U.S. Department of the Interior

U.S. Geological Survey

Professional Paper 1815

Sea-Level Rise Modeling Handbook: Resource Guide for 

Coastal Land Managers, Engineers, and Scientists

Prepared in cooperation with the Department of the Interior Southeast Climate Science Center

Sea-Level Rise Modeling Handbook: 

Resource Guide for Coastal Land Managers, 

Engineers, and Scientists

By Thomas W. Doyle, Bogdan Chivoiu, and Nicholas M. Enwright

Prepared in cooperation with the Department of the Interior

Southeast Climate Science Center

Professional Paper 1815

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015

For more information on the USGS—the Federal source for science about the Earth, its natural and living 

resources, natural hazards, and the environment—visit http://www.usgs.gov or call 1–888–ASK–USGS.

For an overview of USGS information products, including maps, imagery, and publications, 

visit http://www.usgs.gov/pubprod/.

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

U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for 

coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p.,

http://dx.doi.org/10.3133/pp1815.

ISSN 2330-7102 (online)
iii

Acknowledgments

The authors are grateful to student interns Nathan Burks and Cory Groover for data management 

and Internet research in support of this effort. The authors are indebted to the many government 

and university colleagues who contributed invaluable assistance with resource material, data, 

analysis, and model development including Darren Johnson, Rick Putnam, Ken Miller, Lorraine 

Lisiecki, Michelle Kominz, Joseph Donoghue, Laurie Rounds, Gary Mitchum, John Tirpak, and 

Gerald McMahon. Federal scientists and engineers Eric Glisch (U.S. Army Corps of Engineers), 

Martha Segura (National Park Service), and John Tirpak and Kristen Kordecki (U.S. Fish and 

Wildlife Service) provided valuable technical reviews. 

The authors also thank Don DeAngelis and Michael Osland (U.S. Geological Survey) for providing 

valuable technical reviews.
iv
v

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Earth’s Hydrosphere.............................................................................................................................2

Factors, Rates, and Models of Sea-Level Change ...................................................................................3

Ancient Sea-Level Reconstructions..................................................................................................3

Sediment Stratigraphy Geology Models..................................................................................4

Glaciation and Sea-Level Cycles ..............................................................................................5

Holocene Sea-Level Rise ...........................................................................................................5

Contemporary Sea-Level Rates..........................................................................................................5

Relative Sea and Land Motion...................................................................................................5

Thermal Expansion of Seawater...............................................................................................9

Tide Gage Records and Relative Sea-Level Trends ...............................................................9

Satellite Altimetry and Eustatic Sea-Level Rise ...................................................................11

Future Sea-Level Rise Projections...................................................................................................13

Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability ............................................15

Sea-Level Rise Simulation and Inundation Models ......................................................................15

CoastCLIM Sea-Level Simulator .............................................................................................15

NOAA Inundation Frequency Analysis Program ..................................................................16

USGS Sea-Level Rise Rectification Program (SLRRP) ........................................................16

Temperature-Based Sea-Level Rise Model ..........................................................................18

Soil Salinity Models...................................................................................................................18

Expert Hydrodynamic Models for Engineering Applications .............................................18

Geographic Information System (GIS) Sea-Level Rise Mapping Tools .....................................22

OzCoasts Sea-Level Rise Maps ..............................................................................................22

NOAA Digital Coast Sea-Level Viewer...................................................................................24

The Nature Conservancy (TNC) Coastal Resilience Decision-Support Framework ......24

University of Arizona Web Map Visualization Tool...............................................................24

USGS Sea-Level Rise Animation.............................................................................................24

Wetland Change Models ...................................................................................................................29

Barataria-Terrebonne Ecological Landscape Spatial Simulation (BTELSS)....................29

Sea Level Affecting Marshes Model (SLAMM)....................................................................29

Sea Level Over Proportional Elevation (SLOPE) Model.......................................................33

Surface Elevation and Shoreline Erosion Models.........................................................................33

Coastal Vulnerability Index (CVI).............................................................................................38

Marsh Substrate Geochronology Methods...........................................................................38

Saltmarsh Stratigraphy and Evolution Models .....................................................................38

Surface Elevation Tables (SETs)..............................................................................................38

Tidal Channel Network Models (TIGER).................................................................................40

Niche-Based Species Distribution Models....................................................................................40
vi

Leaf to Landscape (L2L) Ecosystem Models..................................................................................43

Coupled Saltmarsh Biogeochemical and Demographic Model.........................................43

Hammock-Mangrove Vadose Zone Model............................................................................43

SELVA-MANGRO Model ...........................................................................................................47

Spatial Relative Elevation Model (SREM)..............................................................................52

WETLANDS.................................................................................................................................52

Summary........................................................................................................................................................53

References Cited..........................................................................................................................................54

Appendixes ...................................................................................................................................................59

1. Effect of Steric Temperature Functions on Ocean Volume ..........................................................61

2. Published Sea-Level Trends for U.S. Tide Gages...........................................................................63

3. Elevation and Vegetation Data Sources for Sea-Level Rise Modeling......................................69

4. Recommended Reading .....................................................................................................................75

Figures

1. Schematic showing the forms, reservoirs, and residency times

of the Earth’s hydrosphere including atmospheric moisture, oceans,

rivers, lakes, groundwater, subterranean aquifers, polar icecaps,

and saturated soil .........................................................................................................................2

2. Graph showing sea-level reconstruction from sediment stratigraphy

dating over geological time.........................................................................................................4

3. Graphs showing sea-level reconstruction of late Quaternary,

illustrating glaciation cycles determined from oxygen-18 temperature

correlated with sea level, Vostok ice core age and thickness, and

carbon dioxide gas concentrations...........................................................................................6

4. Graph showing Holocene and Pleistocene sea-level reconstruction

from composite studies and fossil dates illustrating a general sea-level curve

that is more or less universally accepted and applicable worldwide.................................8

5. Graph showing the change in height for the same water mass

at different temperatures and salinities....................................................................................9

6. Graphs showing relative sea-level records from select tide gages

from the more stable eastern Gulf of Mexico coast in Florida and

from river deltas of the western Gulf Coast in Louisiana and Texas

illustrating land motion differences.........................................................................................10

7. Maps showing range of relative sea-level rates for select tide gages

of the United States, comparable with satellite altimetry period, illustrating

both high and low sea-level change for the same tidal epoch...........................................12

8. Graphs showing slopes of mean monthly observations and

residual differences from select Gulf of Mexico tide gages and

corresponding satellite sea-surface heights for the tidal epoch 1994–2012....................14

9. Historical, observed, and possible future amounts of global

sea-level rise from 1800 to 2100 ...............................................................................................16

10. Screenshots showing Sea-Level Rise Rectification Program graphic

user interface including user options for selecting the tide station and

the GCM model parameters, projected sea-level curve, and flood

inundation chronograph ............................................................................................................19
vii

11. Screenshots showing National Oceanic and Atmospheric

Administration Digital Coast Sea-Level Viewer graphic user interface

for Galveston Bay, Texas ...........................................................................................................25

12. Screenshot showing University of Arizona Web Map Visualization Tool

graphic user interface ...............................................................................................................27

13. Screenshot showing U.S. Geological Survey Sea-Level Rise

Animation graphic user interface ............................................................................................28

14. Screenshots showing Barataria-Terrebonne Ecosystems

Landscape Spatial Simulation model domain and sea-level change map

for the Barataria and Terrebonne Basins, Louisiana............................................................31

15. Screenshot showing Sea Level Affecting Marshes Model 6

display viewer for the St. Marks National Wildlife Refuge application.............................32

16. Screenshots showing Sea Level Over Proportional Elevation model

data layers for the northern Gulf of Mexico coast regional application...........................34

17. Screenshot showing Coastal Vulnerability Index Bayesian Network

application for the U.S. Atlantic Coast....................................................................................39

18. Schematics showing surface elevation tables......................................................................41

19. Map showing Taxodium distichum distribution in relation to

elevation of ancient sea level dating back to highstand shorelines

of Late Cretaceous epoch .........................................................................................................42

20. Map showing Eocene distribution limits of mangrove.........................................................44

21. Maps showing Climate-Envelope Mangrove Model predictions

of mangrove forest presence and relative abundance under future

climate scenarios .......................................................................................................................45

22. Screenshots showing SELVA-MANGRO model application in

south Florida ................................................................................................................................48

23. Graph showing WETLANDS species distribution by elevation ..........................................52

Tables

1. Sea-level rise trends for select Gulf of Mexico satellite and

tide gage data and the residual land motion for tidal epoch 1994–2012 ...........................13

2. Sea-level rise trends by tidal epoch during the period of record,

1918–2012, for select Gulf of Mexico tide gages ...................................................................15

3. Attributes of select sea-level rise simulation and inundation models ..............................17

4. Attributes of select geographic information system sea-level

rise mapping tools ......................................................................................................................23

5. Attributes of select wetland change models.........................................................................30

6. Attributes of select leaf to landscape ecosystem models ..................................................46
viii

Conversion Factors

Inch/Pound to International System of Units

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

yard (yd) 0.9144 meter (m)

Area

acre 4,047 square meter (m2)

acre 0.4047 hectare (ha)

acre 0.004047 square kilometer (km2)

square mile (mi2) 259.0 hectare (ha)

square mile (mi2) 2.590 square kilometer (km2) 

Volume

gallon (gal) 3.785 liter (L) 

gallon (gal) 0.003785 cubic meter (m3) 

Flow rate

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

inch per year (in/yr) 25.4 millimeter per year (mm/yr)

mile per hour (mi/h) 1.609 kilometer per hour (km/h) 

Mass

ton per year (ton/yr) 0.9072 metric ton per year
ix

Conversion Factors—Continued

International System of Units to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd) 

Area

square meter (m2) 0.0002471 acre 

hectare (ha) 2.471 acre

square kilometer (km2) 247.1 acre

square meter (m2) 10.76 square foot (ft2) 

hectare (ha) 0.003861 square mile (mi2) 

square kilometer (km2) 0.3861 square mile (mi2)

Flow rate

millimeter per year (mm/yr) 0.03937 inch per year (in/yr) 

Density

kilogram per cubic meter (kg/m3) 0.06242 pound per cubic foot (lb/ft3) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C=(°F-32)/1.8

Datum

Vertical coordinate information is referenced to the North American Vertical Datum

of 1988 (NAVD 88).
x
Sea-Level Rise Modeling Handbook: Resource Guide for 

Coastal Land Managers, Engineers, and Scientists

By Thomas W. Doyle,1 Bogdan Chivoiu,2 and Nicholas M. Enwright1

1

U.S. Geological Survey.

2

University of Louisiana at Lafayette.

Abstract

Global sea level is rising and may accelerate with 

continued fossil fuel consumption from industrial and 

population growth. In 2012, the U.S. Geological Survey 

conducted more than 30 training and feedback sessions with 

Federal, State, and nongovernmental organization (NGO) 

coastal managers and planners across the northern Gulf of 

Mexico coast to evaluate user needs, potential benefits, current 

scientific understanding, and utilization of resource aids 

and modeling tools focused on sea-level rise. In response to 

the findings from the sessions, this sea-level rise modeling 

handbook has been designed as a guide to the science and 

simulation models for understanding the dynamics and 

impacts of sea-level rise on coastal ecosystems. The review 

herein of decision-support tools and predictive models was 

compiled from the training sessions, from online research, and 

from publications. The purpose of this guide is to describe 

and categorize the suite of data, methods, and models and 

their design, structure, and application for hindcasting and 

forecasting the potential impacts of sea-level rise in coastal 

ecosystems. The data and models cover a broad spectrum of 

disciplines involving different designs and scales of spatial 

and temporal complexity for predicting environmental change 

and ecosystem response. These data and models have not 

heretofore been synthesized, nor have appraisals been made 

of their utility or limitations. Some models are demonstration 

tools for non-experts, whereas others require more expert 

capacity to apply for any given park, refuge, or regional 

application. A simplified tabular context has been developed 

to list and contrast a host of decision-support tools and models 

from the ecological, geological, and hydrological perspectives. 

Criteria were established to distinguish the source, scale, and 

quality of information input and geographic datasets; physical 

and biological constraints and relations; datum characteristics 

of water and land components; utility options for setting sealevel rise and climate change scenarios; and ease or difficulty 

of storing, displaying, or interpreting model output. Coastal 

land managers, engineers, and scientists can benefit from this 

synthesis of tools and models that have been developed for 

projecting causes and consequences of sea-level change on the 

landscape and seascape.

Introduction

One of the more direct and scientifically accepted effects 

of modern-day climate change is that of rising sea levels 

associated with a warming climate and melting of glaciers 

and polar ice sheets. Although the direction of sea level is 

upward and unequivocal, the causes, rate, and periodicity 

are under study and debate. Based on observations from a 

worldwide network of tide gages over many decades, eustatic 

sea level rose at least 1–2 millimeters per year (mm/yr) in 

the 20th century (Melillo and others, 2014). Within the last 

two decades, sea-surface heights, as determined from satellite 

altimetry, have confirmed a global eustatic rise exceeding 3 

mm/yr (Melillo and others, 2014). It is yet unclear whether 

this near doubling rate change in recent years is related to 

natural decadal variability of solar, orbital, or climate systems 

or is indicative of a longer term trend sped by human activity. 

Whether mostly a human or natural consequence, rising sea 

level will affect our coastal ecosystems and infrastructure 

through the consequences of coastal flooding if no action is 

taken. 

The purpose of this guide is to describe and categorize 

the suite of data, methods, and models and their design, 

structure, and application for hindcasting and forecasting 

the potential impacts of sea-level rise in coastal ecosystems. 

Various climate and coastal wetland models have been 

developed to address the interaction and impact of changing 

climate and land use from a sea-level rise perspective. Simple 

models using generic datasets and constructs of the behavior 

of natural systems may provide only limited confidence and 

certainty suitable for instructional or educational purposes, 

whereas more complex models require expert development 

and more site-specific parameterization and validation for 

aiding coastal planning and ecosystem management. In 
2 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

2012, the U.S. Geological Survey (USGS) conducted more 

than 30 training and feedback sessions with Federal, State, 

and nongovernmental organization (NGO) coastal managers 

and planners across the northern Gulf of Mexico coast to 

evaluate user needs, potential benefits, current scientific 

understanding, and utilization of resource aids and modeling 

tools focused on sea-level rise. In response to the findings 

of these sessions, this sea-level rise modeling handbook has 

been designed as a guide to the science and simulation models 

for understanding the dynamics and impacts of sea-level rise 

on coastal ecosystems. The handbook is organized into two 

major sections, the first describing factors, rates, and models 

of observed and projected sea-level change and the second 

categorizing the suite of simulation tools for forecasting 

potential impacts of sea-level rise on coastal ecosystems. First, 

we present the physical properties and forms of the water 

cycle of the Earth’s hydrosphere to constrain the discussion 

of how sea levels change in relation to the forces, data, and 

models that have been developed to reconstruct historical sea 

levels and to predict future sea levels.

Earth’s Hydrosphere

There is a finite supply of water on the Earth, of which 

ocean water and open seas make up the largest portion. Oceans 

cover nearly three-fourths of the Earth’s surface (361 million 

square kilometers [km2]) and hold 97 percent of the total water 

volume. The remaining 3 percent is freshwater that is mostly 

frozen water of different forms and permanency, referred to as 

the “cryosphere,” and includes snow cover, freshwater ice in 

lakes and rivers, sea ice (from saltwater), glaciers, ice sheets, 

and permafrost (frozen ground). The residency times and 

coupling of the different forms and sources of water vary with 

the seasons, decades, and millennia in relation to a warmer 

or cooler planet and geological periods (fig. 1). Ice depth 

and volume are greater in Antarctica (about 77 percent of the 

cryosphere), but more exposed areal extent and ice surface 

(about 23 percent of the cryosphere) lie in the Northern 

Hemisphere as snow, glaciers, frozen tundra, and polar ice.

Sea ice, snow cover, and freshwater ice fluctuate 

seasonally or over years between solid and liquid forms but 

affect sea level only marginally and temporarily by volume 

and cyclic behavior. A large or small block of floating sea 

ice does not affect sea level relative to equivalent weight 

displacement. Snow cover in the Northern Hemisphere 

fluctuates annually from highs of 46.5 million km2 in winter 

to lows of 3.8 million km2 in summer. Long-term observations 

and more current satellite imaging of snow cover over North 

America and Eurasia show little variability of areal cover 

change between years but more change with timing (for 

example, earlier melt) in recent decades. Freshwater frozen 

in glaciers, ice sheets, or ground ice has much longer melt 

periodicities, from 10 to 100,000 years or longer, and for 

deep polar ice the melt periodicity may exceed millions of 

years. Permafrost and seasonally frozen ground occupy the 

What causes the sea level to change?

Terrestrial water storage,

extraction of groundwater,

building of reservoirs,

changes in runoff, and

seepage into aquifers

100–100,000 years Subsidence in

river delta region,

land movements, and

tectonic displacements

100–100,000,000 years

Surface and deep ocean

circulation changes, storm surges

As the ocean warms,

the water expands

0.1–100 years (shallow)

10–10,000 years (deep)

Exchange of the water

stored on land by

glaciers and ice sheets

with ocean water

10–100 years (glaciers)

100–100,000 years (ice sheets)

Figure 1. Forms, reservoirs, and residency times of the Earth’s hydrosphere including atmospheric moisture (snow, rain, and clouds), 

oceans, rivers, lakes, groundwater, subterranean aquifers, polar icecaps, and saturated soil (Intergovernmental Panel on Climate 

Change, 2001).
Factors, Rates, and Models of Sea-Level Change 3

largest areal extent of any component in the cryosphere, with 

approximately 54 million km2. Permafrost thickness can 

extend 500 meters (m) below surface and varies by location, 

prevailing temperatures, and surficial factors of ground 

moisture content, vegetation cover, winter snow depth, and 

aspect. Thawing and freezing of permafrost, and advances and 

retreat of glaciers, have been monitored directly over decades 

and centuries, as well as on millennial time scales based on 

carbon dating of organic debris and tree ring analysis.

Deglaciation, or “icemelt,” accounts for most of the sealevel change and cycles of recent Earth history, which amount 

to about 120 m or more every 100,000 years. As recently 

as about 20,000 years ago, the Earth was undergoing the 

Last Glacial Maximum of covered ice across North America 

and Europe, during which sea level was approximately 

120 m lower than current levels and shorelines. In the last 

8,000 years, sea-level rise has slowed and reached an expected 

maximum extent, having risen 4 m above current levels and 

fallen 4 m below current levels multiple times. Glacier and 

ice sheet melting alters sea levels on decadal and centennial 

cycles, thereby representing variation that is of most concern 

and potential impact to coasts today. Sea level rises or falls in 

concert with glacial retreat and advances at rates that vary by 

hemisphere and region and are not necessarily uniform. Of 

most concern to climate change scientists is the potential rise 

of sea level of 1–60 m based on the available mass balance of 

freshwater in vulnerable glaciers and polar ice if warming and 

icemelt continue. 

A relatively minor storage of the Earth’s hydrosphere 

is on or under the land surface in the form of surface water 

and groundwater. While these water sources have little to do 

with historical changes in sea levels comparable to glaciers 

and other ice forms, they have become a valuable resource 

and commodity for drinking supply, industrial use, and 

agricultural irrigation. In addition to natural lakes and rivers, 

the area of surface water exposed to evaporation has increased 

with the advent of dams and reservoirs built for hydropower, 

recreation, and water supply. The associated effects of 

regulated streamflow have changed the storage, routing, 

timing, and quality of water resources. The withdrawal of 

surface water from rivers and of groundwater and other fluids 

from aquifers for societal needs and consumption has altered 

the timing and magnitude of flow to downstream ecosystems 

and organisms dependent on or adapted to premodern flood 

frequencies. Withdrawal of surface water and groundwater 

has also increased the rate of natural geological subsidence of 

deep sedimentary formations. The connection of sea level to 

freshwater withdrawal is most apparent in receiving estuaries 

that become more saline with less freshwater flow over time 

and in locations where land subsidence from subsurface fluid 

withdrawal accelerates saltwater intrusion.

The atmospheric portion of the water cycle in liquid form 

is precipitation. The interannual and multidecadal variabilities 

of precipitation are evident in wet and dry years or arid versus 

mesic landscapes. Gradients of hydrological, geological, 

and biological significance controlled by precipitation rates, 

forms, patterns, and periodicity exist globally and across 

single continents. The withdrawal of surface water from rivers 

and the creation of man-made reservoirs have effectively 

mimicked a climate change effect equivalent to drought, or 

reduced freshwater flow to coastal estuaries. Although climate 

models agree on increased warming in the future, there is less 

certainty or agreement concerning amounts of precipitation 

in the future. In general, future projections of climate models 

account for less precipitation on the basis of warmer air 

holding more water, delivered in punctuated rain events with 

greater intensity, and longer periods between such events. It 

is important to consider that any climate model projections of 

less precipitation across a given region should account for an 

increase elsewhere because of the mass balance of water in 

the hydrosphere, which only changes in form and distribution 

rather than in overall quantity.

Factors, Rates, and Models of 

Sea-Level Change 

In this section, we outline the various factors, processes, 

and evidence of sea-level change over the centuries and 

cycles of Earth’s climate system. First, we explore long-term 

changes in eustatic sea level from geological evidence and 

models that allow reconstructions of ancient sea level related 

to plate tectonics, sediment geology, and glaciation. We then 

progressively review the rates and models of sea-level change 

of the modern era of deglaciation and contemporary records of 

tide gage and satellite observation. Lastly, we address future 

projections of sea-level rise from predictive climate models to 

understand the probabilities and timing of accelerated sealevel rise of the next century.

Ancient Sea-Level Reconstructions

Sea-level changes over the short and long terms of 

geological periods have been recorded and show in many 

mutually corroborating ways that in deep sediments there 

is a sequenced coastal overlap profile, indicating a globally 

consistent and repetitive pattern of sea-level change over time 

and space. The various disciplines have developed suites of 

models for reconstructing sea-level history that are beneficial 

for forecasting climate change direction and impacts for 

natural and cultural resource assessments. In most cases, we 

are primarily concerned for societal and conservation planning 

to know how and when current shorelines will flood with 

increasing sea level under warming climate. A review of the 

factors and rates of sea-level change over the course of the 

Earth’s history expands the different types and disciplines of 

sea-level rise models.
4 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

Sediment Stratigraphy Geology Models

Deep beneath the ocean floor and land surface are the 

different layers of rock and sediment from terrigenous and 

marine sources that chronicle the interchange of terrestrial 

weathering of the exposed landform at low sea levels and 

aquatic life forms of a submerged condition during high sea 

levels. Together they tell the story of ancient sea levels before 

and since the age of dinosaurs. It was only decades ago that an 

understanding of the Earth’s crustal movement was proposed 

and that the process of plate tectonics, in which continents and 

oceans reside on moving plates crashing into or sliding under 

or spreading from each other, was accepted. Geologists have 

since refined their understanding and constructed tectonic 

models to reconstruct the placement, movement, emergence, 

and submergence of land and sea over geological time. It 

was discovered that the order of geological formations and 

sedimentary layers is shared worldwide by fossil association, 

thereby representing progressive and subsequent time 

periods of varying sea level. Interpretation of these layers of 

associated fossil organisms and their isotopic composition has 

further shown that the Earth has been warmer and cooler than 

at present, with and without glaciation as a contributing factor.

As the Earth has cycled through cooling and warming 

periods, so has sea level cycled between minimum limits or 

levels, called lowstands, and maximum extents, known as 

highstands. Highstands occur during warming and icemelt 

periods when sea levels reach a high plateau before eventually 

reversing direction and falling again to another lowstand 

during cooling periods. Different geological methods and 

models of stratigraphic sequencing have been developed to 

interpret this pattern and history of sea-level change; these 

methods and models agree on concept and direction but differ 

with respect to magnitude, timing, and accuracy. Stratigraphic 

sequencing of common layers of rock type, fossil associations, 

marine terraces, alternating depositional sources (marine and 

terrigenous), and magnetic patterning, among other types 

of evidence, provides an analog of land-sea highstand and 

lowstand extents and periods. What can be determined from 

the various models is the direction of change, which indicates 

the process or contributions of plate tectonics on sea-level 

variation. The long view of hundreds of millions of years is 

that the spread and movement of continental plates have been 

increasing ocean basin volume since the sea-level highstand of 

the Late Cretaceous period, during which time sea level was 

nearly 120 m higher than present day. Two different methods 

for interpreting the stratigraphy of deep sedimentary layers 

largely agree on the long view of increasing and decreasing 

ocean basin volume and continental plate dynamics (fig. 2). 

Plate tectonics constantly changes the number, shape, 

geographic position, and shoreline relations of continents. 

When the continents are more consolidated, the lower extent 

of shoreline contributes to decreasing sea level. When the 

continents are breaking up and adding shoreline margins 

and land area, sea level subsequently rises. Related rifts or 

spreading of the ocean floor below the waterline can cause 

-50

0

50

100

150

200

250

Million years (Ma)

Sea-level elevation relative to current mean sea level, in meters

250 200 150 100 50 0

Ocean

basin

volume Decreasing

Increasing

Current mean sea level

EXPLANATION

Era

Mesozoic

Cenozoic

Triassic (252–201 million years)

Period

Jurassic (201–145 million years)

Cretaceous (145–66 million years)

Paleogene (66–23 million years)

Neogene (23–2.6 million years)

101-point moving average

 (Haq and others, 1987, data)

10 mil yrs moving average

 (Kominz and others, 2008, data)

Datasets of sea level over geologic history

Figure 2. Sea-level reconstruction from sediment stratigraphy dating over geological time (modified from Haq and others, 1987; Kominz 

and others, 2008; used with permission).
Factors, Rates, and Models of Sea-Level Change 5

similar effects on sea-level rise or fall. When spreading rates 

for deep ocean ridges collectively decrease, basin volume 

effectively increases and results in sea-level fall. In the case 

where there is net ridge subduction, a major loss of ocean floor 

features will precipitate a drop in sea level. For hundreds of 

millions of years, tectonic forces have controlled ocean basin 

volumes and long-term sea-level change by accommodation or 

displacement. These stratigraphic models of sea-level change 

show that ocean basin volumes are still increasing after the last 

major highstand more than 90 million years ago. 

Glaciation and Sea-Level Cycles

Global sea level is controlled by geological and 

climatological processes of different scales and timing. In 

the last million years of Pleistocene history, glaciation acted 

as a prominent factor in cycles of sea-level rise and fall 

of 100 m or more over roughly 100,000-year cycles. The 

regulation of these highstand and lowstand sea levels is the 

result of greater or lesser glaciation that has been linked to 

the eccentricity of the Earth’s orbit over a 96,000-year cycle 

known as the Milankovitch theory. Other prominent cycles 

in sea-level fluctuation of 41,000 and 21,000 years are tied to 

the periodicity of the Earth’s tilt and the precession (wobble) 

of its axis, respectively. Acting in concert, these cycles 

interact in complicated phase relations of solar proximity 

and seasonal variance, both regionally and globally, driving 

differential icemelt and freeze, glacial advance and retreat, and 

consequentially, sea level. 

Reconstructions of ancient sea level have been generated 

from multiple data sources, including oxygen isotope 

ratios of Foraminifera (a single-celled protist) deposition 

in ocean sediments, permanent ice core thickness, gas and 

particulate concentrations, sediment stratigraphy and fossil 

grouping, carbon dating, and others. The different methods of 

reconstruction capture similar timing and magnitude of major 

cycle inflections and only vary on fine resolution aspects on 

the basis of robustness of technique. Multiple oxygen-18 (18O) 

reconstructions have been completed worldwide involving 

independent collections, analyses, and publications with 

repeatable results of sea levels, timing, and magnitude. These 

reconstructions demonstrate high rates of sea-level rise from 

lowstand to highstand and relatively low rates of sea-level fall 

over the longer period of the cycle with intermittent cycles 

of lesser magnitude (fig. 3). This pattern implies that orbital 

eccentricity and solar proximity drive a constant warmup that 

more effectively melts extended ice forms readily, whereas 

ice accumulation is a much slower and prolonged process. 

During glaciation periods of regular cyclic sea-level rise, the 

highest eustatic rates were more than an order of magnitude 

higher than current rates of sea-level rise (more than 20 mm/

yr). If the pattern of past highstand and lowstand periods of 

glaciation repeats as expected, notwithstanding overriding 

greenhouse effect projections, sea levels would be subject to a 

cyclic fall for the next 90,000 years. 

Holocene Sea-Level Rise

The recent history of sea-level rise begins less than 

20,000 years ago at the end of the Last Glacial Maximum, 

when a vast ice sheet covered the Northern Hemisphere and 

sea level was about 120 m below present. The evidence of this 

period includes various biological data (for example, tree rings, 

coral remnants and growth rates, marsh and mangrove peat 

age, fossil associations), datable carbon and oxygen isotopes, 

and structural features of past shorelines and marine terraces. 

The numerous biological indicators provide an array and 

history of sea-level effects and conditions reported by many 

authors for many species and sites and include evidence from 

above and below present-day shorelines and ocean surface. A 

composite of calculated dates and heights of sea level during 

the Holocene epoch taken from various techniques and oceans 

shows a general sea-level curve with a slope and shape that is 

universally accepted and applicable worldwide (fig. 4). Since 

the Last Glacial Maximum, sea level has risen at an average rate 

of nearly 8 mm/yr, comparable to some of the highest current 

rates, such as those in high subsidence zones of delta systems 

worldwide. This rate is not uniform over the Holocene epoch; 

the highest rates approached or exceeded 20 mm/yr. For the 

past thousand years or more, sea level has been at a relative 

standstill subject to further icemelt and thermosteric effects of 

an extended warming climate or an eventual cyclic fall in the 

next few thousand years.

Contemporary Sea-Level Rates

Relative Sea and Land Motion

Globally, sea level is relative spatially and temporally 

until referenced by some fixed point, commonly referred to as 

a “datum” or “plane of reference.” Datums are mathematical 

expressions or models of the Earth’s surface and sphere (geoid) 

for aiding navigation and referencing horizontal distance and 

direction and vertical depth or height. Many horizontal and 

vertical datums have been established to approximate flat and 

curved land surfaces; they are not necessarily universal but are 

generally defined by political and geographic boundaries of 

parochial significance. Datums are based on unique empirical 

and theoretical models of the Earth’s shape, size, and density. 

As such, a conversion (or rectification) is required to calculate 

and to compare distance and elevation differences between 

two or more points (or measurements) referenced to different 

datums. Referenced datums have known mathematical 

properties and are adopted into a geodetic control network. 

Over time, new or revised datums have been created for greater 

accuracy and utility. In the United States, horizontal datums, 

such as the North American Datum of 1927 (NAD 27) and the 

North American Datum of 1983 (NAD 83), and vertical datums, 

such as the National Geodetic Vertical Datum of 1929 (NGVD 

29) and the North American Vertical Datum of 1988 (NAVD 
6 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

2.9

3.4

3.9

4.4

4.9

-140

-120

-100

-80

-60

-40

-20

0

20

450 400 350 300 250 200 150 100 50 0

Oxygen isotope ratio, in parts per thousand

Sea-level elevation relative to current mean sea level, in meters

Pleistocene epoch

Holocene epoch

Sea level relative to oxygen isotope ratio

(Lisiecki and Raymo, 2005; Miller and others, 2011)

EXPLANATION

Thousands of years (Ka)

A

Current mean sea level

laf13-CSSC00-0572_fig03A

Figure 3. Sea-level reconstruction of late Quaternary, illustrating glaciation cycles determined from oxygen-18 (18O) temperature 

correlated with sea level, Vostok ice core age and thickness, and carbon dioxide (CO2) gas concentrations (modified from Petit and 

others, 1999; Lisiecki and Raymo, 2005; Imbrie and others, 2011; Miller and others, 2011; used with permission). A, Oxygen isotope ratio 

relative to sea level. B, Modeled ice volume and Vostok carbon dioxide data.
Factors, Rates, and Models of Sea-Level Change 7

Thousands of years (Ka)

3

2

1

0

-1

-2

-3

150

170

190

210

230

250

270

290

400

450 400 350 300 250 200 150 100 50 0

Highstand Highstand

Lowstand

B

Current carbon dioxide, April 2014

EXPLANATION

Pleistocene epoch

Holocene epoch

Quaternary period

Ice volume (Imbrie and others, 2011)

Vostok carbon dioxide, in parts per million

by volume (Petit and others, 1999)

Vostok carbon dioxide, in parts per million by volume

Ice volume, arbitrary scale

laf13-CSSC00-0572_fig03B

Figure 3. Sea-level reconstruction of late Quaternary, illustrating glaciation cycles determined from oxygen-18 (18O) temperature 

correlated with sea level, Vostok ice core age and thickness, and carbon dioxide (CO2) gas concentrations (modified from Petit and 

others, 1999; Lisiecki and Raymo, 2005; Imbrie and others, 2011; Miller and others, 2011; used with permission). A, Oxygen isotope ratio 

relative to sea level. B, Modeled ice volume and Vostok carbon dioxide data.—Continued
8 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

EXPLANATION

7-point floating average

Tahiti Corals

 (Bard and others, 1996)

New Guinea Corals

 (Edwards and others, 1993)

Barbados Corals

 (Fairbanks, 1989, 1990)

Red Sea Forams

 (Siddall and others, 2003)

Holocene epoch (0–11,000 years)

Pleistocene epoch (11,000–25,000 years)

Quaternary period

-130

-120

-110

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

25,000 20,000 15,000 10,000 5,000 0

Sea-level elevation relative to current mean sea level, in meters

Calibrated years (before present)

Datasets of reconstructed sea level

Current mean sea level

laf13-CSSC00-0572_fig4

Figure 4. Holocene and Pleistocene sea-level reconstruction from composite studies and fossil dates illustrating a general sea-level 

curve that is more or less universally accepted and applicable worldwide (modified from Balsillie and Donoghue, 2004; used with 

permission).

88), are commonly used and referenced to assign a location 

and elevation, respectively, that are not interchangeable but 

can be easily rectified to one or the other by use of conversion 

software. Satellite navigation systems are likewise based on 

geoid and ellipsoid models of space that can be expressed or 

converted to a specified datum. The distinction of datums is an 

important aspect of all of the different kinds of models of land 

and water that will be discussed hereafter in this handbook. 

Modeling sea-level rise is a challenge because both 

the land and sea are moving vertically and differentially by 

location, spatially and temporally. We easily envision how 

the ocean moves with the ebb and flow of tides, but it is more 

difficult to observe how the ground beneath our feet moves on 

a daily or seasonal basis or laterally in geological time. Land 

and sea motion together account for the relative sea-level rate 

at a given coastal location. While ocean volume (eustasy) is 

currently increasing and sea level is rising on a global scale, 

some coastal environments and communities are actually 

experiencing uplift of land surface (for example, in the Pacific 

Northwest) and drop in sea level both locally and regionally. 

To effectively model the process and impacts of sealevel rise, models of both land surface and sea surface are 

required. Land surface and sea floors are measured and 

referenced differently, but all are part of the same landform 

that is covered or exposed with rising and falling sea levels 

spatially and temporally. The use of geodetic networks and 

tidal benchmarks provides a basis for referencing height 

or depth in relation to the sea surface. The intersection of 

land and sea may appear obvious (such as a beach), but it 

is much more difficult to ascertain when, where, and how 

much flooding occurs in and across the intertidal zone with 

the changing tide. This intertidal zone is where tide gages 

are so important and why the data are so valuable. Tide gage 

observations are used to predict tide behavior, to establish 

tidal datums of the different tide phases, and to determine 

mean sea level for a period of record. Predictive tide models 

require this information to reliably predict the astronomical 

timing and height of low and high tides. Tidal datums are also 

important for boundary determinations of private and public 

land, navigational aids and infrastructure, coastal development 

ordinances, and other political and economic uses.

A number of factors control the types and range of tides 

for a given coastal reach, and these factors are important for 

sea-level rise models and applications. First and foremost, 

every coastal reach has a unique tidal behavior and magnitude 

that are taken into account in more robust sea-level rise 
Factors, Rates, and Models of Sea-Level Change 9

models. Although the general characteristics of sea and tide 

motion are shared for proximal coastal reaches and tide 

gages, the bathymetric shape of bay and harbor bottoms and 

the coupling to open seas can produce unique tidal dynamics 

locally. The nature, direction, and circulation of nearshore 

currents and the geological properties of the underlying 

sediments also interact to account for higher or lower sea 

levels and the need for more frequent tidal analysis and 

datum updates. 

Thermal Expansion of Seawater

From a global perspective, there are two main factors 

that control sea-level change, icemelt balance and ocean 

temperature, usually concomitant with respect to whether 

the global average or eustasy is relatively static or dynamic 

(rising or falling, slow or fast). Warmer climate periods of 

higher solar insolation and insulation melt the collective ice 

forms, glaciers, ice sheets, permafrost, and snow, effectively 

transferring a balance of icebound water in high latitudes and 

altitudes (that is, mountains) into the seas and oceans. At the 

same time, higher temperatures of air and surface waters over 

the Earth’s hydrosphere cause thermal expansion of ocean 

waters and thus higher sea level. Effectively, a kilogram of 

warmwater displaces more volume than would a kilogram 

of coldwater. The volume (or height) for a given water mass 

and density increases under warmer temperatures (fig. 5). To 

illustrate further, a 1-degree Celsius (°C) change in ocean 

temperature over a depth of 1,000 m equals an expansion 

effect of 23.03 centimeters (cm). Although the properties of 

99

100

101

102

103

104

35 0

Salinity, in parts per thousand

Height, in centimeters

Volume change at 34  ̊C

Volume change at 17  ̊C

Reference volume at 0  ̊C

EXPLANATION

Note: oC, degrees Celsius

Figure 5. Change in height (or volume) for the same water mass 

at different temperatures and salinities.

water have long been known with regard to density and mass 

relations of freshwater and seawater, a complex mathematical 

model is required to calculate and integrate the nonlinear 

response for the range of ocean conditions (app. 1).

Tide Gage Records and Relative Sea-Level Trends

Linear regression models are commonly applied to 

tide gage records to calculate relative sea-level trends based 

on long-term monthly or annual means of observed water 

level. A comparison of select tide gage records from the 

more geologically stable eastern Gulf Coast in Florida (Key 

West, Pensacola) with those from river deltas of the western 

Gulf Coast in Louisiana (Grand Isle) and Texas (Galveston) 

illustrates the extreme slope differences attributed to sustained 

low and high land subsidence, respectively (fig. 6). Tide gage 

records are readily available from two principal sources that 

can now be accessed via the Internet. The National Oceanic and 

Atmospheric Administration (NOAA) National Ocean Service 

maintains a user-friendly data portal of tide gage data and utility 

programs for public access, Tides Online (http://tidesonline.

noaa.gov). Permanent Service for Mean Sea Level (http://www.

psmsl.org) is another organization that maintains global tide 

gage records from contributors and cooperating government 

sources. Detailed descriptions of tide station history, datums, 

benchmarks, and semiprocessed data, such as long-term trend 

rates and seasonal variation, are often available.

Published sea-level trends are also readily available from 

government documents and peer-reviewed journal outlets but 

may be somewhat dated by time of publication. This report 

includes a record of published trends over the decades from 

NOAA tide specialists and sources (Flick and others, 2003; 

Hicks and Crosby, 1974; Lyles and others, 1988; Zervas 2001, 

2009) for the larger set of U.S. tide gages (app. 2). These 

trends are derived from available records for each gage, some 

older and more continuous than others. Although current or 

past rates of sea-level rise by reach or region can be used for 

hindcasting and forecasting purposes, the differences in rate 

change from period to period demonstrate the dynamic nature 

of tides and sea level that warrants careful choice of record 

gap interpolation and trending method. Missing records are 

especially problematic if not rectified or interpolated prior to 

applying any trending process or calculation. Often the missing 

record is related to extreme low and high events or tide gage 

function critical for a representative sea-level trend calculation 

and appropriate gage-to-gage comparisons.

The choice of start and end dates alone can influence the 

slope or trend calculation when using least squares regression 

or quadratic models for fitting purposes. Failing to balance 

the start and end dates by equal months is enough to introduce 

undue weight and error of a disproportional high or low 

seasonal effect on either end of the period of record. This 

issue is more evident for seasonally driven and high-latitude 

locations with greater tidal variance. Removal of seasonal and 

storm effects (for example, hurricanes, cyclones, tsunamis, and 

extratropical fronts) will inherently generate more conservative 
Data with the average seasonal cycle removed Linear mean sea-level trend Higher 95 percent confidence interval Lower 95 percent confidence interval EXPLANATION

Monthly mean sea level, in meters, relative to mean sea-level datum, 1983–2001

Year Year

laf13-CSSC00-0572_fig6

-0.60-0.45-0.30-0.15

0.000.150.300.450.60

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

A. Key West, Fla., 2.31 +/-0.15 month/year

-0.60-0.45-0.30-0.15

0.000.150.300.450.60

1900 1910 1920 1930 1940 1950 1960 1970 198

B. Pensacola, Fla., 2.19 +/-0.23 month/year

-0.90-0.75-0.60-0.45-0.30-0.15

0.000.150.30

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

C. Grand Isle, La., 9.07 +/- 0.47 month/year

-0.75-0.60-0.45-0.30-0.15

0.000.150.300.45

1900 1910 1920 1930 1940 1950 1960 1970 198

D. Galveston, Tex., 6.35+/- 0.25 month/year

Figure 6. Relative sea-level records from select tide gages from the more stable eastern Gulf of Mexico coast in Florida and from river deltas of the in Louisiana and Texas illustrating land motion differences (National Oceanic and Atmospheric Administration, 2014). A, Key West, Florida, tide gage rPensacola, Florida, tide gage record, 1922–2014. C, Grand Isle, Louisiana, tide gage record, 1946–2014. D, Galveston Pier 21, Texas, tide gage record, 1
Factors, Rates, and Models of Sea-Level Change 11

estimates of true slope or trend; this practice is rarely applied 

but is herein recommended. The position of these events in the 

record, earlier or later, and near the start and end dates, can 

add bias of inflated or deflated trending depending on overall 

record length. Longer records with fewer such events will 

tend to mediate the positional bias with nominal effect on rate 

change or magnitude.

There is currently much interest and debate regarding 

whether short-term satellite and long-term tide gage records 

can be used reliably for detecting accelerating and (or) 

decelerating sea-level change. Relatively few tide gages 

have been in operation with maintained continuous readings 

longer than a century. Short gaging records are problematic 

for trend analysis and accounting for confounding factors 

of eustatic and land motion influences that contribute to the 

actual (secular) change over time. The cycles of influence—

climate, subsidence, fault movement, and crustal reaction—

may be longer term than the records themselves, or there 

may be acute events not easily distinguished from seasonal 

variation or storm occurrences. There are, nevertheless, long 

and reliable gage records that provide trends of relevant 

influences (for example, subsidence) and expectations of 

future direction; however, to compare tide gage records with 

more recent satellite observations, it is necessary to compare 

complete records for each tide gage and for the same period 

of record. Considering the sea-level rate for the current tidal 

epoch (1994–2012) with appropriate overlap with satellite 

observations, we provide the range of relative sea-level rates 

for the different coastal reaches of the United States, thereby 

illustrating both high and low rates of sea-level rise and fall 

(fig. 7). 

Satellite Altimetry and Eustatic Sea-Level Rise

The advent of satellites with sensors of different types 

and levels of accuracy has provided means to measure 

changes in the Earth’s land cover, ocean levels, ice layers, and 

other surface characteristics useful for climate and sea-level 

assessment. A specific series of satellites similar to those 

of the Global Positioning System have been launched since 

1992 to monitor global sea-surface height change over time. 

These satellites orbit the Earth many times in a single day on 

tracks that vary to capture a record of sea level and variation 

across the world’s oceans and seas every 10 days. Although 

tide gages are useful for calibrating and validating satellite 

observations of sea-surface height, the gages are not evenly 

distributed across the globe and are subject to shoreline effects 

and tidal variance from differential bathymetric configuration, 

ocean currents, wave lapping, upland runoff, and land motion 

effects that confound determination of a definitive global 

mean sea level. Satellites provide a reference perspective 

that is independent of the Earth’s surface above or below the 

dynamic water line that is commonly referred to as “local 

mean sea level.” Because these satellites have gathered 

sea-surface height data since 1992, affording calibration 

checks and sustained observations for more than 20 years, 

the record length exceeds an entire tidal epoch (18.6 years), 

the astronomical cycle of gravitational tides of planetary and 

lunar orbits. 

A major question with sea-level data from satellites 

concerns how they compare to longstanding, reliable tide 

gage records. Because the first reports of satellite-derived 

sea-level trends were published in the mid-1990s, the 

higher sea-level rate, above 3 mm/yr, supposedly verified 

acceleration in global mean sea level above tide gage rates of 

geologically stable coasts at 1–2 mm/yr over the last century 

(Melillo and others, 2014). Rate comparisons for different 

periods of record, short and long, are problematic, however, 

and warrant an understanding of the differences in sea-level 

measurements and rate calculations from satellite and tide 

gage data. The first difference is that the gage and the satellite 

are observing the water surface but one oriented upward from 

beneath sea surface and the other oriented downward from 

a geocentric orbit, respectively, with different measurement 

intervals and accuracy. Tide gages typically record seasurface height at 6-minute (min) intervals at one location 

on the coast; these single locations are influenced by local 

factors of tidal behavior, upland runoff, and land subsidence 

or uplift. Satellites measure a series of single returns of an 

electromagnetic pulse to the sea surface and back, calculating 

an altimeter height of sea surface on the basis of signal time 

lapse. Both measures are then rectified to a common geoid 

or datum, which involves referencing the height of tidal 

benchmarks on land and satellite orbit in space. The geodetic 

rectification of observations from both tide gages and satellites 

is complex and beyond the scope of this handbook, but 

measurements should be expressed in the same datum and for 

the same period of record for direct comparison. 

Although it is challenging to match a single sea-surface 

observation from an instantaneous satellite reading and a 

simultaneous 6-min reading of an associated tide gage, an 

average of measurements over weeks, months, and years 

is comparable. Satellite observations are not as exact or 

repetitive for a given location as are measurements from a tide 

gage, but instead, satellite observations are area specific and 

offer a wide distribution of locations, which is advantageous 

for continuous global monitoring. Satellite observations 

near coastlines are often not available or usable because of 

the proportion of altimeter readings that are on land rather 

than open water; however, satellite observations of seasurface height provide a comprehensive regional and global 

measurement of absolute sea-level change that is independent 

of local land motion (subsidence or uplift). The difference in 

sea-level rate between satellite-derived records and tide gage 

records for the same observation period provides the degree 

and direction of land motion at that location. In effect, we 

therefore need both satellite observations and tide gage records 

for different purposes that are complementary.

For illustrative purposes, we analyzed satellite data from 

U.S. and European satellite services monitoring sea-surface 

heights for the Gulf of Mexico for comparison with select 

Gulf Coast tide gages. Water-level records for northern Gulf 
DE 

VT N

NY M

PA CT NJ MD VA 70° 50° 40° AZ CA ID NV OR UT WA 130° 125° 120° 45° 40° 35° 30° CANADA MEXICO UNITED STATES 180° 150° 120° 90° 60° 30° 30° 40° 0° AK160° 150° 140° 130° 180° 170° 50° 40° 30° Base from Esri St

Lambert Azimuth

AD D A C 

-

No missing da

in meters 

EX

AL AR FL GA KY LA MO MS NC OK SC TN TX 90° 80° 30° B B C 500 MILES 500 KILOMETERS 0 250 0 250 150 0 150 300 MILES 0 300 KILOMETERS 0 200 200 400 MILES 0 400 KILOMETERS 

0 

0 

laf13-CSSC00-0572_fig7

Figure 7. Range of relative sea-level rates for select tide gages of the United States, comparable with satellite altimetry period, illustrating both high (rise and fall) for the same tidal epoch (1994–2012). A, Pacific coast. B, Gulf of Mexico and southern Atlantic coasts. C, Northern Atlantic coast. D, Ala
Factors, Rates, and Models of Sea-Level Change 13

Coast tide stations can be used to contrast relative sealevel rates of high-subsidence zones in deltaic settings of 

Louisiana and Texas to that in geologically stable coasts of 

western Florida to the concurrent satellite-observed rate for 

the Gulf of Mexico. The rate of sea-level rise for the period 

1994–2012 for the select tide gages exceeded the eustatic 

sea-level rise trend of 3.32 mm/yr for satellite altimetry; both 

the U.S. (Topex/Poseidon/Jason) satellite dataset (National 

Aeronautics and Space Administration Goddard Space Flight 

Center [NASA GSFC], 2013) and the U.S. plus European 

satellite dataset (AVISO+ Satellite Altimetry Data, 2013) 

provided the same estimated rate of eustatic sea-level rise 

for this tidal epoch (1994–2012) (table 1). The residual of 

the difference between the higher sea-level rise rates for 

each gage and the eustatic sea-level rise rate for the satellite 

observations for this tidal epoch shows the degree to which 

the tide gage and landform are sinking (subsiding). In this 

case, the lower rates of subsidence for the eastern Gulf 

of Mexico gages at Key West and Pensacola demonstrate 

a fairly stable landform. In contrast, the higher rate of 

sea-level rise of the Grand Isle gage illustrates the rapid 

subsidence expected of a relatively new and thick deltaic 

platform undergoing compaction and perhaps affected by 

subsurface oil and gas withdrawal. The rate of subsidence 

along the Texas coast is comparatively moderate but shows 

signs of slowing from historical rates related to groundwater 

withdrawal and regulation. The spread and slope of monthly 

values and residual differences from these gages and satellite 

sea-surface heights for the tidal epoch 1994–2012 are 

shown in figure 8. In some cases, quadratic fits match the 

curvilinear trends and residuals better than linear regression 

models (fig. 8).

Table 1. Sea-level rise trends for select Gulf of Mexico satellite 

and tide gage data and the residual land motion for tidal epoch 

1994–2012. 

[SLR, sea-level rise; mm/yr, millimeters per year; -, not applicable.

Data sources: AVISO+, 2013; National Aeronautics and Space Administration 

Goddard Space Flight Center, 2013; National Oceanic and Atmospheric 

Administration, 2014]

Location

SLR trend 

(1994–2012) 

(mm/yr)

Residual 

land motion 

(mm/yr)

Satellite

U.S. Topex/Poseidon/Jason 3.32 -

AVISO dataset 3.32 -

Gage

Key West, Fla., gage 3.45 0.13

Pensacola, Fla., gage 3.47 0.14

Grand Isle, La., gage 7.34 4.01

Galveston, Tex., gage 4.85 1.52

To determine whether the rate of sea-level rise during the 

most recent tidal epoch (1994–2012) is higher than in earlier 

tidal epochs, it is necessary to calculate the rates of sea-level 

rise for each tidal epoch similarly for equal 19-year segments. 

Table 2 shows the different sea-level rise trends by tidal epoch 

for each tide gage over their respective periods of record. 

The Key West and Pensacola tide gages show similar rates, 

ranging from about 1 to nearly 4 mm/yr for preceding tidal 

epochs. Slope calculations for the Galveston tide gage show 

higher rates, up to 10 mm/yr, corresponding with periods of 

high groundwater withdrawal associated with high industrial 

and population growth. Groundwater regulation and aquifer 

monitoring have reduced the effective withdrawal rates and 

related subsidence over the past few decades (Kasmarek 

and others, 2014). Although it is acceptable to compare 

rates of sea-level change over periods no less than 19 years, 

considering the length and effect of an astronomical cycle, it is 

best to calculate trends on even longer time periods or as long 

as possible for use in sea-level rise modeling applications. 

Future Sea-Level Rise Projections

The IPCC has relied on general circulation models 

(GCMs) as the most advanced tools available for simulating 

the response of the Earth-climate system to increasing 

greenhouse gas concentrations. These models represent 

advanced knowledge of the coupled hydrological processes in 

the atmosphere, ocean, and land surface to provide projections 

of future climate change. There are dozens of GCMs from 

different research laboratories, universities, and government 

agencies worldwide. These GCMs similarly simulate the 

Earth-climate dynamics and processes but differ mostly in 

spatial and temporal resolution. The more complex GCMs 

depict the climate system by using a three-dimensional 

grid over the Earth, typically having a horizontal resolution 

between 250 and 600 kilometers (km), 10–20 vertical layers 

in the atmosphere, and sometimes as many as 30 layers in the 

oceans. Model-to-model differences translate into differences 

in the sensitivity to changes in greenhouse gas emission 

scenarios and in their predicted results. 

Predicting future sea-level change from GCM simulations 

is based on a set of likely scenarios of greenhouse gas 

emissions depending on population growth and economic 

considerations of greenhouse gas controls and regulations. 

Emission scenarios are assumptions more than predictions 

or forecasts, and no probabilities are associated with them. 

Projections of future global mean sea level are based on 

thermal expansion of warmer oceans and the degree of 

melting glaciers and ice sheets as predicted from GCMs for 

different emission scenarios with greater or lesser greenhouse 

gas concentrations.

IPCC reports of climate change projections originally 

considered selected model output in the Third Assessment 

Report (2001) but have since reported composite findings for 

host institutions and countries providing GCM contributions. 
14 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

EXPLANATION

Mean sea level (North American Vertical

Datum of 1988 [NAVD 88])

Land motion residual

Linear regression line

A. Key West, Fla.

B. Pensacola, Fla.

C. Grand Isle, La.

Year Year

D. Galveston, Tex.

Land motion residual, in millimeters

Mean sea level (NAVD 88), in millimeters

laf13-CSSC00-0572_fig8

y = 3.451x - 7,155.8 -500

-450

-400

-350

-300

-250

-200

-150

-100

-50

0

50

y = 7.3384x - 14,539 

-200

-100

0

100

200

300

400

500

y = 4.0147x - 7,857.7 

-50

0

50

100

150

200

250

300

350

400

450

y = 0.1273x - 474.43 -400

-350

-300

-250

-200

-150

-100

-50

0

y = 3.4662x - 6,824.8 -200

-100

0

100

200

300

400

500

y = 0.1425x - 143.45 -150

-100

-50

0

50

100

150

200

250

300

350

400

1990 1995 2000 2005 2010 2015

y = 4.8451x - 9,479.4 -100

0

100

200

300

400

500

600

700

y = 1.5214x - 2,798 

0

100

200

300

400

500

600

1990 1995 2000 2005 2010 2015

Figure 8. Slopes of mean monthly observations and residual differences from select Gulf of Mexico tide gages and corresponding 

satellite sea-surface heights for the tidal epoch 1994–2012 (data sources National Aeronautics and Space Administration Goddard 

Space Flight Center [NASA GSFC], 2013; National Oceanic and Atmospheric Administration [NOAA], 2014). A, Key West, Florida.

B, Pensacola, Florida. C, Grand Isle, Louisiana. D, Galveston, Texas.
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 15

Table 2. Sea-level rise trends by tidal epoch during the period of record, 1918–2012, for select Gulf of Mexico tide gages.

[-, data not available]

Tide gage

 Tidal epoch

1918–36 1937–55 1956–74 1975–93 1994–2012

Key West, Fla., gage 2.67 3.14 2.92 3.71 3.45

Pensacola, Fla., gage - 2.62 3.01 0.81 3.47

Grand Isle, La., gage1 - - - - 7.34

Galveston, Tex., gage 3.37 7.52 9.54 7.48 4.85

 1Discontinuous record.

The IPCC Fourth Assessment Report (2007) resulted in a 

comparatively conservative projection for sea-level rise with 

likely low and high ranges of 18 and 59 cm, respectively, by 

2100. The most recent National Climate Assessment report 

(Melillo and others, 2014) raised the upper estimate of sealevel projections from icemelt to 2 m (6.6 feet [ft]) by 2100 

(fig. 9). The low projection of 20 cm (0.66 ft) by 2100 is 

an extension of the historical global mean sea-level rate of 

the last century without acceleration. The two intermediate 

forecasts between the low and high extremes indicate a likely 

sea-level rise of 0.3 m (1 ft) and 1.2 m (4 ft), respectively, 

by 2100. These and similar projections are commonly used 

in vulnerability assessments and as inputs to ecological and 

hydrological models to forecast the impact of sea-level rise on 

coastal infrastructure and habitat.

Predictive Models of Sea-Level Rise 

Impact and Coastal Vulnerability

In this section, we describe a host of simulation models, 

decision-support tools, and analytical techniques that have 

been applied to predict potential impacts of sea-level rise 

on coastal ecosystems and species, or related changes in 

the physical environment. We categorize the suite of data, 

methods, and models from a broad spectrum of disciplines 

involving different designs and scales of spatial and 

temporal complexity for predicting environmental change 

and ecosystem response. Model descriptions are given to 

highlight design type and features, functional attributes of 

input parameters and predictive variables, and characteristics 

of model utility or limitations. Criteria were established 

to distinguish the source, scale, and quality of information 

input and geographic datasets; physical and biological 

constraints and relations; datum characteristics of water and 

land components; utility options for setting sea-level rise and 

climate change scenarios; and ease or difficulty of storing, 

displaying, or interpreting model output. 

Sea-Level Rise Simulation and

Inundation Models

Some hydrological models have been developed 

to generate future sea-level projections for predicting 

inundation of shorelines or for use in other field and modeling 

studies. Model types range from arbitrary scenarios to 

derived projections based on tide gage records, glacial melt 

calculations, or climate change models designed to forecast 

probable increase of future sea level (table 3). Uncertainty in 

the extent of inundation for a particular sea-level rise scenario 

(such as 50 cm by 2100) is related to both the uncertainty 

in transformation of water-level datums (for example, 

orthometric to tidal) and the uncertainty in coastal elevation 

data. The combined amount of uncertainty from datum 

transformation and elevation sources can present critical 

limitations to sea-level rise applications. We exclude from 

this discussion tide simulation models or more sophisticated 

hydrodynamic models used for wave analyses, storm surge, or 

extreme flood forcing because these models are primarily used 

for engineering bridges and other coastal infrastructure.

CoastCLIM Sea-Level Simulator

CoastCLIM (Warrick, 2006; Warrick and Cox, 2007) 

is a database tool for generating predicted sea-level curves 

for any global coastal location. CoastCLIM uses a global 

database of regional grid cells to generate localized rates of 

sea-level change associated with downscaled GCM projections 

of future sea-level rise and CO2

 emission scenarios under 

climate change. A total of six emission scenarios are included 

in the package, and they can be queried for their associated 

changes in temperature, icemelt effects, and CO2

 concentration 

as produced for IPCC projections. CoastCLIM employs a 

user-friendly interface that allows users to select the region 

or coastal reach of interest from a global map. CoastCLIM 

associates selected locations with an overlapping GCM grid 

cell and extracts a normalized index of regional sea-level 

change relative to the global mean sea level. The normalized 
16 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

laf13-CSSC00-0572_fig9 Sea-level change, in feet

Year

1800 1850 1900 1950 2000 2050 2100

7

6

5

4

3

2

1

0

-1

6.6 feet

0.66 feet

4 feet

1 foot

EXPLANATION

Historical estimates (based on

sediment records and other proxies)

Uncertainty

Tide gage measurements

Satellite observations

Intermediate forecasts of possible future amounts

Possible future amounts Intermediate

forecasts

Figure 9. Historical, observed, and possible future amounts of global sea-level rise from 1800 to 2100 (from Melillo and others, 2014). 

Historical estimates (based on sediment records and other proxies) are shown in red (pink band shows uncertainty range), tide gage 

measurements in blue, and satellite observations in green. 

index is derived as a ratio or scaling factor for the average 

pattern of sea-level change for the region of interest divided 

by the global mean sea-level change for the forecast period of 

2071–2100. The user also selects which of six IPCC emission 

scenarios (A1B, A1F1, A1T, A2, B1, or B2) to correspond 

with downscaled GCM sea-level rise projections. CoastCLIM 

displays the predicted outcome in relative sea-level rise above 

zero in tabular and graphical format on an annual basis to 

2100. CoastClim sea-level simulations show that sea-level rise 

can vary with both the selected model and scenario. 

NOAA Inundation Frequency Analysis Program

An inundation frequency analysis program was developed 

by the NOAA National Ocean Service (http://tidesandcurrents.

noaa.gov/inundation/) as a utility tool for coastal planners. 

The program uses observed 6-min water-level records of tide 

gages relating observed times and heights of high-water tides 

for a desired period of record as data input. The data output 

of this program is an Excel spreadsheet that takes each of the 

tabulated high tides in a specified time period relative to the 

user-specified reference datum or threshold elevations and 

calculates the elevations and durations of inundation above 

the reference datum. This inundation frequency analysis 

generates graphs and histograms of frequency occurrences 

by elevation and duration. For each threshold elevation, 

statistical summaries of flooding frequencies are generated 

for total number of high tides, hours inundated and number 

of days inundated, and the percentage of time inundated. 

For application of analyzing various sea-level rise scenarios, 

the reference datum is adjusted by the estimated amount of 

elevation change for a given sea-level rise scenario, and the 

aforementioned statistics are recalculated.

USGS Sea-Level Rise Rectification Program 

(SLRRP)

The Sea-Level Rise Rectification Program (SLRRP) 

(Keim and others, 2008) was developed by the USGS to 

generate future sea-level rise projections based on historical 

tide gage records and user-specified inputs or selections of 

IPCC (2001) GCMs and emission scenarios. SLRRP was 

designed to provide other USGS coastal ecosystem models 

with corresponding sea levels rectified to NAVD 88. SLRRP 

uses the mean monthly tide records of NOAA tide gages 

projected into the 21st century with the addition of curvilinear 
Table 3. Attributes of select sea-level rise simulation and inundation models.[GCM, general circulation model; NOAA, National Oceanic and Atmospheric Administration; SLRRP, Sea-Level Rise Rectification Program]

Model Agency/ organization Appropriate scale Spatial resolution Temporal scale Input parameters Output parameters 

CoastCLIM SeaLevel Simulator 

(component of the SimCLIM system)

CLIMsystems Local, regional, 

global

Varies; 

determined by data availability and computation demands

Variable depending on 

impact model being run

Elevation, climatologies, 

site time-series data, patterns of climate and sea-level changes from GCMs, impact models

Maps of areas/habitats 

potentially vulnerable to inundation. May estimate adaptation costs

NOAA inundation 

frequency analysis program

NOAA Local Not applicable 1 month–5 years Tide station, reference 

elevation, date range

Inundation duration, 

frequency of high water elevation or duration (tabular or graph format)

Sea-Level Rise 

Rectification Program (SLRRP)

U.S. Geological 

Survey

Local, regional Not applicable Historical tide range 

plus projection to 2100, monthly to annual time step

Tide station, local 

subsidence rate (historical or custom), GCM sea-level rise rate

Cumulative sea-level 

rise, flood inundation potential for given elevation

Temperature-based 

sea-level rise model

Potsdam 

Institute for Climate Impact Research

Global Not applicable 1990–2100 Global mean sea level, 

mean temperature, time

Future mean sea level 
18 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

rates of eustatic sea level expected from climate change. The 

historical tide gage record is “wrapped” or extended into 

the future to mimic the natural cycle of high and low tidal 

variations attributed to astronomical and meteorological 

causes of seasonal and interannual variability including wind 

and storm tides. The mean monthly water levels capture both 

short-term seasonal variations and long-term trends of sealevel change for the respective gage and coastal reach selected. 

Projected data records are given in stage heights for different 

tidal datums such as mean low water, mean tide level, and 

mean high water, rectified to NAVD 88 to allow comparison 

with land-based elevations of tidal wetlands, roads, and other 

features of interest. The default tidal parameter simulated 

by SLRRP is the mean higher high water (MHHW), which 

corresponds with the upper boundary of the tidal-upland 

ecotone and higher flood extent to establish flooding potential 

of ecological and cultural importance. 

SLRRP utilizes a graphic user interface of sequential 

pop-up windows to facilitate user selection of a desired tide 

station, GCM, emission scenario, or alternative customized 

inputs (fig. 10). The SLRRP customized mode allows 

users to manually enter a local subsidence rate and eustasy 

rate or elevation over a given time frame instead of using 

model defaults. The program gives the user options for 

saving graphical and digital formats of generated sea-level 

projections. SLRRP prompts the user to execute a seawater 

inundation option that builds a supplemental graph of the 

timing and rate of flooding for an elevation entry of a land 

surface or other feature (fig. 10). In effect, the model shows 

the prospective date and time period for which sea level may 

likely overtop and permanently submerge a given landscape 

feature under a future sea-level projection. Flooding potential 

is the percentage of months within a year when there is 

inundation by seawater for a given elevation determined by 

the user, assuming hydrological connectivity. SLRRP is used 

to generate projected sea-level rise curves for other USGS 

ecological models, such as SELVA-MANGRO, SLOPE, and 

WETLANDS, developed for assessing tidal wetland migration 

under sea-level changes for either a rise or a fall.

Temperature-Based Sea-Level Rise Model

A semi-empirical model of future sea-level rise was 

developed (Rahmstorf, 2007) to project possible changes 

in global sea-surface height from global near-surface air 

temperature. The model is based on the assumption that 

the rate of sea-level change is roughly proportional to 

the magnitude of relative warming above pre-industrial 

temperatures. A semi-empirical approach is a reasonable 

alternative to process-based GCMs given their accuracy 

limitations to reproduce historical sea-level trends of the 20th 

century. Modern climate records show a highly significant 

correlation of global temperature with sea-level change by 

rate and direction. It is expected that, with every 1 °C change 

in global air temperature, sea levels will rise or fall by 10–30 

m. The linear approximation of sea-level change with this 

semi-empirical approach provides reproducible results of 

modern sea-level trends within centimeters. Future projections 

are based on the range of predicted temperature changes that 

will vary according to greenhouse gas controls and conservation 

measures. Projected rates for the 21st century from this model 

fall within the range of other global lows and highs from more 

complex models.

Soil Salinity Models

Salinity conditions within the estuarine gradient of tidal 

channels and wetland soils vary hourly and seasonally with 

changing tides, storms, and freshwater runoff (Morris, 1995; 

Teh and others, 2008; Wang and others, 2007). What wetland 

type and plant species may be found within the intertidal zone is 

controlled by persistent salinity concentrations. Saltmarsh and 

mangroves are known for their tolerance of high soil salinities 

that can well exceed the average or typical concentration of 

open seawater, 35 parts per thousand (ppt). As sea level rises, 

the circulation of seawater penetrates farther inland with each 

tidal cycle, as well as episodically by storm tides and waves 

from tsunamis and hurricanes. A major cause of coastal forest 

retreat is saltwater intrusion by way of major storm tides and 

surge events occurring during a local or regional drought. 

During times of low water, marshes dehydrate and compress 

below normal elevations such that refilling by seawater as 

opposed to rainfall or other freshwater input can elevate 

interstitial pore water salinities in the soil layer. Increases of 1 or 

2 ppt of soil salinity concentrations are sufficient to cause plant 

mortality and shifts in vegetation dominance. 

Expert Hydrodynamic Models for 

Engineering Applications

There are a host of expert hydrodynamic models for 

engineering applications that are used for coastal restoration 

purposes of different kinds that likewise have the capacity 

to evaluate future sea-level rise projections. These models 

generally require expert knowledge and costly data gathering 

and license agreements to execute hydrology applications for 

environmental impact assessment, construction design and 

planning, and other coastal engineering needs. These numerical 

models consist of empirically dependent two- and threedimensional models used for comprehensive flood forcing, 

wave energy, nearshore circulation hydraulics, and storm-surge 

simulations such as Delft3D, MIKE 3FM, HECRas, MIKE 

2, SLOSH, AdCirc, FV COM, and other associated sediment 

and water-quality transport models designed for complex 

engineering applications of oceanic, coastal, riverine, and 

estuarine environments. These model types are not necessarily 

appropriate or useful for generating future sea-level rise 

projections or impact assessments based on design or function 

but are relevant in other ways, such as for parallel efforts to 

better understand the effects of river and coastal management on 

coastal geomorphology and tidal change.
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 19

A

Figure 10. Screenshots showing Sea-Level Rise Rectification Program (SLRRP) graphic user interface (from Keim and others, 2008).

A, User options for selecting the tide station and the GCM model parameters. B, Projected sea-level curve. C, Flood inundation 

chronograph.
20 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

B

Figure 10. Screenshots showing Sea-Level Rise Rectification Program (SLRRP) graphic user interface (from Keim and others, 2008).

A, User options for selecting the tide station and the GCM model parameters. B, Projected sea-level curve. C, Flood inundation 

chronograph.—Continued
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 21

C

Figure 10. Screenshots showing Sea-Level Rise Rectification Program (SLRRP) graphic user interface (from Keim and others, 2008).

A, User options for selecting the tide station and the GCM model parameters. B, Projected sea-level curve. C, Flood inundation 

chronograph.—Continued
22 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

Geographic Information System (GIS) SeaLevel Rise Mapping Tools

Geographic information system (GIS) sea-level 

rise mapping tools are abundant and popular for ease of 

construction and interactive graphical display. These tools 

are becoming commonly available online for public access 

as interactive maps. These tools allow Internet browsers 

to visualize the potential impact of future sea-level rise 

scenarios on land maps at local, regional, and global scales 

as determined by tool design and capability. Tool developers 

include Federal, State, university, and nongovernmental 

organizations utilizing GIS data, software, and graphic 

user interfaces to display developer-specified images or 

user-specified choices of geospatial extent and sea-level 

rise options (table 4). As a rule, these are non-expert tools 

lacking scientific credibility that display maps of potential 

coastal inundation based on public-domain digital elevation 

models (DEMs) (app. 3) and arbitrary sea-level projections. 

These mapping tools are often referred to as “bathtub” 

models for the overly simplified approach of water-over-land 

relations, an approach that disregards important biological, 

hydrological, physiographic, and geodetic considerations. 

At best, these mapping tools provide only a visualization 

of the future extent of tidal wetlands and shorelines 

suitable for facilitating discussion of coastal issues and, 

more importantly, highlighting the need for expert model 

application and accuracy. 

Advantages of GIS sea-level rise mapping tools include 

their relative simplicity and ease of construction such that 

many organizations and individuals are able to produce their 

own versions by relying on the same or similar GIS mapping 

software and public-domain mapping sets of digital land 

elevation, vegetation, and land-use data. In this way, they 

are inexpensive and quick to build but also very limited 

functionally. Limitations of GIS sea-level rise mapping tools 

are numerous in places and situations where accuracy of 

coastal process, built features, differential land movement, 

ecosystem functions, and vertical rectification of water 

and land datums are lacking. Most importantly, these tools 

are almost exclusively unrectified and unverified for use 

or application as predictive models in the same context as 

more sophisticated ecological and engineering models (see 

following sections). 

The more accurate predictive models for simulating 

sea-level change and coastal impacts require datum 

transformations of water and land sources that are rectified 

to the same units and reference plane to make legitimate 

comparison of flooded or unflooded land surface. Tidal 

datum and land datum are measured and reported differently 

and cannot be directly compared or analyzed without 

proper rectification to the same datum type and epoch. As 

discussed previously, there are many reference datums that 

have been established over the years of numerous local and 

regional origins that make rectification somewhat complex and 

subject to expert knowledge. In most cases, GIS sea-level rise 

mapping tools use arbitrary heights or rates of sea-level rise 

that are not based on any datum or measured local or global 

tide history. The more accurate sea-level rise applications by 

contrast consider the relative sea-level rate by coastal reach 

because of the variable conditions, causes, and directions of 

land movement and tidal behavior. In addition to not being 

rectified, GIS sea-level rise mapping tools are not verified 

(verification is the measure to which they have been tested 

for accuracy or function that adds to the sense of predictive 

reliability). Modeling that simply forecasts a predicted future 

condition without verification is akin to extrapolating beyond 

the boundary conditions and data of a statistical model. 

Although it may be useful to apply GIS sea-level rise mapping 

tools to consider “what if” scenarios for limited planning and 

educational purposes, these tools do not have any certainty 

bounds or other verification measures that ensure results, 

particularly given all of the limitations of data sources, design, 

and functionality.

GIS sea-level rise mapping tools may have an appearance 

of greater utility because of the busy display characteristics 

served from the quality and accuracy of DEMs incorporated 

(app. 3). The more detailed DEM sources of higher spatial 

resolution, however, have the advantage or disadvantage 

of being increasingly challenging to utilize because of 

file acquisition, size, and other factors that require greater 

computing skills and capacity to manage properly. Local 

features such as levees, dikes, revetments, and canals may 

not be included in associated public databases, which add 

to limitations of model application. A major assumption of 

GIS sea-level rise mapping tools is that there is uniform 

hydrological connectivity from open bays, oceans, estuaries, 

and rivers to adjacent or inland land units both in accessibility 

and in elevation. Water quality is also assumed to be uniform 

in that salinity or other nutrient constituents are not considered 

in these models. Table 4 identifies select publicly accessible, 

online GIS sea-level rise mapping tools with various 

characteristics and capabilities.

OzCoasts Sea-Level Rise Maps 

The Australian Government has developed a series of 

sea-level rise maps (Australian Government, 2009) to illustrate 

the potential impacts of climate change for key urban areas. 

The online maps are activated by selecting among several 

regional map insets. The maps, prepared by combining a 

sea-level rise projection with a high tide value, illustrate an 

event that could be expected to occur at least once a year, but 

possibly more frequently, by 2100. This mapping tool provides 

an interactive display to visualize and realize the risks to the 

infrastructure and natural areas along the Australian coastal 

zone, where nearly 85 percent of the population resides.
Table 4. Attributes of select geographic information system (GIS) sea-level rise mapping tools.[m, meter; IPCC, Intergovernmental Panel on Climate Change; DEM, digital elevation model; USGS, U.S. Geological Survey; km, kilometer; SRTM, Shuttle Radar Topography Elevation Dataset; NOAA, National Oceanic and Atmospheric Administration; ft, feet; VDatum, vertical datum transformation tool; MHHW, mean higher high water; C-CAP, N

Analysis Program; km2, square kilometer; MHW, mean high water]

Model Agency/ organization Web page Appropriate scale Spatial resolution Sea-level rise range options Input parameters Vegetation classification Outp

parame

OzCoasts sealevel rise 

maps

Australian 

Government

http://www.

ozcoasts.gov.au/climate/sd_visual.jsp

Local, key 

urban areas (Australia)

Unknown 0.5, 0.8, 

and 1.1 m (IPCC projections)

DEM (lidar) None Inundation 

USGS Sea 

Level Rise Animation

USGS http://cegis.usgs.

gov/sea_level_rise.html

Local, 

regional, national, global

30 m (U.S. 

coast), 90 m (regional), 

1 km (global)

0–30 m (U.S. 

coast), 0–80 m (continetal, global)

DEM 

(GTOPO30, SRTM 90 m, NED 30 m), land cover, population

None Inundation 

populatioaffected

NOAA Digital 

Coast SeaLevel Viewer

NOAA http://www.csc.

noaa.gov/slr/viewer/#

Local, 

regional, national (U.S., territories)

Variable 

(3–30 m)

0–6 ft DEM, 

vegetation, VDatum MHHW surface

C-CAP Inundation 

socioecovulnerabindex

University of 

Arizona Web Map Visualization Tool

University of 

Arizona

http://climategem.

geo.arizona.edu/slr/us48prvi/index.html

Continental, 

global

1 km2 (global), 

30 m (U.S.)

0–6 m DEM 

(GTOPO30, NED 30 m), land cover

None Inundation 

Surging Seas Climate 

Central

http://sealevel.

climatecentral.org/surgingseas/

City, county, 

State (lower 48 States)

10 m, 30 m 0–10 ft DEM, VDatum 

MHW, tide gage data

None Inundation 

populatiohousing land affeflooding (downlo
24 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

NOAA Digital Coast Sea-Level Viewer

The NOAA Digital Coast Sea-Level Viewer (http://coast.

noaa.gov/slr/; Marcy and others, 2011; National Oceanic and 

Atmospheric Administration National Ocean Service [NOAA 

NOS], 2010) was designed to be a teaching and planning tool 

for coastal land managers. The online user can slide a sea-level 

rise bar located left of the map to show the potential impact 

to locations where various sea levels may inundate coastal 

communities (fig. 11). The underlying digital elevation from 

the USGS National Elevation Dataset (NED) (1 arc-second) 

supplemented with available and accessible light detection and 

ranging (lidar) elevation data is converted to a vertical datum 

transformation tool (VDatum) surface or local tide station 

MHHW datum depending on location. The Digital Coast SeaLevel Viewer allows the user to select six static scenario heights 

of sea level rise by 1-ft increments up to 6 ft. Wetland data 

portrayed as the initial condition within the viewer are derived 

from the NOAA Coastal Change Analysis Program (C-CAP) 

(app. 3). The model allows marsh migration based on userselected accretion rates such that, as sea level rises and higher 

elevations become more frequently inundated, marsh migrates 

landward. The user is allowed to select from four predetermined 

rates of accretion. These rates are presented as high (6 mm/

yr), medium (4 mm/yr), low (2 mm/yr), and no (0 mm/yr) 

accretion. These rates were determined on the basis of observed 

rates from published field studies. The minimum mapping unit 

of the viewer was modified to remove features smaller than 

0.202 hectares (0.5 acres) in size. Inclusion of additional coastal 

counties is planned for the near future so that all U.S. coastlines 

and coastal communities can be visualized with the tool. 

The Nature Conservancy (TNC) Coastal Resilience 

Decision-Support Framework

The Nature Conservancy (TNC) Coastal Resilience 

Decision-Support Framework (http://maps.coastalresilience.org/

network/) was developed as a Web-based mapping tool to assist 

decision makers with assessing alternative future scenarios that 

address sea-level rise, storm surge, and community vulnerability. 

TNC is using this tool to advance a global network for coastal 

resilience to support adaptation planning and post-storm 

redevelopment decisions, as well as to reduce the ecological and 

socioeconomic risks of coastal hazards. The framework includes 

local to global application for examining social, economic, 

and ecological priorities alongside data on coastal hazards. 

The interactive decision-support framework allows users to 

visualize future flood risks from sea-level rise and storm surge. 

The users can also identify areas and populations at risk and 

gain a better understanding of social, economic, and ecological 

impacts from coastal hazards. This information is particularly 

helpful for officials considering rising sea levels and increased 

storm intensity and frequency when making coastal management 

decisions, such as coastal planning, zoning, and land acquisition.

University of Arizona Web Map 

Visualization Tool

The University of Arizona Web Map Visualization Tool 

(Overpeck and Weiss, 2009; Weiss and others, 2011; Strauss 

and others, 2012) includes both global and conterminous 

U.S. map versions of different elevation data sources and 

scales of resolution. The global map, the Global Elevation 

Model (GEM), is composed of the GTOPO30 dataset at 

1-km horizontal resolution, whereas the conterminous 

U.S. map is based on the USGS NED (1 arc-second) at 

30-m horizontal resolution. The geoprocessing algorithm 

performs iterative cell-by-cell analysis of the selected DEM 

and highlights all land units (cells) with elevation values 

less than or equal to projected sea levels of 1–6 m above 

current sea level. The tool identifies low-lying coastal areas 

expected to be inundated by sea-level rise by 2100. The 

program is composed of ArcGIS Viewer software (Esri, 

Redlands, Calif.) with zoom capability to enlarge the map 

view (fig. 12). The map tool is based on modeled presentday elevations and does not predict future shorelines or 

include processes that may affect local elevation such as 

glacial isostatic adjustment, tectonics, subsidence, or erosion 

and accretion. The municipal boundaries are GIS shapefiles 

that are part of the U.S. Census Bureau Topologically 

Integrated Geographic Encoding and Referencing 

(TIGER) database. All maps are on an Albers Equal-Area 

Conic projection.

USGS Sea-Level Rise Animation

The online USGS Sea-Level Rise Animation (Usery 

and others, 2010) creates visualizations of seawater over 

land, illustrating vulnerability of low-elevation areas (rather 

than predictions of sea-level rise). The animated graphics are 

created by using raster elevation, land cover, and population 

data at 1-km (global), 90-m (regional), and 30-m (regional, 

specifically the U.S. areas) resolutions. Colors reflect land 

cover categories, and the counts are numbers of people 

living or impacted below specific elevations (fig. 13). The 

animated graphics are not meant to be site specific. They 

are based exclusively on elevation and do not attempt to 

account for tidal action, shoreline configuration, and other 

characteristics that might affect how the water would rise in 

a specific coastal zone. Data resolution and accuracy limit 

the ability to access a particular location and get an accurate 

measure of the exact inundation area and number of people. 

Any errors in the data affect the accuracy of the simulations. 

The limits of the rise in the global animated graphics are set 

to 80 m, the theoretical limit of sea-level rise if all glaciers 

and icecaps melt. For regional animated graphics, a limit of 

30 m is used because this was the greatest rise resulting from 

the Indian Ocean tsunami in 2004.
A

Figure 11. Screenshots showing National Oceanic and Atmospheric Administration (NOAA) Digital Coast Sea-Level Viewer graphic user interface fo(http://coast.noaa.gov/slr/). A, Inundation extent for current mean higher high water (MHHW). B, Inundation extent for 6-foot sea-level rise.
B

Figure 11. Screenshots showing National Oceanic and Atmospheric Administration (NOAA) Digital Coast Sea-Level Viewer graphic user interface fo(http://coast.noaa.gov/slr/). A, Inundation extent for current mean higher high water (MHHW). B, Inundation extent for 6-foot sea-level rise.—Continue
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 27

Figure 12. Screenshot showing University of Arizona Web Map Visualization Tool graphic user interface (from Strauss and others, 

2012; used with permission).
Figure 13. Screenshot showing U.S. Geological Survey Sea-Level Rise Animation graphic user interface (from Usery and others, 2010; used with per
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 29

Wetland Change Models

Ecological simulation models used for sea-level rise 

impact assessment vary fundamentally in design and function 

on the basis of the organizational unit, whether at ecosystem, 

species, or organism level. In this section, we describe the 

class of wetland change models designed to predict changes 

in land cover at the ecosystem (habitat) level in terms of 

presence or absence (table 5). In these models, there are no 

quantitative features or attributes applied to the cover type 

other than a descriptive classification. The wetland type or 

habitat cover descriptions vary in detail and discrimination 

from generic classification of terrestrial or aquatic forms to 

surficial qualifiers of tidal flat, beach, low marsh, high marsh, 

swamp, upland, or other cover type. These models are similar 

to GIS sea-level rise mapping tools (“bathtub” models; see 

section “Geographic Information System (GIS) Sea-Level 

Rise Mapping Tools”) in that they predict change on the basis 

of the relation between land and water, but wetland change 

models assign a vegetative cover or habitat change effect. 

Further, whereas GIS sea-level rise mapping tools predict 

which and when land grid cells are overtaken by rising sea 

level to become open water, wetland change models generally 

have a simple rule set that assigns a given habitat type by 

degree of submergence. As the land grid cell becomes more 

inundated over time, these models account for the loss or gain 

of the different habitat types as a measure of impact from 

sea-level rise. Wetland change models differ largely on the 

delineation of habitat types or classification scheme used and 

the degree of inundation at which a habitat switches from 

one type to another. Predicted loss of habitat is generally a 

matter of erosion or submergence by surface inundation when 

a marsh, swamp, or mudflat eventually transitions to open 

water. As with GIS sea-level rise mapping tools, with wetland 

change models the sea-surface height is modeled as a static 

rise in adjoining water cells over annual or longer time steps 

assuming connectivity rather than any dynamic process. Most 

of these models have the capacity to parameterize the land 

elevation dataset with protective features of dikes, seawalls, 

or levees to mimic decoupling from adjacent water bodies, in 

which case the wetland type usually stays the same until the 

protective feature is overtopped by rising sea level. 

Barataria-Terrebonne Ecological Landscape 

Spatial Simulation (BTELSS)

The Barataria-Terrebonne Ecological Landscape Spatial 

Simulation (BTELSS) (fig. 14) (Voinov and others, 1999, 

2007; Martin and others, 2000; Reyes and others, 2000, 2004; 

Binder and others, 2003) and its predecessor, the Coastal 

Ecological Landscape Spatial Simulation (CELSS) (Constanza 

and others, 1990; Sklar and others, 1991), represent a processbased ecological model design of coastal wetland change and 

water constituents within a watershed context for evaluating 

potential impacts of restoration projects, river diversions, 

saltwater intrusion, climate change, and sea-level rise. The 

model framework is composed of raster cells representing 

interconnected 1-km2

 land units for a defined watershed. Each 

cell is connected to neighboring cells on all sides, involving 

a mass balance exchange of water volume and constituents 

of sediment, salt, and nutrients. The buildup of land or 

erosion to open water in a cell depends on the net balance 

of elevation gain by sedimentation and organic accretion or 

net loss from scour or subsidence. The effect of net surficial 

change is critical for predicting how marsh succession is 

affected by natural and human activities. Time-series data of 

Atchafalaya River and Mississippi River discharges, Gulf 

of Mexico salinity, river sediments and nutrients, rainfall, 

sea level, runoff, temperature, and winds are inputs to the 

model affecting water and constituent movement through the 

watershed. The location, dredging date, and characteristics 

of waterways, canals, and levees are also supplied as model 

inputs affecting historical conditions and effects on watershed 

hydrological dynamics. Land elevation in relation to flooding 

pattern determines succession of one habitat type to another 

by way of a simple switching algorithm. The model has been 

validated by hindcasting historical conditions and matching 

vegetation type and conversion with more contemporary 

vegetation maps.

Sea Level Affecting Marshes Model (SLAMM)

The Sea Level Affecting Marshes Model (SLAMM) 

(Park and others, 1989; Galbraith and others, 2002, 2003; 

National Wildlife Federation, 2006; Craft and others, 2009; 

Clough and others, 2010; Geselbracht and others, 2011) is 

a menu-driven, map-based simulation model using discrete 

time steps of 5–25 years. The different model versions 

reflect upgrades in software, data sources, and spatial 

resolution of subsequent site applications more than they 

reflect any substantial changes of functionality or design. 

Spatial resolution ranges from 500-m pixel size, for map 

areas confined to 7.5-min quadrangles prior to the advent of 

DEMs, to 10-m pixel dimensions of more recent versions. 

Orthometric land elevations are converted to tidal datum 

equivalents for selected regional tide gages. Sea-level rise is 

simulated as a static increase of projected eustatic sea-level 

rise for the proportion of years matching model time step. 

Earlier SLAMM versions did not account for marsh accretion, 

whereas version 6, the most updated version, assigns habitatspecific accretion rates that, if not exceeded by sea-level 

rise, allow for habitat migration or conversion. Model output 

consists of graphical displays of habitat change by color 

scheme (fig. 15) and tabular export files for calculating 

summary statistics of land loss and marsh migration with 

rising sea level. Model execution allows non-expert utility 

accomplished by command line and batch mode inputs of 

discrete user options for sea-level scenarios and map view 

based on a data matrix structure. Habitat data are based on 

classification of multispectral Landsat data of early versions 
Table 5. Attributes of select wetland change models. [<, less than; km2, square kilometers; yr, year; NWI, National Wetlands Inventory; m, meter; lidar, light detection and ranging] Model Agency/ organization Appropriate scale Spatial resolution Temporal scale Input parameters Vegetation classification Output parameters Val

Barataria- Terrebonne Ecosystem Landscape Spatial Simulation (BTELSS) Louisiana State University Local, regional (such as <1 km2– 100,000 km2) 1 km2 Variable time steps (daily, annual), simulation time up to 100 yr Elevation and bathymetry, air temperature, wind speed and direction, precipitation, river discharge, sediment load, wetland land cover, regional salinity, plant growth and mortality rates, salinity and flooding tolerances of plants NWI Maps of land change (habitat switching), flooded and eroded areas, plant productivity, salinity, open-water circulation, and sediment transport Hin

Coastal Ecological Landscape Spatial Simulation (CELSS) Louisiana State University Local, regional (such as <1 km2– 100,000 km2) 1 km2 Variable time steps (daily, annual), simulation time up to 100 yr Elevation and bathymetry, air temperature, wind speed and direction, precipitation, river discharge, sediment load, wetland land cover, regional salinity, plant growth and mortality rates, salinity and flooding tolerances of plants NWI Maps of land change (habitat switching), flooded and eroded areas, plant productivity, salinity, open-water circulation, and sediment transport Hin

Sea Level Affecting Marshes Model (SLAMM 1–5) Warren Pinnacle Consulting, Inc. Local, regional (such as <1 km2– 100,000 km2) 10–100 m Time steps of 5–25 yr can be used on the basis of sea-level rise scenario, simulation time up to 100 yr Elevation maps (lidar preferred), wetland land cover (such as NWI), development footprint, and dike location, sea- level rise projections NWI Maps of areas/ habitats potentially vulnerable to inundation (land cover and elevation maps) Non

Sea Level Affecting Marshes Model (SLAMM 6) Warren Pinnacle Consulting, Inc. Local, regional (such as <1 km2– 100,000 km2) 10–100 m Time steps of 5–25 yr can be used on the basis of sea-level rise scenario, simulation time 

up to 100 yr

Elevation maps (lidar 

preferred), wetland land cover (such as NWI), development footprint, dike location, sea-level rise projections

NWI Maps of areas/

habitats potentially vulnerable to inundation (land cover and elevation maps)

Hin

Sea Level Over 

Proportional Elevation (SLOPE)

U.S. 

Geological Survey

Regional, 

national

County Monthly or annual 

time step, simulation time 

up to 100 yr

Saltmarsh/mangrove 

area, monthly tide gage records, sea-level rise projections, tidal range by county

NWI Maps of land change, 

habitat migration and displacement

Hin
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 31

Figure 14. Screenshots showing Barataria-Terrebonne Ecosystems Landscape Spatial Simulation (BTELSS) model domain and sealevel change map for the Barataria and Terrebonne Basins, Louisiana (from Reyes and others, 2000; used with permission).
Figure 15. Screenshot showing Sea Level Affecting Marshes Model 6 (SLAMM 6) display viewer for the St. Marks National Wildlife Refuge applicatWildlife Service, 2012).
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 33

of SLAMM and U.S. Fish and Wildlife Service (FWS) 

National Wetland Inventory (NWI) habitat delineations of 

later versions of SLAMM, including aquatic systems, urban 

developed, terrestrial wetland, and upland forest classes (app. 

3). NWI land cover classes describe generic habitat type more 

than they describe specific species or any metrics of habitat 

quality, density, height, or biomass. Elevations of the grid cells 

in SLAMM are based on interpolated topographic contour 

lines and slopes in early versions to DEMs based on USGS 

NED and supplementary lidar coverage in later versions 

(app. 3). Model predictions are based on rule sets of land 

loss/conversion relative to water-table inundation of the land 

surface and wave erosion on the edge of large water bodies, 

excepting protected land units behind dikes. Although the 

model predicts habitat loss/change on the basis of simple rule 

sets and Boolean decision trees, new site applications usually 

require expert knowledge to parameterize model drivers on the 

basis of tide range and datum relations. 

Sea Level Over Proportional Elevation

(SLOPE) Model

Tidal freshwater forests in coastal regions of the 

southeastern United States are undergoing dieback and 

retreat from increasing tidal inundation and saltwater 

intrusion attributed to climate variability and sea-level rise. 

In contrast, in many areas, tidal saltwater forests (mangroves) 

are expanding landward in subtropical coastal reaches, 

succeeding freshwater marsh and forest zones. Hydrological 

characteristics of these low-relief coastal forests in intertidal 

settings are dictated by the influence of tidal and freshwater 

forcing. The Sea Level Over Proportional Elevation (SLOPE) 

model (Doyle and others, 2010) is a USGS product that 

predicts coastal forest retreat and saltmarsh/mangrove 

migration from projected sea-level rise on the basis of a proxy 

relation of saltmarsh/mangrove area and tidal range (fig. 16). 

The model as applied to the Gulf Coast is subdivided into 

separate reaches defined by each of the 60 coastal counties 

from Texas to Florida. Summaries of saltmarsh/mangrove 

area for each coastal county were obtained from published 

sources on the basis of detailed grid sampling of the NWI 

database and habitat classification scheme. Tidal ranges were 

obtained from NOAA tide stations for more than 300 locations 

and filtered to assign corresponding maximum tide range for 

each coastal county. The SLOPE model assumes that the sum 

area of saltmarsh/mangrove habitat along any given coastal 

reach is determined by the slope of the landform and vertical 

tide forcing. The model assumes that area and boundary 

characteristics of saltmarsh/mangrove ecosystems have been 

defined by the local tidal prism in relation to ambient salinity 

concentrations and circulation suitable to support saltwater 

habitats in a given coastal reach. 

Surface Elevation and Shoreline Erosion Models

Most predictive sea-level rise models previously 

discussed are GIS tools, not dynamic ecological models, 

and they lack any feedback relation of marsh accretion and 

elevation with sea-level change. More often the implicit 

assumption of GIS sea-level rise mapping tools is that, as sea 

level rises above surface elevation, marsh habitat succumbs 

to inundation and is consequently converted to open water. 

Wetland change models work similarly, but some assign fixed 

or static accretion rates by habitat type such that, as sea-level 

rise rate exceeds these values, the habitat type and land unit 

undergo inundation (that is, submersion, erosion), resulting 

in habitat loss and conversion to open seawater. Field studies 

of marsh soil stratigraphy and age demonstrate that saltmarsh 

and mangrove species can keep pace with sea-level change 

of past centuries and millennia even in delta settings subject 

to high rates of land subsidence, such as in the Mississippi 

River coastal platform. This long-term evidence of marsh 

resiliency indicates that we know less about under what 

conditions and for what reasons they erode and at what rates 

of sea-level rise they cannot keep pace. In this section, we 

describe process-based studies and models of marsh-elevation 

change, substrate biogeochemical state, and shoreline erosion 

that are complementary to predictive sea-level rise models, 

particularly species- and ecosystem-based simulation models 

to be discussed in later sections.

There are a number of empirical studies and observation 

systems for monitoring the process and rate of surface 

elevation change, substrate biogeochemical state, and 

shoreline erosion that are important to highlight. Geographic 

position of barrier island and mainland shorelines from maps 

dating to the European discovery of North America has been 

compared with more recent map sources to demonstrate 

shifts, erosion, and aggradation of coastal features linked to 

longshore currents and riverine influences. New observational 

tools and techniques to monitor surface elevation and soil 

salinity change within coastal wetlands have improved 

understanding of the dynamic nature of subsidence, accretion, 

sedimentation, eutrophication, and saltwater intrusion with 

changing hydrological forcing connected with hurricanes, sea 

level, streamflow, climate, and biotic factors. In this section, 

we describe different tools and techniques for monitoring 

and modeling surface elevation, shoreline change, and storm 

influences of coastal systems.
A

Figure 16. Screenshots showing Sea Level Over Proportional Elevation (SLOPE) model data layers for the northern Gulf of Mexico coast regional apothers, 2010; used with permission). A, Total saltmarsh and mangrove area (in hectares) extracted from the National Wetland Inventory (NWI) for coasGulf of Mexico. B, Relative sea-level rate (in millimeters per year) for National Oceanic and Atmospheric Administration (NOAA) tide stations. C, Prediof freshwater forest area (in hectares) for coastal counties under a 10-centimeter rise of sea level from accelerated global eustasy. D, Tidal range estextracted from water level records for 300 NOAA coastal tide stations for coastal counties.
B

Figure 16. Screenshots showing Sea Level Over Proportional Elevation (SLOPE) model data layers for the northern Gulf of Mexico coast regional apothers, 2010; used with permission). A, Total saltmarsh and mangrove area (in hectares) extracted from the National Wetland Inventory (NWI) for coasGulf of Mexico. B, Relative sea-level rate (in millimeters per year) for National Oceanic and Atmospheric Administration (NOAA) tide stations. C, Prediof freshwater forest area (in hectares) for coastal counties under a 10-centimeter rise of sea level from accelerated global eustasy. D, Tidal range estextracted from water level records for 300 NOAA coastal tide stations for coastal counties.—Continued
C

Figure 16. Screenshots showing Sea Level Over Proportional Elevation (SLOPE) model data layers for the northern Gulf of Mexico coast regional apothers, 2010; used with permission). A, Total saltmarsh and mangrove area (in hectares) extracted from the National Wetland Inventory (NWI) for coasGulf of Mexico. B, Relative sea-level rate (in millimeters per year) for National Oceanic and Atmospheric Administration (NOAA) tide stations. C, Prediof freshwater forest area (in hectares) for coastal counties under a 10-centimeter rise of sea level from accelerated global eustasy. D, Tidal range estextracted from water level records for 300 NOAA coastal tide stations for coastal counties.—Continued
D

Figure 16. Screenshots showing Sea Level Over Proportional Elevation (SLOPE) model data layers for the northern Gulf of Mexico coast regional appothers, 2010; used with permission). A, Total saltmarsh and mangrove area (in hectares) extracted from the National Wetland Inventory (NWI) for coasGulf of Mexico. B, Relative sea-level rate (in millimeters per year) for National Oceanic and Atmospheric Administration (NOAA) tide stations. C, Predicof freshwater forest area (in hectares) for coastal counties under a 10-centimeter rise of sea level from accelerated global eustasy. D, Tidal range estiextracted from water level records for 300 NOAA coastal tide stations for coastal counties.—Continued
38 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

Coastal Vulnerability Index (CVI)

The Coastal Vulnerability Index (CVI) (Gutierrez and 

others, 2011) represents an objective (observed) measure 

of shoreline erodibility for the coastlines of the United 

States—Atlantic, Pacific, and Gulf of Mexico—on the basis of 

historical shoreline maps and geological studies. The CVI is 

a probability index designed to assign relative risk and future 

threat of shoreline change under rising sea level. The index 

is based primarily on empirical measures of physical changes 

determined from comparative shoreline surveys or maps 

related to physical forces and features of tidal range, wave 

height, coastal slope, geomorphology, and relative sea-level 

trend by coastal reach. This approach, using combinations of 

measured change, coastal attributes, and physical forcings, 

provides a relative vulnerability, or erodibility, measure for 

guiding coastal planning and research. Prediction of shoreline 

retreat and land loss rates is critical to planning future coastal 

development and management strategies. CVI provides a 

useful construct and decision-support tool on a national and 

regional basis for assessing economic risk and threats of 

coastal erosion, flooding, and storm damage. For example, 

along the U.S. Atlantic Coast, high-vulnerability areas are 

typically barrier islands having small tidal ranges, large 

waves, a low coastal slope, and high landform subsidence, 

such as in the Mid-Atlantic region (fig. 17). Coasts with rocky 

cliffs or steep slopes are generally associated with large tidal 

ranges and low subsidence rates, such as most of the coastline 

in the Northeast, and are represented as low vulnerability 

(that is, low CVI). A Bayesian Network (BN) analysis has 

been developed to complement the historical perspective 

of the CVI to predict long-term shoreline change for the 

different projections of sea-level rise. The BN analysis links 

the relations between driving forces, geological constraints, 

and coastal response for the U.S. Atlantic Coast that include 

observations of local rates of relative sea-level rise, wave 

height, tide range, geomorphic classification, coastal slope, 

and shoreline change rate. Probabilistic predictions of 

shoreline retreat can be generated in response to different 

sea-level rise projections, mostly demonstrating that shoreline 

retreat increases with higher rates of sea-level rise (fig. 17). 

Marsh Substrate Geochronology Methods

A number of well-established methods are used to date 

peat and mineral layers of marsh substrates and biological 

deposits with different radioactive isotopes, mainly carbon-14 

(14C), lead-210 (210Pb), cesium-137 (137Cs), beryllium-7 (7

Be), 

oxygen-18 (18O), and uranium-238 (238U). The concentration 

and ratios of the different radioactive isotopes for each 

element have been used successfully to age the different 

depths of soil and fossil deposits for reconstructing ancient 

sea level, glacial advance and retreat, fossil age, and marsh 

accretion rates (DeLaune and others, 1989; Appleby and 

Oldfield, 1992; Turner and others, 2006; Mudd and others, 

2009). The different radioactive isotopes are used for 

different purposes and time periods but provide similar results 

of identifying dates for organic debris, fossils, and mineral 

layers of marsh soils and bog peats. Geochronology is the 

science of aging (dating) rocks, fossils, peats, and sediments 

that is inherent to the accuracy and limitations of the isotope 

and properties of radioactive decay or half-life. A number of 

radioactive isotopes are used for this purpose and, depending 

on the rate of decay, are used for dating different geological 

periods. More slowly decaying radioactive isotopes are 

useful for dating longer periods of time but are less accurate 

in absolute years. Radiocarbon dating is well known and 

commonly used for reconstructing ancient sea level of the more 

recent Holocene epoch of the last 12,000 years. Cesium-137 

dating of marsh substrate is a reliable method for estimating 

near-term sediment accretion rates as an artificial radionuclide 

product of bomb testing and peak fallout in 1963–64. Isotopic 

analysis, such as 18O and 16O ratios, is also used to relate the 

climate conditions under which fossils grew, which correlate 

with ocean temperature and salinity that can be further 

extrapolated to sea-level equivalents. 

Saltmarsh Stratigraphy and Evolution Models

There are a number of field and modeling studies that 

have attempted to understand the process and feedback 

mechanisms of marsh accretion of different coastal settings 

and environmental drivers (Allen, 1990; French, 1993; Morris, 

1995; Rybczyk and others, 1998; Fagherazzi and others, 

2012; Kirwan and Mudd, 2012). Conceptual, analytical, and 

numerical models have been developed to predict the process 

and rate on the basis of the different biotic and abiotic factors 

that vary with coastal position and degree of marine influence. 

In most applications, vegetation health and productivity are 

important components controlled by degree of inundation and 

submergence by tidal influence relative to mean sea level. 

Model types vary with complexity of processes and parameters 

to predict the genesis of saltmarsh soils controlled by relative 

rates of nutrient mineralization and inputs of organic and 

inorganic matter. Processes include sedimentation of exogenous 

and endogenous organic and inorganic matter, decomposition, 

aboveground and belowground biomass and production, 

and nitrogen and phosphorus mineralization. Models vary 

with vegetation type, tidal relations, and deposition rates as 

a function of surface elevation and distance from a channel. 

Distinctions of refractory and labile forms of organic fractions, 

aboveground and belowground production models, and decay 

rates add to model detail and functionality (fig. 3).

Surface Elevation Tables (SETs)

The surface elevation table (SET) (Cahoon and others, 

2002a, 2002b; Krauss and others, 2010; Rogers and others, 

2012) is composed of a benchmark set in wetland soils and 

environments that is repeatedly resurveyed with a portable 

mechanical device for precise releveling of the soil surface. 
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 39

Figure 17. Screenshot showing Coastal Vulnerability Index (CVI) Bayesian Network application for the U.S. Atlantic Coast (from 

Gutierrez and others, 2011).
40 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

The SET provides a nondestructive method for making highly 

accurate and precise remeasurements of sediment elevation 

of intertidal and subtidal wetlands over long periods of time 

relative to a fixed benchmark attached to a subsurface rod 

usually deep and driven to refusal (fig. 18). This technique 

overcomes many of the limitations of methods currently 

used to estimate elevation, such as sedimentation pins and 

precision surveying. The rod SET can be attached to either 

deep or shallow rods driven to different depths below surface 

into stable geological formations (fig. 18A). This flexible 

design allows the rod SET to be used to monitor elevations 

across different depths of the soil profile. A number of SETs 

may be established in a network for a particular site along 

a landscape or geomorphic gradient or in different wetland 

types to determine rates and processes of elevation change in 

different settings relative to abiotic and biotic factors. SETs 

are typically revisited quarterly, annually, or after storm events 

to monitor change over time and to identify what sources, 

mechanisms, and factors are contributing to elevation loss or 

gain. Clay feldspar pads are used in conjunction with SETs 

to establish a marker horizon of a known date on the wetland 

surface (fig. 18B) from which organic and mineral deposition 

rates can be estimated from subsequent core sampling. A data 

record of SET readings over years provides a definitive picture 

of the various contributing factors of accretion, deposition, 

subsidence, and storms that determine whether a given soil 

surface is either increasing or decreasing in elevation and is 

either keeping pace with or falling behind relative sea-level 

change for a given coastal setting.

Tidal Channel Network Models (TIGER)

Beyond the coarse coastline-change measures on a 

national or regional scale are the local and more fractal 

changes of tidal creeks and river outlets that relate to the 

process of marine and riverine coupling with marsh platforms, 

which is important to hydrological exchange and biophysical 

controls of marsh evolution, species composition, and 

productivity. Various numerical models have recently been 

developed to describe the morphogenesis and long-term 

dynamics of saltmarsh channels and tidal creek networks 

during periods of rising sea levels (Fagherazzi and Sun, 2004; 

D’Alpaos and others, 2005; Kirwan and Murray, 2007). 

Current understanding of coastal landform evolution is still 

more conceptual than certain in ability to validate quantitative 

predictions of how, when, where, and whether saltwater 

and freshwater systems can or will keep pace with different 

rates of sea-level rise. Tidal channels are related conduits 

that shape the marsh landscape and are in turn affected by 

the marsh landscape. The composition and constituency of 

marsh substrate and its exposure to tidal forcing and storm 

surge flooding dictate the proximity and dendritic patterns of 

tidal creeks, as do human influences of coastal modifications, 

dredging, and channelization. Where static models are simple 

to apply and to simulate flooding of marsh surfaces, they 

assume that surface water is delivered instantaneously rather 

than with temporal and spatial variability across the marsh 

platform. More advanced hydrodynamic models consider the 

time-dependent processes of flooding path, depth, and duration 

on the basis of distance, shape, and function of associated tidal 

creeks and marsh platforms. The rate of flooding and draining 

varies sharply at the highest and lowest elevations of the tidal 

range more than around the mean condition because of the 

different astronomical tide types and meteorological extremes. 

Sea-level rise models should be improved or more accurate as 

more realistic tidal hydrodynamics are incorporated rather than 

static “bathtub” treatment.

Niche-Based Species Distribution Models

Niche-based species distribution models describe the 

interrelation of environmental factors or conditions thought 

to define or limit species range on the basis of geographic 

extent. These models assume that modern distribution of 

species is controlled by associated climate and edaphic factors 

that can be quantified and expressed as favored and adapted 

ecological space. Fundamental niche space is theoretically all 

sets of factors controlling species presence, colonization, and 

persistence, whereas realized niche space (actual species range) 

is a limited expression of full ecological potential. Commonly, 

the overlap of precipitation and temperature datasets coincident 

with species range is used to construct niche space and 

project potential spread with changing climate. This approach 

assumes that environment solely controls species spread and 

success apart from event-driven causes or other physiographic, 

hydrological, or biotic factors. 

A prime example of how niche-based modeling may be 

deficient for some plant species is the range extent of Taxodium 

distichum (baldcypress) across the Eastern United States. 

The range boundary is not uniform with latitude or longitude, 

including disjunct distribution and populations. One biological 

attribute of baldcypress is its modest but comparatively elevated 

salt tolerance among freshwater tree species. Baldcypress is 

more readily known for its site preference and dominance in 

flood zones and growth production under high-precipitation 

conditions. Its interrupted range extent in the western Gulf 

Coastal Plain of Texas is indicative of arid conditions less 

suitable for baldcypress sustainability. Studies of tidal 

freshwater forests along coastal margins of the Southeastern 

United States indicate that, as residual soil salinities of these 

systems become elevated above 3 ppt from storm tides, 

prolonged drought, and saltwater intrusion, baldcypress persists 

in degraded, monospecific stands along the marsh-estuarine 

ecotone. The distribution and range of baldcypress correspond 

with the elevation of ancient sea level (about 120 m above 

current sea level) dating back to highstand shorelines of the Late 

Cretaceous epoch around 65 million years ago (fig. 19). State 

records of champion baldcypress trees provide evidence that 

planted baldcypress persists to mature age and size in nearly 

every northern State and Canada, beyond its natural range, 

suggesting that colder climate is not a limitation to growth. 
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 41

Root zone

Rod SET

(about 3–25 meters deep)

Feldspar marker

horizon (surface)

Vertical

accretion

Marker

horizon

Elevation

change

Zone of

shallow subsidence

Deep

subsidence

laf13-CSSC00-0572_fig18

A B

Figure 18. Surface elevation tables (SETs) (from Cahoon and others, 2002b; used with permission). A, Rod SET.

B, Feldspar marker horizon.
95° 90° 85° 80° 75° 40° 35° 30° 25° Base from modified U.S. Geologic Survey Generalized Geologic Map, U.S. National Elevation Dataset (1 arc-second) Gulf of Mexico Atlantic Ocean

laf13-CSSC00-0572_fig19

Generalized cypress data from Forest Inventory and Analysis (2015)

Geo

NO

00 90

90

180 MILES

180 KILOMETERS

MISSOURI

OKLAHOMA

ARKANSAS

ILLINOIS INDIANA

MISSISSIPPI

LOUISIANA TEXAS

ALABAMA

TENNESSEE

KENTUCKY

OHIO

KANSAS

WEST

VIRGINIA

VIRGINIA

NORTH

CAROLINA

SOUTH

CAROLINA

GEORGIA

PENNSYLVANIA

MARYLAND DELEWARE

FLORIDA

Figure 19. Taxodium distichum (baldcypress) distribution in relation to elevation of ancient sea level (about 120 meters above current sea level) datin

shorelines of Late Cretaceous epoch.
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 43

Mangroves are another group of wetland species 

whose range is related to sea level by adaptation to saltwater 

conditions and propagule spread by ocean tides and currents. 

As tropical plant species that dominate coastal areas in tropical 

zones worldwide, mangroves also delineate intertidal zones 

of higher latitudes over geological periods under favorably 

warm climates with an absence of freeze events and of 

lower, equatorial latitudes during cooling periods (fig. 20). A 

weakness, therefore, of many sea-level models is the singular 

factor approach, in which only sea level by submergence 

controls loss or gain of coastal habitat with its projected rise or 

fall, and there is a lack of an interactive or integrated approach 

of allowing mangroves to supersede saltmarsh under warmer, 

frost-free, climate. 

A niche-based species distribution model with sea-level 

controls does not exist, but the related Climate-Envelope 

Mangrove Model (Osland and others, 2013), developed by 

the USGS, demonstrates how projected climate warming 

allows for mangrove expansion to higher latitudes. This 

species distribution model was developed to predict potential 

mangrove expansion latitudinally with rising temperatures 

and lower freeze probabilities under a future climate scenario. 

By using existing mangrove distribution across the northern 

Gulf of Mexico, an empirical relation was derived to explain 

mangrove presence and abundance with a host of multidecadal 

winter climate parameters, and the niche-based modeling 

approach was applied by using current mangrove distributions 

for the five Gulf Coast States—Texas, Louisiana, Mississippi, 

Alabama, and Florida—to determine whether and where 

mangroves might expand in the absence of freeze events 

for different emission scenarios. The resulting ClimateEnvelope Mangrove Model is based on winter severity climate 

parameters to predict either potential loss or injury from freeze 

or population spread and abundance with warming climate. 

Mean annual minimum temperature (MAMT) was identified 

as the winter climate variable that best explained mangrove 

presence and abundance. Model simulations were applied to 

predict where mangrove colonizers can establish and spread 

under different cases of future temperature regimes under 

climate change. The northern Gulf of Mexico represents a 

broad longitudinal expanse within a narrow latitudinal band 

within a subtropical zone where only slight changes in rising 

temperatures will favor spread of tropical species (fig. 21). In 

this case, sea-level rise alone is not sufficient to predict the 

fate of mangrove spread but requires additional predictors 

such as favorable temperature regimes. 

Leaf to Landscape (L2L) Ecosystem Models

Leaf to landscape (L2L) ecosystem models are the most 

sophisticated ecological models developed for sea-level rise 

application. These models predict ecosystem change at the 

species and organism level on the basis of the physiological 

tolerances and ecological requirements of a species and 

specific environmental conditions of an organism or individual 

plant (table 6). L2L ecosystem models incorporate a 

hierarchical integration of ecological processes and response 

from the leaf level, where photosynthesis, water exchange, 

and carbon allocation take place on an individual tree or 

plant basis, to the stand level, where interplant competition, 

biomass, and diversity are measured, to the landscape level, 

where physical factors of soil, watershed, climate, and 

disturbance differences affect system-level response.

Coupled Saltmarsh Biogeochemical and 

Demographic Model

A coupled biogeochemical and demographic model 

(Simas and others, 2001) was developed for saltmarsh in the 

Tagus estuary in Portugal to predict potential effects of sealevel rise on distribution of C3

 and C4 plant species (plants that 

use a carbon fixation pathway with 3- and 4-carbon molecules, 

respectively, in the first stable photosynthetic product). 

Saltmarsh processes are simulated as a function of C3

 and 

C4 species carbon production from light- and temperaturedependent functions of growth, respiration, and leaf mortality. 

A class transition model simulated the demographics of plant 

population density per unit area. A GIS tool was used to track 

changes in elevation with constant sedimentation inputs over 

time and the various production attributes of lower marsh (C4

) 

and upper marsh (C3

) with sea-level rise of 95 cm by 2100. 

The model does not simulate marsh migration upslope but 

rather simulates the loss of marsh habitat cover and production 

over time. 

Hammock-Mangrove Vadose Zone Model

The ecotone of vegetation boundaries between tidal 

freshwater and saltwater wetlands is often fairly discrete but 

can be overlapping and intermingled. In The Everglades, 

mangroves have expanded upslope into zones once dominated 

by freshwater wetlands over the past century concomitant 

with sea-level rise and freshwater drainage effects. Mangrove 

propagule transport by landward wind and storm tides pushing 

up tidal creeks and drainage canals allows for regeneration 

opportunities far inland. Saltwater intrusion simultaneously 

occurs with redirected freshwater drainage and increasing sea 

level. The Hammock-Mangrove Vadose Zone Model (Teh 

and others, 2008) was developed by the USGS to investigate 

the process of species distribution and productivity response 

of changing salinity concentrations with sea-level rise. The 

simulation model is of hypothetical design for a 1-hectare 

landscape (made of 100-by-100 1-m grid cells) at the ecotone 

of hardwood hammock and mangrove vegetation with an 

assumed elevation gradient of 10 cm/km. 
World Geodetic System, 1984

MISSOURI

MISSISSIPPI

ALABAMA

TENNESSEE

ILLINOIS

KENTUCKY VIRGINIA

NORTH CAROLINA

SOUTHCAROLINA

GEORGIA

FLORIDA

KANSAS

OKLAHOMA

TEXAS

ARKANSAS

LOUISIANA

ATLANTIC OCEAN

GULF OF MEXICO

PACIFIC OCEAN

CARIBBEAN SEA

00 300

300

600 KILOMETERS

600 MILES

United States

VENEZUELA

COLOMBIA

PANAMA

NICARAGUA

GUATEMALA

MEXICO

HONDURAS

CUBA

HAITI

100o

30o20o10o

80o 

Mangro

durin

EXPLAN

Figure 20. Eocene distribution limits of mangrove (modified from Sherrod and McMillan, 1985; used with permission).
TEXAS

FLORIDA

GEORGIA ALABAMA

LOUISIANA

MISSISSIPPI

NORTH

CAROLINA

SOUTH

CAROLINA

GULF OF MEXICO

B1

Presence

A2

Presence

ATLANTICOCEAN

ATLANTICOCEANATLANTICOCEAN

GULF OF MEXICO

GULF OF MEXICO GULF OF MEXICO

ATLANTICOCEAN

A2

Abundance

B1

Abundance

A BC D

TEXAS

FLORIDA

GEORGIA ALABAMA

LOUISIANA

MISSISSIPPI

NORTH

CAROLINA

SOUTH

CAROLINA

TEXAS

FLORIDA

GEORGIA ALABAMA

LOUISIANA

MISSISSIPPI

NORTH

CAROLINA

SOUTH

CAROLINA

TEXAS

FLORIDA

GEORGIA ALABAMA

LOUISIANA

MISSISSIPPI

NORTH

CAROLINA

SOUTH

CAROLINA

N

Proba

fo

Man

a

00 

Figure 21. Climate-Envelope Mangrove Model predictions of mangrove forest presence and relative abundance under future climate scenarios (from used with permission). A, Probability of mangrove forest presence with an ensemble B1 scenario climate. B, Probability of mangrove forest presence scenario climate. C, Mangrove forest relative abundance with an ensemble B1 scenario climate. D, Mangrove forest relative abundance with an ense
Table 6. Attributes of select leaf to landscape ecosystem models.[m, meter; yr, year; TM, Landsat Thematic Mapper; NWI, National Wetlands Inventory; <, less than; m2, square meter; km2, square kilometer; lidar, light detection and ranging; cm, cent

Model Agency/ organization Appropriate scale Spatial resolution Temporal scale Input parameters Vegetation classification Output parameters 

Coupled Saltmarsh 

Biogeochemical and Demographic Model

Universidade 

Nova de LisboaQuinta Da Torre, Portugal

Local 30–300 m Hourly time 

step, simulation time up to 110 yr

Elevation and bathymetry, 

air temperature, physiological parameters for marsh plants, TM imagery

NWI Maps of land change, 

flooded areas, biomass and plant density

HammockMangrove 

Vadose Zone Model 

U.S. Geological 

Survey

Hypothetical 

landscape 100 by 

100 m

1 m Daily time step, 

simulation time 50 yr 

Precipitation, tidal 

height, speciesspecific physiological parameters, storm surge data, hypothetical elevation

Hammock 

mangrove

Maps of vegetation 

change, salinity, vegetation density, gross productivity

Spatially Explicit 

Landscape Vegetation Analysis Model (SELVA)

U.S. Geological 

Survey, National Wetlands Research Center

Local, 

regional (such as 

<1 m2–

100,000 km2)

100–10,000 m Time step of 1 

yr, simulation time up to 1,000 yr

Elevation maps (lidar 

preferred), wetland land cover (such as NWI), development footprint, sea-level rise projections, monthly tide gage records

Gap Analysis, 

Landsat TM imagery, NWI

Maps of areas/habitats/

forest structure/biodiversity potentially vulnerable to inundation, hurricanes, lightning strikes, drought, hydrology (land cover and elevation maps)

Mangrove Forest 

Growth and Succession Model (MANGRO)

U.S. Geological 

Survey, National Wetlands Research Center

Tree and 

stand level, local (such as 

<1 cm–

100 m)

1 cm–100 m Time step of 1 

yr can be used on the basis of the sea-level rise scenario, simulation time up to 1,000 yr

Elevation maps (lidar 

preferred), wetland land cover (such as NWI), development footprint, climate data, hydrology, sea-level rise projections

Mangrove 

species, Avicennia germinans, Laguncularia racemosa, Rhizophora mangle

Forest composition, 

biomass, density and individual tree height, diameter, growth, and mortality metrics by species

Spatial Relative

Elevation Model(SREM)

Skagit River 

Systemcooperative, Western Washington University

Local (70 

km2)

50 m Time step of 

0.25 weeks, simulation time 400 years

Bathymetry grid, 

subsidence rate from SET, sea-level rise rate, net primary production and biomass functions, sediment dynamics model parameters

Eelgrass 

species,Zostera marina,Zostera japonica

Bathymetry, annual net 

aboveground primary productivity, standing stock

WETLANDS U.S. Geological 

Survey

Local, 

regional, national

10–30 m Monthly or 

annual time step, simulation time up to 100 yr

Monthly tide gage 

records, sea-level rise projections, climate data, hydrology

Landsat TM 

imagery, NWI

Maps of land change, 

habitat migration and displacement
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 47

The Hammock-Mangrove Vadose Zone Model simulates 

daily changes and diffusion of saltwater intrusion on the basis 

of difference calculations or infiltration rates of precipitation, 

evaporation, and transpiration distinguished by wetland 

type. Daily tide values are compared with cell elevation 

to determine flux additions of salinity that is subsequently 

diffused among neighboring cells and with soil depth based on 

salinity concentrations. A single 1-m grid cell can contain both 

hammock and mangrove species. Physiological parameters 

are species specific to account for species growth rates and 

litter production. The model was developed principally to 

evaluate the process of self-organization under chronic sealevel changes and acute impacts of storm surge synoptics. 

The model predicts species biomass and distribution spatially 

and temporally for exploring the process of regime shifts of 

species dominance. 

SELVA-MANGRO Model

The near-sea-level elevation and flat slope of the 

protected ecosystem of The Everglades account for one of the 

largest contiguous tracts of mangrove forests found anywhere 

in the world and emphasize the potential vulnerability of 

coastal wetlands to rising sea level and changes in freshwater 

management. An L2L ecosystem model known as SELVAMANGRO (Doyle and Girod, 1997; Doyle and others, 2003b; 

Berger and others, 2008) was developed for Neotropical 

mangrove forests by the USGS to investigate the potential 

impacts of climate change on the quality and distribution of 

future coastal wetland habitat of The Everglades. SELVAMANGRO predicts mangrove displacement of freshwater 

marsh and swamp habitat with increasing tidal inundation in 

proportion to the rate of sea-level rise. SELVA-MANGRO 

represents a hierarchically integrated landscape-scale 

vegetation model (SELVA) spatially linked to an individualbased stand simulation model (MANGRO) (table 6) for 

predicting change in the structure and distribution of 

mangrove forests at a park, a refuge, or regional scale. 

Both models are spatially explicit, thereby accounting 

for arrangement of trees within a forest stand and stand 

distribution within a landscape. SELVA-MANGRO comprises 

multiple linked hierarchical relations at the leaf, tree, species, 

stand, ecosystem, and landscape level. The location of each 

individual tree and stand is explicitly mapped within the same 

coordinate system, akin to a GIS. SELVA and MANGRO 

represent trees and space in three-dimensional architecture 

of horizontal and vertical articulation. SELVA contains a 

regionally interpolated DEM of south Florida based on a 1-ft 

contour survey conducted in the 1950s (fig. 22). 

SELVA tracks predicted changes in the biotic and 

abiotic conditions of distributed land units, including forcing 

functions and environmental conditions on an annual time step 

for the entire simulated landscape. SELVA passes necessary 

information of environmental change to the MANGRO 

model at the stand level for each and all land units in the 

landscape profile. MANGRO returns a system condition 

of stand structure and composition to SELVA as predicted 

for each growth season or calendar year. Composite maps 

are produced that exhibit the predicted changes in species 

composition and forest migration, loss, or gain as influenced 

by changes in climate, sea level, hurricane disturbance, and 

freshwater flow (fig. 22). 

SELVA is composed of several primary data layers 

delineating ecosystem boundaries, land elevation, tidal range, 

sea level, river stage, and disturbance conditions. Classified 

vegetation maps of the study area are imported into 

SELVA from all available sources to distinguish ecosystem 

distribution across the simulated landscape including 

mangrove and non-mangrove vegetation of other habitat 

types, such as saltmarsh, freshmarsh, swamp forest, and 

upland forest. Tidal inundation and circulation are key factors 

controlling marsh and forest distribution. Subsidence and 

accretion rates are user-specified inputs that SELVA accepts 

to affect annual elevation change. Boundary zones of major 

habitat classes are used to delineate the lower and upper 

elevations of the intertidal zone as defined by mangrove 

extent relating to mean low tide and mean high tide by 

coastal reach. Tide gage records are used to interpolate tidal 

range across the land-sea interface of the study region and to 

define the intertidal plane.

MANGRO is an individual-based model composed of 

a species-specific set of biological functions predicting the 

growth, establishment, and death of individual trees. Species 

selection and function include mangrove species common 

to Florida and the Neotropics: Avicennia germinans (black 

mangrove), Laguncularia racemosa (white mangrove), and 

Rhizophora mangle (red mangrove). MANGRO includes 

empirically based algorithms for tolerance of each species 

to shade on the basis of light-response experiments and the 

ability of each species to regenerate and grow under flooded 

conditions. MANGRO predicts the tree and gap replacement 

process of natural forest succession as influenced by stand 

structure and prevailing environmental conditions (fig. 

22B). MANGRO assigns attributes to each tree including 

latitude, longitude, species, stem diameter, height, crown 

dimensions, leaf area, and spatial position relative to 

neighbors (distance and azimuth). Growth of individual trees 

is dependent on species, stem diameter, crown size, leaf area, 

light availability, flooding, salinity, and competition from 

neighboring trees. Crown growth and structure are explicitly 

modeled as a function of crown space and preeminence as to 

which tree fills space first for a given crown height and class. 

Canopy structure is modeled as a three-dimensional process 

of crown height, width, and depth in relation to sun angle 

and shading by neighboring trees. Tree growth is modeled as 

a function of growth potential for a given tree size reduced 

by derived crown volume, light availability to the individual 

tree, and species response to shade. Tree death results from 

prolonged growth suppression, senescence, or disturbance by 

hurricane, logging, or lightning strike.
A

Figure 22. Screenshots showing SELVA-MANGRO model application in south Florida (from Doyle and others, 2003b; used with permission). A, South 

model. B, MANGRO stand simulation model. C, Historical mangrove distribution circa 1940. D, Predicted mangrove distribution under sea-level rise by 
B

Figure 22. Screenshots showing SELVA-MANGRO model application in south Florida (from Doyle and others, 2003b; used with permission). A, South 

model. B, MANGRO stand simulation model. C, Historical mangrove distribution circa 1940. D, Predicted mangrove distribution under sea-level rise by 
C

Figure 22. Screenshots showing SELVA-MANGRO model application in south Florida (from Doyle and others, 2003b; used with permission). A, South 

model. B, MANGRO stand simulation model. C, Historical mangrove distribution circa 1940. D, Predicted mangrove distribution under sea-level rise by 
D

Figure 22. Screenshots showing SELVA-MANGRO model application in south Florida (from Doyle and others, 2003b; used with permission). A, South 

model. B, MANGRO stand simulation model. C, Historical mangrove distribution circa 1940. D, Predicted mangrove distribution under sea-level rise by 
52 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

Spatial Relative Elevation Model (SREM)

Seagrass meadows are important habitats for a number 

of fisheries and wildlife taxa. Although aquatic, these 

meadows are also at risk under rising sea levels. Seagrasses 

exist within a restricted depth range as influenced by 

substrate, water quality, and most importantly, turbidity, 

which can limit light penetration and growth. A change in 

sea level, whether rise or fall, affects their distribution and 

survival in nearshore waters. The ability of certain species 

to persist or migrate with rising sea level depends on how 

and where sediment deposition occurs as sea level changes 

over time. The Spatial Relative Elevation Model (SREM) 

(Kairis and Rybczyk, 2010) was developed for Padilla 

Bay, Washington, to predict changes in bathymetry on the 

basis of distribution and productivity of seagrass meadows 

dominated by Zostera marina (eelgrass). SREM is a modified 

version of a marsh soils cohort model for coastal Louisiana 

adapted to consider the processes of sediment deposition 

affecting bathymetry of an aquatic bay system. SREM is 

a spatial model of Padilla Bay at a 50-m grid resolution 

with no interaction between neighboring cells. Each cell 

simulates processes of seagrass productivity, aboveground 

and belowground, and standing stock resulting in net changes 

in bay-bottom elevation. Survival is determined by discrete 

depth ranges, taking into account that water can be too 

shallow or too deep. A number of sea-level rise scenarios 

were applied to forecast bathymetric changes of Padilla 

Bay and the threat to eelgrass communities. Results show 

that, except for the most extreme sea-level rise projections, 

eelgrass communities will not diminish and may even 

flourish because of the proportion of shallow mud flats that 

will become favorable colonization sites.

WETLANDS

The WETLANDS L2L ecosystem model (Doyle 

and others, 2003a) predicts plant-species distribution 

and migration with changing land-water relations. The 

WETLANDS model is a USGS product that uses empirical 

relations of species occurrence by elevation within the tidal 

plane to project marsh regression or transgression with 

sea-level fall or rise, respectively. The model uses data 

from a field study that was conducted to relate plant-species 

distribution and ecotones to surface elevation and tidal 

inundation by using differential leveling surveys (fig. 23). 

The WETLANDS model contains functional probabilities 

of species tolerance to flooding conditions that dictate the 

rate and process of ecological succession and coastal retreat 

Bare

Spartina

alterniflora

Juncus spp.

Sand flat

Palm spp., dead

Palm spp.

Pine

Plant species

EXPLANATION

0

10

20

30

40

50

60

70

80

90

100

-0.5 -0.3 -0.1 0.1 0.3 0.5 0.7 0.9 1.1 >1.2

Percent occurrence

Elevation above mean sea level, in meters

Figure 23. WETLANDS species distribution by elevation (from Doyle and others, 2003a; used with permission).
Summary 53

with sea-level rise. Map information of hypsography and 

bathymetry of the coastal zone was digitized and interpolated 

to construct a regional-scale DEM prior to the availability of 

NED and lidar datasets. Classified Landsat Thematic Mapper 

imagery of aquatic and terrestrial habitat at a community 

level was used to initialize model simulation by species 

and vegetation type. The WETLANDS model, assuming 

hydrological connectivity of adjacent open water cells, tracks 

the process and pattern of coastal inundation spatially and 

temporally. The model predicts migration of marsh habitat 

and transgression of coastal forest habitat as sea level moves 

upslope. Simulated forecasts of the tidal regime are based on 

mean monthly sea-level projections from regionally relevant 

tide gage records. The model contrasts the predicted mean 

sea-surface height with surface elevations of each land unit 

(that is, 30-m pixel) to determine flood height. Flood height 

is then used to predict favored habitat condition on the basis 

of probability functions of species and community tolerance 

to coastal inundation and elevation calibrated from field 

surveys. In years in which flood height exceeds tolerance for 

the prevailing species complement or habitat condition, new 

or updated species cover and community type are assigned to 

the habitat array to reflect a change in ecological succession. 

Model output consists of pixel counts and hectares of 

converted habitat (loss and gain) by calendar year. 

Summary

Changes in climate during past ice ages and warming 

periods have affected sea levels and coastal extent as 

evidenced from ice, sediment, peat, and fossil records. 

Ancient sea-level reconstructions from multiple disciplines 

and techniques demonstrate worldwide consistency of high 

and low sea-level cycles of 120 meters (m) or more for long 

glaciation periods and higher and lower rates during the rise 

and fall segments. Current sea-level rates of more than 3 

millimeters per year (mm/yr) in global ocean volume (eustasy) 

are moderate by historical calculations but when amplified 

by high rates of land subsidence may prove consequential 

to wetland stability and persistence on a local or regional 

basis. Tide gage records show that some coastal reaches are 

undergoing uplift from glacial rebound that accounts for an 

effective sea-level fall counter to the prevailing global rise 

of sea level elsewhere. Satellite altimetry provides a more 

accurate rate of global ocean volume (eustasy) exceeding 

3 mm/yr for the most recent tidal epoch (1994–2012). Tide 

gages for the same time period generally show higher sealevel rise rates where there is active subsidence or crustal 

tilt. Sea-level rise rates for U.S. tide gages along the Gulf 

of Mexico coastline exhibit comparatively lower and higher 

rates in earlier tidal epochs of the 20th century, confounding 

any certainty of short- or long-term acceleration based 

on satellite observations. 

In this handbook, we explain and illustrate proper 

evaluation of sea-level rates between satellite observations and 

tide gages, which requires observed records that are longer 

than a tidal epoch (greater than 19 years), have the same start 

and end dates, are seasonally balanced, have equal frequency 

modulation, and are complete (without data gaps). Otherwise, 

it is improper and problematic to contrast a sea-level rate 

from long-term tide gage trends with short-term satellite 

observations, as has been done in media and scientific reports. 

In effect, tide gage records and satellite observations are 

compatible and complementary, and there is value and need 

for both. Satellites account solely for the change in eustasy, 

whereas tide gages also capture the rate and direction of land 

motion (rise or fall).

No matter the degree of human or natural consequence, 

rising sea level is already affecting our coastal ecosystems 

and infrastructure to an extent that society must deal with the 

problems and setbacks of coastal flooding. Various Earthclimate and coastal wetland models have been developed to 

address the interaction and impact of changing climate and 

land-use from a sea-level rise perspective. Simple models 

utilizing coarse datasets and constructs of the behavior of 

natural systems may only provide limited utility and certainty 

suitable for instructional or educational purposes, whereas 

more complex models require expert development and more 

site-specific parameterization and validation for aiding 

management decisions. The dynamic nature of the components 

of the Earth’s hydrosphere and climate that account for the 

net balance of ocean volumes combined with the interactive 

effects of rebound and tilting of continental plates, as well 

as differential local and regional subsidence, presents a 

complicated process to model easily or with certainty. Models 

help our understanding of these interacting processes and 

provide the basis for forecasting potential change useful for 

land management and conservation planning. 

In 2012, the U.S. Geological Survey conducted more 

than 30 training and feedback sessions with Federal, State, 

and nongovernmental organization (NGO) coastal managers 

and planners across the northern Gulf of Mexico coast to 

educate and to evaluate user needs and their understanding of 

concepts, data, and modeling tools for projecting sea-level rise 

and its impact on coastal habitats and wildlife. It was our goal 

to introduce the non-expert to the broad spectrum of models 

and applications that have been used to predict or forecast 

environmental change and ecosystem response for sea-level 

rise assessments. We have compiled a fairly comprehensive 

treatise of the data, models, and methods from various related 

disciplines to offer a condensed synthesis of the science and 

simulation models of sea-level rise, past, present, and future. 

As demonstrated, the models vary greatly in design and detail, 

functionally and structurally, spatially and temporally, physical 

to biological, from leaf to landscape, and so on. It is our hope 

that coastal managers, engineers, and scientists will benefit 

from a greater understanding of the dynamics and models for 

projecting causes and consequences of sea-level change on the 

landscape and seascape.
54 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

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58 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
Appendixes 59

Appendixes
60 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
Appendixes 61

Appendix 1. Effect of Steric Temperature Functions on Ocean Volume

The change in seawater volume for a given change in temperature can be calculated from the change in the water density 

(ρ, in kilograms per cubic meter [kg/m3]). If the water pressure is constant, the water density depends on the practical salinity (S, 

in parts per thousand [ppt]) and temperature (t, in degrees Celsius [oC]). 

Seawater density is given by the following formula, according to Fofonoff and Millard (1983): 

ρ (S, t, 0) = ρw + (b0 +b1t +b2t

2

 + b3t

3

 + b4t

4

)S + (c0 + c1t + c2t

2

)S3/2 + d0S2 (1)

where 

ρw = density of reference pure water,

b0 = +8.24493 * 10-1, 

b1 = -4.0899 * 10-3, 

b2 = +7.6438 * 10-5, 

b3 = -8.2467 * 10-7, 

b4 = +5.3875 * 10-9, 

c0 = -5.72466 * 10-3, 

c1 = +1.0227 * 10-4, 

c2 = -1.6546 * 10-6, and

d0 = +4.8314 * 10-4.

The density of the reference pure water is given by the following formula: 

ρw = a0 + a1t + a2t

2

 + a3t

3

 + a4t

4

 + a5t

5 (2)

where 

a0 = +999.842594, 

a1 = +6.793952 * 10-2, 

a2 = -9.095290 * 10-3, 

a3 = +1.001685 * 10-4, 

a4 = -1.120083 * 10-6, and

a5 = +6.536332 * 10-9.

The formulas have been implemented as MATLAB code by James Manning (http://globec.whoi.edu/globec-dir/sigmatcalc-matlab.html) and Python code by Bjørn Ådlandsvik (http://www.imr.no/~bjorn/python/seawater/index.html). The two 

implementations are based on the SeaWater library (http://www.cmar.csiro.au/datacentre/ext_docs/seawater.htm) of EOS-80 

seawater properties (Millero and others, 1980). For example, if the reference column of water is 100 centimeters (cm) (1 meter) 

in height at 0 °C and 35 ppt salinity, the height of the column (proportional to the water volume) changes with the temperature 

and the salinity as presented in table 1–1 (computed with Python code).

If the seawater pressure is a variable, then similar polynomial formulas containing 26 coefficients (Fofonoff and Millard, 

1983) are added to the above formulas to compute the water density for a given temperature, salinity, and pressure. 

The SeaWater library of EOS-80 seawater properties has been superseded by the Gibbs SeaWater (GSW) Oceanographic 

Toolbox of the International Thermodynamic Equation of Seawater 2010 (TEOS-10; http://www.teos-10.org/). The density of 

seawater in the GSW toolbox is given by a formula involving 48 coefficients (IOC, SCOR, and IAPSO, 2010). The values in 

table 1–1 remain the same under the new equations.
62 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

Table 1–1. Height of water column at different temperatures and 

salinities.

[cm, centimeter]

Temperature, in 

degrees Celsius

Height of water column

Salinity, in parts per thousand

0 35

0 102.827 cm 100.000 cm

34 103.392 cm 100.765 cm

References Cited

Fofonoff, N.P., and Millard, R.C., Jr., 1983, Algorithms 

for computation of fundamental properties of seawater: 

UNESCO Technical Papers in Marine Science, no. 44, 53 p.

IOC, SCOR, and IAPSO, 2010, The international 

thermodynamic equation of seawater—2010—Calculation 

and use of thermodynamic properties: Intergovernmental 

Oceanographic Commission, Manuals and Guides, no. 56, 

UNESCO (English), 196 p.

Millero, F.J., Chen, Chen-Tung, Bradshaw, Alvin, and 

Schleicher, Karl, 1980, A new high pressure equation of 

state for seawater: Deep-Sea Research, v. 27A, p. 255–264.
Appendixes 63

Appendix 2. Published Sea-Level Trends for U.S. Tide Gages

Table 2–1. Published sea-level trends for U.S. tide gages.

[Measurements are in millimeters per century; -, value not available]

Station 

identificationCity, port, State Start year

End year

1972

(Hicks and Crosby, 

1974)

1986

(Lyles and

others, 1988)

Up to 1999

(Flick and

others, 2003)

1999

(Zervas, 

2001)

2006

(Zervas, 

2009)

East Coast, United States

8410140 Eastport, Maine 1929 360 270 224 212 200

8413320 Bar Harbor, Maine 1947 - 270 219 218 204

8418150 Portland, Maine 1912 230 220 194 191 182

8419870 Seavey Island, Maine 1926 - 180 - 175 176

Portsmouth, N.H. - 242 - 169 - -

8443970 Boston, Mass. 1921 289 290 269 265 263

Sandwich, Mass. - - - 80 - -

8447930 Woods Hole, Mass. 1932 346 271 262 259 261

Buzzards Bay, Mass. - 117 - - - -

8449130 Nantucket, Mass. 1965 - - 315 300 295

8452660 Newport, R.I. 1930 304 270 255 257 258

8454000 Providence, R.I. 1938 237 180 217 188 195

8461490 New London, Conn. 1938 263 210 208 213 225

8467150 Bridgeport, Conn. 1964 - 210 244 258 256

8510560 Montauk, N.Y. 1947 231 190 248 258 278

8514560 Port Jefferson, N.Y. 1957 362 270 215 244 244

8516945 Kings Point, N.Y. 1931 - 240 243 241 235

New Rochelle, N.Y. - 310 60 -57 54 -

8518750 The Battery, N.Y. 1856 287 270 319 277 277

8531680 Sandy Hook, N.J. 1932 492 410 397 388 390

8534720 Atlantic City, N.J. 1911 390 390 415 398 399

8536110 Cape May, N.J. 1965 - - 365 388 406

8545240 Philadelphia, Pa. 1900 267 260 - 275 279

8557380 Reedy Point, Del. 1919 - - - - 346

8551910 Lewes, Del. 1956 354 310 304 316 320

8570283 Ocean City, Md. 1975 - - - - 548

8571892 Cambridge, Md. 1943 - - - 352 348

8573927 Chesapeake City, Md. 1972 - - - - 378

8574680 Baltimore, Md. 1902 339 320 313 312 308

8575512 Annapolis, Md. 1928 423 360 374 353 344

8577330 Solomons, Md. 1937 387 330 - 329 341

8594900 Washington, D.C. 1924 328 320 308 313 316

8632200 Kiptopeke, Va. 1951 - - 367 359 348

8635150 Colonial Beach, Va. 1972 - - - 527 478
64 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

Table 2–1. Published sea-level trends for U.S. tide gages.—Continued

[Measurements are in millimeters per century; -, value not available]

Station 

identificationCity, port, State Start year

End year

1972

(Hicks and Crosby, 

1974)

1986

(Lyles and

others, 1988)

Up to 1999

(Flick and

others, 2003)

1999

(Zervas, 

2001)

2006

(Zervas, 

2009)

East Coast, United States—Continued

8635750 Lewisetta, Va. 1974 - - - 485 497

8637624 Gloucester Point, Va. 1950 - - - 395 381

8638610 Sewells Point, Va. 1927 - - - 442 444

Hampton Roads, Va. - 463 430 435 - -

8638660 Portsmouth, Va. 1935 381 370 - 376 376

8638863 Bridge Tunnel, Va. 1975 - - 744 701 605

8652587 Oregon Inlet, N.C. 1977 - - - - 282

8656483 Beaufort, N.C. 1953 - - - 371 257

8658120 Wilmington, N.C. 1935 - - 201 222 208

8659084 Southport, N.C. 1933 - - - - 207

8661070 Springmaid Pier, S.C. 1957 - - - 517 409

8665530 Charleston, S.C. 1921 361 340 318 328 315

8670870 Fort Pulaski, Ga. 1935 265 300 297 305 298

8720030 Fernandina, Fla. 1897 184 190 216 204 202

8720218 Mayport, Fla. 1928 269 220 234 243 240

8721120 Daytona Beach, Fla. 1925 - - - - 232

8723170 Miami Beach, Fla. 1931 250 230 218 239 239

8723970 Vaca Key, Fla. 1971 - - 227 258 278

Atlantic Caribbean

2695540 Bermuda 1932 - - - 183 204

9731158 Guantanamo, Cuba 1937 - - 285 164 164

9751401 Lime Tree Bay, Virgin Islands 1977 - - - - 174

9751639 Charlotte Amalie, Virgin Islands 1975 - - - 50 120

9755371 San Juan, Puerto Rico 1962 - - 152 143 165

9759110 Mayagues, Puerto Rico 1955 - - - 124 135

Cristobal, Panama - 124 - - - -

Gulf Coast, United States

8724580 Key West, Fla. 1913 210 220 192 227 224

8725110 Naples, Fla. 1965 - - 133 208 202

8725520 Fort Myers, Fla. 1965 - - - 229 -

8726520 St. Petersburg, Fla. 1947 - 230 239 240 236

8726724 Clearwater Beach, Fla. 1973 - - 269 276 243

8727520 Cedar Key, Fla. 1914 204 190 137 187 180

8728690 Apalachicola, Fla. 1967 - - - 153 138

8729108 Panama City, Fla. 1973 - - - 30 75

8729840 Pensacola, Fla. 1923 236 240 214 214 210

8735180 Dauphin Island, Ala. 1966 - - - 293 298
Appendixes 65

Table 2–1. Published sea-level trends for U.S. tide gages.—Continued

[Measurements are in millimeters per century; -, value not available]

Station 

identificationCity, port, State Start year

End year

1972

(Hicks and Crosby, 

1974)

1986

(Lyles and

others, 1988)

Up to 1999

(Flick and

others, 2003)

1999

(Zervas, 

2001)

2006

(Zervas, 

2009)

Gulf Coast, United States—Continued

8761724 Grand Isle, La. 1947 - 1,050 - 985 924

8764311 Eugene Island, La. 1939 921 970 - 974 965

8770570 Sabine Pass, Tex. 1958 - 1,320 - 654 566

8771450 Galveston Pleasure Pier, Tex. 1908 - 640 653 739 684

8771510 Galveston Pier 21, Tex. 1957 595 750 708 650 639

8772440 Freeport, Tex. 1954 - 1,400 1,099 587 435

8774770 Rockport, Tex. 1948 - 400 564 460 516

8778490 Port Mansfield, Tex. 1963 - - -213 205 193

8779751 Padre Island, Tex. 1958 - 510 - 344 348

8779770 Port Isabel, Tex. 1944 - 310 327 338 364

West Coast, United States

9410170 San Diego, Calif. 1906 199 210 231 215 206

9410230 La Jolla, Calif. 1924 191 200 229 222 207

9410580 Newport Beach, Calif. 1955 - 190 - 222 222

9410660 Los Angeles, Calif. 1923 66 80 91 84 83

9410840 Santa Monica, Calif. 1933 - 180 161 159 146

9411270 Rincon Island, Calif. 1962 - - - 322 322

9411340 Santa Barbara, Calif. 1973 - - - 277 125

9412110 Port San Luis, Calif. 1945 - 120 196 90 79

9413450 Monterey, Calif. 1973 - - 302 186 134

9414290 San Francisco, Calif. 1854 197 130 145 141 201

9414290 San Francisco, Calif. 1906 - - - 213 -

9414523 Redwood City, Calif. 1974 - - - - 206

9414750 Alameda, Calif. 1939 45 100 81 89 82

9415020 Point Reyes, Calif. 1975 - - 405 251 210

9415144 Port Chicago, Calif. 1976 - - 727 - 208

9418767 North Spit, Calif. 1977 - - - - 473

9419750 Crescent City, Calif. 1933 -49 -60 -50 -48 -65

9431647 Port Orford, Ore. 1977 - - - - 18

9432780 Charleston, Ore. 1970 - - 207 174 129

9435380 South Beach, Ore. 1967 - - 369 351 272

9437540 Garibaldi, Ore. 1970 - - - - 198

9439040 Astoria, Ore. 1925 5 -30 6 -16 -31

9440910 Toke Point, Wash. 1973 - - - 282 160

9443090 Neah Bay, Wash. 1934 -86 -110 -103 -141 -163

9444090 Port Angeles, Wash. 1975 - - - 149 19

9444900 Port Townsend, Wash. 1972 - - - 282 198
66 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

Table 2–1. Published sea-level trends for U.S. tide gages.—Continued

[Measurements are in millimeters per century; -, value not available]

Station 

identificationCity, port, State Start year

End year

1972

(Hicks and Crosby, 

1974)

1986

(Lyles and

others, 1988)

Up to 1999

(Flick and

others, 2003)

1999

(Zervas, 

2001)

2006

(Zervas, 

2009)

West Coast, United States—Continued

9447130 Seattle, Wash. 1898 193 200 212 211 206

9449424 Cherry Point, Wash. 1973 - - 189 139 82

9449880 Friday Harbor, Wash. 1934 115 140 138 124 113

9450460 Ketchikan, Alaska 1919 0.3 -10 -4 -11 -19

9451600 Sitka, Alaska 1924 -231 -220 -211 -217 -205

9452210 Juneau, Alaska 1936 -1,346 -1,240 -1,248 -1,269 -1,292

9452400 Skagway, Alaska 1944 - -1,730 -1,636 -1,668 -1,712

9453220 Yakutat, Alaska 1940 -533 -460 -552 -575 -1,154

9454050 Cordova, Alaska 1964 - 706 697 257

9454240 Valdez, Alaska 2006 - - 5 -34 -492

9455090 Seward, Alaska 1925 - - 2,069 -146 -174

9455500 Seldovia, Alaska 1964 - -820 -997 -993 -945

9455760 Nikiski, Alaska 1973 - - - -1,071 -98

9455920 Anchorage, Alaska 1972 - - 381 276 88

9457292 Kodiak Island, Alaska 1949 - - - -1,208 -1,042

9459450 Sand Point, Alaska 1972 - - 108 7 92

9461380 Adak Island, Alaska 1943 - 0 -60 -263 -275

9462620 Unalaska, Alaska 1957 - -710 -565 -644 -572

Pacific Islands

1611400 Nāwiliwili, Hawaii 1955 - 200 162 153 153

1612340 Honolulu, Hawaii 1905 156 160 129 150 150

1612480 Mokuoloe, Hawaii 1957 - 360 - 112 131

1615680 Kahului, Hawaii 1947 - - 234 209 232

1617760 Hilo, Hawaii 1927 - - 340 336 327

1619000 Johnston Atoll 1947 - 40 55 68 75

1619910 Midway Atoll 1947 - -60 8 9 70

1630000 Guam, Marianas Islands 1948 - -120 -36 10 845

1770000 Pago Pago, American Samoa 1948 - 150 199 148 207

1820000 Kwajalein, Marshall Islands 1946 - 100 87 105 143

1840000 State of Chuuk, Caroline Islands 1947 - 100 - 68 60

1890000 Wake Island 1950 - 80 155 189 191
Appendixes 67

References Cited

Flick, R.E., Murray, J.F, and Ewing, L.C., 2003, Trends in 

United States tidal datum statistics and tide range: Journal 

of Waterway, Port, Coastal, and Ocean Engineering, v. 129, 

no. 4, p. 155–164. 

Hicks, S.D., and Crosby, J.E., 1974, Trends and variability 

of yearly mean sea level, 1893–1972: Rockville, Md., 

National Ocean Survey, National Oceanic and Atmospheric 

Administration. 

Lyles, S.D., Hickman, L.E., and Debaugh, H.A., 1988, Sealevel variations for the United States 1855–1986: Rockville, 

Md., U.S. Department of Commerce.

Zervas, Chris, 2001, Sea level variations of the United 

States 1854–1999: National Oceanic and Atmospheric 

Administration Technical Report NOS CO-OPS 36.

Zervas, Chris, 2009, Sea level variations of the United 

States 1854–2006: National Oceanic and Atmospheric 

Administration Technical Report NOS CO-OPS 53.
68 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
Appendixes 69

Appendix 3. Elevation and Vegetation Data Sources for Sea-Level Rise Modeling

The recent advent of geospatial software, digital modes 

of cartographical referencing, and remote sensing applications 

has greatly expanded the scope of data visualization and 

spatial modeling critical to the study and analysis of Earthclimate systems and sea-level rise. Fundamental to the process 

of assessing the impact of rising sea levels on a natural or 

cultural feature, park or refuge, ecosystem or species range, or 

coastline or continent is knowing where you are and whether 

you are above or below a past, present, or future waterline. 

Geographic information systems (GIS) and the Global 

Positioning System (GPS) are common tools on the job, in the 

home, or on mobile phones and are useful for placing oneself 

or any physical or natural feature in time and space. Most sealevel rise analyses or applications have some spatial context 

and depend on geospatial data sources and software to make 

assessments and predictions. Fundamental geospatial data 

inputs include model boundaries or study area; land, water, 

or feature elevations; and cover type, usually vegetation, 

species, ecosystem, or cultural. This appendix provides a basic 

overview of commonly used geospatial datasets of sea-level 

rise models and an evaluation of data utility for sea-level 

vulnerability assessments.

Topography and bathymetry are key parameters of the 

process and consequence of coastal change based on elevation 

above or below past, present, or future sea level. Relating the 

surface elevation of the terrestrial to the marine environment 

requires a compatible reference datum with respect to sea 

surface (such as mean high water [MHW] or mean higher high 

water [MHHW]). Historically, the records and datums for land 

and water have been collected and referenced differently in 

orthometric and tidal datums that are not directly comparable 

without rectification or transformation from one to the other. 

Depending on the date, age, and process of data gathering, the 

records also may be of different orthometric and tidal datums 

of different eras, thereby requiring additional rectification to 

match modern datums. The subject of datum transformations 

is critically important but beyond the scope of this handbook 

except to educate potential users and modelers of the need to 

rectify all data sources and elevations into a compatible format 

and the same reference plane. 

The choices of digital data for surface elevations 

and habitat type are becoming increasingly diverse and 

are generally improving in detail, spatial resolution, and 

accuracy. In this appendix, we describe various publicdomain topographic and bathymetric data products of global, 

national, and regional significance that have been or can be 

used for sea-level rise modeling. Important considerations 

are addressed regarding the data source, spatial resolution, 

temporal period, and the data vertical and horizontal 

accuracies (table 3–1). Evaluations of the different data 

sources are contrasted to describe the utility and limitations of 

the data for coastal change analysis. Widespread and readily 

available elevation sources discussed herein include Shuttle 

Radar Topography Mission (SRTM) data, various versions of 

the National Elevation Dataset (NED) (for example, NED 1 

arc-second, NED 1/3 arc-second, and NED 1/9 arc-second), 

and mission-based light detection and ranging (lidar) data 

collections, photogrammetry, and topographic surveys.

Digital Elevation Models

A digital elevation model (DEM) is defined as an 

electronic file or database containing elevation points over a 

contiguous area. The data files are generally raster-based grids 

or vector-based triangular irregular networks (TINs). On the 

basis of models we have reviewed, raster-based DEMs are 

more prevalent in sea-level rise models; TINs are common in 

more advanced engineering applications involving elevation 

analyses and are beyond the scope of this handbook. Two 

types of DEMs are digital surface models (DSMs) and digital 

terrain models (DTMs). DSMs are models that contain the 

elevations of all features found on the surface of the Earth, 

including vegetation, buildings, or other structures. DTMs 

represent elevation data of bare-earth only and are more 

commonly used for sea-level rise modeling applications. 

Each data product is specified to the particular datum on 

which it is based or referenced (horizontal and vertical, as 

previously described).

Shuttle Radar Topography Mission (SRTM)

The SRTM (Bamler, 1999; Farr and others, 2007) was a 

collaborative mission by the National Aeronautics and Space 

Administration (NASA), the National Imagery and Mapping 

Agency (NIMA), the German Space Agency (DLR), and 

the Italian Space Agency (ASI) to collect synthetic aperture 

radar data from two radar antennas on an 11-day mission in 

February 2000 for more than 80 percent of the Earth’s surface. 

Differences in data received from the two radar antennas were 

analyzed to estimate surface topography. 

SRTM data are available in two spatial resolutions: 3 

arc-seconds (approximately 90 meters [m]) globally and 1 

arc-second (approximately 30 m) for the United States and its 

territories. Rodríguez and others (2005, 2006) assessed SRTM 

errors globally. For North America, more than 90 percent of 

errors were within 12.6 m for absolute horizontal accuracy, 

9 m for absolute vertical accuracy, and 7 m for relative 

height accuracy. The errors were roughly consistent globally. 

The vertical datum for SRTM data is the World Geodetic 

System 1984 (WGS 84) ellipsoid. It is important to note that 

SRTM elevation data do not always map the ground surface 

because radar cannot penetrate dense tree canopy or can be 

reflected off man-made structures or objects. The elevation 
70 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

Table 3–1. Sources, attributes, and accuracies of publicly available digital elevation models.

[RMSE, root mean square error; lidar, light detection and ranging]

Elevation dataset Source(s)

Spatial 

resolution,

in meters

Time period(s)

Horizontal 

accuracy,

in meters

Vertical 

accuracy,

in meters

Shuttle Radar 

Topography 

Mission 

(SRTM)

Satellite-based radar data 

collection

30; 90 February 2000 12.6 9

Relative: 7

National Elevation 

Dataset (NED)

1 arc-second

Stereo photogrammetry and 

linear interpolation of 

cartographic contours

30 1925–present Variable on the 

basis of source 

data

2.44 RMSE Relative 

(average): 1.64

NED1/3 arcsecondSimple linear interpolation 

of cartographic contours, 

digital photogrammetry, 

and polynomial 

interpolation of lidar data

10 Variable on the basis 

of source data; 

typically 1960s–

present

Variable on the 

basis of source 

data

Variable on the basis

of source data

NED 1/9 arcsecondPolynomial interpolation 

of lidar data and digital 

photogrammetry

3.3 Variable on the basis 

of source data; 

typically 2000s–

present

Variable on the 

basis of source 

data

Variable on the basis

of source data

Photogrammetry Overlapping aerial 

photography

Variable on 

the basis 

of mission 

specifications

Variable on the 

basis of mission 

specifications

Variable on 

the basis 

of mission 

specifications

Variable on the basis

of mission 

specifications

Lidar Aerial lidar collection Variable by 

mission 

specifications; 

typically 3–5

Variable on the 

basis of mission 

specifications; 

typically 2000s–

present

Variable on 

the basis 

of mission 

specifications; 

typically 

submeter

Variable on the 

basis of mission 

specifications; 

typically submeter

Topographic 

survey

Field data collection Spot elevations Variable Typically several 

centimeters

Typically several 

centimeters

data from SRTM, therefore, can be considered more of a 

DSM than a “bare-earth” DEM. There are multiple versions 

of the SRTM data. Version 1, released in 2003, is considered 

“research grade” data and should be avoided for sea-level 

rise applications for several reasons, including the following: 

(1) these data are unedited and may contain areas with no 

data or anomalous data, (2) coastlines are typically not well 

delineated, and (3) these data have not been evaluated for 

conformance with National Mapping Accuracy Standards. 

Version 2.0, also known as the SRTM finished data, was 

released in 2005 and contains edits to fill voids and to better 

delineate coastlines and water bodies, specifically oceans, 

lakes, and rivers. Version 2.1 was released in 2009 and had a 

low filter (3×3) applied to clean remaining voids from version 

2.0 data. Version 2.1 is available for only SRTM 3-arcsecond (approximately 90-m) data. The accuracy associated 

with SRTM is notable considering the scale of a near global 

mapping effort; however, these data are not recommended for 

local, regional, or national sea-level rise modeling.

National Elevation Dataset (NED)

NED (Gesch and others, 2002) is a seamless DEM 

produced by the U.S. Geological Survey (USGS) for the entire 

United States and its territories. NED is a dynamic dataset 

that is continually updated as new elevation data, particularly 

lidar data, become available. NED data are referenced to a 

1-by-1 degree latitude/longitude index. NED products are 

available at various spatial resolutions. NED 1 arc-second is 

a DEM product with a spatial resolution of 30 m, whereas 

NED 1/3 arc-second and NED 1/9 arc-second are interpolated 

at finer resolutions of 10 m and 3.3 m, respectively. The 

vertical datum for these products is the North American 

Vertical Datum of 1988 (NAVD 88). NED source data 

vary by DEM product and by geographic area. NED 1-arcsecond data are created by using stereophotogrammetry of 

aerial photographs and linear interpolation of cartographic 

contours. NED 1/3-arc-second and 1/9-arc-second data 

are resampled with different degrees and combinations 
Appendixes 71

of simple linear interpolation of cartographic contours, 

digital photogrammetry, and polynomial interpolation of 

lidar data. The accuracy of these NED products is variable 

depending on the actual data sources used by geographic 

location. The absolute vertical accuracy of the NED 1-arcsecond DEM products was assessed by using control points 

from the National Geodetic Survey (NGS) and has been 

found to have a root mean square error (RMSE) of 2.44-m 

horizontal accuracy and 1.64-m vertical accuracy. Although 

this accuracy assessment provides a useful baseline, it was 

conducted by using about 13,000 control points for the entire 

conterminous United States. If possible, a separate assessment 

is recommended for more localized accuracy estimates 

for any NED product. Accuracy assessment is particularly 

important for NED 1/3-arc-second and NED 1/9-arc-second 

data, for which there is not a reported accuracy. Another way 

to determine the vertical accuracy is to investigate the vertical 

accuracy of the source data. To check availability of NED 

products, updates, methods and source data, and currency 

of data, visit the USGS National Elevation Dataset Viewer 

(http://viewer.nationalmap.gov/viewer/?p=ned). 

Light Detection and Ranging (Lidar)

In 1960, the first optical laser was developed by Hughes 

Aircraft, Inc. (the technology is similar to sonar and can gage 

distance below), and shortly thereafter, early lidar technology 

was used to develop topographic profiles (Jensen, 2007). 

Modern lidar system surface elevation data are generated by 

firing a laser beam from an airplane-based instrument and 

recording the time increment required for the beam return. 

Elevations are calculated on the basis of the time it takes 

for the laser beam to bounce from the surface and return 

to the aircraft, the position of which is recorded by highly 

accurate GPS. Lidar data provide both horizontal positions 

in planimetric geographic coordinates (x,y) and vertical (or 

elevation) coordinates (z).

Lidar sensors can receive one or more returns from one 

pulse of light. If multiple returns exist for a sample area, then 

the first return is considered the elevation of the first object 

that the light contacts, and the last return is often some point 

below the first return and sometimes considered to be bare 

earth. Raw data (often received as files in LASer file format 

[.las]) contain all returns. DSMs can be created from lidar data 

containing first returns (that is, tree canopy and structures). 

Lidar products are commonly available in raw format (as 

.las files) containing all returns and as lidar-derived products 

(for example, DEMs or contour maps) that have undergone 

advanced postprocessing to ensure that elevations represent 

bare earth. 

There are two main types of lidar systems. Terrestrial 

lidar systems fire laser pulses that have a spectral resolution of 

near-infrared (0.75–1.4 micrometers [μm]). Near-infrared light 

is absorbed by water and therefore may not record elevations 

below the surface of the water. Bathymetric lidar systems use 

a laser-visible, green band length in addition to the infrared 

band length. The return pulse of the infrared laser indicates the 

surface of the water body, and the return pulse of the green laser 

indicates the bottom of the water body (Fowler, 2001). 

Lidar data availability in the United States is increasing. 

Although much of the lidar data available are topographic lidar 

data collected for mapping floodplains, some bathymetric lidar 

data are available for coastal areas. Lidar data are obtained on a 

mission-by-mission basis by agencies and organizations such as 

Federal agencies (for example, the USGS, National Oceanic and 

Atmospheric Administration [NOAA], and U.S. Army Corps of 

Engineers) or State and local governments (for example, county 

or city governments). The spatial resolution, accuracy, and 

temporal resolution, therefore, vary by dataset. Generally, the 

spatial resolution of lidar-based DEMs is less than 5 m, and the 

reported horizontal and vertical accuracies are highly accurate 

and are 0.25 m or less. For information pertaining to the spatial 

resolution and horizontal and vertical accuracies of individual 

products, check the metadata that accompany the product, 

contact the vendor, or contact the entity that provided the data.

Lidar data have limitations in areas with dense canopy 

or vegetative cover. Generally, if one is standing under dense 

tree canopy and cannot see the sky, then it is unlikely that a 

laser pulse will strike the ground there. Accuracy estimates 

are reported over the entire mission area, which can be large 

and include a large number of different land covers. It is 

recommended, therefore, that if possible the lidar data and 

related products are assessed locally for relevant accuracy 

estimates for the area of interest in any modeling effort. 

Recently, easy to use portals such as the United States 

Interagency Elevation Inventory and the NOAA Coastal Lidar 

Portal have been developed to view and obtain readily available 

lidar data.

Bathymetric Data

Seafloor topographic data are often combined with 

terrestrial DEMs (see section “Digital Elevation Models”) to 

create a DEM that extends above and below the water surface 

(that is, a topobathy DEM). Multiple sources of bathymetry data 

exist, including NOAA nautical charts radar, marine geophysical 

trackline surveys, multibeam sound navigation and ranging 

(sonar) surveys, bathymetric lidar, and satellite altimetry. The 

NOAA National Geophysical Data Center has an extensive 

archive of bathymetric data collected by various sources and 

methods. For some areas, NOAA National Geophysical Data 

Center has created DEMs containing topography and ocean 

bathymetry for coastal areas. Bathymetric data sources can be 

found in the United States Interagency Elevation Inventory. 

Bathymetric data were collected during the SRTM; however, 

the data (known as SRTM Plus) have a coarser resolution (that 

is, 1.85-km resolution) than do the SRTM topographic data. A 

few considerations for selecting bathymetric data include the 

objective(s) of the sea-level rise application, the extent of the 

study area, and the scale and limitations of the existing data used 

(that is, topographic elevation data, land cover data).
72 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

Topographic Survey

Topographic surveys conducted with a total station or 

GPS equipment may be available for certain localized areas 

along a coast or within managed lands where a topographic 

survey may have been conducted. These data may be used to 

enhance an existing DEM, but because of the time-consuming 

nature and cost of these surveys, they would rarely be done at 

a scale large enough for a sea-level rise modeling effort. 

Vertical Accuracy and Uncertainty

Vertical accuracy of elevation data is one of the most 

critical elements in sea-level rise applications. The National 

Standard for Spatial Data Accuracy (NSSDA) uses the RMSE 

to estimate the linear error with a 95-percent confidence level 

by multiplying the RMSE by 1.96. As previously mentioned, 

uncertainty can present critical limitations to sea-level rise 

applications, particularly regarding modeling a sea-level 

rise rate (such as 50 cm by 2100) for relatively small time 

steps (such as 2040, 2060, 2080, and 2100) (Gesch, 2013). 

Elevation data with the lowest amount of uncertainty (that 

is, highest vertical accuracy), therefore, should be used for 

modeling inundation associated with sea-level rise.

Land Cover Data

Numerous sources of land cover data may be available 

for any given area where one may want to conduct sea-level 

rise modeling. In this section, we introduce and discuss a 

few widespread and readily available land cover sources. We 

identify where these datasets may be obtained and sources 

of supplementary information. Important considerations 

regarding land cover data include the classes mapped (that 

is, vegetation types, habitats), scale of source data, spatial 

resolution of the data, temporal period of the data, minimum 

mapping unit (that is, area of the smallest features mapped), 

and the accuracy of the land cover dataset (table 3–2). 

Classification Systems

Classification systems provide standards for a land 

cover mapping product in regards to classes and the 

definitions of classes. A few common classification systems 

include Anderson Level I and II (Anderson and others, 

1976), the classification of wetlands and deepwater habitats 

(Cowardin and others, 1979), and NatureServe’s Terrestrial 

Ecological Systems (http://www.natureserve.org/library/

usEcologicalsystems.pdf). The objectives and focus of the sealevel rise application should determine the level of detail in a 

classification system required. 

Methodology for Commonly Used Land Cover Data

There are numerous land cover products that are 

available throughout the conterminous United States. The 

methodology used to create these land cover datasets can be 

lumped into two different categories: (1) photointerpretation 

of aerial photography (for example, U.S. Fish and Wildlife 

Service National Wetlands Inventory [NWI] and Florida Land 

Use, Cover, and Forms Classification System [FLUCCS]) 

and (2) image classification from automated and manual 

classification of satellite imagery (for example, National Land 

Cover Dataset [NLCD] and NOAA Coastal Change Analysis 

Program [C-CAP]). 

Photointerpretation Land Cover Classifications

Photointerpretation is a manual process that typically 

involves mapping features in a highly detailed manner by using 

expert opinion, aerial photography, and ancillary data. Features 

are delineated by using heads-up digitizing (that is, manual 

digitizing by tracing over features displayed on a computer 

monitor) or created by using a more robust vectorization of 

imagery (that is, segmentation by using image properties such 

as spectral bands, texture, and hue) and then are assigned a 

class in a classification system via photointerpretation. Some 

examples of photointerpretation land cover maps include the 

NWI, some State land cover mapping efforts, particularly 

FLUCCS, and localized habitat maps (for example, park maps 

and maps developed by ecologists of habitat for managed 

lands). By using the Cowardin and others (1979) classification 

system, NWI maps not only differentiate a wide spectrum 

of wetland types but also classify wetland water regime (for 

example, saturated, seasonally flooded, permanently flooded) 

and in some cases apply special modifiers to wetlands to indicate 

special characteristics of the wetland (for example, presence 

of mangroves, salinity concentration). The date and scale of 

photography used for NWI maps vary by area. In some cases, 

the NWI has been updated recently, and in other cases, the NWI 

may be from the 1980s or earlier. If NWI is incorporated into a 

sea-level rise application, it is important therefore to assess the 

date and scale of source imagery for NWI maps available for the 

study area (http://www.fws.gov/wetlands/data/mapper.HTML). 

The spatial resolution of these classifications makes them better 

suited for more localized sea-level rise applications. 

Satellite-Imagery-Based Land Cover Classifications

Many of the other land cover classifications that are widely 

available are created from automated or manual classification 

of satellite-based imagery with a moderate spatial resolution 

of 5–30 m. In most cases, these types of classifications tend 

to be less detailed than those created with photointerpretation 

of aerial photography. Satellite-imagery-based classifications 

include NLCD, NOAA C-CAP land cover, National Gap 
Appendixes 73

Table 3–2. Landcover class descriptions, data attributes, and sources for publicly available datasets.

[<, less than; %, percent; ft, foot; m, meter; TM,Thematic Mapper; ETM+, Enhanced Thematic Mapper Plus; m2, square meter; NOAA, National Oceanic and 

Atmospheric Administration; GAP, National Gap Analysis Program]

Land cover/

vegetation dataset Source(s)

Spatial 

resolution,

in meters

Temporal 

period

Minimum 

mapping unit

Classification 

system

Accuracy (overall 

thematic accuracy 

unless specified 

otherwise)1

National Wetlands 

Inventory

Aerial photography 

at various scales

(<1:40,000, 

1:40,000, 

1:50,000, and 

1:80,000)

Vector data 

(such as 

polygons)

1970s–

present; 

varies by 

geography

1 acre (Cowardin and 

others, 1979)

Thematic: 90% 

features at 95%

Positional: 33 ft/10 m 

Localized habitat maps Variable (such as 

aerial photographs 

and field data 

collection/survey)

Variable Variable Variable Variable Variable

State landcover 

classification (such 

as Florida Land 

Use, Cover, and 

Forms Classification 

System [FLUCCS] 

or Texas Ecological 

Classification 

Systems)

Varies by dataset 

(such as aerial 

photography, 

Landsat TM/

ETM+ Imagery)

Varies by 

dataset; 

Florida: 

Vector data 

Texas: 10

Varies by 

dataset; 

Florida2

: 

2000s 

Texas: 

2005–07

Varies by dataset; 

Florida2

: 2 acres 

Texas: 1 hectare 

(about 2.5 acres)

Variable; Florida 

and Texas: 

NatureServe’s 

Terrestrial 

Ecological 

Systems 

Varies by dataset

National Land Cover 

Dataset (NLCD)

Landsat 5 TM and 

Landsat 7 ETM+

30 1992, 2001, 

2006, 2011

4,500 m2

(1.11 acre)

Modified 

Anderson 

(Anderson and 

others, 1976)

1992: 80%

2001: 79%

2006: 78%

NOAA Coastal 

Change Analysis 

Program

Landsat 5 TM 30 1996, 2001, 

2006, 2011

3,600 m2

(1 acre)

Modified 

Anderson 

(Anderson and 

others, 1976) 

with increased 

number of 

wetland classes

85%

Varies by geography 

and date

GAP Landsat 7 ETM+ 30 1999–2001 Varies by 

geography

NatureServe’s 

Terrestrial 

Ecological 

Systems

Varies by geography

LANDFIRE Existing 

Vegetation Cover

Landsat 7 ETM+ 30 2008 Not specified NatureServe’s 

Terrestrial 

Ecological 

Systems

Eastern United 

States: 63% 

Western United 

States: 40%

Commission for 

Environmental 

Cooperation North 

America Land 

Cover

Moderate Resolution 

Imaging 

Spectroradiometer 

(MODIS)

250 2005 Not specified Modified 

Anderson 

(Anderson and 

others, 1976)

59%

1

Reported accuracy assessments are for a sampled area within data or maps. Actual accuracy may vary by geography within dataset. 

2

May vary by water management district.
74 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists

Analysis Program (GAP), LANDFIRE Existing Vegetation 

Cover, and the Commission for Environmental Cooperation 

North America Land Cover (table 3–2). C-CAP land cover 

was developed for coastal areas of the United States with a 

goal of increasing the level of detail at which coastal wetlands 

were mapped in the NLCD. Table 3–3 contains a comparison 

between Anderson Level I (Anderson and others, 1976), 

NLCD, and C-CAP classification systems for wetlands, 

open water, and barren land classes. Similarly, GAP and 

LANDFIRE Existing Vegetation Cover are classified by using 

NatureServe’s Terrestrial Ecological Systems and contain 

many more wetland classes common in the southeastern 

region of the United States (for example, bottomland forests, 

pondshore, peat swamp, seepage fen). It is important to note 

that GAP data are mapped at a State and regional level by 

different agency offices and personnel; therefore, the accuracy 

and quality of data may vary. 

Table 3–3. Comparison among Anderson Level I, National Land 

Cover Database (NLCD), and National Oceanic and Atmospheric 

Administration (NOAA) Coastal Change Analysis Program (C-CAP) 

classification systems.

Anderson 

Level 11 NLCD category C-CAP category

Wetlands Woody wetlands Palustrine forested wetlands.

Palustrine scrub shrub wetlands. 

Estuarine forested wetlands.

Estuarine scrub shrub wetlands.

Emergent 

herbaceous 

wetlands

Palustrine emergent wetlands. 

Estuarine emergent wetlands.

Open water Open water Open water.

Palustrine aquatic bed.

Estuarine aquatic bed.

Barren land Barren land Unconsolidated shore.

Barren land.

1

Anderson and others (1976).

References Cited

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