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 (m
2
)
acre 0.4047 hectare (ha)
acre 0.004047 square kilometer (km
2
)
square mile (mi
2
) 259.0 hectare (ha)
square mile (mi
2
) 2.590 square kilometer (km
2
)
Volume
gallon (gal) 3.785 liter (L)
gallon (gal) 0.003785 cubic meter (m
3
)
Flow rate
cubic foot per second (ft
3
/s) 0.02832 cubic meter per second (m
3
/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 (m
2
) 0.0002471 acre
hectare (ha) 2.471 acre
square kilometer (km
2
) 247.1 acre
square meter (m
2
) 10.76 square foot (ft
2
)
hectare (ha) 0.003861 square mile (mi
2
)
square kilometer (km
2
) 0.3861 square mile (mi
2
)
Flow rate
millimeter per year (mm/yr) 0.03937 inch per year (in/yr)
Density
kilogram per cubic meter (kg/m
3
) 0.06242 pound per cubic foot (lb/ft
3
)
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. Enwright
1
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 benets, current
scientic understanding, and utilization of resource aids
and modeling tools focused on sea-level rise. In response to
the ndings 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 simplied 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 sea-
level rise and climate change scenarios; and ease or difculty
of storing, displaying, or interpreting model output. Coastal
land managers, engineers, and scientists can benet 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 scientically 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 conrmed 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 ooding 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 condence and
certainty suitable for instructional or educational purposes,
whereas more complex models require expert development
and more site-specic 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 benets, current scientic
understanding, and utilization of resource aids and modeling
tools focused on sea-level rise. In response to the ndings
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 rst 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 nite 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 [km
2
]) 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 (g. 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 uctuate
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 oating sea
ice does not affect sea level relative to equivalent weight
displacement. Snow cover in the Northern Hemisphere
uctuates annually from highs of 46.5 million km
2
in winter
to lows of 3.8 million km
2
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)
laf13-CSSC00-0572_fig1
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 km
2
. Permafrost thickness can
extend 500 meters (m) below surface and varies by location,
prevailing temperatures, and surcial 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 sea-
level 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 streamow have changed the storage, routing,
timing, and quality of water resources. The withdrawal of
surface water from rivers and of groundwater and other uids
from aquifers for societal needs and consumption has altered
the timing and magnitude of ow to downstream ecosystems
and organisms dependent on or adapted to premodern ood
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 ow over time
and in locations where land subsidence from subsurface uid
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 signicance 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 ow 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 sea-
level 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 prole, 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 benecial
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 ood 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 oor 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 rened 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 (g. 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 oor below the waterline can cause
laf13-CSSC00-0572_fig2
-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 oor
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 uctuation 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 inections and only vary on ne resolution aspects on
the basis of robustness of technique. Multiple oxygen-18 (
18
O)
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 (g. 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 (g. 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 xed 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 at and
curved land surfaces; they are not necessarily universal but are
generally dened by political and geographic boundaries of
parochial signicance. Datums are based on unique empirical
and theoretical models of the Earth’s shape, size, and density.
As such, a conversion (or rectication) 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 (
18
O) temperature
correlated with sea level, Vostok ice core age and thickness, and carbon dioxide (CO
2
) 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
HighstandHighstand
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 (
18
O) temperature
correlated with sea level, Vostok ice core age and thickness, and carbon dioxide (CO
2
) 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 rectied 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 specied 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 ow of tides, but it is more
difcult 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 Pacic
Northwest) and drop in sea level both locally and regionally.
To effectively model the process and impacts of sea-
level rise, models of both land surface and sea surface are
required. Land surface and sea oors 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 difcult to ascertain when, where, and how
much ooding 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 (g. 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 (g. 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 rectied 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 inuence the
slope or trend calculation when using least squares regression
or quadratic models for tting 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
10 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
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
0.00
0.15
0.30
0.45
0.60
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
0.00
0.15
0.30
0.45
0.60
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020
B. Pensacola, Fla., 2.19 +/-0.23 month/year
-0.90
-0.75
-0.60
-0.45
-0.30
-0.15
0.00
0.15
0.30
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
0.00
0.15
0.30
0.45
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020
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 western Gulf Coast
in Louisiana and Texas illustrating land motion differences (National Oceanic and Atmospheric Administration, 2014). A, Key West, Florida, tide gage record, 1912–2014. B,
Pensacola, Florida, tide gage record, 1922–2014. C, Grand Isle, Louisiana, tide gage record, 1946–2014. D, Galveston Pier 21, Texas, tide gage record, 1908–2014.
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 inated or deated 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 inuences that contribute to the
actual (secular) change over time. The cycles of inuence—
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
inuences (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
(g. 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 specic 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 conguration,
ocean currents, wave lapping, upland runoff, and land motion
effects that confound determination of a denitive 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 rst reports of satellite-derived
sea-level trends were published in the mid-1990s, the
higher sea-level rate, above 3 mm/yr, supposedly veried
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 rst 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 sea-
surface height at 6-minute (min) intervals at one location
on the coast; these single locations are inuenced 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 rectied to a common geoid
or datum, which involves referencing the height of tidal
benchmarks on land and satellite orbit in space. The geodetic
rectication 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 specic 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 sea-
surface 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
12 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
RI
DE
ME
VT
NH
MA
NY
PA
CT
NJ
MD
VA
70°
50°
40°
AZ
CA
ID
NV
OR
UT
WA
120° 125° 130°
45°
40°
35°
30°
CANADA
ME XICO
UNITED
STATES
30° 60° 90° 150° 120° 180°
30°
40°
0°
AK
160°
150°
140°
130°
170° 180°
50°
40° 30°
Base from Esri State, country, and major lakes
Lambert Azimuthal Equal Area projection
A
D
D
A
C
<-10
-5 to -10
-5 to 0
0 to 2
2 to 4
4 to 6
>6
No missing data,
in meters
Missing less than
3 months of data,
in meters
EXPLANATION
AL
AR
FL
GA
KY
LA
MO
MS
NC
OK
SC
TN
TX
80° 90°
30°
B
B
C
500 MILES
500 KILOMETERS
0 250
250
0
150
0 150 300 MILES
0 300 KILOMETERS
0 200
200
400 MILES
0 400 KILOMETERS
250 MILES
250 KILOMETERS
0 125
125
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 and low sea-level change
(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, Alaskan coast.
Factors, Rates, and Models of Sea-Level Change 13
Coast tide stations can be used to contrast relative sea-
level 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 gure 8. In some cases, quadratic ts match the
curvilinear trends and residuals better than linear regression
models (g. 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 ndings 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., gage
1
- - - - 7.34
Galveston, Tex., gage 3.37 7.52 9.54 7.48 4.85
1
Discontinuous 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 sea-
level projections from icemelt to 2 m (6.6 feet [ft]) by 2100
(g. 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 difculty 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 eld 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 ood 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 CO
2
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 CO
2
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 specied time period relative to the
user-specied 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 ooding 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 Rectication 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-specied inputs or selections of
IPCC (2001) GCMs and emission scenarios. SLRRP was
designed to provide other USGS coastal ecosystem models
with corresponding sea levels rectied to NAVD 88. SLRRP
uses the mean monthly tide records of NOAA tide gages
projected into the 21st century with the addition of curvilinear
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 17
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 Rectication Program]
Model
Agency/
organization
Appropriate
scale
Spatial
resolution
Temporal
scale
Input
parameters
Output
parameters
Citations
CoastCLIM Sea-
Level 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
Warrick (2006); Warrick
and Cox (2007).
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)
http://tidesandcurrents.
noaa.gov/inundation/,
Inundation Analysis
Users’ Guide.
Sea-Level Rise
Rectication
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, ood inundation
potential for given
elevation
Keim and others (2008).
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 Rahmstorf (2007).
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 sea-
level 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, rectied 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 ood extent to establish ooding 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 (g. 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 ooding for an elevation entry of a land
surface or other feature (g. 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 signicant
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 relling 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 sufcient 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 three-
dimensional models used for comprehensive ood 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) Sea-
Level 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-specied images or
user-specied choices of geospatial extent and sea-level
rise options (table 4). As a rule, these are non-expert tools
lacking scientic 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 simplied 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 rectication of water
and land datums are lacking. Most importantly, these tools
are almost exclusively unrectied and unveried 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 rectied
to the same units and reference plane to make legitimate
comparison of ooded or unooded land surface. Tidal
datum and land datum are measured and reported differently
and cannot be directly compared or analyzed without
proper rectication 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 rectication 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
rectied, GIS sea-level rise mapping tools are not veried
(verication 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 verication 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 verication 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
le 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 identies 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.
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 23
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 Mission; NED, National
Elevation Dataset; NOAA, National Oceanic and Atmospheric Administration; ft, feet; VDatum, vertical datum transformation tool; MHHW, mean higher high water; C-CAP, NOAA Coastal Change
Analysis Program;
km
2
, square kilometer; MHW, mean high water]
Model
Agency/
organization
Web page
Appropriate
scale
Spatial
resolution
Sea-level
rise range
options
Input
parameters
Vegetation
classification
Output
parameters
Citations
OzCoasts sea-
level 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 maps Australian
Government
(2009).
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 map,
population
affected
Usery and others
(2010).
NOAA Digital
Coast Sea-
Level 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 map,
socioeconomic
vulnerability
index
NOAA (2010);
Marcy and
others (2011).
University of
Arizona
Web Map
Visualization
Tool
University of
Arizona
http://climategem.
geo.arizona.edu/
slr/us48prvi/
index.html
Continental,
global
1 km
2
(global),
30 m (U.S.)
0–6 m DEM
(GTOPO30,
NED 30 m),
land cover
None Inundation map Overpeck and
Weiss (2009);
Weiss and
others (2011);
Strauss and
others (2012).
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 map,
population,
housing and
land affected,
ooding risk
(downloadable)
Tebaldi and
others (2012);
Strauss and
others (2012).
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 (g. 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 Sea-
Level 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 user-
selected 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 eld studies. The minimum mapping unit
of the viewer was modied 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 ood 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 ofcials 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 identies 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 (g. 12). The map tool is based on modeled present-
day 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 shapeles
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,
specically the U.S. areas) resolutions. Colors reect land
cover categories, and the counts are numbers of people
living or impacted below specic elevations (g. 13). The
animated graphics are not meant to be site specic. They
are based exclusively on elevation and do not attempt to
account for tidal action, shoreline conguration, and other
characteristics that might affect how the water would rise in
a specic 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.
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 25
A
Figure 11. Screenshots showing National Oceanic and Atmospheric Administration (NOAA) Digital Coast Sea-Level Viewer graphic user interface for Galveston Bay, Texas
(http://coast.noaa.gov/slr/). A, Inundation extent for current mean higher high water (MHHW). B, Inundation extent for 6-foot sea-level rise.
26 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
B
Figure 11. Screenshots showing National Oceanic and Atmospheric Administration (NOAA) Digital Coast Sea-Level Viewer graphic user interface for Galveston Bay, Texas
(http://coast.noaa.gov/slr/). A, Inundation extent for current mean higher high water (MHHW). B, Inundation extent for 6-foot sea-level rise.—Continued
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).
28 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
Figure 13. Screenshot showing U.S. Geological Survey Sea-Level Rise Animation graphic user interface (from Usery and others, 2010; used with permission).
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 classication. The wetland type or
habitat cover descriptions vary in detail and discrimination
from generic classication of terrestrial or aquatic forms to
surcial qualiers of tidal at, 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 classication 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 mudat 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) (g. 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 process-
based 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-km
2
land units for a dened 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 surcial
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 ooding
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
reect upgrades in software, data sources, and spatial
resolution of subsequent site applications more than they
reect any substantial changes of functionality or design.
Spatial resolution ranges from 500-m pixel size, for map
areas conned 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 habitat-
specic 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 (g. 15) and tabular export les 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
classication of multispectral Landsat data of early versions
30 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
Table 5. Attributes of select wetland change models.
[<, less than; km
2
, 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
Validation Citations
Barataria-
Terrebonne
Ecosystem
Landscape
Spatial
Simulation
(BTELSS)
Louisiana
State
University
Local,
regional
(such as
<1 km
2
–
100,000
km
2
)
1 km
2
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 ooding
tolerances of plants
NWI Maps of land
change (habitat
switching), ooded
and eroded areas,
plant productivity,
salinity, open-water
circulation, and
sediment transport
Hindcast Reyes and others
(2000, 2004);
Martin and others
(2000); Voinov and
others (1999, 2007);
Binder and others
(2003).
Coastal
Ecological
Landscape
Spatial
Simulation
(CELSS)
Louisiana
State
University
Local,
regional
(such as
<1 km
2
–
100,000
km
2
)
1 km
2
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 ooding
tolerances of plants
NWI Maps of land
change (habitat
switching), ooded
and eroded areas,
plant productivity,
salinity, open-water
circulation, and
sediment transport
Hindcast Costanza and others
(1990); Sklar and
others (1991).
Sea Level
Affecting
Marshes
Model
(SLAMM
1–5)
Warren
Pinnacle
Consulting,
Inc.
Local,
regional
(such as
<1 km
2
–
100,000
km
2
)
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)
None Park and others
(1989); Galbraith
and others (2002,
2003); National
Wildlife Federation
(2006); Craft and
others (2009).
Sea Level
Affecting
Marshes
Model
(SLAMM 6)
Warren
Pinnacle
Consulting,
Inc.
Local,
regional
(such as
<1 km
2
–
100,000
km
2
)
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)
Hindcast Clough and others
(2010); Geselbracht
and others (2011).
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
Hindcast Doyle and others
(2010).
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 sea-
level change map for the Barataria and Terrebonne Basins, Louisiana (from Reyes and others, 2000; used with permission).
32 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
Figure 15. Screenshot showing Sea Level Affecting Marshes Model 6 (SLAMM 6) display viewer for the St. Marks National Wildlife Refuge application (from U.S. Fish and
Wildlife 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 specic 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 inuence 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 (g. 16).
The model as applied to the Gulf Coast is subdivided into
separate reaches dened 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 classication scheme. Tidal ranges were
obtained from NOAA tide stations for more than 300 locations
and ltered 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
dened 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 xed
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 inuences. 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, streamow, climate, and biotic factors. In this section,
we describe different tools and techniques for monitoring
and modeling surface elevation, shoreline change, and storm
inuences of coastal systems.
34 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
A
Figure 16. Screenshots showing Sea Level Over Proportional Elevation (SLOPE) model data layers for the northern Gulf of Mexico coast regional application (from Doyle and
others, 2010; used with permission). A, Total saltmarsh and mangrove area (in hectares) extracted from the National Wetland Inventory (NWI) for coastal counties of the northern
Gulf of Mexico. B, Relative sea-level rate (in millimeters per year) for National Oceanic and Atmospheric Administration (NOAA) tide stations. C, Predicted land loss or retreat
of freshwater forest area (in hectares) for coastal counties under a 10-centimeter rise of sea level from accelerated global eustasy. D, Tidal range estimates (in centimeters)
extracted from water level records for 300 NOAA coastal tide stations for coastal counties.
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 35
B
Figure 16. Screenshots showing Sea Level Over Proportional Elevation (SLOPE) model data layers for the northern Gulf of Mexico coast regional application (from Doyle and
others, 2010; used with permission). A, Total saltmarsh and mangrove area (in hectares) extracted from the National Wetland Inventory (NWI) for coastal counties of the northern
Gulf of Mexico. B, Relative sea-level rate (in millimeters per year) for National Oceanic and Atmospheric Administration (NOAA) tide stations. C, Predicted land loss or retreat
of freshwater forest area (in hectares) for coastal counties under a 10-centimeter rise of sea level from accelerated global eustasy. D, Tidal range estimates (in centimeters)
extracted from water level records for 300 NOAA coastal tide stations for coastal counties.—Continued
36 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
C
Figure 16. Screenshots showing Sea Level Over Proportional Elevation (SLOPE) model data layers for the northern Gulf of Mexico coast regional application (from Doyle and
others, 2010; used with permission). A, Total saltmarsh and mangrove area (in hectares) extracted from the National Wetland Inventory (NWI) for coastal counties of the northern
Gulf of Mexico. B, Relative sea-level rate (in millimeters per year) for National Oceanic and Atmospheric Administration (NOAA) tide stations. C, Predicted land loss or retreat
of freshwater forest area (in hectares) for coastal counties under a 10-centimeter rise of sea level from accelerated global eustasy. D, Tidal range estimates (in centimeters)
extracted from water level records for 300 NOAA coastal tide stations for coastal counties.—Continued
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 37
D
Figure 16. Screenshots showing Sea Level Over Proportional Elevation (SLOPE) model data layers for the northern Gulf of Mexico coast regional application (from Doyle and
others, 2010; used with permission). A, Total saltmarsh and mangrove area (in hectares) extracted from the National Wetland Inventory (NWI) for coastal counties of the northern
Gulf of Mexico. B, Relative sea-level rate (in millimeters per year) for National Oceanic and Atmospheric Administration (NOAA) tide stations. C, Predicted land loss or retreat
of freshwater forest area (in hectares) for coastal counties under a 10-centimeter rise of sea level from accelerated global eustasy. D, Tidal range estimates (in centimeters)
extracted 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, Pacic, 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, ooding, 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 (g. 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 classication, 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 (g. 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
(
14
C), lead-210 (
210
Pb), cesium-137 (
137
Cs), beryllium-7 (
7
Be),
oxygen-18 (
18
O), and uranium-238 (
238
U). 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
Oldeld, 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 articial radionuclide
product of bomb testing and peak fallout in 1963–64. Isotopic
analysis, such as
18
O and
16
O 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 eld 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 inuence.
In most applications, vegetation health and productivity are
important components controlled by degree of inundation and
submergence by tidal inuence 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 (g. 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 xed benchmark attached to a subsurface rod
usually deep and driven to refusal (g. 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 (g. 18A). This exible
design allows the rod SET to be used to monitor elevations
across different depths of the soil prole. 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 (g. 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 denitive 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 ooding dictate the proximity and dendritic patterns of
tidal creeks, as do human inuences of coastal modications,
dredging, and channelization. Where static models are simple
to apply and to simulate ooding 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 ooding path, depth, and duration
on the basis of distance, shape, and function of associated tidal
creeks and marsh platforms. The rate of ooding 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 dene 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 quantied 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
decient 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
ood 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, monospecic 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 (g. 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.
42 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
75°80°85°90°95°
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)
Current cypress location
Generalized 120-meter contour
Geologic age by era
Cenozoic (65.5 mya to present)
Mesozoic (251 to 65.5 mya)
Paleozoic (542 to 251 mya)
Precambrian (4,600 to 542 mya)
Water
NOTE: mya, million years ago
EXPLANATION
0
0 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) dating back to highstand
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 (g. 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 ve 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 Climate-
Envelope 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 identied
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 (g. 21). In
this case, sea-level rise alone is not sufcient 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
specic 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 sea-
level rise on distribution of C
3
and C
4
plant species (plants that
use a carbon xation pathway with 3- and 4-carbon molecules,
respectively, in the rst stable photosynthetic product).
Saltmarsh processes are simulated as a function of C
3
and
C
4
species carbon production from light- and temperature-
dependent 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 (C
4
)
and upper marsh (C
3
) 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.
44 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
World Geodetic System, 1984
MISSOURI
MISSISSIPPI
ALABAMA
TENNESSEE
ILLINOIS
KENTUCKY
VIRGINIA
NORTH CAROLINA
SOUTH
CAROLINA
GEORGIA
FLORIDA
KANSAS
OKLAHOMA
TEXAS
ARKANSAS
LOUISIANA
ATLANTIC OCEAN
GULF OF MEXICO
PACIFIC OCEAN
CARIBBEAN SEA
0
0 300
300
600 KILOMETERS
600 MILES
United States
GUYANA
VENEZUELA
COLOMBIA
PANAMA
NICARAGUA
GUATEMALA
MEXICO
HONDURAS
CUBA
HAITI
100º
30º
20º
10º
80º
60º
Mangrove distribution
during the Eocene Epoch
EXPLANATION
Figure 20. Eocene distribution limits of mangrove (modified from Sherrod and McMillan, 1985; used with permission).
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 45
TEXAS
FLORIDA
GEORGIA
ALABAMA
LOUISIANA
MISSISSIPPI
NORTH
CAROLINA
SOUTH
CAROLINA
GULF OF MEXICO
B1
Presence
A2
Presence
ATLANTIC
OCEAN
ATLANTIC
OCEAN
ATLANTIC
OCEAN
GULF OF MEXICO
GULF OF MEXICOGULF OF MEXICO
ATLANTIC
OCEAN
A2
Abundance
B1
Abundance
A B
C 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
EXPLANATION
N
0 to 25
25 to 50
50 to 75
75 to 100
Probability of mangrove
forest presence, in percent
EXPLANATION
0 to 25
25 to 50
50 to 75
75 to 100
Mangrove forest relative
abundance, in percent
0
0 225
225
450 MILES
450 KILOMETERS
Figure 21. Climate-Envelope Mangrove Model predictions of mangrove forest presence and relative abundance under future climate scenarios (from Osland and others, 2013;
used with permission). A, Probability of mangrove forest presence with an ensemble B1 scenario climate. B, Probability of mangrove forest presence with an ensemble A2
scenario climate. C, Mangrove forest relative abundance with an ensemble B1 scenario climate. D, Mangrove forest relative abundance with an ensemble A2 scenario climate.
46 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
Table 6. Attributes of select leaf to landscape ecosystem models.
[m, meter; yr, year; TM, Landsat Thematic Mapper; NWI, National Wetlands Inventory; <, less than; m
2
, square meter; km
2
, square kilometer; lidar, light detection and ranging; cm, centimeter]
Model
Agency/
organization
Appropriate
scale
Spatial
resolution
Temporal
scale
Input parameters
Vegetation
classification
Output
parameters
Validation Citations
Coupled Saltmarsh
Biogeochemical
and Demographic
Model
Universidade
Nova de
Lisboa-
Quinta
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,
ooded areas,
biomass and plant
density
None Simas and others
(2001).
Hammock-
Mangrove
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, species-
specic physiological
parameters, storm
surge data, hypothetical
elevation
Hammock
mangrove
Maps of vegetation
change, salinity,
vegetation density,
gross productivity
Sensitivity
analysis
Teh and others
(2008).
Spatially Explicit
Landscape
Vegetation
Analysis Model
(SELVA)
U.S. Geological
Survey,
National
Wetlands
Research
Center
Local,
regional
(such as
<1 m
2
–
100,000
km
2
)
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)
Hindcast Doyle and others
(2003b);
Berger and
others (2008).
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
Hindcast Doyle and Girod
(1997);
Doyle and
others
(2003b);
Berger and
others (2008).
Spatial Relative
Elevation Model
(SREM)
Skagit River
System
cooperative,
Western
Washington
University
Local (70
km
2
)
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
Hindcast Kairis and
Rybczyk
(2010).
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
Hindcast Doyle and others
(2003a).
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 inltration rates of precipitation,
evaporation, and transpiration distinguished by wetland
type. Daily tide values are compared with cell elevation
to determine ux 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 specic to account for species growth rates and
litter production. The model was developed principally to
evaluate the process of self-organization under chronic sea-
level 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 at 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 SELVA-
MANGRO (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. SELVA-
MANGRO 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 individual-
based 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 (g. 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 prole. 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 inuenced
by changes in climate, sea level, hurricane disturbance, and
freshwater ow (g. 22).
SELVA is composed of several primary data layers
delineating ecosystem boundaries, land elevation, tidal range,
sea level, river stage, and disturbance conditions. Classied
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-specied 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 dened 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
dene the intertidal plane.
MANGRO is an individual-based model composed of
a species-specic 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 ooded
conditions. MANGRO predicts the tree and gap replacement
process of natural forest succession as inuenced by stand
structure and prevailing environmental conditions (g.
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, ooding, 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 lls space rst 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.
48 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
A
Figure 22. Screenshots showing SELVA-MANGRO model application in south Florida (from Doyle and others, 2003b; used with permission). A, South Florida digital elevation
model. B, MANGRO stand simulation model. C, Historical mangrove distribution circa 1940. D, Predicted mangrove distribution under sea-level rise by 2100.
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 49
B
Figure 22. Screenshots showing SELVA-MANGRO model application in south Florida (from Doyle and others, 2003b; used with permission). A, South Florida digital elevation
model. B, MANGRO stand simulation model. C, Historical mangrove distribution circa 1940. D, Predicted mangrove distribution under sea-level rise by 2100.—Continued
50 Sea-Level Rise Modeling Handbook: Resource Guide for Coastal Land Managers, Engineers, and Scientists
C
Figure 22. Screenshots showing SELVA-MANGRO model application in south Florida (from Doyle and others, 2003b; used with permission). A, South Florida digital elevation
model. B, MANGRO stand simulation model. C, Historical mangrove distribution circa 1940. D, Predicted mangrove distribution under sea-level rise by 2100.—Continued
Predictive Models of Sea-Level Rise Impact and Coastal Vulnerability 51
D
Figure 22. Screenshots showing SELVA-MANGRO model application in south Florida (from Doyle and others, 2003b; used with permission). A, South Florida digital elevation
model. B, MANGRO stand simulation model. C, Historical mangrove distribution circa 1940. D, Predicted mangrove distribution under sea-level rise by 2100.—Continued
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 sheries and wildlife taxa. Although aquatic, these
meadows are also at risk under rising sea levels. Seagrasses
exist within a restricted depth range as inuenced 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 modied
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
ourish because of the proportion of shallow mud ats 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 eld study that was conducted to relate plant-species
distribution and ecotones to surface elevation and tidal
inundation by using differential leveling surveys (g. 23).
The WETLANDS model contains functional probabilities
of species tolerance to ooding 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
laf13-CSSC00-0572_fig23
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. Classied 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 ood 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 eld
surveys. In years in which ood 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 reect 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 amplied
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 sea-
level 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 scientic 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 ooding. Various Earth-
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
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-specic 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 benet
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/m
3
]). 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 [
o
C]).
Seawater density is given by the following formula, according to Fofonoff and Millard (1983):
ρ (S, t, 0) = ρ
w
+ (b
0
+b
1
t +b
2
t
2
+ b
3
t
3
+ b
4
t
4
)S + (c
0
+ c
1
t + c
2
t
2
)S
3/2
+ d
0
S
2
(1)
where
ρ
w
= density of reference pure water,
b
0
= +8.24493 * 10
-1
,
b
1
= -4.0899 * 10
-3
,
b
2
= +7.6438 * 10
-5
,
b
3
= -8.2467 * 10
-7
,
b
4
= +5.3875 * 10
-9
,
c
0
= -5.72466 * 10
-3
,
c
1
= +1.0227 * 10
-4
,
c
2
= -1.6546 * 10
-6
, and
d
0
= +4.8314 * 10
-4
.
The density of the reference pure water is given by the following formula:
ρ
w
= a
0
+ a
1
t + a
2
t
2
+ a
3
t
3
+ a
4
t
4
+ a
5
t
5
(2)
where
a
0
= +999.842594,
a
1
= +6.793952 * 10
-2
,
a
2
= -9.095290 * 10
-3
,
a
3
= +1.001685 * 10
-4
,
a
4
= -1.120083 * 10
-6
, and
a
5
= +6.536332 * 10
-9
.
The formulas have been implemented as MATLAB code by James Manning (http://globec.whoi.edu/globec-dir/sigmat-
calc-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 coefcients (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 coefcients (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
identifi-
cation
City, 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
identifi-
cation
City, 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
identifi-
cation
City, 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 Manseld, 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
identifi-
cation
City, 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, Sea-
level 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 Earth-
climate 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 sea-
level 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 rectication 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 rectication 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 public-
domain topographic and bathymetric data products of global,
national, and regional signicance 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 dened as an
electronic le or database containing elevation points over a
contiguous area. The data les 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 specied 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
reected 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 arc-
second
Simple 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 arc-
second
Polynomial 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
specications
Variable on the
basis of mission
specications
Variable on
the basis
of mission
specications
Variable on the basis
of mission
specications
Lidar Aerial lidar collection Variable by
mission
specications;
typically 3–5
Variable on the
basis of mission
specications;
typically 2000s–
present
Variable on
the basis
of mission
specications;
typically
submeter
Variable on the
basis of mission
specications;
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 nished data, was
released in 2005 and contains edits to ll voids and to better
delineate coastlines and water bodies, specically oceans,
lakes, and rivers. Version 2.1 was released in 2009 and had a
low lter (3×3) applied to clean remaining voids from version
2.0 data. Version 2.1 is available for only SRTM 3-arc-
second (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 ner 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-arc-
second 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-arc-
second 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 rst 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 proles (Jensen, 2007).
Modern lidar system surface elevation data are generated by
ring 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 rst return is considered the elevation of the rst object
that the light contacts, and the last return is often some point
below the rst return and sometimes considered to be bare
earth. Raw data (often received as les in LASer le format
[.las]) contain all returns. DSMs can be created from lidar data
containing rst returns (that is, tree canopy and structures).
Lidar products are commonly available in raw format (as
.las les) 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 re 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 oodplains, 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
Seaoor 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 condence 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
Classication systems provide standards for a land
cover mapping product in regards to classes and the
denitions of classes. A few common classication systems
include Anderson Level I and II (Anderson and others,
1976), the classication 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 sea-
level rise application should determine the level of detail in a
classication 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 Classication System [FLUCCS])
and (2) image classication from automated and manual
classication 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 classication 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) classication
system, NWI maps not only differentiate a wide spectrum
of wetland types but also classify wetland water regime (for
example, saturated, seasonally ooded, permanently ooded)
and in some cases apply special modiers 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 classications makes them better
suited for more localized sea-level rise applications.
Satellite-Imagery-Based Land Cover Classifications
Many of the other land cover classications that are widely
available are created from automated or manual classication
of satellite-based imagery with a moderate spatial resolution
of 5–30 m. In most cases, these types of classications tend
to be less detailed than those created with photointerpretation
of aerial photography. Satellite-imagery-based classications
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; m
2
, 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 eld data
collection/survey)
Variable Variable Variable Variable Variable
State landcover
classication (such
as Florida Land
Use, Cover, and
Forms Classication
System [FLUCCS]
or Texas Ecological
Classication
Systems)
Varies by dataset
(such as aerial
photography,
Landsat TM/
ETM+ Imagery)
Varies by
dataset;
Florida:
Vector data
Texas: 10
Varies by
dataset;
Florida
2
:
2000s
Texas:
2005–07
Varies by dataset;
Florida
2
: 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 m
2
(1.11 acre)
Modied
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 m
2
(1 acre)
Modied
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 specied 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 specied Modied
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 classication systems for wetlands,
open water, and barren land classes. Similarly, GAP and
LANDFIRE Existing Vegetation Cover are classied 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 ofces 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 1
1
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).
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