Commercial—retail, departmentstores, etc.

170

480, 481, 482, 483, 484, 485, 698 Commercial—automotive, servicestations, etc.

180

400, 651, 460, 461, 462, 463, 464, 499 Commercial—other, misc.,communications—all types,financial—banks, ATMs, etc.

190

470, 471, 472, 473, 474, 476, 477 Industrial—warehouse, storage, etc. 200

700, 710, 720, 730, 740, 750, 760, 770,780, 790

Industrial—all others(e.g., manufacturing)

210

450, 452, 453, 454, 455, 496, 520, 530,566, 570

Medical—hospitals, veterinary, daycare, nursing home, etc.

230

000, xxx Land—vacant land/ contiguous landholding

0, 240

564 Emergency service—fire, police,ambulance

250

493, 494, 495, 497, 492, 511 Recreation—sports, art venues, RVparks, etc.

280

445, 550, 560, 561, 562, 563, 564, 565,567, 491

Public services—schools, postoffices, churches, libraries,regional centers

290

* 1.5-story structures were houses with1-room attics

Note: In this table, we have listed every building class (e.g., 1 story, industrial, recreation etc) we examinedin our study. However, in this paper we only report on the vulnerability and likely PMLs of 1- and 1.5-* story residential (hereafter referred to as ‘1 story’) and commercial buildings

Nat Hazards (2010) 53:43–61 49

123

ground truth the taxlot database. Selected ‘representative’ residential and commercial

blocks located within the peninsula area of Seaside (the area of Fig. 1b located between the

Pacific Ocean and the Necanicum River) were chosen and information related to eight key

PTVA model attributes (Table 3) was recorded for every structure. The peninsula region

was selected because it is the area affected by the deepest tsunami inundation (Fig. 1c) and

is therefore, extraordinarily vulnerable. We used the ground-truthed data from 12 resi-

dential and 12 commercial blocks together with a rapid visual assessment of the remaining

building stock in the peninsula area to generate a high resolution dataset of building

attributes.

2.5 Step 5: Calculation of PTVA vulnerability scores for ground-truthed blocks

We restrict our calculation of building vulnerability to the ground-truthed one-story resi-

dential and commercial buildings on the peninsula region of Seaside. The percentage

distribution of these vulnerability scores was then scaled up to all one-story residential and

commercial buildings within the peninsula area.

For each attribute, descriptions of possible alternatives were provided, and these

alternatives were given a raw score. The list of attributes, and thus raw scores, varied

depending upon the particular locality being evaluated. These are shown in Table 4.

To calculate the overall ‘‘vulnerability score’’ for each building, we:

• Weighted the attributes (Table 3) from highest (those most significant in controlling

vulnerability) to lowest (those least significant in controlling vulnerability). The

weighting of the attributes was determined by expert judgement by Papathoma (2003)

from an analysis of numerous tsunami damage assessment surveys and engineering

reports

• Transformed (or standardized) the raw scores for each attribute to a ratio scale to permit

interattribute manipulation. Thus, we take the raw score and divide that number by the

highest possible value for that attribute. For example, for water depth, the raw score

may be 4 (indicating a water depth of between 2.0 and 2.99 m) and 4 is divided by 6

(which is the maximum possible score for this attribute ; see Table 4). So, 4/6 = 0.66.

We then complete this calculation for each attribute

Table 3 Attributes from the PTVA model and their weighting within the study

Attribute Weighted value

Water depth above ground surface 8

Building row number (from the sea) 7

Building material 6

Number of floors 5

Orientation of building 4

Condition of building 3

Building surroundings 2

Land cover 1

These specific attributes were selected for this study because they are (1) known to be important inpredicting/controlling vulnerability and (2) because data related to these attributes was readily available tous. The weighting of the attributes was determined by Papathoma (2003) via a process of ‘expert judgement’

50 Nat Hazards (2010) 53:43–61

123

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Nat Hazards (2010) 53:43–61 51

123

To calculate the vulnerability score (V) of each individual building, we then sum as

follows:

V ¼ 8� að Þ þ 7� bð Þ þ 6� cð Þ þ 5� dð Þ þ 4� eð Þ þ 3� fð Þ þ 2� gð Þ þ 1� hð Þ

where a, b, c, d, e, f, g and h are the standardized scores for water depth, row number, material,

floors, orientation, condition, surroundings and land cover, respectively (Table 4, row 1).

The lowest possible vulnerability score is:

R 8� 0:16ð Þ þ 7� 0:027ð Þ þ 6� 0:25ð Þ þ 5� 0:5ð Þ þ 4� 0:33ð Þ þ 3� 0:33ð Þþ 2� 0:25ð Þ þ 1� 0:33ð Þ ¼ 8:609

The highest possible vulnerability score is:

R 8� 1ð Þ þ 7� 1ð Þ þ 6� 1ð Þ þ 5� 1ð Þ þ 4� 1ð Þ þ 3� 1ð Þ þ 2� 1ð Þ þ 1� 1ð Þ ¼ 36

The ‘vulnerability score’ of each building will lie between these values.

Since we now know that the building vulnerability score will lie between 8.609 and 36,

we divide this range into ‘equal intervals’ (Table 5).

2.6 Step 6: Extrapolation from ground-truthed blocks to whole of Seaside peninsula

and calculation of PML’s

Once we had estimated the vulnerability of buildings in our ground-truthed blocks, we took

the relative distribution of ‘high vulnerability’ and ‘medium vulnerability’ (etc.) structures

and scaled these numbers up to the total number of buildings present on the whole pen-

insula. Finally, we calculated the PMLs (both absolute number of buildings and their $

value) for one-story residential and commercial buildings.

3 Results

There are a total of 4,910 buildings (of different classes) and land plots within Seaside

(Table 6, column 2). Here, we focus on the vulnerability of one-story residential and

commercial buildings only since collectively they represent over 50% of the building

stock, and we only wish to ‘demonstrate’ the capability of our approach (rather than

provide a comprehensive assessment of building vulnerability and PML).

3.1 Summary of building exposure within the 1:500 year tsunami flood layer, spatial

distribution and replacement costs

A total of 3,032 buildings (62% of the total) are located within the 1:500 year tsunami

flood hazard layer. Their spatial distribution is shown in Fig. 2a, and the total number of

Table 5 Range of vulnerabilityclasses and their respectivenumeric distributions

Numeric values Vulnerability class

30.53–36 High vulnerability

25.031–30.529 Medium-high vulnerability

19.56–25.03 Medium vulnerability

14.08–19.55 Medium-low vulnerability

8.609–14.079 Low vulnerability

52 Nat Hazards (2010) 53:43–61

123

buildings (by class) is summarized in Table 6 (column 3). Figure 2b and c shows the

spatial distribution of the one-story residential and commercial structures (by water depth),

respectively. The total market value of all buildings exposed to inundation within the

1:500 year tsunami flood hazard layer is almost US$0.5 billion (Table 6, column 4).

3.2 Summary of exposure of buildings under tsunami flood water [4 m deep, their

spatial distribution and replacement costs

Figures 1c and 2 indicate that some of the peninsula would be under tsunami flood water

[4 m deep. Table 6 (columns 5 and 6) summarizes the total number of buildings (by class)

and the percentage of the total building stock that would be submerged under tsunami water

[4 m deep. The replacement costs for those buildings are summarized in Table 6 (column

7). Severe structural damage is known to have occurred to buildings in flood waters of

significantly lesser depth [e.g., 2004 Indian Ocean Tsunami (Dominey-Howes and Pa-

pathoma 2007) and 1996 Irian Jaya Tsunami (Matsutomi et al. 2001)]. However, we

assume that any structure submerged in tsunami water[4 m deep will be damaged beyond

repair. The total replacement cost in this case would exceed US$87 million (or 18.5% of

the value of all building stock) (Table 6, column 7).

Table 6 Number of buildings and land plots (by class), exposure and replacement cost in the tsunamiinundation zone

Building class Total numberof buildingsand plots

Total numberof buildingsand plots in1:500 yeartsunami flood

Total $ valueof buildings inthe 1:500 yeartsunami flood

Totalnumber ofbuildings intsunamiwater [4 mdeep

Percentageof buildingstock intsunamiwater [4 mdeep

Replacementcosts ($) ofbuildingsdestroyed bytsunami water[4 m deep

1 storyresidential

2,949 1,874 189,954,374 258 13.76 31,159,699

2 ? storyresidential

1,221 577 105,849,803 173 29.98 36,495,884

Plexes(duplex,triplex, etc)

181 133 22,697,035 24 18.04 4,712,938

Commercial 417 355 85,845,670 38 10.70 14,606,689

Industrial 55 29 4,683,521 6 20.69 162,084

Recreation,publicservice andregionalcenters

55 42 59,005,132 3 7.14 283,389

Medical andemergencyservices

32 22 6,530,069 3 13.64 392,364

Vacant land 439a

Total 4,910 3,032 $474,565,604 505 – $87,813,047

a These vacant land parcels have no structures built on them. However, the Clatsop County AssessorDatabase includes these land plots as ‘rateable’ areas. Thus the total number of ‘building plots’ equals 4,910

Nat Hazards (2010) 53:43–61 53

123

Fig

.2

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54 Nat Hazards (2010) 53:43–61

123

3.3 Results of building tsunami vulnerability assessment

Figure 3 shows the spatial distribution of the 1,017 exposed one-story residential and 199

commercial buildings in the peninsula region. We ground-truthed 131 (or 12.88%) of the

one-story residential and 98 (or 49.25%) of the commercial structures.

3.3.1 Residential building tsunami vulnerability assessment

Of the 131 ground-truthed one-story residential structures (Fig. 4a, b), 14.5% have a

vulnerability classification of ‘high’ and 81.7% are ‘medium-high’. The remaining clas-

sifications are shown in Table 7 (columns 1 and 3). These relative percentages were then

scaled up for the entire peninsula region. We assume that a total loss of structure occurs for

buildings classified ‘medium-high’ and ‘high’ (which in the vast majority of cases cor-

related with water depth[4 m). Therefore, the PML is the aggregate replacement costs of

buildings that fall within these classifications. This replacement cost, calculated from the

2006 market value of the residential buildings, amounts to more than US$103 million

(Table 7, column 5).

3.3.2 Commercial building tsunami vulnerability assessment

Of the 98 ground-truthed commercial structures (Fig. 4c), none have a vulnerability

classification of ‘high’, and only 23.5% have a classification of ‘medium-high’. The

remaining classifications are shown in Table 7 (columns 1 and 3). These percentages were

also scaled up for the entire peninsula region, and it was again assumed that a total loss

occurs for buildings classified ‘medium-high’ and ‘high’. Consequently, the PML for these

structures exceeds US$13 million (Table 7, column 5).

4 Discussion

Wood and Good (2004) provide a framework to begin to examine tsunami vulnerability at

the local level. We go well beyond Wood and Good (2004) using a specific technical tool

to identify and quantify one-story residential and commercial building vulnerability and to

estimate PMLs—something that to our knowledge, has never been done for tsunami in the

United States.

We attempted to use data from the HAZUS-MH database to estimate PMLs from the

1:500 year tsunami. However, HAZUS-MH data are at present, too coarse to be used for

this type of detailed tsunami vulnerability assessment. The County Tax Assessor Taxlot

Database on the other hand, is useful, although we are uncertain if other similar databases

are as accurate. This issue would need to be investigated for each community subjected to

similar detailed vulnerability assessment.

There are a large number of buildings (3,032) with a high market value within the

1:500 year tsunami flood layer, and they are densely concentrated on the low-lying pen-

insula (Fig. 2a). This is a significant issue during tsunami inundation since the peninsula is

connected to the mainland by six small bridges with one lane of traffic flow in each

direction. In the event of an evacuation, significant bottlenecks would be expected to

develop at these critical points.

Some 1,874 single-story residential structures with a combined value of more than

US$189 million are located within the 1:500 year tsunami flood layer. However, exposure

Nat Hazards (2010) 53:43–61 55

123

Fig

.3

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istr

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(exposu

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56 Nat Hazards (2010) 53:43–61

123

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.4

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Nat Hazards (2010) 53:43–61 57

123

to tsunami flooding does not automatically mean that these structures will be damaged

beyond repair. If we assume that only those residential structures affected by tsunami water

depths[4 m will be completely destroyed, then the replacement cost of these 258 homes

(14% of the single story residential building stock) alone exceeds US$31 million (Table 6,

column 7).

Three hundred and fifty-five commercial buildings (out of 417) with a combined value

of US$85 million are located within the 1:500 year tsunami flood layer. However, only 38

of these (10.7% of the stock) are located in areas where tsunami water depth will be[4 m.

Assuming that all these buildings would be destroyed, their replacement costs would

exceed US$14 million (Table 6, column 7). Our assumption about water depth and total

building loss needs to be explored in greater detail however.

The vulnerability/resilience of an individual structure is a function of a number of

parameters that include structural engineering, age, condition, material, distance from the

coast, engineering standards, orientation and shape. The PTVA model attempts to account

for these factors and ascribes a vulnerability score to each structure. Thus, two adjacent

building structures both in 4 m of tsunami flood water, may sustain varying degrees of

damage according to these factors. Therefore, when the PTVA model is applied to

buildings in Seaside, the patterns shown in Figs. 2 and 3 vary, and the PMLs change

dramatically.

In our analyses, it is assumed that buildings categorized as ‘high’ or ‘medium-high’

vulnerability will be completely destroyed. Consequently, over 95% (Table 7, column 3)

of one-story residential buildings in Seaside would be destroyed with a PML in excess of

Table 7 Building vulnerability and PML of one-story residential and commercial buildings located on thepeninsula

PTVA vulnerability class Number ofground-truthedstructures

Percentageof buildingstock

Scaled-upmarketvalue $

Probablemaximumloss $

Residential buildings

High 19 14.5 15,568,512 15,568,512

Medium-high 107 81.7 87,675,303 87,675,303

Medium 3 2.3 2,458,186 –a

Medium-low 0 – – –a

Low 2 1.5 1,638,791 –a

Total 131 100 107,340,792 103,243,815

Commercial buildings

High 0 – – –

Medium-high 23 23.5 13,431,515 13,431,515

Medium 51 52.0 29,782,925 –a

Medium-low 5 5.1 2,919,895 –a

Low 19 19.4 11,095,600 –a

Total 98 100 57,229,935 13,431,515

Total PML for one-story residential and commercial buildings in the 1:500 year tsunami 116,675,330

a While buildings with a vulnerability classification of ‘Medium’, ‘Medium-low’ and ‘Low’ will bedamaged by the 1:500 year tsunami flood, we assume that they will not be damaged beyond repair.Therefore, we do not include the likely replacement costs for these structures since we are only concernedwith the replacement costs of structures that are completely destroyed by the tsunami

58 Nat Hazards (2010) 53:43–61

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US$103 million. When the same analysis is repeated for commercial buildings, some 23%

(Table 7, column 3) would be destroyed with a PML of approximately US$13 million.

Therefore, total PML for these two building classes would exceed US$116 million

(Table 7, column 5) out of a total exposure worth close to US$0.5 billion.

The PML of US$116 million is the minimum that would be expected at Seaside for a

1:500 year tsunami. The real figure would be much higher as other classes of buildings

would also be destroyed. Also, our calculations have only considered the costs associated

with the full replacement of destroyed buildings. It does not include the costs associated

with the repair of partly-damaged buildings.

The PTVA model is a useful tool for quantifying the vulnerability of buildings enabling

estimates of PML and could easily be ‘rolled-out’ across the United States to help gain a

high-resolution first-order assessment of exposure, vulnerability and PML. This loss esti-

mation tool should be useful to emergency management and local government officials in

prioritizing disaster mitigation efforts. As fragility curves become available, individual

fragility functions could be added as an attribute field within the PTVA model.

This pilot study was designed to test the capability of the PTVA model rather than

provide a comprehensive assessment of building vulnerability and PML for Seaside (and

the Pacific NW coast). There are several limitations to this work:

• We have deterministically quantified exposure, vulnerability and PML based upon a

probabilistic map that does not directly equate to an actual event. Use of a ‘credible

worst case scenario’ would increase our confidence in the estimates of exposure and

PML. When a ‘credible worst case scenario’ is developed, this study should be repeated

to gain a more comprehensive estimate of PMLs

• PMLs associated with the 1:500 year tsunami do not (in our study) take account of

earthquake-related damage to structures prior to the arrival of the tsunami. Given that

the 1:500 year tsunami flood layer is dominated by a Cascadia type earthquake, such an

event will likely have caused significant structural damage to buildings that would then

be affected by a tsunami. As such, PMLs are likely to be higher than reported here.

Further work should try to incorporate this issue

• Ground truthing was limited to a few blocks on the peninsula. Increased coverage of

blocks on both the peninsula and mainland regions of Seaside would increase our

confidence in the representativeness of the building stock surveyed (even if it did not

change the distribution of building types/vulnerabilities)

• We have only included the peninsula region in our analysis. Further analysis should

focus on the mainland. Additionally, we simplified tsunami inundation to a single wave

running across the region parallel with the shoreline. In reality, the wave would funnel

up the Necanicum River estuary (Fig. 1b, c). Inundation of buildings adjacent to the

estuary could occur before water from the coast reaches them. This might alter the

relative vulnerability (and PMLs) at these locations

• The County Tax Assessor Taxlot Database is useful, although we are uncertain if other

databases are as accurate. This would need checking ‘on the ground’ for each

community and could potentially limit the usefulness of the PTVA model; and

• We have made no attempt to estimate human vulnerability, which is important for a

complete tsunami vulnerability assessment. Databases containing business information

such as number of hotel beds, employees, etc, could also be used to determine the

variability in human exposure and vulnerability

Nat Hazards (2010) 53:43–61 59

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5 Conclusion

It is now known that the Cascadia margin is capable of generating large magnitude seis-

mic-tsunami. Consequently, there is an urgent need to identify and apply quantitative tools

to assess the relative vulnerability of buildings located within expected tsunami inundation

zones and to use the assessments of vulnerability to estimate PML for future possible

events. The results of such analyses may then be used for developing appropriate miti-

gation strategies.

We have used a 1:500 year tsunami hazard flood layer produced during a PTHA as the

input to a study of the vulnerability of one-story residential and commercial buildings in

the low-lying peninsula region of Seaside, OR, USA. We have mapped building exposure,

applied the ‘‘PTVA model’’ to calculate their vulnerability and estimate PMLs associated

with a 1:500 year tsunami flood. Almost US$0.5 billion worth of buildings would be

inundated, 95% of single-story residential and 23% of commercial buildings would be

destroyed with PMLs exceeding US$116 million. This study represents the first time that

PMLs have ever been calculated for a Cascadia type tsunami, and the results have serious

implications for risk management.

It is clear from this pilot study that the PTVA model may be a useful tool for inves-

tigating high resolution vulnerability of structures and for estimating PMLs within United

States coastal tsunami inundation zones and could be applied elsewhere.

Acknowledgments NOAA/NGDC is thanked for providing the resources to enable Dale Dominey-Howesand Papathoma-Kohle to participate in this study. Two anonymous referees and Filippo Dall’Osso arethanked for making numerous helpful comments on an earlier version of this manuscript all of whichsignificantly improved the final version.

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