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.