Seaside, Oregon Tsunami Pilot Study—

Modernization of FEMA Flood Hazard Maps

By Tsunami Pilot Study Working Group

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S.

Government

Open-File Report 2006-1234

U.S. Department of the Interior

U.S. Geological Survey
1

Working Group Participants

Frank    González NOAA/PMEL

Eric     Geist U.S. Geological Survey

Costas   Synolakis Univ. of Southern California

Diego Arcas NOAA/PMEL

Douglas Bellomo FEMA, Dept. of Homeland Security

David    Carlton FEMA, Dept. of Homeland Security

Thomas Horning Horning Geoscience

Bruce    Jaffe U.S. Geological Survey

Jeff     Johnson Northwest Hydraulics Consultants

Utku     Kanoglu Middle East Technical University, Ankara, Turkey

Harold Mofjeld NOAA/PMEL

Jean Newman NOAA/PMEL

Tom Parsons U.S. Geological Survey

Robert   Peters U.S. Geological Survey

Curt     Peterson Portland State University

George   Priest Oregon Dept. of Geology & Minerals

Vasily   Titov NOAA/PMEL

Angie Venturato NOAA/PMEL

Joseph Weber FEMA, Dept. of Homeland Security

Florence Wong U.S. Geological Survey

Ahmet    Yalciner Middle East Technical University, Ankara, Turkey

Front Cover:

Perspective view of 500-year tsunami in the Seaside/Gearhart, Oregon, pilot study area.

Tsunami wave heights (m) with a 0.2% annual probability of exceedance. Wave heights

include the effects of tides. Vertical exaggeration 10x; view looks southeastward.
2

Contents

EXECUTIVE SUMMARY ................................................................................................................................. 4

METHODOLOGY.................................................................................................................................................. 4

RESULTS.............................................................................................................................................................. 5

RECOMMENDATIONS .......................................................................................................................................... 5

BACKGROUND AND INTRODUCTION....................................................................................................... 7

PREVIOUS METHODS USED FOR FIRM TSUNAMI MAPS................................................................... 9

TSUNAMI SOURCES........................................................................................................................................... 10

TRANS-PACIFIC AND NEARSHORE NUMERICAL MODELS ............................................................................... 10

PREDICTED TIDES ............................................................................................................................................. 11

COMPUTING THE 100- AND 500-YEAR TSUNAMI RUNUP HEIGHTS ................................................................ 11

APPLICATION TO SEASIDE, OREGON................................................................................................................ 12

DEVELOPMENT OF GIS DATABASE ........................................................................................................ 13

DIGITAL ELEVATION MODEL ........................................................................................................................... 14

HISTORICAL TSUNAMI EVENT DATA ............................................................................................................... 16

Observations ................................................................................................................................................ 16

Shoreline....................................................................................................................................................... 16

MODEL OUTPUT................................................................................................................................................ 16

TSUNAMI DEPOSITS...................................................................................................................................... 16

METHODS.......................................................................................................................................................... 17

RESULTS............................................................................................................................................................ 18

1964 Tsunami Deposits................................................................................................................................ 18

1700 Tsunami Deposits................................................................................................................................ 19

Older Tsunami Deposits.............................................................................................................................. 19

Paleo-tidal Inlet ........................................................................................................................................... 20

DISCUSSION OF TSUNAMI DEPOSIT RESULTS .................................................................................................. 20

PROBABILISTIC METHOD .......................................................................................................................... 22

PTHA OVERVIEW ............................................................................................................................................ 22

Previous PTHA Studies................................................................................................................................ 22

Application of PTHA to FIRM Specifications............................................................................................. 23

SOURCE SPECIFICATION ................................................................................................................................... 25

Source Magnitude and Geometry................................................................................................................ 25

Far-Field Earthquakes................................................................................................................................ 27

Cascadia Subduction Zone Earthquakes.................................................................................................... 30

PROPAGATION AND INUNDATION MODELING.................................................................................. 33

NUMERICAL MODEL.......................................................................................................................................... 33

Mathematical formulation ........................................................................................................................... 34

Moving boundary condition ........................................................................................................................ 35

Tsunami propagation model........................................................................................................................ 35

Model verification........................................................................................................................................ 36

MODEL SOURCES .............................................................................................................................................. 36

Far-field sources.......................................................................................................................................... 37

Near-field sources........................................................................................................................................ 37

NUMERICAL MODEL SETUP AND TESTING ........................................................................................................ 38

DATABASE OF MODEL RUNS............................................................................................................................. 39
3

EVALUATING TSUNAMI IMPACT METRICS......................................................................................... 41

FORCES ON STRUCTURES ................................................................................................................................. 42

RESULTS............................................................................................................................................................ 46

100-YEAR TSUNAMI MAP ................................................................................................................................ 46

500-YEAR TSUNAMI MAP ................................................................................................................................ 47

DISCUSSION: RESOLVABLE ISSUES THROUGH FUTURE RESEARCH........................................ 47

TSUNAMI EARTHQUAKES ................................................................................................................................. 47

SMALLER CASCADIA SUBDUCTION ZONE EARTHQUAKES.............................................................................. 48

OTHER SOURCES OF EPISTEMIC UNCERTAINTY .............................................................................................. 48

RECOMMENDATIONS................................................................................................................................... 49

ACKNOWLEDGEMENTS .............................................................................................................................. 50

REFERENCES................................................................................................................................................... 51
4

Seaside, Oregon Tsunami Pilot Study —

Modernization of FEMA Flood Hazard Maps

By Tsunami Pilot Study Working Group

Executive Summary

FEMA Flood Insurance Rate Map (FIRM) guidelines do not currently exist for

conducting and incorporating tsunami hazard assessments that reflect the substantial

advances in tsunami research achieved in the last two decades; this conclusion is the

result of two FEMA-sponsored workshops and the associated Tsunami Focused Study

(Chowdhury, et al., 2005). Therefore, as part of FEMA’s Map Modernization Program,

a Tsunami Pilot Study was carried out in the Seaside/Gearhart, Oregon, area to develop

an improved Probabilistic Tsunami Hazard Assessment (PTHA) methodology and to

provide recommendations for improved tsunami hazard assessment guidelines. The

Seaside area was chosen because it is typical of many coastal communities in the section

of the Pacific Coast from Cape Mendocino to the Strait of Juan de Fuca, and because

State Agencies and local stakeholders expressed considerable interest in mapping the

tsunami threat to this area. The study was an interagency effort by FEMA, U.S.

Geological Survey and the National Oceanic and Atmospheric Administration, in

collaboration with the University of Southern California, Middle East Technical

University. Portland State University, Horning Geosciences, Northwest Hydraulics

Consultants, and the Oregon Department of Geological and Mineral Industries. Draft

copies and a briefing on the contents, results and recommendations of this document were

provided to FEMA officials before final publication.

Methodology

The study methodology consisted of a number of important components, each of

which was essential to successfully developing 100- and 500-year tsunami inundation

products required by FEMA for Flood Insurance Rate Maps, including flooding depth

and high velocity zones (V-zones). These components were:

• Source Specification. Review of literature; consultation with expert colleagues;

development of a database of quantitative probabilistic models of local and farfield earthquake tsunami sources in the Cascadia Subduction Zone, the AlaskaAleutian Subduction Zone and the Peru-Chile Subduction Zone.

• Data Acquisition. Performance of a paleotsunami deposit mapping and

interpretation study; acquisition of historical records and eyewitness reports.
5

• Model Development, Testing and Application. Development of a highresolution digital elevation models (DEM) based on the latest available

topography, bathymetry, and tidal information; development of a state-of-the-art,

site-specific tsunami inundation model; testing of the model with all available

tsunami field observations, including paleotsunami data, historical records, and

eyewitness reports; application of the model, using the source database, to

generate the corresponding tsunami inundation database.

• Probabilistic Computations. Development of a systematic procedure to process

the study data and compute the distributions of 0.01 and 0.002 annual rates of

occurrence (100- and 500-year) quantities, including the effect of ocean tides;

application of the procedure to create the site-specific tsunami hazard maps.

• Study-Specific Database Development. Development and documentation of a

comprehensive, study-specific, GIS-compatible database that includes sources,

DEM, model output, field observations, and other information relevant to the

study; creation of web-based interface for database access.

• Analyses and Interpretation. Use of the GIS database for quality control and

error-checking, and to analyze and interpret the primary study results; exploratory

analyses and interpretation of various tsunami impact indices to generalize the

concepts of tsunami hazard levels in general, and tsunami high velocity flood

zones (V-zones) in particular.

Results

Although Seaside suffered inundation and damage as a result of the tsunami

generated by the Great 1964 Prince William Sound earthquake, little inundation is

indicated by the 100-year tsunami hazard map. The interpretation of this result is that, on

the 100-year time scale, Seaside is threatened primarily by tsunamis generated by farfield tsunamis that are not generally as destructive as those generated locally. In contrast,

on the 500-year time scale, Seaside is threatened by large, destructive tsunamis generated

locally by great earthquakes on the Cascadia Subduction Zone, which lies just offshore.

As a consequence, the 500-year tsunami hazard map reflects very large regions of

Seaside inundated to significant depths.

Details of the methodology developed during the course of this project, a

discussion of older tsunami assessment methods, data sources, literature references,

results and other recommendations are provided in the body of the report.

Recommendations

Some important factors influenced our recommendations, as follows. First, the

methodology for probabilistic tsunami hazard assessment (PTHA) developed by this

study, while preliminary, is nonetheless a major advance over previous methods, and

should therefore be applied to upgrade assessments in other coastal areas. However,

considerable work remains to improve the methodology, and these follow-on studies

should also be designed to refine and improve the methodology, as discussed in the

report. Also, truly disastrous local events will inevitably devastate U.S. coastal

communities near known subduction zones, such as the Cascadia and the Alaska-Aleutian

subduction zones; though infrequent, the impact of such events are so catastrophic that

they must somehow be taken into account. Time limitations did not allow development
6

of a “Credible Worst Case” methodology during the course of this study. However, our

results lay the groundwork for the development of this method which, essentially,

answers two fundamental questions: “What is the scientifically defensible and credible

worst case scenario ?” and “What is the probability of occurrence of this scenario ?”

This simple concept has great intuitive appeal and should have very high practical value

as an actuarial tool. Finally, adequate PTHA for all U.S. coastlines is a long-term,

challenging effort that requires an integrated, sustainable national approach, including the

establishment and maintenance of Federal agency partnerships, in collaboration with

State agencies, academic and other institutions. Our specific recommendations are

therefore grouped as Scientific/Technical and Policy/Programmatic, as follows.

Scientific/Technical Recommendations

• Include all reasonable epistemic and aleatory sources of uncertainty in each

Probabilistic Tsunami Hazard Assessment, using the best available science.

• Utilize tsunami hydrodynamic models that meet NOAA standards, to ensure

consistency of Federal agency products

• Test all earthquake and tsunami models by extensive field studies to gather and

exploit all possible paleogeography and paleotsunami data, historical tsunami

measurements, eyewitness reports and other types of field observations.

• Develop and maintain a comprehensive GIS database of all field data, model

results and a comprehensive site- and source-specific tsunami/earthquake

bibliography for the region as an essential and invaluable analysis and product

development tool.

• Publish a report for each PTHA project that documents procedures, data sources

and results, that includes a bibliography, and that is reviewed for consistency

with FEMA standards.

• Publish PTHA results either as a separate Federal Insurance Rate Map, or include

PTHA information as separate, tsunami-specific items on FIRMs. In either case,

include: (a) the 100-year and 500-year events, (b) tsunami-specific V-zones, (c)

measurements available for the worst case historical and/or paleo-tsunami events,

and (d) the "Credible Worst-Case Scenario" event.

•

Policy/Programmatic Recommendations

• Establish a formal FEMA/NOAA/USGS partnership to address national needs for

tsunami hazard assessment products in a Federally consistent and cost-effective

manner.

• Apply PTHA to additional Cascadia Subduction Zone communities as NOAA

inundation models are completed.

• Conduct Pilot Studies to adapt PTHA to other Tsunami Regimes in the Pacific,

Caribbean, Atlantic, and Gulf, using a preliminary assessment of uncertainty.

• Apply PTHA to additional Pacific, Caribbean, Atlantic and Gulf communities as

the corresponding Pilot Studies and NOAA inundation models are completed.

• Establish a systematic Maintenance and Improvement Program to integrate

scientific and technical advances into the PTHA methodology.
7

Background and Introduction

FEMA Flood Insurance Rate Map (FIRM) guidelines do not currently exist for

conducting and incorporating tsunami hazard assessments that reflect the substantial

advances in tsunami research achieved in the last two decades. Thus, current FIRMs rely

heavily on the science, technology and methodologies developed in the 1970s, such as

that of Houston and Garcia (1974) and Houston (1980). This work is generally regarded

as groundbreaking and state-of-the-art for its time, but is now superseded by modern

methods (Table 1).

Table 1: Comparison of pre-1990 and post-1990 tsunami hazard assessment

methodologies.

Component Pre-1990 Post-1990

Runup Modeling No Yes

Far-field Sources Earthquakes. Surface deformation based on

simple elliptic analytic idealizations.

Earthquakes and landslides. Surface

deformation based on geophysical

models

Near-field

Sources No. Importance not recognized. Yes. Importance now recognized as aresult of numerous studies.

Bathymetry &

topography

Low quality coverage and availability. Deep

ocean modeled as constant-depth basin.

Shallow coastal features not adequately

resolved.

Improved quality, coverage and

availability of Pacific deep and coastal

bathymetry and topography.

Computational

grids Coarse-resolution. Fine-resolution, where required.

Probabilistic

Methodology

Based on short-term historical tsunami

record

Based on long-term paleoseismic and

paleotsunami records and short-term,

historical earthquake and tsunami

records.

Hazard Zone

Identification

 Qualitative estimates inferred from offshore

height, only.

Indices can be computed, based on

both runup heights and currents.

Two recent FEMA workshops were held to help develop plans for updating the

existing FIRMs. The approximately 40 workshop participants included FEMA

management, coastal engineering and scientific experts, floodplain management

professionals, and study contractors. FEMA guidance at the first workshop encouraged a

regional approach, in recognition that “one shoe seldom fits all” and that somewhat

different methodologies are frequently required to properly account for regional

differences. The second workshop concentrated on reviewing “Focused Study” plans

developed by Technical Working Groups, including the Tsunami Focused Study.

Tsunamis generated by seismic or other sources near or far from a site of interest

are termed, from the point of view of that site, near-field (or local) and far-field (or

distant) tsunamis, respectively. The Tsunami Focused Study (Tsunami Study Group,

2004) identified two general types of sources as the most common generators of

destructive tsunamis: earthquakes, which might be local or distant from the area of

interest; and slides, which might be coseismic or aseismic, subaerial or subaqueous.

Earthquake sources generally produce a zone of destructive tsunami energy over a

larger geographic scale than slide sources. Differences in the relative importance of local

and distant earthquake sources serve to identify five distinct Pacific Tsunami Regimes:
8

A. Southern and Central California. Local offshore fault systems; distant

subduction zones.

B. Cascadia (Northern California to Northern Washington and Straits of Juan de

Fuca). Local Cascadia Subduction Zone; distant subduction zones.

C. Puget Sound. Local Seattle, Tacoma, and other fault systems.

D. Alaska. Local Alaska-Aleutian Subduction Zone.

E. Hawaii. Distant subduction zones.

Slide sources in all regions can also generate tsunamis that produce destructive

zones, but on a smaller geographical scale, with variations in the type and potential

threat. Upon review and discussion by workshop participants of the Tsunami Focused

Study plan, the following recommendation was made:

“The recommended approach is to perform a comprehensive probabilistic tsunami

hazard assessment at a pilot site in California or Oregon or Washington [that

includes]: (1) recurrence interval estimate[s] of forcing functions and (2)

propagation of tsunamis from Pacific Seismic Subduction Zones, (3) inundation

calculations, [and] (4) probability distributions and integration.”

Subsequently, after a site selection study, this interagency project – the Seaside

Tsunami Pilot Study -- was funded by the FEMA Map Modernization Program. The

purpose of the Study was to develop methods and preliminary guidelines for future

tsunami components of FEMA FIRMs. These specific guidelines would apply to coastal

communities along the coast of the Cascadia Tsunami Regime, extending from Cape

Mendocino to the Strait of Juan de Fuca. Existing FEMA Flood Insurance Studies and the

resulting FIRM maps for this region do not include tsunamis as a flooding hazard. During

the 1970s, a Type 16 Flood Insurance Study was carried out for this region by Houston

and Garcia (1978). Their study was based on the assumption that only far-field tsunamis

impacted this region. Furthermore, their computations did not include actual inundation

of the land. Since that study, compelling evidence from earthquake and paleotsunami

research has shown that great earthquakes occur in the Cascadia Subduction Zone and

that these earthquakes generate major tsunamis. These local Cascadia events, although

infrequent, are not rare. Furthermore, they would cause such widespread and severe

devastation that they need to be considered in developing new FIRM maps for this

coastal region.

The site for the Pilot Study includes the communities of Seaside and Gearhart,

Oregon, and the adjacent unincorporated areas. The site was chosen because it is typical

of coastal communities in the region with development on sand spits and other low lying

areas near the ocean and with coastal rivers flowing through the communities. The study

area was also recommended by Oregon Emergency Managers, who need the results of the

Study for tsunami evacuation planning and public education. Further, stakeholders in the

Seaside/Gearhart area are very interested in tsunamis, due in part to the flooding and

damage caused by the 1964 Alaska and other recent tsunamis that struck these

communities, and to the awareness that major Cascadia Subduction Zone tsunamis have

struck this area in the past.
9

This pilot study directly addresses Task Item 16 “Probabilistic Hazard

Assessment for the open and non-open coastlines of the Pacific States” and Task Item 20

“Tsunami structure debris interaction to define hazard zones”, identified in a series of

workshops in 2004 that were held to plan the development of new tsunami hazard

mapping guidelines for FEMA’s National Flood Insurance Program. A methodology was

recommended for a comprehensive probabilistic tsunami hazard assessment for the

Cascadia Region, considering both far-field events and near-field events triggered by

seismic sources. For both types of events, the tsunamis are generated by coseimic

seafloor displacement and submarine landslides. Far-field events are defined as those

generated a long distance away by sea floor displacement during earthquakes, such as the

1964 Alaska and 1960 Chile earthquakes; and near-field events are those generated by

sea floor displacement from Cascadia subduction zone earthquakes . An example of the

latter is the 1992 Cape Mendocino tsunami that was incident on the northern California

coast (González and others, 1995).

FEMA’s policy has been to incorporate tsunami-induced hazard and other stormrelated coastal hazard into one coastal high-hazard zone, which is defined in the Code of

Federal Regulations, Title 44, Part 59.1 as:

Coastal high hazard area means an area of special flood hazard extending from

offshore to the inland limit of a primary frontal dune along an open coast and any

other area subject to high velocity wave action from storms or seismic sources.

During the course of the present study, it became imperative to address not only the

statistical aspects of tsunami generation but also the associated geological, numerical

modeling, regulatory and institutional aspects as well as the available resources in

NOAA, USGS and academic institutions participating in this study.

Previous Methods Used for FIRM Tsunami Maps

For the FEMA Pilot Study, it is helpful to understand the procedures used by

Houston and Garcia (1978) to develop the previous set of FIRM tsunami maps for the

U.S. West Coast. Like the goals of the new Pilot Study, their procedures produced 100-

and 500-year tsunami runup elevations using numerical models and probabilistic

approaches to both the distribution of tsunami sources, in terms of their intensity and

location, and the effects of tides and other background water levels on the elevations. The

purpose of this section is to summarize the assumptions and methodology used by

Houston and Garcia (1978) in order to provide background for the Pilot Study and to

provide a perspective when comparing their results with those generated by the Pilot

Study. Only a few references are given in this section; an extensive bibliography can be

found in the 1978 report.

When Houston and Garcia (1978) did their study for the Federal Insurance

Administration in the Department of Housing and Urban Development, regional tsunami

sources in the Cascadia Subduction Zone had not been identified as the most likely to

dominate the 100- and 500-year tsunami runup elevations along the middle and northern

portions of the West Coast. Local landslides in the Southern California Bight had also not

been identified as important sources for that region. However, Houston and Garcia (1978)

state that important local sources might eventually be found but that such sources are
10

outside the scope of their study. The sources they use are limited to the Alaska-Aleutian

and Peru-Chile subduction zones, justified by the historical record of damaging tsunamis

along the West Coast.

Tsunami Sources

The tsunamis striking the West Coast are assumed by Houston and Garcia (1978)

to be teletsunamis from the Alaska-Aleutian and Peru-Chile subduction zones. Using

observed tsunamis in the source regions, the tsunami intensities i=log2(21/2 Ravg) are first

computed from the average runup height Ravg in meters using the Imamura-Iida intensity

scale as modified by Soloviev (1970). (Runup is strictly defined as the wave height at

maximum inundation. As used in this case, runup is a more general term that also wave

height measurements within the inundation zone.) A least-square fit to the historical data

along the Peru-Chile subduction zone then gives n(i)=0.074e-0.63i as the probability of

occurrence in a given year for a tsunami of intensity i. (The Houston and Garcia (1978)

technical report lacks the minus sign in the exponential term, which is needed since the

probability n(i) should decrease with increasing tsunami intensity.) Since there is much

less historical information on tsunamis occurring in the Alaska-Aleutian subduction zone,

an assumed exponent coefficient -0.71 is used n(i)=0.113e-0.71i , based on large (i = or >

3.5) tsunamis in the Alaska-Aleutian subduction zone and observed coefficients of other

tsunamigenic regions around the Pacific (Soloviev, 1970). The probabilities are assumed

to be uniform along the respective subduction zones.

The Alaska-Aleutian subduction zone is divided into 12 segments and the PeruChile subduction zone into 3 segments. The fine segmentation along the Alaska-Aleutian

subduction zone honors the observation that the heights of tsunamis along the West Coast

are very sensitive to the location of earthquake in the Alaska-Aleutian subduction zone,

whereas this is much less true for the Peru-Chile subduction zone. The coseismic uplift

patterns due to the tsunamigenic earthquakes are assumed to be ellipses oriented parallel

to the subduction zone trench and are centered on the respective segment.

The shapes and sizes of the uplift ellipses are “standardized” because there is

often a disparity between the intensity, observed uplift extent and the tsunami heights that

occur at impact site. Houston and Garcia (1978) discuss this issue using the 1946 and

1957 Aleutian tsunamis, in which the modest 1946 earthquake had a relatively small

uplift area but large tsunami, whereas the great 1957 earthquake had a very large extent

but a much smaller teletsunami. Other issues of tsunami generation and their implication

for tsunami generation are also discussed.

For each segment, seven tsunami intensities in the range i=2-5 (in increments of

0.5) are used. Here, i=2 is considered a lower limit for dangerous tsunamis along the

West Coast; and i=5 is a credible upper limit based on the history of Pacific tsunamis.

The 15 earthquake segments (12 for the Alaska-Aleutian and 3 for the Peru-Chile

subduction zones) then lead to a total of 105 tsunami sources used in the study by

Houston and Garcia (1970), each with its own probability of occurrence.

Trans-Pacific and Nearshore Numerical Models

A linear finite difference model (1/3 deg by 1/3 deg) is used to propagate the

tsunamis from each source across the Pacific to the vicinity (about the depth contour of

500 m) of the West Coast. The details of the trans-Pacific model are given in Houston
11

and Garcia (1974). A finer-scale nearshore finite difference model (2 min. by 2 min.),

driven by tsunami time series at the open boundaries, is then used to estimate runup along

a vertical-wall coast. The nearshore model is based on that of Leendertse (1967) and

includes advective terms and quadratic drag.

The West Coast is divided into four overlapping segments, each with its own

nearshore model applied to a rectangular domain. Variable bathymetry is used out to the

500 m depth contour, beyond which the depth is set to 500 m. Each domain has a normalto-shore width of approximately 1.5 wavelengths of a 30-min tsunami. This width is

chosen so that at least 3 waves of a major trans-Pacific tsunami have a chance to reach

the coast before re-reflection can occur at the open seaward boundary.

While the southern domain extends across half of the Southern California Bight,

only results for the region west of Santa Barbara are reported. However, 100- and 500-

year tsunami maps for the Southern California Bight are given by Houston and Garcia

(1974). Likewise, Garcia and Houston (1975) show analogous maps for Monterey and

San Francisco Bays and Puget Sound.

Verification of the model time series is limited to a comparison at Crescent City

and Avila Beach (Port San Luis), California. There is significant disagreement at

Crescent City, but this station had only a partial tide gage record. The agreement at Avila

Beach (largest tsunami amplitude reported from uninterrupted 1964 tide gage records

along the West Coast) is good and is taken to be justification for the modeling

procedures.

Predicted Tides

To include the effects of the tides on the maximum tsunami runup elevation,

Houston and Garcia (1978) use as tidal input 15-min sampled time series of predicted

tides for stations along the West Coast. Observed NOAA harmonic constants were used

to compute the predicted tides where these were available. Presumably the predicted tides

were either zoned (constant within a coastal section) or interpolated to give the coastal

tides at the nearshore model grid points. The tidal time series are for the year 1964,

during which nodal factors modifying tidal heights are at or near their average values

during the 18.6-year nodal cycle. Clearly, these are also convenient series to use when

discussing the 1964 Alaska tsunami.

Computing the 100- and 500-Year Tsunami Runup Heights

For each of the 105 tsunami time series at each coastal grid point of the nearshore

model, a 24-hour tsunami series is prepared by adding a sinusoidal series (with an

amplitude equal to 40% of the maximum height of the first model waves) to the 2 hours

of directly modeled series representing the first waves of the tsunami. The factor of 0.4

was determined from observed tsunamis along the West Coast that are observed to decay

slowly in time. Adding a given tsunami time series sequentially to the predicted tide,

stepping every 15 min, and then computing the maximum height of the combined

tsunami and tide leads to a year-long series of maximum runup heights. The largest of

these is selected to give the tsunami runup elevation for that coastal grid point and that

tsunami source location and intensity. This is under the assumption that the linear sum of

the tsunami and tidal time series adequately represents the actual water levels for that

tsunami impacting the coast as the tides vary in time.
12

The 100- and 500-year tsunami runup heights are computed numerically by

Houston and Garcia (1978) from the maximum runup heights and probabilities associated

with the corresponding source segment and tsunami intensity. For a given coastal grid

location, the probabilities are summed over decreasing heights starting with the

maximum one for that location. When the sum reaches 1/500, this is the 500-year runup

height for that coastal location. The summing of the individual probabilities downward in

height then continues until the summed probability reaches 1/100, yielding the 100-year

runup height. Once this is done for all the coastal grid points along the West Coast,

smooth curves are drawn through these to give the alongshore distributions of the 100-

and 500-year runup heights that are shown in the Plates 1-30 of the technical report.

Application to Seaside, Oregon

Shown in Table 2 are the 100- and 500-year tsunami runup heights computed

from Plate 26 of Houston and Garcia (1978) for Seaside, Oregon, which is the site

location for the FEMA FIRM Pilot Study. The heights are for the open coast at lat. 46

00.0N, which passes through Seaside. The original heights are in feet relative to mean sea

level (MSL). For the purposes of the Pilot Study and for other applications, Table 2 also

contains heights relative to other tidal datums on the open coast.

Table 2. 100- and 500-year tsunami runup heights at Seaside, Oregon, (latitude of 46°

00.0’N) relative to various tidal datums. The heights are computed from the Houston and

Garcia (1978) results relative to mean sea level (MSL), using observed tidal datums

interpolated in latitude between Hammond (46° 12.1’N, 123° 56.7’W) and Garibaldi (45°

33.3’N, 123° 45.7’W).

Datum (m)* 100-Year

Runup (m)

500-Year

Runup (m)

MHHW 2.5 2.1 4.7

MHW 2.3 2.3 4.9

MTL 1.4 3.2 5.9

MSL 1.3 3.2 5.9

MLW 0.4 4.2 6.9

MLLW 0.0 4.6 7.3

* 1983-2001 Tidal Epoch

Mean high water (MHW) was used as the background water level for all

inundation modeling performed in the current study. A check on the consequences of

fixing the background water level at MHW was made by performing a statistical analysis

based on linearly superimposed tsunami wavetrains by predicted tides at Seaside. The

tsunami wavetrains are assumed to decay exponentially in time with an e-folding decay

coefficient of 2.0 days, consistent with observed Pacific teletsunamis (Mofjeld and

others, 2000; van Dorn, 1984). Assuming a linear superposition may be regarded as a

first step toward a fuller analysis that includes the dynamical interaction between

tsunamis and the tides. The details of the linear analysis are given by Mofjeld and others

(in press). It is consistent with the way the Seaside tides are included in the estimation of

the 0.01 and 0.002 probability wave heights in this present study (see Appendix E).

Briefly summarizing the results of the Mofjeld and others (in press) analysis, the

probability distribution functions (pdfs) of maximum wave height for small tsunamis
13

(<0.5 m amplitudes) are tightly concentrated around the sum of the tsunami amplitude

and mean higher high water (MHHW). Hence, using MHW as the background water

level in modeling these small-amplitude tsunamis introduces a slight downward bias

relative to the mean height of the pdfs. At Seaside, this bias amounts to MHHWMHW=0.23 m. As the amplitude of the incident tsunami increases, the pdf changes both

in mean maximum height and vertical spread. The total mean is the sum of the tsunami

amplitude at each location and an effective height. The latter decreases from MHHW to

mean sea level (MSL) with increasing amplitude. For very large tsunami amplitudes, the

largest tsunami wave simply selects the stage of the tide at the time of the wave crest.

Statistically, the pdf is then that for the probability distribution of the tide itself.

For the 0.01 and 0.002-probability wave heights shown in Figures 26 and 28, the

effect of not allowing the tides to vary in the tsunami modeling gives an upward bias

(Table 3) of 0.2-0.7 m (7-10%) based on the linear analysis. The bias will be less at other

locations where the tsunami amplitude is less, so the values in Table 3 are estimates of

the maximum bias.

There is a need for future research on non-linear tide/tsunami interactions and

their effects on wave heights, inundation and current strength. Research is also needed

on issues of tsunami-caused erosion during the first waves that might alter the access of

tsunamis and tides to estuaries and coastal rivers.

Table 3. Representative maximum tsunami wave heights when the background water

level in the tsunami inundation model was set to mean high water (MHW) and when the

equivalent mean height for the tsunami wavetrain was superimposed linearly on predicted

Seaside tides (1992).

Annual

Probability

Location Background

MHW

Adjustment

for Tides

(m)

Difference

(m)

Difference

(%)

0.01 Coastal 4.0 3.6 0.4 10

Estuarine 3.0 2.8 0.2 8

0.002 Coastal 10.0 9.3 0.7 8

Estuarine 7.0 6.5 0.5 7

Development of GIS Database

An extensive amount of relational spatial data was collected and developed for the

Study. A geographic information system (GIS) was built to organize these data for

analysis (see accompanying Open-File Report on GIS database). The GIS database

consists of data descriptions, preview images, virtual globe (Google Earth©) views,

metadata, and downloadable files (Table 4). Except for data sets strongly tied to the

Study, such as historic inundation lines and existing FEMA Flood Insurance Rate maps,

the GIS serves only data developed in the course of building the tsunami model.
14

Table 4. Summary of GIS database layers.

Category Dataset

Digital Elevation Model

Development

Coastal Tide Stations

Modeling Grid Limits

Historic Shorelines

Vertical Control Data

Seaside Digital Elevation Model

Historical Tsunami Events

Alaska 1964 Event Deposits, Observations, and Inundation

Cascadia 1700 Event Deposits and Inundation

Photographs of Field Sites

Tsunami Propagation and

Inundation Modeling

Far- and Near-Field Earthquake Sources

Maximum Tsunami Velocity Zones Based on Far- and Near-Field

Sources

Coseismic Vertical Displacement Fields for Near-Field Sources

Maximum Wave Heights Based on Far- and Near-Field Sources

Probabilistic Tsunami Hazard

Assessment Model

Probability Surfaces for Maximum Wave Heights of 0.5 to 10.5 m

Maximum Tsunami Wave Heights for 100- and 500-year Floods

The majority of the data were built using ESRI ArcGIS© software products. All

were georeferenced to the following parameters:

Coordinate system: Geographic decimal degrees or Universal Transverse

Mercator Zone 10 where indicated

Vertical units: Meters

Horizontal datum: North American Datum of 1983

Vertical datum: Mean High Water

The horizontal datum was chosen based on the latest accepted geodetic

references. Mean high water (MHW) was used as the constant background water level for

the tsunami inundation modeling, and this is the reference datum for the tsunami heights

in this report. Current FEMA FIRM maps are based on the horizontal and vertical datums

of the North American Datum of 1927 and the National Geodetic Vertical Datum of

1929, respectively. Mofjeld and others (2004) provides more information on the

determination of vertical datum values for Seaside (Appendix A).

Digital Elevation Model

An accurate DEM is critical to accurate model results. The DEM for inundation

modeling should consist of the best available elevation data at a resolution of 50 m or less

(González and others, 2005). Elevation data available from Federal, State, and local

agencies often consist of disparate horizontal and vertical datums that must be rectified

through accepted conversion methods (see Appendix A).
15

Three nested digital elevation models (DEMs) were created for the MOST model

to simulate tsunami generation at the offshore source, wave propagation nearshore, and

inundation in the region of interest (Figure 1, Table 5). The source and propagation

DEMs consist solely of bathymetric values with land set to a “no data” value. The

inundation DEM consists of both bathymetric and topographic values.

Table 5. DEM summary.

Region Resolution SW/NE Corner Extents

Pacific Northwest

(bathymetry only)

36 arc-seconds ( ~1 km) SW: -132.00, 43.00; NE: -122.00, 53.00

Washington-Oregon Border

(bathymetry only)

6 arc-seconds (~180 m) SW: -124.5, 45.36; NE: -123.5, 47.36

Seaside 1/3 arc-seconds (~10 m) SW: -124.04, 45.90; NE: -123.89, 46.08

These DEMs were developed using a standard four-step process:

1. Data Collection

2. Data Assessment

3. DEM Development

4. Quality Assessment

The best available bathymetric, topographic, orthophotographic, and control data

were obtained from various government agencies and converted to modeler parameters.

Datasets were analyzed for accuracy and consistency. The best available data were used

to build the DEMs.

The inundation DEM was compared to fifteen vertical control points to yield a

RMS error 0.135 m. Detailed procedures, methodologies, and quality assurance analyses

are available in Venturato (2005) (Appendix B).

Significant shoreline differences were discovered when comparing the inundation

DEM with historical shorelines. Coastlines extracted from regional historical aerial

photography obtained from the University of Oregon show a general trend of accretion

averaging 3.2 m/yr on the outer coast north of the Necanicum River mouth (Figure 2)

(Appendix B).

A cyclic pattern of erosion and accretion within the Necanicum River mouth

along its northern and southern Mean High Water extents is apparent (Figure 3). The

northern extent generally shows an accretion rate of approximately 7 m/yr since 1939.

The southern extent varies between accretion and erosion over an estimated 15-year

cycle. The river mouth cycled from a minimum width of 300 m to a maximum width of

800 m over the 65-year period (Appendix B).

The final DEMs were distributed in an ASCII raster format to the modeler. The

modeler converted the DEMs to a format compatible with the model, clipped the DEM to

cover the inundation area, and applied an algorithm to smooth the bathymetry using a

predetermined steepness threshold (refer to the Propagation and Inundation Modeling

Section).
16

Historical Tsunami Event Data

Tsunami deposits, observations, and inundation lines were collected to compare

with model results. Deposits collected in the field (see Tsunami Deposits section) were

converted to GIS files for comparison with model results. Estimated inundation lines

were subsequently created for the 1964 Gulf of Alaska and the 1700 Cascadia Subduction

Zone events. Summaries of observations and historic shoreline are discussed below.

Observations

Over 70 observations at Seaside of the 1964 Gulf of Alaska event were added to

the GIS database for comparison (Fiedorowicz, 1997). (See also Appendix C.) These

observations include estimated runup/wave height values and type (Figure 4).

Shoreline

The apparent Mean Lower Low Water line was digitized (Figure 5) from

orthophotos nearest in time to significant historical tsunami events (1946 East Aleutian

Islands, AK, 7.3 Ms; 1960 Central Chile, 8.5 Ms; and 1964 Gulf of Alaska, 8.5 Ms). The

Necanicum River mouth migrates northward from 1946 to 1964 and then southward from

1964 to 2000. The dynamic nature of the shoreline in this region could vary tsunami

inundation patterns over time.

Model Output

Model runs from the Model Database (see Propagation and Inundation Modeling

section) were converted to GIS-compatible formats and added to the GIS database.

Model setup of the inundation grid introduced a rounding error (RMS error 0.000901 m),

which is reflected in the model runs. Additional error (total RMS error 0.001267 m) was

created during the conversion of the model runs to GIS. This error is considered

insignificant in this study. Probabilistic tsunami wave height data were derived as

described in the “Probabilistic Method” part of this report.

Tsunami Deposits

Tsunamis flooding Seaside have left behind distinctive sheets of sand (hereafter

referred to as tsunami deposits) that can be interpreted to reconstruct the history of

tsunamis. These tsunami deposits are similar in appearance to those found by other

researchers studying tsunamis along the Cascadia margin (Peters and others, 2003). The

spatial distribution of tsunami deposits in Seaside is hard evidence of tsunamis that

establishes minimum inundation areas (Jaffe and Gelfenbaum, 2002). Tsunami deposit

age, combined with this spatial distribution, can be used to estimate the relative

magnitude of near-field and far-field tsunamis and to determine the frequency and

magnitude of tsunami inundation.

The value of incorporating a tsunami deposit component in probabilistic tsunami

hazard assessments is underscored by the fact that the only record of tsunamis generated

by earthquakes on the Cascadia Subduction Zone impacting Seaside are from tsunami

deposits. Tsunami deposits verify the ability of the Cascadia Subduction Zone to

generate large tsunamis that impacted Seaside in the past. Tsunami deposits define the
17

lower limit of the inland extent of inundation. In this study, we have not interpolated

between these point measurements, although in some locations it would be justified

because they are close together. The spatial distribution and ages of tsunami deposits

were used in this study for validation of the hydrodynamic model runs. The focus of

tsunami deposit validation for modeling was on two events—the near-field 1700

Cascadia tsunami and the far-field 1964 Alaska tsunami.

Tsunami deposits also are tangible evidence of tsunamis in Seaside that the public

is able to relate to. Locations of known tsunami deposits overlain on an inundation map,

or, for this study, on a map showing the100 and 500 year tsunami flooding lines, validate

study results for the public, emergency planners, and managers. Digging a hole and

seeing a tsunami deposit makes believers out of even the most skeptical—a tsunami

flooded this location.

This study benefited from extensive research on tsunami deposits in Seaside

conducted by Curt Peterson and his students in the 1990s (Darienzo and Peterson, 1995;

Darienzo and others, 1994; Fiedorowicz, 1997; Fiedorowicz and Peterson, 2002;

Peterson, 1993). It is possible to generate a tsunami deposit record for use in a

probabilistic tsunami hazard assessment without previous studies. When the geometry of

the site is simple, there is less need for preexisting tsunami deposit data. In the case of

Seaside, which has a complex geometry (two shore-parallel rivers bounded by high beach

ridges), without preexisting data the tsunami deposit component of this study would

require additional effort and would not have produced as complete a record of past

tsunamis.

Methods

The methods used in the tsunami deposit component of this study are standard for

the geologic tsunami research community. These methods can be transported to other

sites where FEMA requires a probabilistic tsunami hazard study. It is essential that

scientists performing a tsunami deposit study have experience identifying tsunami

deposits and knowledge of geologic environments where tsunami deposits are likely to be

formed and preserved. Without such experience and knowledge, the quality of a tsunami

deposit study is compromised and the results of such a study are of limited usefulness.

We used a combination of preexisting sedimentary data and new sedimentary data

collected specifically for this study to map out the distribution of past tsunami inundation

in the Seaside area. We examined and re-evaluated logs of cores, trenches, and cut banks

from previous work done in Seaside during 1996 by Brooke Fiedorowicz and Curt

Peterson (Fiedorowicz, 1997; Fiedorowicz and Peterson, 2002). The existing data set,

collected at 236 sites in the 1990s, was supplemented by additional fieldwork to increase

the data density, provide data where existing data was not available or clear, and to

extend the boundaries of the survey farther inland and farther to the south of Seaside.

Cores, trenches, and cutbacks were examined and logged from 76 locations in the Seaside

area during the summer and fall of 2004 (Figure 6). Overall, data from 312 locations

were included in the survey (Figure 7).

In addition to sedimentary data, we used a database that contains 66 observations

by Seaside, Oregon residents of inundation, run-up, and water levels from the 1964

tsunami in Seaside, recorded by Tom Horning, and included in the master’s thesis of
18

Brooke Fiedorowicz (1997). Other locations of possible tsunami sand layers and tsunami

mud layers based on these eyewitness observations were also noted and categorized as

locations where the 1964 tsunami was known to have inundated. We included an

additional five observations of areas with no sedimentary deposits that are presumed to

have not been inundated by the 1964 tsunami.

The ages of the deposits were determined using a combination of radiocarbon

dating, stratigraphic context and, for 1964 tsunami deposits, historical documentation.

Correlations between deposits were based on stratigraphic context and lateral continuity

between deposits. Tsunami deposits stratigraphically below the 1700 event were

deposited by earlier tsunamis.

Results

Deposits from five tsunamis in the past 2000 years were found at 167 sites located

as far as 2 km inland along the 5-km stretch of coast at Seaside. Deposits were found

primarily in marshes fringing the Necanicum River and Neawanna Creek, which flow

parallel to the coast between 5-10 m high beach ridges.

Tsunami deposits in the Seaside area usually occur as anomalous sand layers

within mud or peat layers (Figure 8). The following additional criteria were established

for the Seaside area to determine whether a sand layer had a tsunami origin: normal

grading, presence of organic detritus, particularly as a detrital cap; a noticeable decrease

in the amount of peat from the underlying material to the overlying material; lateral

continuity; presence of rip-up clasts; presence of sand/mud couplets; and historical

documentation. With the exception of historical documentation, no single criterion is

wholly diagnostic. A combination of stratigraphic context and lateral context, combined

with one or more of the identifying criteria were used to assign a tsunami origin to a

particular deposit. See Peters and others (2003) for a discussion of tsunami identification

criteria.

We focused on defining tsunami inundation from tsunami deposits for the 1964

far-field and 1700 near-field tsunamis to develop a dataset for validating hydrodynamic

models.

1964 Tsunami Deposits

Tsunami deposits from the 1964 tsunami were identified at 116 sites (Figure 9;

Appendix D). Tsunami deposits from the 1964 Alaskan tsunami were typically found

within a few tens of centimeters of the surface. In contrast, tsunami deposits from the

1700 Cascadia were typically covered by more than 0.5 m of sediment. The stratigraphic

features of the 1964 tsunami deposits are very different than those of a storm deposit

(Morton and others, in press), allowing discrimination between the two types of deposits.

Inundation during the 1964 tsunami was primarily up channels. Tsunami deposits were

limited to the banks of channels, primarily the Necanicum River, Neawanna Creek, and

Neacoxie Creek. Along Neacoxie Creek, deposits were found as far upstream as the G

Street Bridge in Gearhart (Figure 4c). Residents observed tsunami inundation over the

bridge and beyond it. There was a log jam at the bridge and the water backed up behind

the bridge. In Seaside, deposits from the 1964 tsunami are found along Neawanna Creek
19

as far south as 16th Avenue. Residents observed the tsunami as far south as the 12th

Avenue Bridge. 1964 tsunami deposits on the Necanicum River are found as far south as

Avenue Q. Eyewitness observations indicate that the 1964 tsunami inundation reached

the golf course south of Avenue U (Figure 4c).

Tsunami deposits were found primarily within the inundation line determined

from eye-witness reports (Appendix C, Figures 4 and 9). The distribution of 1964

tsunami deposits was a close approximation of the area of inundation along the

Necanicum River determined from historical observations, but significantly

underestimated the area of inundation along Neawanna Creek.

1700 Tsunami Deposits

Deposits from the1700 tsunami were present at 119 sites in the Seaside area

(Figure 10; Appendix D). The 1700 tsunami deposit is usually found approximately 0.5 –

1 m below the surface and in many places forms sand sheets that are laterally continuous

for tens to hundreds of meters. The sites available for investigation were limited to those

not developed or otherwise disturbed since 1700. Long stretches of the banks of the

Necanicum River and Neawanna Creek have been covered with fill so that residential or

commercial structures could be built or to create pasture land. Armoring of the banks

also made many areas possibly inundated by the 1700 tsunami inaccessible for coring or

trenching.

Deposits from the 1700 tsunami east of Neawanna Creek were found at Stanley

Lake and along Shore Terrace Road as far east as the trailer park. Fiedorowicz (1997)

reconstructed the geography present during the 1700 tsunami by interpreting geologic

deposits. The tsunami probably entered Stanley Lake through the outlet at the north end

of the lake. The deposits along Shore Terrace Road are best explained by the tsunami

overtopping the dune ridge that separates Neawanna Creek from the lowlands to the east.

Deposits from the 1700 tsunami were found along the banks of the Necanicum River as

far south as Avenue U and along the banks of Neawanna Creek in the reaches south of

the millponds. In Gearhart, deposits from the 1700 tsunami were also found along the

banks of the small creek east of the present Neacoxie Creek, but no deposits from the

1700 tsunami were found along Neacoxie Creek. Geological evidence suggests that

Neacoxie Creek is younger than 1700 (Fiedorowicz and Peterson, 2002).

The 1700 tsunami may have overtopped the narrow gravel ridge between the

Necanicum River and Neawanna Creek north of Avenue P. It may also have overtopped

the considerably wider gravel ridge complex at 4th Avenue.

Older Tsunami Deposits

Deposits from tsunamis older than the 1700 tsunami are also found in the Seaside

area (Figure 11). Deposits from tsunamis older than the 1700 event were found at 36

sites. Of particular interest are deposits dated at 1230±30 years B.P. and 2770 years B.P.

that are located south of the bend in the Necanicum River south of Seaside. These

radiocarbon dates are based on spruce cones found within the deposits. This area may

have been a paleo-outlet for the Necanicum River. Deposits from events older than the

1700 event are also found along the banks of the southern portions of Neawanna Creek.
20

In the vicinity of Avenue P, north of the Avenue S Bridge, a deposit from a tsunami that

occurred prior to 1700 can be seen at low tide along the cut banks of Neawanna Creek.

This deposit lies stratigraphically below the 1700 deposit. Deposits from tsunamis older

than 1700 are also found in cores from the Stanley Lake region.

Paleo-tidal Inlet

The distribution of the 1700 tsunami deposits and morphological features

prompted us to hypothesize that the inlet was located further south than its present

location when the 1700 tsunami impacted Seaside. A preliminary investigation using

Ground Penetrating Radar (GPR) found a sediment-filled valley between the Necanicum

River and the Pacific Ocean that could have been formed by an inlet approximately 1 to

1.5 km south of its present location.

North-south GPR lines collected along Downing Street and the full length of

Front St. (north-south parallel to Necanicum River) detected the banks and the bottom of

the paleoinlet. Curt Peterson and David Percy (Portland State University) ran additional

north-south GPR lines to confirm location of the paleo-inlet and west-east lines to check

for channel fill versus beach progradation strata. At the southern portion of the sediment

fill, north-dipping reflectors indicate a northward migration of the paleo-inlet. Change

from northward-dipping reflectors to flat or landward-dipping reflectors occur at

approximately 50 m south of A Street, marking the southern extent of the paleo-inlet.

Paleo-tidal inlet depth was a maximum of 5 m below mean sea level. The inlet fill is

approximately 1.3 km wide; the size of the paleo-inlet was less because the fill is created

by migration or narrowing of the inlet. For comparison, the widest portion of the present

inlet is approximately 0.7 km wide. The north side of the paleo-tidal inlet begins just

south of 15th Street in Seaside (on Franklin S-N extension of Downing Street about 3

blocks south of the waste water treatment plant).

Reflectors imaged by GPR constrain the history of the inlet. At 1st Street, a

progradational beach facies (seaward dipping reflectors) starts 50m east of the present

landward extent of the beach (the boardwalk) indicating that the closing off of the inlet

and building out of the beach there is relatively recent. Moderately-deep reflectors

dipped south (towards the paleo-inlet channel) at the northern end of the inlet fill

confirming that the channel 'jumped' to its present position (north of the waste-water

treatment plant) rather than by a gradual migration north, which would have left north

dipping reflectors. The jump may have occurred immediately after the catastrophic

flooding by the 1700AD tsunami event.

Discussion of Tsunami Deposit Results

The tsunami deposit record for Seaside establishes that near-field tsunamis

generated by great Cascadia Subduction Zone earthquakes are significantly larger than

the 1964 far-field Alaska tsunami. Deposits from the 1700 tsunami are found up to 2 km

inland near the base of the hills on the east side of town (Figure 10). The spatial

distribution and characteristics of 1700 tsunami deposits indicates that the 1700 tsunami

overtopped the ridge east of Neawanna Creek— the 1700 tsunami was large even this

great distance inland. Geological and archeological evidence indicates that this and other
21

high gravel ridges have been present in Seaside for many centuries (Fiedorowicz, 1997).

In contrast to the extensive spatial distribution of 1700 tsunami deposits, deposits from

the 1964 tsunami are confined to the margins of Neawanna Creek and the Necanicum

River—indicating a smaller tsunami that was not able to overtop the high gravel ridges at

Seaside. Geological and archeological evidence indicates that this and other high gravel

ridges have been present in Seaside for many centuries (Fiedorowicz, 1997). In contrast

to the extensive spatial distribution of 1700 tsunami deposits, deposits from the 1964

tsunami are confined to the margins of Neawanna Creek and the Necanicum

River—indicating a smaller tsunami that was not able to overtop the high gravel ridges at

Seaside. The presence of tsunami deposits older than 1700 far inland is evidence that the

1700 tsunami is not an outlier in terms of size. The Seaside area has been inundated by

large tsunamis many times in the past.

The inundation zones derived from the tsunami deposit data in this report are

minimums because of limitations inherent in deriving inundation from tsunami deposits

and limitations in the scope of this study. Where there is a suitable environment for

deposition and preservation of tsunami deposits, data from modern tsunamis

(Gelfenbaum and Jaffe, 2003; Jaffe and others, 2003) indicate that the inland extent of

tsunami deposits and of flooding are usually within 50 m—using tsunami deposits as

proxy for limit of inundation does not introduce significant error. However, inundation

extent is underestimated if a deposit never formed because there was not a source of

sediment. A larger source of error in mapping inundation using only tsunami deposits,

especially for tsunamis that occurred hundreds or thousands of years ago, is erosion of

tsunami deposits. Preservation potential must be carefully evaluated in a probabilistic

analysis of inundation and used as a filter for evaluating tsunami deposit data. Although

a large number of sites (312) have been examined in Seaside for tsunami deposits, it is

probable that further investigations will increase the estimate of the area of inundation,

especially for older tsunamis.

Changes in topography or bathymetry need to be accounted for in using tsunami

deposits to estimate the magnitude of past tsunamis. For Seaside, inlet location is a

primary control on tsunami inundation. Deposits from the 1964 tsunami extended

farthest inland at the inlet, indicating that it served as a conduit for the tsunami.

Preliminary investigations using Ground Penetrating Radar (GPR) found a sedimentfilled valley that could have been formed by an inlet to the south of its present location.

If this inlet was open when the 1700 tsunami impacted Seaside, tsunami deposits could

be expected to extend farther directly inland of the inlet. Additional studies are needed to

determine the time when it was open to the sea.

Shoreline stability must also be taken into account when using tsunami deposits in

a probabilistic tsunami hazard study. Change in shoreline position was observed but not

accounted for in the Pilot Study and does not introduce large errors into the analysis of

the 1700 and 1964 tsunamis, but could for older tsunamis. Estimates of inundation from

tsunami deposits at a site where there is an eroding (prograding) shoreline underestimates

(overestimates) tsunami inundation. For tsunami deposits to be most useful for validation

of hydrodynamic models, paleoshorelines, paleotopography, and paleobathymetry should

be established.

Because of its geologic setting, complex topography, and inlet migration history,

Seaside is not a good location to develop tsunami recurrence intervals. Sites with simple
22

topography and a coastal geologic setting that favors deposition and preservation of

tsunami deposits are best used for developing tsunami recurrence intervals. Tsunami

recurrence intervals have been established for Cannon Beach (Peterson and others, 2004),

which is 13 km south of Seaside. If a site-specific tsunami recurrence interval based on

deposits were required for this study, it could have been developed using a combination

of the Cannon Beach and Seaside tsunami deposit records. Use of tsunami deposits

records from nearby locations is acceptable for developing tsunami recurrence intervals

in a probabilistic tsunami hazard study.

Even with the complexities encountered in the study of tsunami deposits at

Seaside, we were able to develop a robust tsunami record using standard geologic

tsunami research methods. This record established minimum inundation zones from past

tsunamis and was the only data available for validation of near-field tsunamis generated

during Cascadia Subduction Zone earthquakes. An additional benefit of a tsunami

deposit component to this study is that tsunami deposits were useful as an educational

tool for the general public, emergency planners, and managers.

Probabilistic Method

PTHA Overview

Previous PTHA Studies

Probabilistic tsunami hazard analysis (PTHA) is derived from and closely allied

to probabilistic seismic hazard analysis (PSHA); the latter, developed originally by

Cornell (1968) and subsequently described in several reports including (Senior Seismic

Hazard Analysis Committee (SSHAC), 1997). Lin and Tung (1982), Rikitake (1988),

and Downes and Stirling (2001) modified PSHA to develop a PTHA that calculates wave

heights using a simple source specification. In a related effort, a recent Puerto Rico Sea

Grant report (Natural Disaster Research, 2001) used both traditional cumulative runupfrequency statistics and rank-order statistics (Sornette and others, 1996) derived from

hydrodynamic modeling for calculating wave heights at Aguadilla, Puerto Rico.

Included in this Sea Grant report were 1% annual probabilities of exceedance (i.e.,

according to the FIRM specification) as well as a 10% probability of exceedance in 50

years specification.

Geist and Parsons (2005) recently expanded these efforts by comparing empirical

analysis of tsunami probabilities with computational PTHA. For a site such as Seaside

that lacks an extensive historic record of tsunamis, computational PTHA provides a

valuable tool for assessing tsunami risk. For the Cascadia region, Geist and Parsons

(2005) compare end-member models of earthquake magnitude distributions:

characteristic and Gutenberg-Richter. The latter involves a Monte Carlo simulation

where hypocentral location and slip distribution is randomized in the process of building

a tsunami hazard curve (tsunami amplitude vs. probability). They also compare an

empirical estimate of far-field probabilities with the computational PTHA estimates for

local tsunamis. For this study, tsunamis from a characteristic M~9 Cascadia earthquake

are as part of the PTHA for the Seaside pilot study.
23

The PTHA methods described in these previous studies are expanded for the

Seaside tsunami pilot study to develop, for the first time, a probabilistic tsunami

inundation map. In this case, rather than calculating a hazard curve for a point on the

coastline, a high-resolution grid is developed for the region around Seaside (see

Development of GIS Database Section) and a hazard curve is computed for each grid

cell. For each grid cell hazard curve, the exceedance wave heights for the 1% and 0.2%

annual probabilities are interpolated and the results mapped using GIS software.

Application of PTHA to FIRM Specifications

The central point in developing the PTHA maps is determing the joint recurrence

rate for different sources of tsunamis. This problem has been previously addressed in the

development of FIRMs near the mouth of a river where there is combined riverine and

coastal flooding as described in a Tetra Tech Inc. report (1981) to FEMA. If riverine

flooding at a given elevation (η) occurs at an average recurrence rate of 1/m and coastal

flooding at η occurs at an average recurrence rate of 1/n, then the joint recurrence rate is

simply 

€

1

q = 1m

+

1

n

. We can think of this in terms of probabilities such that

(1)

€

Priver(η,T) =1− exp − 1

m

T 



 





and

(2)

€

Pcoastal(η,T) =1− exp − 1

n

T 



 



,

where P is the Poisson, time-independent probability of exceedance and T is the exposure

time.

The combined probability from both sources of flooding is given by

(3)

€

P(η,T) =1− (1− Priver)(1− Pcoastal) =1− exp − 1

m

+

1

n





 



T 









.

Thus, the apparent recurrence rate for the combined source is 

€

1

m

+

1

n

. A similar approach

to determine the probability of ground shaking from multiple sources is described by

Ward (1994).

We can adopt the same methodology (Tetra Tech Inc., 1981) to determine the

joint recurrence rate for multiple tsunami sources. Suppose that in a given source region

(with position vector r0), we can determine the rate at which a tsunamigenic source with

source parameter set (ψs) occurs: 

€

n  ̇ (r0,ψs). Typically, in PTHA the objective is to

determine the total rate 

€

N  ̇ (r,ηcrit) at which wave height exceeds a risk tolerance value

(ηcrit) at a specific coastal site (r). In general, this involves a double integral over the
24

parameter space for a given source and over all source locations (for example, Anderson

and Brune, 1999; Ward, 2001):

(4)

€

N  ̇ (r,ηcrit) = n  ̇ r0 ( ,ψs)

ψs

crit (r,r0 )

∫

A

∫ dψsdA r( 0)

For our probabilistic inundation map at Seaside, the source locations will include

far-field tsunamis from major subduction zone segments around the Pacific and local

sources near Seaside. Focusing first on the far-field sources, for a given subduction zone

there will be a range of source locations (for example, a M=8.0 earthquake could happen

anywhere along the subduction zone), tsunamigenic magnitudes, and recurrence intervals.

For a range of tsunamigenic earthquake magnitudes (Mlow < M < Mmax), each magnitude

will be associated with an average recurrence rate according to the Gutenberg-Richter

relationship 

€

log(n  ̇ (M)) = a − bM , where a and b are empirical constants. Mlow is the lower

cutoff magnitude that would produce a significant tsunami at Seaside. Mmax is the

maximum earthquake magnitude that can occur along a particular subduction zone or

subduction zone segment.

Ideally, we would like to produce many inundation maps for a range of

earthquake magnitudes Mlow - Mmax for each subduction zone. We note, however, that if

we just estimate the Mmax -recurrence pair for each subduction zone, this will provide

adequate constraint for high recurrence rate-low wave height tsunamis (that is, P>0.01).

Additional inundation runs for smaller magnitude earthquakes will not significantly

constrain the P=0.01, P=0.002 exceedance wave heights. The possible exception are

smaller earthquakes M<Mmax in the Prince William Sound segment of the AleutianAlaska subduction zone that are optimally oriented in terms of wave focusing at Seaside.

It is possible that these earthquakes can produce larger tsunamis then Mmax earthquakes

along other subduction zones. Mmax - recurrence pairs are specified for adjacent regions

along all subduction zones considered. For each earthquake source specification, an

inundation map is produced. The production of inundation maps for these far-field

sources is facilitated by the fact that NOAA/PMEL has pre-computed the open-ocean

tsunami wavefield for these sources as part of the FACTS database. Uncertainty in

recurrence rates and Mmax is discussed in the Results Section.

Once a set of far-field and local inundation maps are prepared, Figure 12

illustrates how we can determine the 1% annual probability inundation line, using GIS

software. To create a map, a hazard curve is calculated for each (x,y) point. As shown

below, the tsunami hazard curve plots cumulative frequency of exceedance (ordinate) as

a function of exceedance wave height (abscissa). The exceedance wave height

incorporates the combined tidal and tsunami wave heights as described in Appendix E.

Aggregating the resuts from all of the inundation runs, at each (x,y) point there would be

discrete values plotted in wave height/recurrence rate space. This is shown graphically in

Figure 12, where a hazard curve is derived from all of the inundation runs at each (x,y)

point. A regression analysis is run to fit a straight or other parametric line to determine

each of these hazard curves. The wave height corresponding to the 0.01 yr-1 recurrence

rate is determined from the hazard curve at each (x,y) point and contoured over x-y space

to produce a probabilistic exceedance wave-height map. A similar procedure is used to

also produce the 0.002 yr-1 exceedance wave-height map.
25

PTHA can also accommodate a comprehensive treatment of uncertainties in much

the same way as PSHA (Senior Seismic Hazard Analysis Committee (SSHAC), 1997).

For convenience, two types of uncertainty are often considered: aleatory and epistemic.

In simple terms, aleatory uncertainty is often associated with the natural complexity of

the physical process itself, whereas epistemic uncertainty is associated with incomplete

knowledge about the physical process that can be lessened through the collection of

additional data. Aleatory uncertainty is sometimes called external, objective, random, or

stochastic uncertainty, whereas epistemic uncertainty is sometimes called internal,

subjective, or functional uncertainty (National Research Council (NRC), 2000). A

comprehensive treatment of uncertainty requires scientific guidance from a broad range

of scientific experts. In the past, this has been performed through a Technical Integrator

or Technical Integrator/Facilitator (Senior Seismic Hazard Analysis Committee

(SSHAC), 1997) process that determines the “legitimate range of technically supportable

interpretations” and “the relative importance or credibility that should be given to the

differing hypotheses across that range”.

For this preliminary study, however, we focus on including those uncertainties

that are readily quantifiable. These are primarily aleatory uncertainty of tsunami arrival

time relative to the tidal stage (Appendix E) and the slip distribution of the earthquake.

The only epistemic uncertainty we consider is the two earthquake models for the

Aleutian-Alaska subduction zone set forth by Wesson and others (1999). We compare

the probabilistic results using different rupture models and estimates of recurrence rates

for a local Cascadia earthquake, but do not explicitly include this uncertainty in the

probabilistic calculations. Other possible sources of epistemic uncertainty are indicated

in the Discussion of this report, but not included in this study. To include these sources

of uncertainty would require the assignment of relative weights in a logic-tree approach

and hence, the consensus among a wide range of scientific experts. It is recommended,

that the level of uncertainty analysis be related to the specific objectives of the

probabilistic study as described in Section 3: Structuring and Implementing a PSHA of

the SSHAC (1997) report.

Source Specification

Source Magnitude and Geometry

Typical Interplate Thrust Earthquakes

Earthquake source parameters used in this study include primarily the largest

earthquakes (that is, M=Mmax) along major north Pacific subduction zones and the

southern Chile subduction zone, site of the 1960 M=9.5 earthquake and trans-oceanic

tsunami. The rationale for choosing the largest earthquakes is that, even though the

recurrence rate for each event is low, when combined these earthquakes (along with local

Cascadia earthquakes) should be sufficient to specify the tsunami at Seaside from any

source with an average return time of 100 years and 500 years. Inclusion of smaller

magnitude and more frequent earthquakes will likely not add significantly more

information with which to constrain the 100-year exceedance wave heights. The

exception is inclusion of smaller earthquakes in the Prince William Sound segment of the
26

Aleutian-Alaska subduction zone, that can result in larger runups than M=Mmax

earthquakes in other subduction zones. In using this set of source parameters, it will

appear that we are adopting a characteristic earthquake distribution model (Appendix F).

This may be the case, for example, in the 1964 Alaska source region. In most cases,

however, we are choosing an earthquake magnitude that is at the extreme tail of a

continuous distribution of earthquake magnitudes (that is, that of a modified GutenbergRichter distribution, see Appendix F).

The primary source parameters that are provided are magnitude, width, length,

and slip. Because we are relying on pre-computed solutions from NOAA/PMEL’s

FACTS database for the far-field sources, it is assumed that other source parameters such

as depth, dip, strike, and slip direction have already been determined from the best

available sources. Except for local events, it will be assumed that variations in slip

distribution patterns from event to event will have minimal effect on far-field tsunami

amplitudes (see, Titov and others, 1999). Wherever possible, published references will be

provided for estimates of these source parameters. It should be noted, however, that for

the objectives of the probabilistic study, these source parameters will not be based strictly

on historic events but on events that are thought to be representative of the M=Mmax

earthquake for each subduction zone. Average earthquake return times may be cited for

different cases, though it is important to note that these are estimates subject to epistemic

uncertainty. The Alaska-Aleutian, Kamchatka, Kuril, and southern Chile subduction

zones are discussed in the sections below.

Table 6 below summarizes the earthquake source parameters and recurrence rates

used in this study. Horizontal dimensions have been adjusted according to the pre-set

parameters in the FACTS database constrained by the seismic moment relationship.

Details of each source region are given in the next section.
27

Table 6. Source specification for earthquakes used in this study.

Source

Numb

er

Location Mmx Lengt

h

(km)

Widt

h

(km)

Slip

(m)

Mean Inter-event

Time (yr.)

FACTS Specification

1 AlaskaAleutian9.2 1000 100 17.7 1,313 A0-A9 & B0-B9

(Tsunami model 1-western

zone)

2 AlaskaAleutian9.2 1100 100 18.1 750 A9-A19 & B9-B19

(Tsunami model 1-mid source)

3 AlaskaAleutian9.2 600 100 - 750 Distributed slip:

15m(A20+B20) +

20m(A21+B21) +

25m(A22+B22) + 30m

`(A23+B23+A24+B24)

(Tsunami model 1-eastern

zone)

4 AlaskaAleutian9.2 1200 100 16.3 1,133 A0-A11 & B0-B11

(Tsunami model 2-western

zone)

5 AlaskaAleutian9.2 1200 100 14.8 750 A12-A23 & B12-B23

(Tsunami model 2-western

zone)

6 AlaskaAleutian8.2 300 100 2.1 875 A17-A19 & B17-B19

7 AlaskaAleutian8.2 300 100 2.1 661 A20-A22 & B20-B22

8 AlaskaAleutian8.2 300 100 2.1 661 A23-A25 & B23-B25

9 Kamchatka 8.8 500 100 9.8 100 A1-A5 & B1-B5

10 Kamchatka 8.8 500 100 9.8 100 A6-A10 & B6-B10

11 Kuril 8.5 300 100 5.8 500 A11-A13 & B11-B13

12 Kuril 8.5 300 100 5.8 500 A14-A16 & B14-B16

13 Kuril 8.5 300 100 5.8 500 A17-A19 & B17-B19

14 Southern

Chile

9.5 1000 100 40.0 300 A35-A45 & B35-B45

15-26 Cascadia 9.1 Var. Var. Var. 520 High-resolution fault model

(Flück and others, 1997)

Far-Field Earthquakes

Alaska-Aleutian Subduction Zone

Because the probabilistic tsunami hazard assessment for the Tsunami Pilot Study

closely follows similar efforts used in the National Seismic Hazard Mapping Program, it

is judicious to take advantage of previous work in determining likely source parameters.

The seismic hazard maps for Alaska are described by Wesson and others (1999). They
28

consider 2 hazard models (I and II) for the Alaska-Aleutian megathrust: Model I consists

of a western and an eastern seismic zone in which earthquakes as large as M=9.2 can

occur. The delineation between the zones occurs approximately at the site of the 1946

Aleutian earthquake. Model II consists of a western Aleutian seismic zone (Zone A) as in

Model I, but a smaller eastern Alaska seismic zone (Zone C) with a western boundary

coincident with the rupture boundary for the 1964 earthquake.

Because the intervening zone (Zone B) that includes the Shumagin seismic gap

(Nishenko, 1991) and the source area for the 1938 earthquake can only accommodate

earthquakes up to magnitude 8.5, there is a saddle (low region) in the expected seismic

hazard near Zone B. For the purposes of far-field tsunamis, Model I is probably more

representative of the long-term zonation for large earthquakes. Near the source region for

the 1964 earthquake, paleoseismic studies indicate that return times of great earthquakes

is roughly 600-800 years (Combellick, 1992; Gilpin and Carver, 1992; Wesson and

others, 1999). Average return times for M=9.2 earthquakes along the Aleutian part of the

subduction zone are probably similar, but more work is needed to constrain these times.

The tsunami models for the largest earthquakes would closely follow the seismic model I

above. The first tsunami model consists of three M=9.2 earthquake the adjacent rupture

areas as shown in Figure 13. One of the M=9.2 ruptures would correspond in location to

the 1964 rupture, whereas the other 2 rupture areas would not correspond to any historic

event. The middle event also spans what some may believe is a tectonic segment

boundary at the tip of the Alaska Peninsula. It is important that the rupture areas for the 3

events do not overlap—this would violate an important seismic moment balance along

the subduction zone.

For the purpose of accounting for radiation pattern changes with along-strike

shifts of the rupture area (see Figure 2, gage 3 in Titov and others, 1999), Tsunami Model

2 is introduced with two M=9.2 earthquakes (Figure 14). The western rupture area

approximately corresponds to the 1957 rupture area, whereas the eastern rupture spans

the 1946, 1938 and the western part of the 1964 rupture area. Tsunami Models 1 and 2

are weighted (50 % each) and combined to form a composite model. The result would be

combined as separate, weighted branches of a logic tree. For each of Models 1 and 2, we

are operating under the hypothesis that large earthquakes (and even small earthquakes,

Tanioka and González, 1998) will rupture across segment boundaries defined by tectonic

and geologic structures.

Finally, to accommodate the possibility that a smaller magnitude earthquake

(M=8.2) in Prince William Sound may cause larger inundation at Seaside than other

earthquakes considered in this initial set of source parameters, a third tsunami model

(Figure 15) is included that consists of 3 adjacent M=8.2 ruptures, similar to the 1938

rupture. The recurrence rates for the M=8.2 ruptures are again derived from Wesson and

others (1999).

For the Alaska-Aleutian subduction zone, 8 inundation runs have been completed:

three for Model 1, two for Model 2, and three for Model 3.

Kamchatka Subduction Zone

Like the Alaska-Aleutian subduction zone, large earthquakes have occurred along

the Kamchatka subduction zone with noticeable frequency. Pinegina and others (2003)
29

recently concluded that Kamchatka has been impacted by large tsunamis at a rate of 1

every 100 years for the past 3,000 years, though not all are from local sources (~10-20%

far field). Of note, the Mw=8.8 1952 (Johnson and Satake, 1999) and the Mt=8.8 1923

(Abe, 1979) earthquakes are probably representative of the largest earthquakes of this

subduction zone. A tsunami model for Kamchatka can be constructed as done for the

Alaska Tsunami Model 1 above, where two adjacent M=8.8 earthquake fill the entire

subduction zone.

Kuril Subduction Zone

Continuing south along the Kuril subduction zone, the maximum magnitude

earthquake is likely to be slightly smaller than for the Kamchatka subduction zone,

primarily because of a change in tectonic regime for the overriding plate. From the

analysis of the 10/13/1963 Kuril Islands earthquake (there was also a tsunami earthquake

in the Kuril Islands on 10/20/1963) which Ward (1982) and Ruff and Kanamori (1983)

placed at Mw=8.5 and recent evidence of multi-segment rupture in the southern part of the

Kuril subduction zone by Nanayama and others (2003), it is reasonable to characterize

this subduction zone with a series of M=8.5 earthquakes. Nanayama and others (2003)

indicates that the average return time for these earthquakes is approximately 500 years.

Approximately three M=8.5 earthquakes would fill the Kuril subduction zone up to the

southern extent of the Kamchatka subduction zone.

Southern Chile Subduction Zone

Earthquakes along the Chilean subduction zone are also considered, primarily

because of the size of the M=9.5-9.6 1960 earthquake (Cifuentes, 1989; Cifuentes and

Silver, 1989) and observations of the associated tsunami along the west coast of North

America. The amount of slip that occurred during the 1960 tsunami is difficult to

ascertain because of the complexity of the event. The geodetic models of both Linde and

Silver (1989) and Barrientos and Ward (1990) result in average amounts of slip that

correspond to significantly lower seismic moment estimates than determined from

seismic waveform data, though still at a M~9.5 level. Average slip estimates vary from

17 m to 20 m, though the variable slip models indicate significantly higher amounts of

slip, as much as 40-50 m, are predicted for the offshore extent of rupture and even small

earthquakes (Barrientos and Ward, 1990; Linde and Silver, 1989). For comparison, Liu

and others (1995) use 24 m of slip in their far-field tsunami model.

Chile has been struck by giant earthquakes and tsunamis in the past, including the

1570s, 1730s, 1837 and the M~9.5 1868 earthquake in northern Chile. These dates are

not representative of return times for M~9.5 earthquakes from purely a moment-balance

perspective (Barrientos and Ward, 1990) and from recent paleoseismologic analysis by

Salgado and others (2003). The latter study suggests an average return time for great

earthquakes in Chile of ~250 years. Even so, if the average slip per event is ~20 m, this

results in a seismic slip rate of 8 cm/yr—close to the relative plate convergence rate of

8.4 cm/yr. Although the southern Chile subduction zone is considered the most highly

coupled subduction zone in the world in terms of seismic efficiency (Scholz, 1990), the

repeat time should not be much smaller, nor the average slip per event be much greater,

than these estimates to satisfy the moment balance.
30

Cascadia Subduction Zone Earthquakes

This part of the pilot study is of particular importance because M~9 earthquakes

along the Cascadia subduction zone will likely produce the worst-case tsunami

inundation and be the defining event for the 500-year tsunami flood standard. We first

discuss how the coseismic displacement field can be calculated for variable slip (Geist,

2002), building on previous inundation mapping efforts (Priest and others, 1997). Not

only does the coseismic displacement field provide the initial conditions for tsunami

propagation, but it will also affect inundation estimates because of coseismic subsidence

of coastal regions. We will then discuss different approaches to incorporating

uncertainties caused by incomplete knowledge of rupture geometry and by different slip

distribution patterns into the probabilistic calculations. Discussion of recurrence rates

and time-dependent probability calculations is presented by Petersen and others (2002).

Specification of Cascadia Earthquake Magnitudes

There is mounting evidence that the last major Cascadia subduction zone

earthquake in the year 1700 had a magnitude approximately equal to M~9 (Satake and

others, 2003). Available paleoseismic data indicate that other major earthquakes have

struck the Cascadia margin, though it is unclear whether these earthquakes were

“characteristic” M=9 events or whether they also included smaller magnitude

earthquakes. The method that the National Seismic Hazard Mapping program employed

to characterize earthquakes along the interplate thrust was to use two equally weighted

scenarios: (1) a M=9.0 characteristic earthquake with an average repeat time of 500 years

and (2) a series of M=8.3 earthquakes that fill the seismogenic region of the interplate

thrust every 500 years resulting in a repeat time of 110 years for a M=8.3 earthquake to

occur anywhere in the seismic zone (Frankel and others, 1996; Frankel and others, 2002;

Petersen and others, 2002). These two scenarios represent epistemic uncertainty that is

included in the probabilistic calculations for the seismic hazard maps.

One could now argue, however, that this either/or option has been superseded

with recent analysis of the Japan tsunami records that indicate the 1700 event had a

magnitude of M=8.7-9.2 and ruptured approximate 1100 km (Satake and others, 2003).

That is, the epistemic uncertainty regarding magnitude posed in the National Seismic

Hazard maps has essentially been resolved with the collection of new data (that is,

tsunami records in Japan). This does not exclude the possibility of M<9 earthquakes

occurring along the interplate thrust. Instead, the epistemic uncertainty may now be

whether the magnitude distribution is characteristic or a Gutenberg-Richter (G-R)

distribution (Appendix F). Toward this end, Geist and Parsons (2005) consider two endmember cases of a characteristic M=9 earthquake and a modified G-R distribution of

earthquakes for demonstrating methods to calculate tsunami probabilities. Because of a

lack of information to define a G-R distribution for the Cascadia subduction zone (see

Discussion), we focus primarily on M=9 events as specified by Satake and others (2003)

and Leonard and others (2004), though this is certainly a topic open for future research

and inclusion in future PTHA studies.
31

Calculation of Coseismic Displacement Field

To calculate the coseismic displacement field, we can take advantage of previous

work performed by Priest and others (1997) in developing deterministic tsunami

inundation maps. The source discretization currently implemented in FACTS may not be

fine enough to accommodate slip pattern variations. Optimally, it is best to modify the

fault grid developed by Flück and others (1997) and used by Priest and others (1997).

This grid includes 105 quadrilateral elements with varying dip and strike.

The stochastic source model is modified from that of Herrero and Bernard (1994)

as described by Geist (2002). In basic terms, the model computes a stochastic slip

distribution that conforms to a specific amplitude spectrum in the wavenumber domain

that is constrained by earthquake physics and observations. Randomizing the phase

spectrum and transforming to the spatial domain yields a wide variety of slip distribution

patterns. Scaling constants are adjusted so that the average slip for all slip distributions

produced by the model equal the specified seismic moment or independent slip estimates

from far-field tsunamis and/or coastal subsidence. The slip distribution then can be

mapped to the fault grid without too much distortion of the wavenumber spectrum. It is

important to recognize that the stochastic source is specifically designed to estimate the

aleatory uncertainty related to slip for a given seismic moment. Any single slip

distribution has a low probability of occurring and should not be considered as a

characteristic slip distribution (i.e., one which represents the slip distribution for each

earthquake, with little uncertainty) (cf., Schwartz, 1999). Such a model, not considered

here, is tightly constrained by the overall convergence rate and time since the last event.

An important consideration is how to deal with slip in the transition zone (Flück

and others, 1997) and uncertainty in the rupture width. In past coseismic displacement

studies, slip is assumed to taper in the transition zone from full slip adjacent to the locked

zone to zero at the downdip edge of the transition zone (Flück and others, 1997; Leonard

and others, 2004). In Satake and others (2003), three long-rupture models are considered

for the 1700 C.E. earthquake that are compatible with both the tsunami records and

coastal subsidence estimates: a “Long-Narrow” model with uniform slip in the locked

zone and slip tapered to zero half-way down the transition zone; a “Long-Splayed” model

like the Long-Narrow model, but with a seaward-vergent splay fault at the updip edge of

rupture; and a “Long-Wide” model with uniform slip throughout both the locked and

transition zones.

Because the Satake and others (2003) study could not resolve between the LongNarrow and Long-Wide rupture models at a reasonable level of confidence, there is still

uncertainty related to the rupture width. The National Seismic Hazard Maps handle this

uncertainty by considering five different rupture widths that extend through the transition

zone (Petersen and others, 2002). We make the case that uncertainty related to the

effective width of the rupture zone is in part aleatory uncertainty associated with slip

distribution patterns. The slip distributions used for the local tsunami models are based

on the Long-Wide rupture geometry. Using this geometry, regions where slip is

concentrated up-dip, for example, will have a narrow effective rupture width. Even so,

there is still a level of epistemic uncertainty related to the region where slip can occur for

a Cascadia subduction zone earthquake. If we limit variable slip to a narrow zone

consistent with the Long-Narrow rupture geometry, mean tsunami amplitudes are larger

(Geist, 2005). For this pilot study, we did not consider this latter case.
32

The uncertainty in rupture width is also related to how well the coastal subsidence

predicted using the slip distributions for the local tsunami models compare to the

observations. The event for which there are the most abundant paleoseismic observations

is the 1700 C.E. earthquake (Leonard and others, 2004). Recognizing that the stochastic

slip distributions represent a range of possible rupture modes (i.e., they are not

representative of one particular event) and that there is significant uncertainty associated

with the paleoseismic subsidence measurements themselves, most of the slip distributions

tend to over-predict subsidence in comparison to the estimates from the 1700 C.E.

earthquake (Leonard and others, 2004). There are, however, individual slip distributions

that provide an adequate comparison to the observed subsidence observations. Moreover,

reductions in rupture width as little as 12.5% (still greater than the Long-Narrow

geometry) greatly reduce the subsidence predictions. Thus, for the Cascadia geometry,

coastal subsidence is more sensitive to uncertainty in rupture width than average slip.

Tsunami generation, on the other hand, is more sensitive to average slip and slip

distribution.

Finally, we estimate how many model runs it may take to capture that natural

variability in runup heights caused by variations in slip distribution patterns. To do this,

we track variation in peak nearshore tsunami amplitude (PNTA) at the 100 m isobath,

which can be quickly computed using linear propagation models. In Figure 16, both the

standard deviation of PNTA (blue) offshore Seaside and the difference between

maximum and minimum PNTA values (magenta) are shown as a function of the number

of model runs. As is typical with this type of computational effort, representational gains

in modeling the physical system die off exponentially with the number of model runs. In

this case, a minimum of about 12 model runs would be needed to capture the natural

variability in PNTA.

Specification of Earthquake Recurrence Rates

In the Cascadia region, numerous paleoseismic investigations have been

conducted to determine the recurrence record of great earthquakes (for example, Atwater

and Hemphill-Haley, 1997; Atwater and others, 2004; Kelsey and others, 2002; Witter

and others, 2003). In this section, we show how empirical parameters that define the

regional probability distributions can be determined, taking into account uncertainty in

age-dating methods and the open time intervals before the first and after that last

earthquake dated in the geologic record (Ogata, 1999). We also show how the method

for determining empirical distribution parameters can be applied directly to establishing

tsunami probability distributions.

The age range of geologic horizons representing great earthquakes is a result of

dating samples from multiple sites, each with an associated uncertainty related to the age

dating technique used. These age ranges, as well as open intervals before the first and

after the last geologic horizon and a limited sampling of earthquake recurrence history

introduces uncertainty in determining the mean inter-event time and aperiodicity. To

determine the range of distributions that are consistent with available paleoseismic data, a

Monte Carlo simulation is performed in which a random sample of earthquake times is

drawn from a specified probability distribution with varying mean inter-event times (μ)

and aperiodicities (α) (Parsons, 2004, submitted). Either mean values of μ and α for all
33

random samples that fit the observations can be used, or a range of values can be carried

through the probability calculations enabling an estimate of parameter sensitivity.

Results using paleoseismic data from southwest Washington (Atwater and others,

2004) are shown in Figure 17. The event table shows the age ranges (calibrated years

B.P.) for the identified horizons and the plots show the successful distributions defined in

μ-α parameter space that produce hits in the age ranges for the events. The histogram

shows the 30-year conditional probability resulting for all successful hits, using the

Brownian Passage Time probability distribution (Matthews and others, 2002). The

probability corresponding to the mean of the successful hits is shown by the white star

(μ=566 years, α=0.55).

Propagation and Inundation Modeling

Developing quantitative estimates for site-specific tsunami hazard assessments

requires substantial modeling efforts to simulate potential tsunami impacts. For most

locations, the use of historical data alone is not sufficient to derive long- and short-term

hazard estimates. Such studies demand additional model data to fill in the gaps in the

historical records. Even if a wealth of historical data is available, extra modeling

estimates are warranted to account for changes of coastal infrastructure and/or for

probable but non-historical events. The goals of numerical modeling for such studies

differ substantially from the goals of a typical hindcast simulation, where the model

results are compared with various field data for a specific historical event. In probabilistic

modeling, comparison with historical data is only the first preliminary step of the study,

to ensure reliability of multiple model estimates for probable events. In this respect, the

probabilistic simulations are similar to forecast modeling, which employs a similar

methodology for model use. This section presents the methods, modeling results and

discussions of the modeling study for Seaside, Oregon. This modeling effort produced a

model database for probabilistic tsunami hazard assessment. Multiple simulations have

been performed for a large number of potential far- and near-field tsunami sources using

the MOST numerical model (Titov and González, 1997; Titov and Synolakis, 1996).

Unlike previous tsunami probabilistic studies, high-resolution numerical grids are

employed to resolve details and internal structure of the computed flood zones for each

modeled event. Although Seaside does not have a tide gage to record historical tsunamis,

some historical tsunami inundation data is available in the form of inundation zone

estimates and tsunami sediment data for a limited number of historical events. The

numerical model was tested against available historical tsunami measurements. Full

numerical solutions for the high-resolution grid are retained for each model run to form a

model database that can be used to perform various analyses and probabilistic estimates.

Numerical model

NOAA’s MOST numerical model (Titov and González, 1997; Titov and

Synolakis, 1995) was utilized to produce inundation and propagation simulations for this

study. This model has been extensively tested against a number of laboratory experiments

and was successfully used for many historical tsunami simulations (Bourgeois and others,
34

1999; Titov and Synolakis, 1995, 1996, 1997, 1998; Yeh and others, 1995). The model

includes simulation of inundation dynamics by implementing moving boundary

conditions that allow calculation of the flow dynamics of a wave climbing up dry

topography and water withdrawing from the initial coastline. The employed numerical

scheme also handles wave breaking that has been verified by comparison with a number

of laboratory experiments and historical tsunami observations. Details of the numerical

implementation and testing of the MOST model are described in Titov and Synolakis

(1995; 1996; 1997; 1998) and Titov and González (1997). Here, we present a brief

description of the model and its application for this study.

Mathematical formulation

Two-dimensional shallow-water-wave equations (SW) are used to model this

phenomenon. Despite certain limitations, these equations have proven capable of

modeling many important physical characteristics of tsunami propagation, including

wave breaking and bore runup on mild and steep beaches (Kobayashi and others, 1987;

Peregrine, 1966). Recent studies (Titov and Synolakis, 1995) have shown that this

approximation works reasonably well even in the case of relatively short (length to depth

ratio less then 10) breaking waves. Although the equations cannot resolve the specific

pattern of the breaking front, they adequately model the overall wave behavior and give

accurate estimation of the runup values in a wide range of wave parameters.

The shallow-water-wave equations are

€

ht + (uh)x + (vh)y = 0

ut+uux+vuy+ghx=gdx

vt + uvx + vvy + ghy = gdy .

where 

€

h = η(x, y,t) + d(x, y,t) , 

€

η(x, y,t) is the wave amplitude, 

€

d(x, y,t) is the

undisturbed water depth, 

€

u(x, y,t), v(x, y,t) are the depth-averaged velocities in the x and

y directions respectively.

For arbitrary topography and bottom displacement the system of equations has to

be solved numerically. We use a finite-differences algorithm based on the splitting

method (Titov and Synolakis, 1998). This method reduces the numerical solution of the

two-dimensional problem into the consecutive solution of two locally one-dimensional

problems. The splitting technique allows effective implementation of a variety of

boundary conditions including moving boundary conditions to account for tsunami

inundation of dry topography.

Note that, with regards to wave breaking, the equations cannot resolve the specific

pattern of the breaking front. However, the equations do adequately model the overall

wave behavior and give accurate estimation of the runup values in a wide range of wave

parameters. The MOST model handles wave breaking by modeling it as a shock wave

within the shallow-water wave approximation (without simulating the details of the

breaking front). The numerical dissipation qualities of the scheme allow for stable

computation of the shock dynamics, conserving mass and momentum with good

accuracy. The amplitudes of the simulated breaking waves compare well with a number

of laboratory experiments.
35

Moving boundary condition

To calculate tsunami evolution on a dry bed, it is necessary to use moving

boundary conditions. The Froude number may be greater than 1 near the shoreline point,

implying that all characteristic families have the same inclination in this region. Hence, it

is impossible to use the direct relationships between the Riemann invariants, as is done

for fixed boundary approximations (Titov and Synolakis, 1995). Therefore,

approximations of the boundary values from previous space nodes are used. This is

described in Figure 18.

The shoreline algorithm uses a time-dependent space step ∆x(t) of the last node of

the computational area. The objective is to maintain the shoreline boundary point

(represented consecutively by A, B or C on figure 18) on the surface of the beach during

the computation. We therefore adjust the length of the last space step ∆x(t) every time

step, so that the shoreline point (A) is at the intersection of the beach with the horizontal

projection of the last "wet" point, for example n-1 node on figure 18. The value of the

velocity on the shoreline node is equal to the velocity on the previous "wet" point.

We introduce additional grid points as follows. Referring to figure 18, at the time

interval between times t and t + ∆t, there are n grid points (n-1 fixed grid points and the

instantaneous shoreline, points A or B) in the computation. At time t + 2∆t, when the

shoreline point (C) reaches beyond the next fixed grid point (n-th fixed node of the

constant dry bed grid), this n-th fixed point is introduced between the shoreline point

(C) and the previous internal fixed node (n-1) and η (D) = η(D). Now there are n+1 grid

points in the computational area and we repeat the process. During rundown, we reduce

the number of dry grid points sequentially in an analogous manner.

Tsunami propagation model

To account for a spherical earth during tsunami propagation, the SW equations are solved

in spherical coordinates. The MOST propagation code uses the non-linear shallow water

equation in spherical coordinates with Coriolis force and a numerical dispersion scheme

to take into account the different propagation wave speeds with different frequencies. The

equations, shown below, are numerically solved using a splitting method (Titov and

González, 1997):

( ) ( ) 0

cos

cos = +

+

φ

λ φ

R

uh vh ht

€

ut +

uuλ

Rcosφ

+

vuφ

R

+ ghλ

Rcosφ

− uv tanφ

R = gdλRcosφ

− Cf u u

d

+ fv

€

vt +

uvλ

Rcosφ

+

vvφ

R

+

ghφ

R

+

u

2 tanφ

R = gdφR − Cf v ud − fu

where:
36

€

λ = longitude

φ = latitude

h = η(λ,φ,t) + d(λ,φ,t)

η(λ,φ,t) = amplitude

d(λ,φ,t) = undisturbedwater depth

u(λ,φ,t) = depth averagedvelocity in longitude direction

v(λ,φ,t) = depth averagedvelocity in latitude direction

g = gravity

R = radiusof theearth

f = 2ωsinφ,coriolis parameter

Cf = gn

2 /h1/ 3

, n is Manning coefficient

To account for changing spatial scales during tsunami propagation, several telescoping

grids are used for propagation simulations with dynamic data exchange at the boundaries.

The highest resolution grid simulation includes inundation modeling with moving

boundary conditions applied. The MOST model handles wave breaking by modeling it as

a shock wave within the shallow-water wave approximation (without simulating the

details of a breaking front). The numerical dissipation qualities of the scheme allow for

stable computation of the shock dynamics, conserving mass and momentum with good

accuracy. The amplitudes of the simulated breaking waves compare well with a number

of laboratory experiments (Titov and Synolakis, 1995).

Model verification

MOST model testing against a variety of data is documented in many publications

(Bourgeois and others, 1999; Titov and Synolakis, 1995, 1996, 1997, 1998; Yeh and

others, 1995).

Here, verification of the inundation computations is presented for one historical

tsunami to illustrate the accuracy of inundation estimates using the MOST model. As a

partial test of inundation forecast capability of the MOST model, the simulation of the

1993 Hokkido-Nansei-Oki tsunami has been compared with an independent dataset. The

model scenario of this event is based on the field survey data (Takahashi, 1996). An

independent, much denser dataset of tsunami inundation distances and heights have been

obtained at PMEL from stereo photography data of Okushiri Island. Figure 19 shows a

comparison of the original MOST simulation (Titov and Synolakis, 1997) with the new

stereo data. Inundation values are compared for the west coast of Okushiri Island, where

the highest runup was measured for this event. The MOST runup and inundation

estimates compare well with both stereo and field data.

Model sources

Only earthquake-generated tsunamis are assumed for the Seaside probabilistic analysis.

Landslide sources can potentially be considered for analyses in the future, when the

probability of landslide occurrence is better defined for this location.
37

Tsunamis propagated from distant sources have substantially different dynamics,

duration and intensity when compared with local tsunami events. Simulations of tsunami

propagation across the Pacific from far-field sources need to be combined with the local

high-resolution inundation computations. On the other hand, deformations from local

earthquake sources change the bathymetry and topography of the study area, which needs

to be taken into account during the simulation of inundation dynamics. To account for

those and other differences, the modeling strategy for the tsunami scenarios of the farfield sources was different from the local sources modeling.

Far-field sources

Seaside inundation modeling from far-field sources was divided into two steps: (1)

across-ocean propagation from a source to the U.S. West Coast in the vicinity of the

Seaside and (2) high-resolution inundation simulation using the tsunami propagation

results as input.

A source sensitivity study (Titov et al., 1999) has established that only a few

source parameters are critical for the far-field tsunami characteristics, namely the location

and the magnitude (assuming some typical mechanism for the displacement and typical

size of a given magnitude source). The details of the earthquake deformation are not

important for inundation in the far-field. For example, wide-ranging variations of dip and

slip (rake) angles of an earthquake source do not lead to significant changes in the farfield tsunami signal. Therefore, assuming simplified uniform slip for each

location/magnitude combination of far-field sources accounts for most of the variability

of the tsunami inundation at the Seaside (far-field) location for events of certain

magnitude from a specified geographical area.

Tsunami propagation scenarios for far-field sources are obtained from PMEL’s

model tsunami propagation database that includes sources from all major tsunamigenic

subduction zones (Titov et al, 2005). The database contains a discrete set of unit sources

that can provide the basis for constructing a tsunami scenario from a given source

location and magnitude. Numerical solutions of tsunami propagation from these unit

sources, when linearly combined, provide arbitrary tsunami propagation simulation.

Figure 20 shows the computational area of the propagation simulations and locations of

14 earthquake scenarios considered for this study with earthquake magnitudes varying

from Mw 8.2 to 9.2 . Details of the earthquake parameters and the methodology for

choosing the sources for the Probabilistic Tsunami Hazard Analysis are in the

Probabilistic Method/Source Specification section of this report.

Near-field sources

In contrast to tsunamis arriving from the far-field, details of the local earthquake

deformation source are important for inundation estimates. The local source model for

this study involved a discretized fault surface with variable dip and strike; using average

slip estimates, rupture dimensions, and moment magnitude estimates for the 1700 event.

That allowed the use of different slip distribution patterns and different rupture

geometries to estimate the range of local tsunamis. Figure 21 shows deformation patterns

for two tsunami source scenarios of Mw ~ 9 earthquake used for the Seaside inundation

modeling, as an example of source scenario variability. A total of 14 different local

source scenarios were considered for the study.
38

Numerical model setup and testing

Figure 22 illustrates the numerical grid system setup for the tsunami numerical

model at Seaside. It shows the location and the resolution of 3 telescoping grids (grid A,

B and C with corresponding resolutions of 36, 6 and 1/3 arc seconds) that are used to

compute each simulation of the Seaside tsunami inundation. The propagation results for

the far-field sources (1964 propagation model is shown as example on the Pacific-wide

grid) are used as input through the boundary of the grid A. The deformation data from

local earthquake source scenarios are input directly into all 3 computational grids; the

resulting local tsunamis are computed without additional use of the propagation model.

The MOST numerical model has been extensively tested in many model

comparative studies and in various historical tsunami simulations (Titov et al., 2005;

Titov & Gonzalez, 1997, Titov & Synolakis, 1995, 1998). It is known to accurately

simulate tsunami propagation and inundation for even extreme tsunami events (Titov &

Synolakis, 1995). The goal of the model tests in this study is to verify that the numerical

setup for the Seaside, OR location is adequate for the purpose of this study, i.e. the

accuracy, size and the resolution of the numerical grid is sufficient to resolve details of

the inundation flow for both the far-field and the near-field tsunamis.

Unfortunately, Seaside does not have a tide gage to record tsunami signals from

the 1964 or other smaller tsunamis. It is not feasible to have a standard tide gage at

Seaside because this would have to be located inside the very shallow entrance bar to the

Necanicum River. To serve the needs of the northern Oregon coastal region, NOAA has

installed a tsunami-capable tide gage at Garibaldi, a location that provides more direct

observation of incident tsunamis.

 Nonetheless, eyewitness reports of the 1964 Alaskan tsunami (compiled by Tom

Horning and described in the Eyewitness Observation section of this report) provide

several tsunami runup values for this event at different Seaside locations. These are the

best available tsunami field data for this location. The inundation measurements for this

largest tsunami at Seaside are important, but they are not a comprehensive dataset to

verify model accuracy. Changes in topography and bathymetry since 1964 create an

additional difficulty in interpreting comparisons of the model simulations and field data.

The source of the 1964 Alaskan tsunami is modeled as a two-fault rupture with

fault geometry and average slip values approximately corresponding to the analysis of

Johnson et al. (1996). Detailed modeling of the 1964 event is beyond the scope of this

study. The goal of this exercise is to accurately reproduce the far-field propagation

pattern of this event. The sensitivity study (Titov et al., 1999) implies that a simplified

source model for this event that reflects the geometry, location and the magnitude of this

source generates a tsunami that accurately reproduces the 1964 Prince William Sound

tsunami in the far-field. To verify this assumption, we compared this model of the 1964

tsunami with a tide gage record at Hilo, Hawaii. To accurately reproduce the tsunami

dynamics at Hilo, a high-resolution grid was used for the tsunami simulation. The overall

model setup was similar to that used for the Seaside model, and consisted of three

telescoping grids (Figure 23) that used propagation model output as input via the outer

grid boundary shown in red on Figure 22. The numerical model setup for Hilo has been

tested against many historical events and has shown to be a reliable reference for

verifying general parameters of the tsunami sources. The results shown in Figure 23

demonstrate that our propagation model of the 1964 tsunami compares well with the tide
39

gage record. The amplitude and period of the first two waves match with the measured

data. The first waves carry most of the information about the tsunami source magnitude

and configuration. Therefore, the comparison at Hilo shows that our model source of the

1964 tsunami has proper amplitude and correct location. These are the two most

important parameters of the source for reproducing the tsunami amplitude in the far-field

(Titov et al., 1999). The good comparison with independent data at Hilo provides

additional confirmation of the accurate representation of the 1964 tsunami at Seaside.

The comparison of modeled inundation of the 1964 tsunami at Seaside with the

eyewitness accounts shows a qualitatively consistent picture. Figure 24 shows the

computed inundation and the inundation inferred from the eyewitness accounts. The

computed inundation shows a slightly larger inundated area, however comparisons of the

vertical runup values at the open coast show very good correspondence between

measurements and model. The difference between the model inundation extent and the

field estimates can be explained by many factors, including slight differences of coastal

dune representation in the Digital Elevation Model used for this study and the actual

topography in 1964. The most important value for this study is the predicted vertical

amplitude values. The predicted runup for the 1964 tsunami compares well with

measurements, which ensures the accuracy of the tsunami inundation model predictions.

The Seaside inundation model of the 1964 tsunami was computed with two different grid

resolutions of 30 meters and 10 meters. The comparison of the results shows that

computed vertical runup values are very similar for both simulations, while the horizontal

extent of the inundation differs. The 30-meter grid does not represent the coastal dunes

accurately enough, which leads to extended inundation areas for waves that have smaller

amplitudes than the height of the dunes. Therefore, the 10-meter grid was used for all farfield simulations, in which case the coastal dunes can block the horizontal extent of

smaller amplitude waves. For the local tsunami sources, where the runup values are much

larger, small-scale features like dunes do not make much difference in the horizontal

inundation. The 30-meter grid with coverage of a larger area was used to account for

much greater inundation distances.

Data for the 1700 Cascadia tsunami are much more scarce. Tsunami deposit data

are the only indication of the size of the tsunami inundation area. Since topography and

bathymetry could have changed significantly in 300 years, a direct comparison with the

model data is very difficult. Our inundation results for the local tsunami sources (which

are all potential scenarios for the 1700 event) show that computed inundation areas

encompass the tsunami sediment locations. At least qualitatively, this indicates that our

local inundation estimates are within the constraints of the available field data for such

events.

Database of model runs

Computed tsunami inundation scenarios for Seaside are stored in a WWWaccessible database for further analyses. All computed variables (amplitude and velocity

components) are saved at each time-step for the duration of the simulation.

These model data are available via Web interface using Live Access Server (LAS)

technology (Figure 25). The interface provides full access to all computed variables and

additional tools for project researchers to conduct additional analyses.
40

Discussion of model simulation results.

Analysis of the far-field tsunami source simulations revealed the strong influence

of the directivity of tsunami propagation on runup amplitudes at Seaside. Directivity

determines both the amplitude and the direction of the incident waves and, therefore

affects the degree of refraction and other effects of local and regional bathymetry and

shoreline shape have on the characteristics of tsunamis at Seaside. Potentially important

local and regional features include the Astoria Canyon, the bight between the Columbia

River and Tillamook Head just south of Seaside, the Juan de Fuca Ridge and seamount

chains farther offshore. These bathymetric features are resolved in the DEM used to

model tsunami propagation to Seaside. A detailed analysis of these effects has not been

carried out. However, such an analysis would be helpful for interpreting the tsunami

response in the Seaside area as a function of the source location.

Figure 26 illustrates the overall effects of different sources by comparing three

different simulation results corresponding to Source Numbers 2, 3 and 5 in Table 6. The

figure shows that these three simulation scenarios are for tsunami sources at similar

locations (epicenters for these three earthquakes could very well be at the same location);

all correspond to the same earthquake magnitude of Mw=9.2 . Despite the seeming

similarities of these source scenarios, the inundation simulations at Seaside show very

different amplitudes (lower images). The difference is explained by different propagation

directivity patterns for the three sources (upper images). The largest inundation at Seaside

among the chosen far-field sources is produced by the Alaskan source with the fault near

Kodiak (the middle images). This source roughly corresponds to the rupture area of the

1964 Prince Williams Sound tsunami but have different slip distribution. This large

inundation from the far-field source may not show up in the probabilistic inundation map

for the Seaside due to the lower probability of such source. However, it should be noted

that tsunami sources from this area in Alaska could produce large inundation at Seaside,

if this low probability rupture does occur.

Currents cannot be neglected in tsunami hazard assessments, because the

associated kinetic energy can be the most destructive aspect of a tsunami and very high

currents can be associated with relatively modest wave height. Figure 27 illustrates this

point for the far-field Source Numbers 3 and 5 of Table 6. Note that regions of high

currents frequently do not correspond to regions of high wave heights. The most obvious

examples are in the river entrance and the adjacent bay area for both scenarios and,

especially in the case of Source Number 3, on the peninsula south of the river entrance.

This lack of correspondence between maximum wave heights and currents means that

inundation maps of maximum wave height could be dangerously misleading – i.e., the

overall tsunami hazard and destructive potential could be seriously underestimated in

areas of modest wave height because destructively high currents were not taken into

account. A more complete hazard assessment must employ “impact indices” or “impact

metrics” that take account of both potential and kinetic energy, i.e., both wave height and

currents. This topic is explored in the next section, “Evaluating Tsunami Impact

Metrics.”

For local sources, in Figure 28 we compare the inundation map derived from one

of the slip distributions used for Cascadia subduction zone earthquakes described above

to the inundation map derived from a uniform slip rupture model described by Priest and

others (1997) (their Model 1A). Although the magnitudes for these two scenarios is
41

similar and the average slip used in Figure 28a is similar to the uniform slip used for

Figure 28b, the distributed slip model results in significantly higher maximum wave

heights. This is consistent with theoretical results (Geist and Dmowska, 1999) and the

comparison presented in Priest and others (1997) between the uniform slip model and the

same model with an added Gaussian asperity (Model 1A-Asperity). The difference

between the inundation maps derived from uniform slip and distributed slip source

models is shown in Figure 28c.

Evaluating Tsunami Impact Metrics

Tsunamis can generate large onshore currents that can cause dramatic damage to

structures and move large objects far inland. The 26-December- 2004 Indian Ocean

megatsunami demonstrated tsunami impact on structures in a rather dramatic fashion.

Historic examples of large tsunamis setting large objects in motion abound. The most

notorious is the myth of the USN Watery, the ship moved by the 1868, Arica, Chile

tsunami two miles inland and then moved back to shore during the 1877 Arica tsunami so

that the ship could sail on. Actually, the ship was indeed transported inland, but the 1877

tsunami just moved it closer to the shoreline, where it still rests. During the 26 December

2004 megatsunami, at least two similar-size barges were moved inland in Banda Aceh

and Lhok Nga in North Sumatra.

As a measure of what an even small tsunamis can do, consider the 1994 Mindoro

Philippines tsunami. In an area where the vertical inundation heights did not exceed

3m(10ft), the generated tsunamis floated a 6000 ton generating barge, broke its mooring

lines and carried it one mile inland down the Baruyan River. The impact of tsunamis on

structures can be observed in detail in Discovery Channel’s production “Tidal Wave”

(1998). The estimation of impact forces and currents is still an art and far less understood

than hydrodynamic evolution and inundation computations. In what follows, different

methods and formulae in the literature are described, although none has been truly

validated by comparisons with field data.

In terms of assessing FIRM V zones (zones identifying velocities exceeding

certain thresholds or areas of 100-year coastal floods), in addition to inundation zones, it

is useful to evaluate different combinations of flow parameters. We name them impact

metrics or damage indicators, in an effort to determine a single hazard zone that helps

identify areas where structural safety needs to be considered in greater detail. For

example, existing formulations recommended in FEMA's Coastal Construction Manual

rely on riverine flooding results, and the flow velocity and forces inferred through largely

empirical relationships involving only the flow depth. Tsunami flow patterns can be

counterintuitive even for fairly simple topographies of a plane beach as in Banda Aceh.

During the 2004 megatsunami, particle image velocimeter techniques helped identify

flow velocities 3km inland which suggest that the larger the depth the larger the velocity.

Further, the topography of Seaside is quite unique, particularly because of the presence of

the sand-spit within the broader Seaside bay, which is fronted by another sandspit. The

setting is as different from the canonical geometry of a one-dimensional wave climbing

up a sloping beach, described by Synolakis (1987) as one can be. As shown recently by

Carrier and others (2003), even for the simple geometry of the canonical problem, the

highest velocity does not occur at the same location as the highest inundation depth, and
42

the location of the region of highest velocity depends on the incoming wave, hence on the

particular scenario under study.

We will describe here existing formulations to calculate forces on structures to

help motivate our choices of combinations of flow parameters, acceleration, velocity,

depth, amplitude, front velocity that may be relevant in tsunami V-zone assessment. Not

unexpectedly, perhaps, the momentum flux parameter appears to be the most useful for

engineering applications in identifying regions where the flow forces may possibly be

larger than otherwise anticipated from existing formulations. The present inundation

results for Seaside for specific far-field and near-field inundation events as discussed in

this report have guided this choice of the boundaries of the V-impact zone.

Forces on Structures

In principle, the calculation of wave forces on structures involves the integration

of the pressure and of the shear force over the exposed area of the structure during the

wave motion. To understand the development of the damage metrics, we consider first

the simplest possible geometry, which involves the calculation of the instantaneous wave

force at time t on a cylindrical pile of radius R, in the direction of the wave propagation.

Given a pressure 

€

p(R,θ,z,t) and a tangential shear stress 

€

τ rθ (R,θ,z,t), then the force is

given by

(5)

€

FT (t) = p(θ,z,t)Rcosθdθdz +

0

2π

∫ 0

η p +hp

∫ τ rθ (θ,z,t)Rsinθdθdz

0

2π

∫ 0

η p +hp

∫

Here, 

€

ηp and 

€

hp are the local amplitude and undisturbed water depth at the pile,

respectively, with the assumption that they do not vary significantly over the pile

diameter, hence their dependence on the radius R is not shown in the arguments.

Tsunamis are long waves, and indeed the flow parameters do not vary significantly over

small distances, such as those typically encountered in coastal structures.

In practice, for all but the simplest steady flows, determining either the pressure

or the tangential shear stresses through calculation of the velocity gradients is impossible

at this state of knowledge, as it involves solution of the Navier–Stokes equation.

Shallow-water wave (SW) equations used in inundation mapping are depth–averaged

approximations of the Navier–Stokes equations for inviscid flow, and there are no

velocity gradients perpendicular to the axis of the pile, that is, there is no depth variation.

The classic simplification is to consider a mass coefficient that incorporates some of the

dynamic pressure effects, and a drag coefficient CD which in turn accounts for the form

drag that results from flow separation and incorporates all the effects of the viscous

forces on the cylinder. In terms of these coefficients, the force on a cylinder is given by

(6) 

€

v 

F T (t) = πCM ρR2 d

v 

V 

dt

dz

0

η p +hp

∫ + CDρR v 

V v V dz

0

η p +hp

∫

Here V is the instantaneous horizontal velocity in the direction of wave motion, while

dV/dt is the instantaneous water particle acceleration. Clearly 

€

v 

F is a vector in the same

direction as the velocity vector 

€

v 

V , hence, again the force given by (6) is in the direction

of wave propagation. The absolute value is used to underscore that the force may change

direction as the water particle velocity changes direction. For example, 

€

v 

V = u

v 

i + v

v 

j and

 

€

v 

V = u

2 + v2 with u, v the horizontal particle velocities in x, y. If the flow is primarily

one–dimensional and onshore, 

€

v 

V = u

v 

i , and u is positive under the crest and negative
43

below the crest, if x is pointing towards the pile. Dean and Harleman (1966) note that the

expression (6) for the drag force was determined empirically for steady flows, yet for

lack of better knowledge, the same formulation is used for strongly unsteady flows such

as the impact of bores and surges. In these cases, the coefficients CD and CM have to be

carefully evaluated. The variation of CD with the Reynolds number 

€

Re = uD /ν is shown

in figure 8.2. page 344, in Ippen (1966). In the range of 

€

103 < Re < 5 ×105

, then CD ~ 1.

We note that CD does depend on the roughness of the cylinder, although for tsunamis a

usual assumption is that the pile is hydrodynamically smooth, given the wavelength of

the tsunami wave train.

There are a few cases where (6) can be used directly to calculate tsunami forces.

As an example, consider small amplitude wave theory. This theory is irrotational,

requiring that the curl of the velocity vector is zero ( 

€

∇ × v V = 0), an assumption which is

not correct when waves are breaking. Shallow water wave theory (SW) is also

irrotational, with no vertical velocity gradients perpendicular to the direction of wave

propagation. However, shallow water wave theory is valid for larger amplitudes, for L/d

>> 1, while small amplitude theory applies when a/h << 1. Assuming that the pile is

located at x = 0, and assuming a wavenumber 

€

k = 2π / L and celerity 

€

σ / k = gh then,

(7)

€

FT (t) = −ρg πR2

( akh)CM asin(σt) + ρgCDa

2

Rcos(σt) cos(σt) ,

with the understanding that (7) is valid for small–amplitude long waves. Dean and

Harleman (1966) note that the intertial force is inversely proportional to the period, while

the drag force is independent of the period. Equation 7 might be an adequate

approximation for calculating forces on piles offshore for tsunamis generated by far-field

earthquakes or by landslides, that is, for tsunami wave trains with more than one wave.

For tsunamis generated in the near-field, where there is not sufficient distance for a

wavetrain to fully emerge, then these equations can only be used with caution.

The total moment is formally calculated from 

€

MT (t) = zFT (t)dz

0

η p +h p

∫ , that is, it

is the first moment of the force from the ocean floor to the free surface. To the same level

of approximation as (7), then

(8)

€

MT (t) = 1

2 ρgCD Ra2

hcos(σt) cos(σt) − 1

2 ρgCM πR2

akh2 sin(σt)

Note that consistent to the SW approximation, the moment is equal the force times a

moment arm of h/2, given that SW implies that the force is uniform over the depth. Note

also that the drag force does not depend on the period of the wave motion.

Impact forces on structures are calculated by different methods depending on

whether or not the breaking wave forms a surge or a bore. On the vertical front face of a

structure, they have been traditionally estimated using the classic formula of Cross

(1967). He proposed that the force on a b wide seawall is given by

(9)

€

Fwall(t) = 1

2 ρgbη2

(x = Xw,t) +Cf (t)ρbη(x = Xw,t)C2
44

where 

€

ηw = η(x = Xw,t) is the water surface elevation on the wall located at some x = Xw,

b is the width of the wall, C is the surge or bore velocity and 

€

Cf = 1+ tan1.2 ( θ) is a

computed force coefficient. tan θ is the slope of the front face of the bore as it impacts the

wall. For practical applications, Cross suggested calculating 

€

η(x = Xw,t) as if the wall

were not there, that is, the bore would pass through the wall relatively unchanged. In

computational terms, this implies using a numerical code without the structure present,

and then using the calculated values and applying them on the structure. This may well be

a good assumption if there is sufficient distance between structures. Even if it is not a

good assumption for a densely built town such as Seaside, the objective here is to identify

a damage metric that captures the V-zone and not to calculate individual forces.

Cf (t) could be a function of time, since the front slope of the wave may change as

the wave evolves. Ramsden and Raichlen (1990) reformulated the same equation and

integrated laboratory measurements to calculate values for Cf in terms of the bore

strength H/h, the ratio of the bore height H to the local depth h, recognizing that the wave

height at the front face of the structure might be difficult to calculate a priori.

Ramsden (1993) points out that when b/H ~ 1, then three-dimensional effects

dominate and are believed to reduce the overall force. On the other hand, when b << H,

that is, the width of the wall is smaller than the effective crest length of the wave, the

force is thought to be primarily the drag force. When overtopping occurs, that is, the wall

height is smaller than the expected runup R. The resulting forces might be significantly

less. Since there are no established and validated formulas for overtopping, at least this

theory provides a worst-case scenario for the forces on the wall.

The limitations of calculating tsunami forces and moments from (7) and (8) are

obvious when the tsunami evolves over dry land, which is the region of interest for

developing high–hazard zones. One then has to rely on (8) using results from shallow

water wave theory, with the numerical predictions for the depth–averaged u, v,

substituted directly into (6) with 

€

V 2 = u

2 + v2

.

Calculating the x-y tsunami current distributions and magnitudes and their time

variation is possible using numerical solutions of the SW equations and was done in this

study. However, harbor resonances effects, breakwaters, and seawalls with characteristic

sizes smaller than the grid spacing are transparent to the numerical computations. For

example, a typical grid size Δx, Δy ~ 100m (333ft) will miss all coastal structures smaller

than 100m, unless the grid is positioned appropriately. This is not as simple as it appears,

for numerical grids are calculated so as to model the hydrodynamic evolution correctly

by attempting to maintain a constant number of grid points per wavelength as the

wavelength changes.

Recently, Hughes (2004) revisited the radiation stress parameter Sxx perpendicular

to the wave motion per unit wavelength as proposed by Longuet-Higgins and Stewart

(1964),

(10)

€

Sxx= 1

L pd + ρu

2

( ) 0

h +η (x )

∫ 0

L

∫ dzdx
45

pd is the dynamic pressure and u the horizontal particle velocity. The radiation stress is

averaged over one wave period. For small amplitude wave theory and a periodic wave of

the form 

€

η(x,t) = asin(kx −σt), then

(11)

€

Sxx = 1

2 ρga

2 1

2

+

2kh

sinh2kh





 



.

Hughes (2004) proposed the momentum flux parameter

(12)

€

Mt(x,t) = pd + ρu

2

( ) 0

h +η ( x)

∫ dz,

as characterizing the flow kinematics better than other parameters. He noted that at the

front surface of a perfectly reflecting seawall, Mt is the instantaneous dynamic force.

Note that rewriting the momentum flux parameter Mt(x, t) for SW waves, that is, if one

performs the integration in (12), one obtains

(13)

€

Mt(x,t) = pd (ηp + h) + ρu

2

(ηp + h)

Observing that the instantaneous dynamic pressure gradient in z reflects the instantaneous

fluid acceleration in z, equation (13) is reminiscent of the force equation for the total

force on a pile. Rewriting (7) for shallow water waves where both V and dV/dt are

depth–independent, then

(14)

€

FT (t) = CM ρR2 dV

dt (ηp + h) + CDρRV V (ηp + h)

€

FT (t)=CM ρR2 dV

dt ηp+ h ( )+CDρRV V ηp+ h ( )

.

For the purpose of determining tsunami impact zones, and consistent with both

(13) and (14), we conjecture that tsunami forces can be thought of as consisting of two

parts, an inertial component (proportional to depth times acceleration) and another due to

the dynamic effects of the moving flow (proportional to depth times velocity squared).

Once the accelerations and current velocities are known, drag CD and intertial mass CM

coefficients can be determined for the specific shapes of structures, depending on the

zone boundaries. Damage metrics of use in planning and possibly zoning, must reflect the

distribution of the force over the entire impacted area and identify areas of exceptional

force. Therefore, the following parameters are of interest in assessing tsunami impact,

• the depth h,

• the velocity 

€

V 2 = u

2 + v2

,

• the acceleration dV/dt,

• the inertial component hdV/dt,

• and the momentum flux hV2.

Since V-zones must reflect the hazards, we use the detailed inundation

computations for both nearfield and farfield scenario events that may impact Seaside to

make detailed calculations of the four different damage metrics over the entire region,

and compared their distributions.
46

While one might have expected that regions of large flow depths might correlate

with regions of large velocities, this is not always the case. For example, as a tsunami

evolves over dry land, the flow depth decreases up to the point of maximum runup, and

the velocity of the shoreline tip becomes zero. Here, both h and V are small. During

rundown, the flow depth remains small, but the velocity can be substantial, leading to

higher hV2 values in regions of the flow field that are unexpected, as suggested in a

simple one dimensional setting by Carrier and others (2003). The acceleration dV/dt may

diminish as the wave runs-up, but may be substantial during rundown.

The distribution of the above four parameters for both near-field and far-field

events for Seaside suggests that while individual differences exist among the different

scenario events, the momentum flux represents the most suitable damage indicator. In

contrast to what one might have expected based on 1-dimensional considerations, flow

velocities appear to correlate well with inundation depths over the two sand-spits in

Seaside. Hence, the momentum flux shows a similar distribution as the inundation depths

and currents. While small-scale differences exist, overall, the inertial component appears

to have a similar geographical distribution as the momentum flux. We therefore

recommend that the momentum flux be used as a determinant for the V-zone.

We note that our results are based on calculating the parameter V2/gH, where H is

the entire depth. During the calculation of tsunami propagation and inundation, this

parameter was calculated every time step, allowing direct construction of a map of its

distribution of maximum values to guide the V-zone. We recommend that all future

inundation mapping studies monitor this parameter, in addition to archiving u,v,eta for all

times. We also recommend that whenever possible the moving front velocities be

monitored and that the normalized momentum flux parameter V2/gH be calculated using

the front velocity, and the higher of the two values employed in the maps. Maps of the

V-zone can then be used to identify areas of higher risk, where a detailed analysis using

time histories of u,v,eta at the specific locations would be useful .

Results

100-Year Tsunami Map

Using the methods described above, Figure 29 and Plate 1b shows the tsunami

waves heights, including tides, that are met or exceeded at an annual probability of

P=0.01. For brevity, we will call this the 100-year tsunami map. A common feature in

all the maps is an increase in the offshore wave height as the water depth decreases

toward the outer coast. This is a direct effect of shoaling of the long waves. Inside the

estuary, the hydrodynamics are more complex. For the 100-year tsunami map, there is

little inundation of the developed areas in the study region. The region of coastal dunes

south of the estuary mouth (that is, along the Promenade) are high enough in elevation to

block most of the far-field tsunamis. Although a few of the far-field earthquake scenarios

resulted in significant areas of inundation (for example, Source 3, Table 6, where the

1964 Alaska earthquake occurred), the 100-year tsunami map show little inundation

because the average inter-event times for these earthquakes are substantially longer than

100 years.

As a sensitivity test to see what effect our choice of segmentation models for

Aleutian-Alaska subduction zone earthquakes has on the 100-year tsunami map, we
47

compare the results in Figure 29 with a similar map in which only Segmentation Model 1,

which includes the 1964 source region (Figure 13), is used (Figure 30b).

(Correspondingly, the weight for Segmentation Model 1 was changed from 0.5 to 1.0.)

Figure 30a is the 100-year map shown in Figure 29. Although there is a slight increase in

the wave heights using just Model 1 for Aleutian-Alaska earthquakes, there is no

significant increase in regions that are inundated by the tsunamis.

500-Year Tsunami Map

In a similar manner, the 500-year tsunami map was constructed and represents

wave heights that are met or exceeded at an annual probability of P=0.002 (Figure 31,

Plate 1c). In stark contrast to the 100-year tsunami map, the 500-year tsunami map shows

large regions of inundation with significant wave heights throughout Seaside. The 500-

year tsunami map is dominated by the tsunami generated by a local Cascadia subduction

zone earthquake. For reference, the region of inundation indicated by the 500-year

tsunami map includes most of the localities where tsunami deposits from the 1700

Cascadia tsunami were found (Figure 10).

Because the 500-year tsunami map is dominated by local Cascadia tsunamis,

uncertainties in the corresponding earthquakes are likely to have a large influence. Some

of the epistemic uncertainties associated with these earthquakes are discussed in Geist (in

press) and in the section below. In Figure 32, we explore how uncertainty in the mean

inter-event time for these earthquakes affects the 500-year tsunami map. Figure 32a is

the 500-year map shown in Figure 31. Two other estimates for mean inter-event times,

477 years and 610 years, that fit the paleoseismic observations (Figure 17) are shown in

Figure 32b and 32c, respectively. The shorter mean inter-event time (477 years, Figure

32b) results in significant changes in the 500-year tsunami map because it crosses the

threshold of the exceedance probabilities being mapped (500 years.) In contrast,

increasing the mean inter-event time to 610 years (Figure 32c) results in little change in

the 500-year tsunami map. It is evident that it will be important to gather more accurate

age dates of Cascadia subduction zone earthquakes to constrain the mean inter-event

times shown in Figure 17.

Discussion: Resolvable Issues through Future Research

Tsunami Earthquakes

Although not included as part of this study, it is important to note the possibility

of tsunami earthquakes (Kanamori, 1972; Kanamori and Kikuchi, 1993; Polet and

Kanamori, 2000). These earthquakes typically rupture the shallowest part of the interplate

thrust near the trench and characteristically have a slow rupture velocity. Although we

know of subduction zones where they have occurred (Aleutian, Kuril, Japan, Peru,

Nicaragua), it is unclear how ubiquitous they are nor whether or not their downdip

rupture extent overlaps with that of typical subduction zone earthquakes. It is especially

difficult to assign average return times for these earthquakes, though a minimum return

time can be estimated from the characteristic amount of slip during a tsunami earthquake

and the relative plate convergence velocity.
48

One possible scenario for tsunami earthquakes for the Cascadia subduction zone

is that they occur by rupture on a shallow splay fault near the deformation front (Geist

and Yoshioka, 1996; Priest and others, 1997; Satake and others, 2003). The possibility of

rupture propagation onto splay faults is an area of active research (for example, Kame

and others, 2003). For now, it is difficult to quantify how likely this scenario is.

Smaller Cascadia Subduction Zone Earthquakes

Inclusion of earthquakes that have a magnitude smaller than the idealized

characteristic earthquakes magnitude will likely affect the results of the probabilistic

calculations. Much of the recent paleoseismic research suggests that not all of the

subduction zone earthquakes preceeding the 1700 event ruptured the entire length of the

subduction zone. However, we neither have sufficient information for the paleoseismic

record nor have information from recorded seismicity to determine what the distribution

of earthquake magnitudes is for Cascadia. One of the main questions we addressed

during the beginning of this study was “Do we model earthquakes of M<9 and alternative

rupture models in addition to characteristic M=9 earthquakes?” Depending on the exact

form of the frequency-magnitude distribution, the inclusion of smaller earthquakes will

tend to increase the tsunami hazard for the 0.01 yr-1 recurrence rate and decrease the

tsunami hazard for the 0.002 yr-1 rate (Geist and Parsons, 2005). It is important to

emphasize, however, that it is currently quite difficult to get reliable recurrence rates for

the smaller Cascadia earthquakes from paleoseismic studies to build such a distribution,

thus adding a significant amount of uncertainty to the calculations. Instead, we have

focused on sources of uncertainty that we can quantify with some degree of confidence:

influence of tides (Appendix E) and natural variability in slip distribution.

Other Sources of Epistemic Uncertainty

In any probabilistic study, the distinction between epistemic and aleatory

uncertainties is made because they are handled in different ways in the probability

calculations. Epistemic uncertainties are typically incorporated using logic trees, whereas

aleatory uncertainties, such as the tidal stage at tsunami arrival and slip distribution at the

source, are handled through direct integration in the rate calculations (Appendix E).

Variation in parameters such as rupture width and whether or not splay faults are ruptured

also has a demonstrated effect on local tsunami runup (Priest and others, 1997). A

sample logic tree that includes many of the sources of epistemic uncertainty is given in

Figure 33. As displayed, this would involve a total of 20 branches. The total number of

model runs would be dependent on both the number of branches and the sample size for

the G-R distribution branch—in any case, the number would be excessive. Even just

considering the characteristic distribution branch, if 12 model runs are needed to capture

the uncertainty from slip distribution patterns, then a total of 10x12=120 model runs

would be needed according to the scheme above. Clearly, some judicious choices need to

be made to reduce this computational load. As possible options for future studies, one

can reduce the number of branches (for example, just considering 2 rupture widths) or

randomly sample the different branches of a more complete logic tree.
49

Recommendations

This pilot study was motivated by the previous finding (Chowdhury, et al., 2005)

that the current methodology for including tsunami information on Federal Insurance

Rate Maps is inadequate, because it does not include advances of the last few decades in

tsunami science and tsunami hazard assessment. The study has integrated advances of

the last few decades in the scientific understanding, tools, and methods available for

geophysical and tsunami hazard assessment to develop a Probabilistic Tsunami Hazard

Assessment (PTHA) methodology, and has applied this methodology to the community

of Seaside, Oregon. The resulting products, including 100- and 500-yr tsunami hazard

maps and a comprehensive GIS database, represent a major advance in tsunami hazard

assessment methodology.

The PTHA must now be applied to other Cascadia Subduction Zone communities,

and refined and adapted to other tsunami regimes as part of a formal

FEMA/NOAA/USGS partnership in a systematic, cost-effective, national effort to

upgrade the FEMA series of Federal Insurance Rate Maps. To this end, we make the

following recommendations:

Scientific/Technical Recommendations

• Include all reasonable epistemic and aleatory sources of uncertainty in each

Probabilistic Tsunami Hazard Assessment, using the best available science.

• Utilize tsunami hydrodynamic models that meet NOAA standards, to ensure

consistency of Federal agency products

• Test all earthquake and tsunami models by extensive field studies to gather and

exploit all possible paleogeography and paleotsunami data, historical tsunami

measurements, eyewitness reports and other types of field observations.

• Develop and maintain a comprehensive GIS database of all field data, model

results and a comprehensive site- and source-specific tsunami/earthquake

bibliography for the region as an essential and invaluable analysis and product

development tool.

• Publish a report for each PTHA project that documents procedures, data sources

and results, that includes a bibliography, and that is reviewed for consistency

with FEMA standards.

• Publish PTHA results either as a separate Federal Insurance Rate Map, or include

PTHA information as separate, tsunami-specific items on FIRMs. In either case,

include: (a) the 100-year and 500-year events, (b) tsunami-specific V-zones, (c)

measurements available for the worst case historical and/or paleo-tsunami events,

and (d) the "Credible Worst-Case Scenario" event.

Policy/Programmatic Recommendations

• Establish a formal FEMA/NOAA/USGS partnership to address national needs for

tsunami hazard assessment products in a Federally consistent and cost-effective

manner.

• Apply PTHA to additional Cascadia Subduction Zone communities as NOAA

inundation models are completed.

• Conduct Pilot Studies to adapt PTHA to other Tsunami Regimes in the Pacific,

Caribbean, Atlantic, and Gulf, using a preliminary assessment of uncertainty.

• Apply PTHA to additional Pacific, Caribbean, Atlantic and Gulf communities as

the corresponding Pilot Studies and NOAA inundation models are completed.
50

• Establish a systematic Maintenance and Improvement Program to integrate

scientific and technical advances into the PTHA methodology.

Acknowledgements

The authors of this report gratefully acknowledge the thorough reviews and

constructive comments by Frederic Raichlen and Robert Dean. High-resolution fault

geometry for the Cascadia interplate thrust was generously provided by Kelin Wang.
51

References

Abe, K., 1979, Size of great earthquake of 1837-1974 inferred from tsunami data: Journal

of Geophysical Research, v. 84, p. 1561-1568.

Anderson, J.G., and Brune, J.N., 1999, Probabilistic seismic hazard analysis without the

ergodic assumption: Seismological Research Letters, v. 70, p. 19-28.

Atwater, B.F., and Hemphill-Haley, E., 1997, Recurrence intervals for great earthquakes

of the past 3,500 years at northeastern Willapa Bay, Washington: U.S. Geological

Survey, Professional Paper 1576, p. 108.

Atwater, B.F., Tuttle, M.P., Schweig, E.S., Rubin, C.M., Yamaguchi, D.K., and

Hemphill-Haley, E., 2004, Earthquake recurrence inferred from paleoseismology:

Developments in Quaternary Science, v. 1, p. 331-350.

Barrientos, S.E., and Ward, S.N., 1990, The 1960 Chile earthquake: inversion for slip

distribution from surface deformation: Geophysical Journal International, v. 104,

p. 589-598.

Bourgeois, J., Petroff, C., Yeh, H., Titov, V.V., Synolakis, C.E., Benson, B., Kuroiwa, J.,

Lander, J.F., and Norabuena, E., 1999, Geologic setting, field survey and

modeling of the Chimbote northern Peru, tsunami of 21 February 1996: Pure and

Applied Geophysics, v. 154, p. 513-540.

Carrier, G.F., Wu, T.T., and Yeh, H., 2003, Tsunami runup and drawdown on a plane

beach: Journal of Fluid Mechanics, v. 475, p. 79-99.

Cifuentes, I.L., 1989, The 1960 Chilean earthquakes: Journal of Geophysical Research, v.

94, p. 665-680.

Cifuentes, I.L., and Silver, P.G., 1989, Low-frequency source characteristics of the Great

1960 Chilean earthquake: Journal of Geophysical Research, v. 94, p. 643-663.

Combellick, R.A., 1992, Paleoseismicity of the upper Cook Inlet region, Alaska, in

Wadati Conference on Great Subduction Earthquakes, Fairbanks, Alaska, p. 97-

98.

Cornell, C.A., 1968, Engineering seismic risk analysis: Bulletin of the Seismological

Society of America, v. 58, p. 1583-1606.

Cross, R.H., 1967, Tsunami surge forces: Journal of the Waterways and Harbors

Division, A.C.E., v. 93, p. 201-231.

Darienzo, M.E., and Peterson, C.D., 1995, Magnitude and frequency of subduction zone

earthquake along the northern Oregon coast in the past 3,000 years: Oregon

Geology, v. 57, p. 3-12.

Darienzo, M.E., Peterson, C.D., and Clough, C., 1994, Stratigraphic evidence for great

subduction-zone earthquake at four estuaries in northern Oregon: Journal of

Coastal Research, v. 10, p. 850-876.

Dean, R.G., and Harleman, D.R.F., 1966, Interaction of structures and waves, in Ippen,

A.T., ed., Estuary and Coastline Hydrodynamics, McGraw-Hill Book Co., p. 341-

403.
52

Downes, G.L., and Stirling, M.W., 2001, Groundwork for development of a probabilistic

tsunami hazard model for New Zealand, in International Tsunami Symposium

2001, Seattle, Washington, p. 293-301.

Fiedorowicz, B.K., 1997, Geologic evidence of historic and prehistoric tsunami

inundation at Seaside, Oregon, Portland State University, M.S. Thesis.

Fiedorowicz, B.K., and Peterson, C.D., 2002, Tsunami deposit mapping at Seaside,

Oregon, USA, in Bobrowski, P.T., ed., Geoenvironmental mapping: methods,

theory, and practice: Lisse, The Netherlands, A. A. Balkema Publishers, p. 629-

648.

Flück, P., Hyndman, R.D., and Wang, K., 1997, Three-dimensional dislocation model for

great earthquakes of the Cascadia subduction zone: Journal of Geophysical

Research, v. 102, p. 20,539-520,550.

Frankel, A.D., Mueller, C.S., Barnhard, T., Perkins, D.M., Leyendecker, E.V., Dickman,

N., Hanson, S., and Hopper, M., 1996, National seismic-hazard maps:

Documentation June 1996: U.S. Geological Survey, Open-File Report 96-532, p.

41.

Frankel, A.D., Petersen, M.D., Mueller, C.S., Haller, K.M., Wheeler, R.L., Leyendecker,

E.V., Wesson, R.L., Harmsen, S.C., Cramer, C.H., Perkins, D.M., and Rukstales,

K.S., 2002, Documentation for the 2002 Update of the National Seismic Hazard

Maps: U.S. Geological Survey, Open-File Report 02-420, p. 33.

Garcia, A.W., and Houston, J.R., 1975, Type 16 Flood Insurance Study: Tsunami

Predictions for Monterey and San Francisco Bays and Puget Sound: U.S. Army

Engineer Waterways Experiment Station, Technical Report H-75-17, p. 263.

Geist, E.L., 2002, Complex earthquake rupture and local tsunamis: Journal of

Geophysical Research, v. 107, p. doi:10.1029/2000JB000139.

Geist, E.L., 2005, Local Tsunami Hazards in the Pacific Northwest from Cascadia

Subduction Zone Earthquakes, U.S. Geological Survey Professional Paper 1661B,

p. 17.

Geist, E.L., in press, Local Tsunami Hazards in the Pacific Northwest from Cascadia

Subduction Zone Earthquakes, USGS Professional Paper 1661B.

Geist, E.L., and Dmowska, R., 1999, Local tsunamis and distributed slip at the source:

Pure and Applied Geophysics, v. 154, p. 485-512.

Geist, E.L., and Parsons, T., 2005, Probabilistic analysis of tsunami hazards: Natural

Hazards.

Geist, E.L., and Yoshioka, S., 1996, Source parameters controlling the generation and

propagation of potential local tsunamis along the Cascadia margin: Natural

Hazards, v. 13, p. 151-177.

Gelfenbaum, G., and Jaffe, B.E., 2003, Erosion and sedimentation from the 17 July 1998

Papua New Guinea tsunami: Pure and Applied Geophysics, v. 60, p. 1969-1999.

Gilpin, L.M., and Carver, G., 1992, Paleoseismicity of the SW extent of the 1964

Alaskan earthquake rupture zone, Kodiak Islands, Alaska, in Wadati Conference

on Great Subduction Earthquakes, Fairbanks, Alaska, p. 99-100.

González, F.I., Satake, K., Boss, E.F., and Mofjeld, H.O., 1995, Edge wave and nontrapped modes of the 25 April 1992 Cape Mendocino tsunami: Pure and Applied

Geophysics, v. 144, p. 409-426.
53

González, F.I., Titov, V.V., Mofjeld, H.O., Venturato, A.J., Simmons, S., Hansen, R.,

Combellick, R.A., Eisner, R.K., Hoirup, D., Yanagi, B., Yong, S., Darienzo, M.,

Priest, G.R., Crawford, G., and Walsh, T., 2005, Progress in NTHMP hazard

assessment: Natural Hazards, v. 35, p. 89-110.

Herrero, A., and Bernard, P., 1994, A kinematic self-similar rupture process for

earthquakes: Bulletin of the Seismological Society of America, v. 84, p. 1216-

1228.

Houston, J.R., and Garcia, A.W., 1974, Type 16 Flood Insurance Study: Tsunami

Predictions for Pacific Coastal Commnunities: U.S. Army Engineer Waterways

Experiment Station, Technical Report H-74-3.

Houston, J.R., and Garcia, A.W., 1978, Type 16 Flood Insurance Study: Tsunami

Predictions for the West Coast of the Continental United States: U.S. Army

Engineer Waterways Experiment Station, Technical Report H-78-26.

Hughes, S.A., 2004, Estimation of wave run-up on smooth, impremeable slopes using the

wave momentum flux parameter: Coastal Engineering, v. 51, p. 1085-1104.

Ippen, A.T., 1966, Estuary and Coastline Hydrodynamics, McGraw-Hill Book Co., 744

p.

Jaffe, B.E., and Gelfenbaum, G., 2002, Using tsunami deposits to improve assessment of

tsunami risk, in Solutions to Coastal Disasters '02, ASCE, p. 836-847.

Jaffe, B.E., Gelfenbaum, G., Rubin, D., Peters, R., Anima, R., Swensson, M., Olcese, D.,

Bernales, L., Gomez, J., and Riega, P., 2003, Tsunami deposits: Identification

and interpretation of tsunami deposits from the June 23, 2001 Peru tsunami, in

International Conference on Coastal Sediments 2003, World Scientific Publishing

Corp., p. 13.

Johnson, J.M., and Satake, K., 1999, Asperity distribution of the 1952 Great Kamchatka

earthquake and its relation to future earthquake potential in Kamchatka: Pure and

Applied Geophysics, v. 154, p. 541-553.

Kame, N., Rice, J.R., and Dmowska, R., 2003, Effects of prestress state and rupture

velocity on dynamic fault branching: Journal of Geophysical Research, v. 108, no.

B5, p. ESE 13-11 - 13-21.

Kanamori, H., 1972, Mechanism of tsunami earthquakes: Physics of the Earth and

Planetary Interiors, v. 6, p. 346-359.

Kanamori, H., and Kikuchi, M., 1993, The 1992 Nicaragua earthquake: a slow

earthquake associated with subducted sediments: Nature, v. 361, p. 714-716.

Kelsey, H.M., Witter, R.C., and Hemphill-Haley, E., 2002, Plate-boundary earthquakes

and tsunamis of the past 5500 years, Sixes River estuary, southern Oregon:

Geological Society of America Bulletin, v. 114, p. 298-314.

Kobayashi, M., Otta, A.K., and Roy, I., 1987, Wave reflection and runup on rough

slopes: Journal of the Waterways and Harbors Division, A.S.C.E., v. 113, p. 282-

298.

Leendertse, J.J., 1967, Aspects of Computational Model for Long-Period Water Wave

Propagation: The Rand Corp. RM-5294-PR.

Leonard, L.J., Hyndman, R.D., and Mazzotti, S., 2004, Coseismic subsidence in the 1700

great Cascadia earthquake: Coastal estimates versus elastic dislocation models:

Geological Society of America Bulletin, v. 116, p. 655-670.
54

Lin, I., and Tung, C.C., 1982, A preliminary investigation of tsunami hazard: Bulletin of

the Seismological Society of America, v. 72, p. 2323-2337.

Linde, A.T., and Silver, P.G., 1989, Elevation changes and the Great 1960 Chilean

earthquake: Support for aseismic slip: Geophysical Research Letters, v. 16, p.

1305-1308.

Liu, P.L.F., Cho, Y.S., Yoon, S.B., and Seo, S.N., 1995, Numerical simulations of the

1960 Chilean tsunami propagation and inundation at Hilo, Hawaii, in Tsuchiya,

Y., and Shuto, N., eds., Tsunami: Progress in Prediction, Disaster Prevention and

Warning, Kluwer Academic Publishers, p. 99-115.

Longuet-Higgins, M.S., and Stewart, R.W., 1964, Radiation stress in water waves: a

physical discussion, with applications: Deep-Sea Research, v. 11, p. 529-552.

Matthews, M.V., Ellsworth, W.L., and Reasenberg, P.A., 2002, A Brownian model for

recurrent earthquakes: Bulletin of the Seismological Society of America, v. 92, p.

2233-2250.

Mofjeld, H.O., González, F.I., Bernard, E.N., and Newman, J.C., 2000, Forecasting the

heights of later waves in Pacific-wide tsunamis: Natural Hazards, v. 22, p. 71-89.

Mofjeld, H.O., González, F.I., Titov, V.V., Venturato, A.J., and Newman, A.V., in press,

Effects of tides on maximum tsunami wave heights: Probability distributions:

Journal of Atmospheric and Oceanic Technology.

Mofjeld, H.O., Venturato, A.J., González, F.I., and Titov, V.V., 2004, Background tides

and sea level variations at Seaside, Oregon: NOAA, Technical Memorandum

OAR PMEL-126, p. 15.

Morton, R., Gelfenbaum, G., and Jaffe, B.E., in press, Physical criteria for distinguishing

sandy tsunami and storm deposits using modern examples: Journal of

Sedimentary Geology.

Nanayama, F., Satake, K., Furukawa, R., Shimokawa, K., Atwater, B.F., Shigeno, K., and

Yamaki, S., 2003, Unusually large earthquakes inferred from tsunami deposits

along the Kuril trench: Nature, v. 424, p. 660-663.

National Research Council (NRC), 2000, Risk Analysis and Uncertainty in Flood

Damage Reduction Studies: Washington, DC, National Academy Press, 202 p.

Natural Disaster Research, I., 2001, Probabilistic Tsunami Hazard Analysis: Aguadilla, Puerto

Rico: Sea Grant College Program, University of Puerto Rico, Project R-122-1-97,

p. 28.

Nishenko, S.P., 1991, Circum-Pacific seismic potential: 1989-1999: Pure and Applied

Geophysics, v. 135, p. 169-259.

Ogata, Y., 1999, Estimating the hazard of rupture using uncertain occurrence times of

paleoearthquakes: Journal of Geophysical Research, v. 104, p. 17,995-918,014.

Parsons, T., 2004, Recalculated probability of M ≥ 7 earthquakes beneath the Sea of

Marmara, Turkey: Journal of Geophysical Research, v. 109, p. B05304.

Parsons, T., submitted, Significance of stress transfer in time-dependent earthquake

probability calculations.

Peregrine, D.H., 1966, Calculation of the development of an undular bore: Journal of

Fluid Mechanics, v. 22, p. 321-330.

Peters, B., Jaffe, B.E., Gelfenbaum, G., and Peterson, C., 2003, Cascadia tsunami deposit

database: U.S. Geological Survey, Open-File Report 03-13, p. 19.
55

Petersen, M.D., Cramer, C.H., and Frankel, A.D., 2002, Simulations of seismic hazard

for the Pacific Northwest of the United States from earthquakes associated with

the Cascadia subduction zone: Pure and Applied Geophysics, v. 159, p. 2147-

2168.

Peterson, C.D., 1993, Field trip guide to Cascadia paleoseismic evidence along the

northern Oregon coast: Evidence of subduction zone seismicity in the central

Cascadia margin: Oregon Geology, v. 55, p. 99-114.

Peterson, C.D., Jaffe, B.E., and Peters, B., 2004, Estimating uncertainty and frequency of

high-velocity paleotsunami inundation from geologic records in back barrier

settings, test locality Cannon Beach, Oregon [abs.]: Eos, Transactions American

Geophysical Union.

Pinegina, T.K., Bourgeois, J., Bazanova, L., Melekestsev, I.V., and Braitseva, O.A.,

2003, A millennial-scale record of Holocene tsunamis on the Kronotskiy Bay

coast, Kamchatka, Russia: Quaternary Research, v. 59, p. 36-47.

Polet, J., and Kanamori, H., 2000, Shallow subduction zone earthquakes and their

tsunamigenic potential: Geophysical Journal International, v. 142, p. 684-702.

Priest, G.R., Myers, E., Baptista, A.M., Fleuck, P., Wang, K., Kamphaus, R.A., and

Peterson, C.D., 1997, Cascadia subduction zone tsunamis: Hazard mapping at

Yaquina Bay, Oregon: State of Oregon, Department of Geology and Mineral

Industries, Open-File Report O-97-34, p. 144.

Ramsden, J.D., 1993, Tsunamis: Forces on a Vertical Wall Caused by Long Waves,

Bores, and Surges on a Dry Bed: Pasadena, California, Californiat Institute of

Technology, Ph.D. Thesis, 279 p.

Ramsden, J.D., and Raichlen, F., 1990, Forces on vertical wall caused by incident bores:

Journal of Waterway, Port, Coastal, and Ocean Engineering, v. 116, p. 592-613.

Rikitake, T., and Aida, I., 1988, Tsunami hazard probability in Japan: Bulletin of the

Seismological Society of America, v. 78, p. 1268-1278.

Ruff, L.J., and Kanamori, H., 1983, The rupture process and asperity distribution of three

great earthquakes from long-period diffracted P-waves: Physics of the Earth and

Planetary Interiors, v. 31, p. 202-230.

Salgado, I., Eipert, A., Atwater, B.F., Shishikura, M., and Cisternas, M., 2003,

Recurrence of giant earthquakes inferred from tsunami sand sheets and subsided

soils in south-central Chile [abs.], in 2003 Geological Society of America Annual

Meeting, Seattle, WA.

Satake, K., Wang, K., and Atwater, B.F., 2003, Fault slip and seismic moment of the

1700 Cascadia earthquake inferred from Japanese tsunami descriptions: Journal of

Geophysical Research, v. 108, no. B11, p. ESE 7-1 - 7-17.

Scholz, C.H., 1990, The mechanics of earthquakes and faulting: Cambridge, Cambridge

University Press, 439 p.

Schwartz, S.Y., 1999, Noncharacteristic behavior and complex recurrence of large

subduction zone earthquakes: Journal of Geophysical Research, v. 104, p. 23,111-

123,125.

Senior Seismic Hazard Analysis Committee (SSHAC), 1997, Recommendations for

Probabilistic Seismic Hazard Analysis: Guidance on Uncertainty and Use of

Experts: U.S. Nuclear Regulatory Commission, Main Report NUREG/CR-6372

UCRL-ID-122160 Vol. 1, p. 256.
56

Soloviev, S.L., 1970, Recurrence of tsunamis in the Pacific, in Adams, W.M., ed.,

Tsunamis in the Pacific Ocean: Honolulu, East-West Center Press, p. 149-163.

Sornette, D., Knopoff, L., Kagan, Y.Y., and Vanneste, C., 1996, Rank-ordering statistic

of extreme events: Application to the distribution of large earthquakes: Journal of

Geophysical Research, v. 101, p. 13,883-813,893.

Takahashi, T., 1996, Benchmark Problem 4. The 1993 Okushiri tsunami--Data,

conditions and phenomena, in Yeh, H., Liu, P.L.F., and Synolakis, C.E., eds.,

Long wave runup models: Singapore, World Scientific Publishing Co., p. 384-

403.

Tanioka, Y., and González, F.I., 1998, The Aleutian earthquake of June 10, 1996 (Mw

7.9) ruptured parts of both the Andreanof and Delarof segments: Geophysical

Research Letters, v. 25, p. 2245-2248.

Tetra Tech Inc., 1981, Coastal Flooding Storm Surge Model: Federal Emergency

Management Agency, Part 1: Methodology.

Titov, V.V., and González, F.I., 1997, Implementation and testing of the Method of

Splitting Tsunami (MOST) model: NOAA, Technical Memorandum ERL PMEL112, p. 11.

Titov, V.V., Mofjeld, H.O., González, F.I., and Newman, J.C., 1999, Offshore

forecasting of Hawaiian tsunamis generated in Alaskan-Aleutian subduction zone:

Pacific Marine Environmental Laboratory (PMEL), NOAA Technical

Memorandum ERL PMEL-114, p. 26.

Titov, V.V., and Synolakis, C.E., 1995, Modeling of breaking and nonbreaking long

wave evolution and runup using VTSC-2: Journal of Waterway, Port, Coastal, and

Ocean Engineering, v. 121, p. 308-316.

Titov, V.V., and Synolakis, C.E., 1996, Numerical modeling of 3-D long wave runup

using VTCS-3, in Yeh, H., Liu, P.L.F., and Synolakis, C.E., eds., Long wave

runup models: Singapore, World Scientific Publishing Co., p. 242-248.

Titov, V.V., and Synolakis, C.E., 1997, Extreme inundation flows during the HokkaidoNansei-Oki tsunami: Geophysical Research Letters, v. 24, p. 1315-1318.

Titov, V.V., and Synolakis, C.E., 1998, Numerical modeling of tidal wave runup: Journal

of Waterway, Port, Coastal, and Ocean Engineering, v. 124, p. 157-171.

van Dorn, W.G., 1984, Some tsunami characteristic deducible from tide records: Journal

of Physical Oceanography, v. 14, p. 353-363.

Venturato, A.J., 2005, A digital elevation model for Seaside, Oregon: Procedures, data

sources, and analyses: NOAA, Technical Memorandum OAR PMEL-129, p. 21.

Ward, S.N., 1982, Earthquake mechanisms and tsunami generation: The Kurile Islands

event of 13 October 1963: Bulletin of the Seismological Society of America, v.

72, p. 759-777.

Ward, S.N., 1994, A multidisciplinary approach to seismic hazard in southern California:

Bulletin of the Seismological Society of America, v. 84, p. 1293-1309.

Ward, S.N., 2001, Landslide tsunami: Journal of Geophysical Research, v. 106, p.

11,201-211,215.

Wesson, R.L., Frankel, A.D., Mueller, C.S., and Harmsen, S.C., 1999, Probabilistic

Seismic Hazard Maps of Alaska: U.S. Geological Survey, Open-File Report 99-

36, p. 20.
57

Witter, R.C., Kelsey, H.M., and Hemphill-Haley, E., 2003, Great Cascadia earthquakes

and tsunamis of the past 6700 years, Coquille River estuary, southern coastal

Oregon: Geological Society of America Bulletin, v. 115, p. 1289-1306.

Yeh, H., Titov, V.V., Gusiakov, V., Pelinovsky, E., Khramushi, V., and Kaistrenko, V.,

1995, The 1994 Shikotan earthquake tsunami: Pure and Applied Geophysics, v.

144, p. 569-593.
Figure 1. Study area of the FEMA FIRM pilot project for Seaside, Oregon. Top panel

displays nested grids used by the model. Bottom panel details the study region.
Figure 2. Historical shoreline depicting the apparent Mean High Water line based on

orthophotography from various Federal and state agencies.
Figure 3. Accretion and erosion trends of the Necanicum River mouth (3.2 m/y).
Prom

E

G

Holladay

B

D

Roosevelt 12th

Beach

F

Wahanna

1st

Park

C

Downing Edgewood

Columbia Broadway

3rd 2nd

4th

5th 6th 7th 8

9th

th

Nita

H

Sunset

24th

Ocean

10th

Lincoln

Spruce

Avenue A

11th

Franklin

Avenue B

13th

15th

Pine

Necanicum

26th

Ocean Vista

25th

Cooper

Hilltop

Railroad Lewis and Clark

Oster

14th

Oregon Coast

Cedar

Skyline

Maple

Woodland

Oregon

16th

King 17th

Avenue T

Ridge

Avenue E

Irvine

Hager

Sundquist

Fernwood

Shoreline

Grove

Elk

Lewis

Laurel

Lea

Oceanway

Farrell

Reef

Airport

Jackson

Jeffrey Village

Avenue P

19th

Avenue U

1st

6th

D Park

3rd

H

4th

17th

9th

G

Wahanna

4th

123°57'0"W

123°56'0"W

123°56'0"W

123°55'0"W

123°55'0"W

123°54'0"W

123°54'0"W

45°59'0"N

45°59'0"N

46°0'0"N

46°0 0' "N

46°1'0"N

46°1'0"N

123°57'0"W

1964

Tsunami

Observations

(meters MHW)

(Horning, 1997)

no flood

0.1 - 1.0

1.1 - 2.0

2.1 - 3.0

3.1 - 4.0

4.1 - 5.0

5.1 - 5.9

ak64ln

0 1,000 2,000 4,000

Feet

Necanicum

R.

Neawanna Cr.

Neacoxie Cr.

(a)

(c)

(b)

Figure 4. Observations of the Alaska 1964 tsunami

event as described in Fiedorowicz (1997) and updated

by Horning (see Appendix C). The runup line is

based on observations (a) and tsunami deposits (b).

The values associated with each observation represent

runup elevation in meters based on a vertical datum of

Mean High Water. Meaning of different eyewitness

runup indicators listed in (a) described in Appendix

C. Locations of possible tsunami sand and mud

layers are provided in (b). (c) Major streets in

Seaside and Gearhart shown with tsunami observation

locations.
Figure 5. Estimated shoreline during historic tsunami events depicting Mean High Water

(solid) and Mean Lower Low Water (dashed). Shoreline from the most recent orthophoto

(2000) also displayed in rightmost panel.
(a)

(b)

Figure 6. (a) Bob Peters and Curt Peterson coring at Stanley Lake; (b) Bruce Jaffe digs a

trench while Curt Peterson cores along Neawanna Creek.
Figure 7. Locations of gouge core and trench sites visited by Tom Horning (1996),

Fiedorowicz and Peterson (1996), and Jaffe and others (2004).
(a)

(b)

Figure 8. (a) Deposit from 1964 tsunami in Neawanna Creek trench; (b) Deposit from

1700 tsunami exposed in Neawanna Creek cutbank.
Figure 9. Sites containing 1964 tsunami deposits.
Figure 10. Sites containing 1700 tsunami deposits.
Figure 11. Locations of older tsunami deposits.
(x,y, η)

(x,y, η)

n  ̇ 1

(x,y, n ̇ ) m

n  ̇ 2

n  ̇ 3

€ 

n  ̇ n

flow depth (η)

Recurrence Rate

Hazard Curve

r=0.01

Inundation Run #1

Final Hazard

Map

Aggregate & Combine 

with Tidal Probabilities

Inundation Run #3

Inundation Run #2

Inundation Run #n

n  ̇ 1

n  ̇ 2

n  ̇ 3

100-year inundation line

Scenario inundation line

Gather = 0.01 contours

from exceedance wave height

layers

n  ̇ 

Recurrence

Rate

Exceedance

Wave Height

η1

ηm

η3

η2

 

n  ̇ = 0.01

 

n  ̇ = 0.01

 

n  ̇ = 0.01

(a) (b)

Figure 12. General procedure for developing probabilistic tsunami hazard map of exceedance wave

height for the 0.01 yr

-1 recurrence rate. (a) Inundation runs for each source are associated with a

particular recurrence rate (i.e. earthquake probability). (b) All inundation runs in (a) are aggregated and

combined with tidal probabilities (Appendix E) to produce maps of contoured recurrence rate for a given

exceedance wave height (only 0.01 yr

-1 rate contour shown for each layer). Final hazard map produced

by gathering contours of a particular rate from exceedance wave height layers (Appendix G).
160 ̊E

160 ̊E

170 ̊E

170 ̊E

180 ̊

180 ̊

170 ̊W

170 ̊W

160 ̊W

160 ̊W

150 ̊W

150 ̊W

140 ̊W

140 ̊W

130 ̊W

130 ̊W

50 ̊N

50 ̊N

60 ̊N

60 ̊N

70 ̊N

70 ̊N

Western Zone

M = 9.2

Eastern Zone

M = 9.2

Tsunami Model 1

Figure 13. Model 1 for location of M=9.2 earthquakes along the Aleutian-Alaska

subduction zone. Red lines: faults with predominantly strike-slip motion.
160 ̊E

160 ̊E

170 ̊E

170 ̊E

180 ̊

180 ̊

170 ̊W

170 ̊W

160 ̊W

160 ̊W

150 ̊W

150 ̊W

140 ̊W

140 ̊W

130 ̊W

130 ̊W

50 ̊N

50 ̊N

60 ̊N

60 ̊N

70 ̊N

70 ̊N

Western Zone

M = 9.2

Eastern Zone

M = 9.2

Tsunami Model 2

Figure 14. Model 2 for location of M=9.2 earthquakes along the Aleutian-Alaska

subduction zone. Red lines: faults with predominantly strike-slip motion.
160 ̊E

160 ̊E

170 ̊E

170 ̊E

180 ̊

180 ̊

170 ̊W

170 ̊W

160 ̊W

160 ̊W

150 ̊W

150 ̊W

140 ̊W

140 ̊W

130 ̊W

130 ̊W

50 ̊N

50 ̊N

60 ̊N

60 ̊N

70 ̊N

70 ̊N

Eastern Zone

M = 8.2

Tsunami Model 3

Figure 15. Model for location of M=8.2 earthquakes along the Aleutian-Alaska

sbuduction zone in the Prince William Sound region. Red lines: faults with

predominantly strike-slip motion.
PNTA at Seaside

0

1

2

3

4

5

6

0 20 40 60 80 100

# Model Runs

Max-min,

 stan. dev. (m)

Figure 16. Level of variability in peak nearshore tsunami amplitudes (PNTA) offshore

Seaside as a function of the number of stochastic slip distributions used. Pink: difference

in maximum and minimum values of PNTA. Blue: standard deviation of PNTA va lues.
300

400

500

600

700

800

900

1000

Interevent time (yr)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Aperiodicity

Events:

285-302

810-1220

1279-1314

1590-1660

2470-2670

2895-2975

3355-3440

Interevent models: SW Washington

Mean of possible 

models

Mean from record

1

2

3

4

147 total hits Number

(a)

(b)

1 3 5 7 9 11 13 15 17

0

5

10

15

20

25

30

35

40

45

50

Number

30-year conditional probability (%) in 2005

Time-dependent probability models

5.9%

6.3%

SW Washington

Figure 17. Results of Monte Carlo analysis

of inter-event times and aperiodicity, using

paleoseismic horizons identified and dated

by Atwater and others (2004).
Figure 18. Definition sketch for the shoreline boundary computation.
Figure 19. Comparison of the 1993 Okushiri tsunami inundation model (crosses) with field observations

(circles) and stereo photo data (triangles). Top frame shows aerial photo of the modeled area used for the

stereo analysis of the inundation data. Middle frame illustrates the numerical grid used for the simulation of

the same area (dots are computational nodes, contours show topography data) and compares inundation

distances. Bottom frame compares maximum vertical runup for the same shoreline locations.
Model 14

Model 9-13

Model 1-8

Local Cascadia

sources - 12

Seaside

Figure 20. Source regions from FACTS database used for propagation modeling.
Figure 21. Two examples of coseismic vertical displacement field used as initial

conditions for local tsunami modeling.
tsunami propagarion

time = 3h:48m

after earthquake

Grid A

dx = 36 arc sec

Grid B

dx = 6 arc sec

Grid C

dx = 1/3 arc sec

Propagation grid

dx = 4 min

Seaside, OR

Hilo,HI

Tsunami propagation

time = 3h:48m

after earthquake

Grid A

dx = 36 arc sec

Grid B

dx = 6 arc sec

Grid C

dx = 1/3 arc sec

Propagation grid

dx = 4 min

Seaside, OR

Hilo,HI

Figure 22. Schematic of nesting grids for Seaside, Oregon, used in numerical

computations.
Hilo tide-gage

Hilo bathymetry

Figure 23. Schematic of nesting grids for Hilo, Hawaii, where the modeled 1964 tsunami

is compared with tide gauge records.
Prom

E

G

Holladay

B

D

Roosevelt 12th

Beach

F

Wahanna

1st

Park

C

Downing Edgewood

Columbia Broadway

3rd 2nd

4th

5th 6th 7th 8

9th

th

Nita

H

Sunset

24th

Ocean

10th

Lincoln

Spruce

Avenue A

11th

Franklin

Avenue B

13th

15th

Pine

Necanicum

26th

Ocean Vista

25th

Cooper

Hilltop

Railroad Lewis and Clark

Oster

14th

Oregon Coast

Cedar

Skyline

Maple

Woodland

Oregon

16th

King 17th

Avenue T

Ridge

Avenue E

Irvine

Hager

Sundquist

Fernwood

Shoreline

Grove

Elk

Lewis

Laurel

Lea

Oceanway

Farrell

Reef

Airport

Jackson

Jeffrey Village

Avenue P

19th

Avenue U

1st

6th

D Park

3rd

H

4th

17th

9th

G

Wahanna

4th

123°57'0"W

123°56'0"W

123°56'0"W

123°55'0"W

123°55'0"W

123°54'0"W

123°54'0"W

45°59'0"N

45°59'0"N

46°0'0"N

46°0 0' "N

46°1'0"N

46°1'0"N

123°57'0"W

1964

Tsunami

Observations

(meters MHW)

(Horning, 1997)

no flood

0.1 - 1.0

1.1 - 2.0

2.1 - 3.0

3.1 - 4.0

4.1 - 5.0

5.1 - 5.9

ak64ln

0 1,000 2,000 4,000

Feet

Necanicum

R.

Neawanna Cr.

Neacoxie Cr.

(a) (b)

Figure 24. (a) Predicted peak tsunami amplitudes from far-field sources at Seaside,

Oregon, compared with (b) eyewitness observations for the 1964 tsunami.
Figure 25. Screen-shot from FACTS database.
Figure 26. Example of how tsunami beaming from different earthquakes along the

Aleutian-Alaska subduction zone affect tsunami amplitudes at Seaside.
Figure 27. Maximum wave heights and currents for Sources 3 and 5 in Table 6. (a)

Source 3 maximum wave height, (b) Source 3 maximum current speed, (c) Source 5

maximum wave height, (d) Source 5 maximum current speed,
Figure 28. Comparison of inundation maps using (a) the distributed slip source described

in this study with (b) the uniform slip source (Model 1A) described by Priest and others

(1997). Map (c) is the difference of wave heights between (a) and (b).
3

3.5

4

3.5

4

3.5

3

3

3.5

4

3

3

4

123°57'0"W

123°57'0"W

123°56'0"W

123°56'0"W

123°55'0"W

123°55'0"W

45°59'0"N

45°59 0' "N

46°0'0"N

46°0 0' "N

46°1 0' "N

46°1'0"N

100-year tsunami:

Maximum tsunami

wave height (m)

with 0.010 probability

of exceedance

0.5 - 1.9

2.0 - 2.9

3.0 - 3.4

3.5 - 3.9

4.0 - 10.5

0 m MHW

0 0.5 1

Kilometers

UTM 10, NAD83

Figure 29. Tsunami wave heights (m) with a 1% annual probability of exceedance. Wave

heights include the effects of tides.
1 2 3

3.5

0.5 1.5 2.5 4 0.5

1.5

3.5

1.5

4

1 2.5

0.5

2

2

4

2.5

3

3

1

3.5

123°58'0"W

123°58'0"W

123°56'0"W

123°56'0"W

123°54'0"W

123°54'0"W

45°58'0"N

45°58'0"N

46°N

46°N

46°2'0"N

46°2'0"N

0 0.5 1 2

Kilometers

UTM 10, NAD83

100-year far+near

source tsunami:

maximum tsunami

wave height (m)

with 0.010 probability

of exceedance

exrate010 f+n

0.5 - 1.0 meter

1.1 - 2.0

2.1 - 3.0

3.1 - 4.0

4.1 - 10.5

FACTS polygon

1 2 3

3.5

0.5 1.5 2.5 4 2 1.5

3.5

2

1

0.5

4

2.5

3.5

1

0.5 1.5

4

3

2.5

3

123°58'0"W

123°58'0"W

123°56'0"W

123°56'0"W

123°54'0"W

123°54'0"W

45°58'0"N

45°58'0 N"

46°N

46°N

46°2 0' "N

46°2'0"N

0 0.5 1 2

Kilometers

UTM 10, NAD83

100-year far+near

source tsunami:

maximum tsunami

wave height (m)

with 0.010 probability

of exceedance

exrate010 AAmod1

0.5 - 1.0 meter

1.1 - 2.0

2.1 - 3.0

3.1 - 4.0

4.1 - 10.5

FACTS polygon

(a) (b)

Figure 30. Comparison of (a) the 100-year tsunami map shown in Figure 29 with (b) a

similar map in which only Model 1 for Aleutian-Alaska earthquakes is used.
2

3

4

5

6

8 7

9

10

4

2

3

2

2

4

4

2

6

5 6

7

7

6

5

2

4

6

6

9

6

7

6

3

6

3

6

5

10

9

7

2

10

6

7

9

2

3

3

3

5

9

9

2

4

2

5

4

6

3

4

5

6

8

5

5

7

3

3

4

5

6

6

8

4

4

6

4

5

6

3

8

9

123°57'0"W

123°57'0"W

123°56'0"W

123°56'0"W

123°55'0"W

123°55'0"W

45°59'0"N

45°59 0' "N

46°0'0"N

46°0 0' "N

46°1 0' "N

46°1'0"N

500-year tsunami:

Maximum tsunami

wave height (m)

with 0.002 probability

of exceedance

0.5 - 2.0 meters

2.1 - 4.0

4.1 - 6.0

6.1 - 8.0

8.1 - 10.5

0 m MHW

0 0.5 1

Kilometers

UTM 10, NAD83

Figure 31. Tsunami wave heights (m) with a 0.2% annual probability of exceedance.

Wave heights include the effects of tides.
6

5

7

8

9

10

1

23 4

7

6

1

2

2

2

7

7

4

5

2

6

5

4

6

6

5

8

9

3

7

6

8

3

3

3

6

1

6

7

6

2

5

8

1

4 5

3

9

1

4

7

6

10

6

6

4

3

4

5

8

6

1

6

123°58'0"W

123°58'0"W

123°56'0"W

123°56'0"W

123°54'0"W

123°54'0"W

45°58'0"N

45°58'0"N

46°N

46°N

46°2'0"N

46°2'0"N

0 0.5 1 2

Kilometers

UTM 10, NAD83

500-year far+near

source tsunami:

maximum tsunami

wave height (m)

with 0.002 probability

of exceedance

exrate002 f+n

0.5 - 2.0 meters

2.1 - 4.0

4.1 - 6.0

6.1 - 8.0

8.1 - 10.5

FACTS polygon

1 2 3

4

5

6

8

7 9

10

3

8

2

8

3 2

84

9

6

7

10

7

9

4

3

10

1

5

6

2

9

4

4 2 4 3

5

8 7

2

7

10

1

10

2

10

7

1

5 9

6

8

2

9

8

3

9

7

5

10

10

8 1

2

8

7 6

10 5

4

10

8

7

5

6

5

10

6

4

9 8

4

10

10

6

3

10 5

9

5

3

5

10

10

10

6

1

10

3

7

9

8

5

1

8

10

9

10

6

7

7

9

1

7

10

1

10

1 2

9

7

7

5

4

3

2

8

3

5

1

9

3

123°58'0"W

123°58'0"W

123°56'0"W

123°56'0"W

123°54'0"W

123°54'0"W

45°58'0"N

45°58'0"N

46°N

46°N

46°2 0' "N

46°2'0"N

0 0.5 1 2

Kilometers

UTM 10, NAD83

500-year far+near

source tsunami:

maximum tsunami

wave height (m)

with 0.002 probability

of exceedance

exrate002 TDmeanrec

0.5 - 2.0 meters

2.1 - 4.0

4.1 - 6.0

6.1 - 8.0

8.1 - 10.5

FACTS polygon

6

9

7

10

5

13

24

8

6 2

3

2

8 7

5

5

6

5

4

3

5

8

3

6

7

4

5

9

5

8

2

7

3

1

6

6

7

2

4

1

1

3 1 8

6

1

4

6

2

3

8

123°58'0"W

123°58'0"W

123°56'0"W

123°56'0"W

123°54'0"W

123°54'0"W

45°58'0"N

45°58'0"N

46°N

46°N

46°2'0"N

46°2'0"N

0 0.5 1 2

Kilometers

UTM 10, NAD83

500-year far+near

source tsunami:

maximum tsunami

wave height (m)

with 0.002 probability

of exceedance

exrate002 TDmeanmod

0.5 - 2.0 meters

2.1 - 4.0

4.1 - 6.0

6.1 - 8.0

8.1 - 10.5

FACTS polygon

(a) (b) (c)

Figure 32. Effect of mean inter-event time for Cascadia subduction zone earthquakes on

500-year tsunami map. (a) Map shown in Figure 31. (b) Map using a mean inter-event

time less than 500 years (477 years). (c) Map using a mean inter-event time of 610 years.

(See Figure 17.)
interplate

thrust EQ

characteristic 

Gutenberg-Richter

(p1)

(1-p1)

non-splay

splay

(p2)

(1-p2)

0

1/4

full

3/4

1/2

Magnitude

Distribution

Rupture

Geometry

Fraction of transition zone 

ruptured (as in NSHM)

Figure 33. Sample logic tree to include the effects of epistemic uncertainties for local

subduction zone earthquakes. The ellipses indicate replication of the branches from the

top level.
NOAA Technical Memorandum OAR PMEL

Background tides and sea level variations at Seaside,

Oregon

Harold O. Mofjeld1 and Angie J. Venturato2

1Pacific Marine Environmental Laboratory

7600 Sand Point Way NE

Seattle, WA 98115-6349

2Joint Institute for the Study of the Atmosphere and Ocean (JISAO)

University of Washington

Box 354235

Seattle, WA 98195-4235

August 2004

Contribution 2736 from NOAA/Pacific Marine Environmental Laboratory
NOTICE

Mention of a commercial company or product does not constitute an endorsement by

NOAA/OAR. Use of information from this publication concerning proprietary products

or the tests of such products for publicity or advertising purposes is not authorized.

Contribution No. 2736 from NOAA/Pacific Marine Environmental Laboratory

For sale by the National Technical Information Service, 5285 Port Royal Road

Springfield, VA 22161

ii
Contents iii

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. Tidal Datums . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

3. Tidal Harmonic Constants . . . . . . . . . . . . . . . . . . . . 6

4. Water Level Probability Distributions ............ 6

4.1 Observed Background Water Levels Near Seaside . . . . . . . 6

4.2 Predicted Background Water Levels at Seaside . . . . . . . . 10

5. Sea Level Trends . . . . . . . . . . . . . . . . . . . . . . . . . 12

6. Next Steps: Joint Probability Methods Versus Direct Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

7. Summary and Conclusions . . . . . . . . . . . . . . . . . . . . 13

8. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 14

List of Figures

1 Map showing Seaside and relevant tide stations ............ 2

2 Sample time series of observed water levels near Seaside ....... 3

3 Probability density functions from observed water levels ....... 8

4 Exceedance probabilities from observed water levels ......... 9

5 Same as Fig. 4 but with logarithm of exceedance probability .... 9

6 Probability density functions at South Beach and Seaside . . . . . . 11

7 Same as Fig. 7 but with logarithm of exceedance probability . . . . 11

List of Tables

1 Type of tide = (O1 + K1)/(M2 + S2) and sequence of tide =

M2◦−K1◦−O1◦ .............................. 5

2 M2 and K1 tidal harmonic constants in the Cascadia region ..... 5

3 Tidal datums relative to MLLW .................... 5

4 Tidal harmonic constants at Seaside, Toke Point, and South Beach . 7

5 Percentage heights and ranges from observed water levels . . . . . . 10

6 Observed sea level trends in the Cascadia region . . . . . . . . . . . 13
iv Contents
Background tides and sea level variations at Seaside, Oregon

Harold O. Mofjeld1 and Angie J. Venturato2

1. Introduction

The purpose of this technical brief is to provide a summary of the tides and

other sea level variations at Seaside, Oregon, the site for the FEMA FIRM

Tsunami Pilot Study. Because the tidal range is so large along the U.S.

West Coast, the tides and other sea level variations have a significant effect

on tsunami runup heights and inundation. For this reason, Houston and

Garcia (1978) used predicted tides when computing the 100- and 500-year

tsunami runup heights for the previous tsunami Flood Insurance Rate Maps

(FIRMs). Their method for including the tides is outlined in a July 2004

technical brief summarizing the Houston and Garcia (1978) technical report.

Since Seaside is not served by a long-term tide station, many tidal quantities that are relevant to tsunami mapping must be estimated by other means.

The results in this technical brief are based on inferences from NOAA tide

stations in the region and from the Eastern North Pacific ENPAC 2003 tide

model of Spargo et al. (in press). Tidal datums are available inside the

mouth of the Necanicum River from water level observations taken during

Nov 1971–Sept 1972; as we will see, there are substantial differences between

these and the inferred coastal datums. The locations of the tide stations used

in this technical brief are shown in Fig. 1.

NOAA has designated the 19-year period 1983–2001 as the official U.S.

National Tide Datum Epoch, and we have used observations from this NTDE

when these were available. It is fortunate that the time series of water

levels observed during this NTDE contain the largest El Ni ̃no events of the

Twentieth Century (1982–1983 and 1997–1998), as well as representative

distributions of other water level variations. This allows a useful comparison

between observed and predicted tides in the region.

This technical brief is organized into examples of tidal time series to give

a general characterization of the tides in the region (This section, 1), tidal

datums to provide information to develop DEMs and compare with tsunami

amplitudes (Section 2), tidal harmonic constants that can be used for tidal

prediction (Section 3), probability distributions including the average time

the water level is at or above various heights (Section 4), interseismic sea

level trends (Section 5), next steps to include background water levels in the

estimation of 100- and 500-yr tsunami heights at Seaside area (Section 6),

and conclusions on estimates of tides and other background water levels at

Seaside (Section 7).

The examples of observed water levels in Fig. 2 show that in the section of

the Cascadia region containing Seaside, the tides have very similar temporal

1NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle,

WA 98115-6349 2Joint Institute for the Study of the Atmosphere and Ocean (JISAO), University of

Washington, Box 354235, Seattle, WA 98195-4235
2 Mofjeld and Venturato

Figure 1: Map of the Central Cascadia region showing the locations of Seaside and relevant tide stations.
Background tides and sea level variations 3

Figure 2: Sample time series of observed water levels at the three nearest long-term

tide stations to Seaside.

patterns with some variation in amplitude between the tide stations. These

tides are mixed semidiurnal (Table 1). Substantial differences (Fig. 2) occur

between successive low waters; lesser differences occur between successive

high waters; and low and high waters follow the sequence LLW, LHW, HLW,

HHW. Both the amplitude and shape of the daily tidal curve are modulated

(Fig. 2) over fortnightly (two-week) and monthly time scales. The northward

increase (Table 2) of the harmonic constant amplitudes and phase lags is

consistent with northward propagating tidal waves that turn northwestward

in the Cascadia Bight. Descriptions of the tidal modeling and dynamics

in the Cascadia region, as well as references to previous work, are given by

Mofjeld et al. (1995), Foreman et al. (2000), Myers and Baptista (2001), and

Spargo et al. (in press).

Observations and tidal theory show that the amplitudes and shapes of

the daily tidal curve vary throughout the month due to variations in the

lunar phase (new moon, first quarter...), declination (meridional angle off the

equator), and parallax (distance from the earth). Significant modulations of

the tides also occur on seasonal (solstitial-equinoctal and solar parallax) and

interannual (18.6-year lunar nodal) time scales. For the latter reason, tidal

datums (like those in the next section) are ideally computed from 19 years

of observations (e.g., NTDE 1983–2001).
4 Mofjeld and Venturato

2. Tidal Datums

For the Tsunami Pilot Study, tidal datums are relevant in two ways. The

first is in developing the digital elevation model (DEM) for the tsunami

inundation model. The second is in providing a succinct summary of height

scales for background water levels to compare with tsunami wave heights.

Tidal datums are for the NTDE 1983–2001, computed from observations

during this period or adjusted vertically to be consistent with it (details

documented at NOAA’s www.co-ops.nos.noaa.gov website).

Table 3 gives the official tidal datums for stations in the region surrounding Seaside as reported by NOAA/NOS/CO-OPS (National Ocean

Service/Center for Operational Oceanographic Products and Services) and

NOAA/NGS (National Geodetic Survey). For Seaside itself, three sets of

datums are shown. The first set of coastal datums at Seaside were obtained

by the harmonic constant datum method (Mofjeld et al., 2004) using harmonic constants from the Spargo et al. (in press) tide model. The other set

of coastal datums at Seaside were linearly interpolated in latitude from the

observed datums at Hammond and Garibaldi (Fig. 1), the closest stations

to Seaside. The third set of Seaside datums are based on 11 months (Nov

1971–Sept 1972) of water level observations at the 12th Avenue Bridge over

the Necanicum River.

The coastal datums obtained by the two different methods are in relatively good agreement. For instance, the values of MHW relative to MLLW

(important when merging water depth and land elevation data) agree within

0.2 m. However, there is a significant difference (Table 3) between the coastal

datums and those inside the shallow river mouth. Of particular interest is the

0.76–0.96 m difference for MHW which is relevant to merging water depth

and land elevation data to form the digital elevation model for tsunami modeling. It is worth noting that reduced tidal ranges often occur in bays with

restrictive inlets (e.g., Kjerfve and Knoppers, 1991).

The larger difference (Table 3) between MLLW and the geodetic datum

NAVD 88 observed inside the river mouth is also consistent with restricted

tidal exchange through the inlet, especially near low tide when more water is retained in the estuary than would occur with free exchange. This

hypothesis is corroborated by a personal communication from Maria Little (NOAA/NOS/CO-OPS) in which she reports that a water level station

at the Seaside Sewage Plant (46◦ 0.4

N, 123◦ 55.1W), just inside the river

mouth, showed “...damping of the high waters and flat low waters, caused

by a distortion of the tide signal. There are no Accepted Values [of tidal

datums] for this station.” The tidal exchange may additionally be affected

by the changing nature of the river mouth as observed over the past 30 years.

The variations in the river mouth will be discussed in a technical brief by

Angie Venturato (in preparation). A high-resolution non-linear tide model

would be needed to fully understand the relationship between the tides on

the open coast at Seaside and those inside the Necanicum River mouth.
Background tides and sea level variations 5

Table 1: Type of tide = (O1 + K1)/(M2 + S2) and sequence of tide = M2◦−K1◦−O1◦ at the nearest long-term

tide stations to Seaside and inferred values for the open

coast at Seaside. The Type of Tide is mixed semidiurnal

when the amplitude ratio is between 0.25 and 1.5.

Station Type Sequence

(deg)

Toke Point 0.563 127.2

Astoria 0.549 126.4

Coastal Seaside 0.611 130.7

South Beach 0.621 132.4

Table 2: M2 and K1 tidal harmonic constants observed at coastal and estuarine tide stations in the Cascadia

region. These are the largest semidiurnal and diurnal constituents, respectively.

M2 K1

Station Latitude Longitude Amp Lag Amp Lag

(deg) (deg) H (m) G (deg) H (m) G (deg)

Neah Bay 48.3683 124.6167 0.787 246.3 0.497 248.3

Toke Point 46.7083 123.9650 0.981 253.7 0.435 251.1

South Bend 46.6633 123.7983 1.124 259.7 0.428 254.8

Astoria 46.2083 123.7667 0.945 264.2 0.403 256.2

Coastal Seaside 46.0017 123.9300 0.960 229.1 0.463 238.2

Depoe Bay 44.8100 124.0583 0.890 225.0 0.438 235.0

South Beach 44.6250 124.0433 0.902 231.1 0.443 237.7

Charleston 43.3450 124.3217 0.818 225.3 0.401 235.2

Port Orford 42.7400 124.4967 0.750 216.5 0.428 231.2

Crescent City 41.7450 124.1833 0.714 212.2 0.390 228.2

North Spit 40.7667 124.2167 0.710 220.0 0.411 236.1

Table 3: Tidal datums relative to MLLW at tide stations in the region surrounding Seaside.

The coastal Seaside datums were linearly interpolated in latitude using the datums at Hammond

and Garibaldi. Also shown are the maximum and minimum observed heights at the tide station

(where available) and the geodetic datums NGVD 29 and NAVD 88.

Coastal Coastal Estuarine South

Station Hammond Seaside Seaside Seaside Garibaldi Beach

Source: Observed Model Interpolated Observed* Observed Observed

Latitude 46.2017 46.0017 46.0017 46.0017 45.5550 44.6250

Longitude 123.9450 123.9300 123.9300 123.9200 123.9117 123.0433

To Entrance 7 km 0 km 0 km 2 km 2 km 4 km

(m) (m) (m) (m) (m) (m)

Max Obs 3.45 3.60 3.73

MHHW 2.54 2.74 2.52 1.77 2.48 2.54

MHW 2.33 2.51 2.31 1.55 2.26 2.33

MTL 1.36 1.47 1.35 0.84 1.35 1.38

MSL 1.34 1.46 1.34 0.83 1.33 1.36

MLW 0.39 0.44 0.39 0.12 0.40 0.42

NGVD29 0.23 1.16 1.26

NAVD 88 −0.01 −0.03 to 0.02 −0.88 0.10 0.23

MLLW 0.00 0.00 0.00 0.00 0.00 0.00

Min Obs −0.90 −0.88 −1.07

*Maria Little, NOAA/NOS/CO-OPS, personal communication
6 Mofjeld and Venturato

3. Tidal Harmonic Constants

Tidal predictions rely on harmonic constants (HCs) that are specific to the

location of interest. For the open coast at Seaside (46◦ 00.1

N, 123◦ 55.8W),

the HCs for the O1, K1, N2, M2, and S2 constituents were computed from

the Spargo et al. (in press) model. The minor diurnal and semidiurnal HCs

(Table 4) were inferred from South Beach amplitude ratios and phase differences using the method outlined by Schureman (1976). The long period HCs

are based on those at South Beach. The higher frequency tides (2SM2, ...)

are set to zero amplitude because of lack of information. However, they are

likely to be small because the continental shelf west of Seaside is narrow and

deepens relatively rapidly; this limits greatly the shallow water effects that

generate such constituents. Also shown for comparison are observed HCs at

Toke Point and South Beach (Fig. 1). The 37 constituents in Table 4 are

the same as those used by NOAA for its official tidal predictions.

4. Water Level Probability Distributions

Probability distributions serve to characterize the general behavior of background water levels in terms of the duration of time spent at various ranges

of height. To apply this method, the probability distribution functions (pdfs)

are computed from observed tide gage records when these are available, or

from predicted tides.

4.1 Observed Background Water Levels Near Seaside

Shown in Fig. 3 are the observed pdfs at Toke Point, Astoria, and South

Beach (Fig. 1). The pdfs were computed by first interpolating the verified

hourly observations to 15-minute values using cubic interpolation. The 15-

minute time interval was chosen to allow accurate estimates for the heights

of the individual high and low tides that may not be resolved with 1-hr sampling. The height values in the time series were binned using a 0.1 m height

interval for each bin to form a histogram for each station. The histograms

were then renormalized so that the total (sum) over the full range of heights

is unity (1.0), as required by probability theory.

Figure 3 shows that the pdfs at the three stations tend to be large around

the frequently occurring high and low water stands, when the height is changing slowly, but much smaller at the extreme ranges that occur only rarely

in time. The pdfs at these stations are very similar. The slight upward displacement of the upper Toke Point and Astoria curves relative to the South

Beach pdf is due to the slightly larger tidal ranges at these two stations.

The shapes of the pdf curves are somewhat different near the central peaks

(pdf > 0.3); but overall, the pdfs have very similar shapes and widths (Table 5) and similar maximum values. This is no surprise since the distances

between the stations are small compared with the alongshore variations in

tides in the Cascadia region (Mofjeld et al., 1995; Foreman et al., 2000; Myers and Baptista, 2001, Spargo et al., in press). The same should therefore

be true on the open coast of Seaside since it is located between these tide
Background tides and sea level variations 7

Table 4: Tidal harmonic constants (37 constituents) and the mean relative to MLLW for the open

coast at Seaside and at the Toke Point and South Beach tide stations.

Station: Toke Point Coastal Seaside South Beach

Location: 46.7083, 123.9650 46.0017, 123.9300 44.6250, 124.0433

Constituent Amplitude Phase Lag Amplitude Phase Lag Amplitude Phase Lag

H (m) G (deg) H (m) G (deg) H (m) G (deg)

mean 1.458 1.441 1.358

SA 0.158 289.8 0.123 281.6 0.123 281.6

SSA 0.000 0.0 0.019 258.7 0.019 258.7

MM 0.000 0.0 0.027 174.7 0.027 174.7

MSF 0.000 0.0 0.000 0.0 0.000 0.0

MF 0.024 161.8 0.019 155.0 0.019 155.0

2Q1 0.006 221.0 0.007 202.2 0.007 204.3

Q1 0.047 230.2 0.050 216.7 0.048 213.4

RHO 0.011 208.7 0.011 212.4 0.010 213.8

O1 0.264 235.4 0.288 220.2 0.269 221.0

M1 0.016 270.3 0.016 252.2 0.015 250.7

P1 0.136 249.2 0.144 235.5 0.137 234.2

S1 0.006 40.7 0.012 20.4 0.012 20.4

K1 0.435 251.1 0.463 238.2 0.443 237.7

J1 0.023 267.6 0.029 256.8 0.027 255.0

OO1 0.016 292.6 0.016 276.4 0.015 273.1

2N2 0.022 204.6 0.022 178.2 0.021 183.2

MU2 0.008 253.2 0.016 197.2 0.015 201.1

N2 0.198 229.7 0.196 203.7 0.187 207.2

NU2 0.044 228.6 0.038 206.1 0.036 209.5

M2 0.981 253.7 0.960 229.1 0.902 231.1

LAM2 0.009 269.3 0.006 241.2 0.006 243.8

L2 0.033 266.3 0.026 243.6 0.024 246.4

T2 0.017 270.5 0.015 250.6 0.014 253.8

S2 0.261 284.4 0.269 255.2 0.244 258.6

R2 0.002 285.6 0.002 255.2 0.002 258.6

K2 0.071 279.6 0.070 247.4 0.066 250.2

2SM2 0.000 0.0 0.000 0.0 0.003 63.5

2MK3 0.005 64.3 0.000 0.0 0.000 0.0

M3 0.000 0.0 0.000 0.0 0.000 0.0

MK3 0.004 10.9 0.000 0.0 0.004 172.3

MN4 0.006 335.6 0.000 0.0 0.005 164.8

M4 0.014 352.6 0.000 0.0 0.013 189.0

MS4 0.009 30.5 0.000 0.0 0.007 229.5

S4 0.000 0.0 0.000 0.0 0.000 0.0

M6 0.009 45.3 0.000 0.0 0.008 300.7

S6 0.000 0.0 0.000 0.0 0.000 0.0

M8 0.000 0.0 0.000 0.0 0.000 0.0
8 Mofjeld and Venturato

Figure 3: Probability density functions computed from observed water levels at

the nearest long-term tide stations to Seaside.

gage stations. Other differences in the pdfs are presumably due in part to

local tidal dynamics within the three bays where the tide gages are located

and to different inlet effects on the tides in these bays.

Integrating the pdfs (Fig. 3) downward in height gives the cumulative

exceedance probability P(η), where η is the height above MLLW. P(η) represents the fraction of the time, on average, that the water level is above the

height η. As shown in Fig. 4, the exceedance probabilities decrease approximately linearly with increasing height η from MLLW (η = 0) to P = 0.1

(10% probability of exceedance). The differences in P between the three

tide stations increases upward in height to a 0.19 m difference between Toke

Point and South Beach at P = 0.1.

The values of P at larger heights are relevant to the exceedance probabilities of observed storm surges. When plotted on a log(P) scale, the exceedance curves (Fig. 5) take an asymptotic form that can be fit to extreme

value distributions. Like the tsunami probability distributions, additional

calculations using time series of storm surges, especially their duration in

time, need to be performed before distributions like those in Fig. 5 can be

used to determine event heights for various recurrence intervals (Pugh, 1987,

2004).

The percentage heights and ranges shown in Table 5, as well as the

magnitudes of the tidal datums given in Table 3, indicate that tides are large

enough to have important effects on the height probabilities of tsunamis at

Seaside. The percentage height ranges (Table 5) for the three stations are
Background tides and sea level variations 9

Figure 4: Exceedance probabilities computed from observed hourly water levels

at the nearest long-term tide stations to Seaside.

Figure 5: Same as Fig. 4 but with the logarithm of exceedance probability plotted

against height.
10 Mofjeld and Venturato

Table 5: Percentage heights and ranges, based on the probability distribution functions derived from observed water levels during the National

Tidal Datum Epoch 1983–2001. For a given percentage probability, a

random background water level will occur within the height range between the upper and lower bounds.

Station Height Ranges

Bounds (%): 50% Height 50% 80% 90% 98%

(m) (m) (m) (m) (m)

Toke Point 1.47

Upper 2.00 2.30 2.50 2.82

Lower 0.70 0.21 −0.05 −0.46

Range 1.30 2.09 2.55 3.28

Astoria 1.35

Upper 2.00 2.40 2.60 2.94

Lower 0.70 0.21 −0.02 −0.33

Range 1.30 2.19 2.62 3.27

South Beach 1.36

Upper 2.07 2.49 2.71 3.10

Lower 0.80 0.27 −0.01 −0.45

Range 1.27 2.22 2.72 3.55

very similar, differing by less than 0.2 m for probabilities of 90% or less.

Hence, these statistics are spatially uniform within a few tenths of a meter

at Toke Point, Astoria, and South Beach. By implication, the same is true

along the open coast at Seaside.

4.2 Predicted Background Water Levels at Seaside

Houston and Garcia (1978) used predicted tides to compute the effects of

background water level fluctuations on the 100- and 500-year tsunami exceedance heights along the West Coast. Since observed water levels are

available at only a limited number of sites along the West Coast and these

are typically within embayments, there is an issue as to whether predicted

tides can be used for the next generation of tsunami flood maps.

As a measure of the differences between the observed and predicted water

levels that might occur at Seaside, a comparison between these was made for

the water levels at South Beach. These are then compared with the predicted

tides at Seaside. The pdf and exceedance probabilities were computed from

19-year (NTDE 1983–2001) time series of hourly tidal predictions (cubic

interpolated to 15-minute values to be consistent with the procedures used on

the observed time series). The South Beach and Seaside harmonic constants

used to generate the predictions are shown in Table 4. The height exceedance

probabilities in Figs. 6 and 7 provide a measure of how much error would

occur on average by using predicted rather than observed water levels for

Seaside, since both South Beach and Seaside are located within the same

meteorological and oceanographic regimes. The differences in the Seaside

curves relative to those for South Beach are primarily due to the larger tidal

range at Seaside, as seen in Figs. 3 and 5 for Astoria and Toke Point.

From the standpoint of probabilities (Figs. 6 and 7), there is very close
Background tides and sea level variations 11

Figure 6: Probability density functions (pdfs) computed from predicted water

levels at South Beach and Seaside. Also shown is the pdf computed from the

observed water levels at South Beach.

Figure 7: Same as Fig. 6 but with the logarithm of exceedance probability plotted

against height.
12 Mofjeld and Venturato

agreement between the observations and predictions for South Beach. What

differences there are (Figs. 6 and 7) between the probabilities based on observed and predicted water levels are due primarily to the storm surges and

other non-tidal water level fluctuations that are not included in the tidal

predictions. These give rise to the greater heights at low exceedance probabilities (Fig. 7). However, the chances are much less that the maximum

wave in a tsunami will arrive at Seaside at the same time as a winter storm,

occurring during a major El Ni ̃no, than at lower heights where there is a

very close match between the observed and predicted tides.

5. Sea Level Trends

When presenting the broad spectrum of coastal water levels, it is appropriate to also include a discussion of sea level trends. This is true even though

they are not immediately relevant to the specific issues of computing 1-

percentage-annual-chance and 0.2-percentage-annual-chance quantities that

are FEMA’s definitions for the 100- and 500-yr exceedance values, respectively.

The sea level trends (Table 6) in the Cascadia region vary considerably

between long-term tide stations. This indicates different vertical ground

movement in the vicinity of the tide gages that are significant in magnitude

compared with the present ∼1.4 mm/yr of global sea level rise (Intergovernmental Panel on Climate Change (IPCC), 2001; Pugh, 2004). The upward

ground movement at Neah Bay and Crescent City (Fig. 1) is large enough

to overwhelm oceanic sea level rise and produce negative trends that are

significant at the 95% confidence level. In contrast, the observed trend at

South Beach indicates continuing subsidence of the land near the tide gage.

The variation in trends (Table 6) along the length of Cascadia shows

that the interseismic tectonic processes presently affecting relative sea level

also vary with location along the coast. Given this variability, it is difficult to estimate the sea level trend at Seaside based solely on the observed

trends elsewhere in the region. The observed trends at other sites serve as

placing some bounds of the Seaside trend and, taken together with other information from GPS observations and geological interpretation, may help to

further constrain estimates of the interseismic Seaside trend. There is also

the important issue of coseismic ground movement (subsidence or uplift)

during regional Cascadia SZ earthquakes.

6. Next Steps: Joint Probability Methods Versus

Direct Calculations

One method for including tides and other water level fluctuations in the

probability estimates of coastal flooding is via the joint probability method

(JPM) described by, e.g., Tawn and Vassie (1991) and Pugh (1987, 2004).

This method convolves (integrates their product over height) the pdf of the

background water levels at a given coastal location with the pdf of modeled

storm surges to get the total exceedance probability. The revised method
Background tides and sea level variations 13

Table 6: Observed sea level trends at tide stations in the Cascadia region.

Shown are trends in relative sea level, which are the sum of the separate vertical

movements of the water and the land. Note that the uncertainty in the trend

decreases with increasing series length.

Station Trend Stnd. Dev. 95% C I Start End Length

(mm/yr) (mm/yr) (mm/yr) (years)

Neah Bay −1.41 0.22 0.43 1934 1999 66

Toke Point 2.82 1.05 2.06 1973 1999 27

Astoria −0.16 0.24 0.47 1925 1999 75

South Beach 3.51 0.73 1.43 1967 1999 33

Charleston 1.74 0.87 1.71 1970 1999 30

Crescent City −0.48 0.23 0.45 1933 1999 67

(Tawn and Vassie, 1991) adjusts the probabilities to take into account the

non-random nature of the tides and the persistence in time (redundancy) of

both the tides and the additional flooding event (e.g., storm surge), as well

as possible non-linear interactions between tsunamis and the tides. A major

issue in applying the revised JPM method to the tsunami problem would

be finding the redundancy factors for the model tsunamis and background

water levels.

An alternative to this method is that of Houston and Garcia (1978),

in which each model tsunami is slid along the time series of background

water levels. Since the time series of the model tsunamis are relatively

short compared to the duration of significant waves in observed tsunamis,

the model time series need to be extended in time. This can be done by

assuming exponential amplitude decay, which Mofjeld et al. (2000) have

shown matches closely the observed decay of Pacific tsunamis.

7. Summary and Conclusions

For tides on the open coast at Seaside, Oregon, a comparison has been

made of those predicted by the Spargo et al. (in press) tide model with

the observed and predicted water levels at long-term tide stations in the

region. This comparison shows there is close agreement for many of the

tidal quantities that are relevant to probabilistic tsunami mapping. This is

fortunate, since Seaside is not served by a long-term tide station. The tidal

datums inferred from the model harmonic constants and those interpolated

from observed values at Hammond and Garibaldi (Fig. 1) are found to be

within 0.05 m for MHW relative to MLLW. Having an accurate estimate of

this height difference is essential for generating the digital elevation model

(DEM) for tsunami modeling. There are substantial differences between the

coastal datums at Seaside and those observed within the Necanicum River,

possibly due to restricted tidal exchange through the river mouth.

Close agreement exists between probability distributions at the long-term

South Beach station and those at the Seaside open coast. This is due in part

to the proximity of the South Beach station to the coast and the free tidal

exchange through the inlet to Yaquina Bay. Conversely, the comparison
14 Mofjeld and Venturato

of tidal quantities at Toke Point, Astoria, and South Beach (Fig. 1) reveals

greater differences, probably at least partly due to local tidal dynamics in the

bays where these tide stations are located. However, the exceedance curves

from all the tide stations are very close to each other and, by implication, to

that on the outer coast at Seaside. These results also suggest that predicted

tides should be adequate when linearly combining coastal tsunami heights

with background water levels to estimate probabilities.

The goal of the Tsunami Pilot Study is to develop methods that can

be applied to probabilistic tsunami hazard assessment and mapping along

the U.S. West Coast. The analyses and comparison outlined in this technical brief, together with the distribution of long-term tide stations along the

Coast, suggest that the methods used here will work effectively to characterize background water levels at other locations where direct long-term water

level observations are not available.

8. Acknowledgments

The authors wish to thank Marie Little, Emily Spargo, Ed Myers, and Mike

Foreman for providing datums, harmonic constants, and other useful tidal

information.

References

Foreman, M.G.G., W.R. Crawford, J.Y. Cherniawsky, R.F. Henry, and M.R. Tarbotton (2000): A high-resolution assimilating tidal model for the northeast

Pacific Ocean. J. Geophys. Res., 105 (C12), 28,629–28,651.

Houston, J.R., and A.W. Garcia (1978): Type 16 Flood Insurance Study: Tsunami

predictions for the West Coast of the Continental United States. USACE USACE Waterways Experimental Station Tech. Rprt H-78-26.

Intergovernmental Panel on Climate Change (IPCC) (2001): Climate Change 2001:

Impacts, Adaptation and Vulnerability. Cambridge Univ. Press, 1032 pp.

Kjerfve, B., and B.A. Knoppers (1991): Tidal choking in a coastal lagoon. In Tidal

Hydrodynamics, B.B. Parker (ed.), Wiley, 169–181.

Mofjeld, H.O., F.I. Gonz ́alez, E.N. Bernard, and J.C. Newman (2000): Forecasting

the heights of later waves in Pacific-wide tsunamis. Nat. Hazards, 22 , 71–89.

Mofjeld, H.O., F.I. Gonz ́alez, M.C. Eble, and J.C. Newman (1995): Ocean tides

in the continental margin off the Pacific Northwest Shelf. J. Geophys. Res.,

100 (C6), 10,789–10,800.

Mofjeld, H.O., A.J. Venturato, F.I. Gonz ́alez, V.V. Titov, and J.C. Newman (2004):

The harmonic constant datum method: Options for overcoming datum discontinuities at mixed-diurnal tidal transitions. J. Atmos. Oceanic Tech., 21 , 95–104.

Myers, E.P., and A.M. Baptista (2001): Inversion for tides in the Eastern North

Pacific Ocean. Adv. Water. Resour., 24 (5), 505–519.

Pugh, D.T. (1987): Tides, Surges and Mean Sea-Level. Wiley, 472 pp.

Pugh, D.T. (2004): Changing Sea Levels: Effects of Tides, Weather and Climate.

Cambridge Univ. Press, 265 pp.

Schureman, P. (1976): Manual of Harmonic Analysis and Prediction of Tides. C&GS

Spec. Publ. No. 98, U.S. Government Printing Office, 317 pp.

Spargo, E., J. Westerink, R. Luettich, and D. Mark (2004): Developing a tidal

constituent database for the Eastern North Pacific Ocean. In Proceedings of the
Background tides and sea level variations 15

8th International Conference on Estuarine and Coastal Modeling, Monterey,

CA, 3–5 November 2003, in press.

Tawn, J.A., and J.M. Vassie (1991): Recent improvements in the joint probability

method for estimating extreme sea levels. In Tidal Hydrodynamics, B.B. Parker

(ed.), Wiley, 813–828.

Venturato, A.J. (in preparation): A digital elevation model for Seaside, Oregon:

Procedures, data sources, and analyses. TsuPilot Technical Brief.
UNITED STATES

DEPARTMENT OF COMMERCE

Carlos M. Gutierrez

Secretary

NATIONAL OCEANIC AND

ATMOSPHERIC ADMINISTRATION

VADM Conrad C. Lautenbacher, Jr.

Under Secretary for Oceans

and Atmosphere/Administrator

Office of Oceanic and 

Atmospheric Research

Richard W. Spinrad

Assistant Administrator

NOAA Technical Memorandum OAR PMEL-129

A DIGITAL ELEVATION MODEL FOR SEASIDE, OREGON:

PROCEDURES, DATA SOURCES, AND ANALYSES

Angie J. Venturato

Joint Institute for the Study of the Atmosphere and Ocean (JISAO)

University of Washington, Seattle, WA

Pacific Marine Environmental Laboratory

Seattle, WA

November 2005
NOTICE

Mention of a commercial company or product does not constitute an endorsement by

NOAA/OAR. Use of information from this publication concerning proprietary products

or the tests of such products for publicity or advertising purposes is not authorized.

Contribution No. 2812 from NOAA/Pacific Marine Environmental Laboratory

For sale by the National Technical Information Service, 5285 Port Royal Road

Springfield, VA 22161

ii
Contents iii

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

3. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3.1 Data Sources and Processing . . . . . . . . . . . . . . . . . . 3

3.1.1 Shoreline . . . . . . . . . . . . . . . . . . . . . . . . 3

3.1.2 Bathymetry . . . . . . . . . . . . . . . . . . . . . . 5

3.1.3 Topography . . . . . . . . . . . . . . . . . . . . . . . 6

3.2 DEM Development . . . . . . . . . . . . . . . . . . . . . . . . 10

3.3 Quality Assurance . . . . . . . . . . . . . . . . . . . . . . . . 10

3.4 Historical shoreline analysis . . . . . . . . . . . . . . . . . . . 13

4. Summary and Conclusions . . . . . . . . . . . . . . . . . . . . 14

5. Data Credit . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

6. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 16

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

List of Figures

1 Study area of the FEMA FIRM project for Seaside, Oregon. . . . . . 2

2 Estimated contours used to correct vertical datums to Mean High

Water. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

3 Shoreline vector analysis . . . . . . . . . . . . . . . . . . . . . . . . . 4

4 Estimated depth surface of the Necanicum River estuarine region. . 6

5 Coverage areas of bathymetric data sources used in the DEM . . . . 7

6 Coverage area of topographic data sources used in DEM development 7

7 Topographic data analyses . . . . . . . . . . . . . . . . . . . . . . . . 9

8 Steps of DEM development: (a) bathymetric grid, (b) topographic

grid, and (c) merged grid. . . . . . . . . . . . . . . . . . . . . . . . . 11

9 Spatial density distribution . . . . . . . . . . . . . . . . . . . . . . . 11

10 Historical shoreline depicting the apparent Mean High Water line

based on orthophotography obtained from the University of Oregon 13

11 Accretion and erosion trends of the Necanicum River mouth over a

55-year period. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

List of Tables

1 Parameters set by the modeler for the high-resolution DEM. . . . . . 3

2 Shoreline data sources. . . . . . . . . . . . . . . . . . . . . . . . . . . 5

3 Bathymetric data sources from the National Ocean Service . . . . . 5

4 Topographic data sources . . . . . . . . . . . . . . . . . . . . . . . . 8

5 Comparison of NGS vertical control points with the DEM . . . . . . 12

6 Estimate of total DEM error. . . . . . . . . . . . . . . . . . . . . . . 15

A Digital Elevation Model for Seaside, Oregon: Procedures, Data

Sources, and Analyses

Angie J. Venturato1

1. Introduction

As part of a probabilistic tsunami hazard assessment pilot study to modernize Flood Insurance Rate Maps for the Federal Emergency Management

Agency (Gonz ́alez et al., 2004), the NOAA Center for Tsunami Inundation Mapping Efforts (TIME) developed a digital elevation model (DEM)

for the purpose of modeling tsunami inundation for Seaside, Oregon. The

finite-difference inundation model requires a series of nested computational

elevation grids to simulate tsunami generation, propagation, and inundation

in the region of interest (Fig. 1). To properly simulate the non-linear wave

dynamics of inundation, a high-resolution DEM merging land and seafloor

elevations is required (Gonz ́alez et al., 2005). A merged DEM with a resolution of 1/3 arc-seconds (approximately 10 meters) was developed for the

Seaside, Oregon area. This technical memorandum provides a summary of

the data sources and methodology used.

2. Study Area

The study area covers the coastal communities of Seaside and Gearhart in

Clatsop County, Oregon. The Seaside-Gearhart area has a population of

6900 based on 2000 U.S. Census data, with a projected growth rate of 13%

within the next decade (Clatsop County, 2005). The area’s economy is primarily based on tourism with tens of thousands of visitors during the peak

summer season (Oregon Coast Visitors Association, personal communication). Several tourist accommodations line the promenade, a 2-mile concrete

boardwalk along the ocean beachfront.

Seaside is part of the Clatsop Plains, which is a low-lying coastal area

from the Columbia River to Tillamook Head abutted on the east side by the

hills of the Oregon Coast Range (Fig. 1b). Soils consist primarily of sand

dune ridges and silt loam (USDA Natural Resources Conservation Service,

1994). The sand ridges run parallel to the ocean shore due to littoral accretion from the Columbia River (Fiedorowicz, 1997). Beachgrass and shrubs

along sand ridges and Sitka spruce inland are the main vegetation types

outside of urban areas (Oregon Natural Heritage Program, 1999).

The Necanicum River flows through the center of Seaside where it joins

the Neawanna and Neacoxie Creeks, forming an estuary bay before draining

into the Pacific Ocean (Fig. 1c). The Necanicum River bar-built estuary has

a low water volume with a watershed of approximately 225 square kilometers

(Oregon Ocean-Coastal Management Program, 2000).

1Joint Institute for the Study of Atmosphere and Ocean (JISAO), University of Washington, Box 354235, Seattle, WA 98195-6349
2 Venturato

Figure 1: Study area of the FEMA FIRM project for Seaside, Oregon. (a) The nested grids used by the

model. (b) Display of the Columbia River littoral cell with associated NOS water-level stations. (c) Details

of the study region.
Digital Elevation Model for Seaside, Oregon 3

3. Methodology

The modeler set parameters for the grid based on input requirements for

the inundation model (Table 1). Other criteria, such as obtaining the best

available data, were also established in the planning process using recommended practices from prior inundation modeling efforts (Gonz ́alez et al.,

2005). Data processing, grid assembly, and quality assurance are described

in the following subsections.

3.1 Data Sources and Processing

Bathymetric, topographic, shoreline, control, and orthophotographic data

were obtained from various government agencies. The primary data sources

were obtained from the U.S. Geological Survey (USGS), NOAA National

Ocean Service (NOS), and the Oregon Geospatial Data Clearinghouse

(OGDC). Datasets were converted into formats compatible with ESRI

ArcView GIS© 3.3.

Data sets were corrected to the common data framework established by

modeler parameters. The ArcView Projection Utility was used to convert

projected horizontal coordinates and datums to geographic coordinates and

NAD83. Vertical datum transformation was applied based on the methodology described in Mofjeld et al. (2004). A vertical datum surface was

developed using linearly interpolated values from official NOS datums at

Hammond and Garibaldi, Oregon and a historical tertiary NOS water-level

station in the Necanicum River estuary (Fig. 2).

3.1.1 Shoreline

Vector data representing the Mean High Water line were collected from the

NOAA Shoreline Data Explorer and OGDC (Table 2). Significant discrepancies were found when comparing datasets (Fig. 3a). Orthorectified photographs conducted in 2000 by the National Aerial Photography Program

(NAPP) were obtained from OGDC to perform a visual analysis (Table 2). A

Table 1: Parameters set by the modeler for the high-resolution

DEM.

Pilot study site Seaside, Oregon

Coverage area West boundary: −124.04

East boundary: −123.89

North boundary: 46.08

South boundary: 45.90

Coordinate system Geographic decimal degrees

Horizontal datum North American Datum of 1983 (NAD83)

Vertical datum Mean High Water

Vertical units Meters

Grid resolution 1/3 arc-seconds

Grid format ASCII raster grid
4 Venturato

Figure 2: Estimated contours used to correct vertical datums to Mean High Water. Values are based on

Mofjeld et al. (2004).

Figure 3: Shoreline vector analysis. (a) Shoreline vectors from the Bureau of Land Management (BLM)

in green, National Ocean Service (NOS) in red, and U.S. Geological Survey (USGS) in blue. (b) Corrected

shoreline vectors used in DEM development. The dashed line represents apparent Mean Lower Low Water

based on recent orthophotos. The solid line represents Mean High Water.
Digital Elevation Model for Seaside, Oregon 5

Table 2: Shoreline data sources.

Source Survey(s) Year Scale

NOAA Shoreline Data Explorer OR43C04 1926 1:20,000

USGS Hydrographic Vectors 45123h8 1973 1:24,000

46123a8

Bureau of Land Management Various 1999 1:24,000

National Aerial Photography Program 45123h8 2000 1:24,000

46123a8

Table 3: Bathymetric data sources from the National Ocean

Service. No survey data were available for the Necanicum River

region.

Source Survey Year Scale Spatial Resolution

NOSHDB H04611 1926 1:20,000 50–200 m

NOSHDB H04612 1926 1:20,000 50–200 m

NOSHDB H04635 1926 1:40,000 50–200 m

NOSHDB H08417 1958 1:20,000 50–200 m

LDART Spring 1998 1998 N/A 5 m

resulting shoreline file with vectors representing both Mean High Water and

Mean Lower Low Water was created using the Bureau of Land Management

(BLM) vectors with revisions made based on photography (Fig. 3b). Polygonal files representing water and land were derived from data corresponding

to the Mean High Water line.

3.1.2 Bathymetry

Bathymetric datasets consisted of four hydrographic surveys obtained from

the NOS Hydrographic Database (NOSHDB) and LIDAR data from the

NOS Coastal Services Center (CSC) (Table 3). Various Federal, State,

and local agencies were contacted for recorded depth information on the

Necanicum River estuary, yielding no results. Tom Horning of Horning Geosciences provided an estimated depth based on field observations (personal

communication). This estimation was combined with limited LIDAR data

within the intertidal zone to develop an estimated depth surface for the

estuary (Fig. 4).

NOSHDB surveys were conducted using a single-beam digital echo sounder

or lead line sounding method. The database processing system converted

sounding depths to NAD83 and corrected meters using the National Geodetic Survey (NGS) NADCON software and Carter’s tables (Carter, 1980),

respectively. The accuracy of these surveys is difficult to determine. Accuracy standards were not established until 1965, and the values were digitized

from hand-drawn maps. The surveys within the immediate vicinity of Seaside were based on the North American Datum of 1913, which required an
6 Venturato

Figure 4: Estimated depth surface of the Necanicum River estuarine region.

additional transformation based on a single pair of datum shift values before

applying NADCON (National Geophysical Data Center, 2004).

LIDAR data were obtained using the CSC LIDAR Data Retrieval Tool

(LDART) to cover the intertidal zone. The Topography section provides

details on these data.

Approximately 75,000 bathymetric points were selected from the surveys

and surface model with recent data superseding older data in overlapping

areas. Four anomalous points were removed from the selection. Spatial

polygons were created to display selected coverage areas (Fig. 5) within

the area of interest. 95% of the project area was covered by NOS survey

soundings of variable density.

3.1.3 Topography

LIDAR data from LDART, a 10-meter DEM from the USGS National Elevation Dataset (NED), and a 10-meter DEM used in prior tsunami hazard

modeling (Priest et al., 1998) were obtained as topographic datasets. Fifteen
Digital Elevation Model for Seaside, Oregon 7

Figure 5: Coverage areas of bathymetric data sources used in the DEM. The Necanicum River region

was estimated from limited LIDAR data and estimates from Horning (see Fig. 4). The coverage areas were

clipped to the study area boundaries for display purposes; however, the selected data sources extend beyond

the boundary to avoid interpolation edge errors during grid development.

Figure 6: Coverage area of topographic data sources used in DEM development (clipped to boundary for

display). Vertical control points are depicted by identification number.
8 Venturato

Table 4: Topographic data sources. The associated root mean square (RMS) error is based

on source metadata and NGS control point comparison.

Horizontal Vertical Vertical

RMS Error RMS Error RMS Error

Spatial based on based on based on

Source Survey Year Resolution metadata metadata comparison

LDART Spring 1998 1998 5 m 0.80 m 0.15 m 0.21 m

LDART Fall 1997 1997 5 m 0.80 m 0.15 m 0.37 m

David C. Smith 1996 1996 10 m N/A N/A 0.77 m

& Associates, Inc.

USGS NED 45123h8 1973 10 m 15 m 15 m 1.24 m

46123a8

vertical survey control datasheets were collected from NGS for comparison

with each dataset (Fig. 6). Table 4 provides information on each dataset.

The LIDAR surveys were conducted as part of the Airborne Topographic

Mapper Mission to cover coastal areas. The data were collected at low tide

using a pulsed laser with a wavelength of 550 nanometers. Quality assessment of the survey data is limited to internal consistency checks with filtering

to remove outliers of first returns. A detailed analysis of the LIDAR data was

made to remove vegetation by selecting points with the highest likelihood

of being on the surface. This analysis was conducted using Spatial Analyst

and 3D Analyst (Fig. 7b). Rectified orthophotos were used to remove points

representing apparent vegetation or structures (Fig. 7c) to create a corrected

LIDAR dataset.

The USGS NED DEM was developed in 1999. The dataset was derived

using bilinear interpolation of hypsography and hydrography contours based

on surveys conducted in 1973. The majority of the Seaside area is below the

hypsography contour interval of 25 feet (7.6 m). A comparative analysis of

the USGS data with the corrected LIDAR survey data revealed significant

differences in these low-lying areas. The USGS DEM was edited to select

only those values above the 250-foot contour and outside of the boundary

of the available LIDAR data. Further edits were made to contours derived

from the USGS DEM to remove boundary discrepancies (Fig. 7d).

David C. Smith and Associates, Inc., developed the 10-meter DEM used

in prior tsunami hazard modeling. It was based on 1996 orthorectified aerial

photography. DEM values did not compare well with LIDAR or USGS data

(Fig. 7e). Comparison with control data along the coast also showed an average error of 3 m. Since little information was available on the development

procedure for this dataset, it was not considered a viable input option.

The corrected 1998 LIDAR survey and USGS NED DEM data were

selected as final input for the DEM (Fig. 6). LIDAR covered approximately

45% of the project area.
Digital Elevation Model for Seaside, Oregon 9

(b) (c)

Figure 7: Topographic data analyses. (a) Red boxes depict sample areas as detailed in associated panels.

(b) Analysis of LIDAR data using 3D Analyst with 2 m vertical exaggeration. Peaks suggest possible

vegetation or infrastructure. (c) LIDAR data is corrected to remove vegetation and infrastructure using

orthophotography as a reference. Polygons (yellow boxes) representing buildings are drawn to select and

remove data values that do not represent the “bald earth.” (d) Contour comparison of LIDAR (black) and

USGS NED (white) data along the boundary of the LIDAR survey. A vector file (yellow) is created to correct

disparities between datasets with reference to orthophotography. (e) Contour comparison of corrected data

(black) with DEM used in prior tsunami hazard modeling efforts (blue).
10 Venturato

3.2 DEM Development

A simple method of Delauney triangulation and natural neighbor interpolation was used to build the DEM. Delauney triangulation, also known as the

triangular irregular network (TIN) method, constructs a surface of continuous non-overlapping triangles based on mass points and breaklines. A TIN

maintains data values within the range of vertices and will not extend beyond

boundaries. Its main disadvantage is the creation of triangular plateaus due

to all vertices having the same elevation value. This usually occurs when

using contours as an input. The inputs for this study consisted primarily of

mass points.

Natural neighbor interpolation helps create a smoother raster grid by

using an areal weighting scheme on the nearest TIN vertices to the output

raster cell. Other methods such as spline interpolation with tension or inverse

distance weighting are also viable options, but were not investigated.

DEM development consisted of three steps: building the bathymetric

grid, building the topographic grid, and then merging the two grids into the

final DEM (Fig. 8). This process was chosen based on modeler requirements

to ensure a definitive distinction between land (positive) and water (negative)

values. Zero or near-zero values cause anomalies in the inundation model.

Consequently, all land values were restricted to ≥0.01 m and all water values

to ≤−0.01 m.

The bathymetric grid was built using selected bathymetric data and the

land polygon (with a value of −0.01 m) as input for the interpolation. The

resulting grid was clipped to the water polygon. The topographic grid used

selected topographic data sources and the water polygon (with a value of

0.01 m) as input and then clipped to the land polygon in the same manner.

Spatial analysis was used to find problem areas, which were corrected by

re-interpolating after removing anomalous points or adding supplementary

points and contours.

The topographic and bathymetric grids were merged into a final DEM

and analyzed for consistency. Any null values were set to 0.01 m. The DEM

was exported to an ASCII raster grid and distributed to the modeler.

3.3 Quality Assurance

The quality of the DEM is difficult to determine. A number of different

factors contribute to cumulative DEM error, including inherent error within

the various selected data sources, conversion error, error due to interpolation

of spatially varying data sources, and error due to subjective interpretation.

An attempt to quantify some of these factors is provided below.

Inherent errors in the data sources are provided in the Data Sources

and Processing section. The spatial density of selected bathymetry and

topography data show high-density in low-lying and intertidal areas with

sparse data offshore (Fig. 9). Offshore values of the DEM are based on

interpolation of distant data points, whereas near-shore values are based on

data points that support the requested 1/3 arc-second resolution.

Horizontal datum conversions were primarily made using NADCON trans-
Digital Elevation Model for Seaside, Oregon 11

(a) (b) (c)

Figure 8: Steps of DEM development: (a) bathymetric grid, (b) topographic grid, and (c) merged grid.

(a) (b)

Figure 9: Spatial density distribution. (a) Spatial density of selected bathymetric (black) and topographic

(gray) data sources. Red box depicts (b) detailed display of distribution. The topographic data had a much

higher density (ranging from 5–10 m) than the bathymetric data (ranging from 5–200 m).
12 Venturato

formation, which is based on a minimum-curvature model (Dewhurst, 1990).

The conversion from NAD27 to NAD83 introduces uncertainty of 0.15 m at

a 67% confidence level. Converting from older North American datums to

NAD83 leads to a conversion error of 0.20 m (National Geodetic Survey,

2004).

Converting from a map projection to geographic coordinates also introduces error. A few data sources were in Oregon Lambert Projection or

Oregon State Plane Projection. The conversion error for both of these projections is 0.10 m (Snyder, 1987).

Vertical datum conversion based on the interpolation of tidal and geodetic datums obtained from NOS tidal benchmarks produced an error of 0.05 m

(Mofjeld et al., 2004). Additional vertical control error was created based

on estimating the interaction between the open coast and the Necanicum

River estuary. The estuarine region is not well defined (see Bathymetry section 3.1.2); thus, the vertical datum surface (Fig. 2) does not account for

the possibility of a deltaic sill affecting tidal circulation. This may add a

vertical error of up to 0.35 m within the estuary.

Assessing the quality of the DEM was based on comparison with vertical

control and source data. Vertical control data existed only for land elevation

values, yielding a RMS error of 0.135 m (Fig. 6, Table 5). A direct difference

between bathymetric source data and the DEM yielded a RMS error of

0.01 m.

Subjective interpretation may also introduce error. The construction of

shoreline and contours based on disparate sources are two of the primary

components that could affect DEM quality. Descriptions of these data provided in the methodology section provide some guidance on the quality of

the interpretation.

Table 5: Comparison of NGS vertical control points with the DEM. Average error

was 0.404 m with a RMS error of 0.135 m. See Fig. 6 for a spatial display of the

control points.

Control Control DEM Absolute

Point Latitude Longitude Elevation (m) Elevation (m) Difference (m)

AA3536 −123.929722 45.993056 8.210 7.715 0.495

RD1141 −123.929551 45.995082 7.297 6.990 0.307

RD1422 −123.924444 45.989167 5.490 5.374 0.116

RD1423 −123.926111 45.981944 6.074 5.992 0.082

RD4368 −123.923333 45.988056 5.640 5.232 0.408

SC0609 −123.914167 46.060278 8.152 8.923 0.771

SC0611 −123.916698 46.056681 20.911 20.494 0.417

SC0617 −123.920805 46.028119 11.834 11.754 0.080

SC0618 −123.920833 46.027778 9.562 9.881 0.319

SC1034 −123.913889 46.047778 6.190 6.764 0.574

SC1035 −123.915000 46.035556 6.964 8.292 1.328

SC1036 −123.911667 46.023333 7.095 7.043 0.052

SC1037 −123.913889 46.009444 4.164 4.185 0.021

SC1038 −123.920833 46.001111 6.103 5.683 0.420

SC1041 −123.925000 46.001389 5.362 4.697 0.665
Digital Elevation Model for Seaside, Oregon 13

Figure 10: Historical shoreline depicting the apparent Mean High Water line based on orthophotography

obtained from the University of Oregon. The northern outer coast has an accretion rate of approximately

45 meters every 14 years. The southern outer coast varies little over the same period.

3.4 Historical shoreline analysis

The discovery of significant shoreline differences with source data and recent aerial photography from 1996 and 2000 led to an analysis of historical

shoreline for the Seaside area. Past aerial photographs were obtained from

the University of Oregon. Apparent mean high water and mean lower low

water were digitized and georeferenced in ArcGIS©.

The resulting files show a very dynamic shoreline pattern likely due to

the Seaside area residing within the Columbia River littoral cell. A general trend of accretion averaging 3.2 m/y on the outer coast north of the

Necanicum River mouth is apparent (Fig. 10). These values nearly match

the historical accretion rates of the Clatsop Plains sub-cell, which averages

3.3 m/y (Woxell, 1998).

There is also a cyclic pattern seen within the Necanicum River mouth

(Fig. 11). The northern extent of the mouth shows an accretion rate of approximately 7 m/y since 1939. The southern extent varies between accretion

and erosion over an estimated 15-year cycle. Over 55 years, the mouth span

ranged from 300–800 m width.
14 Venturato

Figure 11: Accretion and erosion trends of the Necanicum River mouth over a

55-year period.

4. Summary and Conclusions

A digital elevation model consisting of merged bathymetry and topography

and covering the Seaside, Oregon region was built for use in a tsunami inundation model as part of a FEMA FIRM Pilot Study. The DEM consisted

of disparate data sources from various Federal, State, and local agencies.

Data were collected and processed according to requested parameters. Best

available data were selected as input for the grid. A simple interpolation

method based on Delauney triangulation and a natural neighborhood filter

was used to build the DEM.

The DEM was analyzed for quality by making comparisons with input

data sources and vertical control points. Total DEM error is difficult to

quantify due to subjective factors in development; however, an estimated

total error range is provided in Table 6. The methodology described in this

report helps provide a qualitative assessment of the DEM.

The DEM provides a snapshot of a dynamic region. Varying shoreline
Digital Elevation Model for Seaside, Oregon 15

Table 6: Estimate of total DEM error. These estimates are based on a quantitative

assessment of the DEM. The total error is sum of the quantitative values. Subjective

interpretation adds unknown error to the DEM.

Error Type Horizontal Error Range Vertical Error Range

(meters) (meters)

Projection/Datum conversion 0.35–0.45 0.05–0.40

Comparison with vertical control 0.14–1.33

Comparison with original data sources 0.80–10 0.01–1.24

Total known quantitative error 1.15–10.45 0.20–2.97

patterns may affect maximum credible tsunami inundation. Further analysis

and subsequent updates are necessary to ensure accurate tsunami hazard

assessments.

The inherent uncertainty based on disparate data sources and the dynamic nature of the shoreline led to a set of recommendations to improve

the DEM. These recommendations are listed below in no particular order:

• New bathymetric multibeam surveys should be conducted for the Necanicum River and the offshore region of northern Oregon. This could

significantly reduce error in the DEM by providing greater spatial density and better information on the current state of the seafloor.

• Recent 2002 LIDAR surveys should be used to update the topography

and intertidal zone. Given the dynamic nature of the shoreline, these

data could also be used to further analyze Columbia River littoral exchange patterns. Ideally, data with vegetation already removed would

be available to reduce processing time.

• A high-resolution tidal model to explore the tidal relationship between

the Necanicum River estuary and open ocean should be developed.

Tertiary tide gages should be installed to provide a recent observational record of current tidal trends, thereby reducing vertical datum

conversion errors.

• The latest orthophotos and high-resolution vectors representing Mean

High Water to more accurately depict the shoreline should be obtained.

New false-color orthoimagery is being collected by the State of Oregon

(Oregon Geospatial Enterprise Office, 2005).

• Different interpolation schemes should be tested to assess the best

method for the inundation model.

5. Data Credit

David C. Smith and Associates, Inc. (1996): 10-meter DEM. Portland,

Oregon.
16 Venturato

NOAA Coastal Services Center (2004): Aircraft Laser/GPS Mapping

of Coastal Topography, LDART. Charleston, South Carolina. http:

//www.csc.noaa.gov/lidar/

NOAA National Geodetic Survey (2004): Vertical Geodetic Control

Data. Silver Spring, Maryland. http://www.ngs.noaa.gov/

NOAA National Geodetic Survey (2004): OR43C04 1926 Shoreline,

Shoreline Data Explorer. Silver Spring, Maryland. http://www.ngs.

noaa.gov/newsys_ims/shoreline/

NOAA National Geophysical Data Center (2002): NOS Hydrographic

Database, GEODAS Version 4.1.18. Boulder, Colorado. http://

ngdc.noaa.gov/

NOAA National Ocean Service (2004): Regional Water-Level Station

Benchmarks. Silver Spring, Maryland. http://tidesandcurrents.

noaa.gov/benchmarks/

Oregon Bureau of Land Management (2001): Oregon Watershed Boundaries. Portland, Oregon. Data obtained through the Oregon Geospatial Data Clearinghouse. http://www.gis.state.or.us/

USGS EROS Data Center (1973): Hydrographic Vectors. Sioux Falls,

South Dakota. Data obtained through the Oregon Geospatial Data

Clearinghouse. http://www.gis.state.or.us/

USGS EROS Data Center (1999): National Elevation Dataset. Sioux

Falls, South Dakota. http://gisdata.usgs.net/ned/

USGS National Aerial Photography Program (2002): 2000 Digital Orthophoto Quads. Reston, Virginia. Data obtained through the Oregon

Geospatial Data Clearinghouse. http://www.gis.state.or.us/

6. Acknowledgments

This research is funded in part by the Federal Emergency Management

Agency Map Modernization Program. This publication is partially funded

by the Joint Institute for the Study of the Atmosphere and Ocean (JISAO)

under NOAA Cooperative Agreement No. NA17RJ1232, Contribution #1136.

The author thanks George Priest (Oregon Department of Geology and

Mineral Industries), Tom Horning (Horning Geosciences), Colin Kelly (University of Oregon), Kirk Waters (NOAA Coastal Services Center), and Harold

Mofjeld (NOAA Pacific Marine Environmental Laboratory) for assistance

with data sources and processing.

References

Carter, D.J.T. (1980): Echo-Sounding Correction Tables, third edition. Hydrographic Department, Ministry of Defence, Taunton Somerset.
Digital Elevation Model for Seaside, Oregon 17

Clatsop County (2005): Clatsop County Community Profile. Clatsop County Community Development, Astoria, Oregon.

Dewhurst, W.T. (1990): NADCON—The application of minimum-curvaturederived surfaces in the transformation of positional data from North American

Datum of 1927 to the North American Datum of 1983. NOAA Tech. Memo.

NOS NGS-50, National Ocean Survey, 30 pp.

Fiedorowicz, B.K. (1997): Geologic evidence of historic and prehistoric tsunami

inundation at Seaside, Oregon. M.S. thesis, Portland State University.

Gonz ́alez, F.I., E. Geist, C. Synolakis, and V.V. Titov (2004): Probabilistic tsunami

hazard assessment: the Seaside, Oregon pilot study. In 2004 Fall AGU Conference Proceedings, OS22B-04.

Gonz ́alez, F.I., V.V. Titov, H.O. Mofjeld, A.J. Venturato, R.S. Simmons, R. Hansen,

R. Combellick, R.K. Eisner, D.F. Hoirup, B.S. Yanagi, S. Yong, M. Darienzo,

G.R. Priest, G.L. Crawford, and T.J. Walsh (2005): Progress in NTHMP hazard

assessment. Nat. Hazards, 35(1), 89–110.

Mofjeld, H.O., A.J. Venturato, F.I. Gonz ́alez, and V.V. Titov (2004): Background

tides and sea level variations at Seaside, Oregon. NOAA Tech. Memo. OAR

PMEL-126, NOAA/Pacific Marine Environmental Laboratory, 15 pp.

National Geodetic Survey (2004): NADCON Version 2.10 Documentation. Boulder,

CO.

National Geophysical Data Center (2004): GEODAS Help Manual. National Geophysical Data Center, Marine Geology and Geophysics Department, Boulder,

CO.

Oregon Geospatial Enterprise Office (2005): 2005 Orthoimagery Update. http:

//www.oregon.gov/DAS/IRMD/GEO/.

Oregon Natural Heritage Program (1999): Oregon GAP analysis project—A geographic approach to planning for biological diversity. Oregon Department of

Fish and Wildlife, Portland, OR, 168 pp.

Oregon Ocean-Coastal Management Program (2000): Oregon Coastal Atlas. http:

//www.coastalatlas.net/.

Priest, G.R., E. Myers, A. Baptista, R.A. Kamphaus, B.K. Fiedorowicz, C.D. Peterson, and T.S. Horning (1998): Tsunami Hazard Map of the Seaside-Gearhart

Area, Clatsop County, Oregon. IMS-3, Scale 1:12,000, Oregon Department of

Geology and Mineral Industries, Portland, OR.

Snyder, J.P. (1987): Map Projections—A working manual. USGS Professional Paper 1395, 383 pp.

USDA Natural Resources Conservation Service (1994): Soil survey of Clatsop

County, Oregon, OR007. U.S. Department of Agriculture, Portland, OR, 189 pp.

Woxell, L.K. (1998): Prehistoric beach accretion rates and long-term response to

sediment depletion in the Columbia River Littoral System, USA. M.S. thesis,

Portland State University, Portland, OR, 113 pp.
1

Appendix C: Summary of Eyewitness Observations from 1964 Alaska

Tsunami in Seaside, Oregon, Compiled by Tom Horning

The 1964 Good Friday Alaska far-field tsunami struck Seaside on Friday, March 27, at

11:35 PM, approximately one hour before an approximate +5.4 ft MLLW high tide. It took about 4

hours for the tsunami to reach Seaside from its source area near Prince William Sound, arriving 15

minutes after the wave struck Neah Bay, Washington, and 5 minutes before it reached Crescent

City, California. The first surge caused the greatest damage. A second surge probably occurred

prior to 1 AM, but caused significantly less damage. Surges continued for at least 12 hours. Surf

was exceptionally low, averaging about 3 ft high, at a time of year when 8 to 20 ft waves are

common. The moon was full on a balmy windless night and the night sky was cloudless,

permitting good illumination. Damage totaled $276,000 from flooded homes, damaged bridges,

and ruined cars. Two houses were damaged beyond repair. Evacuation sirens were not blown until

the first surge was spreading through neighborhoods and up rivers. The decision to evacuate the

city was withheld until urgent radio messages from the Mendocino County (California) sheriff’s

office were received recommending evacuation of the coast. One fatal heart attack occurred during

the sirens.

Initial Drawdown

The first surge was preceded by a negative phase that drew down the tide to an inferred

elevation of less than minus 2 ft MLLW. The Necanicum River was observed at two locations to

be lower and narrower than ever seen before. Near the Avenue A bridge, the river was observed to

be only about 45 ft wide in a 135-ft-wide channel that is always filled with water, even during tides

of minus 2.0 ft MLLW. A police observer crossing the river at the 12th Avenue Bridge during his

evacuation claims that the mud bottom of the river was visible. These observations have not been

corroborated. Sand flats in the bay were exposed immediately prior to the beginning of the first

surge, according to another police observer along the north side of the bay. The flats normally

would have been covered by the high tide.

First Surge on the Beach

Few people observed the first surge arriving on the ocean beach on the west side of Seaside.

Near the Cove, at the south end of town, the ocean was seen to withdraw and develop a trough-like

depression about 1200 yards west of the beach. Beyond the trough, the ocean rose higher than

normal, as judged by the elevation of breakers along the boulder beach of Tillamook Head. As the

trough deepened, small breakers between the trough and Seaside diminished until the ocean surface

became smooth and glassy, apparently as water withdrew off the strand. Large waves then surged

past observers on Tillamook Head and toward the beach, much like 20-ft winter storm waves. It

appears that the first surge arrived along the Cove as a breaking bore, in contrast to its more passive

arrival farther to the north, perhaps due to effects from the headland. The water surged over the

cobble berm and passed between houses south of Avenue W, carrying large driftwood against

houses and filling basements with water.

According to newspaper accounts in the Seaside Signal, the arrival of the first surge on the

beach near Broadway, midway between Tillamook Head and the river mouth, was characterized by
2

waves running up the beach, followed by additional waves rapidly overtaking the first, all of which

chased strollers off the beach. There are no eyewitness reports of a prior drawdown of the ocean

along this part of the beach, but it probably occurred, based on the reduction of water elevation

observed in the estuary. Apparently, a bore advanced onto the beach near 12th Avenue, just south

of the estuary at the north end of town, because the police observer there heard the roar of very loud

surf, felt an anomalous strong wind from the west, and observed wads of foam blowing over his

head from out of the darkness. At this point, he departed in haste, sped eastward across the 12th

Avenue Bridge, and observed the unusual drawdown of the river, apparently beating the incoming

tsunami to the Necanicum River.

Run-up elevations along the oceanfront ranged from 19 to 23 ft NGVD. Allowing for about

3.4 ft between MLLW and 0.0 ft NGVD, and given that the tide was predicted to be at 4.9 ft

MLLW (+1.5 ft NGVD) at the 12th Avenue Bridge at that time, the first surge was from about 17 to

21 ft in height (there is some question as to MLLW versus NGVD in the Seaside area). Water

crossed the oceanfront Promenade at 12th and 9th Avenues, at Broadway, and Avenue A, at locations

where elevations ranged from 20 to 23 ft. It rose to 19.5 ft at Avenue S, but did not cross the

boardwalk. There is an anecdotal account of foam on the boardwalk one or two blocks farther to

the north. Water was observed flowing down 12th Avenue and Broadway, perhaps 6 inches deep,

for a distance of about one block. One eyewitness directly observed water less than 6 inches deep

quietly flowing onto the Promenade near Avenue A. To the west, the ocean surface was glassy

with minor roiling “like a full glass of water just poured”; free of significant foam and without any

expression of the prevailing 3 ft surf. The water stood for “a few moments”, and then withdrew

rapidly into the moonlit darkness to well beyond standard low tide. Abundant debris, small logs,

sticks, and other detritus were left on the concrete walk of the Promenade and in front yards.

Larger stumps and logs were stranded on access ramps to the beach, several feet below the

Promenade surface.

Water flowed onto Ocean Vista Drive south of Avenue W, near the Cove, establishing runup at about 21 ft NGVD. Accounts are inconsistent as to whether water crossed Sunset Boulevard

along the south side of the Cove. From the most conservative accounts, water appears to have risen

to at least 19 ft NGVD. Possibly it may have been 2 to 3 ft higher and may have carried large logs

and boulders onto the road, which some observers remember blocking the road. One neighbor

noted that the ocean surface was largely free of foam and appeared ready to break over the berm,

but then withdrew into the night. Confusion arises with memories for residents of this area after 30

to 40 years, because winter storms frequently have surged onto the road and into the marshes on the

lee side of the cobble berm on which the road is built. There are no newspaper accounts of debris

on this road, nor do emergency responders and public works employees recall clearing debris. In

general, it appears that run-up generally may have been limited south of Avenue S, as compared to

points farther north along the beach. In part, this can be explained by direct observation that the

tsunami water readily drained into the rounded cobble deposits that underlie the south part of

Seaside, in contrast to less permeable dune sands and organic soils farther to the north. This

enhanced drainage prevented more extensive inundation of the upland terrain in the southern part of

the city.

Surge Enters the Bay

The primary surge apparently entered the estuary as waves propelled by a rapid rise in sea

level, but it became focused as a series of bores within the narrow confines of the river channels.
3

Observers along the east side of the bay, at Mason Street and 25th Avenue, recount that water

elevations rose over a period of several minutes, allowing time to evacuate. Card-players near 24th

Avenue heard the growl of cobbles in the river channel west of their house, like a distant jet engine,

and observed wild and chaotic conditions in the estuary with waves and foam moving

incomprehensively in the moonlight. Water and foam barely surmounted the forested dune on

which their residence was built, establishing run-up at about 21 ft NGVD from about 20th to 24th

Avenues.

Surge Enters Neawanna Creek and Venice Park Neighborhood

An observer at 26th Avenue and Queen Street observed a bore heading straight toward his

house up the lower channel of Neawanna Creek. The family dog first heard the dull roar of the

water. The family had less than one minute to evacuate upstairs before water and 5-ft diameter

driftwood logs crashed through the west side of their house, filling it seven feet deep, pushing out

the back wall, and carrying away their cars. The surge banked against Highway 101 north of the

Neawanna Creek Bridge, partly flooding over it, depositing sand on the highway and running

between houses to the east. The surge spread back to the southwest, inundating the eastern twothirds of Venice Park neighborhood under one to three feet of water from 24th to 26th Avenues, and

lapping onto a low dune ridge on the west side of the neighborhood. Freezers, coolers, furniture,

and spewing propane tanks, were consistently found southwest of their homes of origin. Similarly,

large driftwood logs floated into the neighborhood on southwesterly-directed currents. Several

families put their children into attic spaces and onto roofs to avoid the swift rise of water. Surges

constrained by dense forest vegetation and brush along narrow roads rose as high as seven feet,

only to drop to less than three in open intersections. Many heard the evacuation sirens and stepped

out of their homes to find swift currents in the streets. Several mobile homes were swept into the

highways or into the river. In this neighborhood, static water run-up averaged about 12.5 ft NGVD,

or about 2.5 ft higher than the elevation of highest flooding due to storm surge in the following 40

years. Surges locally increased run-up by 6 to 12 inches, primarily near the river channel.

It is likely that run-up was increased a small amount by debris dams lodged against bridges.

Run-up elevations upstream from the Neawanna Creek (Hwy 101) Bridge were about 1.5 ft lower

than in Venice Park, below the blockage. Pilings beneath the Seattle, Portland, and Spokane train

trestle, just upstream from the Highway 101 Bridge, were washed away by the floodwaters, leaving

the ties and rails, which hung as a suspension bridge between the riverbanks. Apparently, debris

dams against the pilings forced water to accelerate near the bed of the river, and this washed away

sand into which the pilings had been driven, allowing them to be swept away with the logjam.

Heavy foam and strong, groaning currents were observed by evacuees who crossed the 12th

Avenue Bridge over Neawanna Creek, but residents along the river bank at the bridge did not

observe evidence of flooding out of the flood plain. Between 12th Avenue and Broadway, water did

not rise any higher than typical winter floods. Run-up in this vicinity appears not to have been

higher than about 9 ft NGVD. There is very little evidence of the extent of flooding south of

Broadway due to the absence of damage. Water is inferred to have flooded at least to the Avenue S

Bridge, primarily within the channel. In general, flooding affected marshland of the Neawanna

Creek drainage for a distance of about one mile upstream from the bay.
4

Surge in the Necanicum River

Inundation within the Necanicum River extended from its mouth more than two miles

upstream to at least the ninth fairway of the Seaside Golf Course, just south of Avenue U. An eyewitness between Broadway and Avenue A observed multiple bores advancing over the nearlyempty river bottom, each about 2 ft high and each followed by another within 10 to 30 seconds,

until the 14-ft-deep river channel was filled flush to the edge of its banks by a final 4-ft culminating

bore. The culminating bore was observed sweeping up the Necanicum Channel near 4th Avenue

beneath a thick mantle of dense foam, essentially as a quiet plateau of water moving at about 10

miles per hour. The bore struck the main timber decking of the abandoned 4th Avenue Bridge and

tore it from its pilings with the sound of “giant eggshells cracking”. The decking broke into

multiple pieces and floated upstream, the largest to lodge beneath the east end of the First Avenue

Bridge and tip up to erect a cofferdam to the on-rushing water. As a result, water and foam were

directed beneath the west end of the bridge and beyond. Water flowed over the bridge railings and

flooded westward across First Avenue and Necanicum Boulevard, filling parking lots and streets,

and blocking evacuation by many who thought it would be a safer crossing than farther down

stream. Water was several inches deep in Broadway west of the river. Foam was as high as seven

feet along the sides of buildings along North Edgewood, apparently having been skimmed off by

the deck of the Broadway Bridge. As the foam broke down later, only silt from the river was left in

the streets, in contrast to beach and bay sands in neighborhoods closer to the river mouth.

Debris from the 4th Avenue Bridge, or large driftwood, continued upstream beneath the

Broadway Bridge to the First Avenue Bridge, where it struck the bridge railings and knocked them

into the street. Swift currents, possibly with the assistance of debris dams, swept two sets of pilings

from beneath the Avenue G Bridge, farther to the south. The concrete decking of the bridge

fortunately was able to span the gap and support the loads of evacuating cars without collapsing.

Low areas on the west side of the river flooded with several inches of water from about Avenue I to

about Avenue N, and as far west as South Downing Street.

Water and/or foam stood on the decks of all bridges from 12th Avenue to Avenue G. Runup more than 14 ft NGVD at 12th Avenue, about 13 ft at 1st Avenue, and about 12 ft at Avenue A.

Water entered the first floor of the hospital at Avenue S, flooding only several inches deep and

establishing run-up at about 10.5 ft. Run-up reached 9.8 ft NGVD on the ninth green of the golf

course at Avenue U. Run-up near bridges tended to be higher than farther from the channel,

apparently because of surges and splashing related to higher velocities in the channels.

A Second Surge

There are few recollections of the second surge of the Good Friday tsunami and no

reference to it can be found in the news clippings from that event. Hearsay accounts of disaster

responders suggest that barrels had moved about the Venice Park neighborhood during the night by

waves; however, large driftwood logs were not affected and no residents of the neighborhood

noticed anything unusual. The primary observation of a second surge was made at the Seaside Golf

Course, where the owner observed, at about 1:30 AM, that a slightly lower run-up debris line had

formed on the ninth green well after the first surge had withdrawn.

An eyewitness who saw bores moving up the Necanicum channel later moved to the north

side of the estuary in Gearhart. There he saw what must have been the second surge enter the

estuary. Easily visible in the moonlight, he saw multiple waves pouring into the bay from the

ocean to strike the east side of the bay near the high school, between 17th and 24th Avenues. The
5

waves rebounded and met oncoming surges to amplify and explode upward higher than the homes

along the shore. Run-up was as high as 21 ft NGVD in this area, or nearly 8 ft higher than run-up

to the north or south. One homeowner adjacent to the amplification zone observed that driftwood

logs had floated into her glass-enclosed patio without any damage to the panes, implying gentle

forces at the extreme edge of the run-up. She also reported that considerable rock fill along the

shore had been washed away and, further, that the bank had slumped enough to break irrigation

pipes in the lawn and distort some walls in the house a tiny amount. Another resident at 24th

Avenue estimated that the waves eroded the 15-ft-high bank of dune sand by about 10 ft.

Sand and Debris Deposits

Although significant sand was transported by the tsunami into marshes and neighborhoods,

there was little evidence of intense channel erosion or scouring. Noticeable scouring occurred only

along shorelines where rock fill projected into the bay. These projections allowed eddying, which

created shallow depressions of less than 2 ft deep and no more than 70 ft long and 15 ft wide. Sand

deposition tended to occur along topographic shelves, such as riverbanks and yards, where water

velocities slowed abruptly. Streets and wide level fields tended not to accumulate much sand, but

yards adjacent to streets could accumulate up to 14 inches of sand. Sand suspended in 30 to 36

inches of water was able to settle out in houses to form layers of about 2 to 3.5 inches thick. Many

ghost shrimp (Callianassa californiensis) were transported from the sand flats and river bottoms

into yards and streets with the sand. The shrimp probably were sucked from their burrows by

Venturi forces, rather than excavated by erosion, as their burrows can be more than two feet deep

and such extensive erosion was generally not visible. However, significant erosion in sandy river

channels could have occurred. Many fish were stranded as the water withdrew. Worms came to

the surface of the ground to escape the salt water.

Surges for Twelve Hours

Several surges entered the estuary the next day. They were perhaps as much as 2 to 4 ft

high and appeared as rapid increases in the tide that took about 10 minutes to peak. Water sped up

the main river channels at speeds of 5 to 10 miles per hour. Standing waves with breaking crests

stood perhaps 2 to 3 ft high. Logs riding in on the water rose out of the water as they crossed the

waves, slapping the water surface as they came back down. Stumps rolled and tumbled in the

rapidly moving water. As water spilled out of the channels and onto the sand flats, it spread

outward at a moderate walking pace without cessation as dark turbulent sand-filled water several

inches deep, crossing about 150 yards in under 2 minutes. Shoaling occurred over sand bars in the

channels and bores were observed as far up the Necanicum River as Broadway.

Wrap-up

Had the first surge arrived during low tide, much of the damage in Seaside would most

likely not have occurred, as later surges in the tsunami train were apparently smaller than the

earliest ones. It is important to note that had the tide, the first surge, or the ocean surf of that day

been higher, the damage in Seaside would have resulted in significantly greater property damage.

Water would have poured across the Promenade and into the core of town, flowing down nearly all

streets. Many homes would have been swept from their foundations and many lives could have
6

been lost. Large numbers of people evacuated across bridges and roads that were already flooded

or were adjacent to raging rivers. Had the water been any higher, many would have been washed

into deep, swift water, and most likely would have drowned.

This account of the 1964 tsunami in Seaside is based on interviews with over 75 people,

personal observations, at least four sets of excellent photos, and clippings from various newspapers.
Appendix D: Location of sites used in this study and presence of 1964, 1700 and older tsunami

deposits

Source site # UTM_X (NAD 83) UTM_Y (NAD 83) date 1964 1700 older

Jaffe et al., this study SS6-04-1 428207.00000 5092839.00000 06/24/04 yes no no

Jaffe et al., this study SS6-04-2 428244.00000 5092869.00000 06/24/04 yes ? no

Jaffe et al., this study SS6-04-3 428095.00000 5092851.00000 06/24/04 no no no

Jaffe et al., this study SS6-04-4 428094.00000 5092851.00000 06/24/04 no no no

Jaffe et al., this study SS6-04-5 428401.00000 5092858.00000 06/25/04 yes yes no

Jaffe et al., this study SS6-04-6 428401.00000 5092858.00000 06/25/04 no no no

Jaffe et al., this study SS6-04-7 428653.00000 5092807.00000 06/25/04 no no no

Jaffe et al., this study SS6-04-8 428683.00000 5092799.00000 06/25/04 no yes no

Jaffe et al., this study SS6-04-9 428742.00000 5092818.00000 06/25/04 no yes no

Jaffe et al., this study SS6-04-10 428757.00000 5092818.00000 06/25/04 no yes yes

Jaffe et al., this study SS6-04-11 428757.00000 5092828.00000 06/25/04 no yes no

Jaffe et al., this study SS6-04-12 429043.00000 5093031.00000 06/25/04 no yes no

Jaffe et al., this study SS6-04-13 429043.00000 5093026.00000 06/25/04 no no no

Jaffe et al., this study SS6-04-14 429288.00000 5095951.00000 06/26/04 yes no no

Jaffe et al., this study SS6-04-15 429297.00000 5095165.00000 06/26/04 yes no no

Jaffe et al., this study SS6-04-16 429294.00000 5095174.00000 06/26/04 yes no no

Jaffe et al., this study SS6-04-17 429289.00000 5095197.00000 06/26/04 yes no no

Jaffe et al., this study SS6-04-18 429297.00000 5095197.00000 06/26/04 yes no no

Jaffe et al., this study SS6-04-19 429279.00000 5095204.00000 06/26/04 yes no no

Jaffe et al., this study SS6-04-20 429273.00000 5095213.00000 06/26/04 yes no no

Jaffe et al., this study SS6-04-21 428595.00000 5094778.00000 06/26/04 yes no no

Jaffe et al., this study SS6-04-22 428139.00000 5092869.00000 06/27/04 no yes no

Jaffe et al., this study SS6-04-23 429663.00000 5094968.00000 06/27/04 no no yes

Jaffe et al., this study SS6-04-24 429506.00000 5094739.00000 06/27/04 no yes yes

Jaffe et al., this study SS6-04-25 429261.00000 5095231.00000 06/27/04 yes yes no

Jaffe et al., this study SS6-04-26 429303.00000 5095155.00000 06/27/04 yes yes no
Jaffe et al., this study SS9-04-30 428773.00000 5092845.00000 09/24/04 no yes yes

Jaffe et al., this study SS9-04-31 428808.00000 5093005.00000 09/24/04 no yes no

Jaffe et al., this study SS9-04-32 428736.00000 5092992.00000 09/24/04 no no no

Jaffe et al., this study SS9-04-33 428756.00000 5092500.00000 09/24/04 no yes no

Jaffe et al., this study SS9-04-34 429660.00000 5094931.00000 09/24/04 no yes no

Jaffe et al., this study SS9-04-35 429647.00000 5094894.00000 09/24/04 no yes no

Jaffe et al., this study SS9-04-36 429647.00000 5095033.00000 09/24/04 no yes no

Jaffe et al., this study SS9-04-37 427301.00000 5091607.00000 09/25/04 no no yes

Jaffe et al., this study SS9-04-38 427288.00000 5091519.00000 09/25/04 no no yes

Jaffe et al., this study SS9-04-39 427363.00000 5091396.00000 09/25/04 no no yes

Jaffe et al., this study SS9-04-40 427402.00000 5091267.00000 09/25/04 no no yes

Jaffe et al., this study SS9-04-41 427495.00000 5091107.00000 09/25/04 no no yes

Jaffe et al., this study SS9-04-42 429272.00000 5096272.00000 09/25/04 yes yes yes

Jaffe et al., this study SS9-04-43 428744.00000 5092758.00000 09/26/04 no yes no

Jaffe et al., this study SS9-04-44 428761.00000 5092819.00000 09/26/04 no yes no

Jaffe et al., this study SS9-04-45 428890.00000 5092944.00000 09/26/04 no yes yes

Jaffe et al., this study SS9-04-46 429182.00000 5093416.00000 09/26/04 no yes yes

Jaffe et al., this study SS9-04-47 429181.00000 5093445.00000 09/26/04 no yes no

Jaffe et al., this study SS9-04-48 429165.00000 5093502.00000 09/26/04 no yes no

Jaffe et al., this study SS9-04-49 429002.00000 5094032.00000 09/26/04 no no no

Jaffe et al., this study SS9-04-50 429433.00000 5094076.00000 09/26/04 no yes no

Jaffe et al., this study SS9-04-51 429216.00000 5094168.00000 09/26/04 no yes no

Jaffe et al., this study SS9-04-52 428466.00000 5093918.00000 09/27/04 yes no no

Jaffe et al., this study SS9-04-53 428466.00000 5093918.00000 09/27/04 yes no no

Jaffe et al., this study SS9-04-54 428540.00000 5094150.00000 09/27/04 yes no no

Jaffe et al., this study SS9-04-55 428576.00000 5094279.00000 09/27/04 yes no no

Jaffe et al., this study SS9-04-56 428609.00000 5094400.00000 09/27/04 yes no no

Jaffe et al., this study SS9-04-57 428752.00000 5092329.00000 09/27/04 no yes no

Jaffe et al., this study SS9-04-58 428695.00000 5092256.00000 09/27/04 no yes no

Jaffe et al., this study SS9-04-59 428330.00000 5091895.00000 09/27/04 no yes no

Jaffe et al., this study SS9-04-60 429307.00000 5093988.00000 09/27/04 no yes no

Jaffe et al., this study SS9-04-61 429196.00000 5094244.00000 09/27/04 no? no

Jaffe et al., this study SS9-04-62 429260.00000 5094331.00000 09/27/04 no? no

Jaffe et al., this study SS9-04-63 429288.00000 5094387.00000 09/27/04 no? no

Jaffe et al., this study SS9-04-64 429216.00000 5094168.00000 09/27/04 no yes no
Jaffe et al., this study SS9-04-65 429239.00000 5094260.00000 09/27/04 no

? no

Jaffe et al., this study SS9-04-66 429278.00000 5094155.00000 09/27/04 no? no

Jaffe et al., this study SS9-04-67 429259.00000 5094162.00000 09/27/04 no? no

Jaffe et al., this study SS9-04-68 429171.00000 5094278.00000 09/27/04 no? no

Jaffe et al., this study SS9-04-69 429251.00000 5094326.00000 09/27/04 no? no

Jaffe et al., this study SS9-04-70 428156.00000 5092146.00000 09/28/04 no? no

Jaffe et al., this study SS9-04-71 428170.00000 5092345.00000 09/28/04 no yes no

Jaffe et al., this study SS9-04-72 428297.00000 5092714.00000 09/28/04 yes no no

Jaffe et al., this study SS9-04-73 429394.00000 5094075.00000 09/29/04 no yes no

Jaffe et al., this study SS9-04-74 429511.00000 5094120.00000 09/30/04 no yes no

Jaffe et al., this study SS9-04-75 429624.00000 5094156.00000 09/30/04 no yes no

Jaffe et al., this study SS9-04-76 429682.00000 5094125.00000 09/30/04 no yes no

Fedorowitcz and Peterson, 1997 SSX_I 429141.14990 5096568.21467 07/18/96 no no no

Fedorowitcz and Peterson, 1997 SSX_II 429139.15015 5096542.21502 07/18/96 no no no

Fedorowitcz and Peterson, 1997 SSX_III 429136.15039 5096517.21535 07/18/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_IV 429134.15063 5096492.21569 07/18/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_V 429131.15086 5096467.21602 07/18/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_VI 429105.15111 5096441.21622 07/18/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_VII 429127.15135 5096416.21670 07/18/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_VIII 429124.15157 5096393.21701 07/18/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_IX 429122.15180 5096368.21734 07/18/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_X 429121.15227 5096319.21802 07/18/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_XI 429119.15223 5096323.21795 07/18/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_XII 429118.14988 5096570.21450 07/18/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_XIII 429115.15009 5096548.21479 07/18/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_XIV 429111.15034 5096522.21513 07/19/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_XV 429108.15058 5096497.21546 07/19/96 no no no

Fedorowitcz and Peterson, 1997 SSX_XVI 429091.15006 5096551.21460 07/18/96 no no no

Fedorowitcz and Peterson, 1997 SSX_XVII 429087.15029 5096527.21491 07/18/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_XVIII 429083.15054 5096501.21524 07/18/96 no no no

Fedorowitcz and Peterson, 1997 SSX_XIX 429079.15078 5096476.21557 07/18/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_XX 429104.15081 5096472.21578 07/19/96 no no no

Fedorowitcz and Peterson, 1997 SSX_XXI 429100.15105 5096447.21610 07/19/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_XXII 429096.15128 5096423.21641 07/19/96 yes no no

Fedorowitcz and Peterson, 1997 SSX_XXIII 429092.15152 5096398.21674 07/19/96 yes no no
Fedorowitcz and Peterson, 1997 SSX_XXIV 429085.15045 5096510.21513 07/19/96 no no no

Fedorowitcz and Peterson, 1997 SSX_XXV 429085.15198 5096350.21737 07/19/96 no no no

Fedorowitcz and Peterson, 1997 SSX_XXVI 429089.15219 5096328.21770 07/19/96 no no no

Fedorowitcz and Peterson, 1997 SSX_XXVII 429116.15253 5096292.21836 07/19/96 no no no

Fedorowitcz and Peterson, 1997 SSX_XXVIII 429114.15270 5096274.21860 07/19/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_I 429415.15956 5095544.23052 07/27/96 no no no

Fedorowitcz and Peterson, 1997 SSW_II 429410.15982 5095516.23088 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_III 429405.16013 5095484.23129 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_IV 429400.16036 5095459.23161 07/27/96 no no no

Fedorowitcz and Peterson, 1997 SSW_V 429395.16065 5095429.23200 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_VI 429366.16067 5095428.23184 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_VII 429362.16096 5095397.23225 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_VIII 429359.16124 5095367.23264 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_IX 429332.16098 5095396.23209 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_X 429332.16125 5095367.23249 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XI 429335.16260 5095224.23448 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XII 429334.16281 5095202.23478 07/27/96 no no no

Fedorowitcz and Peterson, 1997 SSW_XIII 429330.16296 5095186.23498 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XIV 429302.16309 5095174.23499 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XV 429302.16282 5095202.23460 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XVI 429269.16311 5095173.23482 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XVII 429270.16281 5095205.23438 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XVIII 429238.16309 5095176.23460 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XIX 429242.16282 5095205.23422 07/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XX 429305.16387 5095091.23615 07/30/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XXI 429302.16410 5095067.23647 07/30/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XXII 429304.16410 5095067.23648 07/30/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XXIII 429277.16388 5095091.23600 07/30/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XXIV 429276.16435 5095042.23667 07/30/96 no no no

Fedorowitcz and Peterson, 1997 SSW_XXV 429276.16456 5095019.23699 07/30/96 no no no

Fedorowitcz and Peterson, 1997 SSW_XXVI 429250.16459 5095017.23687 07/30/96 no no no

Fedorowitcz and Peterson, 1997 SSW_XXVII 429252.16435 5095043.23652 07/30/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XXVIII 429253.16413 5095066.23621 07/30/96 yes no no

Fedorowitcz and Peterson, 1997 SSW_XXIX 429248.16483 5094992.23720 07/30/96 no no no

Fedorowitcz and Peterson, 1997 SSW_XXX 429228.16503 5094972.23737 07/30/96 no yes no
Fedorowitcz and Peterson, 1997 SSW 712 429224.16817 5094147.24199 06/20/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 713 429243.16815 5094146.24209 06/20/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 714 429229.16732 5094730.24073 06/21/96 no no no

Fedorowitcz and Peterson, 1997 SSW 715 429259.16732 5094729.24091 06/21/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 716 429145.16594 5094880.23819 06/21/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 717 429148.16635 5094837.23880 06/21/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 718 429300.16419 5095057.23660 06/21/96 yes no no

Fedorowitcz and Peterson, 1997 SSW 719 429287.16397 5095081.23619 06/21/96 yes no no

Fedorowitcz and Peterson, 1997 SSW 720 429272.16391 5095088.23601 06/21/96 yes no no

Fedorowitcz and Peterson, 1997 SSW 721 429275.16416 5093559.23656 06/21/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 722 429401.16034 5095461.23159 06/22/96 yes no no

Fedorowitcz and Peterson, 1997 SSW 723 429318.16131 5095362.23248 06/22/96 yes no no

Fedorowitcz and Peterson, 1997 SSW 724 429306.16121 5095372.23227 06/22/96 yes no no

Fedorowitcz and Peterson, 1997 SSW 725 429298.16317 5095165.23509 06/22/96 yes no no

Fedorowitcz and Peterson, 1997 SSW 726 429248.16263 5095225.23398 06/22/96 yes yes no

Fedorowitcz and Peterson, 1997 SSW 727 429086.16720 5094001.23978 06/22/96 no no no

Fedorowitcz and Peterson, 1997 SSW 728 428786.15976 5092942.22762 06/22/96 no no no

Fedorowitcz and Peterson, 1997 SSW 729 428618.15363 5092096.21823 06/25/96 no yes yes

Fedorowitcz and Peterson, 1997 SSW 730 428570.15327 5092053.21750 06/25/96 no yes yes

Fedorowitcz and Peterson, 1997 SSN 731 428244.15900 5092874.22369 06/25/96 yes? no

Fedorowitcz and Peterson, 1997 SSN 732 428269.15858 5092815.22325 06/25/96 no yes no

Fedorowitcz and Peterson, 1997 SSS 733 429768.15954 5095536.23269 06/26/96 no no no

Fedorowitcz and Peterson, 1997 SSS 734 429758.16065 5095417.23425 06/26/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 735 428810.15618 5092432.22274 06/26/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 736 428843.15738 5092600.22459 06/26/96 no yes yes

Fedorowitcz and Peterson, 1997 SSN 737 428261.16010 5093022.22527 06/27/96 yes? no

Fedorowitcz and Peterson, 1997 SSN 738 428262.16056 5093083.22589 06/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 739 428256.16069 5093101.22603 06/27/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 740 428238.16039 5093062.22554 06/28/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 741 428221.16000 5093010.22492 06/28/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 742 428171.15961 5092961.22413 06/28/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 743 428261.16091 5093131.22636 06/28/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 744 428359.16248 5093340.22903 06/28/96 yes yes no

Fedorowitcz and Peterson, 1997 SSN 745 428369.16268 5093368.22937 06/28/96 yes yes no

Fedorowitcz and Peterson, 1997 SSN 746 428345.16220 5093303.22858 06/28/96 yes yes no
Fedorowitcz and Peterson, 1997 SSN 747 428378.16226 5093310.22884 06/28/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 748 428393.16233 5093319.22902 06/28/96 yes yes no

Fedorowitcz and Peterson, 1997 SSN 749 428405.16222 5093304.22894 06/29/96 yes yes no

Fedorowitcz and Peterson, 1997 SSN 750 428406.16266 5093364.22955 06/29/96 yes no no

Fedorowitcz and Peterson, 1997 SSX 751 429127.15069 5096485.21574 06/29/96 yes no no

Fedorowitcz and Peterson, 1997 SSX 752 429130.15053 5096502.21552 06/29/96 yes no no

Fedorowitcz and Peterson, 1997 SSX 753 429133.15031 5096525.21522 06/29/96 yes no no

Fedorowitcz and Peterson, 1997 SSX 754 429138.15019 5096538.21507 06/29/96 yes no no

Fedorowitcz and Peterson, 1997 SSW 755 429121.16595 5093821.23822 07/02/96 no no no

Fedorowitcz and Peterson, 1997 SSW 756 429126.16610 5093843.23847 07/02/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 757 429218.16663 5093921.23976 07/02/96 no yes no

Fedorowitcz and Peterson, 1997 SSN 758 428447.16330 5093450.23064 07/03/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 759 428452.16342 5093467.23084 07/03/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 760 428326.16182 5093252.22796 07/03/96 no no no

Fedorowitcz and Peterson, 1997 SSN 761 428356.16043 5093061.22623 07/03/96 yes no no

Fedorowitcz and Peterson, 1997 SSW 762 429145.16461 5093626.23646 07/03/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 763 429150.16446 5093604.23627 07/03/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 764 429198.16475 5093646.23696 07/03/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 765 429197.16460 5093624.23674 07/03/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 766 429191.16448 5093607.23654 07/10/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 767 429207.16429 5093579.23636 07/11/96 no no no

Fedorowitcz and Peterson, 1997 SSW 768 429274.16404 5093541.23638 07/11/96 no yes no

Fedorowitcz and Peterson, 1997 SSX 769 429114.15045 5096510.21531 07/11/96 yes no no

Fedorowitcz and Peterson, 1997 SSX 770 429125.15141 5096409.21679 07/11/96 yes no no

Fedorowitcz and Peterson, 1997 SSX 771 429105.15113 5096439.21625 07/11/96 yes no no

Fedorowitcz and Peterson, 1997 SSX 772 429097.15077 5096477.21567 07/11/96 yes no no

Fedorowitcz and Peterson, 1997 SSX 773 429081.15052 5096503.21520 07/11/96 yes no no

Fedorowitcz and Peterson, 1997 SSX 774 429086.15026 5096530.21486 07/11/96 yes no no

Fedorowitcz and Peterson, 1997 SSW 775 429306.16642 5093893.23999 07/12/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 776 429275.16646 5093898.23986 07/12/96 no? no

Fedorowitcz and Peterson, 1997 SSW 777 429292.16645 5093897.23995 07/12/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 778 429306.16696 5093973.24077 07/12/96 no? no

Fedorowitcz and Peterson, 1997 SSW 779 429326.16694 5093970.24085 07/12/96 no? no

Fedorowitcz and Peterson, 1997 SSW 780 429239.16918 5094296.24353 07/12/96 no no no

Fedorowitcz and Peterson, 1997 SSW 781 429142.16963 5094492.24356 07/12/96 no yes no
Fedorowitcz and Peterson, 1997 SSW 782 429180.16248 5093314.23361 07/12/96 no no no

Fedorowitcz and Peterson, 1997 SSW 783 429237.16239 5093299.23380 07/12/96 no no no

Fedorowitcz and Peterson, 1997 SSW 784 428750.15733 5092602.22405 07/13/96 no yes yes

Fedorowitcz and Peterson, 1997 SSW 785 428775.15735 5092602.22420 07/13/96 no no no

Fedorowitcz and Peterson, 1997 SSW 786 428827.15736 5092599.22449 07/13/96 no no no

Fedorowitcz and Peterson, 1997 SSW 787 428825.15793 5092679.22526 07/13/96 no yes yes

Fedorowitcz and Peterson, 1997 SSW 788 428851.15799 5092686.22549 07/13/96 no yes yes

Fedorowitcz and Peterson, 1997 SSW 789 428834.15836 5092740.22592 07/13/96 no yes yes

Fedorowitcz and Peterson, 1997 SSW 790 428836.15898 5092828.22680 07/13/96 no yes yes

Fedorowitcz and Peterson, 1997 SSW 791 429012.16225 5093287.23236 07/17/96 no no no

Fedorowitcz and Peterson, 1997 SSW 792 429121.16247 5093314.23327 07/17/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 793 428963.16117 5093133.23056 07/25/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 794 429010.16055 5093041.22993 07/25/96 no no no

Fedorowitcz and Peterson, 1997 SSW 795 428980.16070 5093064.22998 07/25/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 796 428955.16014 5092986.22907 07/25/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 797 428740.15717 5092580.22377 07/26/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 798 428731.15732 5092602.22393 07/26/96 no yes yes

Fedorowitcz and Peterson, 1997 SSW 799 428746.15832 5092742.22541 07/26/96 no? no

Fedorowitcz and Peterson, 1997 SSW 800 428741.15887 5092820.22615 07/26/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 801 428883.16050 5093042.22919 07/31/96 no no no

Fedorowitcz and Peterson, 1997 SSW 802 429000.16253 5093327.23268 07/31/96 no no no

Fedorowitcz and Peterson, 1997 SSW 803 429025.16265 5093344.23299 07/31/96 no no no

Fedorowitcz and Peterson, 1997 SSW 804 429062.16285 5093371.23348 07/31/96 no no no

Fedorowitcz and Peterson, 1997 SSW 805 429100.16305 5093400.23398 07/31/96 no no no

Fedorowitcz and Peterson, 1997 SSW 806 429145.16301 5093392.23417 07/31/96 no no no

Fedorowitcz and Peterson, 1997 SSW 807 429136.16329 5093434.23453 07/31/96 no no no

Fedorowitcz and Peterson, 1997 SSW 808 429202.16351 5093465.23522 07/31/96 no no no

Fedorowitcz and Peterson, 1997 SSW 809 429165.16378 5093505.23539 07/31/96 no no no

Fedorowitcz and Peterson, 1997 SSW 810 429149.16399 5093536.23560 07/31/96 no no no

Fedorowitcz and Peterson, 1997 SSW 811 428806.16118 5093142.22971 07/31/96 no no no

Fedorowitcz and Peterson, 1997 SSW 812 428877.16171 5093214.23084 07/31/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 813 428919.16162 5093199.23094 07/31/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 814 428945.16164 5093201.23112 07/31/96 no yes yes

Fedorowitcz and Peterson, 1997 SSW 815 428927.16186 5093234.23134 07/31/96 no no no

Fedorowitcz and Peterson, 1997 SSW 816 428937.16204 5093259.23164 07/31/96 no no no
Fedorowitcz and Peterson, 1997 SSW 817 428769.15711 5092568.22383 08/07/96 no no no

Fedorowitcz and Peterson, 1997 SSW 818 428856.15710 5092558.22426 08/07/96 no yes yes

Fedorowitcz and Peterson, 1997 SSW 819 429172.16033 5093000.23050 08/07/96 no no no

Fedorowitcz and Peterson, 1997 SSW 820 429259.16160 5093182.23279 08/07/96 no no no

Fedorowitcz and Peterson, 1997 SSW 821 429241.16121 5093125.23213 08/07/96 no no no

Fedorowitcz and Peterson, 1997 SSW 822 428927.16009 5092980.22884 08/07/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 823 428945.16003 5092971.22886 08/07/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 824 429051.15989 5092943.22922 08/07/96 no no no

Fedorowitcz and Peterson, 1997 SSW 825 429049.16048 5093029.23005 08/07/96 no yes no

Fedorowitcz and Peterson, 1997 SSN 826 427303.14815 5091572.20470 08/07/96 no no yes

Fedorowitcz and Peterson, 1997 SSN 827 427272.14763 5091511.20388 08/08/96 no yes yes

Fedorowitcz and Peterson, 1997 SSW 828 428439.15262 5091982.21598 08/08/96 no yes yes

Fedorowitcz and Peterson, 1997 SSW 829 428446.15293 5092024.21644 08/08/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 830 428405.15230 5091943.21538 08/08/96 no yes yes

Fedorowitcz and Peterson, 1997 SSW 831 428335.15215 5091933.21484 08/08/96 no no yes

Fedorowitcz and Peterson, 1997 SSW 832 428277.15175 5091887.21402 08/08/96 no yes yes

Fedorowitcz and Peterson, 1997 SSW 833 428676.15398 5092138.21900 08/08/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 834 428690.15427 5092177.21948 08/08/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 835 428692.15459 5092222.21993 08/08/96 no yes yes

Fedorowitcz and Peterson, 1997 SSW 836 428692.15493 5092269.22040 08/09/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 837 428704.15474 5092241.22020 08/09/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 838 428676.15493 5092271.22032 08/09/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 839 428715.15505 5092283.22068 08/09/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 840 428729.15540 5092331.22124 08/09/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 841 428743.15545 5092336.22138 08/09/96 no no no

Fedorowitcz and Peterson, 1997 SSW 842 428752.15567 5092367.22174 08/09/96 no no no

Fedorowitcz and Peterson, 1997 SSW 843 428764.15609 5092424.22237 08/09/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 844 428756.15666 5092506.22314 08/09/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 845 429046.16109 5093117.23089 08/09/96 no no no

Fedorowitcz and Peterson, 1997 SSW 846 429063.16151 5093177.23158 08/10/96 no no no

Fedorowitcz and Peterson, 1997 SSW 847 429249.16518 5093709.23787 08/10/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 848 428973.16807 5094124.24034 08/14/96 no no no

Fedorowitcz and Peterson, 1997 SSXa 849 429279.15242 5096303.21921 08/14/96 yes no no

Fedorowitcz and Peterson, 1997 SSXa 850 429266.15277 5096266.21964 08/14/96 yes yes no

Fedorowitcz and Peterson, 1997 SSXa 851 429295.15302 5096239.22020 08/14/96 yes? no
Fedorowitcz and Peterson, 1997 SSXa 852 429272.15307 5096234.22012 08/14/96 yes no no

Fedorowitcz and Peterson, 1997 SSXa 853 429268.15305 5096236.22007 08/14/96 yes yes no

Fedorowitcz and Peterson, 1997 SSXa 854 429256.15306 5096235.22001 08/14/96 yes yes no

Fedorowitcz and Peterson, 1997 SSXa 855 429227.15200 5096347.21828 08/14/96 yes? no

Fedorowitcz and Peterson, 1997 SSXa 856 429247.15199 5096348.21839 08/14/96 yes no yes

Fedorowitcz and Peterson, 1997 SSXa 857 429263.15162 5096387.21795 08/15/96 no no no

Fedorowitcz and Peterson, 1997 SSXa 858 429276.15373 5096164.22112 08/15/96 yes? no

Fedorowitcz and Peterson, 1997 SSN 859 428645.16626 5094873.23552 08/15/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 860 428638.16636 5094863.23562 08/15/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 861 428591.16726 5094772.23665 08/15/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 862 428542.16805 5094694.23748 08/15/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 863 428535.16769 5094731.23692 08/15/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 864 428617.16637 5094863.23551 08/15/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 865 428608.16625 5094876.23527 08/15/96 no no no

Fedorowitcz and Peterson, 1997 SSS 866 429723.15977 5095512.23275 08/15/96 no yes no

Fedorowitcz and Peterson, 1997 SSS 867 429717.16023 5095463.23339 08/16/96 no yes no

Fedorowitcz and Peterson, 1997 SSS 868 429694.16053 5095432.23368 08/16/96 no yes no

Fedorowitcz and Peterson, 1997 SSN 869 428847.16462 5095033.23439 08/16/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 870 428887.16343 5095155.23291 08/16/96 yes no no

Fedorowitcz and Peterson, 1997 SSN 871 428642.16539 5094963.23423 08/16/96 yes no no

Fedorowitcz and Peterson, 1997 SSS 872 429638.16593 5094855.24126 08/16/96 no no yes

Fedorowitcz and Peterson, 1997 SSS 873 429676.16484 5094970.23990 08/16/96 no no yes

Fedorowitcz and Peterson, 1997 SSS 874 429699.16377 5095084.23847 08/16/96 no yes no

Fedorowitcz and Peterson, 1997 SSS 875 429677.16255 5095216.23654 08/17/96 no yes no

Fedorowitcz and Peterson, 1997 SSS 876 429506.16700 5094748.24200 08/17/96 no yes yes

Fedorowitcz and Peterson, 1997 SSN 877 428173.15573 5092439.21891 08/23/96 no yes no

Fedorowitcz and Peterson, 1997 SSN 878 428201.15567 5092427.21896 08/23/96 no no no

Fedorowitcz and Peterson, 1997 SSS 879 429681.15870 5095628.23092 08/23/96 no yes no

Fedorowitcz and Peterson, 1997 SSS 880 429704.15832 5095668.23051 08/23/96 no yes no

Fedorowitcz and Peterson, 1997 SSS 881 429670.15803 5095700.22987 08/23/96 no yes no

Fedorowitcz and Peterson, 1997 SSN 882 428337.15890 5092854.22405 08/23/96 no yes no

Fedorowitcz and Peterson, 1997 SSN 883 428353.15892 5092855.22416 08/23/96 yes yes no

Fedorowitcz and Peterson, 1997 SSN 884 428326.15888 5092851.22396 08/23/96 no yes no

Fedorowitcz and Peterson, 1997 SSS 885 429553.16868 5094567.24474 08/23/96 no no no

Fedorowitcz and Peterson, 1997 SSS 886 429503.16870 5094568.24445 08/23/96 no? no
Fedorowitcz and Peterson, 1997 SSS 887 429483.16868 5094571.24430 08/24/96 no yes no

Fedorowitcz and Peterson, 1997 SSS 888 429459.16867 5094574.24413 08/24/96 no ? no

Fedorowitcz and Peterson, 1997 SSS 889 429505.16844 5094595.24409 08/22/96 no yes no

Fedorowitcz and Peterson, 1997 SSW 890 428193.15121 5091827.21290 08/22/96 no yes yes

UTM: Universal Transverse Mercator coordinates in North American Datum 1983, zone 10, in meters.

yes = tsunami deposit of specified age present

no = tsunami deposit of specified age not present

? = evidence for tsunami origin for deposit of specified age inconclusive
1

Appendix E: Incorporating Tides into Probability Calculations

 Including tides in the probability calculation is essentially a source of aleatory uncertainty,

because it is unknown at what tidal stage the tsunami wavetrain will arrive at Seaside (Mofjeld and

others, 1997; Mofjeld and others, in press). First, consider the far-field case where only one rupture

scenario (magnitude, slip, area) is considered per source region (j). For each tsunami amplitude Aj

(corresponding to for each j source), the tides produce a probability density function f(y,A) that

describes the distribution in height of the maximum waves, where y is the total wave height

including tsunami and tidal amplitudes. To get the exceedance rate for the jth source as a function

of y, we integrate the corresponding probability density function (PDF) as follows:

(1)

€

Fj(y, Aj) ≡ f(y ′, A)

y

∞

∫ dy ′.

To get an explicit form for F(y,A), a Gaussian distribution (Mofjeld and others, in press) is a

reasonable approximation to the PDF:

(2)

€

f (y, A) = Bexp −(y − η0 )

2 2σ2

[ ], B−1 = 2πσ,

where

(3)

€

η0 (A) = A + MSL + C(MHHW − MSL) exp −α(A /σ0 )  β

  and

(4)

€

σ(A) = σ0 −C ′σ0 exp −α′(A /σ0 )  β′

 

The parameters C, α, β, C', α' and β' are specific to the tides at a given location (see Appendix A).

The integral F(y,A) is then

(5)

€

F(y, A) = 1

2

erfc[(y −η0 ) 2σ ], erfc(z) ≡1− erf(z).

Here, 

€

erf(z) = 2 π ( ) exp −t

2

( )dt

0

z

∫ is the standard form of the error function.

The rate at which 

€

y ′ ≥ y for a given source (λj) is the mean recurrence rate of that design

earthquake (νj) multiplied by Fj:

(6)

€

λ j(y ′ ≥ y) = υjFj(y ′ ≥ y)

If for all source regions, there is only one design earthquake per region, then the aggregate

exceedance rate would be (since we a dealing with discrete regions):
2

(7)

€

λ(y'≥ y) = λ j(y'≥ y)

j

∑

The exceedance probability at Seaside, assuming that the arrival times are Poissonian is

(8)

€

P(y'> y) =1− e−λT ,

where T is the exposure time of interest (1 year).

Next, consider just the local case where a distribution of tsunami amplitudes is obtained

from multiple slip distributions available for a specified magnitude, average slip, area. If 

€

p(Alocal)

is the PDF for local tsunami amplitudes then

(9)

€

Elocal(y'≥ y) = p(Alocal) 0

∞

∫ F(y,Alocal)dA.

€

p(Alocal) can be determined from a normalized histogram as in Mofjeld and others (in press) or by

approximation to a Gaussian distribution, similar to f(y,A). Inclusion of multiple realizations for

local rupture (aleatory uncertainty of the source) is discussed further in the Source Specification

Section for Cascadia subduction zone earthquakes below. It follows that the local exceedance rate

is given by

(10)

€

λlocal(y'≥ y) = υlocalElocal(y'≥ y).

Therefore, the “Grand Total” exceedance rate (local + far field) can be given by

(11)

€

λ(y'≥ y) = λlocal(y'≥ y) + λ j(y'≥ y)

j

far− field

∑ .

The objective, of course, is to find y corresponding to λ=0.01 yr-1 and λ=0.002 yr-1.

References

Mofjeld, H.O., Foreman, M.G.G., and Ruffman, A., 1997, West Coast tides during Cascadia

subduction zone tsunamis: Geophysical Research Letters, v. 24, p. 2215-2218.

Mofjeld, H.O., González, F.I., Titov, V.V., Venturato, A.J., and Newman, A.V., in press, Effects of

tides on maximum tsunami wave heights: Probability distributions, Journal of Atmospheric

and Oceanic Technology.
1

Appendix F: Determining Earthquake Recurrence Rates

For each earthquake, a set of source parameters must be chosen that determines the initial

condition for propagation modeling. Earthquake magnitude is the primary parameter that links

other source parameters such as rupture area and average amount of slip. Unlike deterministic

modeling in which recurrence rates are not considered, probabilistic modeling requires the

determination of recurrence rate for each source used in inundation modeling. Recurrence rates can

be determined from frequency-magnitude distributions as follows.

Characteristic vs. Gutenberg-Richter

A persistent controversy in seismology is whether the frequency-magnitude distribution for

earthquakes follows a characteristic model at the largest magnitudes or a modified GutenbergRichter (G-R) distribution. Although broad generalizations are not always accurate, observational

seismology tends to support the modified G-R model whereas paleoseismology studies tend to

follow, at least implicitly, the characteristic model. Figure F-1a, modified from Wesnousky (1994),

shows the two models for both the discrete and cumulative form, where Ma is the magnitude of the

largest aftershock for the characteristic distribution. Note that for both models, smaller earthquakes

follow a power-law scaling relationship. In Wesnousky’s (1994) conception that is typical for

paleoseismic studies, there is a significant gap in intermediate earthquake magnitudes between Ma

and Mmax for the characteristic model such that a given fault typically ruptures in Mmax earthquakes

with the rest of the distribution M < Ma representing foreshocks, aftershocks and background

seismicity. In contrast, the G-R model predicts a power-law scaling relationship that is valid for all

magnitudes up to Mmax.

Both models are often employed in the development of the USGS National Seismic Hazard

Maps. For example, in the seismic hazard maps for Alaska (Wesson and others, 1999) the Aleutian

segment of the Pacific-North American interplate thrust is characterized by a G-R distribution for

M=7-9.2, whereas the Prince William Sound segment (1964 source region) is characterized by both

a G-R distribution (M=7-8) and a characteristic rupture model (M=9.2) with an average return time

of 750 years derived from paleoseismology studies (400-500 years estimated by Bartsch-Winkler

and Schmoll, 1992). For earthquakes along the Cascadia interplate thrust, the National Seismic

Hazard Maps use two different models that are equally weighted (Frankel and others, 2002): (1) a

M=9.0 characteristic earthquake with an average repeat time of 500 years and (2) a series of M=8.3

earthquakes that fill the seismogenic region of the interplate thrust every 500 years resulting in a

repeat time of 110 years for a M=8.3 earthquake to occur anywhere in the seismic zone. A G-R

distribution is not used for Cascadia interplate thrust earthquakes. It should be stressed, however,

that the paleoseismic data used in characteristic earthquake models needs to be carefully analyzed

with respect to the possible magnitude range that geologic markers represent and to account for

open intervals in calculating average repeat times (Parsons, 2004, submitted) (see also Savage,

1991; Schwartz, 1999 for specific concerns regarding the characteristic model).

A different definition of characteristic and Gutenberg-Richter models is presented more

recently by Kagan (2002a) as shown in Figure F-1b. The characteristic distribution is a power-law

distribution truncated at the characteristic magnitude (cumulative form). This is similar to

Wesnousky’s (1994) definition of Gutenberg-Richter distribution (Figure F-1a) and hence is an

obvious source of confusion . The other distributions shown above are modified Gutenberg-Richter

distributions where there is an accelerating fall-off in earthquake recurrence rates with increasing
2

magnitude. The tapered Gutenberg-Richter and Gamma distributions have a “soft” corner

magnitude, whereas the characteristic and truncated Pareto (also called truncated GutenbergRichter because the probability density distribution is truncated) have “hard” cutoff magnitudes

(Kagan, 2002b).

For tsunamis, we a primarily concerned with establishing recurrence rates for large

magnitude earthquakes that occur at the tail of these distributions. The distributions derived by

Kagan are statistically more defensible using observed seismicity and seismic moment balances

(Bird and Kagan, submitted; Kagan, 2002a; Pisarenko and Sornette, 2004; Sornette and Sornette,

1999). Even so, there is substantial uncertainty in establishing recurrence rates at the distribution

tails because of a lack (thankfully) of very large magnitude earthquakes (hence, the reason there are

multiple distributions).

Modified G-R Distributions

The original form of the G-R distribution is

(1)

€

logN(m) = a − bm,

where N(m) is the number of earthquake with magnitude ≥ m and a and b are scaling parameters.

The parameter a is often associated with the seismic activity of a particular region and b is the

power-law exponent of scaling. Kagan (2002a), Sornette and Sornette (1999) and other earlier

studies indicate that source finiteness requires that there must be an upper bound to the G-R

distribution (Equation 1). This leads to the modified G-R distributions described by Kagan (2002a)

and shown in Figure F-1b.

In a simplified version involving the truncated G-R distribution, Ward (1994) derives an

expression for the average repeat time for earthquakes of magnitude m.

(2)

€

T(m) = b

1.5 + b













10(1.5+b)mmax +9.05

M  ̇ s 10bmmax −10bm [ ]

,

where 

€

M  ̇ s is the seismic moment release rate in Nm/yr. Note that the a-value (Equation 1) does not

appear in this expression. The seismic activity is determined by 

€

M  ̇ s as described below.

Recurrence rates calculated from (2) are associate with a sharp distribution corner with a hard

cutoff at mmax.

An equivalent expression for the tapered G-R (TGR) distribution can be derived from

Kagan (2002b, eqn. 7):

(3)

€

T(M0) = 1

1−β













M0

β

Mcm

1−β

M  ̇ s

Γ(2 −β)ξm ,

where 

€

β = 2

3 b, Γ is the gamma function and 

€

ξm = exp(M0 /Mcm ). M0 and Mcm are given in Nm

(corresponding magnitudes m0 and mcm are given by 

€

m = 2

3 log M − 6.0). Similar expressions can be

derived for the other distributions.
3

Seismic Moment Conservation

The seismic moment rate 

€

M  ̇ s in Equations (2) and (3) can be determined for a particular

seismic zone from historic earthquakes, as long as the catalog contains the largest seismic moment

events. Otherwise, 

€

M  ̇ s can be determined from fault slip rates (

€

s  ̇ geol) and estimates of geometric

parameters. The formula for geologically-determined

€

M  ̇ s is given below (Ward, 1994):

(4)

€

M  ̇ s = μLHss  ̇ geol

where μ is the shear modulus, L is the fault length, and Hs is the effective seismogenic thickness.

The latter parameter is the most difficult to estimate because it involves determination of the base

of seismicity (transition from unstable to stable frictional properties) and the seismic coupling

coefficient (χ) that accounts for the portion of fault movement that occurs aseismically. Studies by

(Bird and Kagan, submitted; Kagan, 2002b; Ward, 1994) all seek to ensure that the seismic moment

rates are consistent with plate tectonic rates and physical parameters.

Seismic Zonation

In calculating frequency-magnitude distributions, a seismic zonation scheme is used such

that the scaling parameters can be considered more or less uniform. For example, the zonation

scheme used by Ward (1994) for evaluating onshore seismic hazards in southern California entailed

defining 66 regions with definable characteristics. For the purpose of the tsunami pilot study, much

larger zones need to be considered. For far-field sources, the Flinn-Engdahl regions are probably

most suitable. Kagan (1997) and Sornette and Sornette (1999) determine b-values, moment rate,

and corner magnitudes for Flinn-Engdahl regions relevant to far-field tsunamis. In a later paper,

Kagan (2002b) considers smaller circum-Pacific seismic zones defined by McCann and others

(1979) and Nishenko (1991). For the smaller zones, however, the statistics are not as reliable and

accommodation for interacting zones (cf., Ward, 1994) needs to be included for larger earthquakes

that span multiple zones.

A question may be raised as to whether the estimated scaling parameters are applicable for

the subset of tsunamigenic earthquakes. The earthquakes analyzed by Kagan (1997; 2002b) are in

the depth range of 0-70 km, though most are in the 0-40 km range. The primary condition that

limits the transfer of seismic to tsunami energy is deformation that occurs onshore rather that

offshore. This effect should be accounted for in existing tsunami propagation models that use

realistic bathymetry and topography over the source region.

Approach Used in This Study

As outlined above, there are several choices of frequency-magnitude distributions that one

could choose to determine earthquake recurrence rates. Whereas the scaling and recurrence rates of

small earthquakes are usually well defined, the tail of the distribution in difficult to constrain with

statistical confidence (Pisarenko and Sornette, 2004). Unfortunately, it is the large earthquakes that

exist at the distribution tail that is of primary interest for far-field tsunami studies.

One possible approach is to calculate inundation maps based Mmax, some of which have

historic precedent, such as the 1964 Alaska, 1960 Chile, 1957 Aleutian, and 1700 Cascadia

earthquakes. This serves the dual purpose of constraining the aggregate 0.01 or 0.002 annual

probability from high return-period events and using these historic events for model validation.

However, how do we establish the return period for these events? Sykes and Quittmeyer (1981)
4

use the time-predictable model (Shimazaki and Nakata, 1980) to estimate recurrence rates for large

earthquakes in most of the regions we are interested. Murray and Segall (2002), however, recently

call into question the time-predictable model. As another option, we can use characteristic rates for

the 1964 Alaska source and 1700 Cascadia source regions (similar to the National Seismic Hazard

Maps) estimated from paleoseismology and use a modified G-R distribution for other regions

defined by Flinn-Engdahl zonation. If characteristic sources are used, however, it would be most

consistent to also use historic and paleoseismic studies for other regions such as Japan which are

considerably more complex (e.g., Nanayama and others, 2003; Rikitake, 1999; Utsu, 1984). This

also introduces the previously mentioned problems in using paleoseismic data to establish

recurrence rates for specific earthquake magnitudes. A third approach would be to simply use a

modified G-R distribution for all regions that are constrained by plate tectonic/fault slip rates. In

this case, the seismic moment rate for the Cascadia interplate thrust would have to be derived from

fault slip rates (Equation 4) and an assumed b-value. In contrast to the paleoseismic/characteristic

earthquake approach, both the magnitude and recurrence rates are well defined, though there is

significant uncertainty in estimating recurrence rates for the largest earthquakes.

Summary

One of the obvious difficulties with specifying far-field sources for this and any tsunami

probability study is obtaining accurate estimates for Mmax and the associated recurrence rates for

each source region. Several problematic examples include wide variations in the estimated seismic

moment for the 1957 Andreanof earthquake (Boyd and others, 1995) and differences between

tsunami and seismic estimates for events such as the 1952 Kamchatka and 1960 Chile earthquakes.

One way to incorporate these uncertainties is to estimate a reasonable distribution of source

parameters and run additional models to determine the effect on inundation at Seaside. Given the

time constraint of the pilot study, however, we rely on sensitivity studies such as Titov and others

(1999) to determine if and how this type of analysis should proceed.

Finally, an example of how these sources may combine for determining the 100-year and

500-year inundation zones is given below. If we assume a time-independent (Poissonian)

probability model and that the largest inundation zone will be from a M=9 Cascadia earthquake

with an average return time of 500 years, then the Cascadia earthquake alone (along with its

variations) will determine the 500-year tsunami flood at Seaside. Suppose furthermore, that the 4

largest inundation zones at Seaside were from a M=9.0 Cascadia event (500 yr.), a M=9.2 Gulf of

Alaska event (750 yr.), a M=8.8 Kamchatka event (300 yr.), and a M=9.5 Chile event (300 yr.)

Then, the 100-year tsunami flood would be where the inundation zones from all four of these

earthquakes overlap (i.e., 

€

1

500 + 1750 + 1300 + 1300 = 1100 ). These numbers are approximate for the

purposes of the example. Also, the combination of events constraining the 100-year flood may

change when time-dependent probabilities are considered.
5

References

Bartsch-Winkler, S., and Schmoll, H.R., 1992, Utility of radiocarbon-dated stratigraphy in

determining late Holocene earthquake recurrence intervals, upper Cook Inlet region,

Alaska: Geological Society of America Bulletin, v. 104, p. 684-694.

Bird, P., and Kagan, Y.Y., submitted, Plate-tectonic analysis of shallow seismicity: apparent

boundary width, beta-value, corner magnitude, coupled lithosphere thickness, and coupling

in 7 tectonic settings: Bulletin of the Seismological Society of America.

Boyd, T.M., Engdahl, E.R., and Spence, W., 1995, Seismic cylces along the Aleutian arc: Analysis

of seismicity from 1957 through 1991: Journal of Geophysical Research, v. 100, p. 621-644.

Frankel, A.D., Petersen, M.D., Mueller, C.S., Haller, K.M., Wheeler, R.L., Leyendecker, E.V.,

Wesson, R.L., Harmsen, S.C., Cramer, C.H., Perkins, D.M., and Rukstales, K.S., 2002,

Documentation for the 2002 Update of the National Seismic Hazard Maps: U.S. Geological

Survey, Open-File Report 02-420, p. 33.

Kagan, Y.Y., 1997, Seismic moment-frequency relation for shallow earthquakes: Regional

comparison: Journal of Geophysical Research, v. 102, p. 2835-2852.

---, 2002a, Seismic moment distribution revisited: I. Statistical Results: Geophysical Journal

International, v. 148, p. 520-541.

---, 2002b, Seismic moment distribution revisited: II. Moment conservation principle: Geophysical

Journal International, v. 149, p. 731-754.

McCann, W.R., Nishenko, S.P., Sykes, L.R., and Krause, J., 1979, Seismic gaps and plate

tectonics: seismic potential for major boundaries: Pure and Applied Geophysics, v. 117, p.

1082-1147.

Murray, J., and Segall, P., 2002, Testing time-predictable earthquake recurrence by direct

measurement of strain accumulation and release: Nature, v. 419, p. 287-291.

Nanayama, F., Satake, K., Furukawa, R., Shimokawa, K., Atwater, B.F., Shigeno, K., and Yamaki,

S., 2003, Unusually large earthquakes inferred from tsunami deposits along the Kuril

trench: Nature, v. 424, p. 660-663.

Nishenko, S.P., 1991, Circum-Pacific seismic potential: 1989-1999: Pure and Applied Geophysics,

v. 135, p. 169-259.

Parsons, T., 2004, Recalculated probability of M ≥ 7 earthquakes beneath the Sea of Marmara,

Turkey: Journal of Geophysical Research, v. 109, p. B05304.

---, submitted, Significance of stress transfer in time-dependent earthquake probability calculations.

Pisarenko, V.F., and Sornette, D., 2004, Statistical detection and characterization of a deviation

from the Gutenberg-Richter distribution above magnitude 8: Pure and Applied Geophysics,

v. 161, p. 839-864.

Rikitake, T., 1999, Probability of a great earthquake to recur in the Tokai district, Japan:

reevaluation based on newly-developed paleoseismology, plate tectonics, tsunami study,

micro-seismicity and geodetic measurements: Earth Planets Space, v. 51, p. 147-157.

Savage, J.C., 1991, Criticism of some forecasts of the National Earthquake Prediction Evaluation

Council: Bulletin of the Seismological Society of America, v. 81, p. 862-881.

Schwartz, S.Y., 1999, Noncharacteristic behavior and complex recurrence of large subduction zone

earthquakes: Journal of Geophysical Research, v. 104, p. 23,111-123,125.

Shimazaki, K., and Nakata, T., 1980, Time-predictable recurrence model for large earthquakes:

Geophysical Research Letters, v. 7, p. 279-282.
6

Sornette, D., and Sornette, A., 1999, General theory of the modified Gutenberg-Richter law for

large seismic moments: Bulletin of the Seismological Society of America, v. 89, p. 1121-

1130.

Sykes, L.R., and Quittmeyer, R.C., 1981, Repeat times of great earthquake along simple plate

boundaries, in Simpson, D.W., and Richards, P.G., eds., Earthquake Prediction an

International Review: Washington, D.C., American Geophysical Union, p. 217-247.

Titov, V.V., Mofjeld, H.O., González, F.I., and Newman, J.C., 1999, Offshore forecasting of

Hawaiian tsunamis generated in Alaskan-Aleutian subduction zone: Pacific Marine

Environmental Laboratory (PMEL), NOAA Technical Memorandum ERL PMEL-114, p.

26.

Utsu, T., 1984, Estimation of parameters for recurrence models of earthquakes: Bulletin of the

Earthquake Research Institute, v. 59, p. 53-66.

Ward, S.N., 1994, A multidisciplinary approach to seismic hazard in southern California: Bulletin

of the Seismological Society of America, v. 84, p. 1293-1309.

Wesnousky, S.G., 1994, The Gutenberg-Richter or characteristic earthquake distribution, which is

it? Bulletin of the Seismological Society of America, v. 84, p. 1940-1959.

Wesson, R.L., Frankel, A.D., Mueller, C.S., and Harmsen, S.C., 1999, Probabilistic Seismic Hazard

Maps of Alaska: U.S. Geological Survey, Open-File Report 99-36, p. 20.
(a)

Mmax M

Cumulative Frequency

Ma Mmax M

Cumulative Frequency

Gutenberg-Richter Distribution Characteristic Distribution

(b)

Mcm M

Cumulative Frequency

Mmax M

Cumulative Frequency

Tapered Gutenberg-Richter Distribution Characteristic Distribution

Figure F-1. Characteristic and Gutenberg-Richter earthquake distributions as described

in different studies: (a) Wesnousky (1994); (b) Kagan (2002a).
1

Appendix G: Surface and Area Calculation for the 100-year and 500-

year Floods

The results of the probabilistic tsunami hazard analysis (PTHA) are grids of probability

values for a series of tsunami wave heights in 0.5-m increments from 0.5 m to 10.5 m. The cell

size is defined by the near-field tsunami source models and is 0.000558 degree in geographic

coordinates or approximately 60 m in projected coordinates. The cell values were exported from

PTHA as xyz (longitude, latitude, probability) values and reformatted as ArcGIS grids. The grid

for each wave height was contoured with an interval of 0.2% or 0.002 probability (Figures G-1 ac). The 1.0% contours for each wave height were merged into one file representing an annual

probability of exceedance of 1.0% or the 100-year tsunami (Figure G-1d). The 0.2% contours were

similarly merged to represent the annual probability of exceedance of 0.2% or the 500-year tsunami

(Figure G-1e).

The wave height contours overlap or are closely spaced (sub-pixel separation) in coastal and

inshore areas (Figure G-2d). The overlaps of the 100-year contours were clarified in the plate that

accompanies this report by selective labeling, but overlaps for the 500-year contours are more

complex and were processed to smooth the surface taking into account the maximum wave height

values at grid cell resolution (Figure G-2d-2f).

Several ArcGIS surface interpolation methods were tested (with the overlapping and nonoverlapping contours separated or combined) to isolate one that most closely reproduced the

maximum grid values. Combining contour data from the non-overlapping area and the extracted

maximum points from the overlapping area in the TOPOGRID tool provided final wave heights

with a standard deviation 0.25 m around the maximum PTHA wave heights. The TOPOGRID

command is an interpolation method designed for the creation of digital elevation models (DEMs)

(Hutchinson, 1993) from both line and point input.

Each of the 500-year wave height contours was converted to a grid, the grids were merged

by descending wave height to capture the maximum value in each grid cell. The maximum grid

values were converted to points and clipped to the overlap area for input to TOPOGRID (Figure G2b). Contours that do not overlap are clipped and input to TOPOGRID as line features without

modification. Contours of the resulting grid are used in the final representation of these flood

surfaces (Figure G-2c, Plate 1).

The eastern edge of the data portrays a maximum wave height of no less than 2.5 m. This

artificial thickness of water may be addressed by going to a smaller grid spacing and/or modeling

finer increments of wave height exceedance from PTHA

The extents of the 100- and 500-year maps are combined with the mean lower low water

line (or_seaside1_3navd) (Venturato, 2005) to produce the tsunami-based flood zone map (Figure

G-3).
2

References

Hutchinson, M.F., 1993, Development of a continent-wide DEM with applications to terrain and

climate analysis, in Goodchild, M.F., Parks, B.O., and Steyaert, L.T., eds., Environmental

Modeling with GIS: New York, Oxford University Press, p. 392-399.

Priest, G.R., Myers, E., Baptista, A.M., Fleuck, P., Wang, K., Kamphaus, R.A., and Peterson, C.D.,

1997, Cascadia subduction zone tsunamis: Hazard mapping at Yaquina Bay, Oregon: State

of Oregon, Department of Geology and Mineral Industries, Open-File Report O-97-34, p.

144.

Venturato, A.J., 2005, A digital elevation model for Seaside, Oregon: Procedures, data sources,

and analyses: NOAA, Technical Memorandum OAR PMEL-129, p. 21.
z = 0.5 m z = 4.0 m z = 6.5 m

Probability of

exceedance for

selected tsunami

wave heights (z)

0.002 (500 yr)

0.010 (100 yr)

0

0.001 - 0.002

0.003 - 0.004

0.005 - 0.006

0.007 - 0.008

0.009 - 0.01

1 23 4

4

4

6

9

7

10

5

1 2 3

4

8

6

3

6

6 8

5

5

7

8

2

6

18 5

6

4

1

3

4

7

7

8

7

5

6

4

9

5

2

5

3

6

3

8

Maximum

wave height

0.5 - 2.0 m

2.1 - 4.0

4.1 - 6.0

6.1 - 8.0

8.1 - 10.5

100-year

flood

500-year

flood

(a)

(d) (e)

(b) (c)

Figure G-1. Probability grids from probabilistic tsunami hazard analysis (PTHA) for

selected wave heights (a) 0.5 m, (b) 4.0 m, and (c) 6.5 m with the 0.002 and 0.010

contours where they exist. 100-year (d) and 500-year (e) flood maps of wave heights

compiled from 0.010 and 0.002 probability contours, respectively.
6

9

7

10

5

12

3 4

8

6

4

6

8

6

2

5

3

7

4

8

5

6

2

8 5

1

1

6

4

7

7

8

7

6

1

1

6

4 5

5 2

5

3

9

3 8

7

8

6

5

4

9

3

10

4 6

6

5

7

6

6

7

6

6

7

8

5

4

500-year flood

0.5 - 2.0 m

2.1 - 4.0

4.1 - 6.0

6.1 - 8.0

8.1 - 10.5

clip002ov

clip002

unmodified

contours

modified

contours

5

4

3

2 1 4.5

3.5

5.5

1.5

0.5

2.5

6

5

3

0.5

4.5

2.5

4.5

5.5

1

3.5

2

5

4

4.5

3.5

5.5

3

6

5

3 3

4 3 4

4 4 4

4 4 4 4

5 5 4 4 5 4

6 5 5 5 5

5 5 5

5 5

3.5 3.5

3.5 3.5 3.5 3.5

3.5 3.5 3.5

4.5 3.5 4.5

4.5

6.5 4.5

5.5 5.5 5.5 4.5

5.5 5.5 5.5 4.5 4.5

0 50 100

Meters

0 1

Kilometers

grid cell

maximum

(a)

(d) (e) (f )

(b) (c)

Figure G-2. Maps of 500-year flood data for entire study area with accompanying

enlargements of a selected area. (a) 500-year flood contours as extracted from PTHA

model. Enlargement (b) shows that contours lie close together at the grid-cell level. (c)

Maximum wave height values extracted for each grid cell in the inshore area. (d)

Enlargement shows values for each grid cell in relation to original contours. (e)

Contours recalculated by Arc TOPOGRID are generalized, but generally honor the

original data (f).
123°57'0"W

123°57'0"W

123°56'0"W

123°56'0"W

123°55'0"W

123°55'0"W

45°59'0"N

45°59 0' "N

46°0'0"N

46°0 0' "N

46°1 0' "N

46°1'0"N

0 0.5 1

Kilometers

UTM 10, NAD83

100- and 500-year

flood zones

Cascadia 1700

100-year

100-500 year

Figure G-3. Map of 100- and 500-year flood zones based on PTHA overlaid on FEMA Flood

Insurance Rate Maps for Seaside-Gearhart area. Seaward limit is mean lower low water (NAVD88).

Landward boundary of 500-year flood zone may extend eastward because maximum wave height is

2.5 m at eastern edge. Line showing extent of deposits from tsunami generated by 1700 Cascadia

earthquake (Priest and others, 1997) is included for comparison.