Marshes on theMove

A Manager’s Guide to Understanding and Using 

Model Results Depicting Potential Impacts 

of Sea Level Rise on Coastal Wetlands
Marshes on the Move

A Manager’s Guide to Understanding and Using Model Results 

Depicting Potential Impacts of Sea Level Rise on Coastal Wetlands

The Nature Conservancy

Global Marine Team

URI Narragansett Campus

South Ferry Road

Narragansett, RI 02882

Phone: (401) 874-6871

E-mail: marine@tnc.org

www.nature.org/ourinitiatives/

habitats/oceanscoasts

NOAA National Ocean Service

Coastal Services Center

2234 S. Hobson Avenue

Charleston, SC 29405

Phone: (843) 740-1200

E-mail: csc.info@noaa.gov

www.csc.noaa.gov 

Writing Team

Roger Fuller (Co-Lead)

Zach Ferdaña, Adam Whelchel

Nancy Cofer-Shabica (Co-Lead)

Nate Herold, Keil Schmid, Brian Smith, 

Doug Marcy, Dave Eslinger 

Writing and Design: 

Peter Taylor, Waterview Consulting

waterviewconsulting.com

Acknowledgements

Special thanks to Bethney Ward, Ben Gilmer, Nicole Maher, Mark Hoover, 

Sarah Newkirk, Brian Boutin, Laura Geselbracht, and Jorge Brenner for 

providing valuable input and inspiration. This document was made possible 

in part by the support of the Collins Northwest Conservation Fund.

October 2011
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Introduction

T

he scientific community is generally in agreement that

global sea level is rising and coastal wetlands are changing as 

a result. Depending on local conditions, coastal wetlands may 

disappear under the rising seas, persist in their current locations, or 

migrate inland. In many places, these changes 

have important ramifications for the ecosystem 

and economy. Understanding where and how 

coastal environments could change in response 

to sea level rise, however, is a complex challenge 

dependent upon many factors—from interdependent ecological processes to data quality 

and availability. As a result, resource managers 

and other coastal decision-makers need appropriate tools that can help them to anticipate and 

prepare for the future effects of sea level rise on coastal wetlands.

Many models and methods are being used for this purpose. 

Managers and other professionals with oversight responsibility for 

coastal resources are often presented with model outputs in the form 

of maps that illustrate projected sea level rise and potential loss or 

migration of coastal wetlands. These maps or visualizations may 

appear to provide a definitive picture, when in fact each represents a 

collection of assumptions, compromises, and simplifications based on 

the amount and quality of available data and information and on the 

model’s purpose. As a result, it can be challenging to interpret model 

results and to use the information appropriately. 

This document is intended for people who need to use model 

outputs for decision-making but who do not 

build models themselves. It provides a basic 

understanding of the parameters and uncertainties involved in modeling the future impacts 

of sea level rise on coastal wetlands. This is a 

first step toward informed use and communication of these models to support a range 

of sea level rise adaptation activities—from 

stakeholder education to habitat management to land conservation. Equipped with this conceptual understanding, managers and 

planners will be able to more effectively 

• ask the right questions of technical specialists regarding model 

use and results, 

• evaluate the real-world implications of model results, and

• incorporate modeling results into management initiatives. 

This document is intended for

people who need to use model

outputs for decision-making

but who do not build models

themselves. 

Information in this document 

applies to a wide range of 

coastal wetlands, including 

• tidal freshwater, brackish, 

and salt marshes, and

• coastal freshwater forested 

and riparian wetlands.

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Summary of Key Messages

• Wetland migration models can help guide managers 

and planners in land use planning (e.g., protection, 

restoration, adaptation) as well as emergency 

management (e.g., understanding where wetlands 

may reduce the risk of hazards to vulnerable coastal 

communities). 

• Wetland migration models are valuable tools in the 

management decision-making toolkit. Models do not 

make decisions or predictions.

• Key parameters in wetland migration modeling are 

rate of sea level rise, tides, salinity, elevation, sediment 

dynamics, and habitats and species. Models may 

include some or all of those parameters, which affects 

their complexity, their data requirements, and the types 

of information they can provide.

• The best use of a given model may be to investigate 

a range of possible future scenarios and to identify 

the key influences on the ecosystem, rather than 

attempting to define one “most likely” scenario. Various 

plausible scenarios can be explored with multiple 

model runs. 

• Maps based on model results contain inherent 

uncertainties that may not be evident but may alter the 

effectiveness of management decisions.

• To provide appropriate information for management, 

outputs of a model need to be interpreted based on the 

assumptions, simplifications, and uncertainties included 

in the model.

Refer to page 18 for a list of 

“Good Questions to Ask About Model Results”

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Why Use Models?

T

he future is uncertain, and yet management decisions must

be made. What are the best ways to sustain coastal wetlands 

in the face of sea level rise? How will wetlands change, how will 

those changes affect people living nearby, and what steps can coastal 

towns take to adapt? How will changes in coastal wetlands affect 

fish and wildlife that depend on marshes for habitat, and how can 

resource managers plan? These sorts of management questions are 

daunting in part because so many factors are uncertain—magnitude 

and rate of sea level rise, effects on wetland vegetation, and amount 

of wetland area that will be lost, to name just a few. 

Models provide a framework for understanding the possibilities, and this knowledge can feed into the management decisionmaking process. For example, if models indicated that a particular 

section of coast was especially vulnerable to loss of wetlands, or that 

certain marshes provided 

a substantial buffer 

between incoming storms 

and vulnerable coastal 

communities, managers 

could use this information when prioritizing their management activities. The modeling 

information would help guide managers to direct their effort to where 

it is most needed. Without the benefit of modeling, the managers in 

this example would have much less certainty about where to direct 

their effort.

Types of Questions that Models Can Help Answer

Models can be useful for many management purposes. They can 

be tools for understanding how a complicated ecosystem functions. 

They can provide insight into the future state of particular components of an ecosystem. They can be used to examine the sensitivity 

of the ecosystem to specific types of human impacts. They can be 

used to compare the potential outcomes 

of different management options. They 

can determine the relative vulnerability 

of habitats and can also indicate where 

human communities may benefit from the 

presence of these habitats. Each of these 

uses can help managers take actions today to avoid any negative 

consequences that model outputs suggest may be in store if no action 

is taken. However, it is important to note that the models provide 

guidance, not the answers.

Given the many possible uses of models, managers and planners 

Given the many possible uses of

models, managers and planners

need to focus on what it is that 

they seek to accomplish with them. 

models provide a framework for understanding

the possibilities, and this knowledge can feed

into the management decision-making process. 

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need to focus on what it is that they seek to accomplish with them. 

A clear focus on specific management questions—and the types of 

answers needed—is invaluable for guiding the selection of a model, 

deciding what information is needed, and choosing how to present 

the model outputs. The following are examples of management 

questions that models can help to address about impacts of sea level 

rise on coastal wetlands:

• Which wetlands in the management area are most vulnerable 

under different sea level rise scenarios?

• What areas of a present-day wetland may become open water, 

and what areas of upland may become wetland?

• Where could wetlands migrate inland without encountering 

natural or anthropogenic barriers?

• How would a wetland’s vulnerability to sea level rise be affected 

by changes in factors such as river discharge, sediment inputs, 

and adjacent land use? Where could restoration reduce a 

wetland’s vulnerability?

• How much economic damage from increased flooding is likely 

to occur because of loss of wetlands in a given geographic area? 

Which populated areas are most vulnerable?

• How much would it cost to implement various adaptation 

strategies?

• Are wetlands in the management area likely to continue 

protecting coastal properties from storm damage as sea level 

rises? What management actions could be taken to sustain and 

enhance the role of wetlands in coastal hazard mitigation?

Using and Communicating Model Results

Statistician george e. p. box said, “all models are wrong,

but some are useful.” Box’s quip neatly sums up the basic 

truths of models, including models of sea 

level rise and impacts on coastal wetlands. 

Models, by their very nature, are simplifications 

of the real world. They are artificial constructs 

designed to help people understand what is 

happening within a limited set of circumstances. 

Ecological models simplify complex natural processes. By being 

aware of modeling pitfalls and limitations, managers can make better 

use of these important tools. There will always be more complexity 

and unpredictability in natural systems than can be captured in an 

ecological model. For simplicity’s sake, models remove some realworld processes from the equation entirely. Modelers include specific 

processes and interactions about which the model is intended to 

ecological models simplify

complex natural processes. 

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answer questions. For those processes that are included, models 

make simplifying assumptions about how the real world operates. 

Furthermore, the data on elevation, vegetation, and other factors that 

are used to create models are simplified representations of reality, 

and data may not exist for every desired model parameter.

All models are wrong in the sense that they cannot reproduce 

the real world. Nonetheless, some models provide a good approximation of the potential outcomes of particular scenarios, and they 

are valuable as decision 

support tools for management. When communicating model results 

publicly, it is a good idea 

to convey the message 

that models are a useful tool for helping with decisions, but they are 

a simplification of natural processes and although they can be used 

to explore various scenarios, the actual future real-world outcomes 

will be different.

A range of model types is available for investigating the potential 

impacts of sea level rise on coastal wetlands. The models vary greatly 

in their complexity, their data requirements, and the types of information that they can provide. The simplest models show land areas 

that would be inundated if sea level were to increase by a specified 

amount. Somewhat more complex are models that indicate potential 

effects of sea level rise 

on habitats, including 

loss and inland migration of wetlands, through 

a limited set of ecological 

and geological processes. 

Very complex models are 

available that simulate 

many ecological, hydrological, and geological 

processes over time, 

such as hydrodynamics, 

vegetation growth, and 

sediment transport, as 

well as feedbacks among 

these processes, to 

provide a more nuanced 

view of changes in 

wetland vegetation and 

habitats. Other models 

integrate socioeconomic 

factors along with ecology 

and geology to assess 

vulnerability of human 

Defining Uncertainty 

When addressing sea level rise and marsh migration, managers often need to communicate with 

non-scientists about scientific findings. One term 

that often leads to misunderstanding is “uncertainty”. 

A non-scientist may interpret uncertainty as meaning 

that the answer is not known and that any projection 

is just a guess. Scientists think of uncertainty as a 

specific, quantifiable measure of how well something 

is known. Scientific uncertainty comes in many 

shades of gray, not just black (“we are absolutely 

certain this is correct”) or white (“we have no idea 

what the answer is”). In the context of wetland migration modeling, for example, a model result could 

have a confidence level of 95%, meaning that for all 

practical purposes it can be considered correct, even 

though there is a small amount of uncertainty associated with it. Talking in terms of “confidence levels” 

instead of “uncertainty” is one way to convey the 

intended meaning for both scientific and nonscientific audiences. 

models are a useful tool for helping with decisions, 

but they are a simplification of natural processes

and although they can be used to explore scenarios,

actual future real-world outcomes will be different.

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communities to sea level rise and costs of adaptation. 

The simplest models may require only topographic data and a 

value for projected sea level rise. To use a complex model, however, 

it may be necessary to obtain large quantities of detailed data on 

wetland plant species, vegetation growth rates, tides, sediment 

erosion and deposition, human demographics, land use, and other 

factors. Existing datasets may be available for some of these data 

types, while in other cases original data may need to be acquired. The 

best choice of model depends on the question being asked, the types 

of data available, the level of effort and expertise required to use the 

model, and the time and financial resources available. 

Models Are Only One Tool

While models are valuable tools, they do not make decisions, and 

they provide only certain types of information. People engaged in 

the management process need access to a range of different models 

and other tools in order to make well-informed, effective decisions. 

Modeling can be extremely insightful, 

but it is only one tool in the management 

toolkit. Other types of data and analysis 

tools are usually needed to inform management decision-making. For example, 

decisions about where to protect uplands in order to accommodate 

wetland migration could not be made based solely on model-generated maps of projected paths of wetland migration. These decisions 

would require information about factors such as stakeholder priorities and goals, actual environmental conditions in the management 

area, and land prices and ownership.

While Models are valuable tools, 

They do not make decisions

or predictions. 

The Right Tool for the Job 

When models are used outside the scope of their design, then they can cease to be 

useful and become liabilities instead. It is not always easy to recognize the actual 

scope of a model. Consider, for example, a very simple descriptive seasonal model 

that states, “The air temperature is colder in winter than in summer,” a reasonable, 

simple description of seasonal dynamics. This simple seasonal model reduces the 

world down to only two seasons: summer and winter. Using this model to predict 

daily weather instead of seasonal cycles would be to use this model well outside its 

intended scope. For example, a user of the seasonal model might say, “Based on 

the proposed model, and given that it is past mid-winter now, but not yet summer, 

then tomorrow should be warmer than today.” The seasonal model, however, 

contains no information about daily temperature dynamics. And in reality, we 

know that there can be large variability on shorter time scales than a seasonal one, 

and temperatures can vary greatly from day to day. Models of wetland migration 

are far more complex than this example, but the same principle applies. Users of 

the model need to make sure they are not pushing beyond the model’s capabilities.

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Importance of Multiple Model Scenarios

By using a variety of models or running them multiple times with 

different parameter values, it is possible to explore a range of 

plausible future scenarios. Key drivers of wetland change, such as 

sea level rise, accretion, and salinity intrusion, can vary in complex 

ways across the landscape and over time. But models greatly simplify 

these processes. Rather than trying to identify one scenario that 

represents future conditions and basing management decisions 

on that scenario, managers can 

reduce uncertainty by using multiple 

model runs to explore a range of 

possible futures. This will help 

reveal the most important variables 

affecting the long-term persistence 

of their wetlands. Comparing many 

alternative scenarios is an excellent 

way to improve understanding of 

wetland vulnerabilities and make 

better management decisions. 

Since the results of any single 

modeling scenario may contain 

considerable uncertainty, the patterns 

and trends revealed by multiple runs 

can be especially insightful when few 

data are available to configure the 

model. For example, good-quality data 

on sediment accretion is often scarce. 

Running multiple scenarios using 

different accretion rates could help 

a manager to better understand the 

conditions under which the management area is vulnerable and to target 

monitoring efforts. 

Comparing many alternative scenarios is

an excellent way to improve understanding

of wetland vulnerabilities and make 

better management decisions.

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Sample outputs from a model depicting coastal 

wetland response to sea level rise. Wetland types 

are indicated in magenta, orange, and green 

colors. Top shows current conditions. The bottom 

two images show how two different accretion rates 

affect the outcome of a four-foot rise in sea level.
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Maps May Look More Certain than They Are

Managers and stakeholders often see the results of wetland migration models in the form of maps. 

Although these maps may be quite detailed, giving the appearance of great certainty and precision, 

they contain all of the uncertainties, assumptions, and limitations of the model results and the data 

used to produce them. Maps A and B below illustrate how maps may contain uncertainty that is not 

evident to the person viewing it and that leads to an incorrect understanding of sea level rise impacts. 

If managers and stakeholders were to see only Map A, they might reasonably conclude that not much 

of this marsh would be inundated by sea level rise. However, Map B, which is based on more accurate 

LiDAR data, shows that in fact sea level rise poses a major threat to this marsh. Awareness of the 

invisible uncertainties contained in maps can lead to more effective management decisions.

These maps show an area of beach, marsh, and uplands in Edisto Beach, South Carolina. Map 

A shows the area in blue that would be inundated by three feet of sea-level rise based on data 

from National Elevation Dataset (NED), which has a vertical accuracy of ±2 meters. The NED data 

suggest that little of the marsh will be inundated under a three-foot rise. Map B shows the same 

scenario based on more accurate data collected with LiDAR, which has a vertical accuracy of ±20 

centimeters. The LiDAR data suggest that nearly all of the marsh will be inundated. The difference between the maps is due solely to the difference in vertical accuracy of the elevation data. 

The bottom two maps show the uncertainty associated with the results depicted in Maps A and 

B. Green and blue indicate areas in which there is high confidence in the model’s determination 

that the area would be land or water. Orange indicates areas in which there is less than 80% 

confidence in the model’s determination. Map C shows that the results based on NED data had 

a low confidence level across the entire marsh. In contrast, Map D shows that the results based 

on LiDAR data had a high confidence level across most of the area. The difference in the accuracy 

of the elevation data makes a tremendous difference in the apparent impacts of sea level rise, 

but most people viewing only Map A would likely assume that it presents a reliable picture of the 

impacts. Any map based on modeling outputs contains some degree of uncertainty, which needs 

to be understood in order to interpret the map correctly.

A. Based on National Elevation Dataset (NED) B. Based on LiDAR data, which are more accurate

C. Large area of low confidence using NED D. Smaller area of low confidence using LiDAR

Projected Inundation

Confidence in Projected Inundation

Blue: Inundated Area

Orange: Low Confidence

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Tides

Rate of Sea 

Level Rise

Obstacles to 

Wetland Migration

Habitats and Species

Salinity

Elevation

Sediment Dynamics

Conceptual Diagram

This figure illustrates key parameters for modeling wetland migration in response to sea level 

rise. The parameters are discussed on the following pages. 

• Rate of Sea Level Rise: Not only the amount but the rate of sea level rise is a key factor.

• Tides: Tide levels are important in determining wetland extent and persistence.

• Salinity: If the salinity regime changes, vegetation and wetland functioning may change.

• Elevation: Elevation is one of the most important data components for modeling sea level rise.

• Sediment Dynamics: If sediment accumulates, or accretes, on the wetland surface as fast as 

the sea level rises, then the wetland may avoid being submerged under the rising sea.

• Habitats and Species: Land cover data and habitat change rules can be used to project the 

effects of sea level rise on the locations of habitats and species. Obstacles to marsh migration 

are an important consideration when modeling these changes. Note that the locations of other 

intertidal and shallow subtidal habitats will shift, too, but this report does not address models 

that account for changes in those habitats.

• Additional Complicating Factors: Many other factors complicate the process of modeling and 

projecting changes in coastal wetlands into the future. These factors are difficult or impossible 

to address in quantitative or numerical models, so they may be best addressed in conceptual 

models or qualitatively in the management process. 

Key Parameters and Dynamics for 

Modeling Sea Level Rise and Coastal 

Wetland Migration 

T

his section presents an overview of key factors that affect

the accuracy of models used to project wetland migration. 

It highlights considerations about data quality and inherent 

uncertainty in datasets that may be used to represent environmental variables and processes. Managers can use this information 

as a starting point for assessing the strengths and weaknesses of a 

modeling approach and for understanding the real-world implications of model outputs. The following discussion of parameters begins 

with sea level rise and other physical and chemical drivers of habitat 

change, and then discusses the habitats themselves. To assist with 

evaluating the results of a model, a number of example questions are 

provided at the end of the section.

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Rate of Sea Level Rise

Not just the height of sea level but the rate at which sea level rises is 

an important factor affecting wetland migration processes and model 

results. When sea level rises, tidal wetlands may migrate landward 

to an extent dictated by the slope of the land and rates of vertical 

sediment accretion and peat 

formation, which may enable 

wetlands to keep pace with 

sea level rise (see Sediment 

Dynamics). A combination of 

the amount and the rate of 

sea level rise will determine 

if a wetland will drown or if it will keep up with sea level rise and 

migrate landward. Scientific projections indicate that sea level may 

rise up to or more than two meters by 2100 with an increasing rate of 

rise over time. Wetland migration models can be used to investigate 

the management implications of various scenarios 

covering a range of projections. Because of the 

uncertainty associated with 

climate models themselves 

and projections of sea level 

rise, using multiple runs of 

wetland migration models 

is useful for exploring 

a range of sea level rise 

scenarios and for identifying a plausible range of 

future states of the wetland 

ecosystem. Another important factor for wetland 

migration modeling is that 

the local (relative) rate of 

sea level rise in a given area 

may differ from the global 

(eustatic) rate because 

of a number of factors. 

Modelers, managers, and 

planners should consult 

the latest science to obtain 

the best available projections of sea level rise in 

their area. 

Calculating Local Rates of Sea Level Rise

Wetland migration models can be tailored to a particular study area in part by using a local rate of sea 

level change. The rate of sea level rise is not the same 

everywhere. It differs from place to place because of 

many factors. For example, uplifting of tectonic plates 

in a region can reduce the local rate of sea level rise, 

whereas subsidence can increase the local rate. A local 

rate of future sea level rise can be calculated based 

on a scientifically determined global rate and adding 

or subtracting a local correction factor. Historic data 

from tide stations can be used to determine the local 

correction factor by finding the difference between the 

measured local trend in water level and the historic 

global trend from the same period. NOAA’s Sea Levels 

Online website (tidesandcurrents.noaa.gov/sltrends) 

shows the historical sea level trends around the globe 

based on tide station records.

A combination of the amount and the rate 

of sea level rise will determine if a wetland

will drown or if it will keep up with sea level

rise and migrate landward.

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Tides

Because tide levels are important 

in determining wetland extent 

and persistence, and tidally 

influenced salinity regimes are 

important in determining wetland type (salt, brackish, or fresh), tidal 

fluctuations are a key factor to consider in models of wetland migration. For example the landward edge of a salt marsh typically lies at 

the elevation flooded by spring tides. The approximate seaward edge 

usually occurs near the elevation of mean tide. In some models, the 

projected future elevations of spring tide and mean tide approximate 

the land area into which a wetland may migrate. Depending on the 

size and characteristics of the study area, data from nearby tide 

stations may be adequate for modeling small areas. Another approach 

for larger areas is to use VDatum software (vdatum.noaa.gov) to 

produce a spatially varied model of tidal variability. The method used 

to include tides in a wetland migration model affects the level of confidence in model outputs.

Salinity

Potential changes in the salinity regime are a key factor to consider 

when modeling the future responses of a coastal wetland to climate 

change. If the salinity regime changes, the plant community and 

overall ecological functioning of the wetland may change. In river 

deltas, for example, tidal wetlands range from very low salinity in the 

higher reaches to very high salinity in more distant salt marshes. 

Different plant and animal species occur in the various kinds of 

tidal wetlands, and this is important as each wetland type provides 

different ecosystem services to human communities. 

In the context of wetland migration models, the focus is not on 

short-term salinity fluctuations such as those caused on a daily basis 

by tides, but on long-term shifts in salinity, which may be associated 

with changes in precipitation patterns, watershed land use, groundwater extraction, dam construction or removal, and other factors. 

For example, projected changes in the seasonality and amount of 

precipitation could greatly affect the 

input of freshwater from watersheds into 

coastal wetlands, resulting in droughts 

that increase salinity or high flows 

that decrease it. Some models identify 

how each wetland type will respond to 

changes in both sea level and salinity. Other models do not attempt 

to address salinity changes at all, which could mean that important 

management questions are overlooked. Regardless of the model used, 

understanding how it addresses salinity is important when evaluating 

model outputs to inform management decisions. 

the projected future elevations of spring tide 

and mean tide may approximate the land area

into which a wetland may migrate.

If the salinity regime changes, 

the plant community And ecological

functioning of the wetland may change.

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Elevation

Elevation is one of the most important data components for modeling 

sea level rise and its impacts on coastal wetland systems. Model 

parameters are often compared to, calculated from, or influenced 

by elevation, and elevation ultimately determines which places will 

be wet or dry as sea level rises. Using the most accurate elevation 

dataset, or Digital Elevation Model (DEM), available is one of the best 

ways to improve results of wetland migration models. 

When reviewing the outputs of wetland migration models, it is 

important to consider the accuracy of the elevation data. Vertical 

(how high is this place?) and horizontal (where is this place located?) 

accuracy indicate how closely a DEM resembles actual conditions 

on the ground. The vertical accuracy, or the correctness of the elevation value for each cell, is likely the more influential DEM aspect 

for sea level rise and wetland migration modeling. The resolution or 

size of the grid cells is also influential because all of the area within 

the cell is set to the same elevation, which determines the degree of 

detail that will be depicted. The 

appropriate resolution can be 

determined based on the size of 

the smallest features that must 

be captured in the model in 

order to answer the management 

questions. A rule of thumb is that the DEM resolution should be no 

more than half of the smallest dimension of the feature in question. 

For example, if it is necessary to resolve berms that measure six feet 

wide and one hundred feet long, then the DEM resolution should be 

no larger than three feet by three feet to reliably identify and capture 

these features.

Collecting High-Resolution Elevation Data

Light Detection and Ranging (LiDAR) is a remote-sensing technology that has been used 

to collect high-resolution, high-accuracy elevation data in coastal areas for many years. 

An aircraft equipped with LiDAR flies over the area and captures measurements that can 

be used to create detailed topographic maps typically accurate in marshes to within ±10 

to 35 centimeters. While LiDAR data is a valuable tool, managers should use care when 

interpreting LiDAR maps, especially those of coastal wetlands. LiDAR data tend to have 

more uncertainty in wetlands than in uplands, where the technology has been tested more 

extensively. Although LiDAR can measure the surface elevations of bare earth or vegetation, 

it is difficult to measure ground elevations through vegetation, particularly in wetlands. It 

is common for less than 5% of the “ground points” to have actually hit the ground surface 

in wetlands. Standing water can also reduce LiDAR’s accuracy. Consequently, LiDAR 

measurements in wetlands may have less vertical accuracy than measurements in adjacent 

uplands. While LiDAR data usually are not suitable for examining centimeter-scale elevation changes in wetlands, they are far better than older, lower-accuracy types of data for 

modeling sea level rise and wetlands migration. 

Using the most accurate elevation dataset 

available is one of the best ways to improve

results of wetland migration models.

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Sediment Dynamics

Areas within a tidal wetland change in elevation as sediment builds 

up or erodes away. The rate at which sediment accumulates, or 

accretes, can vary greatly over short distances, so that two spots 

in the same wetland may have different accretion rates. Accretion 

rates can also vary over time, such as when 

changes in land use or precipitation cause 

more sediment to be carried into the wetland. 

If sediment accumulates on the wetland 

surface as fast as the sea level rises, then the 

wetland may avoid being submerged under the 

rising sea. 

Therefore, accretion is an important 

process to consider when projecting wetland migration in response 

to sea level rise. Data on accretion are difficult to obtain, however, 

and one important issue in models that incorporate accretion is the 

source and quality of the accretion data. The data quality for accretion rates used in a model will affect the level of uncertainty in the 

model output. When 

possible, it is preferable 

to use accretion data 

that have been collected 

at many different places 

within the study area, 

using the same methods at 

each location, over a long 

time period. Frequently, 

such data are not available, making it necessary 

to use data from one or 

a few sites, or published 

accretion rates from other 

places. Running a model 

multiple times with a range 

of different accretion rates 

can provide insight into 

whether and under what 

conditions accretion may 

be a major concern at the 

study site. 

If sediment accumulates

on the wetland surface

as fast as the sea level rises,

then the wetland may avoid being

submerged under the rising sea.

13

Some wetland migration models incorporate sediment accretion, and the way in which accretion 

is represented can greatly affect the modeled habitat outcome. For example, these images both 

show model outputs after 100 years with 3 feet of sea level rise and the same average accretion 

rate. However, in the figure at left accretion occurred at a constant rate across the low marsh 

(aqua color), while in the figure at right the accretion rate in the low marsh varied according to 

elevation. That difference in model assumptions resulted in 25% more low marsh area (purple). 

This example illustrates the important ways that a model’s assumptions can affect the results 

and the benefit of conducting multiple model runs in order to see a range of potential outcomes.
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Habitats and Species

Habitat Maps and Land Cover

Data on the current presence and distribution of plant and animal 

species provide a baseline for modeling the potential impacts of sea 

level rise on coastal wetland systems. Because maps are a modeled 

representation of reality, there are a number of factors to consider 

regarding the vegetation data used in wetland migration models. 

Key among these are collection date (recent or potentially outdated), 

spatial resolution, number and types of 

vegetative classes identified, and stated 

accuracy of the dataset. 

Several national land cover data 

products have been used in wetland 

migration models (see box for two 

examples). Local habitat maps, if available, are another possibility and may be of better quality. In general, 

it is important to understand the specifications, characteristics, and 

potential limitations of the land cover data source used. It is also 

important to be mindful of the types, scale, and variety of land cover 

classes considered by a model. Some models are built to relate 

to one geographic area or wetland type, while others are built 

to function based upon generalized categories of wetlands 

(freshwater, brackish, saltwater). Other models make no use 

of current wetland condition, such as simple “bathtub” 

models that are intended only to show which 

areas of land above the tidal 

range could be inundated by 

sea level rise. 

The National Wetlands Inventory (NWI) dataset 

is the national standard for mapping wetland 

types within the United States. While the 

dataset is of a detailed resolution (derived from 

1:24,000-scale aerial photography) and is available in digital format for most of the country, 

it is important to check the date of collection. 

Some of the current NWI data reflect wetlands 

as they existed in the mid-1980s and so are not 

necessarily representative of current conditions 

or sea levels. This can be an issue in areas that 

have experienced a large amount of change. 

In addition, the NWI covers only wetlands and 

provides no information about the condition of 

uplands, which can be particularly important to 

determine if inland wetland migration is viable.

The NOAA Coastal Change Analysis 

Program (C-CAP) is another source of 

land cover and wetland information. The 

data include many classes of uplands and 

impervious surfaces, but these data are 

mapped at a coarser resolution (derived 

from 1:100,000-scale satellite imagery). 

The data are updated on a regular cycle, 

but they do not enable detailed mapping. 

The coarser scale and fewer wetland 

classes can be advantageous in that the 

user is not given a false sense of accuracy 

by viewing model outputs in greater detail 

than the model accommodates or data 

accuracy warrant.

Examples of national Land Cover Datasets

it is important to understand

the specifications, characteristics, 

and potential limitations of the 

land cover data source used. 

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Habitat Change Rules 

Some types of wetland migration models contain a set of rules that 

determine which habitat occurs at any particular geographic location, 

or “cell” in a model. These rules are usually based on specific data 

ranges for one or more critical parameters affecting wetland habitat. 

The most common is elevation. For example, some models are set up 

so that cells within 

a certain elevation 

range are designated 

as having high marsh 

vegetation and cells 

within a slightly lower elevation range are designated as low marsh 

vegetation. A cell that initially was high marsh changes to low marsh 

if sea level increases a specific amount in the model, putting that 

cell into the lower elevation range. This change would be reflected 

in maps of habitat types based on the modeling results. Plant and 

animal species respond in complex ways to different physical, 

chemical, and biological aspects of their environment, and habitat 

change rules are a very simplistic way to try to determine when 

a species will shift its distribution as a result of changes in those 

parameters. Because of the many assumptions and simplifications 

involved, habitat change rules are a major source of uncertainty 

in modeling. Rules can 

be very simplistic (based 

on elevation only) or more 

complex (based on salinity, 

sediment type/chemistry, 

relative vegetation density, 

distance to a sediment 

source, biophysical interactions, and other factors). 

Both approaches have their 

advantages and disadvantages. For example, using 

a model with complex rules 

may be valuable for management if it provides better 

information, but the data 

requirements will be greater, 

demanding a higher level of 

effort from the project team. 

Interpreting model output 

requires an understanding 

of whether habitat change 

rules were incorporated into 

the model and what the 

rules include and ignore.

habitat change rules are a very simplistic way 

to determine when a species will shift its distribution

as a result of changes in environmental parameters.

Obstacles to Wetland Migration

Models of coastal wetland migration make 

assumptions about how habitats change 

and how plant and animal species respond by shifts in 

location. Over time these shifts may allow coastal wetlands 

to migrate inland as sea level rises. However, it is critical 

to recognize that natural obstacles such as rocky cliffs 

and anthropogenic obstacles such as buildings, roads, 

seawalls, berms, dikes, and other forms of shoreline 

armoring can block wetland migration. When one of these 

barriers lies along the inland edge of a wetland, it prevents 

the wetland vegetation from shifting farther inland. Over 

time, the wetland becomes narrower, until eventually it 

may disappear under the rising sea. Models must take 

these barriers into account when calculating the inland 

movement of wetlands. The potential for increases or 

decreases in barriers is a source of uncertainty when 

modeling wetland migration. Analysis of land development 

can reveal places where more shoreline armoring is 

likely to occur in the future, but if land-use regulations 

are revised—as part of a coastal adaptation effort, for 

example—development patterns and the rate of armoring 

could change.

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Additional Complicating Factors

Many other factors complicate the process of modeling and projecting 

changes in coastal wetlands into the future. These factors are difficult or impossible to address in quantitative or numerical models, so 

they may be best addressed in conceptual models or qualitatively in 

the management process. Some examples of complicating factors are 

listed below. Every management area will differ with respect to which 

factors are important and how important they are.

•	 Thresholds. Ecological thresholds exist that, once crossed, are 

essentially irreversible and trigger additional changes that 

accelerate wetland loss. 

• Human behavior. People will respond in many different ways 

to climate change and sea level rise, and their behavior could 

affect the persistence and migration of tidal wetlands (see box).

• Disguised rates of change. Some habitat changes happen rapidly 

over a period of one or a few years, but when averaged over 

many years they appear to be gradual. 

• Unexpected key players. Species can unexpectedly take on 

new ecological importance and alter the wetland system. For 

example, wintering populations of snow geese have expanded 

tremendously in some places where the geese now act as 

ecosystem engineers by overgrazing on marsh plant rhizomes, 

resulting in increased erosion and reduced capacity to accrete 

sediment.

• Major storms. Storms can cause rapid, large changes in wetland 

habitats, such as rerouting river channels and eroding barrier 

beaches. By their nature, storms are difficult to predict and a 

storm event can significantly change conditions on the ground 

from those predicted by a well-executed model. 

How Will People Influence Wetland Migration? 

For centuries, people have impacted coastal wetlands, and they will continue to 

do so. When looking at model projections of wetland migration, it is important to 

consider the many ways that people could influence the outcome—and that are not 

included in the model. Land use and development patterns around wetlands may 

continue on their present trajectories, or they may change. Some communities may 

choose to pursue an increase in coastal armoring, while others retreat from the 

coast. Some may proactively implement adaptive management plans, while others 

delay action. In some watersheds, people may build more dams to store drinking 

water and generate electricity, while in other watersheds they may remove dams. 

Land-use regulations affecting wetland health may stay the same, or they may 

be revised to be more or less wetland friendly. These types of behaviors should be 

considered both as factors that add uncertainty to projections of wetland migration 

and as factors that could be targeted in management efforts to sustain wetlands.

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• Status of other stressors. Nutrient and toxic pollution, invasive 

species, habitat fragmentation, and other stressors affect a 

wetland’s resilience. A stressed marsh may succumb more 

easily to sea level rise.

• Cyclical climate patterns. Phenomena such as El Niño, the North 

Atlantic Oscillation, and the Pacific Decadal Oscillation cause 

cyclical effects on local sea level, wave regimes, precipitation, 

storm conditions, and other factors. When there is a regime 

shift in any of these climate patterns, local influences on 

habitats can likewise shift, sometimes across thresholds that 

reverse or accelerate key processes of change.

• Unknowns and surprises. Much is still unknown about the ecological functioning of tidal wetlands. Interactions among biology, 

physics, and chemistry can be especially difficult to understand 

and model in coastal wetlands. Some changes now happening 

in the environment may result in ecological responses that 

we simply cannot conceive of yet. In addition, models are 

often based on observations over recent decades, while future 

changes in ecosystem processes may extend beyond the 

recently observed range of values. As a result, surprises are to 

be expected as unanticipated changes occur.

These complicating factors can be addressed in decision-making 

by incorporating other data such as local development trends. In 

some cases, models for different factors can be explicitly linked. 

For example, development trends can feed into a wetland migration 

model through alternative scenarios for hardened shoreline. Even if 

quantitative data are 

not available on some 

complicating factors, 

they can be incorporated qualitatively into 

decision-making. By 

consulting with local 

experts, a manager can 

evaluate which factors 

are likely to be important in a particular 

area and how these 

factors could affect the 

wetland ecosystem. 

This information can 

be used in combination with model results 

to make management 

decisions. 

17

Wintering snow goose populations have increased their 

numbers unexpectedly in some areas, sometimes leading 

to major changes in wetland ecosystems. Models of wetland 

migration do not usually address this type of unanticipated 

change. However, if these kind of “what if” scenarios can be 

identified in advance, models are excellent tools to explore 

the potential implications. 

© Bridget Besaw
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Good Questions to Ask About Model Results

How can managers and planners evaluate modeled wetland migration results? 

Asking these types of questions will help.

Objectives and audience

• What management question was the model intended to address? 

• For what audience and purpose were the model results produced?

• What are the limitations regarding appropriate use of the model results for 

management decision-making?

Assumptions and dynamics

• What are the basic assumptions of the model? For example: Does the model 

assume a constant or an increasing rate of sea level rise over the time period 

modeled? 

• What parameters does the model include and not include?

• Does the model adequately include ecological and/or socioeconomic factors 

important to the management question or specific to the type of wetland 

being examined?

Underlying data and model functions

• Were any key datasets not available? 

• What is known about the quality of the data used? For example: Are the data 

sufficiently recent? Were the data collected over a spatial area and/or time 

period appropriate to the model?

• Are the data used to drive the model appropriate for the management 

question being addressed and the context in which the outputs are being 

used? For example: What type of land cover data were used, and do they 

capture features of interest in the management area?

Spatial scale and geography

• Is the model appropriate for the management area? If it was developed in a 

different area, are the parameters included appropriate, and are any necessary parameters missing? 

• Does the model cover an appropriate spatial scale? 

• Is the spatial resolution of the model high enough to show the features of 

interest, but not higher than the available data and computing power can 

support? 

Time scale and duration

• Are processes modeled through time or as if they occurred all at once? 

• Are processes modeled with time increments relevant to management 

decision-making?

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Conclusion

Modeling is a valuable tool for addressing the important

management issue of coastal wetland migration, which 

involves complex ecological and socioeconomic processes. 

The purpose of this document is to assist managers and planners in 

using wetland migration models by being aware of the models’ limitations and appropriate uses. Appropriate use of models could lead to 

more effective management decisions. Given all the complexities and 

complications, how can managers make use of models effectively? 

How can model results be used appropriately in decision-making? 

How can model results be communicated accurately and effectively to 

stakeholders engaged in the management processes? These general 

guidelines, based on the preceding sections, can help point the way:

• Think of modeling as one tool of many for management 

decision-making. Models do not provide the answer or make 

decisions. They provide additional information that managers 

can use in making a more-informed decision.

• Remember that models do not forecast or predict the future. 

They make only projections based on the rules and inputs that 

they are given.

• Every map generated based on modeling outputs should be 

viewed as one approximation of potential future conditions, 

which may or may not come to pass. The map does not represent “the way it will be”.

• The best model to use depends on the management questions 

being asked and the management context, including the 

time and funding available for data collection and modeling. 

Examining how models and their outputs have been applied 

in other situations helps to reveal a model’s strengths and 

limitations.

• The level of confidence needed in model outputs depends on 

how the information will be used. For example, more confidence 

is needed when purchasing property to conserve wetlands than 

when teaching general concepts about wetlands in an educational setting. Achieving greater confidence in modeling results 

usually requires a greater investment of time and funding, 

which needs to be weighed against the intended use of 

the model.

• By being aware of potential sources of uncertainty in the 

modeling approach, one can gauge how much confidence to 

place in the maps or other outputs.

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• By reviewing the results of multiple model runs, different types 

of models, and other kinds of information, one may find consistency in some elements of future projections, suggesting that 

one can be reasonably confident in these elements.

• Models can offer a framework for integrating the knowledge 

and input of experts and stakeholders, as well as a jumping off 

point for discussions and decision-making at meetings 

and workshops.

By being aware of the complexities and areas of uncertainty 

in wetland migration models, managers and planners are better 

equipped to ask the right questions of technical experts before, 

during, and after modeling, and they can apply model results more 

effectively to decision-making. They can utilize models in raising 

community awareness about future impacts on wetlands, identifying 

where the presence or wetlands can reduce hazard-related vulnerabilities to coastal communities, setting management priorities, crafting 

ecosystem-based adaptation strategies, determining areas in need of 

further study, and numerous other management applications.
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Further Reading

Cahoon, D.R., D.J. Reed, A.S. Kolker, M.M. Brinson, J.C. Stevenson, S. Riggs, R. Christian, 

E. Reyes, C. Voss, and D. Kunz. 2009. Coastal wetland sustainability. In: Coastal 

Sensitivity to Sea-Level Rise: A Focus on the Mid-Atlantic Region. A report by the U.S. 

Climate Change Science Program and the Subcommittee on Global Change Research. 

[J.G. Titus (coordinating lead author), K.E. Anderson, D.R. Cahoon, D.B. Gesch, S.K. 

Gill, B.T. Gutierrez, E.R. Thieler, and S.J. Williams (lead authors)]. U.S. Environmental 

Protection Agency, Washington, DC, pp. 57–72. Available at www.climatescience.gov/

Library/sap/sap4-1

Day, J.W., R.R. Christian, D.M. Boesch, A. Yaneq-Arancibia, J. Morris, R.R. Twilley, L. 

Naylor, L. Schaffner, C. Stevenson. 2008. Consequences of climate change on the 

ecogeomorphology of coastal wetlands. Estuaries and Coasts 31:477–491. Available at 

www.springerlink.com/content/l4k14hr13hw65u53

Ferdaña, Z.A., S. Newkirk, A. Whelchel, B. Gilmer, and M.W. Beck. 2010. Adapting to 

Climate Change: Building Interactive Decision Support to Meet Management Objectives 

for Coastal Conservation and Hazard Mitigation on Long Island, New York, USA. 

In: Andrade Pérez, A., Herrera Fernandez, B. and Cazzolla Gatti, R. (eds.). Building 

Resilience to Climate Change: Ecosystem-based Adaptation and Lessons from the 

Field. Gland, Switzerland: IUCN. 164 pp. Available at http://data.iucn.org/dbtw-wpd/

edocs/2010-050.pdf 

Gesch, D.B., 2009. Analysis of lidar elevation data for improved identification and 

delineation of lands vulnerable to sea-level rise. Journal of Coastal Research. 

SI(53):49–58. Available at http://topotools.cr.usgs.gov/pdfs/jcr_gesch_SI53.pdf

IPCC (Intergovernmental Panel on Climate Change). 2007. Climate Change 2007: The 

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

Report of the Intergovernmental Panel on Climate Change [S. Solomon, D. Qin, M. 

Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H. L. Miller (eds.)]. 

Cambridge, UK, and New York, Cambridge University Press. Available at www.ipcc.ch/

publications_and_data/ar4/wg1/en/contents.html

Kirwan, M.L., G.R. Guntenspergen, A. D’Alpaos, J.T. Morris, S.M. Mudd, and S. 

Temmerman. 2010. Limits on the adaptability of coastal marshes to rising sea 

level. Geophysical Research Letters. 37:L23401. Available at www.agu.org/pubs/

crossref/2010/2010GL045489.shtml 

McLeod, E., B. Poulter, J. Hinkel, E. Reyes, R. Salm. 2010. Sea-level rise impact models 

and environmental conservation: A review of models and their applications. Ocean 

and Coastal Management 53:507–517. Available at www.sciencedirect.com/science/

article/pii/S0964569110000852

Morris, J.T., P.V. Sundareshwar, C.T. Nietch, B. Kjerfve, and D.R. Cahoon. 2002. Responses 

of coastal wetlands to rising sea level. Ecology 83:2869–2877. Available at www.ias.

sdsmt.edu/staff/sundareshwar/Reprints/morris%20et%20al%202002.pdf

National Oceanic and Atmospheric Administration (NOAA). 2010. Technical Considerations 

for Use of Geospatial Data in Sea Level Change Mapping and Assessment. NOAA 

Technical Report NOS 2010-01. Available at www.csc.noaa.gov/digitalcoast/

inundation/_pdf/SLC_Technical_Considerations_Document.pdf

Pendleton, L. [ed.]. 2007. The Economic and Market Value of Coasts and Estuaries: What’s 

at Stake? Restore America’s Estuaries Report. Arlington, VA. Available at www.

estuaries.org/the-economic-value-of-coasts-a-estuaries.html 

Scavia, D., J.C. Field, D.F. Boesch, R.W. Buddemeier, V. Burkett, D.R. Cayan, M. Fogarty, 

M.A. Harwell, R.W. Howarth, C. Mason, D.J. Reed, T.C. Royer, A.H. Sallenger, 

and J.G. Titus. 2002. Climate change impacts on U.S. coastal and marine 

ecosystems. Estuaries 25:149–164. Available at www.springerlink.com/content/

v07168r234685087

Schmid, K., B.C. Hadley, and N. Wijekoon. In press. Vertical accuracy and use of 

topographic LIDAR data in coastal marshes. Journal of Coastal Research. Available at 

www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-10-00188.1

Titus, J.G. 1988. Greenhouse Effect, Sea Level Rise, and Coastal Wetlands. U.S. 

Environmental Protection Agency, EPA 230-05-86-013. 186 pages. Available at http://

epa.gov/climatechange/effects/coastal/SLRLandUse.html
Joe Andrews