Sea-Level Rise, Inundation, and Marsh Migration: Simulating Impacts on Developed Lands and Environmental Systems

Abstract

Sea-level rise is expected to affect natural and urban areas by shifting habitats and inundating infrastructure. To plan for a sustainable future, it is important to identify both human and ecological vulnerabilities to sea-level rise. Here, we simulate impacts to urban, developed lands and environmental systems from sea-level rise by analyzing land cover (surface cover) and land use (land purpose) in the Matanzas River study area in NE Florida. The Sea Level Affecting Marshes Model (SLAMM) simulated land-cover change through wetland migration under three sea-level rise scenarios. Parcel data, including land use classification and land valuation, was overlaid on the simulated, future land cover. Our analysis describes a 2- to 5-km-wide longitudinal band along the NE coast of Florida of expected land-cover change where sea-level rise will likely cause inundation and wetland migration. Under a 0.9-m scenario by 2100, 5,332 ha of land (5% of the study area) will be threatened by some type of land-cover change, and inundation was estimated to affect approximately US$177 million in present property value. The migration of wetlands out of current areas and into new areas is of particular concern because (1) those wetlands will have to keep pace with sea-level rise, and (2) accommodation space must be available for new wetlands to move into. Developed lands have the possibility of hindering up to 6% of the area that wetlands may migrate into. These methods and findings are important for sustainable planning under future climate change.

Full text

Sea-Level Rise, Inundation, and Marsh Migration:
Simulating Impacts on Developed Lands and Environmental
Systems
Anna C. Linhoss
†
*, Greg Kiker
‡
, Michael Shirley
§
, and Kathryn Frank
††
†
Department of Agricultural and Biological
Engineering
Mississippi State University
Starkville, MS 39762, U.S.A.
‡
Department of Agricultural
and Biological Engineering
University of Florida
Gainesville, FL 32611, U.S.A.
§
Guana Tolomato Matanzas
National Estuarine Research Reserve
Ponte Vedra Beach, FL 32082, U.S.A.
††
Department of Urban and Regional Planning
University of Florida
Gainesville, FL 32611, U.S.A.
ABSTRACT
Linhoss, A.C.; Kiker, G.; Shirley M., and Frank K., 2015. Sea-level rise, inundation, and marsh migration: simulating
impacts on developed lands and environmental systems. Journal of Coastal Research, 31(1), 36–46. Coconut Creek
(Florida), ISSN 0749-0208.
Sea-level rise is expected to affect natural and urban areas by shifting habitats and inundating infrastructure. To plan
for a sustainable future, it is important to identify both human and ecological vulnerabilities to sea-level rise. Here, we
simulate impacts to urban, developed lands and environmental systems from sea-level rise by analyzing land cover
(surface cover) and land use (land purpose) in the Matanzas River study area in NE Florida. The Sea Level Affecting
Marshes Model (SLAMM) simulated land-cover change through wetland migration under three sea-level rise scenarios.
Parcel data, including land use classification and land valuation, was overlaid on the simulated, future land cover. Our
analysis describes a 2- to 5-km-wide longitudinal band along the NE coast of Florida of expected land-cover change where
sea-level rise will likely cause inundation and wetland migration. Under a 0.9-m scenario by 2100, 5,332 ha of land (5% of
the study area) will be threatened by some type of land-cover change, and inundation was estimated to affect
approximately US$177 million in present property value. The migration of wetlands out of current areas and into new
areas is of particular concern because (1) those wetlands will have to keep pace with sea-level rise, and (2)
accommodation space must be available for new wetlands to move into. Developed lands have the possibility of hindering
up to 6% of the area that wetlands may migrate into. These methods and findings are important for sustainable planning
under future climate change.
ADDITIONAL INDEX WORDS: Land cover change, land use change, Matanzas, Sea Level Affecting Marshes Model,
SLAMM, wetlands.
INTRODUCTION
Coastal communities and ecosystems are forecasted to be
profoundly affected by sea-level rise (SLR). Global forecasts
project a 33%area loss in coastal wetlands between 2000 and
2080 with 36 cm of SLR (IPCC, 2007). SLR will also affect
urban, developed lands by inundating infrastructure, with
2.4%of the global population forecasted as being displaced in
this century (Nicholls et al., 2011). Coastal adaptation is
‘‘urgently required’’ (IPCC, 2007), so to plan for a sustainable
future, it is critical to identify the areas that are most
vulnerable to SLR. Assessing the impacts to urban, developed
lands and environmental systems from SLR by analyzing land
cover (surface cover) and land use (land purpose) is one step in
that direction.
The threat posed by SLR is especially important for Florida
because of its low topography, extensive coastline, valuable
natural areas, and large coastal populations. Ten percent of the
land in the state is less than 1 m above sea level, and Florida is
one of four states in the United States that account for 80% of
the total low-lying land that will likely be affected by SLR
(Titus and Richman, 2001; Weiss and Overpeck, 2003). Florida
has one of the highest numbers of endemic species in North
America (n¼413) (LandScope, 2014; Noss, 2011), including
federally listed species, such as the Florida scrubjay (Aphe-
locoma coerulescens), the Florida scrub lizard (Sceloporus
woodi), and the Florida golden aster (Chrysopsis floridana),
each of which depends on coastal habitat. Additionally, more
than 75% of Florida’s human population lives in counties that
abut the coast, 86% of Florida’s gross domestic product is from
coastal-related economics (Wilson and Fischetti, 2010;Wyman,
Carter, and Weber, 2010), and 80% of the contributions to
Florida’s economy come from counties that abut Florida’s
coastline (Kildow, 2008). Thus, planning for SLR in Florida is
especially crucial for ensuring both the environmental and
economic stability of the state.
Models have been widely used to simulate a wide variety of
effects from SLR. Bathtub-style models simulate simple
inundation (conversion to open water) based on topography
and SLR scenarios (Dasgupta et al., 2009; Titus and Richman,
DOI: 10.2112/JCOASTRES-D-13-00215.1 received 10 December 2013;
accepted in revision 14 May 2014; corrected proofs received
10 June 2014; published pre-print online 9 July 2014.
*Corresponding author: alinhoss@abe.msstate.edu
ÓCoastal Education & Research Foundation 2015
Coconut Creek, Florida January 2015Journal of Coastal Research 31 136–46

2001). Models that assess additional ecological processes, such
as the Sea Level Affecting Marshes Model (SLAMM), can
simulate both inundation and wetland responses to SLR (Chu-
Agor et al., 2011; Geselbracht et al., 2011; Glick et al., 2013;
Temmerman et al., 2004). Several other modelsalso explore the
combined biophysical and socioeconomic effects from SLR
(Feagin et al., 2010; Hinkel and Klein, 2009; Hinkel et al.,
2012; Linhoss et al., 2013), and other models assess species
level responses to SLR (Mendoza-Gonza
´lez et al., 2013; Morris
et al., 2002). Mcleod et al. (2010) provide a review of SLR
models and describes their physical, ecological, and socioeco-
nomic inputs and outputs.
We extend the body of SLR research to assess spatial wetland
dynamics and inundation as a result of SLR within the context
of natural and human communities and economics. This
approach allows for a more integrative assessment of the
effects of SLR through assessing the combined impacts from
changes to habitat composition, the aerial extent of influence,
and the cost per acre of affected areas.
The goal of our study was to assess the effects of SLR on
urban, developed lands as well as environmental systems in the
Matanzas area in NE Florida. We assessed impacts to land use
and land cover by considering the net change in area for
wetlands, the migration or relocation of wetlands into and out
of areas, and the migration of wetlands onto developed lands.
We assessed the impacts to developed lands by calculating the
area of inundation and wetland migration and the associated
cost per area for individual land uses. The specific objectives of
this research were to (1) simulate inundation and wetland
migration under a range of SLR scenarios in Matanzas, Florida
using SLAMM, (2) use land cover (surface cover) to infer
impacts to environmental systems, and (3) use existing land
use (land purpose) to infer impacts to human communities.
METHODS
This study integrates the use of an off-the-shelf model that
simulates wetland land-cover changes from long-term SLR
with land-use and land-valuation data to provide an assess-
ment of human and environmental vulnerabilities to SLR in
NE Florida. Land-cover types include fresh and saltwater
wetlands as well as developed (urban) and undeveloped lands.
Land-use data includes land use classifications as well as
parcel valuation, which allows an economic assessment of the
impacts from SLR. These methods present a logical next step in
the field of SLR research and an important progression in our
ability to assess the integrated impacts from SLR.
Study Area
The study area is the Matanzas River Basin including an
additional 5-km buffer surrounding the basin (106,864 ha)
(Figure 1). The Matanzas Basin is located in NE Florida on the
U.S. Atlantic Coast. The buffer is included in this study to
account for adjacent habitat corridors and municipalities.
Elevation in the study area ranges from 0 to 32 m above sea
level (Figure 1). There is approximately 62 km of low-lying
ocean coastline within the site. The NE portion of the study
area includes the cities of St. Augustine and St. Augustine
Beach (Figure 1). St. Augustine is the oldest continuously
occupied city in the United States and is, therefore, rich in
cultural value. St. Augustine straddles the intercostal water-
way and has a mean elevation of 0.25 m, making the city and its
cultural resources extremely vulnerable to SLR. The mideast-
ern portion of the study area includes the Guana Tolomato
Matanzas National Estuarine Research Reserve (GTMNERR),
which is dedicated to the conservation of natural and cultural
resources. The reserve provides habitat for 56 protected plant
and animal species and has an average elevation of 0.6 m above
sea level. The SE portion of the study area includes the cities of
Beverly Beach and large portions of Palm Coast and Flagler
Beach. These cities are important population centers and
tourist destinations for the state of Florida. The western
portion of the study area is composed of primarily agricultural
and forestry land uses. Land use throughout the study area is
primarily composed of agriculture followed by residential and
government land uses (per Florida Department of Revenue
land use classification codes [Florida Department of Revenue
Staff, 2010]) (Figure 2).
Model Description
SLAMM is a spatially explicit model in which land cover and
elevation data are assigned to each cell. SLAMM can simulate
25 land-cover categories, each associated with parameters and
Figure 1. Map of the Matanzas Watershed study area showing relative
elevation, cities, and the Guana Tolomato Matanzas National Estuarine
Research Reserve (GTMNERR). (Color for this figure is available in the
online version of this paper.)
Journal of Coastal Research, Vol. 31, No. 1, 2015
Impacts of Sea-Level Rise on Human and Environmental Systems 37

boundary conditions (e.g., elevation, salinity, tidal range). SLR
causes wetland land-cover types to convert according to
parameter boundary conditions that are set for each land-
cover category. This conversion occurs by switching from one
land-cover type to another on a cell-by-cell basis in a given time
step. For example, a saltmarsh cell may switch to a tidal flat in
a time step based on rising water levels. The SLAMM technical
documentation details the wetland switching functions
(Clough, Park, and Fuller, 2010). Depending on the study area
and available data, not all wetland types may be used. Here, we
used the following SLAMM land-cover categories: developed
uplands, undeveloped uplands, saltmarsh, tidal flat, inland
fresh marsh, swamp, cypress marsh, beach, and open water.
SLAMM simulates regional SLR by comparing local-to-global
historical trends. The model considers several second-order
effects beyond a simple bathtub model, such as computing the
exposure to wave action and assuming additional erosion when
cells are adjacent to open ocean. SLAMM also calculates
vertical accretion according to an empirical relationshiprelated
to elevation, the accretion rate, the distance to a river or tidal
channel, and salinity. SLAMM produces annual maps of
inundation and land cover for the simulation period (Clough,
Park, and Fuller, 2010). The quality of the parameter values,
model assumptions, and model structure are all important
factors that should be considered when interpreting the model
results.
Previous work using SLAMM described its advantages and
disadvantages and its most important inputs. McLeod et al.
(2010) compared several SLR models and noted that the
advantages of SLAMM include its flexibility in scale, ability to
describe the vulnerability of habitats and species, and ability to
simulate the effect of inland saltwater intrusion on land cover.
Kirwan and Guntenspergen (2009) described the disadvantag-
es of SLAMM, including declining accretion rates in the
seaward direction and the lack of positive feedbacks in
response to sediment supply, and they also questioned the
validity of the exact amount of change in wetland area. Craft et
al. (2009) responded to Kirwan and Guntenspergen citing the
vertical accuracy of Light Detection and Ranging (LIDAR), how
accretion is strongly related to salinity, the fact that the broad
spatial scale of SLAMM does not accommodate simulating
mechanistic processes at specific locations, and emphasized not
only the outputs related to wetland area but also the ecosystem
services provided by various wetland types. Recently, SLAMM
results and model structure have been rigorously tested to
build greater confidence in the model’s algorithmic structure
and performance. Chu-Agor et al. (2011) conducted a compre-
hensive sensitivity and uncertainty analysis of SLAMM in an
application to the Florida Panhandle. They showed that
approximately 90% of the variability in the model results can
be attributed to uncertainty in inputs regarding the digital
elevation model, historical SLR, accretion, and sedimentation.
Geselbracht et al. (2011) conducted a hindcast to validate
SLAMM’s competence in depicting changes in wetland cover
from SLR in Florida. They found that the simulations showed a
pattern in land-cover changes that was consistent with the
pattern observed. Glick et al. (2013) also conducted a hindcast
along the coast of Louisiana and found that SLAMM predicted
total marsh lost within 4% of the observed loss. Overall,
although limitations do exist within the SLAMM modeling
framework, the model is a useful tool for developing a general
understanding of the trends that a region may face under SLR.
Model Inputs
SLAMM requires spatial and nonspatial inputs to drive the
model runs. The two spatial inputs that SLAMM requires are
land cover and elevation. We used a composite of land-cover
data from the St. Johns River Water Management District
Land Use and Cover data set (SJRWMD staff, 2004) and the
emergent vegetation data set from GTMNERR (Kinser et al.,
2007). The SJRWMD data are a regional land use land cover
(LULC) data set that comprises 88 categories, including 15
wetland categories within the study area. The emergent
vegetation data set was developed to provide a more detailed
spatial description of wetland land cover along the coastal
fringe of GTMNERR. This data set comprises 26 dominant
wetland-vegetation classifications. The SJRWMD data set was
used wherever the emergent vegetation data was missing.
Land-cover classifications from both data sets were trans-
formed to SLAMM land-cover classifications by matching the
Figure 2. Composition of land use within the study area, including
residential, commercial, industrial, agriculture and timber, institutional,
government, and miscellaneous land uses. Note that agriculture and timber
areas are largely located in the western portion of the study area, outside of
the impacts from sea-level rise. (Color for this figure is available in the online
version of this paper.)
Journal of Coastal Research, Vol. 31, No. 1, 2015
38 Linhoss et al.

definitions of the SLAMM categories with those of the
GTMNERR composite LULC (GTMNERR LULC). In cases
where the match was not straightforward, we created a map
using National Wetlands Inventory data translated to SLAMM
land-cover categories (a crosswalk is provided by the SLAMM
developer) and then overlaid the GTMNERR LULC layer. That
allowed an analysis of how the categories aligned. For example,
97% of lands that the GTMNERR LULC data set calls ‘‘Bays
and Estuaries’’ align with the SLAMM category ‘‘Estuarine
Water’’ and so this category was cross-walked to ‘‘Estuarine
Water.’’
Here, we refer to wetlands according to the SLAMM wetland
land-cover types including: saltmarsh, tidal flat, inland fresh
marsh, swamp, cypress marsh, and beach (Clough, Park, and
Fuller, 2010). For this analysis, developed land refers to any of
the following land uses: government, industrial, institutional,
commercial, and residential property with medium and high
densities (Florida Department of Revenue Staff, 2010). Resi-
dential properties with low densities (,2 units per acre) were
considered undeveloped land. Undeveloped dry land refers to
agriculture and miscellaneous land uses as well as residential
properties with low densities (,2 units per acre).
Elevation data were produced as a composite from St. Johns
County LIDAR (St. Johns County, 2008), Palm Coast LIDAR
(Palm Coast, 2008), and Florida Fish and Wildlife–Fish and
Wildlife Research Institute 5-m digital elevation maps (FWCC
Staff, 2009). This composite data set allowed the use of the best-
available data to describe elevation. We used a 10-m spatial
grid for all data and simulations.
Nonspatial model inputs in SLAMM include tidal ranges,
historical SLR, and accretion (Table 1) as well as SLR
scenarios. Tidal range was determined from the National
Oceanic and Atmospheric Administration (NOAA) Fort Mata-
nzas River station (Station 8720686). Historical sea level was
determined from the NOAA Fernandina Beach station (Station
8720030). Accretion, erosion, and sedimentation rates were
parameterized with data from the SLAMM technical docu-
mentation and existing literature. Swamp accretion was based
on default values listed in the technical documentation and
measured values along the coast of Georgia (Clough, Park, and
Fuller, 2010; Craft et al., 2008). Saltmarsh and inland fresh
marsh accretion was based on mean values collected along the
SE Atlantic Coast (Craft, 2007). Erosion and sedimentation
rates were based on SLAMM default values (Clough, 2006;
Clough, 2011). SLAMM 6.0 has a soil-saturation option that
raises the freshwater table with SLR. This option was turned
off because of the simple unidirectional algorithm that
produced striping in the mapped results.
Land use was used to assess the impacts to developed lands
and, in turn, anthropogenic vulnerability. Counties in Florida
provide the Florida Department of Revenue (FDR) geographic
information system (GIS) parcel maps that include a land-use
classification and economic value for each parcel. Although the
FDR data are a state standard, counties such as Flagler,
Florida, keep up-to-date records that are more recent than the
state data. The land use data for this study was based on parcel
data from St. Johns County (Florida Department of Revenue
Staff, 2010) and Flagler County (Flagler County Staff, 2012).
As per the FDR land-use classification codes, parcels are
categorized as residential (14,128 ha; 13.4%), commercial
(4,334 ha; 4.1%), industrial (850 ha; 0.8%), agricultural and
timber (49,202 ha; 46.6%), institutional (616 ha; 0.6%),
government (19,699 ha; 18.6%), and miscellaneous (16,866
ha; 16.0%) (Florida Department of Revenue Staff, 2010) (Figure
2). The miscellaneous category is largely composed of water
bodies, wetlands, utilities, rights-of-way, and unzoned areas.
SLR scenarios in SLAMM are based on the IPCC 2013
Climate Change Report (Church et al., 2013) and are scaled to
user specifications. Estimating the effects of SLR is a
fundamentally uncertain task because of the uncertainty in
the forecasts of SLR. Historical data, from the National Oceanic
and Atmospheric Administration (NOAA) Fernandina Beach
Florida station (Station 8720030),show a linear rise inlocal sea
level during the past 100 years of 2.02
3
m/y (National Oceanic
and Atmospheric Adminitration, 2012). Assuming no future
acceleration of that rate, sea level would rise by 0.2 m between
2008 and 2100. Current projections of global SLR range
between 0.2 and 1.6 m by 2100 (Church et al., 2013; Grinsted,
Moore, and Jevrejeva, 2010; Horton et al., 2008; Vermeer and
Rahmstorf, 2009). Because of that uncertainty in SLR
forecasts, we considered three SLR scenarios (low, medium,
and high), including 0.2, 0.9, and 1.6 m SLR by 2100.
Interpreting Effects to Developed Areas and the
Environment
Land cover was used to assess ecological impacts and
environmental vulnerability from SLR. We investigated the
pattern of land-cover change between the initial condition and
the SLAMM outputs in 2100. We analyzed both the net change
in area per land-cover type as well as the area of relocation or
migration per land-cover type. That is, does a land-cover type
relocate and move to an entirely new location or does it stay in
generally the same place, perhaps losing or gaining some
portion of its area. A wetland land-cover type that relocates to
an entirely new area while maintaining its overall size should
be considered more vulnerable than one that stays in place but
loses some small portion of its overall area.
To assess potential human interventions under SLR, we
simulated the treatment of developed lands in two ways: (1)
changes to developed lands, and (2) no changes to developed
lands. The model was first run assuming that wetlands can
migrate onto developed lands, thus changing the land cover
from developed to wetland. However, we realized that it is
likely that development and human intervention will inhibit
some degree of wetland migration. Therefore, in assessing
environmental effects, we also blocked out the developed lands
excluding them from any type of land-cover change. The
combination of those two approaches allowed us to show the
area of wetland migration that occurred on developed lands
and the potential loss of wetlands because of development.
To describe the impacts to developed lands, we assessed the
area where inundation occurred and the associated property
value. The analysis was based on the difference between the
initial condition (year 2008) and the simulated results in year
2100 for all three SLR scenarios. The change in property value
from SLR was calculated using the just value (JV) (Zhang et al.,
2011). The JV for each parcel within the study area was
assigned using the parcel data from St. Johns County (Florida
Journal of Coastal Research, Vol. 31, No. 1, 2015
Impacts of Sea-Level Rise on Human and Environmental Systems 39

Department of Revenue Staff, 2010) and Flagler County
(Flagler County Staff, 2012) (Appendix Figure A1). According
to section 193.011 of the Florida constitution, JV ‘‘is synony-
mous with fair market value, i.e. the amount a purchaser,
willing but not obliged to buy, would pay a seller who is willing
but not obliged to sell.’’ Equation 1 shows how JV is affected by
SLR, where JV
i
is the original JV for each parcel i,A
i
is that
parcel’s original area, A
SLRi
is the area of each parcel that is
affected by SLR, and JV
SLR
is the resulting total JV as affected
by SLR. Thus, Equation (1) calculates the total proportion of
the JV affected by SLR in the study area on an aerial basis.
JVSLR ¼RASLRi
Ai
JVið1Þ
Because each parcel is associated with a single land-use
category, the JV of each land use classification can also be
assessed. Equation (1) assumes that property value is uniform
throughout each parcel, regardless of the various land-cover
types within a given parcel. We also do not account for the
location of building footprints within parcels and their
associated values. Those assumptions are considered accept-
able because our calculations sum across the study area, which
averages out variations in individual properties. The JV for the
parcels ranges from $0 to more than $60 million dollars. Parcels
with a value of $0 may be under a conservation easement or a
part of community property. While those properties have an
important economic value, this is an unfortunate limitation of
the data. Although the use of JV does not account for future
inflation, it does allow for an assessment of the relative effects
within the study area.
RESULTS
General results from the SLAMM simulations in all SLR
scenarios showed a band of change in land cover running
longitudinally along the coast and extending approximately 2
to 5 km inland (Figure 3). Effects were seen even further inland
along three main rivers (up to 10.5 km). Those changes were
due to wetland migration and inundation, and they apply to
changes to developed and undeveloped lands. In the N and S
portions of the study area (occupied by the cities of St.
Augustine and Palm Coast), land-cover change extended
approximately 3.5 to 5 km inland. In the midsection of the
study area, which is occupied by GTMNERR, land-cover
change generally extended 2 to 2.5 km inland. On an area
basis, the simulations showed potential changes in land cover
(upland to wetland, upland to water, wetland xto wetland y,or
wetland to water) of 433 ha, 5332 ha, and 11,534 ha by 2100
under the 0.2-, 0.9-, and 1.6-m SLR scenarios, respectively.
That accounts for up to 11% of the total study area. Maps of the
initial condition and the results of the 0.2-, 0.6-, and 1.9-m
scenarios are shown in the Appendix (Figures A2–A5).
The difference between allowing wetlands to migrate onto
developed lands and blocking changes to developed lands was
found to be important. If wetlands were allowed to migrate onto
developed lands, the overall change in wetland coverage was
minimal. Under that assumption, the model simulated 37,361,
36,695, and 37,084 ha of wetlands in year 2100 under the 0.2-,
0.9-, and 1.6-m SLR scenarios, respectively. That can be
compared with the initial condition of 36,665 ha of wetlands.
Therefore, in every SLR scenario where wetlands were allowed
to migrate onto developed lands, the study area actually gained
in overall wetland area, although that gain represents a
maximum change of only 1%. However, it is likely that humans
will act to protect developed land from either being inundated
or from converting to wetlands in an effort to protect
infrastructure and property values. If developed lands were
not allowed to change, then higher rates of wetland losses were
Table 1. SLAMM inputs: These inputs are used in the model to describe
local, historical sea-level rise, tidal ranges, erosion, and accretion. They are
parameterized based on NOAA data and literature.
Input Value
Historical SLR (m y
1
) 0.00202
Great diurnal tidal range (m) 1.33
Salt elevation above mean tide (m) 0.86
Marsh erosion (horizontal m y
1
)2.0
Swamp erosion (horizontal m y
1
)1.0
Tidal flat erosion (horizontal m y
1
)0.5
Saltmarsh accretion (vertical mm y
1
)2.3
Inland fresh marsh accretion (vertical mm y
1
)6.0
Swamp accretion (vertical mm y
1
)0.3
Beach sedimentation (vertical mm y
1
)0.5
Figure 3. The locations of land-cover changeas a result of wetland migration
and inundation shown for each sea-level rise scenario (0.2, 0.9, and 1.6 m)
between 2008 and 2100. This map shows a band of change running
longitudinally along thecoastline varying inwidth from 2 to 5 km. Areas are
affected further inland along tidal rivers. (Color for this figure is available in
the online version of this paper.)
Journal of Coastal Research, Vol. 31, No. 1, 2015
40 Linhoss et al.

seen. Under that assumption, the model simulated 37,282,
35,911, and 34,907 ha of wetlands in year 2100 under the 0.2-,
0.9-, and 1.6-m SLR scenarios, respectively. That represents a
maximum change in wetland area of 6% compared with the
initial conditions.
Ecological Impacts
The net change in area for the wetland types illustrates the
range of losses and gains for the variety of wetlands under the
different SLR scenarios (Figure 4). There are important
decreases in saltmarshes under the 0.9- and 1.6-m SLR
scenarios (7to28%). Tidal flats lost 4 to 32% of their area
under the three SLR scenarios. Beaches lost 8% of their area
under the 0.9-m scenario but gained 39% of their area under
the 1.6-m scenario. Developed uplands lost 0 to 14% of their
area under the three SLR scenarios. Undeveloped uplands lost
0 to 5% of their area under the 0.9- and 1.6-m SLR scenarios.
The 0.2-m SLR scenario showed relatively little change in all of
the land-cover categories (maximum 64%). That analysis
assumed that wetlands cannot migrate onto developed lands.
The interpretation of vulnerability shown through the net
changes to wetland area (Figure 4) is in sharp contrast to the
assessment of the degree of movement that the wetlands
experience as they migrate (Figure 5). For example, beaches
gained 39% in net area under the 1.6-m scenario (Figure 4).
However, assessing how that wetland type moved out of old
areas and into new areas presented a deeper understanding of
those dynamics. Beaches actually relocated, moving out of 51%
of their initial location and into new locations for 90% of their
original area (Figure 5). So, although beaches gained in total
area, that scenario includes important losses to exiting
beaches. Tidal flats and saltmarshes were also shown to be
very dynamic, gainingup to 83% in new area while losing up to
92% of their original area between the three SLR scenarios
(Figure 5). That analysis assumed that wetlands could not
migrate onto developed lands.
If we allowed wetlands to migrate onto developed lands, then
we saw additional areas for migration for saltmarshes and
beaches (Table 2). In 2100, under the 0.2-m SLR scenario,
SLAMM simulated that 2% of saltmarshes and 1% of beaches
migrated onto developed uplands. Under the 0.9-m SLR
scenario, 15% of saltmarshes and 20% of beaches migrated
onto developed uplands. Under the 1.6-m SLR scenario, 38% of
saltmarshes and 36% of beaches migrated onto developed
uplands.
Impacts to Human Systems
To assess impacts to human systems, we calculated the area
that was newly inundated under each SLR scenario and the
associated parcel value of that land. Under the 0.2-m scenario,
the total loss of area from inundation was 73 ha, and the
associated JV was $10.2 million. Under the 0.9-m scenario, the
total loss of area from inundation was 2224 ha, and the
associated JV was $177 million. Under the 2-m scenario, the
total loss of area from inundation was 4444 ha, and the
associated JV was $322 million.
Land uses were similarly affected by aerial inundation and
wetland migration within the study area (Figure 6a). Impacts
from inundation ranged between 0 and 2300 ha for each land-
use category, whereas impacts from wetland migration ranged
between 0 and 2139 ha foreach land-use category. On an aerial
basis, the most affected land-use categories from inundation
and wetland migration were residential and government lands,
with commercial and miscellaneous land-uses following. Aerial
impacts to agricultural, industrial, and institutional land uses
were small in comparison.
In terms of economic effects, the simulations showed that
wetland migration affected more valuable land (JV/area) than
inundation did (Figure 6b). Impacts from inundation ranged
between $0 and $1.5 million/ha, whereas impacts from wetland
migration ranged between $0 and $12.2 million/ha. Residential
lands were the most-expensive land-use category to be affected,
Figure 4. The net change in wetland area under the 0.2-, 0.9-, and 1.6-m sea-
level rise scenarios. These data assume that wetlands cannot migrate onto
developed lands. The values in parenthesis on the x-axis represent the area
for the land-cover type under the initial condition.
Figure 5. The migration of wetland out of old areas and into new areas. This
figure shows the percentage of area gained (þ) and lost () for each wetland
land-cover type under the 0.9- and 1.6-m sea-level rise scenarios by 2100.
Data are not shown for 0.2 m because of the relatively small effects under
that condition. The positive values (cool tones) describe gains to land-cover
types through migration into new areas and the negative values (warm
tones) describe losses to land-cover types as they move out of their original
area, as described in the initial condition. These data assume that wetlands
cannot migrate onto developed lands. The values in parenthesis on the x-axis
represent the area for the land-cover type under the initial condition.
Journal of Coastal Research, Vol. 31, No. 1, 2015
Impacts of Sea-Level Rise on Human and Environmental Systems 41

with commercial, industrial, and institutional land-uses
following.
DISCUSSION
The trend of impacts along the coast extending 2 to 5 km
inland (Figure 3) is important for community and ecological
planning purposes. Human adaptation to SLR may be seen as a
choice between protection (via barriers) vs. retreat (Fankhaus-
er, 1995). As such, planning for SLR first entails the
identification of the areas that will most likely be affected.
The band of change shows areas where protection may be
necessary and also areas that are outside of the area of change
and, therefore, safe for retreat. Similarly, environmental
conservation plans need to detect areas and systems that are
vulnerable to SLR. Ecosystems and habitats that are located
within the band of change should be considered vulnerable to
future SLR.
Feagin et al. (2010) concluded that some wetland land-cover
types gain area, whereas others lose area, and that a nuanced
approach is necessary to account for changes in individual
wetland types. Our results agree with those of Feagin et al
because we showed that beaches gained in net area, and the
rest of the wetlands lost net area in most of the scenarios
(Figure 4). We build on the nuanced approach and show the
migration of those wetland types as they moved out of old areas
and into new areas (Figure 5). Our analysis showed that some
wetlands are very dynamic moving into new areas and out of
old areas. The degree of wetland relocation is an important
factor when considering the vulnerability of land-cover
categories for two reasons. First, will wetland migration be
able to keep pace with accelerated SLR?
Accelerated SLR represents a nonstationary condition under
which we do not have field data. If wetlands are not able to
move into new areas fast enough, additional losses will beseen.
For example, under the 1.6-m scenario, if saltmarshes cannot
keep pace with SLR, they could lose up to 90% of their area
(Figure 5). Second, how will existing developed lands inhibit
wetland migration? Table 2 shows that developed lands are an
important factor in potential losses in wetlandarea, and Figure
6b shows that the area that wetlands will migrate into is more
expensive than the area that will be inundated. It is likely that
humans will act to protect expensive parcels from converting to
wetlands.
In addition, the results also show that wetland types do not
necessarily consistently lose or gain area with increasing SLR.
In fact, within just one SLR scenario, we can see individual
wetland types gaining and then losing area over time (or vice
versa). That dynamic is a result of thresholds in elevation and
salinity, and that nonlinear response is an important point in
understanding the ecological impacts from SLR.
The U.S. policy of ‘‘no net loss of wetlands’’ is enforced by
the Environmental Protection Agency through permitting,
mitigating, and restoring wetlands under the Clean Water
Act (EPA, 1997). The Clean Water Act is effectively a
stationary policy that does not account for processes that
mayoccurduringSLR.Thereisnopolicythatexplicitly
protects the accommodation space that is needed for the
migration of wetlands onto uplands because of SLR (NWF
staff, 2006). Specifically, our study showed that the existence
Table 2. The area of migration onto developed uplands for each land cover type. This table gives the area in hectares and the percentage of the total area under
each sea-level rise scenario.
0.2 m 0.9 m 1.6 m
ha % ha % ha %
Inland fresh marsh 0 0 0 0 0 0
Swamp 0 0 0 0 0 0
Cypress swamp 0 0 0 0 0 0
Saltmarsh 78 2 737 15 2013 38
Tidal flat 0 0 0 0 3 0
Beach 1 1 47 20 161 36
Open water 0 0 0 0 3 0
Figure 6. (a) Area of land affected from inundation and wetland migration
per land-use category under the 0.2-, 0.9-, and 1.6-m sea-level rise scenarios.
(b) Cost per area in million U.S. dollars ($) per hectare of areas affected from
inundation and wetlandmigration per land-use category under the 0.2-, 0.9-,
and 1.6-m sea-level rise scenarios. Abbreviations: Ag ¼agriculture, Comm ¼
commercial, Govt¼government, Ind¼industrial, Inst ¼institutional,Misc ¼
miscellaneous, Res ¼residential.
Journal of Coastal Research, Vol. 31, No. 1, 2015
42 Linhoss et al.

of coastal, developed lands can hinder wetland migration.
Those developed lands may result in a 2 to 6% overall loss of
wetlands in the study area under the 0.9- and 1.6-m SLR
scenarios.
The results did not show gains in freshwater marshes. An
initial test run showed that the soil saturation module caused
significant striping and unrealistic results; therefore, the
module was turned off. The striping occurs because the
individual cells to do not communicate with each other in all
directions, which is a limitation of the model.
The results of the land-use analysis showed that residential
lands consistently ranked as the most or the second-most
affected land-use category on both an aerial basis and on a cost-
per-area basis. That is an indication of the economic sector that
is most vulnerable to SLR. Government land suffered the
second-greatest loss in area. Although a significant portion of
government land was affected, much of that land is in parks
and conservation areas that are already devalued because of
their current status as wetland. As a result, the value of the
affected government land was less. Residential and govern-
ment lands are the second and third largest land-use categories
within the study area. Although, agriculture is the most
common land use in the study area (49,202 ha; 46.6%), it
suffered the least in both areal and economic impacts.
Agricultural land generally occurs in the western portion of
the study area outside of the urban areas. Commercial lands
are mostly located in the N and S portions of the study area.
Although there was little effect to commercial land by
inundation, the low-lying commercial areas in the eastern-
most portion of the study area will affect the accommodation
space that is necessary for the inland migration of wetlands.
Though industrial and institutional land uses suffered less
through aerial impacts, the cost per area of effects to these land
uses was high.
The results also showed that overall, wetland migration will
occur on more expensive land than inundation will, likely
because wetland migration will occur on dry uplands that are
close to the coast and, therefore, desirable. On the other hand,
inundation will occur on land that is currently devalued
because of either existing wetlands or a low-lying status. These
findings highlight the cost–benefit for protecting wetlands vs.
inundated lands and have important implications for manage-
ment decisions.
Declining tidal-marsh habitat may lead to losses in the
critical ecosystem services that are provided by those wetlands.
Salt marshes provide valuable economic benefits to coastal
communities, including food, coastal protection, sediment
stabilization, water purification, sustenance of recreational
and commercial fisheries, carbon sequestration, tourism, and
education (Barbier et al., 2011). In Florida, coastal tourism and
recreation account for $67.2 billion (22%) of the state’s sales tax
revenue and employs nearly 1 million Floridians (Florida
Ocean Alliance Staff, 2013). There are more than 100,000
visitors annually to GTMNERR alone (Kildow, 2008). In New
Jersey, Costanza et al. (2006) found that wetlands were worth
more than $10 billion per year. A cost–benefit analysis for
allowing marsh migration onto developed and undeveloped
uplands is essential and requires additional scrutiny beyond
the scope of the present study.
CONCLUSIONS
To plan responsibly for future SLR, it is crucial that we
jointly plan for human and ecological systems. A failureto do so
will result in a lopsided planning process. In this study, we
presented an integrated approach to analyzing vulnerability to
SLR through assessing land use and land cover through parcel
data and wetland habitat. In doing so, we were able to consider
both human and environmental aspects of SLR. We showed
changes in land cover and how those changes were incurred. A
2- to 5-km band of change was identified in which SLR will
inundate areas and cause inundation and wetland migration.
Beaches, tidal flats, and saltmarshes were the most affected
land covers. Depending on the SLR scenario, those land-cover
types were simulated to both gain and lose area. Developed
lands decreased the accommodation area that is available for
wetland migration. The migration of wetlands into new areas
and out of old areas added an additional layer of vulnerability
to the system because if these wetlands cannot keep up with
SLR important additional losses to wetlands will be seen. We
also assessed the areal and economic impacts to land-use
categories. Residential land was the most affected land use of
the categories that were assessed, and wetland migration was
shown to consistently occur on more-expensive land than
inundation does (cost per area). An important limitation of this
work is the lack of information regarding freshwater marshes
because of turning offthe soil saturation module. The tools that
we describe are useful for planners and managers to strategi-
cally procure conservation lands and plan for future develop-
ment.
ACKNOWLEDGMENTS
We would like to thank the National Estuarine Research
Reserve System Science Collaborative for funding this
project. We also acknowledge the generous help from the
Matanzas Guana Tolomato National Estuary Research
Reserve for their help. In addition, we would like to thank
Dr. Paul Zwick, Dr. Thomas Hoctor, and Michael Volk for
their involvement in the project. This material is based on
work supported by the University of Florida, Mississippi
State University, and the National Institute of Food and
Agriculture, U.S. Department of Agriculture, under Project
No. 160000-010-300-02700.
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APPENDIX
Figure A1. Parcel land valuation in the study area in million U.S. dollars of
just value per hectare. (Color for this figure is available in the online version
of this paper.)
Figure A2. Land cover for the initial condition (year 2008) within the study
area. (Color for this figure is available in the online version of this paper.)
Journal of Coastal Research, Vol. 31, No. 1, 2015
Impacts of Sea-Level Rise on Human and Environmental Systems 45

Figure A3. Land cover from the SLAMM simulation in year 2100 under the
0.2-m sea-level rise scenario. This figure assumes thatwetlands can migrate
onto developed lands. (Color for this figure is available in the online version
of this paper.)
Figure A4. Land cover from the SLAMM simulation in year 2100 under the
0.9-m sea-level rise scenario. This figure assumes thatwetlands can migrate
onto developed lands. (Color for this figure is available in the online version
of this paper.)
Figure A5. Land cover from the SLAMM simulation in year 2100 under the
1.6-m sea-level rise scenario. This figure assumes that wetlands can migrate
onto developed lands. (Color for this figure is available in the online version
of this paper.)
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46 Linhoss et al.