NOAA NOS Technical Report

Technical Considerations for Use of Geospatial 

Data in Sea Level Change Mapping and 

Assessment

U.S. DEPARTMENT OF COMMERCE 

National Oceanic and Atmospheric Administration

National Ocean Service 

Silver Spring, Maryland 

September 2010
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This report prepared by:

NOAA Center for Operational Oceanographic Products and Services

Richard Edwing, Director

www.tidesandcurrents.noaa.gov

NOAA Coastal Services Center

Margaret Davidson, Director

www.csc.noaa.gov

NOAA National Geodetic Survey

Juliana Blackwell, Director

www.ngs.noaa.gov

NOAA Office of Coast Survey

Captain John Lowell, Director

www.nauticalcharts.noaa.gov

Acknowledgments:

A special thank you to the writing team: Allison Allen (lead), Stephen Gill, Doug Marcy, 

Maria Honeycutt, Jerry Mills, Mary Erickson, Edward Myers, Stephen White, Doug 

Graham, Joe Evjen, Jeff Olson, Jack Riley, Carolyn Lindley, Chris Zervas, William 

Sweet, Lori Fenstermacher, Dru Smith.

And to those who reviewed the document: W. Michael Gibson, Gretchen Imahori, Paul 

Scholz, Mary Culver, Jeff Payne.

Technical editing by Helen Worthington, NOAA Center for Operational Oceanographic 

Products and Services.
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Table of Contents

List of Figures ............................................................................................................... v

Executive Summary ..................................................................................................... ix

Chapter 1.0 Introduction ........................................................................................... 1

Chapter 2.0 General Information.............................................................................. 3

2.1 What is Sea Level Change? .......................................................................................................................3

2.1.1 Global and Relative Sea Level Change .........................................................................................3

2.1.2 Geologic History of Sea Level ......................................................................................................4

2.1.3 Present Day Global Sea Level .......................................................................................................5

2.1.4 The Latest 16-Year Trends in Global Mean Sea Level from Satellite Altimetry ..........................6

2.1.5 Projected Acceleration in Global Mean Sea Level........................................................................7

2.1.6 Present-Day Trends in Relative Mean Sea Level ..........................................................................8

2.1.7 Annual and Decadal Sea Level Variations..................................................................................10

2.2 Considerations for Sea Level Applications .............................................................................................12

2.2.1 Datums ........................................................................................................................................12

2.2.2 Datum Transformations...............................................................................................................17

2.3 Accuracy Needed For Various Sea Level Applications...........................................................................19

2.4 Use of Sea Level Rise Information..........................................................................................................22

2.5 Projection of Future Sea Level Trends ....................................................................................................23

2.5.1 Background Discussion ...............................................................................................................23

2.5.2 Integration of Projections and Scenarios.....................................................................................25

2.6 Relevance of Geospatial Data to Sea Level Applications........................................................................27

Chapter 3.0 Existing Data and Access................................................................... 31

3.1 Important Types of Geospatial Data Used for Sea Level Change Mapping and Assessment Projects....31

3.1.1 Base Surface Elevation: Topographic Data and How Shorelines Are Related To Topographic 

Datasets .......................................................................................................................................32

3.1.2 Base Surface Elevation: Bathymetric Data..................................................................................36

3.1.3 Water Surface Elevation..............................................................................................................38

3.1.4 Reference Elevation: How it Factors Into Sea Level Change Projects........................................45

3.2 Data Sources............................................................................................................................................49

3.2.1 Base Surface Elevation Data: Where to Find Topographic Data, Including Shoreline Data.......49

3.2.2 Base Surface Elevation Data: Where to Find Bathymetric Data .................................................51

3.2.3 Water Surface Elevation: Where to Find Water-Level Data and Sea Level Trends....................52

3.2.4 Reference Elevation: Where to Find Data or Other Resources to Establish or Maintain a 

Reference Elevation.....................................................................................................................52

Chapter 4.0 New Data Acquisition ......................................................................... 57

4.1 Type of Data Needed...............................................................................................................................57

4.2 How to Acquire the Data to Enable Multiple Uses..................................................................................57

4.3 How to Acquire Specific Data Sets .........................................................................................................59

4.3.1 Long-Term Sea Level and Sea Level Trends ..............................................................................59

4.3.2 Topographic Data ........................................................................................................................61

4.3.3 Bathymetric Data.........................................................................................................................63

4.3.4 Orthometric Heights....................................................................................................................64
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Chapter 5.0 Data Integration and Interpolation..................................................... 69

5.1 Error Quantification.................................................................................................................................69

5.1.1 Sea Level .....................................................................................................................................69

5.1.2 Elevation......................................................................................................................................72

5.1.3 Topographic Mapping .................................................................................................................74

5.1.4 Bathymetry ..................................................................................................................................76

5.1.5 Composite Error Budget Considerations.....................................................................................78

5.2 Integration of Multiple Data Sources to Better Address Sea Level Issues ..............................................78

5.2.1 CORS and Tide Stations..............................................................................................................78

5.2.2 VDatum and How It Can Be Used ..............................................................................................79

5.3 How to Build Integrated Data Products Such as a Digital Elevation Model (DEM)...............................88

5.3.1 Methodology ...............................................................................................................................88

5.3.2 Considerations When Generating DEMs.....................................................................................89

5.3.3 Review of the DEM Surface........................................................................................................90

Chapter 6.0 Applications......................................................................................... 95

6.1 Common Applications for Sea Level Change Data .................................................................................95

6.2 Digital Elevation Model in Mapping and Assessing the Impacts of Sea Level Change..........................99

6.3 Amount of Sea Level Change to Use in Specific Projects.....................................................................102

6.4 What Vertical Reference Datum to Use ................................................................................................104

6.5 Measure and Quantify Shoreline Change ..............................................................................................105

6.6 Applications of Ecosystem/Marsh Change Models...............................................................................106

6.7 Interaction of Sea Level Rise, Episodic Flooding, and Extreme Events................................................107

6.7.1 Statistical Approaches to Integrate SLC and Extreme Events...................................................107

6.7.2 Other Approaches to Integrate SLC and Extreme Events .........................................................108

6.7.3 Resources for Extreme Event Information ................................................................................109

6.8 Considerations for Sea Level Change Visualization .............................................................................110

6.8.1 How to Create SLC Visualizations............................................................................................110

6.8.2 How to Interpret SLC Visualizations ........................................................................................111

6.8.3 Quantify and Show Error in SLC Visualizations.......................................................................112

Chapter 7.0 Case Studies...................................................................................... 115

7.1 California IOCM....................................................................................................................................115

7.2 North Carolina Sea Level Project ..........................................................................................................116

7.3 East Coast Anomaly ..............................................................................................................................119

7.4 Southeast Florida Sea Level Rise Mapping Consensus-Building Workshop ........................................120

Chapter 8.0 Additional Resources.......................................................................... 123

8.1 Organizations/Programs ........................................................................................................................123

8.2 Publications ...........................................................................................................................................123

8.3 Data/Models ..........................................................................................................................................125

8.4 Software/Tools ......................................................................................................................................125

Chapter 9.0 Acronyms and Abbreviations .......................................................... 127
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List of Figures

Figure 2.1 Global sea level change from 400,000 years ago to the present (Williams et al., 

2009)........................................................................................................................... 4

Figure 2.2 The rise in global mean sea level over the last 18,000 years (Williams et al., 2009). . 5

Figure 2.3 Global mean sea level change since 1860 (Williams et al., 2009). .............................. 6

Figure 2.4 The estimated rate of global sea level rise since 1992 using satellite altimeter data ... 6

Figure 2.5 Regional rates of sea level change from overlapping satellite altimeter missions. ...... 7

Figure 2.6 Observed and projected sea level rise since the late 1800s (Williams et al., 2009)..... 8

Figure 2.7 Relative sea level trends around the globe computed from tide gauge records. NOAA 

website at: http://tidesandcurrents.noaa.gov/sltrends/index.shtml. ........................... 9

Figure 2.8 The long term relative mean sea level trend for San Francisco. .................................. 9

Figure 2.9 The variability of relative mean sea level trends over a long period record using over 

lapping 50-year segments......................................................................................... 10

Figure 2.10 Average annual variation in monthly mean sea level at San Francisco. .................. 11

Figure 2.11 The interannual variation in monthly mean sea level at San Francisco ................... 12

Figure 2.12 Accepted 1983-2001 NTDE datums for San Francisco, CA. From: 

http://tidesandcurrents.noaa.gov/. NAVD refers to the North American Vertical 

Datum of 1988.......................................................................................................... 13

Figure 2.13 History of updates to the National Tidal Datum Epoch due to sea level change ..... 14

Figure 2.14 95% confidence interval for linear mean sea level trend versus series length (Zervas 

2009)......................................................................................................................... 20

Figure 2.15 Table 2.4 from Gesch et al., (2009).......................................................................... 21

Figure 2.16 How a sea level rise of 1 m is mapped onto the land surface using two digital 

elevation models with differing vertical accuracies, from figure 2.2 in Gesch et al., 

(2009). ...................................................................................................................... 21

Figure 2.17 Projected temperature change and global sea level rise by 2100 (from IPCC (2007).

.................................................................................................................................. 24

Figure 2.18 Composite of sea level rise scenarios from IPCC 2007 and Rahmstorf (2007). Blue 

curve is the A1B scenario from IPCC 2007, and the grey curve is from Rahmstorf 

after including accelerated ice flow (Williams et al., 2009)..................................... 25

Figure 2.19 Taken from USACE (2009) comparing the modified NRC (1987) curves compared 

to the IPCC (2007) model results. ............................................................................ 26

Figure 2.20 Extension of present relative sea level trends using various accelerated global sea 

level rates of rise....................................................................................................... 26

Figure 3.1 This image shows high-resolution lidar data with an extracted shoreline in blue.......36

Figure 3.2. Schematic of a modern water level station................................................................ 39

Figure 3.3. NOAA tide station installations at (left) Calcasieu Pass, LA and (right) Corpus 

Christi, TX hardened to withstand major storm surge for continuous long-term 

operation. .................................................................................................................. 40

Figure 3.4. The relative mean sea level trend for San Diego, CA ............................................... 41

Figure 3.5. Schematic of a satellite altimeter configuration. ....................................................... 42

Figure 3.6. The ground track coverage for the Jason-1 satellite altimeter mission ..................... 43

Figure 3.7. Surface Elevation Table conceptual diagram, courtesy of Don Cahoon, USGS....... 47
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Figure 3.8. This conceptual diagram shows how geodetic connections between local tide 

stations, SETs, and geodetic control can provide a measurement of wetland 

elevation dynamics relative to locally-expressed (e.g. ―relative‖) sea level change.48

Figure 5.1. Estimated errors in tidal datums (including MSL) for tide stations in the Tampa Bay 

Region.........................................................................................................................71

Figure 5.2. Confidence intervals and record length dependencies for relative sea level trend 

(Zervas 2009)............................................................................................................ 72

Figure 5.3. Sample output from OPUS, showing the x-y-z positional values and their associated 

errors......................................................................................................................... 74

Figure 5.4. The VDatum software interface for converting between vertical datums................. 80

Figure 5.5. Current VDatum coverage as of July 16, 2010 ......................................................... 81

Figure 5.6. Schematic shows how VDatum handles the transformation (arrows) of a value from 

an ITRFxx ellipsoid to several vertical datums (boxes) through the core datums 

(ovals) for the Chesapeake Bay VDatum region. Estimated errors in the 

transformations are shown as standard deviation values (σ) and are placed next to 

the arrow relating to each transformation. The estimated uncertainties for each 

individual vertical datum, shown as the σ values inside the ovals/boxes are also 

included. ................................................................................................................... 82

Figure 5.7. Illustrated are the areal extents of where VDatum allows/restricts tidal datum 

transformations. ........................................................................................................ 84

Figure 5.8. Assuming a constant vertical datum transformation offset to be extended inland.... 85

Figure 5.9. Locations (shown in stars) for which extrapolating tidal datums inland is more 

complex due to proximity to waters of different tidal regimes. The left figure shows 

a portion of Chesapeake Bay, and the right figure shows some of the barrier islands 

off North Carolina. ................................................................................................... 87

Figure 6.1. Estimating wetland impacts, specifically, which areas will become marginal or lost 

(i.e., converted to open water) under three SLC scenarios (CCSP, 2009). ................95

Figure 6.2. Map shows potential geomorphologic responses to SLC for four coastal 

compartments along the Mid-Atlantic region (NY, NJ, Delmarva Peninsula, VA/NC 

coast) (CCSP 2009). Potential responses shown in the inset table based on work by 

Gutierrez et al. (2007)............................................................................................... 96

Figure 6.3. Johnson et al. (2006, as cited in CSP, 2009) examined SLC inundation potential for 

agricultural land in Worcester County, Maryland. On the left, blue areas show 

present-day inundation. On the right, inundation expected in 2100 using IPCC‘s 

(2001) accelerated rate of SLC is shown.................................................................. 98

Figure 6.4. USACE (2009) decision-making process to account for SLC in project planning and 

design........................................................................................................................ 99

Figure 7.1. Construction of the continuous bathy/topo DEM.....................................................118

Figure 7.2. Predicted shoreline change due to sea level rise by use of the Coastal Flooding 

Model...................................................................................................................... 119
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List of Tables

Table 4.1 IOCM standards..............................................................................................................58

Table 5.1. The regression equations and parameters for estimating uncertainties in tidal datums..70

Table 6.1. Potential population to be affected by SLC in the Mid-Atlantic region (CCSP, 2009)..97

Table 6.2. Potential number of residences in the Mid-Atlantic region at risk with 1 meter of SLC 

(CCSP, 2009)................................................................................................................97

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Executive Summary

Understanding trends in sea level, as well as the relationship between global and local sea level, 

provides critical information about the impacts of the Earth's climate on our oceans and 

atmosphere. Changes in sea level are directly linked to a number of atmospheric and oceanic 

processes. Changes in global temperatures, hydrologic cycles, coverage of glaciers and ice 

sheets, and storm frequency and intensity are examples of known effects of a changing climate, 

all of which are directly related to, and captured in, long-term sea level records. Sea levels 

provide an important key to understanding the impact of climate change, not just along our 

coasts, but around the world. By combining local rates of relative sea level change for a specific 

area based on observations with projections of global sea level rise (from IPCC, 2007), coastal 

managers and engineers can begin to analyze and plan for the impacts of sea level rise for longrange planning. 

This document is intended to provide technical guidance to agencies, practitioners, and coastal 

decision-makers seeking to use and/or collect geospatial data to assist with sea level change 

assessments and mapping products. There is a lot of information available today regarding sea 

level change and navigating this information can be challenging. This document seeks to clarify 

existing data and information and provide guidance on how to understand and apply this 

information to analysis and planning applications by directing readers to specific resources for 

various applications.

There is no single approach to sea level change mapping and assessment. The specific data and 

information requirements of any user are unique depending on their application, location, and 

need. It is important to understand what to look for and what questions to ask when applying 

existing information or collecting new data.

The discussion in this document is structured around four key questions to address the required 

technical considerations:

What is sea level change and how is it measured?

What are the considerations for sea level applications with respect to data standards?

How can users understand and apply geospatial data and information to support sea level 

rise mapping and assessment and aid in coastal decision making?

What are the limitations and gaps with respect to sea level measurement, and what are the 

implications of those gaps?

The document is divided into eight distinct chapters to assist readers in quickly locating the most 

relevant information:

The Introduction and General Information Chapters pose the key questions to ask when 

approaching mapping/analysis amidst sea level change, and provide background 

information on past and projected sea level trends.
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Chapter 2 provides details about the definition of sea level change, the status of sea level 

research and data today, how to use this information, and how geospatial data are related 

to sea level applications.

Chapter 3 discusses existing types and sources of geospatial data available for sea level 

mapping projects.

Chapter 4 addresses more specific types of data sets, how to acquire them, and how they 

can serve multiple uses.

Chapter 5 provides details on error and uncertainty within the data, and how to use 

specialized tools such as VDatum, as well as how to integrate data products for maximum 

utility.

Chapter 6 outlines the array of applications for sea level change data, including various 

models (DEM), and using those applications to measure and quantify changes in sea 

levels, as well as ecosystems and wetlands.

Chapter 7 presents case studies that deal with extreme events and anomalies and offers 

insights from workshops and conferences.

Chapter 8 offers a wide range of additional resources for the user who wishes to delve 

deeper into the subject of sea level change.

In summary, this document amasses the most up-to-date and useful information from NOAA and 

others to provide the user with access to a wide range of potential solutions to assist with 

planning for sea level change.
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Chapter 1.0 Introduction

This document is designed to support the climate community in conducting sea level rise 

assessments, as well as communities involved in coastal development and restoration, habitat 

assessment and protection, coastal hazard planning and mitigation, and more. Critical products 

affected by sea level considerations include: navigational, National Shoreline and National

reference system, marine boundaries, integrated bathymetric/topographic (bathy/topo) models, 

and other geospatial products and tools that support a variety of practical application and 

research projects. With this resource, users can assess the utility of existing data and inform the 

acquisition of new data against standards of the National Oceanic and Atmospheric 

Administration (NOAA). The document provides links to NOAA standards for bathymetry, 

topography, and vertical control as defined by NOAA‘s National Ocean Service (NOS). 

Four NOAA NOS Program Offices collaborated to produce this document: the Center for 

Operational Oceanographic Products and Services (CO-OPS), the Coastal Services Center 

(CSC), the Office of Coast Survey (OCS), and the National Geodetic Survey (NGS).

Approach

This technical document pulls from and links together existing standards and documents and 

incorporates additional appropriate documents and reference material. The document provides a 

series of technical references with executive summary-level syntheses to link them together and 

facilitate their use. The references include elements of bathymetry, topography, and vertical 

control as they relate to sea level measurement, mapping, assessment, and the impacts of sea 

level change. 

Key Questions

The discussion is structured around four key questions to address the required technical 

considerations:

What is sea level change and how is it measured?

What are the considerations for sea level applications with respect to data standards?

How can users understand and apply geospatial data and information to support sea level 

rise mapping and assessment and aid in coastal decision making?

What are the limitations and gaps with respect to sea level measurement, and what are the 

implications of those gaps?

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Chapter 2.0 General Information

The purpose of this chapter is to provide fundamental background in understanding sea level

change, appropriate terminology for describing sea level variations at local and global scales, and 

basic concepts of reference datums critical for assessing impacts of sea level change. Some 

considerations of accuracy and for datum transformation are also included to lay the foundation 

for subsequent chapters.

2.1 What is Sea Level Change?

2.1.1 Global and Relative Sea Level Change

The level of the sea observed along the coast changes in response to a wide variety of 

astronomical, meteorological, climatological, geophysical, and oceanographic forcing

mechanisms. From the highest frequency wind waves and sea swell to tsunamis and local 

seiches, to the daily tides, to monthly, seasonal, and annual variations, to decadal and multidecadal variations, and finally, to changes over hundreds of millions of years, sea level is 

constantly changing at any given location.

For purposes of this document, the time scales of concern with respect to sea level change 

include the monthly through the multi-decadal time frames. Multi-decadal change in sea level is 

often described as indicated by long-term sea level trends or shorter time periods, or monthly sea 

level anomalies, both of which are discussed in this document. Sea level change has geospatial 

and temporal variations such that sea level can be rising or falling depending upon location and 

time scale. Therefore, this document focuses on sea level change in general, rather than sea level 

rise, which is a specific type of sea level change.

In addition, there is a subtle, but significant distinction to make when discussing sea level change 

and the context for which estimation of the change is required. This distinction is one between 

global sea level change and relative sea level change (Williams et al., 2009). 

Global (Eustatic) sea level change is often caused by the global change in the volume of 

water in the world‘s oceans in response to three climatological processes: 1) ocean mass 

change associated with long-term forcing of the ice ages ultimately caused by small 

variations in the orbit of the earth around the sun; 2) density changes from total salinity; 

and most recently 3) heat content of the world‘s ocean, which recent literature suggests

may be potentially accelerating due to global warming. Global sea level change can also 

be caused by basin changes, through such processes as seafloor spreading. Thus global 

sea level, also sometimes referred to as global mean sea level, is the average height of all 

the world‘s oceans1. Global sea level rise is a specific type of global sea level change 

 

1 Note that rates of global sea level change vary per region as discussed in later sections of Chapter 2. 
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that climate models are forecasting to occur at an accelerated rate and is the topic of 

much of the discussion in this document.

Relative sea level change is the local change in sea level relative to the elevation of the 

land at a specific point on the coast. Relative sea level change is a combination of both 

global and local sea level change due to changes in estuarine and shelf hydrodynamics, 

regional oceanographic circulation patterns, hydrologic cycles (river flow) and local 

and/or regional vertical land motion (subsidence or uplift). Thus, relative sea level

change is variable along the coast depending upon the local and regional factors 

previously described. Relative sea level rise is a specific type of sea level change that 

affects many applications, since the contribution to the local relative rate of rise from 

global sea level rise is expected to increase. Some areas, as discussed later in this 

chapter, are experiencing relative sea level fall, which can also have ecological and 

societal impacts. Some localized areas exhibit a more dramatic relative sea level change 

trend than is generally observed globally unless data are filtered to account for local 

geophysical anomalies.

2.1.2 Geologic History of Sea Level 

Figure 2.1 shows large variations in global mean sea level elevation over the last 400,000 years

resulting from four natural glacial and interglacial cycles. Global mean sea level was 

approximately 4 meters (m) to 6 m higher than it was during the last interglacial warm period

125,000 years ago and 120 m lower during the last Ice Age, approximately 21,000 years ago. 

Figure 2.1 Global sea level change from 400,000 years ago to the present (Williams et al., 2009).

The generalized plot in figure 2.2 illustrates the rise in global mean sea level at variable rates 

over the last 18,000 years as the Earth moved from a glacial period to the present interglacial 

warm period. The rise was rapid but highly variable, slowing about 3,000 years ago. Recent 

acceleration is not noticeable at this scale. All human development has occurred in the last 3,000 

years, when the average rate of global sea level rise has been relatively flat. We are not used to 

witnessing significant changes in rates of global sea level rise, but these changes necessitate 

adaptation and mitigation planning.
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Figure 2.2 The rise in global mean sea level 

over the last 18,000 years (Williams et al., 2009).

2.1.3 Present Day Global Sea Level

Figure 2.3, taken from the Intergovernmental Panel on Climate Change‘s (IPCC) 2007 report, 

shows annual averages of global mean sea level in millimeters (mm). The red curve shows sea 

level variation from tide gauge observations since 1870 (updated from Church and White, 2006); 

the blue curve displays adjusted tide gauge data from Holgate and Woodworth (2004), and the 

black curve is based on satellite observations from Leuliette et al., (2004). The red and blue 

curves represent deviations from their averages for 1961 to 1990, and the black curve is a 

deviation from the average of the red curve for the period 1993 to 2001. Vertical error bars show 

90% confidence intervals for the data points. The estimated trend over the past century, based on 

analyses of tide gauge records around the globe, is 1.7 mm/yr - 1.8 mm/yr. 
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Figure 2.3 Global mean sea level change since 1860 (Williams et al., 2009).

2.1.4 The Latest 16-Year Trends in Global Mean Sea Level from Satellite 

Altimetry

Figure 2.4 shows an estimate of the present trend in global sea level rise based on a series of 

overlapping satellite altimeter missions performed since 1992, capturing a rate of 3.0 mm/yr for 

the global oceans (http://ibis.grdl.noaa.gov/SAT/SLC/index.php), implying an acceleration of the 

rates compared to the last century. A description of sea level measurements from altimetry is 

found in subsequent sections (section 3.1).

Figure 2.4 The estimated rate of global sea level rise since 1992 

using satellite altimeter data.

Figure 2.5 illustrates the significant geospatial variability of the global sea level trends around 

the world (http://ibis.grdl.noaa.gov/SAT/SLC/index.php). Although the composite global trend 

in sea level change is 3.0 mm/yr from 1993 to the present, regional trends show variations from 
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over 10 mm/yr to less than −10 mm/yr. It is important to understand this regional variability in 

the global signal when estimating local and regional rates. These regional patterns and the 

limited duration of the time series may reflect decadal variability rather than long-term trends. 

Note, for instance, the obvious geographic pattern similar to that observed during normal to La 

Niña conditions. See section 3.1 for additional discussion on how satellite altimeters measure 

sea level.

Figure 2.5 Regional rates of sea level change from overlapping satellite altimeter 

missions.

2.1.5 Projected Acceleration in Global Mean Sea Level

Figure 2.6 shows a plot of the recent rise in global sea level and the estimated acceleration in the 

rate of global sea level rise from several future sea level projections to the year 2100 based on 

various computer models. The blue shaded area represents the sea level rise projection by Meehl 

et al., (2007), which corresponds to the A1B emissions scenario and is part of the basis for the 

IPCC (2007) estimates. The higher gray-and-dashed-line projections are from Rahmstorf (2007)

and consider the factors used in the IPCC estimates, but they also include effects of potential 

increased ice flow rates and associated melting of ice sheets in Greenland and Antarctica.
8

Figure 2.6 Observed and projected sea level rise since the late 1800s

(Williams et al., 2009).

2.1.6 Present-Day Trends in Relative Mean Sea Level

Rates of relative sea level change are highly variable along the coasts because they are the 

combination of many effects (in addition to those from global sea level change) and have 

significant contributions from local and regional rates of vertical land motion. Local trends in 

relative sea level are estimated using long-term tide gauge records (Zervas, 2009). A discussion 

of tide gauge data used for local mean sea level determination is found in section 3.1.3. The

important point is that tide gauges measure variations of the water relative to the land, thus 

providing key information on the land-water interface required for many applications. Although 

tide gauge records are considered key data sources for developing sea level trends world-wide, 

special consideration must be given to gauge zeros, datums, and the fact that gauges are typically 

connected directly to land and land-based monumentation. The records alone cannot distinguish 

among components whether changes are due to global sea level change or land movement but do 

provide rates of actual change relative to the land. Figure 2.7 shows NOAA‘s Sea Levels Online 

website, depicting the relative sea level trends around the globe based on tide gauge records 

(http://tidesandcurrents.noaa.gov/sltrends/index.shtml). The various lengths, colors, and 

directions of the arrows illustrate the variability of the sea level trends around the globe. 

Extreme rates of relative sea level rise are found in the northern Gulf of Mexico due to regional 

and local land subsidence. Extreme rates of relative sea level fall are found in the Gulf of 

Alaska, where there is local rebound of the land due to loss of the land-based glaciers and/or 

uplift response to plate tectonics (including large earthquakes).
9

Figure 2.7 Relative sea level trends around the globe computed from tide gauge records.

NOAA website at: http://tidesandcurrents.noaa.gov/sltrends/index.shtml.

Figure 2.8 provides an example of a long-term tide gauge record and the computed relative mean 

sea level trends for San Francisco, CA, which is the longest continuously-operating tide gauge in 

the U.S. Note that the 95% confidence interval trend lines are also depicted. Trend calculations 

are decoupled for discrete events such as major earthquakes and station relocations so that the 

observations prior to the specific disturbance are not computed into the long-term trend.

Figure 2.8 The long-term relative mean sea level trend for San Francisco.
10

Relative mean sea level trends are typically computed at a tide gauge using the longest record 

available without known discontinuities. For climate applications, the variability of sea level

trends is also of interest, and the records are being analyzed for evidence of acceleration of the 

trends. Figure 2.9 shows the variability of overlapping 50-year trends for the record at San 

Francisco. Using this methodology, the latest 50-year segment was centered 25 years ago;

however, the nature of the variability is still of interest.

Figure 2.9 The variability of relative mean sea level trends over a long period 

record using over lapping 50-year segments.

2.1.7 Annual and Decadal Sea Level Variations

Understanding annual and decadal sea level variations can also be important for context and 

correct application to a particular coastal zone project. Several factors affect the water level 

measured at a tide station over various time scales. For shorter time scales, these include local 

and regional wind stress, changes in barometric pressure, and astronomical tidal forcing. At 

seasonal time scales, the larger-scale seasonal changes in atmospheric pressure, wind, river 

discharge, water density, and seasonal changes in circulation patterns have a greater impact. It is 

important to distinguish which factors are contributing to the sea level trends and account for 

those that are seasonal, tidal, or meteorological. Whereas some signals, such as astronomical 

tide, can be eliminated from the long-term record, others are not as easy to distinguish and 

remove properly. Depending on the region of study, there may be greater impacts on water level 

from physical forcing than the astronomical tide. Some areas are more significantly affected by 

wind-driven circulation and storm surge, which are harder to predict or account for. On a longer 

time scale, sea level observations also exhibit interannual and decadal variations, which are often 

difficult to remove because they can be intertwined with seasonal variations as well as oceanatmospheric interactions. However, if a sea level series from which a trend is being computed 

begins or ends in a significant crest or trough of an interannual ―event‖ (e.g., El Niño Southern 
11

Oscillation, ENSO), calculated trends can become biased. Interannual and decadal trends are 

typically not removed from monthly mean sea level records (monthly averages of hourly sea 

level heights), but should be recognized, and short-term records should be avoided, as they do 

not sufficiently capture long-term trends and therefore create bias (Parker, 1992). The 

relationship between water level and atmospheric processes makes sea level records an important 

part of understanding global teleconnections. The NOAA Sea Levels Online website

(http://tidesandcurrents.noaa.gov/sltrends/index.shtml) is also a source for annual and decadal

variation information using long-term tide gauge records. For example, figure 2.10 shows the 

average annual variability in monthly mean sea level (computed over the period of record, 1897-

2006) using San Francisco with high sea levels in January, February, August, September, and 

October of each year.

Figure 2.10 Average annual variation in monthly mean sea level at San Francisco.

Figure 2.11 shows the interannual variability in mean sea level for San Francisco. This analysis 

first subtracts the annual seasonal cycle from the record and removes the linear sea level trend so 

that the resulting sea levels can be examined for anomalous time periods. The anomalously high 

sea levels are found during the El Niño periods (e.g., 1982-1983 and 1997-1998).
12

Figure 2.11 The interannual variation in monthly mean sea level at San 

Francisco

2.2 Considerations for Sea Level Applications

2.2.1 Datums

Tidal Datums and the National Tidal Datum Epoch (NTDE)

Generally, a datum is a base elevation used as a reference from which to reckon heights or 

depths. A tidal datum is a standard elevation defined by a certain phase of the tide. Tidal 

datums are used as references to measure local sea levels near the tide gauge at which the 

measurements were collected and should not be extended into areas with differing oceanographic 

characteristics without substantiating measurements. So that they may be recovered when 

needed, such datums are referenced to fixed monuments known as bench marks near the tide 

gauge. Tidal datums are also the basis for establishing marine boundaries, delineating privatelyowned land, state-owned land, territorial sea, exclusive economic zone, and high seas 

boundaries.

Tidal datums are based on averaged stages of the tide, such as mean high water (MHW) and 

mean lower low water (MLLW). To minimize all the significant daily, monthly, and yearly sea 

level variations, a tidal datum such as MHW is defined as the average of all the high water 

elevations tabulated over an 18.6-year period (rounded to 19 years to obtain closure on the 

annual cycle). The 19-year period encompasses all significant variations in the mean range of 

tides due to variations in lunar and solar orbits, including the 18.6-year regression of the moon‘s 

nodes. It also averages out most meteorological effects on water level, which could bias a tidal 

datum computed from a shorter length data time series. The National Tidal Datum Epoch 

(NTDE) is a specific 19-year period defined by NOAA. Water level observations obtained 

during this cycle are used to calculate official tidal datums. The present Epoch is the 1983-2001 

NTDE. Figure 2.12 shows the accepted 1983-2001 NTDE tidal datum elevations relative to an 
13

arbitrary station datum at San Francisco, CA. This Web-based presentation also includes the 

elevation of the geodetic North American Vertical Datum of 1988 (NAVD 88) relative to the 

same station datum and includes the tabulated highest and lowest tides of record. NOAA 

reference manuals on applications of tidal datums (NOAA, 2001) and computation of tidal 

datums from short series of measurements (NOAA, 2003) provide more detail. See also

http://tidesandcurrents.noaa.gov/ for products and information.

Figure 2.12 Accepted 1983-2001 NTDE datums for San Francisco, CA. From: 

http://tidesandcurrents.noaa.gov/. NAVD refers to the North American Vertical Datum of 1988.

NOAA‘s Center for Operational Oceanographic Products and Services (CO-OPS) periodically

updates the Nation‘s tidal datum elevations to new NTDE periods. The most recent such update 

was in April 2003 in which the 1983-2001 NTDE superseded the 1960-1978 NTDE. The NOS 

policy is to revise the NTDE every 20-25 years to account for changes in relative mean sea level 

due to global sea level change and long-term local and regional vertical land mass movements. 

The new NTDE calculations provide the most accurate up-to-date tidal datum information 

required to support essential Federal, state, and private sector coastal zone management projects 
14

including navigation safety, storm surge monitoring, coastal engineering, ecosystem research, 

hazard mitigation, and other critical issues confronting coastal communities. Previous tidal 

epochs were determined for the periods 1924-1942, 1941-1959, and 1960-1978 (figure 2.13). 

New NTDEs are adopted so that all tidal datums throughout the United States are based on one 

(and most recent) specific common reference period. The NDTEs do not need to be consecutive 

19-year periods without gaps. The change in relative mean sea level drives the timing. The 

latest NTDE update was officially announced in the Federal Register on May 28, 2003 (volume 

68, Number 102). 

It should be noted, however, that in areas experiencing high rates of relative sea level change, 

datums must be updated more frequently than the national NTDE. NOAA has adopted a 

―Modified Tidal Datum Epoch‖ procedure for updating datums in areas of rapid sea level change 

due to rapid subsidence and uplift. Instead of using a full 19-years of monthly mean sea level, 

datums computed from the modified epoch are based on the most recent 5-years of data and are 

not the same as the standard NTDE datums. The procedure still adheres to the NTDE concept; 

however, since the monthly mean ranges of tide are computed using the full 19-years of data. 

Long-term variations in monthly mean sea level are due to oceanographic change and vertical 

land motion, while variations in range of tide are due to the changing moon‘s declination and 

nodal cycle. Special care must be taken in describing these areas on a station-by-station basis 

and care must be taken in the use of them for controlling the development of 19-year equivalent 

datums at nearby short-term stations. Example areas where the Modified Tidal Datum Epoch is

used are in southeast Alaska, where recent glacial melt has resulted in rapid local land rebound 

or uplift and relative sea level fall, and in the Louisiana Mississippi Delta area, where regional 

and local land subsidence have resulted in rapid sea level rise. Rates of relative sea level change 

in these areas are typically greater than 7-8 mm/yr.

Figure 2.13 History of updates to the National Tidal Datum Epoch due to sea level change.
15

Geodesy and Geodetic Datums

Geodesy is the branch of applied mathematics concerned with determination of the size and 

shape of the Earth, its gravity field, the precise determination of positions on the Earth‘s surface 

and the measurement of geodynamic phenomena, such as the motion of the magnetic poles, tides 

and tectonic plate motion. The National Geodetic Survey (NGS) defines a geodetic datum as: ―A 

set of constants used for calculating the coordinates of points on the Earth.‖ Generally, a datum 

is a reference from which measurements are made (such as a surface of zero elevation for 

referencing heights or the origin and orientation of a Cartesian coordinate frame used to 

reference Cartesian coordinates, as well as latitudes and longitudes, if an ellipsoid model is also 

included). Traditionally, horizontal datum describes a datum in which latitude and longitude are 

referenced. A vertical datum references elevations or heights.

Types of Vertical Datums:

There are three primary types of vertical datums in use in United States. These are orthometric, 

ellipsoidal, and dynamic. Other types of datums (such as ―normal‖ and ―normal orthometric‖) 

also exist in other countries, but will not be discussed further. Dynamic datums are generally 

used only in large landlocked bodies of water (such as the Great Lakes) but do not have a 

significant role in this document and will not be further discussed. The remaining two, 

orthometric and ellipsoidal, are outlined in the following paragraphs.

However, before proceeding, a primer on certain terminology is helpful.

Geoid: The geoid is the surface of constant gravity potential which best fits (in the least squares 

sense) global mean sea level. By this definition, at any given point in time, which represents a 

given distribution of mass on Earth (and in the Universe), there is one and only one geoid.

Ellipsoid: Usually meaning an ―ellipsoid of revolution,‖ it is a three-dimensional surface that

would be described by the rotation of an ellipse about its semi-minor axis. An ellipsoid, being an 

arbitrary shape (defined by only 2 variables) is non-unique, and various groups have adopted 

different ellipsoids of reference for various reasons.

Plumb Line: A curved line in space that is always tangent to the local direction of gravity. It is 

also perpendicular to any surface of constant gravity potential (an equipotential surface) through 

which it passes.

Ellipsoidal Normal: A straight line perpendicular to the surface of an ellipsoid.

Geoid Undulation: The distance along the ellipsoidal normal from a chosen ellipsoid to the 

geoid. If the reference ellipsoid is chosen so as to ―fit the geoid‖, the magnitude of geoid 

undulations ranges from approximately −100 to +100 meters (m), globally. 
16

Orthometric Datums

Orthometric datums are used for referencing orthometric heights. An orthometric height is the 

distance between the geoid and a point on the Earth‘s surface (measured along the plumb line). 

In general, orthometric heights are impossible to determine through a direct measurement, since 

this would require full knowledge of both the plumb line and the geoid, which are generally 

within Earth‘s crust. As such, a variety of approximations are used estimate orthometric heights. 

One of the most common is called a ―Helmert orthometric height‖ and relies solely on surface 

leveling measurements and surface gravity measurements. 

Also, orthometric heights are colloquially, but incorrectly, called heights above mean sea level 

(MSL). Oceanographic MSL, however, departs from the geoid through both periodic effects 

(such as tides) and non-periodic effects (such as western boundary currents). Furthermore, MSL 

is defined over the surface of the oceans only, whereas the geoid is a continuous surface, 

approximating the ocean‘s surface over the oceans, but slicing under the continents at land areas. 

As such, heights ―above mean sea level‖ are meaningless over land. North American Datum of 

1988 (NAVD 88): The current official vertical datum for all surveying and mapping activities of 

the Federal government. The datum is defined as the surface of equal gravity potential to which 

orthometric heights shall refer in North America, and which is 6.271 m (along the plumb line) 

below the geodetic mark at ―Father Point/Rimouski‖ (PID TY5255 in the NGS Integrated 

Database). However, it is realized (i.e., its primary method of access is) through over 500,000 

geodetic bench marks across North America with published Helmert orthometric heights, most 

of which were originally computed from a minimally constrained adjustment of leveling and 

gravity data, holding the geopotential value at ―Father Point/Rimouski‖ fixed.

National Geodetic Vertical Datum of 1929 (NGVD29)

The predecessor of NAVD 88, the National Geodetic Vertical Datum of 1929 (NGVD29) served 

as the official vertical datum for all surveying and mapping activities of the Federal Government 

for the U.S. until it was superseded. It was defined as the surface of equal gravity potential, to 

which orthometric heights shall refer in North America, and which is 0.000 m above mean sea 

level at 26 chosen tide gauges on the East and West Coasts of the United States and Canada 

(below the geodetic mark at ―Father Point/Rimouski‖ (PID TY5255 in the NGS Integrated 

Database). However, it was realized (i.e., its primary method of access was) through the 

National network of geodetic bench marks across North America with published normal

orthometric heights, most of which were originally computed from a constrained adjustment of 

leveling data, holding the mean sea level heights at 29 tide gauges as fixed to be zero in NGVD 

29. A superseded synonym for NGVD29 was Sea-level Datum of 1929. 

Both NAVD 88 and NGVD29 were ―fixed‖ and did not take into account the changing stands of 

sea level or vertical land motion (except in sporadic cases of re-leveling). Because there are 

many variables affecting sea level, and because the geodetic datum represents a best fit over a 

broad area, the relationship between the geodetic datum and local mean sea level is not 

consistent from one location to another in either time or space. 
17

Ellipsoidal Datums/Geometric Reference Frames

Ellipsoidal datums (or, more recently ―geometric reference frames‖) have become important with

the development of GPS. They often include an origin and orientation of a Cartesian coordinate 

system, overlain with an ellipsoid that approximates the geoid. Ellipsoidal heights are the 

distances along the ellipsoidal normal to a point on the Earth‘s surface. While GPS is a purely 

Cartesian (i.e., XYZ) system, the introduction of a simple ellipsoid model allows for the fast 

determination of latitude, longitude, and ellipsoid height indirectly from GPS observations. 

There are various geometric reference frames in use, but for scientific applications, especially 

sea level change, the International Terrestrial Reference Frame (ITRF) is preferred. The ITRF is 

a regularly updated (e.g. ITRF2000, ITRF2005, ITRF2008) realization of the International 

Terrestrial Reference System (ITRS), produced under the auspices of the International Earth 

Rotation and Reference Frame Service (IERS). Each ITRF is purely Cartesian, therefore any 

convenient ellipsoid may be superimposed to convert to latitude, longitude, and ellipsoid height. 

The convention most frequently used is the ellipsoid known as GRS-80 

(http://www.ncgs.state.nc.us/pdf/FAQs_for_NAD_83_NSRS2007.pdf). 

It is critical to understand the geometric reference frame in use, as its origins and varied 

ellipsoids will directly impact height measurements, such as those used in SLC detection. For

example, the official geometric reference frame (still frequently called a horizontal datum) for 

the U.S. is NAD83, whose origin is known to be offset from ITRF2008 by approximately 2.2 m. 

National Reference Systems

The NGS defines, maintains, and provides access to the National Spatial Reference System 

(NSRS) that is a nationally consistent coordinate system for determining latitude, longitude, 

height, scale, gravity, and Earth orientation parameters. See:

http://www.ngs.noaa.gov/INFO/OnePagers/NSRSOnePager.pdf.

The NSRS also tracks how these parameters change with time. The major components of NSRS 

are:

A consistent, accurate, and up-to-date National Shoreline 

The National CORS, a set of GNSS Continuously Operating Reference Stations meeting 

NOAA geodetic standards for installation, operation, and data distribution

A network of passive control monuments including the Federal Base Network (FBN), the 

Cooperative Base Network (CBN), and the User Densification Network (UDN)

A set of accurate models describing dynamic geophysical processes affecting spatial 

measurements

2.2.2 Datum Transformations

There are numerous horizontal and vertical datums used for a variety of geospatial applications. 

Topographic maps (e.g., from USGS) generally have elevations referenced to orthometric 

datums, either the NAVD 88 or to the older NGVD29. Source engineering documents and maps 
18

are typically referenced to a variety of horizontal and vertical datums, depending upon agency 

and surveyor. All GPS positioning data are referenced to one of several 3-D/ellipsoid datums. 

Depths on NOAA‘s nautical charts are typically referenced to MLLW, while bridges and 

overhead obstructions are referenced to MHW. In estuaries that are described as non-tidal (no 

tide or no significant tide compared to non-tidal hydrodynamic influences) local water level 

datums are determined from mean water level measurements and are augmented by safety 

factors for low and high water datum references for similar practical charting applications. The 

legal shoreline in the U.S., the shoreline represented on NOAA‘s nautical charts, is defined as 

the MHW shoreline; that is, the land-water interface when the water level is at an elevation equal 

to the MHW datum. Caution is advised in the use of shoreline data because, in reality, due to sea 

level variability and the frequency with which tidal datums are updated as well as limitations in 

survey technologies, the charted shoreline may not represent the true land-sea interface at the 

elevation of the 19-year epoch MHW for all areas or points along the shoreline data set. The 

MLLW line is also the nautical chart datum depicted on NOAA‘s charts. Lidar data are

collected using GPS vertical control (thus referenced to the ellipsoid) and are converted to 

NAVD 88 referenced elevations for many applications, though for charting applications, a 

relationship to local tidal datums, such as MLLW and MHW, must also be established.

For application to climate change scenarios and to inundation analyses and modeling, it is critical 

that all elevations and depths be referenced to the same datum, regardless of source. Thus, 

integrating data that are referenced to different datums from multiple sources requires 

appropriate datum transformation tools. Historically, differences between datums were available 

only at a particular location or point at a bench mark or at a tide station, where an actual survey 

had been performed using leveling techniques or static GPS observations. Manual interpolation 

of datum relationships between observation locations is often extremely difficult and the results 

inaccurate, depending upon the location and the spatial variability of the parameter being 

interpolated. NOAA‘s National Ocean Service provides two unique tools, NADCON (North 

American Datum Conversion) and VDatum, for transformation across horizontal and vertical 

datums, respectively. 

To convert data between NAD27 and NAD83, the NGS developed NADCON, a program that

uses minimum curvature to relate coordinate differences between the two systems. Visit the 

NGS NADCON Web page at www.ngs.noaa.gov/TOOLS/Nadcon/Nadcon.html. Tidal datum 

elevations vary significantly with horizontal (geographic) distance, especially in shallower 

waters and usually vary more rapidly than the horizontal variation in orthometric or 3-D/ellipsoid 

vertical datums. The variations are often correlated with spatial changes in the range of tide and 

type of tide. These changes can occur within a short distance in the more complex tidal 

hydrodynamic estuaries and river systems. 

NOS developed a vertical datum transformation tool called VDatum (www.vdatum.noaa.gov) to 

facilitate the easy transformation of elevation data between any two vertical datums among a 

choice of 36 orthometric, tidal and ellipsoid vertical datums (See chapter 5 discussion on

VDatum). The transformations within VDatum employ models (such as a geoid or a tidal 
19

model) to deliver the transformations. VDatum offers point location and batch file output, which 

can be put into a gridded model output or a GIS file thus providing interpolated information 

away from the observation locations.

The decision to use either a transformation tool or a more direct measurement depends upon the 

application and the desired accuracy. The published point elevations from NGS for geodetic 

datums at bench marks and from CO-OPS at tide stations offer the most accurate elevations. 

Performing a new static GPS survey, leveling between existing bench marks in the survey area, 

establishing a new tide station and computing new tidal datums, or performing a new 

bathymetric or shoreline survey may be the only ways to meet the most stringent accuracy 

requirements for a local project. That decision interplay between transformation models and 

observations requires understanding of project accuracy requirements, as well as uncertainties 

and limitations of the interpolation and transformation tools being used. A discussion of datum 

uncertainties and datum transformation uncertainties can be found on the NOAA VDatum 

website at: http://vdatum.noaa.gov/docs/est_uncertainties.html.

2.3 Accuracy Needed For Various Sea Level Applications

The best way to address accuracy very much depends upon the application. How are the data to 

be used and in what context? In most instances, the user must determine the total accuracy 

requirements of the final product and how certain he/she needs to be to make a decision or 

assume a significant amount of risk in final statements and conclusions?

There are a few basic statements on accuracy of relative sea level trends that can be made,

however. For instance, the accuracy of relative sea level trends computed from tide gauge 

records is highly dependent upon the record length as detailed by Zervas (2009). Figure 2.14 

shows the relationship of the 95% confidence level (an expression of uncertainty) in a sea level 

trend with record length. The confidence limits of a 20-year record have almost a 3.0 mm/yr 

uncertainty, while a trend from a 40-year record has only a 1.0 mm/yr uncertainty. For most 

applications, the uncertainty of the trend is much less than the value of the actual trend itself. 

NOAA publishes relative trends in mean sea level for only those stations with greater than 30 

years of record. 
20

Figure 2.14 95% confidence interval for linear mean sea level trend 

versus series length (Zervas 2009).

If it is found that shorter record lengths are required, then the trend must always be presented 

with an estimate of its uncertainty. When trends among a region are compared, the trends from 

greater than 30 years of record should be used. When comparing trends using shorter record 

lengths, it is important to use trends determined from simultaneous time periods.

There are also some basic statements that can be made on the accuracy required for topographic 

elevations when merging them with sea level rise trends or scenarios. CCSP 4.1 (Gesch et al.,

2009) found that in many instances, users showing impacts of sea level rise on elevation surfaces 

were overextending their findings because they ignored the underlying accuracy of the source

elevation data and reported results in increments that could not be supported by the data. Some 

of the CCSP4.1 findings were: 

The accuracy with which coastal elevations have been mapped directly affects the 

reliability and usefulness of sea level rise impact assessments. Although previous 

studies have raised awareness of the problem of mapping and quantifying sea 

level rise impacts, the usefulness and applicability of many results are hindered by 

the coarse resolution of available input data. In addition, the uncertainty of 

elevation data is often neglected.

Existing studies of sea level rise vulnerability based on currently available 

elevation data do not provide the degree of confidence that is optimal for local 

decision making.

There are important technical considerations that need to be incorporated to 

improve future sea level change impact assessments, especially those with a goal 

of producing vulnerability maps and statistical summaries that rely on the analysis 

of elevation data. The primary aspect of these improvements focuses on using
21

high-resolution, high-accuracy elevation data, and consideration and application 

of elevation uncertainty information in development of vulnerability maps and 

area statistics. 

Studies that use elevation data as an input for vulnerability maps and/or statistics 

need to have a clear statement of the absolute vertical accuracy. There are 

existing national standards for quantifying and reporting elevation data accuracy.

Figure 2.15 provides CCSP4.1 guidance on the minimum sea level rise increment supportable by 

various accuracies of source elevation data. Topographic lidar data are being increasingly used; 

however, 15.0 cm Root Mean Square Error (RMSE) accuracy is often ignored when applying it 

to various incremental rates of sea level rise or for any inundation mapping purposes.

Figure 2.15 Table 2.4 from Gesch et al., (2009).

Figure 2.16 further explains the on-the-ground difference various uncertainties in source 

elevation can make. The more accurate lidar-derived DEM (±0.3 m at 95% confidence) results 

in a delineation of the inundation zone with much less uncertainty than when the less accurate 

topographic map-derived DEM (±2.2 m at 95% confidence) is used. 

Depending on the slope of the topography, that uncertainty can translate into a significant 

horizontal distance. Care must be taken not to overstate the resolution of area impacts of 

inundation for given sources of data.

Figure 2.16 How a sea level rise of 1 m is mapped onto the land surface 

using two digital elevation models with differing vertical accuracies, from 

figure 2.2 in Gesch et al., (2009). 
22

2.4 Use of Sea Level Rise Information

Understanding trends in sea level, as well as the relationship between global and local sea level, 

provides critical information about the impacts of the Earth's climate on our oceans and 

atmosphere. Changes in sea level are directly linked to a number of atmospheric and oceanic 

processes. Changes in global temperatures, hydrologic cycles, coverage of glaciers and ice 

sheets, and storm frequency and intensity are examples of known effects of a changing climate, 

all of which are directly related to, and captured in, long-term sea level records. Sea levels 

provide an important key to understanding the impact of climate change, not just along our coasts, 

but around the world. By combining local rates of relative sea level change for a specific area 

based on observations with projections of global sea level rise (IPCC, 2007), coastal managers 

and engineers can begin to analyze the impacts of sea level rise for long-range planning. 

The sea level change information, along with the fundamental water level and geodetic data 

sources described in this document, can be used to:

1) Obtain a basic understanding of how sea level rise affects the physical environment:

Shoreline change and erosion or deposition (sediment transport)

Coastal flooding impacts of coupling with storm events (frequency and duration)

Coastal wetland sustainability

Effects on coastal habitats

Local and regional vertical land motion

Adequacy/accuracy of existing navigational charts 

Proper use of historical nautical charts, which are compiled from various surveys, 

typically conducted over several decades.

2) Understand potential societal impacts of sea level rise, such as:

Shore protection and retreat

Impacts on population, land use planning and infrastructure

Public access

Floodplain management and coastal zone management

3) Perform vulnerability studies and risk assessments on impacts of sea level rise, for example:

USGS/Coastal Vulnerability Index Maps (Theiler et al., 2009) 

EPA/Coastal Elevations at Risk Maps (EPA 2009)

U.S. Army Corps of Engineers (USACE)/ Engineering Planning and Design Guidance 

(USACE 2009)

DOT/Impacts on Transportation Infrastructure (DOT 2008)

4) Perform basic research in estimating global sea level rise (Douglas et al., 2001 and Church 

and White, 2006).
23

The present relative mean sea level trends derived from tide gauge records are often used as 

baseline rates for addressing future impacts as if there would be no acceleration in present day 

rates of global sea level rise.

The global climate models (IPCC 2000) are used to project the increased elevation in global 

mean sea level by a certain time (2100) and do not use estimates of changing rates. The USACE

(2009) estimates the change in the rates of global sea level rise using a mathematical curve for 

practical application to get interim values.

Sea level change information in the form of existing trends, projected trends, existing and 

projected extremes, and the nature of the time and spatial variability of the information can be 

used in a variety of applications. Chapter 6 describes many of these applications in more detail.

2.5 Projection of Future Sea Level Trends

2.5.1 Background Discussion

If the period of interest in projected sea level change is only from one to five years from now 

(2010), then the linear trends in relative mean sea level computed from tide station records 

probably suffice as a baseline estimate for most applications (see other chapters of this 

document). Climate models project accelerated contributions to global warming and global sea 

level rise from 10 years out to the end of the century. If projections for longer time scales are 

required, then information from the ongoing climate research needs to be applied and integrated 

with actual present day trends. 

Projection of future global sea level change is dependent upon climate change models that 

predict the impacts of various scenarios for greenhouse gas emissions. The series of efforts 

underway by Intergovernmental Panel on Climate Change (IPCC) provides the latest scientific 

consensus on impacts of global warming. The latest report (IPCC 2007) made projections of sea 

level rise for various climate model scenarios. Ongoing research in climate modeling and in 

global sea level rise has continued since the 2007 report and will be included in the next IPCC 

assessment. A table from the IPCC (2007) report (figure 2.17) shows the relationship of 

projected average surface warming and sea level rise from 1980-1999 to 2090-2099 for various 

climate model scenarios. The table also shows a range of temperature change scenarios resulting 

in several sea level rise scenarios, from 0.18 m to 0.59 m by 2100. Note that the uncertainty in 

the model results is expressed by a range of values for each scenario.
24

Figure 2.17 Projected temperature change and global sea level rise by 2100 (from 

IPCC (2007).

The IPCC is careful to caveat these results and explain their limitations. The results do not 

contain full effects of ice sheet flow because published peer reviewed literature was not available 

at the time of the report. Thus, the upper values for each scenario are not necessarily the upper 

bounds for potential sea level rise. A significant amount of research to determine rates of sheet 

flow that would increase the ocean volume is ongoing, and the next IPCC report could be 

expected to have new rates that explicitly include such effects. In the mean time, researchers 

have published their individual estimates of sea level rise that include increased contribution 

from melting of the land-based ice masses. For instance, Rahmstorf (2007) used results from the 

IPCC 2007, as well as increased rates of melting of the ice sheets in Greenland and Antarctica, to 

show their effect in comparison to the published IPCC results (figure 2.18). Upper bounds 

scenarios for sea level rise by 2100 are over 1 m.
25

Figure 2.18 Composite of sea level rise scenarios from IPCC 2007 and Rahmstorf 

(2007). Blue curve is the A1B scenario from IPCC 2007, and the grey curve is from 

Rahmstorf after including accelerated ice flow (Williams et al., 2009).

Also important to note is that IPCC (2007) did not include graphics of sea level rise showing 

exponential curves of sea level rise until 2100. They simply provide values at 2100 with no 

values implied or inferred before that time. The report did indicate that the climate models, not 

the time resolution of the impacts, allowed for inferring such a curve. There is no scientific basis 

for inferring an exponential function with the curvatures shown. Figure 2.19 uses curves for 

graphical comparison and visual understanding to make a point. Users should interpolate values 

with caution and allow for uncertainty. The U.S. Army Corps of Engineers (USACE, 2009) 

issued an interim guidance document for incorporating projected sea level change into

engineering planning and design. That document uses sea level rise scenario curves established 

in a National Research Council (NRC) report several years ago (NRC 1987) as a best estimate 

for practical application to their requirements for planning and design.

2.5.2 Integration of Projections and Scenarios

The USACE guidance document (previously referenced) uses the present local rate of sea level 

change from the tide station network as an initialization point to estimate future sea level rise 

using the formulas describing each curve. USACE uses a curve equation modified from NRC

(1987) to account for a more recent estimate of global sea level rise, even with the caveats 

described earlier, because most coastal engineering project lifetimes are 50 years in length, and 

interim information is required prior to 2100, which is all that IPCC (2007) provides. Figure 

2.19 compares the NRC modified curves to two of the IPCC 2007 scenarios. The modified 

NRC-III curve approximates the updated curves suggested by Rahmstorf (2007) in figure 2.18 

and the text in USACE (2009) describing the equations is excerpted as follows:
26

Using the current estimate of 1.7 mm/year for global mean sea level change as presented by the 

IPCC (IPCC 2007) results in this USACE modified equation:

E(t) = 0.0017t + bt2 (2)

Figure 2.19 Taken from USACE (2009) comparing the modified NRC 

(1987) curves compared to the IPCC (2007) model results.

The three scenarios proposed by the NRC result in global (eustatic) sea level rise values of 0.5 

m, 1.0 m, and 1.5 m by the year 2100. Adjusting the equation to include the historic global mean 

sea level change rate of 1.7 mm/yr updates the values for the variable—b being equal to 2.36E-5 

for modified NRC Curve I, 6.20E-5 for modified NRC Curve II, and 1.005E-4 for modified NRC 

Curve III.

Another method, but just as arbitrary, is to construct discrete rates of sea level rise for different 

periods as opposed to a smoothly varying function, as shown in figure 2.20 (Gill, 2010).

Figure 2.20 Extension of present relative sea level trends using various 

accelerated global sea level rates of rise.

1

1.2

1.4

1.6

1.8

2

2.2

2.4

1940 1980 2020 2060 2100

Annual Mean Sea Level above 

Station Datum (meters)

Time (years)

Extension of Port Isabel 

Sea Level Record under 

Varying SLR Scenarios

observed 

annual 

mean sea 

level 1944-

2009
27

For many applications, the time dimension may not be required. In these instances, a value of 

projected sea level rise is used to demonstrate a particular effect, such as an inundation map. 

This value could help to answer questions of potential risk, for instance, ―Will my sewage

treatment plant infrastructure become inundated if there is a 0.5 m sea level rise?‖

Notably, all of the climate models project global sea level to rise over the next century, with 

some projections with very high increases of over 1.0 m. There is a considerable amount of 

recent research examining historical tide gauge records for evidence of acceleration in sea level 

rise and in reconciling the altimeter trends with global trends determined from tide gauges. This 

has proven difficult because water level records from tide gauges capture variability from many 

different types and geographic scales of physical oceanographic and meteorological forcing with 

a wide variety of overlapping time dimensions (Church and White, 2006). For local application, 

assumptions on the local and regional rates of vertical land movement need to be made. 

Typically, regional land motion rates change slowly and can be assumed to be linear over 

century time scales; however, the local rates may change significantly over time if due to local 

ground water or oil withdrawal or after major earthquakes..

Long-term tide gauge records have been used over the last few decades to estimate rates of 20th

century sea level rise, primarily based on the historical tide gauge data (Douglas, 2001). Tide 

gauges measure the height of the sea surface relative to coastal land-based bench marks. 

However, these measurements include a signal from large spatial-scale secular trends in glacial 

isostatic adjustment (GIA) and possibly also regional and local tectonic motions. To estimate the 

change in eustatic sea level (i.e., changes in the volume of the ocean), the tide gauge records 

must be corrected for ongoing GIA and tectonic motions. This correction uses geological data to 

infer long-term motions or geophysical models to estimate the GIA. Selected tide gauges with 

the best long-term records located on fairly stable landforms are used. The global distribution of 

these records is significantly biased toward the northern hemisphere, however. Once adjusted 

for vertical land motion, the residual trends are compiled to produce a composite estimate of 

global sea level rise. Rates using this methodology are approximately 1.8 mm/yr for the 

twentieth century.

Snay et al. (2009) uses local rates of local vertical land motion as estimated from Continuously 

Operating Reference Stations (CORS), which is a nationwide network of GPS stations. Using 

CORS located near tide stations, the report cites a composite trend of approximately 1.8 mm/yr 

as well. The CORS data suffer from relatively short record lengths (less than 10 years for most), 

as the GPS technology is relatively new. 

2.6 Relevance of Geospatial Data to Sea Level Applications

Geospatial data are especially relevant to sea level applications for describing impacts of sea 

level rise in a visual and practical sense. The time series plots of variations in sea level from tide 

gauges and altimeter systems provide valuable information but do not immediately provide the 

―so what‖. The examples that are shown in previous sections of the CCSP4.1 report attempt to 

explain this ―so what‖. Amounts and impacts of sea level change need to be put in terms that the 
28

users can understand and put into the context of their ―language‖. Thus, for the USACE, as 

described earlier, sea level projection curves were developed with a mathematical description for 

practical application to engineering planning life cycles. CCSP4.1 attempts to provide 

information on potential risk to the population and the economy. Maps with sea level scenarios 

and visualizations are among the most effective ways to communicate risk. However, having the 

best possible baseline and source data and having accurate geospatial information to show their 

distribution are extremely important. Having an inaccurate depiction for the sake of an attentiongetting show is dangerous and bad science. Realistic geospatial depictions can still be attentiongetting, along with a clear statement of the uncertainty bounds and caveats of the material.

In particular, the geospatial information of the following parameters could be required for many 

sea level applications:

Water level data and datum elevations, water level extremes, and derived sea level trends

Geodetic data, geoid, ellipsoid, orthometric elevations, gravity, topography

Vertical land motion, subsidence, uplift

Other geophysical data, such as hydrology and river flow/stage

Coastal water temperature and density 

Coastal meteorological data

Bathymetry

In addition to fundamental point sources of this information listed above, gridded geospatial data 

fields are extremely useful but typically require modeling of the parameters, as is explained for 

transformation tools such as VDatum.

References Cited:

Church, J.A. and N.J. White, 2006: A 20th Century acceleration in global sea level rise 

Geophysical Research Letters, 33(1), L01602, doi:10.1029/2005GL024826.

DOT, 2008: The potential impacts of global sea level rise on transportation infrastructure, Part1: 

Methodology, U.S. DOT Center for Climate Change and Environmental Forecasting, 

accessed 8/2010 at: http://climate.dot.gov/impacts-adaptations/sea_level_rise.html.

EPA, 2009: Chapter 1. Coastal Elevations in the Mid-Atlantic. In: Background documents 

supporting Climate Change Science Program Synthesis and Assessment Product 4.1, J.G, 

Titus and E.M. Strange, eds., U.S. Environmental Protection Agency, FPA 430-R-07-

004.

Gesch, D.B., B.T. Guiterezz, and S.K.Gill, 2009: Coastal Elevations. In: Coastal Sensitivity to 

Sea level Rise: A Focus on the Mid-Atlantic Region. A report by the U.S. Climate Change 

Science Program, and the Subcommittee on Global Change Research, U.S. 

Environmental Protection Agency, Washington, DC, pp25-42.

Gill. S.K., 2010: Sea level measurement, determination, and application in the Gulf of Mexico, 

presentation at the Sea Level Rise 2010 Conference, Corpus Christi, TX, March 1-3, 2010.
29

Gill, S.K., et al., 2009: Population, land use, and Infrastructure. In: Coastal Sensitivity to Sea 

level Rise: A Focus on the Mid-Atlantic Region. A report by the U.S. Climate Change 

Science Program and the Subcommittee on Global Change Research, U.S. Environmental 

Protection Agency, Washington DC, pp105-116.

Holgate, S.J. and P.L. Woodworth, 2004: Evidence for enhanced sea level rise during the 1990‘s. 

Geophysical Research Letters, 31, L07305, doi:10.1029/2004GL019626.

IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working 

Group 1 to the Fourth Assessment Report of the Intergovernmental Panel on Climate 

Change. Cambridge University Press, Cambridge U.K. and New York, 996pp.

Leuliette, E. W., R.S. Nerem, and G.T. Mitchum, 2004: Calibration of TOPEX/Poseidon and 

Jason altimeter data to construct a continuous record of mean sea level change. Marine 

Geodesy,27(1-2), 79-94.

Meehl, G.A. et al, 2007: Global climate projections. In: Climate Change 2007: The Physical 

Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the 

Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, 

U.K. and New York, pp. 747-845.

NOAA, 2003: Computational Techniques for Tidal Datums Handbook, NOAA Special 

Publication NOS CO-OPS 2, NOAA National Ocean Service, Center for Operational 

Oceanographic Products and Services, 90pp.

NOAA, 2001: Tidal Datums and Their Applications, NOAA Special Publication NOS CO-OPS 

1, S.K. Gill and J.R. Schultz, eds. NOAA National Ocean Service Center for Operational 

Oceanographic Products and Services, 111pp.

NRC, 1987: Responding to Changes in Sea Level, Engineering Implications. Committee on 

Engineering Implications of Changes in Mean Sea Level, marine Board, Commission on 

Engineering and technical Systems, National Research Council. National Academy Press, 

Washington, D.C. 1987.

Parker, B.P., 1991: Sea level as an indicator of climate and global change. Marine Technology 

Society Journal, Vol. 25, No. 4.

Rahmstorf, S., 2007: A semi-empirical approach to projecting future sea level rise. Science,

315(5810), 368-370.

Thieler, E.R. et al, 2009: National Assessment of Coastal Vulnerability to Sea level Rise. Fact 

sheet accessed at: http://woodshole.er.usgs.gov/project-pages/cvi/ in August 2010. USGS 

Woods Hole Field Center, MA.

USACE, 2009: Water Resource Policies and Authorities, Incorporating Sea level Change 

Considerations in Civil Works Programs, EC 1165-2-211, Expires July 2011, CECW-CE 

Circular No. 1165-2-211, Department of the Army, U.S. Army Corps of Engineers, 

Washington, DC, 1 July 2009.
30

Williams et al., 2009: Sea level rise and its effects on the coast. In: Coastal Sensitivity to Sea 

level Rise: A Focus on the Mid-Atlantic Region. A report by the U.S. Climate Change 

Science Program and the Subcommittee on Global Change Research. U.S. 

Environmental protection Agency, Washington, DC, pp 25-42.

Zervas, C., 2009: Sea Level Variations of the United States 1854-2006, NOAA Technical 

Report NOS CO-OPS 053, NOAA National Ocean Service Center for Operational 

Oceanographic Products and Services, 78pp.
31

Chapter 3.0 Existing Data and Access

As outlined in chapters 1 and 2, sea level change (SLC) mapping and assessment projects rely on 

a wide variety of datasets that reflect existing physical conditions, as well as projections of future 

conditions and impacts. This chapter focuses on geospatial data needed to accurately determine 

SLC impacts, including references to existing data sources and further technical guidance. 

3.1 Important Types of Geospatial Data Used for Sea Level Change 

Mapping and Assessment Projects

The primary datasets needed to accurately map and assess the impacts of SLC can be broadly 

grouped into the following types (summarized from NRC 2009):

Base Surface Elevation

Two types of base surfaces are important to sea level change projects: land surface elevation 

(topography) and its underwater equivalent (bathymetry). Topography is expressed as the height 

of a location above the geodetic datum and is in most cases a positive value. Bathymetry is 

expressed as the depth of the land surface below rivers, lakes, and oceans; positive depth implies

negative elevation. Subsequent sections in this chapter provide more detail on the different types 

of topographic and bathymetric data (including shorelines), as well as where to access them.

Water Surface Elevation

SLC projects are concerned with examining the impacts of differing water levels on the base 

surface. Therefore, the final primary data type needed is information about water bodies, 

particularly the location of the air/water boundary surface relative to the base surface elevations. 

At a coastal tide gauge, this data type is ―sea level‖ for mapping and assessment purposes. The 

height of water surfaces is measured with stream and tide gauges. The location and elevation of 

the gauges themselves must be determined accurately to correctly relate water surface 

measurements to other elevations. Later sections provide more detail on accurately measuring 

water surface heights. 

Elevation Reference

Before elevation can be measured or the data used in engineering analysis, a measurement 

system must be established. The location of ―zero‖ elevation (in other words, a vertical datum)

and a method of measuring heights relative to that zero elevation must be established on the 

Earth, where it can be used for all types of height measurements. Chapter 2.2.1 describes the 

three different types of vertical datums in use, while this chapter (section 3.1.3) explains the 

types of data that contribute to establishing and monitoring these datums. More information on 

how to select an appropriate reference frame for SLC projects is available in chapter 6.4.
32

While not a stand-alone dataset, metadata are vital to ensuring the accuracy and utility of SLC 

mapping and assessment products. A key way to locate and discover the origin and quality of a 

particular dataset is to refer to its metadata—that is, data about the data. Metadata should be 

regarded as a critical component of any dataset. Generally, metadata contains the dataset‘s 

definition, structure, and administration of data files, with all contents provided in context to 

facilitate data use and archive. For geospatial datasets, metadata should contain information 

sufficient to answer the following questions: 

Who created the data?

Who maintains it?

When were the data collected? When were they published?

Where is the geographic location?

What is the content of the data? The structure?

Why were the data created?

How were they produced (data acquisition and processing methodologies)?

Where are the data stored?

What are the vertical and horizontal datums/reference systems?

How are accuracy, precision, and uncertainty (total propogated error for vertical and 

horizontal) defined?

Before investing significant time and effort in obtaining or applying dataset that pertains to SLC, 

users should critically review the metadata. If metadata are incomplete or absent, or there is no 

readily apparent way to collect the missing information from the data originator(s), users may 

reconsider use of that dataset or qualify their project results accordingly. 

3.1.1 Base Surface Elevation: Topographic Data and How Shorelines Are 

Related To Topographic Datasets

Topography is defined as the general shape or form of the land surface, determined by analyzing 

the elevation of the land. Topography specifically involves recording the relief or terrain, which 

is the three-dimensional quality of the surface, and identifying specific landforms. This involves 

generation of elevation data in electronic form, including graphic representation of landforms on 

a map by a variety of techniques, including contour lines and relief shading. 

Topography is a crucial dataset for determining the impacts of sea level change because the 

shape of the physical landscape influences the direction that water flows over it, where it 

accumulates, and how and where it drains. The accuracy with which coastal topography has 

been mapped directly affects the reliability and usefulness of SLC impact assessments (CCSP

2009), and is the most important factor in determining accuracy of flood maps (NRC 2009). In 

coastal areas characterized by flat topography, small changes in sea level cause greater changes 

in the extent of areas inundated by sea level rise or exposed by sea level fall. 

As the boundary between water and dry land, shorelines are an important component of SLC 

projects. Changes in shoreline position are, in large measure, driven by changes in water levels 
33

(see section 3.1.3 for further explanation), and shoreline movement is one of the effects of SLC 

that is most readily understandable and easy to communicate to a wide range of audiences. 

Because delineation of shorelines is often done through analysis of elevation data (topographic 

or bathymetric, sometimes both), information about shorelines is provided in the context of 

topographic data throughout this document.

Topographic Data Types

Topographic data are available in several different forms (raw points, rasters, triangular irregular 

networks, contours, regularly gridded digital elevation models) and can be collected using 

different sensors and methods. Among the more common sources of topographic data are: 

Land Survey (captures centimeter-scale elevation changes)

Land surveying is the technique and science of accurately determining the terrestrial or threedimensional position of points and the distances and angles between them. These points are 

usually on the surface of the Earth, and they are often used to establish land maps and boundaries 

for ownership or governmental purposes. Land surveying involves using traditional surveying 

equipment such as levels and theodolites. More recent instruments include total stations that 

combine leveling, ranging, and angle measurement. Today, most survey-grade equipment uses 

Global Positioning System (GPS) data in a kinematic differential mode to obtain relative 

ellipsoidal or orthometric heights precise to 30 mm-40 mm (root mean square error or RMSE), in 

areas of a few tens of kilometers in radius. GPS is the most accurate way to obtain heights but 

can only be done one point at a time, which is very labor intensive and costly.

Aerial Image (Photogrammetry) (10 cm)

Stereo aerial imagery is and has commonly been used to derive elevations for use in generating 

digital elevation models (chapter 5.3). The technique provides accurate information and is used 

extensively in highway and road projects. However, it is less cost effective when working on 

larger areas, and its accuracy suffers in areas of dense vegetation. This method can yield 

elevations with vertical accuracy on the order of 10 cm (RMSE).

Topographic Lidar (10 cm)

Lidar (light detection and ranging) is an active sensor, similar to radar, which transmits laser 

pulses to a target and records the time it takes for the pulse to return to the sensor receiver. This 

technology is currently being used for high-resolution topographic mapping by mounting a lidar 

sensor, integrated with GPS and inertial measurement unit (IMU) technology, to the bottom of 

aircraft and measuring the pulse return rate to determine surface elevations. Lidar yields vertical

accuracy of 10 cm (RMSE).
34

IfSAR (1 m)

Interferometric Synthetic Aperture Radar (IfSAR or InSAR) is an aircraft or satellite-mounted 

sensor designed to measure surface elevation, though its primary strength is in measuring 

elevation change. IFSAR derives a surface height by correlating two coherent radar images, 

which are acquired by two antennae separated by a known distance. The radar images are 

derived from electromagnetic energy returned to each antenna from the first surface it 

encounters. An interferogram that represents the phase difference of the corresponding pixels of 

the two radar images is generated. The height of the pixel is calculated from this phase 

difference and the airborne navigation information. IfSAR generally only yields 1m (RMSE) 

vertical accuracy, though it can detect elevation change on the order of millimeters (see

http://www.csc.noaa.gov/crs/rs_apps/sensors/ifsar.htm#apps).

National Elevation Dataset (NED) (varying accuracy based on geography)

A derived topographic dataset that is nationally available is the USGS National Elevation 

Dataset (NED) (http://ned.usgs.gov/) has been developed by merging the highest-resolution, best 

quality elevation data available across the U.S. into a seamless raster format. NED has a 

consistent projection (geographic) and elevation units (meters). Nationwide coverage is 

available for data at a 1 arc-second (30-meter [m]) post spacing; there also is substantial 

coverage at 1/3 arc-second (10-m) post spacing. The horizontal datum is NAD83, except for 

AK, which is NAD27. The vertical datum is NAVD 88, except for AK, which is NAVD29. 

NED is a living dataset updated bimonthly to incorporate the ―best available‖ DEM data. As 

more 1/9 arc-second (3 m) post spacing data covering the U.S. is available, it will be added to the 

seamless dataset.

Shoreline Data

As stated earlier, a shoreline can most simply be defined as a linear boundary that marks the 

transition from water to dry land. In practice, given the variety of datums in use (chapter 2.2)

and the data sources and delineation methods available, identifying and using shorelines in SLC 

mapping and assessment projects require careful work. 

The National Shoreline

NOAA‘s National Geodetic Survey (NGS) sustains a Coastal Mapping Program with the goal of 

providing the National Shoreline for the U.S. and its territories (http://shoreline.noaa.gov/). NGS 

and its predecessors have conducted shoreline mapping activities since the original ―Survey of 

the Coast‖ in 1807 (Shalowitz, 1964), and the shoreline depicted on National Ocean Service 

(NOS) nautical charts is treated as the legal shoreline by many U.S. agencies (Graham et al., 

2003). In addition to its primary use on nautical charts to assist in safe navigation, the National 

Shoreline also serves numerous other purposes, ranging from determination of legal boundaries 

to coastal management and environmental applications, such as climate change studies (Morton 

et al., 2004; Scavia et al., 2002; Titus and Richman, 2001).

Universally-accepted methodologies and definitions for a standard shoreline do not currently 

exist. Indeed, numerous indicators or proxies for shoreline position have been used in shoreline 

mapping and described in the literature, including: vegetation lines; dune lines; dune toes; bluff 
35

or cliff lines; beach scarps; berm crests; the high water line (HWL), interpreted as the wet/dry 

line from the last high tide; coastal structures, such as seawalls or bulkheads; in addition to 

datum-based shorelines (e.g., Boak and Turner, 2005; Crowell et al., 1991; Leatherman, 2003; 

Moore, 2000; Moore et al., 2006; Morton, 1991; Morton and Speed, 1998; Pajak and 

Leatherman, 2002). 

Historically, the shoreline depicted on NOS topographic sheets (T-Sheets) was an interpreted 

HWL (Boak and Turner, 2005; NRC, 2004; Moore et al., 2006). After the 1930s, the component 

of the U.S. Coast and Geodetic Survey that later became the NOS/NGS Remote Sensing 

Division adopted procedures for shoreline mapping from tide-coordinated aerial photography 

(Smith 1981). The current procedures are designed to produce lines representing the intersection 

of the land (at the specific time of data acquisition) and the water surfaces of the mean high 

water (MHW) and mean lower low water (MLLW) tidal datums (NRC 2004). These procedures 

entail compiling the land/water interface in stereo photography flown within a time window 

calculated from the predicted or observed (via water level stations) time of MHW or MLLW plus 

or minus a specified vertical tolerance, which is a function of the tidal range (Graham et al.,

2003). However, consideration must also be given to the effects of the vertical profile of the 

beach or shoreline, which will impact the horizontal shoreline accuracy.

While the photogrammetric procedures remain NGS‘ primary methodology for mapping the 

National Shoreline, over the course of the past decade NGS has worked with numerous partners 

to develop, test, and refine new airborne light detection and ranging (lidar) shoreline mapping 

procedures. One of the main benefits of using lidar is that the tide-coordination requirements are 

not as intensive during survey as with the photogrammetric procedure; it is typically only 

necessary to acquire the data below a certain stage of the tide, rather than within a very narrow 

tide window. Thus, the efficiency of data acquisition is increased greatly. Furthermore, the 

lidar-based procedures assist in eliminating some of the subjectivity associated with the manual 

photogrammetric compilation methods and providing multi-use data that can benefit other 

coastal projects and programs (Scott et al., 2009; White, 2007). It should be noted that lidar still 

requires tide control; however, with advanced acquisition of water level data and the 

development of an ellipsoid to tidal datum transformation (such as VDatum) prior to lidar 

survey, the tide requirements are often addressed prior to survey data collection. 

Other Shoreline Sources

While the National Shoreline serves as a nationally consistent dataset that is used for a variety of 

applications, anyone with access to the necessary computing technology (e.g., Geographic 

Information System [GIS] software) and suitable source data (e.g., high-resolution topographic 

and bathymetric data, aerial imagery, GPS-aided surveying data) can ―map‖ a shoreline. The 

graphic in figure 3.1 shows one such example. Shorelines may be made publicly available by a 

wide range of sources, such as academic/research institutions, government agencies, or private 

consultants. When considering such data for use in an SLC mapping project, it is vitally 

important to obtain the accompanying metadata. The metadata provides information on how the 
36

shoreline was developed, by whom, when, and most importantly, what elevation the line 

represents (e.g., HWL). 

Figure 3.1 This image shows high-resolution lidar data with an extracted shoreline in blue. As opposed to aerial 

photography, where a shoreline would be derived based on visual identity of a feature (such as the wet/dry line), 

lidar data provides the advantage of deriving a true datum-based shoreline (such as the mean high water line or 

specific elevation contours). Note: To derive a true-datum-based line, accurate, consistent geospatial determinations 

of ellipsoid to reference datums must be derived across the extents of the survey. 

3.1.2 Base Surface Elevation: Bathymetric Data

Bathymetry is the general configuration of the seafloor represented by depth data, or the 

underwater equivalent of topographic data. Nautical charts from hydrographic surveys support 

safety of surface or subsurface navigation and usually show seafloor relief or terrain as contour 

lines (called depth contours or isobaths) and selected depths (soundings), and also provide 

surface navigational information. Bathymetric maps (a more general term where navigational 

safety is not necessarily a concern) may also use a Digital Terrain Model (DTM) and artificial 

illumination techniques to illustrate the depths being portrayed.

Bathymetric data are crucial in determining SLC impacts for several reasons. First, the shape 

and depth of the seafloor influence how water moves onto the topographic surface. For example, 

coastal areas with a flatter, shallower continental shelf experience higher tidal ranges due to 

SLC. Coastal areas with concave shorelines and shallow bathymetry are more vulnerable to 

higher storm surges as well; with SLC, the areas vulnerable to high surges may change over 

time. Bathymetry also directly influences wave patterns, which can cause increased wave energy 

on certain portions of the coast, leading to enhanced coastal erosion. Last, bathymetric data are 

needed to examine the effects of dropping water levels. For example, if sea level falls (as is 

expected for the Great Lakes), bathymetric information is needed to identify the future location 

and configuration of the shoreline. 

Common bathymetric data types include the following: 

Lead Line Surveying

Early techniques used pre-measured heavy rope or cable lowered over a ship's side. The greatest 

limitation of this technique is that it measures the depth only a single point at a time, and so is 
37

inefficient. It is also subject to movements of the ship and currents moving the line out of true 

plumb and therefore is inaccurate.

Sound Navigation and Ranging (SONAR)

The data used to make bathymetric maps today typically come from an echosounder (sonar) 

mounted beneath or over the side of a boat, ―pinging‖ a beam of sound downward at the seafloor 

or from remote sensing lidar or Laser Detection and Ranging (ladar) systems. The amount of 

time it takes for the sound or light to travel through the water, bounce off the seafloor, and return 

to the sounder is what the equipment uses to calculate the distance to the seafloor.

Single-Beam SONAR collects discrete points along track lines. The data coverage is 

sparse and requires a greater degree of interpolation between transects. Since the early 

1930s and more commonly from the 1940s onward, the occasional pings of a single-beam 

sounder might be averaged to create a map.

Multibeam SONAR collects continuous point data throughout survey area. The data 

coverage is greater than single-beam and has higher resolution. The coverage is limited in 

shallow waters. Multi-beam collection features hundreds of narrow adjacent beams 

arranged in a fan-like swath of perhaps 90 degrees to 170 degrees across. The tightly 

packed array of narrow individual beams provides high angular resolution and accuracy. 

The wide swath, which is depth dependent, generally allows a boat to map more seafloor 

in less time than a single-beam echosounder by making fewer passes. 

A number of different outputs are currently generated, including a subset of the original 

measurements that satisfy some conditions (e.g., most representative likely soundings, shallowest 

in a region, etc.) or integrated DTMs (e.g., a regular or irregular grid of points connected into a 

surface). Historically, selection of measurements was more common in hydrographic

applications, while DTM construction was used for engineering surveys, geology, flow modeling, 

etc. Since 2003-2005, DTMs have become more accepted in hydrographic practice.

Bathymetric Lidar

Bathymetric lidar systems operate in a manner similar to their topographic lidar counterpart, with 

one notable exception. Bathymetric systems transmit two light waves, one in the infrared and 

one in the green spectrum, and are capable of detecting two returns that delineate the water 

surface and seabed. The infrared band is quickly absorbed and is therefore used to detect the 

water surface, while the green band is used as the optimum light frequency to achieve maximum 

penetration in shallow water. Lidar bathymetry systems operate at a much slower rate, currently 

around 1000 soundings per second, due to the requirements for a much longer laser pulse and 

higher power. Bathymetric lidar mapping can be conducted in clear water in depths up to 50 m. 

This is a function of water clarity, and performance will decrease with increased water turbidity.

Satellite Altimetry 

Satellites are also used to measure deep-sea bathymetry. Satellite radar data are used to model 

deep-sea topography of the ocean bottom by detecting the subtle variations in sea level caused by 

the gravitational pull of undersea mountains, ridges, and other masses, and inferring the size and 
38

location of these features. Sea level is generally higher over sea mounts and ridges than abyssal 

plains and trenches.

Users obtaining historical nautical chart products must not only be aware of the various 

technologies used to obtain bathymetric soundings, but must also be aware of the various 

reference datum changes over time. For instance, as noted in previous sections, tidal datum 

NTDE periods are updated over time to account for sea level change, and soundings taken during 

one period may have a different NTDE reference than later soundings. In addition, formal datum 

changes have taken place, for instance the change from mean low water (MLW) to mean lower

low water (MLLW) back for the East Coast in the 1980s. Thus, depending upon the accuracy

desired, users cannot assume the depths from a particular nautical chart were taken at the same 

time using the same technologies and reference datum.

3.1.3 Water Surface Elevation

Measurement of Water Levels and Determination of Sea Level Trends

Water level measurements for most coastal applications are made at a water level station. They 

are typically called tide stations when located in an area in which the tide dominates the daily

rise and fall of the water level and simply called water level stations when located in non-tidal 

areas such as the Great Lakes. 

For application to climate studies and research, especially in estimating relative mean sea level 

trends, long-term continuous measurements are required. The networks should have 

characteristics of a true ―end-to-end‖ system that includes data collection through data delivery

to and application by the user community. To ensure this application, NOAA long-term water 

level networks have been configured to ensure long-term sustained measurements.

Water level stations consist of a water level sensor(s), any required ancillary sensors (i.e., water 

density to correct pressure sensor data), a data collection platform (DCP), a data transmission 

system (satellite radio, line-of-sight radio, telephone, internal recording device), and a network of 

local reference points (bench marks) surveyed into the water level sensor (leveled in). Backup 

sensors and DCPs are also used. NOAA water level stations collect and distribute 6-minute 

interval water level elevations relative to documented reference zeros (arbitrary station datum) or 

datum elevations (such as MLLW or NAVD 88) (NOAA 2001). NOAA water level stations also 

have geodetic datum connection to geodetic reference systems using either direct leveling to 

geodetic marks or static GPS surveys.

Starting in the early 1990s, the Next Generation Water Level Measurement System replaced the 

older technology systems that largely went unchanged since the mid-1800s 

(http://celebrating200years.noaa.gov/transformations/tides/welcome.html#is). Water level 

measurement sensor systems were changed from the float/wire stilling well systems to newlyengineered air acoustic and pressure systems that reduced known error sources of the old 

systems. The new system configuration underwent extensive laboratory and field testing prior to 
39

implementation and side-by-side operation with the old systems prior to their independent 

operation. This testing ensured continuity of the long-term tidal record. The new water level 

sensors are directly leveled to local survey marks, so local observers are no longer needed to 

manually observe and record independent tide staff readings for controlling the automatic tide 

gauge. The new systems contain an electronic data acquisition system. Instead of paper tape, 

the data are now stored in computer memory chips. These systems were designed to operate 

unattended for a full year without requiring maintenance. Additionally, the systems collect a 

wide variety of environmental measurements, such as wind speed, air /water temperature, 

barometric pressure, and conductivity. Figure 3.2 shows a schematic of a NGWLMS station. 

Figure 3.2. Schematic of a modern water level station

Another major advancement with this system is the method for sending water level data to 

NOAA headquarters for processing. Instead of mailing a data tape once a month, data are 

transmitted over the Geostationary Operational Environmental Satellite (GOES) system every 6-

minutes. Headquarters automatically receives the satellite transmissions just minutes after it is 

transmitted from the gauge. Data quality control and processing are automated, eliminating the 

manually intensive and time-consuming review of strip charts and the punch paper tapes that 

required conversion to digital format.

For information on how other countries measure sea level, the Intergovernmental Oceanographic 

Commission (IOC) Manual on Sea Level Measurement and Interpretation (IOC, 2006) reviews 

several different types of water level sensors and their configurations, including the new 

technology radar gauges and GPS buoy technology, in a series of manuals and discusses the 

requirements of long-term sustained measurements of sea level. Figure 3.3 shows new NOAA 

tide station installations in the Gulf of Mexico, specifically hardened and configured to withstand 

storm surges and high winds during storm events, thus ensuring long-term sustained operation 

without major gaps in data. These stations contain backup sensors and data collection modes, 

local networks of bench marks that are leveled to every year, and have ongoing sensor 

calibration checks and active continuous data quality control.
40

Figure 3.3. NOAA tide station installations at (left) Calcasieu Pass, LA and (right) Corpus Christi, TX 

hardened to withstand major storm surge for continuous long-term operation.

Use of Water-Level Measurements to Determine Relative Sea Level Trends

Relative sea level trends are computed from carefully compiled observations at long-term tide 

stations. Monthly mean sea level values are computed from the observed hourly heights over 

each calendar month. Time series of monthly mean sea levels are created, quality controlled, and 

referenced to a documented reference datum for the entire time series. 

The monthly data can also be used to obtain the average seasonal cycle for each station 

represented as 12 mean values. The residual time series after the trend has been removed 

contains valuable information about the correlation of the interannual variability between 

stations, which is better defined by a monthly residual series than by an annual residual series. 

Trends derived from monthly mean sea level (MSL) data also have smaller standard errors as 

was shown in Zervas (2009). The NOAA sea level trends are computed using the methodology 

found in section 3.2.3 on derivation of MSL trends found in Zervas (2009). The least squares 

solution incorporates knowledge of the average seasonal cycle. 

A simple least squares linear regression gives an accurate MSL trend but can substantially 

underestimate the standard error or uncertainty of that trend. The reason is that, for sea level 

data, the residual time series is serially auto-correlated even after the average seasonal cycle is 

removed. Each month is partially correlated with the value of the previous month and the value 

of the following month. There are actually fewer independent points contributing to the standard 

error of a linear regression, which assumes a series of independent data. Therefore, following 

Zervas (2009), the monthly MSL data are characterized as an auto-regressive process of order 1. 

This is the recommended treatment for computing relative sea level trends from long-term 

monthly mean sea level data form tide gauge observations.
41

Each calculated linear trend has an associated 95% confidence interval that is primarily 

dependent on the year range of data for each station. A derived inverse power relationship 

indicates that 50-60 years of data are required to obtain a trend with a 95% confidence interval of 

±0.5 mm/yr. This dependence on record length is caused by the interannual variability in the 

observations.

Figure 3.4, the calculated trend for San Diego California, shows the monthly mean sea level 

without the regular seasonal fluctuations due to coastal ocean temperatures, salinities, winds,

atmospheric pressures, and ocean currents. The long-term linear trend is also shown, including 

its 95% confidence interval. The plotted values are relative to the most recent NTDE mean sea 

level (1983-2001).

Figure 3.4. The relative mean sea level trend for San Diego, CA

Use of Satellite Data to Determine Sea Level Trends

Whereas tide stations provide point coverage of relative sea level variability over long time 

periods, they must be combined in a global network mode with appropriate corrections for vertical 

land motion in order to generate spatial estimates of global sea level variations (Douglas, 2001). 

Satellite altimetry provides significant spatial coverage of the world‘s oceans due to the satellite 

orbital confirmations and repeat cycles. Continuous coverage of the oceans in the latitude band 

±66 degrees has been in place since the launch of the TOPEX/Poseidon mission in late 1992, using 

overlapping missions with the Jason series of altimeter missions. For climate applications, the 

overriding drawback is the relatively short duration of the continuous, uninterrupted altimeter 

record (17 years), which is much shorter than the tide gauge records that go back a century or more 
42

and the record length is somewhat close to known major decadal cycles such as ENSO, thus 

making it difficult to estimate long-term global trends in sea level.

As shown in figure 3.5, satellite altimeters basically determine the distance from the satellite to a 

target surface by measuring the satellite-to-surface round-trip time of a radar pulse. The satellite 

orbits must be accurately tracked and the satellite position is determined relative to an arbitrary 

reference surface, an ellipsoid. The sea surface height is the range from the sea surface to a 

reference ellipsoid. The magnitude and shape of the echoes (or waveforms) also contain 

information about the characteristics of the surface that caused the reflection. The best results are 

obtained over the ocean, which is spatially homogeneous and has a surface that conforms to known 

statistics. Surfaces that are not homogeneous, which contain discontinuities or significant slopes, 

such as some ice, rivers or land surfaces, make accurate interpretation more difficult. The 

TOPEX/Poseidon and Jason altimeters used 10-day repeat cycles, thus giving time series 

measurements at every point along a track. Figure 3.6 shows the ground track coverage of the 

Jason-1 altimeter. Note that the coverage does not extend to the polar oceans (although other 

altimeter missions, particularly, ERS-1 and ERS-2 did extend into those regions).

Figure 3.5. Schematic of a satellite altimeter configuration.
43

Figure 3.6. The ground track coverage for the Jason-1 satellite altimeter mission

Satellite altimeters rely upon ongoing point source verification (calibration) using tide stations 

located directly under the ground tracks (Saxena, 1995) and ongoing calibration and estimates of 

altimeter drift using a global network of tide gauges (Mitchum, 2000).

To be useful for sea level applications, numerous corrections are systematically made to the 

altimeter data to account for the effects of various physical phenomena:

1) propagation corrections: the altimeter radar wave is affected during atmosphere 

crossing:

 ionospheric correction 

 wet tropospheric correction 

 dry tropospheric correction

2) ocean surface correction for the sea state which directly affects the radar wave: 

electromagnetic bias

3) geophysical corrections for the tides (ocean, solid earth, polar tides, loading effects) 

4) atmospheric corrections for the ocean's response to atmospheric dynamics: inverse 

barometer correction (low frequency), atmospheric dynamics correction (high frequency)

To calculate global mean sea level, the global or basin mean sea level time series must be 

distinguished from the regional maps of mean sea level slopes. In both cases, these calculations 

are available for the period of each mission being considered, or by combining several altimetry 

missions covering the entire altimetric period. 

For each mission (TOPEX/Poseidon, Jason-1, Jason-2), a mean grid of sea level anomalies for a 

2°×2° grid is first calculated for each cycle (approximately 10 days) to distribute the 

measurements equally across the surface of the oceans. Sea level anomalies are computed by 
44

subtracting the gridded mean sea levels for each cycle from a mean sea reference surface. The 

global or basin mean for each grid is calculated by weighting each box according to its area, to 

give less significance to boxes at high latitudes, which cover a smaller area. This then gives the 

time series per cycle, which is then filtered with a low-pass filter to remove signals of less than 2 

months or 6 months. The annual and semi-annual periodic signals are also adjusted. The slope 

in mean sea level is deduced from this series using a least squares method. 

The global mean sea level for the entire altimetric period is calculated by combining the time 

series from all three TOPEX/Poseidon, Jason-1 and Jason-2 missions before filtering out the 

periodic signals. The three missions are linked together during the verification phases of the 

Jason-1 and Jason-2 missions to calculate precisely the bias in global MSL between these 

missions. The global MSL reference series is obtained by filtering out the periodic signals for 

the entire altimetric period.

The regional slopes in MSL for each mission are estimated using sea level anomaly grids for 

each cycle and each mission as previously defined for the time series. The regional slopes are 

estimated using the least squares method at each grid point after adjusting the periodic signals 

(annual and semi-annual). The map of these points is deduced from the slope grid, as well as the 

map of the corresponding formal adjustment error. Figure 2.4 is the multi-mission map of the 

overall global mean sea level anomaly since 1992, clearly showing the upward trend on nearly 

3.0 mm/yr. Figure 2.5 is the global map showing the high degree of spatial variability of the 

regional trends that go into the make-up of the global trend. 

Use of Very Long Baseline Interferometry to Study Long-Term Sea Level 

Variations

Very Long Baseline Interferometry (VLBI) also plays a role in studying global sea level 

changes. From Robertson (2002),

VLBI is a novel observing technique for measuring the relative positions of 

widely separated points on the surface of the Earth with centimeter-level 

accuracy. Such accuracy is two or three orders of magnitude better than was 

available with classical techniques only a few decades ago. This enormous 

improvement in accuracy has opened up for study a broad new spectrum of 

geophysical phenomena. The new measurements allow direct observation of the 

tectonic motions and deformations of the Earth's crustal plates, observations of 

unprecedented detail of the variations in the rotation of the Earth, and direct 

measurement of the elastic deformations of the Earth in response to tidal forces. 

These new measurements have placed significant constraints on models of the 

interior structure of the Earth; for example, measurements of the variations in the 

Earth's rotation have been shown to be particularly sensitive to the shape of the 

core-mantle boundary. The VLBI measurements, coupled with other space-based 

geodetic observing techniques such as the Global Positioning System, allow 

construction of a global reference frame accurate at the centimeter level. Such a 
45

frame will be essential to studying long-term global changes, especially those 

changes related to sea-level variations as recorded by tide gauge measurements.

3.1.4 Reference elevation: How it Factors Into Sea Level Change Projects

As described at the start of this chapter, SLC projects relying on elevation datasets (including 

topographic, bathymetric, and water-level data, primarily collected for navigation, boundaries, 

engineering and other practical uses) must have established the location of ―zero‖ and a physical 

reference for elevation zero (in other words, a vertical datum must be both defined and 

accessible) for all types of height measurements. Chapter 2, section 2.2 provides detail on the 

different types of vertical datums that are available, while this section discusses methods to 

accurately determine and monitor these datums, including how to accurately measure elevations 

of marsh surfaces.

Continuously Operating Reference Stations (CORS): How They Tie ElevationRelated Datasets into the National Spatial Reference System

NOAA‘s National Geodetic Survey (NGS) manages a network of Continuously Operating 

Reference Stations (CORS) that provide Global Navigation Satellite System (GNSS) data,

consisting of carrier phase and code range measurements to support three dimensional 

positioning, meteorology, space weather, and geophysical applications throughout the United 

States, its territories, and a few foreign countries. 

Surveyors, GIS users, engineers, scientists, and the public at large who collect GPS data can use 

CORS data to improve the precision of their positions. CORS-enhanced, post-processed 

coordinates are within a few centimeters of accuracy within the National Spatial Reference 

System (NSRS), both horizontally and vertically. 

The CORS network is a multi-purpose cooperative endeavor involving government, academic, 

and private organizations. The sites are independently owned and operated. Each agency shares 

its data with NGS, and NGS in turn analyzes and distributes data free of charge. As of May 2010, 

the CORS network contains over 1,450 stations contributed by over 200 different organizations, 

and the network continues to expand. 

For additional information on CORS, see http://www.ngs.noaa.gov/CORS/Articles/, particularly 

the following entries:

Snay, R., et al., 2007. Using global positioning system-derived crustal velocities to 

estimate rates of absolute sea level change from North American tide gauge records, J. 

Geophys. Res., 112(B04409), 1-11 http://www.ngs.noaa.gov/CORS/Articles/Snay-et-alJGR2007.pdf.

Schenewerk, M.J., et al., 2001. Vertical ocean-loading deformation derived from a global 

GPS network, J. Geodetic Soc. of Japan, 47(1)

http://www.ngs.noaa.gov/GRD/GPS/Projects/OLT/Ets.00aug31/ets.html.
46

Schwarz, C.R., et al., 2009. Accuracy assessment of the National Geodetic Survey's 

OPUS-RS utility, GPS Solutions, 13(2), 119-132

http://www.ngs.noaa.gov/CORS/Articles/SchwarzetalGPSSOL09.pdf

GravD and Its Role in the Future of Elevation Referencing

NOAA‘s NGS is leading the GRAV-D project (http://www.ngs.noaa.gov/GRAV-D/), an effort to 

model and monitor Earth‘s geoid (a surface of the gravity field, closely related to global mean sea 

level) to serve as a zero reference surface for all orthometric heights in the nation. 

The GRAV-D project has a decadal-scale gravity monitoring component, which is directly related 

to two components of climate-driven changes to sea level. First, the GRAV-D project aims to 

comprehensively re-survey the entire gravity field of the United States, enabling the modeling of 

the geoid to 1 centimeter of accuracy in much of the United States. Such a model, mixed with 

approximately 1 centimeter of GNSS ellipsoid height accuracy, would allow users in much of the 

U.S., particularly coastal regions, to access orthometric heights through a GNSS receiver to below 

2 cm of accuracy. Second, the primary shape of the ocean‘s surface is driven by Earth‘s gravity

field, with tides and currents having almost two orders of magnitude less impact. As such, 

changes to the sea surface are directly linked to changes to the gravity field. As NGS monitors 

changes to the gravity field, these changes reflect sea level change.

In addition, climate change affects more than sea level. For instance, changes to water tables 

have been seen through their small but measurable changes to the local gravity field. Therefore,

basin-scale changes to freshwater resources are potentially detectable through the monitoring 

aspect of GRAV-D. These data could be used to analyze both the climate-driven impacts of the 

change and its long-term implications.

Use of Surface Elevation Tables (SETs) to Establish a Reference Surface in 

Marshes to Measure Elevations

Surface Elevation Tables (SETs) are portable measuring instruments deployed atop wetland 

vertical bench marks, allowing millimeter-level changes in surface elevation and occurring at one 

point to be measured over time (figure 3.7). SETs integrate both surface and subsurface processes 

that affect elevation change down to the depth of the bench mark (typically 3-20 m). They have 

been used for over a decade by coastal ecologists to investigate processes leading to wetland 

development, sustainability, or loss. NGS has recently developed guidelines to tie SET bench 

marks to the National Spatial Reference System (NSRS) and is working on projects to incorporate 

SETs into a system of high accuracy vertical control in coastal wetland settings

(http://www.ngs.noaa.gov/PUBS_LIB/ProceduresForConnectingSETBMsToTheNSRS.pdf).
47

Figure 3.7. Surface Elevation Table conceptual diagram, 

courtesy of Don Cahoon, USGS.

One benefit of tying SETs to the NSRS is the ability to monitor local changes in sea level at the 

local coastal intertidal level. Until now, estimates of local SLC were based solely on tide 

gauges, which are connected to land elevation through a series of upland bench marks. 

However, most coastal intertidal surfaces are highly dynamic, and many are affected by shallowdepth subsidence. SETs provide a measure of the elevation change over such shallow depths;

combining SET data with local sea level data gives a more accurate picture of locally-expressed 

sea level within the dynamic intertidal zone. Obtaining accurate heights on SET bench marks 

also allows the stability of the wetland bench marks themselves to be monitored, which is very 

important to calibrate observations over long periods of time, providing an estimate of deep 

subsidence (figure 3.8).
48

Figure 3.8. This conceptual diagram shows how geodetic connections between local tide stations, SETs, and 

geodetic control can provide a measurement of wetland elevation dynamics relative to locally-expressed (e.g. 

―relative‖) sea level change.

NGS is also assisting in the development of SET technology by defining the sources and 

magnitude of error associated with the use of SETs as geodetic instruments. Error is present at 

many levels, and no comprehensive study has addressed the sources of error under field 

conditions. NGS has developed a new design for the SET to overcome common sources of error. 

NGS is producing guidelines on the statistical design and analysis of SET data, to assist the 

community of SET users with a rigorous statistical framework for developing studies and 

interpreting the data. All of these steps pave new ground for the use of SETs as geodetic tools to 

bring accurate elevations to coastal intertidal habitats.

Current resources on the Internet relative to SET technology are:

The US Geological Survey‘s SET web site: http://www.pwrc.usgs.gov/set/

The NOAA National Geodetic Survey‘s guidelines document for obtaining GNSS-derived 

orthometric heights on SET foundations:

http://www.ngs.noaa.gov/PUBS_LIB/ProceduresForConnectingSETBMsToTheNSRS.pdf

The original SET cooperative data site: http://ecoinformatics.uvm.edu/SET/
49

3.2 Data Sources

3.2.1 Base Surface Elevation Data: Where to Find Topographic Data, 

Including Shoreline Data

Public domain elevation data are available in a range of extents, accuracy, and formats. The 

following list of websites, while not all-inclusive, may be used as a guide for users interested in 

acquiring elevation data sets for their areas of interest. Those interested in obtaining elevation data 

are encouraged to also contact their state and local GIS staffs regarding available elevation data.

Digital Coast (NOAA Coastal Services Center)

The NOAA Coastal Services Center‘s on-line data are provided via Digital Coast. Data are

available in several point (.txt, LAS), line (.shp, .dxf), and raster (geotiff, floating point, ASCII

grid) formats. http://www.csc.noaa.gov/lidar/

NOAA Coastal Services Center Topographic and Bathymetric Data Inventory

The Topographic and Bathymetric Data Inventory serves as an index to the best-available

elevation data sets by regions. Users can use the interactive viewer to locate and learn about

available data sets http://www.csc.noaa.gov/topobathy/viewer/index.html.

National Elevation Dataset (NED)

NED data are publicly available from the U.S. Geologic Survey (USGS). The data resolution 

varies by location; the type of data can be reviewed at

http://gisdata.usgs.net/website/usgs_gn_ned_dsi/viewer.htm.

Published accuracy of the NED can be found here: 

http://ned.usgs.gov/downloads/documents/NED_Accuracy.pdf.

Center for Lidar Information Coordination and Knowledge (CLICK)

The USGS site CLICK provides access to publicly available lidar point file data sets. The goal of

CLICK is to facilitate data access, user coordination, and education about lidar remote sensing

for scientific needs (http://lidar.cr.usgs.gov).

USGS Topobathy Viewer

The topobathy viewer provides a dynamic on-line map interface that can be used to view U.S.

Geological Survey topobathy DEMs (http://edna.usgs.gov/TopoBathy_Viewer/).

National Center for Airborne Laser Mapping (NCALM)

NCALM is the National-Science-Foundation-supported Center for Airborne Laser Mapping.

The NCALM@Berkeley website provides public access to high-resolution airborne laser

mapping data, documentation, and tools to analyze digital elevation data sets

(http://calm.geo.berkeley.edu/ncalm/links.html).

National Geophysical Data Center (NGDC)

NGDC compiles, archives, and distributes bathymetric data from coastal and open ocean areas,
50

and acts as the long-term archive for NOAA National Ocean Service data collected in support of

charting and navigation (http://www.ngdc.noaa.gov/mgg/bathymetry/relief.html).

Laboratory for Coastal Research at International Hurricane Research Center

The International Hurricane Research Center's Laboratory for Coastal Research data were

produced as part of the Windstorm Simulation Modeling Project in a contract agreement between 

Florida International University International Hurricane Research Center (IHRC), Palm Beach 

County, Broward County, Manatee County, and Miami-Dade County

(http://www.ihrc.fiu.edu/lcr/data/data.htm).

LiDARDATA.com

Lidardata.com provides an easy way to see where lidar has already been collected and to order

off-the-shelf archives of the freshest data (http://www.lidardata.com).

North Carolina Floodplain Mapping Program

This website is a free service provided by the State of North Carolina. The latest information on

the Floodplain Mapping Program is provided here (http://www.ncfloodmaps.com).

Atlas: The Louisiana Statewide GIS

The objective of this website is to make data and information related to GIS in Louisiana, GIS 

data documentation, and data sharing available to the public (http://atlas.lsu.edu).

Puget Sound Lidar Consortium

The Puget Sound LIDAR Consortium (PSLC) is an informal group of local agency staff and 

Federal research scientists devoted to developing public-domain high-resolution lidar topography 

and derivative products for the Puget Sound region (http://pugetsoundlidar.ess.washington.edu).

USGS Alaska Topobathy DEM

A seamless topographic–bathymetric surface model has been created for the area around the 

coastal town of Seward, Alaska. The DEM was developed to study submarine landslides and 

tsunamis produced by the 1964 earthquake and for generating computer models of tsunami wave 

propagation and inundation (http://pubs.usgs.gov/ds/374/).

Texas Topobathy DEM

This data set is composed of topobathy DEMs that cover the coastal region and continental shelf 

of Texas. It was sponsored by Texas Sea Grant and the Texas Parks and Wildlife Department, 

and work was completed by scientists at Texas A&M University

(ftp://ftp2.tnris.state.tx.us/Elevation/BathyTopo/).

Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX)

JALBTCX performs operations, research, and development in airborne lidar bathymetry and 

complementary technologies to support the coastal mapping and charting requirements of the 

U.S. Army Corps of Engineers (USACE), the U.S. Naval Meteorology and Oceanography 

Command, and NOAA. JALBTCX staff includes engineers, scientists, hydrographers, and 
51

technicians from the USACE Mobile District, the Naval Oceanographic Office 

(NAVOCEANO), the USACE Engineer Research and Development Center (ERDC), and NOAA 

National Geodetic Survey. JALBTCX research and development supports and leverages work in 

government, industry, and academics to advance airborne lidar and coastal mapping and charting 

technology and applications (http://shoals.sam.usace.army.mil/).

Shoreline Data

NOAA‘s National Shoreline website (http://shoreline.noaa.gov) provides access to the data and 

many other resources, including applications of shoreline data, definitions of terms, and data 

resources for Federal shorelines. 

As described in section 3.2.1., shoreline datasets representing various data sources and delineation 

methods are available from myriad other sources, including other Federal and state government 

agencies, academic or research institutions, and others. These sources are too numerous and 

diverse to note here; however, for evaluation of the origin and appropriateness of these data, 

consult the Federal Geographic Data Committee (FGDC) standards for both metadata and 

content: 

FGDC Shoreline Metadata Profile:

http://www.csc.noaa.gov/metadata/sprofile.pdf

FGDC Shoreline Data Content Standard:

http://www.fgdc.gov/standards/projects/FGDC-standards-projects/shoreline-datacontent/index_html

See section 6.4 for information on shoreline-change analyses, including sources for shorelinechange rates across the U.S. 

3.2.2 Base Surface Elevation Data: Where to Find Bathymetric Data

NOAA‘s Office of Coast Survey (OCS) is responsible for charting the coastal waters of the 

United States and its Territories and has acquired bathymetric data in these areas for nearly 200 

years. Its extensive archive of data is maintained by NOAA‘s National Geophysical Data Center 

(NGDC) in Boulder, Colorado (http://www.ngdc.noaa.gov/mgg/bathymetry/relief.html) which 

also operates a worldwide digital data bank of oceanic soundings on behalf of the Member 

Countries of the International Hydrographic Organization (IHO). 

NOAA‘s NGDC bathymetric data holdings include both single beam and multibeam sonar 

measurements, gridded data from these measurements, and estimated depths derived from satellite 

altimetry. http://www.ngdc.noaa.gov/mgg/bathymetry/hydro.html

NGDC also builds and distributes high-resolution, coastal DEMs that integrate ocean bathymetry 

and land topography to support NOAA's mission to understand and predict changes in Earth's 

environment, and conserve and manage coastal and marine resources to meet our Nation's 

economic, social, and environmental needs. DEMs can be used for modeling coastal processes 
52

(tsunami inundation, storm surge, sea level rise, contaminant dispersal, etc.), ecosystems 

management and habitat research, coastal and marine spatial planning, and hazard mitigation and 

community preparedness. (http://www.ngdc.noaa.gov/mgg/coastal/coastal.html)

NOAA‘s Coastal Services Center (CSC) maintains a topographic and bathymetric data inventory 

that provides an index to high-accuracy topographic and bathymetric data sets. The Inventory 

Viewer can be accessed at: http://www.csc.noaa.gov/topobathy/.

3.2.3 Water Surface Elevation: Where to Find Water-Level Data and Sea 

Level Trends

Tide Stations

Long term, local, relative sea level trends and variations at NOAA stations are published in NOS 

CO-OPS Technical Report 53, Sea Level Variations of the United States 1854-2006, located at: 

http://www.tidesandcurrents.noaa.gov/publications/Tech_rpt_53.pdf. 

The CO-OPS website also contains ―Sea Levels Online,‖ providing sea level analyses at all the 

long-term National Water Level Observation Network (NWLON) stations and at a select set of 

non-U.S. stations, using data obtained from the Permanent Service for Mean Sea Level (PSMSL; 

www.tidesandcurrents.noaa.gov/sltrends/). PSMSL, the global data bank for sea level data from 

tide stations, maintains a list of relative sea level trends at hundreds of stations worldwide 

(www.pol.ac.uk/psmsl/datainfo/rlr.trends). 

Satellite Altimetry

Mean sea level trends and variations calculated from satellite altimetry data are documented at 

the NOAA National Environmental Satellite, Data and Information Service (NESDIS) 

Laboratory for Satellite Altimetry website: http://ibis.grdl.noaa.gov/SAT/SeaLevelRise/. A 

series of satellite missions estimate global mean sea level every 10 days. These sea level trends 

are calculated and compiled using radar measurements, spacecraft orbits and tide station 

calibrations from 1992 onward and do not include glacial isostatic adjustment (GIA) effects on 

the geoid. 

3.2.4 Reference Elevation: Where to Find Data or Other Resources to Establish or 

Maintain a Reference Elevation

Existing Reference Elevation Information

Basic elevation data are available on the NGS website for individual bench mark locations:

http://www.ngs.noaa.gov/cgi-bin/datasheet.prl

Tools for using and applying geodetic information are found at:

http://www.ngs.noaa.gov/TOOLS/
53

Data Needed to Account for Vertical Land Motion

The CORS coordinate sheets corresponding to an SLC project site can be downloaded from the 

NGS web site (http://www.ngs.noaa.gov/CORS/). These sheets provide velocities at CORS 

sites. For example, the CORS sheet for ―GODE‖ (GODDARD SPACE CTR), contains the 

following values:

NAD_83 VELOCITY 

Transformed from ITRF00 velocity in Mar. 2002. 

 VX = 0.0016 m/yr northward = -0.0007 m/yr 

 VY = 0.0017 m/yr eastward = 0.0019 m/yr 

 VZ = -0.0019 m/yr upward = -0.0022 m/yr .

Alternatively, the Online User Positioning System or OPUS (http://www.ngs.noaa.gov/OPUS/) 

determines a user‘s rover positions using static (or rapid-static) observations and CORS data. 

For purposes of submitting files to OPUS, ―static‖ means more than 4 hours of data; ―rapidstatic‖ means 15 minutes to 4 hours of data.

For investigating sea level change, the ―upward‖ one of the three components of velocity, which 

is associated with the local horizon, is the main point of interest. The VX, VY and VZ values 

relate to the earth-centered earth-fixed Cartesian reference frame. The component of interest 

needs to project forward from the date given to the date of interest.

By sampling velocities at discrete CORS in and around an area of interest (assuming their 

availability) a general idea of regional velocity can be developed. Substantial variation from one 

site to another within a general area may indicate, and/or be explained by, geologic or man-made 

forces in the area—tectonic strain, post-glacial rebound, fluid withdrawal, etc.

Stability and vertical motion at specific CORS sites can be estimated by reviewing multi-year

plots of daily CORS positions at ftp://www.ngs.noaa.gov/cors/Plots/plots.html.

References Cited

Boak, E.H., and Turner, I.L., 2005. Shoreline Definition and Detection: A Review. Journal of 

Coastal Research, 21(4), 688-703.

Brock, J.C., and Purkis, S.J. 2009. The Emerging Role of Lidar Remote Sensing in Coastal

Research and Resource Management. Journal of Coastal Research: Fall 2009, Vol., No. , 

pp. 1-5.

CCSP, 2009: Coastal Sensitivity to Sea level Rise: A Focus on the Mid-Atlantic Region. A 

report by the U.S. Climate Change Science Program and the Subcommittee on Global 

Change Research. [James G. Titus (Coordinating Lead Author), K. Eric Anderson, 

Donald R. Cahoon, Dean B. Gesch, Stephen K. Gill, Benjamin T. Gutierrez, E. Robert 

Thieler, and S. Jeffress Williams (Lead Authors)]. U.S. Environmental Protection 

Agency, Washington D.C., USA, 320 pp.
54

Crowell, M., Leatherman, S.P., and Buckley, M.E., 1991. Historical Shoreline Change: Error 

Analysis and Mapping Accuracy. Journal of Coastal Research, 7(3), 839-852.

Gesch, D.B., 2009. Analysis of Lidar Elevation Data for Improved Identification and Delineation 

of Lands Vulnerable to Sea level Rise. Journal of Coastal Research: Fall 2009, Vol., No. , 

pp. 49-58.

Graham, D.; Sault, M., and Bailey, J., 2003. National Ocean Service Shoreline—Past, Present,

and Future. Journal of Coastal Research, Special Issue No. 38, 14-32.

IOC, 2006: Manual on Sea level Measurement and Interpretation, Volume IV: An Update to 

2006, JCOMM Technical Report No. 31, WMO/TD. No. 1339, Manuals and Guides 14, 

Intergovernmental Oceanographic Commission, 80pp.

Leatherman, S.P., 2003. Shoreline Change Mapping and Management Along the U.S. East 

Coast. Journal of Coastal Research, Special Issue No. 38, 5-13.

Maune, D.F., 2007. Digital Elevation Model Technologies and Applications: The DEM Users

Manual, 2nd Edition. American Society for Photogrammetry and Remote Sensing. 

Bethesda, MD.

Moore, L.J., 2000. Shoreline Mapping Techniques. Journal of Coastal Research, 16(1), 111-124.

Moore, L.J.; Ruggiero, P., and List, J.H., 2006. Comparing Mean High Water and High Water 

Line Shorelines: Should Proxy-Datum Offsets be Incorporated into Shoreline Change 

Analysis? Journal of Coastal Research, 22(4), 894-905.

Morton, R.A.; Miller, T.L.; and Moore, L.J.; 2004. National Assessment of Shoreline Change: 

Part 1:Historical Shoreline Changes and Associated Coastal Land Loss Along the U.S. 

Gulf of Mexico. U.S. Geological Survey Open-file Report 2004-1043, 45 p. 

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Gingerich, K.J., and Kriebel, D.L. (eds.), Coastal Sediments ’91, American Society of 

Civil Engineers, pp. 997-1010.

Morton, R.A. and Speed, M.F., 1998. Evaluation of Shorelines and Legal Boundaries Controlled 

by Water Levels on Sandy Beaches. Journal of Coastal Research, 14(4), 1373-1384.

NOAA, 2001: Tidal Datums and Their Applications, NOAA Special Publication NOS CO-OPS 

1, S.K. Gill and J.R. Schultz, eds. NOAA National Ocean Service Center for Operational 

Oceanographic Products and Services, 111pp.

National Research Council (NRC), 2004. A Geospatial Framework for the Coastal Zone: 

National Needs for Coastal Mapping and Charting, Washington, D.C.: National 

Academy Press, 149 p. 

NRC (National Research Council), 2009, Mapping the zone: Improving flood map accuracy, 

National Academy Press, Washington, DC., 121 pp.
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Pajak, M.J. and Leatherman, S.P., 2002. The High Water Line as Shoreline Indicator. Journal of 

Coastal Research, 18(2), 329-337.

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National Oceanic and Atmospheric Administration, Rockville, Maryland 20852.

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Harwell, M.A.; Howarth, R.W.; Mason, C.; Reed, D.J.; Royer, T.C.; Sallenger, A.H.; and 

Titus, J.G., 2002. Climate Change Impacts on U.S. Coastal and Marine Ecosystems, 

Estuaries and Coasts, 25(2), 149-164.

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for Coastal Science and Management. Hydro International, 13(2), 20-23.

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Geodetic Survey, U.S. Government Printing Office, 420p.

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the National Ocean Survey, 1919-79. U.S. Department of Commerce, National Oceanic 

and Atmospheric Administration, National Ocean Service, 486p. 

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White, S., 2007. Utilization of LIDAR and NOAA‘s Vertical Datum Transformation Tool 

(VDatum) for Shoreline Delineation. Proceedings of the Marine Technology Society / 

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Zervas, C., 2009: Sea Level Variations of the United States 1854-2006, NOAA Technical 

Report NOS CO-OPS 053, NOAA National Ocean Service Center for Operational 

Oceanographic Products and Services, 78pp.

57

Chapter 4.0 New Data Acquisition

4.1 Type of Data Needed

While the type and accuracy of data needed for a specific sea level rise assessment or other 

geospatial project will be dependent on the application of that product, the four most critical 

geospatial data elements for any sea level rise assessment are sea level information, topographic 

data, bathymetric data, and geodetic (or land-based) heights. Chapter 3 discusses the use of

existing data, while chapter 4 addresses the collection and/or acquisition of new geospatial data. 

Once accuracy requirements are determined and existing data availability explored, data 

collection may be the ideal or only option to meet project needs. As long as all the data sets used 

in a project can be referenced to a common datum, data are largely modular. For instance, if a 

new, high-resolution topographic data set exists, but bathymetry data are outdated, new 

bathymetry can be collected and integrated with the topographic data set using common datum 

reference. The following sections provide details on collection standards for the four primary 

data sets.

4.2 How to Acquire the Data to Enable Multiple Uses

Federal agencies are committed to conducting ocean and coastal mapping in a way that permits 

easy access and use by the greatest range of users. NOAA is adopting these practices, entitled 

Integrated Ocean and Coastal Mapping (IOCM), throughout its mapping programs with the goal 

of ―map once, use many times.‖ 

Key principles of IOCM include acquiring data to commonly agreed-to standards, ensuring the 

data acquired is documented thoroughly with metadata, and ensuring the data are archived and 

stored in a way that is accessible to many users. Several state entities (e.g. California, Oregon, 

and North Carolina) have already coordinated efforts to collect data for both ecosystem and 

coastal decision making requirements. 

NOAA and other Federal and state agencies continue to refine common standards for data 

accuracy to support multiple uses. The consensus is that all data acquisition should meet or 

exceed International Hydrographic Organization (IHO) Order 1 standards and be carried out at 

the maximum resolution obtainable using state-of-the-industry tools. For maximum utility, 

coverage would ideally include all ―lands‖ from the shore strand line (mean higher high water or 

MHHW) out to the 3 nautical miles (nm) state water limit. However, obtaining this coverage

often requires the application of multiple acquisition sensors, including acoustic (e.g. multibeam 

echosounder or MBES) and optical (e.g. lidar, hyperspectral, multispectral, and water level 

measuring systems and systems for other oceanographic parameters affecting acoustic and 

optical sensor measurements).

The best available positioning instrumentation (e.g. high precision kinematic GPS) should be 

used when acquiring data, and a common vertical datum needs to be agreed to and used. 
58

Experienced surveyors generally do all bathymetric and topographic surveying on the ellipsoid 

(e.g. ITRF or WGS84), thereby facilitating more accurate tidal corrections, data fusion and 

conversion to other datums.

Table 4 lists the agreed-upon IOCM standards proposed by 2006 IOCM workshop participants. 

These standards would apply in water depths greater than 5 m and extend to the limits of the 

continental margin. The standards are applicable for surveys conducted with multibeam sonars, 

interferometric (phase) swath sonars, and, except for coverage and resolution, airborne lidar. 

Individual programs may specify more stringent standards for their own seafloor mapping projects. 

Table 4.1 IOCM standards

Requirement Specification

Horizontal accuracy of a 

seafloor feature*

5 m + 5% of depth

Depth accuracy at any 

specified location*

[ ( ) ]

2 2

a b d

, where a = 0.5 m, 

b = 0.013, and d is the depth in meters

Resolution 2 m to 40 m depth; 5% of depth beyond 40 m

Coverage Full coverage is the standard; but achievement may depend on 

program constraints or requirements†

Backscatter Best backscatter mode (typically full-beam time-series) recorded 

(dependent on sonar)

Ground truth of bottom 

character backscatter

Optical or grab sample at lateral spacing sufficient to ground truth

backscatter segmentation, and not to exceed 2000 m. Unless 

required by the primary program, bottom sampling is not required in 

depths exceeding 100 m.

Horizontal Reference Positions referenced to WGS 84 (NAD83)

Vertical Reference Depths referenced to mean lower low water datum and/or WGS 84 

ellipsoid††

Metadata FGDC compliant 

Archiving Raw and processed data (with metadata) submitted to NGDC within 

1 yr of acquisition

*At the 95% probability level, 

after application of all 

systematic corrections 

including water level

† There may be situations where the requirement for mapping of large areas or the 

minimal availability of time demands reconnaissance-style mapping that of 

necessity is not full-bottom coverage

†† This requires coordination in advance with NOS CO-OPS for the preparation 

of tidal zoning charts and may require the installation of water level gauges. In 

areas where water level gauges or shore-based kinematic GPS are not available, 

this will require the installation of specialized GPS equipment on the survey 

vessel and subscription to specialized globally-corrected GPS (GcGPS) services.

The availability of seafloor mapping systems to meet the above requirements efficiently in depths less than 5 m is 

limited. In those depths, mapping by interferometric swath sonar, lidar, single-beam echosounder, side scan sonar, or 

aerial/satellite imagery should be used to produce the best mapping products practicable.
59

One common usage of mapping data, in addition to inundation, is habitat classification. To 

allow data to be most useful for habitat determination, seafloor mapping information should 

include seabed geomorphology (relief via XYZ digital elevation model—DEM) and texture 

(substrate type). These two data sets are the minimum needed to support basic habitat 

classification. In addition, ground truthing (e.g. via video or physical samples) of acoustic and 

optical remote sensing data used to create the DEM and surface texture data sets would be 

needed to verify the classifications. Where appropriate and possible, subsurface structure 

(sediment thickness and stratigraphy via subbottom profiles and coring) is highly desirable.

4.3 How to Acquire Specific Data Sets

4.3.1 Long-Term Sea Level and Sea Level Trends

Many agencies other than NOAA/CO-OPS have requirements for long-term water level 

measurements and have their own observing systems in place to meet project goals and mission 

requirements. To effectively monitor sea level change (SLC) and accrue the length of data 

necessary to compute relative sea level trends, a long-term sustainable observing system strategy 

must be implemented. This involves determining if existing data and observation locations meet 

requirements, determining the number of and location of new water level measurement stations, 

determining the cost requirements for long-term installation and annual operation and 

maintenance, and determining the requirements for data quality control (QC), processing and 

data base management ,and finally, for data dissemination. The sustainability of the observations 

are typically a major impediment to long-term sustained operation, so any planning and design of 

new observation stations or networks must include annual operation and maintenance costs, and 

long-term administrative and infrastructure costs (such as information technology or IT). 

NOAA/CO-OPS has collaborated with other agencies, such as USGS and the U.S. Army Corps 

of Engineers (USACE), and with state agencies in Florida and Texas, as well as internal 

organizations, such as the National Estuarine Research Reserve (NERRS) Program, assisting 

them with long-term monitoring guidance.

For application to climate studies and research, especially in estimating relative mean sea level 

trends, long-term continuous measurements are required. The networks should have 

characteristics of a true ―end-to-end‖ system that includes data collection through to data 

delivery to and application by the user community. To ensure this application, long-term water 

level networks must be managed in-line with some basic principles of climate monitoring:

Management of Network Change. Water level networks can change with new 

technology or new scientific information on data gaps or requirements for ancillary 

measurements and configurations.

Parallel Testing. When new technology systems are implemented into water level 

networks, they must first be tested in wave tanks and in field tests and then operated 

simultaneously alongside old technology systems for a duration sufficient to establish 

transfer functions and instrument bias before removing the older systems.
60

Metadata. Active metadata systems must be in place and maintained in real time to 

update sensor configurations, calibration coefficients, and datum offsets. Historical 

metadata records must be made available and converted from hard copy to electronic 

format. Access to metadata must be open and public.

Data Quality and Continuity 

 Water level data must continually undergo automated and manual data 

quality control as close to real time as possible. 

 Data must be continuous with only small breaks tolerated, and breaks are 

filled only under controlled and documented standard operating 

procedures. 

 Water level data must have documented reference datum continuity and 

vertical stability.

 Water level station supporting structures should be constructed to ensure 

vertical stability and minimum damage during large storm events and be 

high enough to withstand most storm surge elevation.

Integrated Environmental Assessment. Water level data should be examined in

context with other ancillary measurements to explain anomalous events and 

unexpected phenomena. Water level stations should have a geodetic datum 

connection to geodetic reference systems using either direct leveling to geodetic 

marks or static GPS surveys.

Historical Significance. Preserve the most important climate sites data by 

maintaining critical long-term station operation and maintaining data archival 

systems.

Complementary Data. Water level stations must have backup sensors deployed and 

alternate data collection and transmission systems in place, and the sensors must be 

capable of measuring the extreme highs and extreme lows in water level. 

Continuity of Purpose. To prevent stations or networks from being dropped due to 

changing priorities, continuous outreach and training for partners, users, and upper 

management must be available. The data applications must remain relevant and 

ongoing. New product development and applied research must be sponsored.

Similar international standards for sea level measurement are detailed by international groups. 

For instance, the IOC Manual on Sea Level Measurement and Interpretation (IOC, 2006)

discusses requirements for long-term sustained measurements of sea level. Specifications used 

by NOAA for new tide station installation, operation and maintenance are found online at: 

http://tidesandcurrents.noaa.gov/publications/COOPS_Specifications_and_Deliverables_for_installation_operation_and_removal_of_water_level_

stations_updated_November2008.pdf

http://tidesandcurrents.noaa.gov/publications/users_guide_for_installation_of_Bench_Mark.pdf

http://tidesandcurrents.noaa.gov/publications/Users_Guide_for_Electronic_Levels_Janua

ry_2003.pdf
61

http://tidesandcurrents.noaa.gov/publications/COOPS_Water_Level_and_Meteorological_Site_Recon_Procedures,_Updated_May_2009.pdf

New installations also need a strong geodetic datum connection, and those specifications are 

found in:

http://tidesandcurrents.noaa.gov/publications/Users_Guide_for_GPS_Observations_updated_Dec

ember_2009.pdf

http://www.ngs.noaa.gov/PUBS_LIB/NGS-58.html

4.3.2 Topographic Data

Several considerations need to be accounted for when acquiring topographic data. When 

collecting digital elevation data, the following questions should be addressed:

What is the specific application for which the data will be used? (While applications 

guide standards, there is a benefit to collecting all data to a minimum set of standards to 

use data sets for multiple applications.)

What is the horizontal and vertical accuracy of topographic data needed for the intended 

application?

What sensor can best meet this requirement?

How should the acquisition be tailored to achieve the desired data?

In which datum does the user need the data, and does the sensor and software provide the 

correct methodologies for working in that datum?

Is the user looking for a Digital Terrain Model/bare-earth surface, a Digital Surface 

Model (DSM) depicting the elevations of the top surfaces of buildings, trees, towers, and 

other features above the bare-earth surface, or very shallow nearshore submerged 

topography? 

Answers to these questions can lead to designing very different types of acquisition 

specifications. The following list, although not exhaustive, details requirements and 

specifications that should be considered before acquiring topographic data:
62

Specific Project Details:

 Project Area/Extents

•Mission/Data Acquisition Details

 Sensor (Type, Maintenance, Certification)

 Aircraft

 Flight Clearances

 Enabling Technologies (GPS/IMU)

 GPS/CORS Base Stations

 GPS Base Line Lengths

 Aircraft Positioning and Orientation System

 PDOP/VDOP

 Calibration

 Factory Calibrations (Radiometric/Geometric)

 In-situ Calibrations

 Determination of sensor-to-GPS-antenna offset vector components (―lever arm‖)

 Data Post Spacing/Resolution

 Sensor Acquisition Parameters

 Coverage and Overlap

 Flight Direction/Height

 Weather Conditions

 Time of Day

 Tide Coordination

•Production Details

 Data Formats/Data Model Type/Deliverables

 Horizontal/Vertical Datums 

 Specific Processing Instructions

 Accuracy Standards, Assessment, and Reporting

 Metadata

 Data Labeling/Shipment/Notifications

 Delivery Schedule/Completion Dates

 Reason for Data Rejections

Additional Information on topographic data acquisition can be found at:

NOAA’s Coastal Mapping Program Scope of Work (SOW) for Lidar:

http://www.ngs.noaa.gov/RSD/SOW_LIDAR.shtml

NOAA’s Data Acquisition Contracting Vehicle 

Through established contracting vehicles with geospatial industry leaders, state and local 

agencies can work with NOAA‘s Coastal Service Center (CSC) staff to contract for 

coastal data collection and other geographic information system services. Fund transfers 

are coordinated through an established memorandum of understanding process. The 

Center does not charge overhead; therefore, 100% of state and local dollars applied to the 
63

contracts go to the service requested. For more information, e-mail the Center at 

csc.info@noaa.gov.

U.S. Geological Survey National Geospatial Program Lidar Guidelines and Base 

Specification:

http://lidar.cr.usgs.gov/USGSNGP%20Lidar%20Guidelines%20and%20Base%20Specification%20v13%28ILMF%29.pdf

Chapter 13, DEM User Requirements: assist with the decision making process for 

developing standard requirements and also provides an example SOW:

Maune, D.F., 2007. Digital Elevation Model Technologies and Applications: The DEM 

Users Manual, 2nd Edition. American Society for Photogrammetry and Remote Sensing. 

Bethesda, MD.

4.3.3 Bathymetric Data

Before acquiring new bathymetric data, it is important to consider many of the same questions as 

those asked for topographic data acquisition; ―What is the exact application you will be utilizing 

the data for?‖ (like topographic data, while applications guide standards, there is a benefit to 

collecting all bathymetric data to a minimum set of standards in alignment with the principles of 

the Integrated Ocean and Coastal Mapping initiative, in order to utilize data sets for multiple 

applications), ―What is the needed horizontal and vertical accuracy of bathymetric data for the 

intended application?‖, ―What sensors can best meet this requirement?‖, and ―How does the 

acquisition need to be tailored to achieve the desired data?‖ 

The accuracy of bathymetric data is dependent on a number of factors: the sonar system and 

positioning system being used, calibrations of these systems, corrections for errors resulting from 

environmental conditions (tides, sound speed variability in water, vessel motion), and the 

processing and quality control of the acquired data sets. While accurate depths may be obtained 

from single beam sonar, multibeam sonar provides much greater detailed bathymetry of the sea 

floor. The Global Positioning System (GPS) is currently the predominant means of positioning 

vessels, but care must be taken to obtain the high accuracy position required for quality 

bathymetric data. Both systems must be tested and calibrated to ensure that all systematic errors 

have been minimized. 

The description of the methods and procedures for acquiring high accuracy bathymetric data can 

be found in the NOS Hydrographic Surveys Specifications and Deliverables document ,which is 

located at http://www.nauticalcharts.noaa.gov/hsd/specs/SPECS_2010.pdf and the NOAA OCS 

Field Procedures Manual, 

(http://www.nauticalcharts.noaa.gov/hsd/docs/Field_Procedures_Manual_April_2010.pdf). 

Additional information can be found in the U.S. Army Corps of Engineers Hydrographic 

Surveying Manual (http://140.194.76.129/publications/eng-manuals/em1110-2-1003/toc.htm).
64

4.3.4 Orthometric Heights

Orthometric heights can be determined using either or both of two different methods: differential 

leveling and satellite observations. Each method is a substantial topic in its own right. The 

choice of one over the other depends on particular circumstances. A compare-and-contrast 

overview follows.

Differential leveling is most suited to relatively small areas, such as connecting a tide gauge to 

nearby bench marks. Satellite observations (referred hereafter as GNSS or Global Navigation 

Satellite System) are most suited to connecting two or more sites distant from each other, such as 

two tide stations. Leveling is labor intensive and hands-on, whereas GNSS is largely set-it-upand-guard-it. Leveling is typically more precise than GNSS over short distances up to several 

kilometers, but the distance-dependent precision of the two methods tend to equalize in the range 

of 50 km to 100 km; beyond 100 km the uncertainty of leveling begins to exceed that of GNSS, 

although not alarmingly so. 

The heights directly determined by GNSS are referenced to the ellipsoid, a geometric reference,

rather than the physical one determined by leveling with reference to a gravitational surface (see 

chapter 2). For GNSS-derived ellipsoid heights to be converted to orthometric heights, a quantity 

known as the geoid height must be added to the ellipsoid height. The result is an orthometric 

height known within the combined uncertainty of the ellipsoid height and the geoid model.

Each method is useful in its own way for monitoring long-term trends. At a tide station, the 

gauge itself is established, as well as several nearby permanent marks. These marks should be in 

different settings (if possible) and of different character to aid stability. Then, leveling from the 

gauge (if possible) to each nearby mark in succession is performed, first in one direction (gauge 

to A to B to C etc), then in the opposite direction (C-B-A-gauge) for a comparison. The result of 

this work is a set of height differences between marks, with the starting elevation referred to the 

gauge. (Standards for the forward-backward comparison of the two directions of leveling are 

found in several publications (http://www.ngs.noaa.gov/PUBS_LIB/Geodeticleveling_nos_3.pdf

pg 3-7 and http://www.ngs.noaa.gov/FGCS/tech_pub/Fgcsvert.v41.specs.pdf , pg. 6). The type 

of leveling known as second-order class I, for example, has an accuracy expectation of 1.0 mm 

per square root of the number of kilometers of leveling performed. Thus, two level runs, forward 

and backward, between two marks 2.0 km apart would be expected to agree within 1.4 mm. This 

sort of uncertainty propagates with distance when moving along a line of leveling to connect two 

sites.) Technicians can then return annually, repeating the process of leveling from gauge to 

mark to mark, looking for the original height differences to be repeated within the accuracy 

specification. If differences exceed this specification, personnel should first confirm that they 

are not a result of an observational blunder; if differences are legitimate, they indicate relative 

vertical movement that needs to be investigated.

A similar monitoring process can be used with GNSS, where vectors can be determined between 

marks at one site and marks at a distant site at one point in time, then re-determined in 
65

subsequent occupations. Repeatability of the vector components at an expected accuracy level 

indicates stability (no relative vertical movement).

A campaign-style GNSS project can be carried out repetitively at several sites to monitor heights 

simultaneously. At secure sites, GNSS receivers can be left to collect data for several days at a 

time, or even continuously. Specifications for this type of multi-station survey for accurate 

height determination are given in http://www.ngs.noaa.gov/PUBS_LIB/NGS-58.html. 

Leveling is generally carried out with a digital barcode level rather than with the human eye,

where a technician looks through a level at a rod. If a connection to the nationwide datum, North 

American Datum of 1988 (NAVD 88), is desired, observation begins at a published bench mark, 

(see following paragraph). The most reliable method is to level between two published marks and 

confirm the difference of elevation within acceptable standards before continuing to new work.

Heights (elevations) referenced to the national vertical datum, currently NAVD 88, are available 

on data sheets from the National Geodetic Survey (NGS) at http://www.ngs.noaa.gov/cgibin/datasheet.prl. From the introductory page, the user can navigate to subsequent pages. These 

give options of retrieving data sheets for bench marks in a given area and of a desired accuracy 

and stability. The top of each file of retrieved data sheets contains a link to further information, 

http://www.ngs.noaa.gov/cgi-bin/ds_lookup.prl?Item=DSDATA.TXT.

Tying two tide gauges to NAVD 88 enables a comparison of heights between the two stations, 

similar to the result obtained when using GNSS to occupy the two sites. The NAVD 88

comparison is subject to the propagated leveling errors between the two sites, as well as any 

movement of the marks being compared either between epochs or since the bench marks were 

originally installed. A GNSS tie is usually more current and therefore less affected by passage of 

time. However, the historic reference to older leveling can be very useful to get at least an orderof-magnitude estimate of any movement at one or both sites.

For specific instructions to accomplish a leveling or GNSS project, manuals and textbooks in 

surveying literature, as well as training, are available from various government agencies and 

private firms.

References Cited

2005 California Strategic Mapping Workshop: 

http://euclase.csumb.edu/DATA_DOWNLOAD/StrategicMapgWrkshp05/MappingWorkshop12_

12-13/Final_Report/CA%20Habitat%20Mapping%20Rpt.pdf

2006 NOAA Integrated Ocean and Coastal Mapping Seafloor Mapping Standards Workshop, 

April 18 –19, 2006, NOAA/UNH Joint Hydrographic Center; Durham, New Hampshire

Federal Geodetic Control Committee, September 1984; Standards and Specifications for 

Geodetic Control Networks, Section 3.5, pages 3-6 through 3-8.

http://www.ngs.noaa.gov/FGCS/tech_pub/Fgcsvert.v41.specs.pdf
66

Hicks, S.D.; Morris, P.C.; Lippincott, H.A.; and O‘Hargan, M.C., October 1987: User‘s Guide 

for the Installation of Bench Mark and Leveling Requirements for Water Level Stations, 

73pp. 

http://tidesandcurrents.noaa.gov/publications/users_guide_for_installation_of_Bench_Mark.pdf

Maune, D.F., 2007. Digital Elevation Model Technologies and Applications: The DEM Users 

Manual, 2nd Edition. American Society for Photogrammetry and Remote Sensing. 

Bethesda, MD.

NOAA, August 1981: Geodetic Leveling – NOAA Manual NOS NGS 3, NOAA National 

Ocean Service National Geodetic Survey, 3-7. 

http://www.ngs.noaa.gov/PUBS_LIB/Geodeticleveling_nos_3.pdf

NOAA, January 2003: User‘s Guide for Electronic Levels, NOAA National Ocean Service Center 

for Operational Oceanographic Products and Services, 27pp.

http://tidesandcurrents.noaa.gov/publications/Users_Guide_for_Electronic_Levels_January_2003.pdf

NOAA, November 2008: CO-OPS Specifications and Deliverables for Installation, Operation, 

and Removal of Water Level Stations, NOAA National Ocean Service Center for 

Operational Oceanographic Products and Services, 26pp. 

http://tidesandcurrents.noaa.gov/publications/COOPS_Specifications_and_Deliverables_for_installation_operation_and_removal_of_water_level_

stations_updated_November2008.pdf

NOAA, December 2009, Draft: Light Detection and Ranging (LIDAR) Requirements – Scope 

of Work for Shoreline Mapping Under the NOAA Coastal Mapping Program, NOAA 

National Ocean Service National Geodetic Survey Remote Sensing Division, 30pp. 

http://www.ngs.noaa.gov/RSD/LIDAR_SOW_NGSDec2009.pdf

NOAA, December 2009: User‘s Guide for GPS Observation at Tide and Water level Station 

Bench Marks, NOAA National Ocean Service Center for Operational Oceanographic 

Products and Services, 25pp.

http://tidesandcurrents.noaa.gov/publications/Users_Guide_for_GPS_Observations_updated_Dec

ember_2009.pdf

NOAA, April 2010: NOS Hydrographic Surveys Specifications and Deliverables, NOAA 

National Ocean Service Office of Coast Survey Hydrographic Surveys Division, 149pp. 

http://www.nauticalcharts.noaa.gov/hsd/specs/SPECS_2010.pdf

NOAA, April 2010: Field Procedures Manual, NOAA National Ocean Service Office of Coast 

Survey Hydrographic Surveys Division, 325pp.

http://www.nauticalcharts.noaa.gov/hsd/docs/Field_Procedures_Manual_April_2010.pdf

Servary, B.L. Jr., May 2009: CO-OPS Water Level and Meteorological Site Reconnaissance 

Procedures, NOAA National Ocean Service Center for Operational Oceanographic 

Products and Services, 6pp. http://tidesandcurrents.noaa.gov/publications/COOPS_Water_Level_and_Meteorological_Site_Recon_Procedures,_Updated_May_2009.pdf
67

U.S. Army Corps of Engineers, Jan 2002 updated April 2004: EM 1110-2-1003 Engineering and 

Design– Hydrographic Surveying, http://140.194.76.129/publications/eng-manuals/em1110-

2-1003/toc.htm

U.S. Geological Survey, February 2010: USGS National Geospatial Program Lidar Guidelines 

and Base Specification, Version 13 – ILMF 2010; 18pp. http://lidar.cr.usgs.gov/USGSNGP%20Lidar%20Guidelines%20and%20Base%20Specification%20v13%28ILMF%29.pdf

Zilkoski, D.B.; D‘Onofrio, J.D.; Frakes, S.J., November 1997: Guidelines for Establishing GPSDerived Ellipsoid Heights (Standards: 2cm and 5 cm), Version 4.3, NOAA Technical 

memorandum NOS NGS-58. http://www.ngs.noaa.gov/PUBS_LIB/NGS-58.html

69

Chapter 5.0 Data Integration and Interpolation

Chapter 5 provides a fundamental understanding of the potential error sources in the various 

input elevation layers and discusses how these errors can be treated when interpolating or 

integrating various data sets for a decision-support tool. The vertical accuracy is the main point 

being addressed, although horizontal accuracy is also important.

5.1 Error Quantification

This section addresses information on error sources of the fundamental data sets needed for data 

integration and interpolation, which are necessary for application of impacts of sea level rise 

assessments. Summaries of various sources of error are provided for control point sources and 

datum transformation, as well as geospatial x-y fields. Whether tidal control points or geodetic 

control points, strategic use of their number and location is required to obtain accurate geospatial 

interpolation for use in mapping.

5.1.1 Sea Level

Local Mean Sea Level and Other Tidal Datums

As discussed in chapter 2, tidal datum elevations are computed from time series of observed tides 

at specific tide station locations. By legal definition used by NOAA, tidal datum elevations are 

computed relative to specific 19-year periods called National Tidal Datum Epochs (NTDEs). 

The current official NTDE is the 1983-2001 period (except in areas with special 5-year datums 

as discussed in chapter 2). Local mean sea level (LMSL) is also computed as a tidal datum 

relative to the NTDE based on the observed hourly heights.

NOAA manages a nation-wide network of long-term continuously operating water level stations 

called the National Water Level Observation Network (NWLON). The NWLON is the 

fundamental observing system necessary to compute and maintain a tidal datum reference 

framework for the U.S. For many NWLON stations, tidal datum elevations were computed 

directly by performing the averaging over the 1983-2001 NTDE. For practical application, the 

error in datum elevation for these ―first-reduction‖ averages is zero by definition. However, tidal 

datum elevations have been determined from hundreds of short-term tide stations along the coast 

that were established for periods much less than 19-years—typically only for 3-months to a year, 

depending on the project or application. The NOAA accepted procedure is to compute 

equivalent NTDE tidal datums at these short-term stations by comparing simultaneous 

observations with an appropriate NWLON control (or reference) station. This method of 

simultaneous comparison for tidal datum determination is detailed in NOS CO-OPS 2 (2003). 

This comparison process for determining equivalent 19-year NTDE datums results in an error in 

the tidal datum elevations because they were not based on full NTDE. NOAA estimates errors in 

tidal datums at short-term subordinate stations using the set of Bodnar equations (Bodnar, 1981) 
70

and the procedures outlined in the NWLON Gaps Analysis (Gill and Fisher, 2008). Datum errors 

are generally higher in areas that contain NWLON gaps. These errors are a function of the 

distance between the short-term and the control station, of the difference in time of high and low 

waters between the short-term station and the control, and of the ratio of the mean ranges of the 

tide between the short-term and control stations. Thus, the errors are spatially variable 

depending on the number and density of good NWLON stations and the complexity of the tidal 

hydrodynamics. Table 5.1 provides the equations used to estimate errors in tidal datums at the 

one-standard deviation level based in geographic distance and tidal differences between the 

control land subordinate stations. The equations for MLW datum are shown but are the same as 

those used for MLLW. Generically and in practice, this set of equations is used to express the 

estimated error for all datums at a particular tide station and represents an upper bound, as errors 

for the low water datums are slightly higher than for the high water datums.

Table 5.1. The regression equations and parameters for estimating uncertainties in tidal datums

for mean low water (Bodnar, 1981)

S1M = 0.0068 ADLWI + 0.0053 SRGDIST + 0.0302 MNR + 0.029

S3M = 0.0043 ADLWI + 0.0036 SRGDIST + 0.0255 MNR + 0.029

S6M = 0.0019 ADLWI + 0.0023 SRGDIST + 0.0207 MNR + 0.030

S12M = 0.0045 SRSMN + 0.128 MNR + 0.025

Where:

S is the standard deviation (in feet),

M is the number of months of subordinate station observation, 

ADLWI is the absolute time difference of the low water intervals between control and 

subordinate stations (in hours), 

SRGDIST is the square root of the geographic distance between control and subordinate stations 

(in nautical miles), 

MNR is a mean range ratio that is defined as the absolute value of the difference in mean 

range between control and subordinate stations divided by the mean range of tide at 

the control station (using range values in feet), and 

SRSMN is the square root of the sum of the mean ranges at the control and subordinate 

stations (in feet).

Geospatially, the tidal datum errors can be visualized by plotting historical tide station locations 

and their error estimates using the previous equations. Figure 5.1 illustrates this distribution 

compiled for a VDatum model assessment for Tampa Bay, FL. These errors are used as part of 

the VDatum error budget analysis described in section 5.1.5 of this report.
71

Figure 5.1. Estimated errors in tidal datums (including MSL) for tide stations in the 

Tampa Bay Region.

Sea Level Trends

As discussed in chapter 2, relative sea level trends can be computed from observed monthly 

mean sea level observations at tide stations. Assuming that the trends are linear over the period 

of the observations, a least squares linear fit can be used to determine the trends and estimate the 

standard error of the trends. Zervas (2009) discusses how standard errors can be computed while 

determining linear least squares fits. Accounting for the average seasonal cycle by using an 

autoregressive formula and a time series over 30 years minimizes the standard error, and 

uncertainty is the trend estimate. 

As mentioned in previous sections, the standard errors can be transformed into 95% confidence 

intervals that, for application to relative sea level trends, are highly dependent upon record length 

(figure 5.2).

Sea level trends should always be provided as the computed trend, along with information on the 

uncertainty of the trend, and are usually expressed as 95% confidence intervals.
72

Figure 5.2. Confidence intervals and record length dependencies for relative sea 

level trend (Zervas 2009).

Relative sea level trends can be highly variable within geospatial regions of significant vertical 

land motion, especially in area of localized subsidence due to fluid withdrawal (fossil fuels or 

water), isostatic rebound (recent retreat of glaciers), or post earthquake response. Depending 

upon a particular area of interest or project, the sea level trends from several locations in the 

region should be assessed for regional consistency and to understand any particular anomalous 

trend.

5.1.2 Elevation

Geodetic Leveling

The National Geodetic Survey (NGS) publishes and maintains guidelines for performing 

accurate surveys to establish ellipsoidal and geodetic elevations on the land. Errors in elevation 

are often associated with the methodology and equipment used to perform a survey. 

The NGS Manual on Geodetic Leveling (NGS, 1981)2provides information on elevation error 

sources using classic geodetic leveling techniques between bench marks. Updated information 

on survey elevation errors in the context of NAVD 88 is found in Zilkoski (1992)3.

Geodetic leveling determines the height differences between adjoining points. It can be extended 

over large distances and/or densified in a local area. Hundreds of thousands of kilometers of 

 

2

See: various sections and sections 3.1.2 and 3.1.3 in 

http://www.ngs.noaa.gov/PUBS_LIB/Geodeticleveling_nos_3.pdf

3

See http://www.ngs.noaa.gov/PUBS_LIB/NAVD88/navd88report.htm
73

leveling have been observed and adjusted throughout the United States to compute Helmert 

orthometric heights for thousands of bench marks. All bench marks have some uncertainty 

associated with their published height. While the uncertainty in geodetic leveling is directly 

proportional to the square root of the distance leveled, the actual uncertainty in published North 

American Vertical Datum of 1988 (NAVD 88) bench mark heights is much more complicated. 

A recent study (Wang 2009) shows that the error built up in NAVD 88 has a dependence on 

topography and may indicate second-order corrections that were not accounted for in the original 

adjustment of the leveling data, which led to the original datum realization. 

Leveling between bench marks ―near‖ each other, such as at one particular tide station, can be 

expected to agree under 1 cm (often much more precisely than that). If no vertical land motion is 

present, repeated leveling surveys that re-visit these marks will maintain this integrity, predicated 

on lack of disturbance at each mark and presuming proper field procedures are followed. For 

example, using geodetic leveling standards, two points 40 km apart with second-order/class one 

heights have an uncertainty between them of [1.0 mm x (SqRt(40)) = 6.3 mm]. Numerous 

factors combine to make it likely that two points may not agree that well today, years after the 

most recent leveling. For example, one or both points could have been disturbed since their last 

leveling. 

GPS Surveying

With the completion of the general adjustment of the NAVD 88 (Zilkoski et al., 1992), 

computation of an accurate national high-resolution geoid model (currently GEOID 09, and 

publication of NGS‘ Guidelines for Establishing GPS-Derived Ellipsoid Heights (Standards: 2 

cm and 5 cm) NGS-58 (Zilkoski et al., 1997), NGS has demonstrated that GPS-derived 

orthometric heights can provide a viable alternative to classical geodetic leveling techniques for 

many applications. 

NGS-58 issued guidelines for performing GPS surveys intended to achieve ellipsoid height 

network accuracies of 5 cm and ellipsoid height local accuracies of 2 cm or 5 cm (Zilkoski et al., 

1997). The official definitions of ―local‖ and ―network‖ accuracy are those adopted by the 

Federal Geodetic Control Subcommittee (FGCS, 1998) of the Federal Geographic Data 

Committee. NGS-59 (Zilkoski et al, 2008) developed guidelines for performing GPS surveys 

intended to achieve orthometric height network accuracies of 5 cm and orthometric height local 

accuracies of 2 cm or 5 cm. The guidelines were developed in partnership with Federal, state, 

and local government agencies, academia, and independent surveyors. 

NGS-59 also addresses errors from the NGS Online Positioning User Service (OPUS) in the

context of the need to have higher accuracies: ―Readers may rightly ask why campaign-style 

guidelines are needed in an era when NGS‘ OPUS yields ―peak-to-peak‖ consistency in ellipsoid 

heights at the 2-3 cm level, with as little as 15 minutes of GPS data (roughly equal to a network 

accuracy of 4-6 cm), and the latest hybrid geoid models of NGS have ―local accuracies‖ as small 

as 1 cm over 10 km in some states. The answer is that using tools such as OPUS and hybrid 

geoids to achieve NAVD 88 Helmert orthometric heights achieve 5-cm network accuracies only 
74

rarely and 2-5 cm local accuracies occasionally (due to the combined errors of both the GPS and 

geoid). In contrast, the campaign-style guidelines contained in this document are intended to 

achieve those accuracies almost always. When tools such as OPUS and hybrid geoid models can 

single-handedly achieve a similar success rate, these guidelines should certainly be modified to 

reflect those results‖ (Zilkoski et al., 2008).

OPUS can be found at: http://www.ngs.noaa.gov/OPUS/about.html

Figure 5.3. Sample output from OPUS, showing the x-y-z positional values and their associated errors.

5.1.3 Topographic Mapping

When quantifying error in topographic map data, it is important to know whether you are using

data where an accuracy assessment has already been performed. If the data error has been 

quantified, the first place to look is in the metadata record. Within Federal Geographic Data 

Committee (FGDC)-compliant metadata, a section titled ―Data Quality‖ provides a general 

quality assessment of the data set. This section may provide horizontal and vertical positioning 

accuracies, attribute accuracy, lineage, processing steps, and contact information. If an accuracy 

assessment has not been performed for the dataset, the data must be evaluated for acceptability.

A quantitative approach is the most common method for assessing/validating the horizontal and 

vertical accuracies of a topographic data set. This process initially begins with the collection of 

independent, higher accuracy ground control data or check points to be used for assessing the 

topographic data set of interest. Generally, the accuracy of the check points should be an order 

of magnitude better than the data being evaluated. The check points are then used for computing 
75

errors and performing accuracy test. There are a myriad of ways to assess and specify 

accuracies. The following documents provide different guidelines and specifications for 

performing quantitative accuracy assessments on topographic data.

NMAS:

Bureau of the Budget, 1947. National Map Accuracy Standards (NMAS), Office of 

Management and Budget, Washington, DC.

National Standard for Spatial Data Accuracy 

(NSSDA)

FGDC, 1998. Geospatial positioning accuracy standards, Part 3: National standard for 

spatial data accuracy (NSSDA), Federal Geographic Data Committee (FGDC), URL: 

http://www.fgdc.gov/standards/standards_publications/.

FEMA Guidelines and Specifications:

FEMA, 2003. Appendix A, Guidance for aerial mapping and surveying, in Guidelines 

and Specifications for Flood Hazard Mapping Partners, Federal Emergency 

Management Agency (FEMA), April 2003, URL:

http://www.fema.gov/plan/prevent/fhm/gs_main.shtm.

National Digital Elevation Program (NDEP) Guidelines:

NDEP, 2004. Guidelines for digital elevation data, Version 1.0, National Digital 

Elevation Program (NDEP), May 10, 2004, URL: http://www.ndep.gov.

ASPRS Guidelines for Lidar:

ASPRS, 2004. ASPRS guidelines, vertical accuracy reporting for lidar data, American 

Society for Photogrammetry and Remote Sensing (ASPRS), May 24, 2004, URL: 

http://www.asprs.org/society/committees/standards/standards_comm.html.

Chapter 3, Accuracy Standards and Chapter 12 DEM Quality Assessment: 

Maune, D.F., 2007. Digital Elevation Model Technologies and Applications: The DEM 

Users Manual, 2nd Edition. American Society for Photogrammetry and Remote Sensing. 

Bethesda, MD.

Although a topographic data set may pass a quantitative accuracy assessment, anomalies can still 

be present and can be identified through a qualitative assessment. Technologies used today for 

acquiring topographic data produce enormously dense data sets. The quantitative assessment 

usually samples only a small portion of the data set. Visualizing the dataset can provide a 

method for finding anomalies such as voids or a data holiday, biases between flight line swaths 

or tile seam lines, striping or banding, data density issues, and outliers. Viewing the topographic 

data set in a three-dimensional (3-D) perspective or creating by-products, such as slope or shaded 

relief surfaces, can facilitate the detection of problematic issues. Therefore, a blend of 

quantitative and qualitative methods should be employed to assess the validity, completeness, 

and cleanliness of the dataset to be exploited.

USGS National Elevation Dataset (NED)

The accuracy of the USGS National Elevation Dataset (NED) (Maune, 2007) varies spatially 

because of the variable quality of the source DEMs. As such, the NED inherits the accuracy of 
76

the source DEMs. In an effort to provide more information to users on the vertical accuracy of 

the NED, the data set has been tested by comparing it to an independent reference source of high 

accuracy. The reference data are the geodetic control points that NGS uses for hybrid geoid 

modeling (known as ―GPS on bench marks‖, or GPSBMs (Wang et al., 2010). The overall 

absolute vertical accuracy, expressed as the root mean square error (RMSE), is 2.44 meters (m). 

As better sources of data are incorporated, the accuracy improves. 

For some applications of elevation data, the relative, or point-to-point, vertical accuracy is more 

important than the absolute vertical accuracy. Whereas absolute accuracy accounts for the 

combined effects of systematic and random errors, relative accuracy is a measure of only random 

errors. Averaged over all 9,187 point pairs, the relative vertical accuracy is 1.64 meters for 

points separated by less than 50 km.

One caveat to note about the accuracy assessment presented here is that, even though the 

reference control point data set is large, the number of quadrangle-based USGS DEMs on which 

the points are located is relatively small. Thus, if users have a need for very specific accuracy 

NED information for a local area, a separate assessment should be done with suitable reference 

data only for that area. 

5.1.4 Bathymetry

NOS Bathymetry Terminology

Sounding: A measurement from the sea surface to the seafloor, regardless of method—acoustic 

or otherwise; e.g., echosounder, lidar, lead line, diver‘s least depth gauge, etc. A ―sounding‖

may be corrected for factors (e.g., for echosounder soundings: sound speed, vessel draft, and 

water levels for normalizing to a common datum), but remains the product of a single 

measurement sample.

Depth: A fully-processed seabed elevation value relative to an established vertical datum, 

calculated from each sounding or otherwise formulated into a gridded dataset or a navigation 

product surface. A hydrographic survey ―depth‖ may be computed based on statistical analyses 

and uncertainty estimates from a sample set of ―soundings‖. Rounding of depth values may be 

unbiased (round half up) or biased (e.g., shoal-biased rounding).

Bathymetry Attributed Grid (BAG): A gridded bathymetry product that includes coregistered data uncertainty measures and built-in FGDC-compliant metadata documentation.

Charted Depth: A depth destined for portrayal on a nautical chart. Navigation product surface 

―charted depths‖ typically have some amount of shoal-biasing applied (e.g., NOAA cartographic 

rounding) in the interest of safety-of-navigation.
77

Error and Uncertainty

―Errors‖ in the three-dimensional (3D) values of bathymetry are typically partitioned into 

components of the one-dimensional (1D) uncertainty in depth value and the two-dimensional 

(2D) uncertainty in depth horizontal position. A given component of bathymetric uncertainty 

may be reported about the reference value (the depth- or position-value estimate) in terms of a 

scalar residual at a specified confidence interval. Whether such a uncertainty value is a 

veracious gauge of (true) error is dependent upon the inclusion of all systematic biases, in 

addition to all the random errors involved with the bathymetric measurement system (accuracy 

versus precision). Scalar confidence interval estimates assume a certain statistical error 

distribution. Root mean square (RMS) error or standard deviation simplifies the otherwise 

bivariate distribution of the (2D) horizontal position statistic, and scaling of the RMS for some 

stated confidence level percentage entails an assumption about the (fixed) correlation between 

horizontal components. For example, two-times distance RMS (2dRMS; here, d means

"distance‖, to emphasize the scalar or 1D nature of the statistic) may be a pessimistic estimate of 

the 95% confidence interval.

When the bathymetry is packaged in terms of a surface model, the 1D uncertainty in depth value 

may be the single relevant statistical assessment of bathymetric error. That is, a surface model of 

bathymetry may be constructed using a set of a priori locations that do not involve any 

measurement process; rather, sounding measurements and uncertainty are statistically 

assimilated to most-probable depth estimates at desired ―perfect‖ horizontal grid locations. The 

Combined Uncertainty and Bathymetric Estimator (CUBE) method used by NOAA is an 

example of how such a bathymetric surface model may be assimilated (Calder and Wells 2007). 

NOS-finalized CUBE surfaces are packaged into the BAGs and archived at NGDC. BAG 

uncertainties may be propagated through the additional data integration and interpolation steps 

involved in the sea level change synthesis.

Uncertainty Standards

NOS specifications for bathymetric uncertainty are based partly on the International

Hydrographic Organization (IHO) Standards for Hydrographic Surveys as outlined in Special 

Publication 44 (S-44), 5th Edition (IHO, 1998). IHO S-44 specifications are suggested 

minimum standards that member states may choose to follow. The IHO minimum standards for 

uncertainty are used in the NOS Hydrographic Survey Specifications and Deliverables (NOS 

2009) as a convenient point of reference. NOS standards for uncertainty in hydrographic surveys 

apply to general water depths and least depths over wrecks and obstructions. By extension, they 

also apply to the elevations of rocks or other features that uncover at low water and to the 

measurement of overhead clearances. Additionally, the NOS standards apply regardless of the 

method of determination; whether by single-beam echosounder, multi-beam echosounder, lidar,

lead line, diver investigation, or other method.
78

The NOS standard for the maximum allowable Total Horizontal Uncertainty (THU) in position 

of soundings shall not exceed a radial measure of 5 meters + 5% of the depth, at the 95% 

confidence level.

The NOS standard for the maximum allowable Total Vertical Uncertainty (TVU) for depth 

values included in processed bathymetric data at the 95% confidence level, after application of 

―correctors‖ for all system-specific random and systematic errors is according to the formula:

Where:

a represents that portion of the uncertainty that does not vary with depth

b is a coefficient which represents that portion of the uncertainty that varies with depth

(b x d) represents that portion of the uncertain that does vary with depthd is the depth

The variables a and b shall be defined as follows:

In depths less than 100 meters, a = 0.5 meters and b = 0.013 (IHO Order 1)

In depths greater than 100 meters, a = 1.0 meters and b = 0.023 (IHO Order 2)

5.1.5 Composite Error Budget Considerations

Each of the errors for the data layers previously discussed (water level data, tidal datums and sea 

level trends; topographic data and bathymetric data) must be considered when applying them to a 

sea level risk assessment, map, or data integration process. Sea level trends are determined at the 

millimeter per year level and their impacts are determined in terms of centimeters by 2100, or in 

some cases1 meter and above, depending upon climate scenario. Tidal datums errors are described 

in terms of a few centimeters. The topographic and bathymetric data sets have errors of several 

centimeters. Users of these data must consider an overall target error budget, depending upon the 

application and desired outcome, and be careful in their conclusions not to overstate or imply 

accuracy that cannot be supported by the accuracies of the fundamental layers described above. A 

baseline DEM built on the fundamental data sets cannot have accuracies implied at the few 

centimeter or millimeter level. Considering the accuracy of the source data and the limitations of 

graphical representation, realistic impacts of sea level rise generally cannot be depicted on 

bathymetric and topographic elevation layers for incremental changes in sea level of a few 

millimeters but must be visualized using increments of several centimeters. The following 

sections in this chapter describe integrating data sources in the context of error and uncertainty.

5.2 Integration of Multiple Data Sources to Better Address Sea Level 

Issues

5.2.1 CORS and Tide Stations 

The Continuously Operating Reference Station (CORS) network is an international network of

GNSS stations established for long-term operation (section 3.2.1) and serves as one of the 
79

fundamental observing systems of the National Geodetic Survey (NGS). CORS data can be used 

to compute continuous precise time series of land movement, both horizontally and vertically, at 

the location of the instrument. The ability to provide millimeter per year resolution in vertical 

land movement is most useful to the sea level community. When co-located with a long-term 

tide station, the two ―signals‖ for the land and the ocean can be combined for a better 

understanding of impacts of both local and global sea level variations. Snay (2007) estimates 

that the standard error for a GPS-derived vertical velocity is reduced from over 3 mm/yr with a 

two-year observation period to just above 0.5 mm/yr with a 12-year period. The NGS CORS 

network has been established only over the last two decades as the technology matured, so data 

sets of several decades have not yet been accrued. Zervas (2009) estimates that the standard 

error for a linear relative mean sea level trend from tide gauge data is reduced from 

approximately 3.0 mm/yr for a 20-year record to less than 0.5 mm/yr for a 60-year record length. 

The benefit of a CORS is that very accurate rates of local land motion that have previously only 

been estimated from local leveling or the inferred geologic models and indirect measurements can 

now be obtained. Subtracting out the land movement provides a method for converting the relative 

sea level change into a point estimate of the regional signal of global sea level change. No matter 

the source of the motion (local fluid withdrawal, continental glacial isostatic adjustment (GIA), 

earthquakes and related relaxation, etc), a CORS will detect the composite motion and allow it to 

be removed from the relative sea level change record that the tide gauge provides.

Once subtracted from the relative sea level trends from a co-located or nearby tide gauge record, 

the composite analysis with similar analysis of other tide gauges can also provide an estimate of 

absolute global sea level change. Snay (2007) estimates a 1.80 ±0.18mm/yr rate of change using 

tide gauge data from a 1900–1999 period from 50 stations in North America and the Pacific 

Islands and assuming constant vertical velocities found from the nearby CORS data. 

5.2.2 VDatum and How It Can Be Used

VDatum is a free software tool that is being developed jointly by NOAA‘s National Geodetic 

Survey (NGS) (http://www.ngs.noaa.gov), Office of Coast Survey (OCS)

(http://nauticalcharts.noaa.gov), and Center for Operational Oceanographic Products and 

Services (CO-OPS) (http://tidesandcurrents.gov). VDatum (figure 5.4) is designed to transform 

geospatial data between a variety of vertical (and horizontal) datums. This allows users to 

convert their data from different vertical references into a common system, which enables the 

fusion of disparate geospatial data, particularly in coastal regions. 

VDatum currently supports vertical datum transformations that can be placed into three

categories: 

Ellipsoidal: realized through GNSS systems.

Orthometric: defined relative to a geopotential surface and realized through geodetic

leveling from bench marks with published heights.

Tidal: based on a tidally-derived surface in the vicinity of a tide gauge.
80

The software is currently available for certain areas of the U.S. (figure 5.5) and supports many 

diverse applications. The VDatum tool allows for transformation of a single height/depth or 

file/files of points from one vertical datum to another. Uncertainties associated with VDatum are 

currently being made available to inform users when transforming heights/soundings among the 

various supported vertical datums.

Figure 5.4. The VDatum software interface for converting between vertical datums.

VDatum enables:

The extraction of consistent, non-interpreted tidal datum based shoreline from lidar.

Vertically referencing hydrographic surveys collected relative to the ellipsoid, 

eliminating time-consuming water level corrections.

Enabling the fusion of diverse geospatial datasets on one common vertical datum.

Additional information, the software, user‘s guides, education material, and references to reports 

and papers can be found:

On the Web:

http://vdatum.noaa.gov

By email:

vdatum.info@noaa.gov
81

Figure 5.5. Current VDatum coverage as of July 16, 2010

Limitations

Estimation of Vertical Uncertainties in VDatum

Users of VDatum should be aware of the potential uncertainties, or errors, in computed heights 

when applying the software to convert values between vertical datums. Random errors in 

VDatum may arise from inaccuracies in either the gridded fields employed in the datum 

transformations, such as GEOID 09 or the mean sea level (MSL)-to-mean higher high water 

(MHHW) transformation, or in the source observational data used to create VDatum, such as the 

elevation of the tidal datums or the height of the North American Vertical Datum of 1988 

(NAVD 88). Users should also be aware of the existence of measurement errors in their own 

vertical elevation data, i.e., uncertainties related to bathymetric measurement, GPS measurement, 

leveling, etc.

There are, however, inconsistencies in the datum field input resolutions. Additionally, some 

areas present higher unresolved hydrodynamic and datum relationship complexities that would 

require additional gauging to achieve some required minimum ―full model‖ accuracy. Tide 

data/datum and geodetic relationship requirements were not targeted at a prescribed accuracy 

prior to the development of the initial models and therefore inconsistencies exist in the resolution 

of these data. Future operational plans will address data needed to develop models to meet some 

minimum accuracy across the gridded models. Also, subsequent quality assessments are limited 

to validating transformations at tide gauge sites only, whereas assessing the value of VDatum 

should occur along shore and offshore where tide gauges are not generally available. Formal 

VDatum model evaluation procedures are being developed to ensure user knowledge of the 

variable uncertainties and observational plans include use of GPS buoy technology to enhance 

offshore measurements for model calibration and evaluation. For the evaluation of VDatum, the 

standard deviation (SD) is the primary statistical variable used to quantify the random 

uncertainty in both the vertical datums (i.e., the source data) and the transformations between 
82

them. Standard deviation is a measure of the average size of the errors in a data set (when errors 

are normally distributed) and is denoted by the Greek letter sigma (σ). Uncertainties for the 

source data and transformations in the Chesapeake Bay VDatum region are shown in figure 5.6 

as an example.

Figure 5.6. Schematic shows how VDatum handles the transformation (arrows) of a value from an ITRFxx

ellipsoid to several vertical datums (boxes) through the core datums (ovals) for the Chesapeake Bay VDatum region. 

Estimated errors in the transformations are shown as standard deviation values (σ) and are placed next to the arrow 

relating to each transformation. The estimated uncertainties for each individual vertical datum, shown as the σ 

values inside the ovals/boxes are also included.

Total random uncertainty for a sequence of conversions such as those used in VDatum is 

obtained by taking the square root of the sum of the squares of the individual uncertainties. Total 

uncertainty also includes systematic errors such as those due to land subsidence or sea level rise. 

(The present study currently does not include these systematic errors.). A preliminary 

assessment of VDatum uncertainty using the Chesapeake Bay region as an example (figure 5.6),

reveals that the uncertainty due to only the transformations from the International Terrestrial 

Reference Frame of year XX (ITRFxx) through the North American Datum of 1983 (NAD83), 

NAVD 88, and local mean sea level (LMSL) to a tidal datum (i.e., MHHW) can be as large as 

8.36 cm (the maximum in Chesapeake Bay occurs when the tidal conversion is to either MHHW 

or MLLW). An explanation of how this number was computed appears in the ―Accuracy of 

Transformations‖ section of the uncertainty documentation available on the VDatum website 

(http://vdatum.noaa.gov/docs/est_uncertainties.html). In addition, the uncertainty due to only the 

source data is 5.84 cm. An explanation of how this number was computed appears in the 

―Accuracy of the Source Data‖ section of the Web documentation. 

The maximum cumulative uncertainty (MCU) is the value of cumulative uncertainty for the 

transformation from ITRFxx to the tidal datum whose transformation has the greatest 

uncertainty. For the Chesapeake Bay region, that tidal datum transformation is to MHHW or 
83

MLLW. The maximum cumulative uncertainty therefore represents uncertainty, expressed as 

the standard deviation of the error. If the errors are normally distributed, then 68% of the errors 

will be smaller than the MCU when using VDatum, and 95% of the errors will be smaller than 

1.96 times the MCU. The MCU values for most VDatum regions have been computed and are 

shown in the website text. NOAA is actively engaged in updating this methodology, adding new 

regions of coverage and improving the VDatum files for the various existing regions.

Further in-depth information about the uncertainties associated with VDatum can be found at:

http://vdatum.noaa.gov/docs/est_uncertainties.html

Limited Coverage for Tidal Datum Transformations:

Another limitation of the VDatum software is the limited areal coverage where tidal datum 

transformations are permitted inland from the land/water interface. Transformations between 

ellipsoid heights in different geometric reference systems are available worldwide. 

Transformations between NAD83 ellipsoid heights and NAVD 88 Helmert orthometric heights are 

currently available in regions where NGS has developed an appropriate hybrid geoid model. Tidal 

transformations are only available for select regions (figure 5.5) of the U.S. where tidal datum 

fields have been created. Figure 5.7 demonstrates where the VDatum software will allow/restrict 

the transformation to a tidal datum in the La Jolla, CA vicinity. Areas of the image that are shaded 

green are where height/sounding data can be transformed to a tidal datum. Transforming elevation 

data in a red shaded area of the image returns a value of −999999, informing the user that a tidal 

transformation is invalid (not allowed). Tidal datum transformations are usually allowed 

approximately 100 meters landward from the land/water interface. Research is currently being 

performed to extend the validity of tidal datum transformations further inland. More detail can be 

found in section 5.2.2 ―Extrapolating Beyond the Grid.‖
84

Figure 5.7. Illustrated are the areal extents of where 

VDatum allows/restricts tidal datum transformations.

Further VDatum limitations being addressed by the NOAA VDatum team:

Transition of VDatum into operational hydrographic surveying and GIS software 

packages. The VDatum team is working toward coordination with software 

manufacturers on VDatum coverage, dynamic-link libraries, file formats, etc.

Development of improved uncertainty estimates, including the possibility of spatiallyvarying uncertainties.

More automatic and real-time updating of VDatum transformation fields to account for 

new data (tidal, ellipsoidal, orthometric), updates to data (epoch adjustments, data 

corrections), and updates to models (tidal and geoid).

Transformations between tidal datums and ellipsoidally-referenced datums currently 

require intermediate transformations through the geoid. The VDatum team is evaluating 

the possibility of having direct transformation fields between the tidal datums and 

ellipsoidally-referenced datums through the use of GPS data on tide gauges.

VDatum is evaluating the possibility of incorporating offshore data and models that could 

help improve the offshore accuracy of the sea surface topography.
85

Extrapolating Beyond the Grid

Tidal datum transformations in VDatum extend only slightly beyond the mean high water 

(MHW) shoreline, but many applications seek to reference to tidal datums further inland. One 

example is the application of VDatum to lidar data to compute a shoreline referenced to a tidal 

datum. Lidar data collected during low tides can be processed in VDatum and referenced to 

MHW. The resulting contours of the MHW-referenced data at a value of zero will then represent 

the MHW shoreline. If the tidal datum transformations do not extend far enough inland to make 

this transformation, though, users must make decisions about how to manually readjust the 

datum transformations to enable the shoreline computation to be made. 

Another example of a need to extend tidal datum transformations further inland can occur with 

sea level change studies that are making static assumptions to look at which areas of a digital 

elevation model (DEM) would be influenced if sea level were adjusted by a fixed amount. If the 

DEM heights are referenced to a tidal datum, then transformations are necessary in inland areas 

affected by sea level rise scenarios. 

Variations in the nearshore tidal datums influence the method of approximating their inland 

extension, no matter whether the extension is for shoreline determination studies or static sea 

level change scenarios. Tidal datums along a coastline can vary locally for many reasons, some 

of which include bathymetry, tidal flats, river interactions, presence of barrier islands, 

geographic/volumetric changes in the shoreline and associated embayments, and the presence of 

shoreline engineering structures. If none of these factors affects a given area (e.g. straight 

coastline, absence of other factors mentioned above), extrapolation of the tidal datums can 

usually be made by assuming a constant datum difference to be extended inland. For example, 

Figure 5.8 shows how a constant offset between NAVD 88 and local mean sea level could be 

extended inland. 

Figure 5.8. Assuming a constant vertical datum transformation offset 

to be extended inland.
86

However, after evaluation of the extension of tidal datum fields inland, most scenarios encounter 

some level of complexity due to the factors affecting local tidal datum variations. Therefore, 

some assumptions need to be made, while also acknowledging the uncertainty that can arise due 

to the associated factors. For example, figure 5.9 illustrates several inland locations for which 

decisions would be made on how to extrapolate tidal datums inland. The star symbols in these 

figures indicate the inland locations in question, with sections of Chesapeake Bay on the left and 

of North Carolina on the right. Questions that might arise in such an extrapolation include:

Should the user extrapolate from the nearest water point, from the nearest river 

location, or from the nearest bay/ocean value?

Because there is more uncertainty in tidal datums in rivers, marsh areas, and tidal 

flats, should the user extrapolate from a VDatum location in one of these regions?

If the location is on a barrier island, should the user extrapolate from the ocean or 

inland waterway side?

Does the user want the extrapolation routine to blend the tidal datums between the 

river and bay sides, and if so, is that physically realistic?

The common element in all of these questions is related to the fact that tidal datums have no 

physical meaning inland (until or unless that inland location becomes inundated), but the 

applications need to make some assumption about how to vertically reference them based on the 

issue being resolved, such as preparing for potential inundation. The user must be aware of local 

tidal datum variations due to a variety of factors, and any extrapolation/interpolation routine used 

to extend them inland should evaluate those factors as part of the decision process.

No matter what decision is made on how to extrapolate the tidal datums in such situations, it is 

suggested that statistics be computed on the variability of the datum transformations in the user‘s 

area of interest. This information can then be used to document the uncertainty introduced by 

assuming a certain interpolation scheme or even by using a constant offset value. In some cases, 

the variability in transformation values between different regimes (river, bay, ocean, marsh, tidal 

flats, etc.) may be small enough in relation to the coastal issue being addressed.
87

Figure 5.9. Locations (shown in stars) for which extrapolating tidal datums inland 

is more complex due to proximity to waters of different tidal regimes. The left 

figure shows a portion of Chesapeake Bay, and the right figure shows some of the

barrier islands off North Carolina.

In extending tidal datums inland for sea level change (SLC) studies, the assumptions of how that 

change will occur need to be considered. For example, if sea level rises by a constant amount in 

a region, the tides will also change due to new areas now being flooded or dried. The local tide 

patterns used to construct VDatum may no longer be completely applicable if significant changes 

occur as a result of a sea level change scenario. For example, a barrier island may be so low that 

in some sea level rise scenarios some parts of the island would be breached. This changes the 

flow of water significantly, allowing the tides to more easily propagate into the inland waterway. 

Marshes and tidal flats can likewise be flooded and/or dried in sea level change scenarios, thus 

changing the local tidal datum patterns accordingly. Aside from the static effects of these SLC

scenarios, dynamic influences related to morphological changes to the marshes and shoreline 

environments would also be present. Sediment erosion and marsh accretion would change in 

ways that could only be evaluated through detailed studies that link processes across a variety of 

scales together in a physically realistic manner.

Quantifying how all of these static and dynamic effects could change tidal datums extended

inland is a difficult task. In many cases, it may be more appropriate to make an assumption of a 

constant datum transformation offset to be used for the area of interest, while acknowledging 
88

these effects in the documentation of the uncertainty. A constant offset, (e.g. between NAVD 88 

and local mean sea level) could be determined by examining an offshore average difference 

between these two datums, far enough away from local factors affecting the nearshore tidal 

datum patterns. This offset could then be applied to transform topographic land heights from 

NAVD 88 to local mean sea level.

What to Do When VDatum is Not Available

The report Topographic and Bathymetric Data Considerations: Datums, Datum Conversion 

Techniques, and Data Integration; Part II of a Roadmap to a Seamless Topobathy Surface, 

included in the references, outlines different methods for applying vertical datum transformations 

to data. While the report describes VDatum and recommends it for areas where available, it also 

provides analysis of the benefits and limitations of other methods that may be used when 

VDatum is not available. These options include interpolation algorithms and the harmonic 

constant datum method. This report is available on the Web at the following address: 

http://www.csc.noaa.gov/topobathy/topographic-and-bathymetric-data-considerations.pdf

If cost effective for the desired application and accuracies, it may be advisable to obtain new data 

to interpolate between existing locations. This may require installing a tide station and 

determining new/or updated tidal datums on bench marks, as well as using GPS surveying

methods to establish a geodetic datum connection (see chapter 3). For topographic elevation 

requiring better accuracy than VDatum and lidar, it may be advisable to conduct a localized 

survey using Kinematic GPS surveying procedures, along with the point measurements 

established by a tide station and static GPS survey.

As a note, almost all valid applications requiring the use of datums and transformations of datums 

have some prescribed accuracy for respective applications. Users should perform an error 

assessment of the desired product or outcome to see if VDatum may be of limited use as a tool.

5.3 How to Build Integrated Data Products Such as a Digital 

Elevation Model (DEM)

5.3.1 Methodology

Several different methods exist for building Digital Elevation Models (DEM), and there is no 

one perfect process for deriving the optimal elevation surface. The DEM can be only a 

topographic data model or a blended topo/bathy surface. A topo/bathy DEM can be thought of 

as a surface where land elevation is combined with the sea surface floor elevation information. 

The first step in building a DEM is to obtain the elevation data that is correct for the specific

application. The horizontal and vertical accuracy of the data should be appropriate for the 

application. Another consideration is obtaining data with the needed density, for DEM creation 

that is suitable for the application. Note that the post spacing of elevation information obtained 

can be quite different between topographic and bathymetric surveys. A greater the density of 
89

data will allow for a higher resolution DEM to be created, which can enhance the ability to 

accurately realize sea level change results.

Attaining the necessary coverage or areal extent of elevation data is important, especially when 

creating topo/bathy DEMs. However, obtaining shallow water submerged topography has been a 

challenge in the past. Large gaps can be present from the land water interface where the 

topographic surveys end, seaward to where it is safe to perform the bathymetric or hydrographic 

surveys. Bathymetric lidar is one tool/technology that can be used to assist with acquiring this 

often neglected swath of elevation data when environmental conditions are feasible. Such 

systems as the former NASA, and current USGS Experimental Advanced Airborne Research 

Lidar (EAARL) fills the niche of collecting highly accurate shallow water topography, aided by 

the laser‘s short pulse width and narrow beam divergence. Additionally, obtaining data

processed to the appropriate level should also be considered. For a particular sea level change 

scenario, the user could ask, ―What is the appropriate model for this scenario? Is it a bare earth 

model, (elevations of the ground free from vegetation, buildings, and other anthropogenic 

structures) or a Digital Surface Model (DSM), (depicting the elevations of the top surfaces of 

buildings, trees, towers, and other features above the bare-earth surface)?‖ 

Once the user has the data that fits the specific application, having the data in a point format 

assists with easing datum transformations, blending, and gridding. The next step is to transform 

all of the data into a common horizontal and vertical datum. For the vertical component, the 

topography is usually in an ellipsoid or orthometric datum, while the bathymetry is more likely 

referenced to a tidal surface. Converting the diverse datasets to a common reference system 

helps minimize the discontinuities or stair-step effect between the data sources. After the data 

are commonly referenced, the next step is gridding the data for a combined surface. 

Considerations should include what data model to use, the appropriate resolution, and what 

construction method or interpolation technique to employ. Again, there are several different 

pathways for gridding the elevation information, and the method of choice should be based on 

the exact application for which the data are being used.

5.3.2 Considerations When Generating DEMs

Often, elevation data are only available as point data, and the user needs to create a raster data set 

for use in modeling and visualization. Software and methods for performing this step are varied 

and can range from easy and free to complicated and expensive. Tools to handle software and 

create elevation surfaces range from freeware to costly commercial packages. When selecting a 

software package, cost is an important factor. Increased cost often results in increased 

functionality and analysis power, but a trade-off may be complexity of use. Inexpensive or free 

software may have fewer sophisticated analysis capabilities but may provide the needed tools in 

a simple interface. 

Many statistical approaches exist to generate a DEM (surface) from point data; they include 

nearest neighbor, kriging, binning (min, max, average, most common), inverse distance 

weighted, and gridding a TIN (triangulated irregular network) surface. Several papers and 
90

textbooks discuss these approaches in detail; one suggested resource is Digital Elevation Model 

Technologies and Applications: The DEM Users Manual, 2nd Edition (Maune, 2007). The most 

common are the gridded TIN and inverse distance weighted approaches. Use of the other 

techniques is valid but may require additional information for a specific use. 

The cell size selected should be chosen to accurately represent the elevation data while 

considering the cost and time needed to build and run through inundation models. DEMs are 

commonly in raster format, largely because of its efficiency, but they can have other structures or 

forms as well.

The structure of the DEM grid (structured or unstructured) should also be considered. A structured 

grid has a uniform grid cell shape—a rectangle—with elevation values at each of the cell‘s four 

nodes or at the center of the cell. An unstructured grid (such as a TIN) has grid cells with a 

triangular shape so that elevation values are at each of the three nodes. Cell size can be highly 

variable in an unstructured grid and therefore show more detail in areas of a DEM where elevation 

change may be variable, such as at the shoreline, and less detail in areas of uniform elevation.

When creating elevation surfaces, special care must be taken to use the proper horizontal and 

vertical datums. Neglecting this step introduces avoidable error into the final elevation surface. 

For example, individual terrain (topography and bathymetry) data sets for topobathy surfaces may 

be referenced to different vertical datums, including orthometric, tidal, and ellipsoidal datums. 

Each of these datums is best suited for particular applications, such as water flow, navigation, and 

satellite positioning, respectively. A thorough discussion on resolving datum issues can be found 

in the NOAA Coastal Services Center‘s publication, Topographic and Bathymetric Data 

Considerations: Datums, Datum Conversion Techniques, and Data Integration

(www.csc.noaa.gov/topobathy/topographic-and-bathymetric-data-considerations.pdf).

5.3.3 Review of the DEM Surface

Quantitative Accuracy

The accuracy values are calculated by comparing surveyed ground control points (GCP) to the 

elevation surface. A TIN surface generated from the lidar elevation data is compared to the GCP

data. A TIN surface is used because there is very little chance that the GCPs will exactly 

coincide with the lidar elevation data points, and a TIN is a straightforward method for 

interpolating a value from the nearest points.

In most cases, 20 GCPs are collected per land cover or classification category, and five different 

land covers or terrain types are chosen. Use of the data for specific applications may depend on 

the accuracy of the data for specific land covers. For example, shoreline delineation requires 

only a high level of accuracy in the bare-earth category, whereas flood mapping requires that 

both bare earth and forested areas have accuracies suitable for creating a specific contour 

interval. If a data set has a high bare-earth accuracy but was poorly classified for vegetation, 
91

then it may not be usable for flood mapping; however, the data set will still work well for 

shoreline delineation.

For lidar data sets, The American Society for Photogrammetry and Remote Sensing (ASPRS) 

has published guidelines for analyzing and reporting on lidar‘s vertical accuracy. The report can 

be downloaded from: 

www.asprs.org/society/committees/lidar/Downloads/Vertical_Accuracy_Reporting_for_Lidar_D

ata.pdf.

Qualitative Accuracy

Unlike the clearly-defined statistical accuracy requirements, the qualitative aspect of the data is

more subjective. While it does not commonly receive the same amount of attention on the front 

end, attention to the qualitative side is a critical check for the successful use of the data. In 

essence, the accuracy assessment tests only 200 to 300 points in a data set of a billion points, so 

the qualitative review can be seen as a test of the other billion or so points. There are, however, 

no specified qualitative accuracy procedures, so familiarity with lidar data in general and the 

location and intended use in particular are necessary. This ―fuzzy‖ analysis is generally best 

performed by a third party, the purchaser, or a user group. Some of the most common qualitative 

―errors‖ are flight line mismatches, high frequency noise (also called ―corn rows‖), formatting, 

misclassification, and data holidays or voids. While many of these problems can be fixed, corn 

rows are more difficult to remedy. Ultimately, there are no ―perfect‖ data sets, but there is 

generally a level at which the data lose some of their usability, and that threshold should be 

considered when specifying the data.

Sources of Integrated Products

NOAA National Geophysical Data Center (NGDC)

NGDC builds and distributes high-resolution, coastal digital elevation models (DEMs) that 

integrate ocean bathymetry and land topography to support NOAA's mission.

Coastal Relief & Tsunami Inundation

http://www.ngdc.noaa.gov/mgg/coastal/coastal.html

Coastlines & Coastline Extractor

http://www.ngdc.noaa.gov/mgg/shorelines/shorelines.html

Digital Elevation Model (DEM) Discovery Portal

http://www.ngdc.noaa.gov/mgg/dem/demportal.html

Global Relief (ETOPO1, ETOPO2, ETOPO5)

http://www.ngdc.noaa.gov/mgg/global/global.html

SRTM DEMs (tied to EGM96)
92

NOAA Coastal Services Center (CSC)

Integrated bathymetric- and topographic-elevation lidar data are available from CSC as part of 

the NOAA Digital Coast Project (Digital Coast Elevation

http://www.csc.noaa.gov/digitalcoast/data/index.html#elevation).

References Cited

Bodnar, A.N. 1981: Estimating accuracies of tidal datums from short term observations, Draft 

Report, Tides and Water levels Division, NOAA National Ocean Survey, 44pp.

Calder, B.R. and Wells, D.E., 2007: CUBE User‘s Manual, version 1.13, University of New 

Hampshire (UNH), Center for Coastal and Ocean Mapping (CCOM)/Joint Hydrographic 

Center (JHC), Durham, NH. URL: http://www.ccomjhc.unh.edu/publications/Calder_07_CUBE_User_Manual.pdf.

FGDC, 1984: Standards and Specifications for Geodetic Control Networks, Federal Geodetic 

Control Committee, Rockville, MD, September 1984. 

http://www.ngs.noaa.gov/FGCS/tech_pub/1984-stds-specs-geodetic-control-networks.htm.

Gill, S.K. and K.M. Fisher, 2008: A network gaps analysis for the national Water Level 

Observation Network, NOAA Technical memorandum NOS CO-OPS 0048. 50pp.

http://tidesandcurrents.noaa.gov/publications/Technical_Memorandum_NOS_COOPS_0048.pdf.

IHO, 1998: International Hydrographic Organization (IHO) Standards for Hydrographic 

Surveys, Special Publication No. 44, 4th Edition.

Maune, D.F., 2007: Digital Elevation Model Technologies and Applications: The DEM Users 

Manual, 2nd Edition. American Society for Photogrammetry and Remote Sensing. 

Bethesda, MD.

NGS, 1981: Geodetic Leveling, NOAA Manual NOS NGS 3, NOAA, NOS, National Geodetic 

Survey, August 1981, reprinted June 2110. Available on-line at:

http://www.ngs.noaa.gov/PUBS_LIB/Geodeticleveling_nos_3.pdf

NOAA, 2003: Computational Techniques for Tidal Datums Handbook, NOAA Special 

Publication NOS CO-OPS 2, NOAA National Ocean Service, Center for Operational 

Oceanographic Products and Services, 90pp

http://tidesandcurrents.noaa.gov/publications/Computational_Techniques_for_Tidal_Datums_han

dbook.pdf.

NOAA, 2001: Tidal Datums and Their Applications, NOAA Special Publication NOS CO-OPS 

1, S.K. Gill and J.R. Schultz, eds. NOAA National Ocean Service Center for Operational 

Oceanographic Products and Services, 111pp 

http://tidesandcurrents.noaa.gov/publications/tidal_datums_and_their_applications.pdf

NOS, 2009: NOS Hydrographic Specifications and Deliverables. 

http://www.nauticalcharts.noaa.gov/hsd/specs/SPECS_2010.pdf

Zervas, C., 2009: Sea Level Variations of the United States 1854-2006, NOAA Technical

Report NOS CO-OPS 053, NOAA National Ocean Service Center for Operational 
93

Oceanographic Products and Services, 78pp. 

http://tidesandcurrents.noaa.gov/publications/Tech_rpt_53.pdf

Zilkoski, et al., 2008: Guidelines for Establishing GPS-Derived Orthometric Heights, NOAA 

Technical Memorandum NOS NGS 59, National Geodetic Survey, Silver Spring, MD. 

15pp.

Zilkoski et al., 1997: Guidelines for Establishing GPS-Derived Ellipsoid Heights (Standards: 2 

cm and 5 cm) Version 4.3, NOAA Technical Memorandum NOS NGS-58, National 

Geodetic Survey, Silver Spring, MD. 

95

Chapter 6.0 Applications

The preceding chapters outlined the characteristics of the myriad datasets typically used in sea 

level change (SLC) mapping and assessment projects, including information on how to obtain 

existing or collect new data. This chapter provides select examples of these data in action—how 

these datasets and other resources can be harnessed to examine particular issues or challenges 

that scientists, engineers, and decisions makers face when identifying and dealing with changing 

sea levels. 

6.1 Common Applications for Sea Level Change Data

As briefly noted in chapter 2, there are a wide range of potential applications for SLC mapping 

and assessments, from detailed evaluation of SLC impacts on critical infrastructure or sensitive 

ecological resources to regional or state-level climate adaptation or hazard mitigation planning 

efforts. For example, the U.S. Climate Change Science Program (CCSP; 2009), particularly Gill 

et al. (2009), examined the impacts of SLC along the Mid-Atlantic coast; figures 6.1 and 6.2

show application of data to evaluate the sensitivity of the region‘s coastal environments and 

resources to various scenarios of SLC. 

Figure 6.1. Estimating wetland impacts, specifically, which

areas will become marginal or lost (i.e., converted to open 

water) under three SLC scenarios (CCSP, 2009).
96

Figure 6.2. Map shows potential geomorphologic responses to SLC for 

four coastal compartments along the Mid-Atlantic region (NY, NJ, 

Delmarva Peninsula, VA/NC coast) (CCSP 2009). Potential responses 

shown in the inset table based on work by Gutierrez et al. (2007).

CCSP (2009) also applied SLC information to improve understanding of the socioeconomic 

consequences of SLC. A geographic information system (GIS) analysis in the study evaluated 

the intersection of inundation caused by SLC of 1 m against U.S. Census population data and a 

land-use layer. Tables 6.1 and 6.2 show a subset of the results, specifically estimates of how 

many people and residential housing units are likely to be affected by 1 m of SLC. Note that the 

results are expressed in high and low estimates because of the uncertainty in the base elevation 

layer and in the data interpolations made in the GIS analysis. One meter of SLC is the minimum 

increment in the analysis due to the inherent uncertainties in the various data layers, especially 

the base elevation layer.
97

Table 6.1. Potential population to be affected by SLC

in the Mid-Atlantic region (CCSP, 2009).

Table 6.2. Potential number of residences in the MidAtlantic region at risk with 1 meter of SLC (CCSP, 2009).

SLC data are increasingly being used in the development of land-use plans. The Maryland 

Department of Natural Resources (MD DNR) and the U.S. Geological Survey (USGS)

completed the development of the Worcester County (MD) Sea Level Rise Inundation Model in 

November 2006 (Johnson et al., 2006). Using lidar data recently collected for the county, a 

Digital Elevation Model (DEM) was produced as the base elevation layer upon which results 

from various SLC scenarios model for three periods (2025, 2050, and 2100) were overlain. The 

three scenarios were: 1) the historic rate of regional SLC estimated from tide station records (3.1 

mm/year), 2) the average accelerated rate of SLC projected by the IPCC (2001), and 3) the 

worst-case scenario using the maximum projection of accelerated SLC by the IPCC (2001) (85

cm to 90 cm by 2100). The scenarios were applied to present-day elevations of mean sea level 

(MSL), mean high water (MHW), and spring tides derived at local tide stations. Figure 6.3
98

shows a typical result for 2100 using the worst-case (accelerated) SLC scenario from the IPCC 

(2001). 

1 -

Figure 6.3. Johnson et al. (2006, as cited in CSP, 2009) examined SLC inundation 

potential for agricultural land in Worcester County, Maryland. On the left, blue areas 

show present-day inundation. On the right, inundation expected in 2100 using IPCC‘s 

(2001) accelerated rate of SLC is shown.

Incorporation of sea level change data is also becoming more prevalent in coastal engineering 

design and construction. The U.S. Army Corps of Engineers (USACE; 2009) recently published 

a new guidance document concerning use of SLC in project planning and design. This report 

assesses the risks associated with specified project designs based on regional mean sea level 

trends. It provides a step-by-step process (figure 6.4) to determine an extrapolated base-line rate, 

an intermediate rate, and a high rate of future SLC in five-year increments.
99

Figure 6.4. USACE (2009) decision-making process to account for SLC in project planning and design.

6.2 Digital Elevation Model in Mapping and Assessing the Impacts 

of Sea Level Change

SLC mapping can be viewed as a four-step process. The following outlined consist of a 

framework to create inundation maps, with each step explained in detail in the NOAA Coastal 

Services Center‘s Coastal Inundation Mapping Guidebook

(http://www.csc.noaa.gov/digitalcoast/inundation/_pdf/guidebook.pdf). The guidebook 

discusses the mapping process and some of the limitations, such as how the resolution of the data 

will drive the scale of the planning, and explains why the accuracy of the data needs to be known 

and communicated to the users.

1) Obtain and Prepare Elevation Data:

(http://www.csc.noaa.gov/digitalcoast/inundation/map/obtain.html)

Elevation data (including nearshore bathymetry) serve as the base data layer for 

mapping coastal inundation. Before using elevation data for inundation mapping, it is 

important to understand requirements and specifications of the data, how to assess 

data quality, and where to obtain accurate data for a community. Chapters 2 through 

4 of this document provide supplemental information on these points.

2) Prepare Water Levels:

(http://www.csc.noaa.gov/digitalcoast/inundation/map/prepare.html)

To map inundation, a water surface must be generated. That surface can be based on 
100

model output or a change in water height using a single value. Both approaches 

require different inputs and technical skill, and each has advantages. Information in 

chapters 3 and 4 of this document provide additional information on identifying and 

obtaining appropriate water-level data. 

3) Map Inundation: (http://www.csc.noaa.gov/digitalcoast/inundation/map/map.html)

Using a digital elevation model (DEM) and water-level information, GIS processes 

can create layers that represent inundation extent and depth. 

4) Visualize Inundation: 

(http://www.csc.noaa.gov/digitalcoast/inundation/map/visualize.html)

Visualizing the inundation results is important for assessing exposure and impacts 

and serves as a powerful tool for education and awareness. Visualizations may range 

from simple maps to interactive Web viewers. See section 6.7 for further information 

on this topic. 

Both the number and complexity of SLC mapping projects grow daily, providing a rich resource 

of experience for those newly engaged in similar efforts. The feature boxes in figures 6.1 and 6.2 

show two such examples. Additional coastal inundation case studies are available on NOAA‘s 

Digital Coast website (http://www.csc.noaa.gov/digitalcoast), under the ―In Action‖ area. 

For additional information on building Digital Elevation Models see:

http://www.csc.noaa.gov/topobathy/topographic-and-bathymetric-data-considerations.pdf
101

Project 1: Ecological Effects of Sea level Rise in North Carolina

Rising sea level has worldwide consequences because of its potential to alter ecosystems and 

habitability of coastal regions. The vulnerability of coastal areas varies with shoreline physical 

attributes and the amount of development. Low-lying areas in the Mid-Atlantic, Southeast, and 

Gulf Coast are especially at risk.

To examine the complex relationships between SLC and coastal ecosystems, the Ecological Effects 

of Sea Level Rise (EESLC) program was initiated in North Carolina. The program brings together 

University researchers and NOAA scientists to help coastal managers and planners better prepare 

for changes in coastal ecosystems due to land subsidence and SLC. Specialists in biology, 

geomorphology, and coastal modeling joined forces to integrate storm surge models with 

ecological models for more precise predictions of how future sea level will affect coastal wetlands, 

submerged aquatic vegetation, sub-tidal habitat, and oyster reefs. For more information on the 

study, see: http://www.cop.noaa.gov/stressors/climatechange/current/sea_level_rise.aspx

http://www.cop.noaa.gov/stressors/climatechange/current/SLC/default.aspx

A major component of this study was the development of a topographic/bathymetric DEM for a 

portion of eastern North Carolina. The adjacent image shows a portion of this DEM, with the 

extent of 1 m of SLC above the Mean High Water (MHW) datum shown in the inset. VDatum was 

used to adjust the source bathymetric and topographic data, which were collected with different

survey techniques, to a common vertical datum. This step ensured that there was no artificial 

jump in the DEM at the topo/bathy interface, an area of vital importance to many species and 

habitats that are highly sensitive to small changes in water level.
102

Project 2: Shallow Flooding in Downtown Charleston, SC

Shallow coastal flooding occurs when seawater inundates low-lying areas. Higher than average 

high tides, sometimes worsened by heavy rainfall and onshore winds, can cause damage to 

buildings and homes and temporarily restrict traffic. 

DEMs developed from highresolution lidar elevation data allow 

managers to map predicted 

inundation with enough accuracy to 

estimate impacts from relatively 

small changes in sea level. For 

example, in Charleston, South 

Carolina, low-lying portions of the 

downtown can be flooded by tides 

that reach a threshold of 2.1 meters 

(7 feet). (For comparison, 

Charleston’s highest high tides are 

typically less than 1.8 meters [6 feet] 

above the mean lower low water 

datum.)

This map, based on a highly accurate 

lidar-derived DEM, shows areas 

susceptible to tidal flooding (dark blue areas). An additional sea level rise of 0.5 meters (1.6 feet), 

which may occur over the next 100 years according to IPCC, would make the extent of flooding 

much greater (light blue areas).Rising sea levels will also increase the expected frequency of 

flooding. For example, with a 0.5 meter sea level rise, the areas currently susceptible to a 2.1 meter 

tide twice per year would be susceptible to flooding on 289 days—and twice on 66 of those days.

6.3 Amount of Sea Level Change to Use in Specific Projects

When choosing which SLC level or range to depict on a map, the user has many options. 

Published SLC projections are available through a variety of sources, including the IPCC (see 

http://www.ipcc.ch/ or http://www.ipcc.ch/publications_and_data/publications_and_data.htm). 

In some cases, states or local governments have chosen SLC increments as planning targets, such 

as 1 m by 2100. The increment chosen should be relevant to the timescale of decisions being 

made based on the project results. For example, different values of anticipated SLC would be 

needed to evaluate placement of new housing developments over the next 20 years versus what 

would guide similar decisions for water and sewer infrastructure expected to function for 50-75 

years. The ability of evacuation routes to serve their purpose over decades must consider both 

SLC as well as vertical land motion throughout the route corridor.

The following references are select examples of either recent scientific projections of SLC (1-5) 

or specific studies or assessments where the sponsoring organization selected SLC increments 

suitable for their project goals (6-7). 
103

1. Intergovernmental Panel on Climate Change. 2000. Special Report on Emissions Scenarios. 

Cambridge University Press, U.K.

2. Meehl, G. A., T. F. Stocker, W. D. Collins, P. Friedlingstein, A. T. Gaye, J. M. Gregory, A. 

Kitoh, R. Knutti, J. M. Murphy, A. Noda, S. C. B. Raper, I. G. Watterson, A. J. Weaver, 

and Z.- C. Zhao. 2007. ―Global Climate Projections.‖ In Climate Change 2007: The 

Physical Science Basis. Contribution of Working Group I to the Fourth Assessment 

Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. 

Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H. L. Miller (eds.). 

Cambridge, United Kingdom and New York, New York, USA: Cambridge University 

Press.

3. Rahmstorf, S., A. Cazenave, J. A. Church, J. E. Hansen, R. F. Keeling, D. E. Parker, and R. 

C.J. Somerville. 2007. ―Recent Climate Observations Compared to Projections.‖ Science. 

Volume 316. Number 5825. Page 709.

4. Rahmstorf, S. 2007. ―A Semi-Empirical Approach to Projecting Future Sea level Rise.‖ 

Science. Volume 315. Number 5810. Page 368.

5. Vermeer and Rahmstorf, 2009. ―Global Sea level Linked to Global Temperature.‖ Proceedings 

of the National Academy of Sciences, Volume 106, Number 51, Page 21527-21532.

6. Cayan, D., M. Tyree, M. Dettinger, H. Hidalgo, T. Das, E. Maurer, P. Bromirski, N. Graham, 

and R. Flick. 2009. Climate Change Scenarios and Sea Level Rise Estimates for 

California 2008 Climate Change Scenarios Assessment. California Climate Change 

Center. In preparation.

7. Cayan, D., P. Bromirski, K. Hayhoe, M. Tyree, M. Dettinger, and R. Flick. 2006. Projecting 

Future Sea Level. California Climate Change Center. California Energy Commission, 

Public Interest Energy Research Program. CEC-500-2005-202-SF.

Regardless of source, the selected SLC increment should be carefully chosen and supported by 

the vertical accuracy of the elevation data. The root mean square error (RMSE) of the elevation 

data is a useful guide, generally equal to 1 standard deviation or a confidence interval of 

approximately 66%. For example, when mapping a 10-cm inundation event using elevation data 

that have an RMSE of 10 cm, the mapped area of inundation is, on the whole, approximately

66% correct, where some areas shown to be inundated in the analysis should not have been and 

vice versa. Mapping inundation levels below the RMSE of the data returns lower confidence, 

and borders on a 50-50 chance that it is correct. Mapping an inundation level of twice the RMSE 

increases the confidence of accuracy at any one location to approximately 90-95%, depending on 

the surrounding topography.

The following example illustrates how RMSE should determine the SLC increments used. In 

2007, the State of Florida collected lidar for the entire state to the Category 5 Hurricane Storm 

Surge Line. These data were reported to have a vertical accuracy of 9.3 cm RMSE, which 

corresponds to a linear error of 18.2 cm at 95% confidence (9.3cm × 1.96). A rule of thumb to 

get the minimum useful SLC increment for inundation mapping is to multiply the elevation error 
104

value (at a 95% confidence level) by 2. Thus, in this example, 18.2 cm is multiplied by 2, which 

results in 36.4 cm or 1.2 ft. Therefore, the smallest SLC increment to consider for useful 

planning purposes should be 1.2 ft (which can be rounded off to the nearest foot). In the absence 

of reliable error estimates, 1ft is a reasonable SLC increment to apply in mapping projects that 

use most current lidar data collection specifications. 

6.4 What Vertical Reference Datum to Use

When developing analyses using elevation, bathymetry, or datum elevations, it is extremely 

important to know the vertical reference datum being used for your source documents or data 

source, as well as to obtain metadata documenting information on the reference datum.

When blending elevation data and datum elevation information for a map depiction or GIS layer 

display, the various layers must be displayed appropriately relative to a common reference datum 

so that any subsequent analyses are not subject to datum shifts along the land-water interface that 

will bias the final analysis. This usually involves a datum transformation of one or more layers.

The specific vertical reference datum used also depends upon the application and analyses to be 

performed, as well as the accuracy desired. For inundation studies for which estimates are 

required to determine the amount of land affected by sea level inundation, the elevation of a tidal 

datum (such as MHW, or MHHW in areas with diurnal tides) is often used as the base elevation. 

This is because the high water datum represents the elevation of the normal daily excursion of 

the tide where the land area is normally inundated. Taking this normal extent of inundation into 

account is important when trying to delineate land areas inundated by abnormal events such as 

storm surge, tsunami run-up, or SLC.

In the past, many users have assumed that the base vertical reference datum for topographic 

information was mean sea level. Some of this confusion comes from colloquialism and some from 

actual naming issues. Specifically, the datum originally used for the USGS Quadrangle was the 

National Geodetic Vertical Datum of 1929 (NGVD29), but that datum was originally called the 

―Sea Level Datum of 1929.‖ The USGS topographic quadrangle maps now have heights in the 

North American Vertical Datum of 1988 (NAVD 88). That datum should be used when 

performing analyses involving only land elevation data. Any older source topographic information 

should be transformed to NAVD 88 using a datum transformation tool such as VDatum. 

Recent bathymetric data from NOAA represent depths and soundings relative to the NOAA 

Chart Datum of MLLW. Older bathymetric data prior to 1980 are relative to MLW on the East 

Coast and need to be transformed to MLLW. Older bathymetric data are also relative to tidal 

datums from previous NTDE time periods and should be updated to the latest NTDE prior to use;

however, this difference (0.10 ft) between adjoining NTDEs is typically small with respect to the 

accuracy and resolution of the soundings.

The accuracy of the elevation data and the accuracy of the desired product are also important, 

and the incremental elevations being used for the product come into play as discussed elsewhere 
105

in this document. For instance, between LMSL and NAVD 88, for some areas of the country 

where that general difference may only be a few centimeters, a source transformation may not be 

necessary if the source elevation data only have accuracies to several centimeters.

6.5 Measure and Quantify Shoreline Change

Shoreline change is the analysis of shoreline variability and shoreline erosion-accretion trends 

through time. Many factors influence the evolution of the shoreline in response to SLC, 

including geologic framework, physical processes, sediment supply, and human activity. Not 

only do these factors influence the response of coastal landforms to changes in sea level, but they

also contribute to the local and regional variations of sea level rise impacts that are often hard to 

quantify using prediction methods. For more on the physical processes that influence shoreline

change and response to SLC, see both chapter 3 in CCSP (2009) and the papers in a special issue 

of the Journal of Coastal Research edited by Byrnes et al. (2003). 

Changes in shoreline position through processes of accretion and erosion can be analyzed in a 

GIS by measuring differences in past and present shoreline locations that were derived from a 

variety of potential sources (e.g., National Ocean Service raster shoreline manuscripts [T-sheets],

aerial photography, and high-resolution, lidar-based elevation data sets). As discussed in chapter 

3, NOAA maintains a National Shoreline, which was originally intended to support NOAA

nautical chart production but has also been used for shoreline-change analysis, boundary 

determination, and cartographic representation. However, determination of sea level change 

using shoreline data is only valid if the data sets being compared were acquired to the same 

accuracy and are on the same datum.

Other Federal agencies have developed datasets and other resources that support assessment of 

shoreline change along the U.S. coast. For example, the Coastal and Marine Geology Program 

of the USGS conducts analyses of historical shoreline changes along open-ocean sandy shores of 

the conterminous U.S. and parts of Alaska and Hawaii. A primary goal of this USGS work is to 

develop standardized methods for mapping and analyzing shoreline movement so that internally 

consistent updates can periodically be made to record shoreline erosion and accretion. Results 

from these shoreline change studies are available at http://coastal.er.usgs.gov/shoreline-change/. 

Additionally, Thieler et al. (2005) developed a suite of tools for both extracting shoreline 

positions and quantifying shoreline change; an updated version of this toolkit is available at: 

http://woodshole.er.usgs.gov/project-pages/DSAS/version4/index.html

States have also undertaken efforts to map shorelines and understand shoreline-change trends in 

their jurisdictions, often in partnership with Federal agencies. The Coastal Management 

Program, which is authorized by the Coastal Zone Management Act of 1972 and is administered 

by the NOAA Office of Ocean and Coastal Resource Management (OCRM), is a partnership 

among OCRM and 34 coastal and Great Lakes states, territories, and commonwealths. Each 

participating coastal program monitors shoreline change in its locality and, in some cases, uses 

historical erosion rates to establish building setbacks. These setbacks are typically used to 
106

permit new construction (residential and commercial) and hard shoreline-stabilization efforts, 

and to promote a policy of retreating from historically erosive coasts. 

To aid coastal programs, OCRM created the Shoreline Management Technical Assistance 

Toolbox as an online guide for state coastal managers. It provides centralized access to 

information, resources, and tools to address shoreline erosion and management, focusing on 

alternatives to traditional shoreline hardening. The website is organized into four main sections: 

Planning, Policy, and Regulatory Tools; Economics of Shoreline Management; Soft/Alternative 

Stabilization Methods; and Resources (http://coastalmanagement.noaa.gov/shoreline.html).

While state coastal programs may share common goals related to identifying and managing 

eroding coastlines, the quantity and quality of source data available and the methods used to 

determine shoreline-change rates are highly variable across the U.S. (Honeycutt et al., 1999; 

Byrnes et al., 2003). This heterogeneity in erosion analyses complicates efforts to compare 

results from one region to another, which makes products like the USGS‘s regional shorelinechange assessments (referenced earlier) such valuable resources for those needing to understand 

the range of potential shoreline-change impacts triggered by SLC. 

6.6 Applications of Ecosystem/Marsh Change Models

While the scientific community agrees that sea level is rising and coastal marshes are changing 

as a result, it is a challenge to predict the result, since natural systems are inherently 

unpredictable. Empirical models can serve as a guide to help us understand when and where 

impacts have the potential to occur and as a gauge as to how severe they may be.

Existing models, such as the Sea Level Rise Affecting Marsh Model (SLAMM) 

(http://warrenpinnacle.com/prof/SLAMM/) attempt to serve as such a guide to understanding. 

For example, the U.S. Fish and Wildlife Service uses SLAMM for wildlife refuge management 

(http://www.fws.gov/slamm/). SLAMM simulates the dominant processes involved in wetland 

conversions and shoreline modifications resulting from long-term sea level rise. Map 

distributions of wetlands are predicted under conditions of accelerated sea level rise, and results 

are summarized in tabular and graphical form. SLAMM is free, user-friendly software that is

capable of being run by operators with basic GIS experience. SLAMM models potential impacts 

and the resulting changes in marsh-type distribution that could result due to user-identified level 

of predicted sea level rise. SLAMM attempts to account for six primary processes: inundation, 

erosion, overwash, saturation, accretion, and salinity.

Because SLAMM handles these processes simply, and users of SLAMM often will have little or 

no information to input for some of these variables, default values or incorrect values are used, 

resulting in inaccurate or incorrect model output. For instance, the spatial processing for 

saturation can result in linear ―streaks‖ of wetland migration onto uplands, often into unrealistic 

elevations, and in unnatural ways. 
107

Other GIS-based methods that use a similar elevations-based rule set for marsh change 

prediction are being developed but ignore erosion, overwash, saturation, and the more complex 

salinity models (see draft at:

http://www.csc.noaa.gov/beta/slr/assets/pdfs/Marsh_Migration_Methods.pdf). These methods

rely instead on predicted SLC and estimated accretion only. Salinity and saturation are handled 

as a function of elevation and tide ranges. Accretion is also handled in a simple way. Results 

are easier to understand and more consistent through time and in many different geographies, 

and there is less room for user-introduced error in this approach. 

Regardless of which modeling method is used for predicting marsh impacts from SLC, these 

model outputs have the potential to be used by coastal managers who do not fully understand the 

limitations and uncertainty of predictive models (i.e. there is always a high level of uncertainty in 

such models because natural systems are inherently unpredictable). Unfortunately, the general 

public often does not understand this uncertainty either, especially when only maps and charts of 

model outputs are shown. To ensure that the end user is properly informed, certain precautions 

must be taken when interpreting the results.

6.7 Interaction of Sea Level Rise, Episodic Flooding, and Extreme 

Events

One of the most significant consequences of sea level rise is the impact of increased water level 

on the height and extent of inundation during extreme events (e.g., hurricanes; nor‘easters and 

other extratropical storms; tsunamis). Projected water levels and impacts associated with SLC

and extreme events are generally considered independently. For example, the data and technical 

analyses used to produce the most commonly available, storm-related inundation maps (e.g., 

FEMA‘s probabilistic Flood Insurance Rate Maps, NOAA‘s SLOSH inundation maps, USACE‘s 

hurricane evacuation maps) consider only present-day water-level conditions or past conditions 

(e.g., historical tide gauge records, storm high water marks). SLC analyses and maps typically 

only consider changes relative to some fair-weather condition (e.g., projected change relative to 

MHW or other tidal datum). 

Increasingly, coastal officials and decision-makers, including emergency and floodplain 

managers, recognize the need for inundation products that integrate across all physical 

inundation processes. As described below, a range of techniques for assessing inundation from 

both SLC and extreme events are available, each with benefits and shortcomings depending on 

the intended use of the results. The science behind these techniques is rapidly evolving, so ―best 

practices‖ cannot be presented at this time. The approaches outlined in the remainder of this 

section, therefore, reflect the current state-of-practice. 

6.7.1 Statistical Approaches to Integrate SLC and Extreme Events

NOAA‘s Center for Operational Oceanographic Products and Services (CO-OPS) provides mean 

sea level (MSL) trend information at tide stations based on monthly data 

(http://www.tidesandcurrents.noaa.gov/sltrends). These trends can be applied to published bench
108

mark elevations for specified tide stations to infer future water level datum elevations and can be 

used as the basis for other statistical and probability analyses for risk assessment of extreme events. 

CO-OPS is also currently developing a Web-based tool to display exceedance probability 

statistics for each tide station with sufficient historical data. This product will become available 

in 2011 at http://tidesandcurrents.noaa.gov. CO-OPS uses the generalized extreme value (GEV) 

theory to determine the statistical probability that water levels will exceed a specific elevation 

every 1 year, every 2 years, every 10 years, and every 100 years. GEV theory describes the 

expected statistical distribution of the extreme values of a sequential process or set of 

observations. GEV analyses are done on monthly highest and lowest water level data at NOAA 

tide stations to determine exceedance probability statistics at the 99%, 50%, 10%, and 1% 

elevations. The highest monthly water levels are referenced to mean higher high water 

(MHHW) and the lowest monthly water levels are referenced to MLLW. Current MSL trends are 

subtracted from the monthly series. The resulting series consists of levels of the extreme events 

beyond the normal diurnal tide range, as if they had all occurred in the same year. This 

normalizes the sea level data to account for long-term sea level change. 

CO-OPS has also developed a frequency-and-duration-of-inundation tool, which analyzes past 

elevations of high tides and produces statistical profiles of the distribution of elevations and their 

associated duration of inundation above a user-defined elevation surface, such as MHW, NAVD 

88 or a local marsh surface elevation. These profiles can also be adjusted for various sea level 

change scenarios to estimated changes in future distributions. This tool is scheduled to become 

available on the CO-OPS website in 2011.

All of these statistical tools attempt to blend analyses of the historical observation record with 

future projections of sea level rise. Observation statistics are often used as baseline ―present 

condition‖ information. The value of statistics is also highly dependent upon the length of the 

observed series. For instance, computation of relative sea level trends requires record lengths 

longer than 30 years to obtain reasonable standard errors. Frequency and duration of inundation 

events are seasonally-dependent, and record lengths of less than one year need to be adjusted by 

comparison with nearby stations with longer record lengths.

6.7.2 Other Approaches to Integrate SLC and Extreme Events

Beyond these statistical tools, other methods have been developed to permit joint consideration 

of SLC with storm-related water levels. One of the earliest approaches developed is the adding a 

single value for SLC onto existing storm model output, and remapping the combined inundation 

extent. For example, see the work by The Nature Conservancy (in partnership with NOAA and 

others) that examines the impact of SLC on inundation hazards along Long Island, NY 

(http://www.csc.noaa.gov/digitalcoast/inundation/longisland.html). This approach is very 

effective at providing a coarse-scale assessment of combined flooding risk, which is useful for 

vulnerability assessments, strategic resource planning, and similar applications. That said, the 

simplifications inherent to this approach (i.e., considering SLC and storm water levels 

independently and combining later) does not capture the complex geomorphic (landform) 
109

changes along the coast that are expected to occur in response to the many physical (water- and 

sediment-transport) processes operating over a variety of spatial and temporal scales. 

More recently, storm-surge modelers and researchers have examined the utility of running a

given storm-surge model (including the underlying topo/bathy grid) with both present-day and 

future sea levels. For example, NOAA‘s study on the ecological impacts of SLC in North 

Carolina (http://www.cop.noaa.gov/stressors/climatechange/current/SLC/default.aspx) includes 

SLOSH storm surge modeling that incorporates projected SLC. While this approach does bring 

SLC-induced higher water levels into the surge model, the results still fail to reflect the complex 

geomorphic changes that are expected over the long-term in response to SLC and other processes 

operating over shorter timescales. In the end, this shortcoming may not be important in light of 

the intended application of the study results, but efforts to apply the specific results or the 

approach in other settings or toward solution of other problems may not be appropriate. 

Another emerging approach is to consider geomorphic change related to SLC within the storm 

surge model, specifically via changes to the model‘s topo/bathy grid. Such an approach is being 

applied in a SLC Risk Management Study sponsored by the Federal Emergency Management 

Agency and the State of North Carolina (http://www.ncsealevelrise.com/). In this study, a team 

of coastal engineers and geologists are attempting to identify changes in barrier island and 

mainland shoreline morphology, including number and size of tidal inlets along the Outer Banks, 

which can be incorporated into the grid for the ADvanced CIRCulation (ADCIRC) storm surge 

model. Again, even with the addition of geomorphic changes to the model, this approach still 

has shortcomings with respect to considering the full suite of water- and sediment-transport 

processes that operate over temporal and spatial scales beyond the storms and SLC that are being 

considered explicitly. That said, this project and others like it represent a significant step 

forward in the evolution of techniques to integrate inundation due to SLC and extreme events. 

6.7.3 Resources for Extreme Event Information

CO-OPS has published a series of data and technical reports of the station records during some 

of these events. Data reports, such as Hurricanes Katrina and Ike, provide the maximum 

observed water level and meteorological parameters recorded at each station, along with a brief 

storm synopsis. Technical reports supply a more detailed analysis of storm-induced water levels, 

tides, currents and meteorological conditions, in addition to historical storm comparisons. For 

example, the Hurricane Isabel report includes corrections for sea level rise on maximum 

observed water level recorded at select stations. Various CO-OPS reports are available at 

http://tidesandcurrents.noaa.gov/publications/.

In addition, CO-OPS provides the time, date, and value of the highest (and lowest) water levels 

recorded over a station‘s history, on the datums webpage of each station. For example, the 

highest recorded water level at the Grand Isle station was due to Hurricane Katrina 

(http://www.tidesandcurrents.noaa.gov/data_menu.shtml?stn=8761724%20Grand%20Isle,%20L

A&type=Datums). These records are becoming increasingly more important, as CO-OPS 

stations that once would have been damaged or have malfunctioned at the peak of the storm are 
110

now routinely capturing the complete oceanographic and meteorological records during a storm. 

All CO-OPS stations can be found at www.tidesandcurrents.noaa.gov.

Tropical storm surge forecasts and recorded water level observations for historical storms have 

been compiled (where available) within the National Weather Service (NWS) Sea Lake and 

Overland Surges from Hurricanes (SLOSH) model. SLOSH is a computerized model run by the 

National Hurricane Center (NHC) to estimate storm surge heights and winds resulting from 

historical, hypothetical, or predicted hurricanes. More information on SLOSH can be found at

http://slosh.nws.noaa.gov/sloshPub/.

The NHC‘s Tropical Cyclone Reports contain comprehensive information on each tropical 

cyclone, including synoptic history, meteorological statistics, casualties and damages, and the 

post-analysis best track (six-hourly positions and intensities). The NHC reports are located in its 

webpage archives found at http://www.nhc.noaa.gov/pastall.shtml. Finally, post-storm surveys 

are often carried out by FEMA, NHC, and other government agencies after an unusually 

damaging event in order to collect and measure the high water marks on the inside and outside of 

structures to characterize the spatial variability of the peak water levels. 

6.8 Considerations for Sea Level Change Visualization

A wide array of techniques and tools are available to take the results from an SLC project and 

present (or visualize) them. Visualizations include maps, computer animations, or other products 

that graphically depict the technical results, whether they are anticipated inundation areas, 

inundation depths, expected impacts on the built or natural environment, or other information. 

This following section provides information about developing the most common type of 

visualizations (principally maps), as well as guidance for interpreting the source data and 

understanding the uncertainty inherent to the mapping process. 

6.8.1 How to Create SLC Visualizations

With prepared digital elevation model (DEM) and water-level information, geographic 

information system (GIS) processes can be used to create mapping layers that represent 

inundation extent and depth. This includes: 

Single Value Surfaces (simple, ―flood the bathtub‖ approach)

1. Use GIS tools to create inundation depth rasters.

2. Use GIS tools to convert depth rasters to polygons representing inundation extent only.

Modeled Surfaces

1. Use GIS tools to extract points from gridded model output.

2. Use GIS tools to create a water surface by interpolating points.

3. Use GIS tools to subtract the DEM from the water surface to create the inundation depth 

raster.
111

4. Use GIS tools to convert depth rasters to polygons representing inundation extent only.

The NOAA Coastal Services Center‘s Coastal Inundation Mapping Guidebook

(http://www.csc.noaa.gov/digitalcoast/inundation/_pdf/guidebook.pdf) , gives specific examples 

of how to perform both of these analyses.

6.8.2 How to Interpret SLC Visualizations

When interpreting and using SLC impact maps, it is important to consider various aspects or 

caveats about the data in order to understand what has or has not been considered in their 

creation. The following issues and solutions represent a sample of those that should be 

considered when evaluating this and other SLC impact data.

Hydrology. SLC impact maps may not completely capture the area‘s hydrology, such as 

canals, ditches, storm water infrastructure, hardened shoreline, and/or dikes. Data may 

not take into account the amount of freshwater inputs, or how such upstream flows might 

be affected in the future, such as impacts to these water resources from change 

precipitation or human uses. All impacts may be based on elevation values and relative 

location within the tidal range of the area. This is true even if these areas are not directly 

connected to any other wetland or water feature.

Erosion and Storms. SLC maps may not consider several natural processes, such as 

erosion and the impacts of coastal storm. These factors can have significant impact on 

location of shoreline and sediment dynamics. Local knowledge should be used to 

evaluate these data in light of these other potential impacts.

Accretion. Accretion can be accounted for, but such accretion does vary by region and is 

specific to local geography. Such variation is based on a number of factors, including 

plant health, location in tide range/frequency of flooding, distance and source of input 

sediments, etc. Users are responsible for selecting the appropriate values for these 

factors, given their individual interests and application.

Error. There is uncertainty (or potential error) in the elevation data, as well as the tidal 

correction and mapping process. The presentation of these predictions can include some 

representation of that uncertainty, by showing transition areas as a graduated color 

transition (instead of a hard line location). This transition zone can be based on the 

approximate RMSE associated with the data and mapping process.

Uncertainty. Uncertainty associated with the exact amount of sea level rise or the timing 

associated with each scenario may not be considered. It may be up to the users to select 

the scenario with which they feel most comfortable and evaluate how they will treat the 

predicted impacts and timing associated with each scenario. When looking at marsh 

impacts, it is important to not focus on the exact timing or location of class transitions, 

but rather to use the timeframe and areas highlighted as a guide to when and where such 

impacts have the potential to occur, and a gauge as to how severe they may be. 

Wetland Data. Wetland data portrayed as the initial condition in many maps can be 

derived from several sources. One such source is from NOAA‘s Coastal Change 

Analysis Program (C-CAP). C-CAP produces a nationally standardized database of land 
112

cover and land change information for the coastal regions of the U.S. Data used in this 

analysis reflect conditions as they existed when mapped in 2005 to 2006 timeframe. 

More information on C-CAP land cover data can be found at 

http://www.csc.noaa.gov/digitalcoast/data/ccapregional/. Different wetland source data 

can cause different results.

6.8.3 Quantify and Show Error in SLC Visualizations

Mapping inundation using elevations as the sole variable places a high dependency on the 

accuracy of the elevation data. This type of mapping has only two variables, the water height 

and the ground elevation. More complex hydraulic and geomorphic models are also used to 

depict inundation and contain additional variables. These models have their own error budgets, 

which can be complex depending on model assumptions. This section focuses mainly on a

simple elevations analysis and how its associated errors affect the resulting inundation maps 

A simple, elevation-based analysis uses a defined water elevation overlaid on the topography 

(elevation). The water surface grid elevations, the first source of uncertainty, has variable error 

depending on the vertical datum used, area extents, and, most importantly, location. The simplest 

datum to use is the North American Vertical Datum of 1988 (NAVD 88), although this choice can 

be an issue when dealing with coastal inundation that is inherently tied to local tidal variability. 

Use of tidal datums will help adjust elevations to a uniform tidal stage (e.g., MHHW) across an 

area, but they create some uncertainty because tidal elevations are not constant, and there are only 

a limited number of gauge stations from which to interpolate. For example, MHHW at Station X

is 3 ft NAVD 88, while 15 miles away at Station Y, it is 3.5 ft NAVD 88. Between these two 

stations, there can be additional differences, which depend on geometry and location. VDatum is 

one tool that helps provide the tidal values for an area and is available in many locations 

(http://vdatum.noaa.gov/). VDatum converts elevation values between NAVD 88 and tidal values

but has a level of error on the order of 5 cm to 20 cm, depending on location in the U.S. 

(http://vdatum.noaa.gov/docs/est_uncertainties.html).

The second, and potentially higher and spatially variable, source of uncertainty with a modified 

single value surface model is the elevation data. Digital Elevation Models (DEMs), derived from 

lidar data, are used in most inundation mapping applications. Lidar is among the most accurate 

of the elevation remote sensing techniques, but the data from lidar can have limitations in certain 

land covers. In addition, not all lidar data are collected to the same accuracy standards; the 

vertical accuracy of the lidar can vary from 5 cm (RMSE) to more than 30 cm (RMSE), even 

within any one collection area. To quantify this variability, accuracy assessments are often 

performed with lidar collections. The results of the accuracy assessments help to document the 

errors and the statistical properties of the errors. The process of determining these values and the 

tests that are run are not covered in this text, but additional information on accuracy assessments 

can be found at http://www.csc.noaa.gov/digitalcoast/data/coastallidar/What_is_Lidar.pdf. For 

inundation mapping, the most important result of the accuracy assessments is the vertical root 

mean square error (RMSE) that depicts the accuracy of the data. 
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The varying level of elevation surface water level accuracy affects the accuracy of inundation 

mapping. There are several techniques used to depict this uncertainty. A technique used by the 

USGS details the linear error of the  ̳inundation extent‘ (i.e., the line depicting the extent of 

inundation) based on the 95% confidence level of the elevation data (Gesch, 2009). In its 

simplest form, the 95% accuracy value is added to the mapped inundation extent to depict 

additional areas above the mapped area that may be flooded (figure 2.15). This technique is used 

to represent data above the mapped area of inundation, although it could also be used to show 

areas below the mapped area. This technique does not include water surface uncertainties.

Gesch (2009) (CCSP 2009) illustrates how to map this uncertainty and shows a comparison of 

the uncertainty in the 1 arc-second NED elevation data and the 1/9 arc-second NED elevation

data (based on lidar) in eastern North Carolina.

New Techniques

The techniques used to generate  ̳areas of high uncertainty‘ in the NOAA Coastal Services 

Center‘s Sea Level Rise and Coastal Flooding Impacts viewer (see draft at:

http://www.csc.noaa.gov/beta/slr/assets/pdfs/Elevation_Mapping_Confidence_Methods.pdf) are 

similar in principle to that used by the USGS. The major differences are in the use of an 80% 

confidence level instead of a 95% confidence level, use of a cumulative percentage, and mapping 

this interval both above and below the inundation extent. Water level surface inaccuracies are 

also included; for many parts of the U.S. where VDatum coverage exists, the standard deviation 

of the water level error has been documented (http://vdatum.noaa.gov/about/availability.html).

In areas without VDatum, the errors may be greater and may have to be estimated. For more 

information on mapping uncertainty refer to this document (pending posting on the NOAA 

Digital Coast website (http://www.csc.noaa.gov/digitalcoast/).

References Cited:

Byrnes, M., Crowell, M., and Fowler, C., (eds), 2003. Shoreline Mapping and Change Analysis: 

Technical Considerations and Management Implications. Journal of Coastal Research, 

Special Issue no. 38. 

Maune, D.F., 2007. Digital Elevation Model Technologies and Applications: The DEM Users 

Manual, 2nd Edition. American Society for Photogrammetry and Remote Sensing. 

Bethesda, MD.

Gesch, D.B., B.T. Gutierrez, and S.K. Gill, 2009. Coastal elevations. In: Coastal Sensitivity to 

Sea level Rise: A Focus on the Mid-Atlantic Region. A report by the U.S. Climate 

Change Science Program and the Subcommittee on Global Change Research, U.S. 

Environmental Protection Agency, Washington DC, pp. 25-42.

Gill, S.K., Wright, R., Titus, J.G., Kafalenos, R., and Wright, K., 2009. Population, land use, 

and Infrastructure. In: Coastal Sensitivity to Sea level Rise: A Focus on the Mid-Atlantic 

Region. A report by the U.S. Climate Change Science Program and the Subcommittee on 
114

Global Change Research, U.S. Environmental Protection Agency, Washington DC, 

pp105-116.

Gutierrez, B.T., Williams, S.J., and Thieler, E.R., 2007. Potential for Shoreline Changes Die to 

Sea level Rise along the U.S. Mid-Atlantic Region. Open file report 2007-1278. U.S. 

Geological Survey, Reston, VA, 26 pp. 

Honeycutt, M.G., Crowell, M., and Hatheway, D., 1999. Coastal erosion hazards study: Phase 

one mapping: Journal of Coastal Research, Special Issue no. 28, p. 10-20. 

Intergovernmental Panel on Climate Change (IPCC), 2001. Climate Change 2001: The Scientific 

Basis. Contribution of Working Group I to the Third Assessment Report of the 

Intergovernmental Panel on Climate Change [Houghton, J.T., Ding, Y., Griggs, D.J., 

Noguer, M., van der Linder, P.J., Dai, X., Maskell, K., Miller, H.L. (eds.)]. Cambridge 

University Press, Cambridge, UK, and New York, 881 pp.

Johnson, Z. et al, 2006: Worcester County Sea Level Rise Inundation Model, Technical Report, 

Maryland Department of Natural Resources, DNR Publication No. 14-982006-166, 15pp

Thieler, E.R., E.A. Himmelstoss, J.L. Zichichi, and T.L. Miller. 2005. Digital Shoreline Analysis 

System (DSAS) Version 3.0: An ArcGIS Extension for Calculating Shoreline Change.

Open-File Report 2005-1304. U.S. Geological Survey. Accessed April 4, 2008, at 

http://pubs.usgs.gov/of/2005/1304/.

U.S. Army Corps of Engineers (USACE), 2009. Water Resource Policies and Authorities, 

Incorporating Sea level Change Considerations in Civil Works Programs, EC 1165-2-

211, Expires July 2011, CECW-CE Circular No. 1165-2-211, Department of the Army, 

U.S. Army Corps of Engineers, Washington, DC.
115

Chapter 7.0 Case Studies

7.1 California IOCM

The California Seafloor Mapping Program (CSMP) is a cooperative program to create a 

comprehensive coastal/marine geologic and habitat base map series for all of California‘s state 

waters. The Ocean Protection Council authorized funds to establish the CSMP in 2007 (OPC, 

2007) and assembled a team of experts from state and Federal agencies, academia, and private 

industry to develop the best approach to mapping and classifying estuarine and marine geologic 

habitats, while also updating all nautical charts. Initiated in 2008, the CSMP is collecting 

bathymetry (underwater topography) and backscatter data (providing insight into the geologic 

makeup of the seafloor) that will be turned into habitat and geologic base maps for all of 

California‘s state waters mean high water (MHW) line out to three nautical miles). Although the 

CSMP was originally developed to support the design and monitoring of marine reserves through 

the Marine Life Protection Act (CDFG, 2007), accurate statewide mapping of the seafloor will also: 

Improve climate change and ocean circulation models

Help evaluate the potential for ocean energy

Improve our understanding of ecosystem dynamics

Identify submerged faults and improve our understanding of tsunami potential

Enable more effective regulation of offshore development. 

Improve maritime safety

Improve our understanding of sediment transport and sand delivery

CSMP focus is to fund ship-based collection of high-resolution sonar data, which is the undersea 

equivalent of satellite remote sensing data in terrestrial mapping. The CSMP plan is based 

largely on earlier findings and recommendations of a State-wide Marine Mapping Planning 

Workshop (Kvitek et al., 2006: http://seafloor.csumb.edu/StrategicMappingWorkshop.htm) 

attended by coastal and marine managers and scientists. That workshop established geographic 

priorities for a coastal mapping project and identified the need for coverage of ―lands‖ from the 

strand line (MHHW) out to the 3 nm (5.6 km) State water limit. A subsequent USGS-hosted 

Coastal Map Development Workshop held in May 2007 helped define the CSMP comprehensive 

mapping approach. 

The CSMP is a cooperative partnership between state, Federal agencies, universities, and 

industry including:

California Coastal Conservancy (http://scc.ca.gov/internal-search/)

California Ocean Protection Council (http://opc.ca.gov)

California Department of Fish and Game (http://dfg.ca.gov)

California Geological Survey (http://www.conservation.ca.gov/CGS)

California State University, Monterey Bay - Seafloor Mapping Lab

(http://seafloor.csumb.edu)
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Moss Landing Marine Laboratories - Center for Habitat Studies

(http://habitat.mimi.calstate.edu)

Fugro Pelagos Inc. (http://www.fugro.com)

Pacific Gas and Electric (http://www.pge.com)

US Army Corps of Engineers (http://www.usace.army.mil)

Office of Coast Survey, NOAA (http://nauticalcharts.noaa.gov)

National Geophysical Data Center, NOAA (http://www.ngdc.noaa.gov)

National Marine Fisheries Service, NOAA (http://www.nmfs.noaa.gov)

National Marine Sanctuaries, NOAA (http://sanctuaries.noaa.gov)

Coastal Services Center, NOAA (http://www.csc.noaa.gov)

Minerals Management Service (http://www.mms.gov)

Western Coastal and Marine Geology Team, U.S. Geological Survey 

(http://walrus.wr.usgs.gov)

Data collected during this project reveal the sea floor offshore of the California coast in 

unprecedented detail and provide an ecosystem context for the effective management of this 

precious marine resource. The partnership with NOAA Office of Coast Survey will also result in 

updated digital nautical charts for all state waters. This website monitors the progress of the 

project and provides a background to the different data collection operations and mapping 

products. 

7.2 North Carolina Sea Level Project

A model to examine the impacts of long term sea level rise (SLR) has been implemented in the 

coastal North Carolina ecosystem http://www.nauticalcharts.noaa.gov/csdl/sealevelrise.html. 

This area, as a fragile system of barrier islands that protect an extensive but sensitive estuarine 

system, is particularly vulnerable to sea level change (SLC). The primary impact of SLC is on 

the hydrodynamic response of the system: circulation, tidal amplitude, and inundation patterns 

due to tides, winds, and storms that can all change in response to rising sea level. Rates of SLC

in the region are nearly 3 mm/year and are increasing; furthermore, inundation is tied to inlet 

conveyance, which can be modified by SLC. A two-dimensional hydrodynamic model is being 

used to simulate tidal response, regional synoptic wind events, and hurricane storm surge 

propagation to study changes due to SLC. Accurate simulation of inundation patterns is 

accomplished by high localized resolution in the coastal zone, continuous bathy/topo data, and 

an accurate wetting/drying algorithm. The model will be validated against observational data 

before modification of initial and boundary water levels to represent eustatic SLC. Shoreline 

migration can be dynamically computed from the model simulation output as a function of SLC. 

Finally, the hydrodynamic model will be coupled to submodels that characterize the ecological 

impact of SLC. This work comprises the Ecological Effects of Sea Level Rise project being led 

by NOS‘ National Centers for Coastal Ocean Science

(http://www.cop.noaa.gov/stressors/climatechange/current/slr/default.aspx).
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Model Development

The Coast Survey Development Lab (www.nauticalcharts.noaa.gov/csdl/welcome.htm) has 

implemented a hydrodynamic model of the Pamlico/Albemarle Sound of North Carolina. The 

two-dimensional version of the ADCIRC finite element model is used. A triangular grid was 

created to cover the entire domain and a water level time series was produced at each node in the 

grid. The semidiurnal tidal high water and low water marks were extracted from the modeled 

time series and used to calculate tidal datums (e.g., MHW, MLLW). The calculated tidal datums 

were compared to NOS water level station data at locations throughout the domain. The model 

results were adjusted to match the station data at those locations by spatially interpolating the 

error, so the corrected model results match the published NOS datum information in the region. 

The final tidal datum results were used to populate regularly-spaced grids that were created as a 

component to the VDatum software (http://vdatum.noaa.gov). The VDatum tool allows the 

transformation (http://www.nauticalcharts.noaa.gov/csdl/learn_datum.html) between ellipsoidal,

orthometric, and tidal datums. After the VDatum tool was created, it was used to transform the 

bathymetry data in the region to the North American Vertical Datum of 1988 (NAVD 88). The 

adjusted bathymetry was combined with topographic LIght Detection and Ranging (LIDAR) 

data (also referenced to NAVD 88) to create a seamless elevation field. A 6-meter horizontal 

resolution continuous bathymetric/topographic (bathy/topo) Digital Elevation Model (DEM) was 

constructed for accurate modeling of inundation (figure 7.1). The final DEM covers a subset of 

the VDatum region with the focus at Beaufort, NC. 

A Coastal Flooding Model (CFM) has been developed for the region by combining the tidal finite 

element hydrodynamic model with the continuous bathymetric and topographic elevation dataset. 

The CFM domain extends from 90 km offshore of the Outer Banks to the 15 m topographic 

contour and from northern Currituck Sound south to the New River. The CFM provides high 

resolution of coastal features down to 50 m. The CFM is relative to the NAVD 88 vertical datum 

and is populated with DEM elevations where available and other topographic and bathymetric data 

relative to NAVD 88 elsewhere to create a continuous bathy/topo elevation field. 
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Figure 7.1. Construction of the continuous bathy/topo DEM

Model Application

The CFM will model different scenarios of sea level rise, as well as inundation from high 

intensity storms that sweep through the region. First, changes in tidal harmonic constants can be 

calculated to simulate the effect of SLC. Second, changes in tidal datum shorelines can be 

calculated throughout the study area as demonstrated for the local test region. For example, the 

Figure 7.2 shows a nautical chart of the Morehead City and Beaufort, NC area. The dark black 

line shows the charted MHW shoreline. The red line shows the modeled present day MHW 

shoreline. The green line shows the modeled MHW shoreline with a 30 cm rise in sea level. 

Third, the impact of synoptic wind events can be examined by forcing the CFM with wind fields 

and validating with water level records. This is an important process in the North Carolina 

sounds, since much of the system is non-tidal, and the primary inundation events are winddriven, such as northeasters. The range and extent of inundation will be affected by SLC, which 

can be shown by model output. Fourth, the CFM can be used to study hurricane storm surge 

flooding of the NC system and the significance of changes in flooding with SLC. Fifth, the 

model output will provide input to other models being developed for the project. 

The CFM was developed to support the ecological effects of SLC study

(http://www.coop.noaa.gov/stressors/climatechange/current/slr/welcome.html). Using the CFM 

to study changes in inundation with SLC is not complete without considering ecological

processes, which include: erosion and deposition; marsh evolution; productivity of oyster reef, 

submerged aquatic vegetation, and benthic habitats; and anthropomorphic change. Therefore, 
119

the CFM is used to drive a suite of ecological submodels of these processes. These submodels 

and the CFM can provide iterative updates to each other to generate an overall prediction of the 

ecological effects of SLC. The impacts of anthropomorphic changes, such as shoreline 

hardening in response to SLC, are included in these ecological submodels and can provide 

coastal managers with key modeling and mapping tools to assess the risk of SLC to the NC 

coastal environment.

Figure 7.2. Predicted shoreline change due to sea level rise by use of the Coastal Flooding Model

7.3 East Coast Anomaly

Sea level variability occurs on many different time and space scales as highlighted in chapter. 2. 

Anomalies, both high and low in nature, occur as significant deviations from a defined 

climatology. Short-period anomalies that garner most attention are events in which water levels 

are significantly different than those predicted by local tidal dynamics. Along the U.S. East Coast, 

these events are usually noticed as surges from regional wind storms such as East Coast winter 

storms (nor‘easters) or as more localized impacts from tropical storm strikes. They can affect a 

coastal region over a period ranging from hours to several days. Post-storm surveys are often 

carried out by FEMA and other government agencies after an unusually damaging event to collect 

and measure the high water marks on the inside and outside of structures in order to characterize 

the spatial variability of the peak water levels. This procedure is especially important when 

NOAA tide stations become damaged or malfunction at the peak of the storm and do not capture 

the complete record of water levels. CO-OPS and its predecessors have published a series of 
120

relevant reports containing station records for particularly large events, which can be found at

http://tidesandcurrents.noaa.gov/publications/NOAA_NOS_Storm_Reports.pdf. 

Longer-period sea level anomalies, such as those normal along the U.S. Pacific Coast from El 

Niño Southern Oscillation (ENSO) forcing, appear as regional deviations from the long-term 

mean seasonal and annual cycles. Long-period anomalies are less noticed, but when positive,

can effectively raise the level on which the spring-neap tidal cycle and incident storm surges 

occur, increasing the potential for coastal flooding. An example of an important seasonal-scale 

sea level anomaly occurred during June and July 2009 when NOAA tide stations recorded 

sustained higher than normal levels along the U.S. East Coast. A detailed report can be found at 

http://tidesandcurrents.noaa.gov/publications/EastCoastSeaLevelAnomaly_2009.pdf. The event 

unfolded when near-peak levels in the latter half of June coincided with a perigean-spring tide,

which added to the observed sea level anomaly, produced minor coastal flooding, and caught the

attention of many coastal communities because of the absence of coastal storms that normally 

cause such anomalies. In terms of monthly mean sea levels, the event was not particularly

abnormal, as many locations have higher levels in the late-summer. The sea levels were

anomalous because of their unexpected geographic scope and timing, unaccounted for within the 

normal seasonal cycles of the winds and atmospheric pressure, ocean currents, and 

heating/cooling of coastal waters. The two probable mechanisms responsible for the anomaly 

was sustained northeasterly wind forcing north of Cape Hatteras, and a reduced transport of the 

Gulf Stream system south of Cape Hatteras that reduced the eastward-rising cross-current slope,

effectively raising coastal sea level. The June-July 2009 sea level anomaly is unique in that the 

winds were not at a multi-year high or transport at its low. But the coupled effect of the two 

forces created high sustained sea levels between North Carolina and New Jersey in the region of 

greatest overlap of the two forces.

7.4 Southeast Florida Sea Level Rise Mapping Consensus-Building 

Workshop

Southeast Florida Inundation Mapping Criteria Workshop - April 20–21, 2010

Southeast Florida is highly vulnerable to sea level rise (SLR) due to its peninsular nature and low

topography. Mapping different sea level rise inundation scenarios helps to identify areas at 

potential risk and aids in planning for a climate-resilient community. At the October 23, 2009 

Southeast Florida Regional Climate Leadership Summit, the local diversity in the data sources, 

methods, and criteria used to generate the currently available sea level change (SLC) inundation 

scenarios was highlighted as a concern and barrier to achieving regionally consistent 

vulnerability analyses. The National Oceanographic and Atmospheric Administration (NOAA) 

Coastal Services Center (CSC) worked closely with Broward County and the South Florida 

Water Management District (SFWMD) to coordinate a two-day technical workshop in April 

2010. The purpose of the workshop was to develop a unified set of methodologies and criteria 

for creating sea level inundation maps in the Southeast Florida region.
121

Workshop participants were Geographic Information System (GIS) practitioners representing 

Monroe, Miami-Dade, Broward, and Palm Beach, as well as the SFWMD, local universities and 

Federal agencies. Using information gained by surveying the participants in advance of the 

workshop, NOAA and its partners brought significant resources that helped in understanding 

inundation mapping methodologies currently in use, defining the local challenges, and working 

toward creating a consensus set of methods and criteria. Through a facilitated process, the group 

agreed to:

Use Florida Division of Emergency Management (FDEM) Light Detection And Ranging 

(lidar) elevation data where it is available

Recognize that the United States Geological Survey (USGS) SGS High Accuracy 

Elevation Dataset (HAED) is the best available dataset for its extent and scale in areas 

not covered by FDEM

Use regionally-consistent digital elevation models (DEMs) provided by SFWMD

Use 10-foot cell size DEMs at the county level for inundation/vulnerability analysis

Use larger cell-size DEMs as appropriate at regional level

Use Mean Higher High Water (MHHW) tidal datum relative to NAVD 88 as the starting 

elevation for inundation scenarios

Use the VDatum MHHW tidal grid surface in NAVD 88 to be provided by NOAA to 

ensure smooth transitions across county boundaries

Map SLR inundation on 1-ft increments

Map scenarios not to exceed a maximum of 6 ft of SLC

Calculate uncertainty (75/25) using NOAA's recommended methodology

Show inundation polygons as areas at or below MHHW for the given scenario, including

unconnected low-lying areas and without differentiation from hydrologically-connected 

areas

Use a minimum mapping unit of 1⁄2 acre

Explore disclaimer language for maps

Commitments following the workshop included:

Participants:

 Abide by the agreed upon methodologies for the generation of sea level rise inundation

modeling

 Attend future workshops to further refine vulnerability analysis methods for Southeast

Florida

USGS Representative:

 Report back to the group the status of HAED elevation information (See chapter 5)
122

SFWMD:

 Prepare and share DEMs using FDEM lidar for each of the four county areas, including 

any future updates

 Assist with planning and hosting the next inundation mapping workshop

NOAA:

 Supply methodology details to calculate uncertainty

 Generate the VDatum MHHW tidal surface for all coastal counties within SFWMD 

boundary

 Act as a resource in the future as the counties begin to develop local inundation maps

The workshop participants agreed to reconvene in 3-4 months to further discuss pending items 

and to outline the specific parameters to include in a regionally consistent vulnerability analysis. 

The project FTP site provides post workshop deliverables including additional contact 

information related to HAED elevation coverage, tidal datum reference material, sample 

disclaimer language, information regarding the VDATUM tidal surface, and notes on 

uncertainty.

A NOAA Digital Coast In Action article briefly explaining this workshop and the process that 

was used to come to a consensus regarding mapping methods can be found at: 

http://www.csc.noaa.gov/digitalcoast/action/slr-seflorida.html.
123

Chapter 8.0 Additional Resources

8.1 Organizations/Programs

NOAA Coastal Services Center:

http://www.csc.noaa.gov/

NOAA Office of Coast Survey:

http://www.nauticalcharts.noaa.gov/

NOAA Center for Operational Oceanographic Products and Services:

http://www.tidesandcurrents.noaa.gov/

National Geodetic Survey:

http://www.ngs.noaa.gov

USGS National Assessment of Coastal Change Hazards Program:

http://coastal.er.usgs.gov/national-assessment/

USGS Center for Lidar Information Coordination and Knowledge (CLICK):

http://lidar.cr.usgs.gov/

National Geophysical Data Center (NGDC):

http://www.ngdc.noaa.gov/

National Digital Elevation Program (NDEP):

http://www.ndep.gov/

Joint Airborne Lidar Bathymetry Technical Center of Expertise:

http://shoals.sam.usace.army.mil/

NOAA CSC Training:

http://www.csc.noaa.gov/bins/training.html

8.2 Publications

FEMA Guidelines and Specifications for Flood Hazard Mapping Partners:

http://www.fema.gov/plan/prevent/fhm/dl_cgs.shtm#volume1

Appendix D (Coastal Mapping):

Appendix A: Guidance for Aerial Mapping and Surveying

FEMA FAQ’s for Digital Flood Data:

http://www.fema.gov/plan/prevent/fhm/fq_main.shtm

Seaside, Oregon Tsunami Pilot Study:

http://pubs.usgs.gov/ds/2006/236/index.shtml

FEMA Coastal Construction Manual:

http://www.fema.gov/rebuild/mat/fema55.shtm

National Standard for Spatial Data Accuracy (NSSDA):

http://www.fgdc.gov/standards/standards_publications/

International Hydrographic Association Standards for Hydrographic Surveys:

http://www.iho.shom.fr/
124

NDEP Guidelines for Digital Elevation Data:

http://www.ndep.gov/

NOAA Coastal Services Center. “IfSAR Data: Notes and Considerations”.

Contact: Kirk Waters

NOAA, Office of Ocean and Coastal Resource Management, 2010. Adapting to 

Climate Change: A Planning Guide for State Coastal Managers.

http://coastalmanagement.noaa.gov/climate/docs/adaptationguide.pdf

NOAA Coastal Services Center, 2007. “Topographic and Bathymetric Data 

Considerations: Datums, Datum Conversion Techniques, and Data Integration.”.

Part II of A Roadmap to aSeamless Topobathy Surface. Technical Report No. 

NOAA/CSC/20718-PUB.

Contact: Kirk Waters

NOAA, 2007. “FAQ / State of the Science: Inundation”. Forthcoming.

Additional References:

Airborne Laser Hydrography, 1985: Gary Guenther, U.S. Department of Commerce, NOAA, 

National Ocean Service, Charting and Geodetic Services, 1985, 385pp.

Coastal Mapping Handbook, 1978: Melvin Y. Ellis, Editor. U.S Department of the Interior 

Geological Survey and U.S Department of Commerce National Ocean Survey Office of 

Coastal Zone Management, U.S Government Printing Office, 1978, 199pp.

Decomposition of Sea Level Variations, 1990. W.D. Sherer. An Approach, National Ocean 

Service, Oceanography Workshop, unpublished manuscript, 1990.

NOAA Hydrographic Manual, Fourth Edition, 1976 by Melvin J. Umbach, U.S. Department of 

Commerce, NOAA, National Ocean Survey, July 4, 1976, 400pp.

Manual of Photogrammetry, Fifth Edition, 2004, Editor J. Chris McGlone, American Society of 

Photogrammetry and Remote Sensing, 1151pp.

Manual of Photogrammetry, Fourth Edition, 1980, Editor Morris M. Thompson, American 

Society of Photogrammetry and Remote Sensing 1168pp.

Understanding Sea-level Rise and Variability, Edited by J.A. Church, P.L. Woodworth, T. 

Aarup, and W.S. Wilson, Wiley-Blackwell, 2010, 428pp.

Sea Surface Topography from Space, Proceedings, 1972, Editor John R. Apel, NOAA Technical 

Report ERL 228-AOML 7

Shore and Sea Boundaries, Volume One, 1962. 

http://www.nauticalcharts.noaa.gov/hsd/shalowitz.html

Shore and Sea Boundaries, Volume Two, 1964. 

http://www.nauticalcharts.noaa.gov/hsd/shalowitz.html
125

Shore and Sea Boundaries, Volume Three, 2000. 

http://www.nauticalcharts.noaa.gov/hsd/shalowitz.html

The American Practical Navigator, 1995. Originally by Nathaniel Bowditch, LL.D. Defense 

Mapping Agency Hydrographic/Topographic Center. Ch. 2. pp.63-163.

8.3 Data/Models

NOAA National Weather Service Probabilistic Hurricane Storm Surge Mapping:

www.weather.gov/mdl/psurge/

FEMA Coastal Recovery Maps:

http://www.fema.gov/hazard/flood/recoverydata/index.shtm

Sea, Lake and Overland Surges from Hurricanes (SLOSH) Model:

http://www.nhc.noaa.gov/HAW2/english/surge/slosh.shtml

Contact: Arthur Taylor*

Advanced Circulation (ADCIRC) Model: ADCIRC Development Group:

http://www.nd.edu/~adcirc/index.htm

Method of Splitting Tsunami (MOST) Model:

http://nctr.pmel.noaa.gov/model.html

USGS National Elevation Dataset:

http://ned.usgs.gov/

Global Land 1 Kilometer Base Elevation (GLOBE) Dataset:

www.ngdc.noaa.gov/mgg/topo/globe.html

National Ocean Service (NOS) Hydrographic Database:

www.ngdc.noaa.gov/mgg/bathymetry/hydro.html

NGDC Topo/Bathy Grids:

http://www.ngdc.noaa.gov/mgg/bathymetry/relief.html

Extratropical Storm Surge Forecasts:

http://www.nws.noaa.gov/mdl/etsurge/

USGS National Water Information System (NWIS):

http://waterdata.usgs.gov/nwis

NWS Flood Severity Inundation Mapping:

http://www.weather.gov/ahps/inundation.php

8.4 Software/Tools

HURREVAV Storm Surge Module:

www.hurrevac.com

Digital Coast Data Access Viewer (DAV):

http://csc-s-maps-q.csc.noaa.gov/dataviewer/viewer.html?keyword=lidar

North American Datum Conversion (NADCON) Tool:

http://www.ngs.noaa.gov/TOOLS/Nadcon/Nadcon.html
126

Vertical Conversion (VERTCON) Tool:

http://www.ngs.noaa.gov/TOOLS/Vertcon/vertcon.html

VERTCON PC Version:

http://www.ngs.noaa.gov/PC_PROD/VERTCON/

Vertical Datum Transformation (VDATUM) Tool:

http://vdatum.noaa.gov

Corpscon Conversion Tool:

http://crunch.tec.army.mil/software/corpscon/corpscon.html

Java Runtime Environment (requirement for VDATUM):

http://java.sun.com/

NOAA CSC Risk and Vulnerability assessment Tool (RVAT):

http://www.csc.noaa.gov/rvat/

NOAA CSC Community Vulnerability Assessment Tool (CVAT):

http://www.csc.noaa.gov/products/nchaz/startup.htm
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Chapter 9.0 Acronyms and Abbreviations

A

ADCIRC Advanced Circulation (model)

AHPS Advanced Hydrologic Prediction Service

ASFPM Association of State Floodplain Managers

ABFE Advisory Base Flood Elevation

B

BAG Bathymetry Attributed Grid

BFE Base Flood Elevation

C

CBN Cooperative Base Network

C-CAP Coastal Change Analysis Program

CCSP Climate Change Science Program

CFM Coastal Flooding Model

CHARTS Compact Hydrographic Airborne Rapid Total Survey

CLICK Center for Lidar Information Coordination and Knowledge

cm centimeter

CO-OPS Center for Operational Oceanographic Products and Services

CORS Continuously Operating Reference Stations

CSMP California Seafloor Mapping Program 

CUBE Combined Uncertainty and Bathymetric Estimator

D

DEM Digital Elevation Model

E

ENSO El Niño Southern Oscillation

F

FBN Federal Base Network

FDEM Florida Division of Emergency Management

FEMA Federal Emergency Management Agency

FGDC Federal Geographic Data Committee

FIS Flood Insurance Study

FIRM Flood Insurance Rate Map

G

GCP Ground Control Point

GEV Generalized Extreme Value

GFS Global Forecast System

GIA Glacial Isostatic Adjustment

GIS Geographic Information System

GPS Global Positioning System

GLOBE Global Land One-Kilometer Base Elevation (dataset)

GNSS Global Navigation Satellite System
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GOES Geostationary Operational Environmental Satellite

H

HAED High Accuracy Elevation Dataset

HES Hurricane Evacuation Study

HURREVAC Hurricane Evacuation (computer program)

HWL High Water Line

I

IDW Inverse Distance Weighting

IERS International Earth Rotation and Reference Frame Service

IfSAR Interferometric Synthetic Aperture Radar

IHO International Hydrographic Organization

IOC Intergovernmental Oceanographic Commission

IOCM Integrated Ocean and Coastal Mapping

IPCC Intergovernmental Panel on Climate Change

ITRF International Terrestrial Reference Frame

J

JALBTCX Joint Airborne Lidar Bathymetry Technical Center of Expertise

K

L

LDART Lidar Data Retrieval Tool

Lidar Light Detection and Ranging

M

m meter

MBES Multibeam Echosounder

MCU Maximum Cumulative Uncertainty

MDL Meteorological Development Lab

MEOW Maximum Envelope of Water

MHW Mean High Water

MHHW Mean Higher High Water 

MLW Mean Low Water

MLLW Mean Lower Low Water

mm millimeter

MOM Maximum of MEOW

MSL Mean Sea Level

N

NADCON North American Datum Conversion 

NCEP National Centers for Environmental Prediction

NTDE National Tidal Datum Epoch

NED National Elevation Dataset (USGS dataset)

NERRS National Estuarine Research Reserve

NESDIS National Environmental Satellite Data and Information Service

NFIP National Flood Insurance Program
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NGDC National Geophysical Data Center

NGS National Geodetic Survey

NHC National Hurricane Center

nm nautical mile

NMAS National Map Accuracy Standards

NOAA National Oceanic and Atmospheric Administration

NOS National Ocean Service

NRC National Research Council

NWS National Weather Service

NSRS National Spatial Reference System

NSSDA National Standard for Spatial Data Accuracy

NWIS National Water Information System

NWLON National Water Level Observation Network

NWLP National Water Level Program

O

OPUS Online User Positioning System

ORRI Orthorectified Radar Image

P

PDOP Position Dilution of Precision

PMEL Pacific Marine Environmental Laboratory

PSMSL Permanent Service for Mean Sea Level

Q

QC Quality Control

R

RMS Root Mean Square

RMSD Root Mean Square Difference

RMSE Root Mean Square Error

RVA Risk and Vulnerability Assessment

S

SD Standard Deviation

SET Surface Elevation Tables

SIFT Site-Specific Inundation Forecasting of Tsunamis

SFHA Special Flood Hazard Area

SLAMM Sea Level Rise Affecting Marsh Model

SLC Sea Level Change

SLOSH Sea, Lake, and Overland Surges from Hurricanes (model)

SONAR Sound Navigation and Ranging

SOW Scope of Work

T

TIN triangulated irregular network

THU Total Horizontal Uncertainty

TVU Total Vertical Uncertainty
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U

UDN User Densification Network

URL Universal Resource Locator

USACE United States Army Corps of Engineers

USGS United States Geological Survey

V

VDOP Vertical Dilution of Position

VLBI Very Long Baseline Interferometry