Application of the Sea-Level
Affecting Marshes Model
(SLAMM) to the Lower
Delaware Bay, with a Focus
on Salt Marsh Habitat
6EPA
United States
Environmental Protection
Agency
EPA/600/R-18/385 April 2019 www.epa.gov/ord
Office of
Research and Development
National Center for
Environmental Assessments

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EPA/600/R-18/385
Final
April 2019
www.epa.gov/research
Application of the Sea-Level Affecting Marshes Model
(SLAMM) to the Lower Delaware Bay, with a Focus on
Salt Marsh Habitat
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC 20460
Photo Credits:
LeeAnn Haaf/PDE (front cover: wetlands, back cover: blue crab and wetlands)
Brian C. Harris (front cover: saltmarsh sparrow)
Steve Nanz/Audobon (back cover: saltmarsh sparrow)
Steve Stanne/NYSDEC (front cover: blue crab)
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DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental Protection Agency policy and
approved for publication. Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.

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CONTENTS
LIST OF FIGURES	v
LIST OF TABLES	vii
ACRONYMS AND ABBREVIATIONS	viii
ACKNOWLEDGEMENTS	x
APPENDICES	xi
EXECUTIVE SUMMARY	xii
1	BACKGROUND	1
1.1	Coastal Wetlands of the Delaware Estuary	1
1.2	Model Summary	3
1.3	Case Study Scope	4
2	METHODS	5
2.1	Study Area and Site Delineation	5
2.2	Input Parameters and Data Preparation	7
2.2.1	Raster Data Preparation	7
2.2.2	Model Timesteps	13
2.2.3	Sea Level Rise Scenarios	13
2.2.4	Vertical Land Movement	14
2.2.5	Tide Ranges	14
2.2.6	SLAMM Salt Elevation Parameter	17
2.2.7	Wetland Elevation-Change Rate	19
2.2.8	Erosion Rates	25
2.3	SLAMM Model Setup and Calibration	26
2.3.1 Model Protection Scenarios	27
2.4	Sensitivity Analysis	28
3	RESULTS	28
3.1	Projected changes in all SLAMM land cover categories	29
3.2	Projected changes in high, low and total marsh acreage	33
3.2.1	High Marsh	34
3.2.2	Low Marsh	36
3.2.3	Total Marsh	38
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3.2.4 Gain/Loss Patterns	40
3.3 Sensitivity Analysis	48
4	CONCLUSIONS	51
5	REFERENCES	54
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LIST OF FIGURES
Figure 1. Map of the seven sites in the Lower Delaware Bay	6
Figure 2. VDATUM-derived correction values	8
Figure 3. The NWI date and corresponding boundary of the study sites	10
Figure 4. Initial land cover. The study sites are outlined in black	11
Figure 5. Great diurnal tide (GT) range data were derived from the NOAA buoys shown as black triangles
in this map	16
Figure 6. Relationship between tides, wetlands, and reference elevations for an example estuarine shore
profile. Source: Titus and Wang 2008	17
Figure 7. HTUs were plotted against the mean salt elevations for the three buoys to derive the linear
regression formula that was used to calculate salt elevations for the seven sites	18
Figure 8. Locations of SET sites in the New Jersey marshes (Dividing, Maurice and Dennis). Values equal
elevation change (mm/yr) averaged across the period of record	21
Figure 9. Locations of SET sites in the Delaware marshes (St. Jones (upper map) and Broadkill (lower
map). Values equal elevation change (mm/yr) averaged across the period of record	22
Figure 10. RFM and IFM SLAMM Elevation-Change Models	23
Figure 11. SLAMM land use categories from early- to late-century for the Delaware sites (Broadkill,
Mispillion and Lower St. Jones) under the intermediate SLR scenario (based on Sweet et al. 2017) and
"protect dry developed land" modeling scenario	30
Figure 12. SLAMM land use categories from early- to late-century for the New Jersey sites (Dennis,
Reeds Beach, Dividing and Lower Maurice) under the intermediate SLR scenario (based on Sweet et al.
2017) and "protect dry developed land" modeling scenario	31
Figure 13. Scatterplot of mean percent change in high marsh acreage versus mean SLR (across four time
steps - 2025, 2050, 2075, 2100), based on the intermediate SLR scenario and (model) protection of dry
developed land	35
Figure 14. Scatterplot of mean percent change in low marsh acreage versus mean SLR (across four time
steps - 2025, 2050, 2075, 2100), based on the intermediate SLR scenario and (model) protection of dry
developed land. The y-axis has been log-transformed	37
Figure 15. Scatterplot of mean percent change in total marsh acreage versus mean SLR (across four time
steps - 2025, 2050, 2075, 2100), based on the intermediate SLR scenario and (model) protection of dry
developed land	39
Figure 16. Gain/loss maps for the Broadkill (DE) site, based on the intermediate SLR scenario and
(model) protection of dry developed land	41
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Figure 17. Gain/loss maps for the Mispillion (DE) site, based on the intermediate SLR scenario and
(model) protection of dry developed land	42
Figure 18. Gain/loss maps for the Lower St. Jones (DE) site, based on the intermediate SLR scenario and
(model) protection of dry developed land	43
Figure 19. Gain/loss maps for Dividing (NJ) site, based on the intermediate SLR scenario and (model)
protection of dry developed land	44
Figure 20. Gain/loss maps for Lower Maurice (NJ) site, based on the intermediate SLR scenario and
(model) protection of dry developed land	45
Figure 21. Gain/loss maps for Dennis (NJ) site, based on the intermediate SLR scenario and (model)
protection of dry developed land	46
Figure 22. Gain/loss maps for Reeds Beach (NJ) site, based on the intermediate SLR scenario and (model)
protection of dry developed land	47
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LIST OF TABLES
Table 1. Acreage (and percentage of total acreage) of initial land cover categories	12
Table 2. Global mean sea level rise projections by scenario (Sweet et al. 2017)	14
Table 3. Great diurnal tide range (GT) inputs for the seven sites	15
Table 4. Salt elevation calculations were based on data from three NOAA buoys	18
Table 5. Salt elevations for the seven sites	19
Table 6. Locations of the SET stations, elevation change rate (averaged across the period of record) and
first and last SET measurement dates (dates cover the period of record available at the time of the
analysis; more data may now be available)	24
Table 7. Accretion rate inputs that were used for the other marsh types (at all sites)	25
Table 8. Average marsh erosion rates for each site (Demberger et al. 2017)	26
Table 9. Inundation models for "Traditional SLAMM" Categories (when cells fall below their lower
elevation boundaries, these are generally what they convert to)	27
Table 10. Example of a percent change table typically found in SLAMM reports	32
Table 11. Summary of results from the sensitivity analysis	49
Table 12. Principal factors affecting vulnerability to SLR	50
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ACRONYMS AND ABBREVIATIONS
DE
Delaware
GIS
Geographic Information System
DEM
Digital Elevation Model
GMSL
Global mean sea level
GT
Great Diurnal Tide Range
HTU
Half-tide Units
IFM
Irregularly-Flooded Marsh
LiDAR
Light Detection and Ranging
NAVD88
North American Vertical Datum of 1988
NJ
New Jersey
NWI
National Wetlands Inventory
MHHW
Mean Higher High Water
MLLW
Mean Lower Low Water
MTL
Mean Tidal Level
NJ
New Jersey
NOAA
National Oceanic and Atmospheric Administration
PDE
Partnership for the Delaware Estuary
RFM
Regularly-Flooded Marsh
SET
Surface Elevation Table
SLAMM
Sea-Level Affecting Marshes Model
SLR
Sea level rise
SSIM
Site-Specific Intensive Monitoring
USEPA
U.S. Environmental Protection Agency
USGS
U.S. Geological Survey
VDATUM
Vertical Datum Transformation Tool
VLM
Vertical land movement

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PREFACE
This report was prepared by the U.S. Environmental Protection Agency (USEPA) Office of Research and
Development, as part of the Air and Energy (A-E) research program, with support from Tetra Tech, Inc.,
and in collaboration with the Partnership for the Delaware Estuary (PDE). The A-E research program
provides scientific information and tools to support USEPA's commitment to clean air, clean water and
sustainable natural resources, even as environmental conditions change. A key component of this is the
development of sound science to support adaptation. Adaptation involves preparing for and adjusting to
the effects of expected future environmental changes. Because these effects are diverse, interactive,
and difficult to predict, adapting management of natural resources in this context can be very
challenging.
In the case of coastal salt marshes—which provide valued ecosystem services such as flood control,
water purification and critical habitat-sea level rise (SLR) is interacting with physical and biological
attributes of the system to induce complex changes in different salt marsh habitats. In this report,
projected changes for seven salt marsh areas of the Delaware Bay are examined using the Sea Level
Affecting Marshes Model (SLAMM, v. 6.7). These areas were chosen because they are of key
management concern to PDE and its partners. SLAMM simulates the dominant processes involved in
determining distributions of wetlands across space and time under conditions of accelerated SLR. This
report uses SLAMM to generate and interpret critical information for assessing the relative
vulnerabilities of different salt marshes to SLR. Besides fulfilling the immediate information needs of PDE
and its partners, these projections also serve an additional purpose; namely, as inputs to a larger study
on how to interpret and use this type of vulnerability information for robust analysis and design of
effective adaptation practices for protecting, restoring and/or enabling migration of valued salt marsh
ecosystems.
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
The Air and Energy (A-E) research program of EPA's Office of Research and Development was responsible for
producing this report. The report was prepared by Tetra Tech, Inc., under EPA Contract No. EP-C-12-060 and
EP-C-17-031. Jordan M. West served as the Task Order Project Officer, providing overall direction and
technical assistance, and was a contributing author.
AUTHORS
Jenifer Stamp, Tetra Tech, Inc.
Anna Hamilton, Tetra Tech, Inc.
Marissa Liang, ORISE Fellow at U.S. EPA
Jonathan Clough, Warren Pinnacle Consulting, Inc.
Marco Propato, Warren Pinnacle Consulting, Inc.
LeeAnn Haaf, Partnership for Delaware Estuary, Inc.
Jordan M. West, U.S. EPA, Office of Research and Development
INTERNAL REVIEWERS
Thomas Johnson, U.S. EPA, Office of Research and Development
Regina Poeske, U.S. EPA, Region 3
Matthew Konfirst, U.S. EPA, Region 3
EXTERNAL REVIEWERS
Patty Glick, National Wildlife Federation
David M. Kidwell, National Oceanic and Atmospheric Administration
ACKNOWLEDGEMENTS
We would like to thank Denice Wardrop and Mike Nassry from Penn State University for their advice
and participation throughout this project. We also thank Kari St. Laurent from the Delaware National
Estuarine Research Reserve, and Danielle Kreeger and Josh Moody from the Partnership for the
Delaware Estuary, for providing data and feedback at key points during the process.
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APPENDICES
A: Model Setup and Calibration
B: NWI Classes and SLAMM Categories
C: Elevation change data from SET stations
D: Full set of SLAMM outputs for the Broadkill
E: Full set of SLAMM outputs for the Mispillion
F: Full set of SLAMM outputs for the Lower St. Jones
G: Full set of SLAMM outputs for the Dennis
H: Full set of SLAMM outputs for the Reeds Beach
I: Full set of SLAMM outputs for the Dividing
J: Full set of SLAMM outputs for the Lower Maurice
K: Full set of SLAMM outputs for the Sensitivity Analysis
L: Comparison of outcomes under different model protection scenarios

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EXECUTIVE SUMMARY
This report presents results from the Sea-Level Affecting Marshes Model (SLAMM, v. 6.7), which was
used to generate spatially explicit projections for sea level rise (SLR)-induced changes in acreage for
seven salt marshes in the Lower Delaware Bay. Four of the marshes are located in New Jersey (Dividing,
Lower Maurice, Dennis, Reeds Beach) and three are located in Delaware (Broadkill, Mispillion, Lower St.
Jones). SLAMM is widely recognized as an effective model to study and predict wetland response to
long-term SLR (Park et al. 1991) and has been applied in every coastal U.S. state. Prior SLAMM work has
been performed in the Delaware Bay (Kassakian et al. 2017), but our methods differ in that we derive
results for specific marsh areas and utilize more recent, higher resolution elevation data, the most
recent SLR projections, and site-specific accretion data. These SLAMM simulations were performed as
part of a larger project by the USEPA on frameworks and methods for characterizing relative wetland
vulnerabilities in order to inform adaptation of management programs and practices (Wardrop et al.
2019).
We ran SLAMM simulations for early to late century time periods under three SLR scenarios (low,
intermediate and high), based on projections in Sweet et al. (2017). We also generated results for three
different model protection scenarios, ranging from no protection (where all cells are subject to
inundation) to protection of all dry land (where all cells designated as dry land are protected from
inundation). In addition, we ran a sensitivity analysis to better understand the influence of each input
variable on the projected changes in salt marsh acreage.
Results are reported in three sections:
1.	Projected changes in all SLAMM land cover categories, with specific attention given to the
three SLAMM land cover categories that are considered to be salt marsh habitat: regularly-
flooded marsh, irregularly-flooded marsh and transitional salt marsh (Warren Pinnacle
Consulting 2016). Outputs include tables and maps typically found in other SLAMM reports.
2.	Projected changes in high, low and total salt marsh acreage. High marsh was defined as the
aggregation of irregularly-flooded marsh and transitional salt marsh, low marsh was defined as
regularly-flooded marsh and total marsh was the aggregation of all three salt marsh habitats.
Outputs diverge from traditional SLAMM outputs and include: scatterplots of response (mean
percent change in acreage) versus exposure (mean relative SLR); and site-specific gain/loss maps
that highlight areas where changes are projected to occur. The intent was to explore new ways
of visualizing patterns across salt marsh habitats and to compare results across specific sites and
time periods, which could help inform management actions.
3.	Sensitivity analysis to assess the relative effect on model outputs of key input variables: Great
Diurnal Tide Range (GT), salt elevation, marsh erosion, and accretion rates.
The SLAMM simulations projected that all sites will experience loss of high marsh acreage by late
century and gains in low marsh and total salt marsh acreage. Rates of change varied across sites, time
periods and SLR scenarios. The Broadkill and Mispillion sites in Delaware were projected to experience
higher percent loss of high marsh sooner (early to mid-century). By late century, particularly under the
high SLR scenario, the New Jersey sites (which have higher rates of vertical land movement and
subsidence) were projected to experience large losses in high marsh habitat. By late century, areas
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initially categorized as low marsh were also projected to be lost at many sites (via conversion to tidal
flats or open water). The conversion/loss of low marsh is projected to occur at a slower rate than
conversion of high marsh; low marshes are assumed to have higher accretion rates since they are
inundated more frequently and collect more sediment. In the sensitivity analysis, the tide range (GT)
was the most dominant factor driving the gain and loss of regularly- and irregularly-flooded marshes,
and salt elevation had the greatest impact on transitional salt marsh. The marsh erosion and accretion
variables had much smaller effects (<1%).
SLAMM is a useful tool for projecting SLR-induced changes in salt marsh acreage; however, factors such
as marsh condition, stressors (e.g., hydrologic alteration, nutrient enrichment) and impacts from large
storms are not taken into account and need to be considered in concert with the SLAMM results to best
inform decisions. There are also uncertainties associated with the input data (e.g., limited tide range
data and variable Surface Elevation Table data). Despite these limitations, the SLAMM results have both
immediate and longer-term applications for informing wetlands and land-management decisions in
coastal areas, such as where to prioritize conservation or restoration efforts, where to plan for change,
and where to set up long-term monitoring sites to detect whether changes are occurring as projected.

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1	BACKGROUND
This report presents model simulations of projected sea level rise (SLR)-induced changes in acreage of
seven salt marsh areas in the Lower Delaware Bay, with a particular focus on changes in high (irregularly
inundated) versus low (regularly inundated) marsh. The projections were generated using the Sea Level
Affecting Marshes Model (SLAMM, v. 6.7), which is widely recognized as an effective tool to study and
predict wetland response to long-term SLR (Park et al. 1991) and has been applied in every coastal U.S.
state (Craft et al. 2009; Galbraith et al. 2002; Glick et al. 2007, 2013; Glick and Clough 2006; Park et al.
1993; Titus et al. 1991, Warren Pinnacle Consulting, Inc. 2015). While there have been prior SLAMM
efforts in the Delaware Bay (Kassakian et al. 20171), our results differ in that we focus on seven specific
marsh areas and utilize more recent, higher resolution elevation data, the most recent SLR projections,
and site-specific accretion data. These SLAMM simulations were designed to be of interest not only in
their own right, but also as a component of a larger U.S. Environmental Protection Agency (USEPA)
project that is developing frameworks and methods for characterizing relative wetland vulnerabilities
and assessing implications for wetlands management activities (Wardrop et al. 2019).
1.1 Coastal Wetlands of the Delaware Estuary
The vulnerability2 of coastal wetlands to SLR is evidenced by their loss due to more frequent inundation.
Coastal wetlands of the Delaware (DE) Estuary are considered especially vulnerable to SLR (Kreeger et al.
2010, Callahan et al. 2017); recent studies indicate that rates of SLR along the U.S. mid-Atlantic coast
have accelerated in recent decades faster than the global mean (Sallenger et al. 2012). SLR is one of
many factors contributing to the loss and degradation of coastal wetlands in the DE Estuary. Other
factors include conversion of wetlands to agricultural or other land uses, land subsidence due to
groundwater withdrawal, mosquito control ditching, incremental filling, hydrological alterations such as
dredging, nutrient enrichment and spread of invasive species (Sun et al. 1999, Haaf et al. 2015, USEPA
2015, Haaf et al. 2017). From 1996-2010, the acreage of estuarine wetlands declined across the
Delaware Estuary (-1.77%; -194 acres; -79 hectares per year), with the largest losses occurring in the
lower New Jersey Bayshore (-3.08%; -1,915 acres; -775 hectares per year) (Haaf et al. 2017).
While SLAMM simulations provide outputs for all wetland types, our primary focus in this report is on
salt marshes. Salt marshes are, by definition, inundated periodically by the tides. They are typically
divided into high and low zones. The low salt marsh is normally inundated by tidal water at least once
per day and in the Mid-Atlantic is predominantly covered by the tall form of Smooth Cordgrass (Spartina
1ln an earlier study, Industrial Economics generated SLAMM results for the Delaware Estuary (IE 2010), which were
later used in the Kassakian et al. 2017 article. These earlier results were generated with an older version of
SLAMM, coarser-resolution elevation data, one SLR scenario (aim increase by 2100) and different output subsites
(they generated results for 27 subsites/rectangular blocks that cover the tidal portion of the Delaware Bay).
Vulnerability is the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate
change (including sea level rise) with accompanying variability and extremes. It is a function of the character,
magnitude and rate of variation to which a system is exposed, its sensitivity, and its adaptive capacity. (Adapted
from Climate Change Science Program 2008)
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alterniflora). The high salt marsh is covered by water only sporadically (once per day or less). There are
two SLAMM land cover categories that are flooded at this frequency: irregularly-flooded marsh and
transitional salt marsh. Irregularly-flooded marsh, which generally borders low marsh habitat, is
characterized by the short form of Smooth Cordgrass, Spike Grass (Distichlis spicata), and Saltmeadow
Rush (Juncus gerardii), while transitional salt marsh is located at the landward edge and has more
woody vegetation such as scrub-shrub habitat. For our purposes, we considered both irregularly-flooded
marsh and transitional salt marsh to be high marsh habitat. For some managers, it is important to
distinguish between high and low marsh habitat because there are some differences in the types of
ecosystem services each provide. For example, low marsh provides habitat for mussels and crabs (Able
et al. 2007); high marsh provides critical habitat for species of conservation concern, such as the salt
marsh sparrow (Gjerdrum et al. 2005) and the American black duck; and total marsh (high and low
combined) provides protection from coastal flooding and erosion.
Survival of salt marshes in rising waters will depend on their natural ability to maintain surface
elevations relative to sea level, which is governed by whether net vertical accretion of the marsh surface
occurs at a rate at least equal to that of relative SLR (Reed 1995). Net vertical accretion is influenced by
sediment deposition, which when combined with vegetation processes, results in accumulation of
organic and inorganic matter. Relative SLR is generally considered to be the net combination of eustatic
(global) SLR, local oceanic currents, and land subsidence. Although salt marshes can adapt to these
changes, there is a threshold of SLR at which a marsh can no longer sustain natural feedbacks (D'Alpaos
et al. 2011). When this threshold is reached, death of marsh vegetation occurs (Raposa et al. 2015,
Watson et al. 2015, 2016, 2017). In addition, the combination of greater inundation with wave action
can lead to increased shoreline erosion (Ashton et al. 2008), as well as greater susceptibility to storm
surge that causes interior wetland erosion and breakup (Raposa et al. 2015, Wigand et al. 2017). The
degree to which a particular coastal wetland may be impacted by these related factors can vary due to
differences in coastal geology and the wave climate (the distribution of wave characteristics averaged
over a period of time for a particular location) (Ashton et al. 2008, Leonardi et al. 2018).
The loss of coastal wetlands poses a very serious problem in the DE Estuary because of the ecosystem
services they provide. These services are wetland functions that are economically valuable and
important for human health and well-being, such as: protecting inland areas from tidal and storm
damage; providing water storage; protecting against flooding; providing important habitat for a wide
variety of wildlife, including waterfowl; filtering contaminants and helping to sustain water quality;
capturing and sequestering carbon; providing spawning and nursery habitat for commercial fisheries;
supporting recreation; and providing aesthetic value (Kreeger et al. 2015, Partnership for the Delaware
Estuary 2017). Therefore, SLAMM-generated information on potential changes in wetland area and
habitat types can have important implications for decision making regarding land use and wetland
management priorities, strategies and techniques.
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1.2 Model Summary
SLAMM (see Box 1 for specifications) projects when and where marshes are likely to experience a
change in inundation due to SLR based on SLR rates, elevation data, accretion/sedimentation rates, tidal
data, and erosion rates. SLAMM also identifies locations where marshes may migrate upland in response
to changes in water levels, based on the relationship between marsh types and their frequency of
inundation. The model simulates the dominant natural processes that affect shoreline modifications
during long-term SLR and uses a complex decision tree incorporating geometric and qualitative
relationships to predict changes in coastal land cover classes.
SLAMM is not a hydrodynamic model. Rather,
SLAMM projects long term shoreline and
habitat class changes based upon a succession
of equilibrium states with sea level. Model
outputs include mapped distributions of
wetlands at different time steps in response
to sea level changes as well as tabular and
graphical data. Mcleod et al. (2010) state in
their review of sea-level rise impact models
that "... the SLAMM model provides useful,
high-resolution insights regarding how sea-
level rise may impact coastal habitats".
SLAMM assumes that wetlands occupy a range of vertical elevations that are a function of the tide
range. Because of this, rather than expressing marsh elevation in absolute values (e.g., meters, feet,
etc.), SLAMM computes units relative to the local tide datum for each cell at each time step (section
2.2.5.1). SLAMM can also calculate relative SLR as a function of global SLR scenarios offset by local
factors such as subsidence and isostatic adjustment (section 2.2.4). SLR is offset by marsh accretion and
other factors affecting marsh surface elevation.
When the model is applied, each study site is divided into cells of equal area (5 m x 5 m for these
simulations) that are treated individually. The conversion from one land cover class to another is
computed by considering the new cell elevation at a given time step with respect to the class in that cell
and its inundation frequency. Default wetland elevation ranges are available as a function of tidal
ranges, or ranges may be entered by the user if site-specific data are available. The connectivity module
determines salt-water flow pathways under normal tidal conditions using the method of Poulter and
Halpin (2008). In general, when a cell's elevation falls below the minimum elevation of the current land
cover class and is connected to open water (or an adjacent connected cell), then the land cover is
converted to a new class according to a decision tree.
Accretion, or the accumulation of organic and inorganic matter, is one of the most important processes
affecting marsh capability to respond to SLR. The SLAMM model was one of the first landscape-scale
models to incorporate the effects of vertical marsh accretion rates on projections of marsh fates, having
done so since the mid-1980s (Park et. al.1989). Since 2010, SLAMM has incorporated dynamic
relationships among marsh types, wetland elevations, tide ranges, and predicted rates of change in
Box 1. SLAMM software is free and has modest data
requirements (Appendix A). It is helpful if the user has
prior experience working with Geographic Information
System (GIS) software (such as ArcGIS or QGIS) and high
resolution elevation data like Light Detection and Ranging
(LiDAR). Prior to running SLAMM, all spatial data must be
converted into raster inputs with identical cell sizes and
dimensions. SLAMM also requires a certain amount of
computer processing power. As a general rule, a
minimum of 4GB RAM is recommended, as well as a 64-
bit version of Windows OS. Exact requirements vary
depending the resolution of the input data files as well as
the size of the study area.
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wetland elevations. The SLAMM application presented here utilizes feedbacks among marsh elevations,
water level, and elevation-change rates derived from local data to parameterize rates of accretion and
shallow subsidence or compaction. This feedback is also supported by similar results from mechanistic
accretion and shallow-subsidence models (e.g., Morris et al. 2002, Morris 2013).
As with any numerical model, SLAMM has limitations. Since SLAMM is not a hydrodynamic model, cell-
by-cell water flows are not projected as a function of topography and hydrological processes (e.g., water
diffusion and advection). Furthermore, it does not capture known/potential feedback mechanisms
between hydrodynamic and ecological systems. Suspended sediments in water are not accounted for via
mass balance, which may affect accretion (e.g., local bank sloughing does not affect nearby
sedimentation rates). The erosion model is also very simple and does not capture more complicated
processes such as new channel development. SLAMM has the capability to apply a salt-wedge model in
an estuary and an overwash model for barrier islands to account for second order effects that may occur
due to changes in the spatial relationships among the coastal elements; each of these model processes
is rather simple and has not been applied in these simulations. A more detailed description of model
processes, underlying assumptions, and equations can be found in the SLAMM Technical Documentation
(Warren Pinnacle Consulting, Inc. 2016: http://warrenpinnacle.com/prof/SLAMM/).
1.3 Case Study Scope
For this case study, we ran SLAMM simulations for four sites in New Jersey (NJ) (Dividing, Lower
Maurice, Dennis, Reeds Beach) and three in Delaware (DE) (Broadkill, Mispillion, Lower St. Jones). We
used four early- to late-century time periods (2025, 2050, 2075, 2100) and three SLR scenarios (low,
intermediate and high). We also generated results for three different model protection scenarios: no
protection (where all cells are subject to inundation); protection of developed dry land (where cells
designated as dry land with development are managed to prevent changes in inundation); and
protection of all dry land (where all cells designated as dry land are managed to prevent changes in
inundation). Because of the large volume of results, in the main report we only present results from: the
intermediate SLR scenario (1 m global mean sea level (GMSL) rise by 2100), which is considered "very
likely" (>90% probability) under future simulations of moderate rates of ocean warming (Sweet et al.
2017); along with the "protect developed dry land" scenario (which seems most likely, based on
feedback from local practitioners). The full set of results for all scenarios are included in the appendices.
In addition, we ran a sensitivity analysis to evaluate how much influence each input variable has on the
projected changes in salt marsh acreage.
The sections that follow explain our methods for site delineation, input parameters and data, and model
setup and calibration. These are followed by results presented in three sections: 1) projected changes
for all SLAMM land cover categories (standard SLAMM outputs); 2) projected changes in high, low and
total marsh (new ways of visualizing patterns); and 3) sensitivity analysis on key input variables (GT, salt
elevation, marsh erosion, accretion rate).
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2	METHODS
2.1 Study Area and Site Delineation
The study areas include four sites in NJ (Dividing, Lower Maurice, Dennis, Reeds Beach) and three in DE
(Broadkill, Mispillion, Lower St. Jones) (Figure 1). Marsh areas were delineated with assistance from the
Partnership for the Delaware Estuary (PDE) based on:
•	Locations of salt marshes: polyhaline areas were targeted to reduce complexities associated
with freshwater inputs (mean salinity values ranged from approximately 14 to 26 ppt)
•	Monitoring and management units (as per PDE's convention)
•	Watershed basins
•	Locations of Mid-Atlantic Coastal Wetland Assessment (MAWCA) sites; this includes Tidal Rapid
Assessment Method (TRAM) data and Site-Specific Intensive Monitoring (SSIM) data (Kreeger
and Padeletti 2013).
5

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Dennis
Dividing
MauriceLower
Mispillion
Reeds
StJones Lower
Miles
Figure 1. Map of the seven sites in the Lower Delaware Bay. Note that the partially-overlapping Dennis
and Reeds sites were modeled separately as per PDE's convention to view them as different units for
monitoring and management.
Broadkill
6

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2.2 Input Parameters and Data Preparation
2.2.1 Raster Data Preparation
SLAMM is a raster-based model that utilizes input cells that are equally-sized squares arranged in a grid.
This section describes the sources and steps used to process the raster data for use in SLAMM. Data
types reviewed in this section include elevation, wetland land cover, dikes and impoundments, and
impervious land cover.
2.2.1.1	Elevation Data
High vertical-resolution elevation data may be the most important SLAMM data requirement. SLAMM
uses elevation data to demarcate where salt water is projected to penetrate, and then combines this
information with tidal data to determine the extent and frequency of saltwater inundation.
Elevation data for the Lower Delaware Bay were downloaded from the National Oceanic and
Atmospheric Administration (NOAA) Digital Coast Data Viewer
(https://coast.noaa.gOv/dataviewer/#/lidar/search/). We used the 2015 U.S. Geological Survey (USGS)
Coastal National Elevation Database Topobathymetric Digital Elevation Model (DEM): NJ and DE dataset,
which is a composite of the best available high-resolution elevation data through 2014 (based on the
North American Vertical Datum of 1988 (NAVD88). The DE data and coastal NJ data are based on 2014
(Post-Hurricane Sandy) surveys, while data for the inland areas in the Dividing and Maurice watersheds
are derived from a 2008 statewide survey. The dataset is a mix of light detection and ranging (LiDAR)
and bathymetric data, which were compiled into a common database and aligned both vertically and
horizontally to a common reference system. The data have a vertical accuracy of 20 cm and were tested
to meet vertical root mean square error in open terrain. It should be noted, however, that LiDAR has
limited penetration ability in marsh areas with dense vegetation, which reduces the accuracy of LiDAR to
estimate bare surface elevations in those areas (Medeiros et al. 2015, Buffington et al. 2016). To reduce
file sizes and GIS processing times, elevation data were split into four separate blocks prior to analysis
(Appendix A, Figure Al).
2.2.1.2	Vertical Elevation Transformation
NOAA's Vertical Datum Transformation Tool (VDATUM, version 3.2; NOAA 2013) was utilized to convert
elevation data from the NAVD88 vertical datum to Mean Tide Level (MTL), which is the vertical datum
used in SLAMM. This is required as coastal wetlands occupy elevation ranges related to tide ranges as
opposed to geodetic datums (where elevations are computed in relation to a specific zero point; e.g.,
NAVD88 is referenced to a point in Quebec, Canada) (McKee and Patrick 1988). VDATUM does not
provide vertical corrections over dry land; dry-land elevations were corrected using the VDATUM
correction from the nearest open water. Corrections in the study areas ranged from -0.135 m to -0.003
m. A spatial map of corrections is shown in Figure 2.
7

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Broadkill
Dennis
Maurice_Lower
~	Dividing
Mispillion
Reeds
~	StJones_Lower
0 2.5 5
10
VDATUM correction (m)
] -0.003 to -0.039
~	-0.039 to -0.069
[ j -0.070 to -0.094
~	-0.095 to -0.113
f| -0.114 to-0.135
15
20
I Miles
Figure 2. VDATUM-derived correction values. Note that the partially-overlapping Dennis and Reeds sites
were modeled separately as per PDE's convention to view them as different units for monitoring and
management.

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2.2.1.3 Slope
Accurate slopes (elevation gradients) of the marsh surface are used in the calculation of the fraction of a
cell that is lost (transferred to another class). In this study, the slope raster was derived from the DEM
elevation data layer described in Section 2.2.1.1 using QGIS software (QGIS Development Team 2016).
The same analysis can also be conducted by using the surface slope tool of ArcGIS (a license-based
geographic information system).
2.2.1.4 Wetland Land Cover Data and Translation to SLAMM
Wetland raster layers were created from National Wetlands Inventory (NWI) GIS shapefiles for DE and
NJ (https://www.fws.gov/wetlands/Data/Data-Download.html). The NWI maps are based on photo
interpretation. Image dates for the study area range from 1995-2009 (Figure 3).
NWI land coverage codes were translated into SLAMM codes per the translation table in Appendix B
(which was produced by Warren Pinnacle (Clough et al. 2016) with assistance from Bill Wilen of the
NWI). Since dry land (developed or undeveloped) is not classified by NWI, SLAMM classifies cells as dry
land if they are initially blank (in the wetland inventory) but have an assigned elevation (above mean-
tide level). It should be noted that there is some uncertainty in land-cover inputs3. For example, the
input photography is not tidally-coordinated, so the boundary lines between "tidal swamp" and fresh
water "swamp" categories can be arbitrary (which in turn makes cells in these areas more prone to
misclassification). Thus, it is important to ground truth local areas where management activities are
being planned and to use the SLAMM model as a tool for looking at overall trends in an area (versus
focusing on individual cells).
After the translation was performed, the resulting raster was checked visually to ensure the projection
information was correct, the number of rows and columns was consistent with the other rasters in the
project area, and to ensure that the data looked complete based on the source data. The resulting land
cover for the area is shown in Figure 4.
Initial land cover areas for the seven sites are summarized in Table 1. Study areas range from 70,748.1
acres (Mispillion) to 14,917.7 acres (Reeds Beach). On average, undeveloped dry land comprised the
largest percentage of study area (43%) followed by estuarine open water (16%). Other land use
categories (listed in descending order, based on percent of total acreage averaged across sites) include
irregularly-flooded marsh (13%), swamp (12.5%), regularly-flooded marsh (7%), developed dry land
(4%), tidal swamp (2%), inland open water (1.5%), transitional salt marsh (1%), inland fresh marsh
(0.4%), tidal fresh marsh (0.2%), estuarine beach (0.1%), inland shore (0.1%), riverine tidal (0.1%), tidal
flat (0.03%), flooded developed dry land (0.02%) and ocean beach (<0.01%).
3There are several sources of uncertainty with NWI maps. For example, maps are not tidally coordinated, so there
is uncertainty at the water to beach/wetland interface; and it is often difficult to discern where forested swamps
end and forested upland habitats begin based on photo interpretation. Based on the NWI source data accuracy
file, there is 98% feature accuracy distinguishing wetland versus upland (meaning 2% may be upland instead of
wetland or vice-versa); there is 85% classification accuracy (meaning 15% of the classified wetlands may have an
incorrect attribution , e.g., high marsh instead of low marsh; and the NWI does not classify marsh systems that are
less than 0.2 hectares (https://www.fws.gov/wetlands/Documents/FGDC-Wetlands-Mapping-Standard.pdf).
9

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Broadkill
Dennis
Dividing
Maurice_Lower
Mispillion
Reeds
StJones Lower
0 2.5 5
10
15
20
Miles
Figure 3. The NWI date and corresponding boundary of the study sites. Note that the partially-overlapping
Dennis and Reeds sites were modeled separately as per PDE's convention to view them as different units
for monitoring and management.
10

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Salt marsh habitats
I I Regularly-Flooded Marsh
:	] Irreg.-Flooded Marsh
Trans. Salt Marsh
N
Other SLAMM categories
I I Tidal Flat
| Estuarine Open Water & Riverine Tidal
H Undeveloped Dry Land
| Developed Dry Land
| Tidal Swamp
I I Swamp
I I Inland Open Water
I I Inland-Fresh Marsh
I I Tidal-Fresh Marsh
| Flooded Developed Dry
I I Estuarine Beach
I I Ocean Beach
| Inland Shore
Figure 4. Initial land cover. The study sites are outlined in black. Note that the partially-overlapping Dennis
and Reeds sites were modeled separately as per PDE's convention to view them as different units for
monitoring and management.
0 2.5 5	10	15 20
I Miles
11

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Table 1. Acreage (and percentage of total acreage) of initial land cover categories across the seven sites.
SLAMM Category
Acrea
ge (% of total)
Broadkill (DE)
Mispillion (DE)
St. Jones Lower
(DE)
Dividing (NJ)
Maurice
Lower (NJ)
Dennis (NJ)*
Reeds (NJ)*
Developed Dry
Land
3232.2 (5.5%)
2827.5 (4%)
1973.8 (10.6%)
128.6 (0.6%)
394.8 (1.6%)
747.8 (1.8%)
257.6 (1.7%)
Undeveloped Dry
Land
36833.4 (62.2%)
40211.5 (56.8%)
9876.2 (53.1%)
5857.3
(25.4%)
5431.5
(22.4%)
15648.3
(37.3%)
4553.4
(30.5%)
Swamp
2348.6 (4%)
4683.6 (6.6%)
732.2 (3.9%)
4599.5 (20%)
3288 (13.5%)
11409.2
(27.2%)
3515.5
(23.6%)
Inland-Fresh Marsh
167.2 (0.3%)
162.2 (0.2%)
42.8 (0.2%)
316.4 (1.4%)
80.3 (0.3%)
53.6 (0.1%)
34.1 (0.2%)
Tidal-Fresh Marsh
164.3 (0.3%)
40.3 (0.1%)
48.3 (0.3%)
54.1 (0.2%)
17.6 (0.1%)
39.3 (0.1%)
3.9 (0%)
Trans. Salt Marsh
69.8 (0.1%)
369.3 (0.5%)
2.5 (0%)
399.3 (1.7%)
440.2 (1.8%)
838.9 (2%)
263 (1.8%)
Regularly-Flooded
Marsh
3284.3 (5.5%)
6440 (9.1%)
1859.6 (10%)
1977.7
(8.6%)
1550.8
(6.4%)
400.8 (1%)
213.1 (1.4%)
Estuarine Beach
148.9 (0.3%)
165.1 (0.2%)
36.2 (0.2%)
0 (0%)
25.4 (0.1%)
1.9 (0%)
5 (0%)
Tidal Flat
0 (0%)
93.5 (0.1%)
10.1 (0.1%)
4.4 (0%)
11.5 (0%)
11.4 (0%)
0 (0%)
Ocean Beach
0 (0%)
0 (0%)
0 (0%)
0 (0%)
0 (0%)
13.3 (0%)
19.6 (0.1%)
Inland Open Water
897.7 (1.5%)
668.2 (0.9%)
349.1 (1.9%)
880.2 (3.8%)
32.2 (0.1%)
320.6 (0.8%)
72.8 (0.5%)
Riverine Tidal
208.9 (0.4%)
137.1 (0.2%)
2.6 (0%)
7.2 (0%)
6 (0%)
0.9 (0%)
0 (0%)
Estuarine Open
Water
8106.8 (13.7%)
11159.9 (15.8%)
2194 (11.8%)
2827.7
(12.3%)
7543.7 (31%)
4127 (9.8%)
2657.3
(17.8%)
Irreg.-Flooded
Marsh
2261.9 (3.8%)
2622.4 (3.7%)
1357.3 (7.3%)
4788.7
(20.8%)
4854.1 (20%)
8348.4
(19.9%)
3290.7
(22.1%)
Inland Shore
37 (0.1%)
40 (0.1%)
8.6 (0%)
100.6 (0.4%)
3.8 (0%)
8.3 (0%)
1.8 (0%)
Tidal Swamp
1463.1 (2.5%)
1127.6 (1.6%)
101 (0.5%)
1082.6
(4.7%)
584.6 (2.4%)
17.8 (0%)
17.1 (0.1%)
Flooded Developed
Dry Land
0 (0%)
0 (0%)
0 (0%)
8.1 (0%)
35.9 (0.1%)
9.6 (0%)
12.8 (0.1%)
Total acres
59223.8
70748.1
18594.4
23032.4
24300.4
41997
14917.7
*Due to the partial overlap in the Dennis and Reeds monitoring and management units, they are not fully independent (9,850 total acres overlap).

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2.2.1.5 Dikes and Impoundments
Dikes, levees and other barriers to inundation were taken into account so that water flow could be
simulated more realistically. Dike rasters were created using NWI data. All NWI wetland polygons with
the "diked or impounded" attribute "h" were selected from the original NWI data layer, and these lands
were assumed to be permanently protected from flooding. This procedure has the potential to miss dry
lands that are protected by dikes and seawalls, as contemporary NWI data contains wetlands data only.
2.2.1.6 Impervious Land Cover
Impervious land cover data describe artificial surfaces and structures through which water cannot
penetrate. In SLAMM, dry land is categorized as developed or undeveloped. If a dry-land cell is covered
by more than 25% impervious surfaces, it is assumed to be "developed" dry land. In this study, percent
impervious rasters were derived from the 2011 National Land Cover Dataset (Xian et al. 2011). The cell
size was resampled from the original 30 m resolution to a 5 m resolution in order to match the cell
resolution of the other rasters in the project.
2.2.2	Model Timesteps
SLAMM simulations were run from either 2007 or 2014 (depending on years of the initial wetland cover
layers, which varied depending on the NWI photo dates and DEM dates; see Appendix A) to 2100 with
model-solution time steps at 2025, 2050, 2075 and 2100. Although equal time intervals may be
desirable for management and planning purposes, under higher SLR scenarios, significant changes will
likely occur over shorter time steps. Thus, a shorter time interval may be needed to capture rapid
changes. This may be particularly important in the U.S. mid-Atlantic region, where rates of SLR have
been occurring at an accelerated rate compared to the global mean (Sallenger et al. 2012).
2.2.3	Sea Level Rise Scenarios
The SLR scenarios used in this analysis are based on the most recent global SLR projections published by
NOAA (Sweet et al. 2017), which came from a joint effort of the Sea Level Rise and Coastal Flood Hazard
Scenarios and Tools Interagency Task Force. These projections incorporate the most up-to-date science
(specifically the improved understanding of complex behaviors of the large, land-based ice sheets of
Greenland and Antarctica), and utilize the most up-to-date methodologies for making regional
adjustments to global mean SLR scenarios. The projections include six scenarios ranging from low (0.3
m by 2100) to extreme (2.5 m by 2100). Table 2 shows details of SLR relative to the base year
of 2000. For this case study, we generated SLAMM results for three SLR scenarios: low, intermediate and
high, each of which is considered to be "very likely" (>90% probability) under future simulations of low,
moderate and high rates of warming, respectively (Sweet et al. 2017). These encompass the SLR
scenarios recommended by the DE SLR Technical Committee4 (Callahan et al. 2017). Recent studies
4 For planning purposes, the DE SLR Technical Committee has decided to use SLR scenarios of 0.52 m, 0.99 m,
and 1.53 m by 2100, relative to year 2000. These three scenarios closely correspond to the intermediate-low,
intermediate and intermediate-high scenarios from the Sweet et al. 2017 report (Table 2).
13

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suggest that the "extreme" rate (2.5 m GMSL rise by 2100) is possible, although the probability of this
extreme outcome cannot currently be assessed (Sweet et al. 2017).
Table 2. Global mean SLR projections by scenario (Sweet et al. 2017). Base year is 2000. For this case study,
we generated results for the low, intermediate and high SLR scenarios (highlighted in green).
Scenario
Global mean sea level (m)
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Low
0.03
0.06
0.09
0.13
0.16
0.19
0.22
0.25
0.28
0.30
Intermediate-Low
0.04
0.08
0.13
0.18
0.24
0.29
0.35
0.40
0.45
0.50
Intermediate
0.04
0.10
0.16
0.25
0.34
0.45
0.57
0.71
0.85
1.00
Intermediate-high
0.05
0.10
0.19
0.3
0.44
0.60
0.79
1.00
1.20
1.50
High
0.05
0.11
0.21
0.36
0.54
0.77
1.00
1.30
1.70
2.00
Extreme
0.05
0.11
0.24
0.41
0.63
0.90
1.20
1.60
2.00
2.50
2.2.4	Vertical Land Movement
The relative SLR at the local scale can differ significantly from the global mean SLR. Vertical land
movement (VLM), or subsidence, is an important factor that contributes to this discrepancy. For our
case study, we utilized data from Zervas et al. (2013) to adjust for VLM. Their rates are based on an
oceanographic analysis of long-term (30-60 year) NOAA tide gauge station measurements along the
coasts of DE and NJ. Zervas et al. (2013) found greater subsidence rates in NJ than in DE (2.1mm/yr
versus 1.7mm/yr, respectively) likely due to artificial groundwater withdrawal over decades (Sun et al.
1999). Within SLAMM, we added these VLM rates to the historic eustatic trend (1.7 mm/yr) to get
historic relative SLR trend inputs of 3.4 mm/yr at the DE sites and 3.8 mm/yr at the NJ sites, and
assumed that the VLM rate would remain the same through the end of this century.
2.2.5	Tide Ranges
SLAMM requires Great Diurnal Tide Range (GT)5 as an input. Tide range data were collected from the
NOAA Tides and Currents website (www.tidesandcurrent.noaa.gov) and were based on the present
National Tidal Datum Epoch (1983-2001). GT values across the seven study sites ranged from 1.42 m at
Lewes to 1.96 m at Fortescue (Table 3, Figure 5).
5GT is the difference between mean higher high water (MHHW) and mean lower low water (MLLW) levels.
14

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Table 3. Great diurnal tide range (GT) inputs for the seven sites.
State
Site
Great Diurnal Tide
Range (m)
NOAA station ID
NJ
Dividing Creek
1.96
8536931, Fortescue
NJ
Maurice River
1.96
8536931, Fortescue
NJ
Dennis Creek
1.92
8536581, Bidwell Creek
NJ
Reeds Beach
1.92
8536581, Bidwell Creek
DE
Broadkill
1.42
8557390, Lewes
DE
Mispillion
1.81
8554399, Port Mahon
DE
St. Jones
1.81
8554399, Port Mahon

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Ship John Shoal
GT 1.90 m
Port Mahon
GT 1.81 m
Bidwell Creek
GT 1.92 m
Lewes,
GT 1.42 m
NOAA buoys
Broadkill
Dennis
Miles
Dividing
Maurice_Lower
Mispillion
Reeds
StJones Lower
Figure 5. GT data were derived from the NOAA buoys shown as black triangles in this map. Two additional
NOAA buoys (Cape May and Ship John Shoal; shown as gray triangles) were used to generate salt elevation.
Note that the partially-overlapping Dennis and Reeds sites were modeled separately as per PDE's
convention to view them as different units for monitoring and management.
16

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2.2.5.1 Elevations expressed in half tide units (HTU)
In general, wetlands occupy a range of vertical elevations that is a function of the tide range (Titus and
Wang 2008); one conceptual example of this is shown in Figure 6. Because of this, rather than
expressing marsh elevation in absolute values (e.g., meters, feet, cm, etc.), SLAMM uses units relative to
the local tide range or "half-tide units". A half-tide unit (HTU) is defined as half of the great diurnal tide
range (GT/2). A numerical example follows:
•	If a marsh elevation is X meters above MTL, its elevation in HTU is given by X/(GT/2),
•	For example, consider a marsh with an elevation 1 m above MTL, with a GT of 1.5 m. The height
of the marsh in HTU is equal to 1/(1.5/2)=1.33 HTU.
•	This set of units is straightforward to understand if you consider that MTL is defined as 0.0 HTU,
MHHW is defined as 1.0 HTU, and MLLW is defined as -1.0 HTU. A marsh with an elevation
above 1.0 HTU falls above the high tide line regardless of the absolute value of the tide.
Transition
Upland
Open Water
(subtidal)
High Marsh
Low Marsh
Tidal Flat
100 Year Storm
- - 8.00
-- 7.00
-- 6.00
-- 5.00
f I Annual Storm
i
3
-- 4.00
-- 3.00
-- 2.00
Spring High'Water
Mean HighjWater
ean Sea Leve
NAVD ,089m
NGvD -0.172m
Mean Low Wate
Tidal
Range
Figure 6. Relationship between tides, wetlands, and reference elevations for an example estuarine shore
profile. Source: Titus and Wang 2008.
2.2.6 SLAMM Salt Elevation Parameter
The salt-elevation parameter in SLAMM defines the boundary between coastal wetlands and dry lands
(or fresh-water wetlands). This elevation, relative to MTL, is determined through analysis of "higher
high" water levels in NOAAtide records. Warren Pinnacle, the consulting firm that has been developing
SLAMM software since 1998, has found that the elevation that differentiates coastal wetlands and dry
lands is approximately the height that is inundated once every 30 days.
Therefore, the 30-day inundation level was determined for the most proximate buoys in the study area
that had NOAA-verified water-level data, which were: Lewes, Cape May and Ship John Shoal (Table 4;
17

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Figure 5). We downloaded hourly water level data from these three buoys from 2012-2016 and
calculated: 1) the monthly maximum water level; 2) the minimum of the monthly maximum water levels
in each year; and 3) the mean value across years (Table 4). We then obtained the GT for the three buoys
from the NOAA Tides and Currents website (as described in Section 2.2.5) and calculated the (HTUs (=
GT/2). We plotted the HTUs against the mean salt elevations for the three buoys and did a linear
regression analysis (Figure 7). We then used the formula from the regression analysis to calculate salt
elevations for each of the seven sites (Table 5).
Table 4. Salt elevation calculations were based on data from three NOAA buoys.
Tide Gauge Station
Mean salt elevation (m above MTL)
GTU
(m)
HTU
(GT/2)
(m)
2016
2015
2014
2013
2012
Overall
(2012-
2016)
Lewes, DE (8557380)
1.076
1.070
0.981
1.051
1.015
1.039
1.418
0.709
Cape May, NJ (8536110)
1.204
1.185
1.117
1.209
1.130
1.169
1.659
0.830
Ship John Shoal, NJ (8537121)
1.262
1.142
1.177
1.209
1.130
1.184
1.899
0.950
Salt Elevation (SE)
1.400
1.200
_j 1.000
i—
2
H 0.800
o
-Q
™ 0.600
0.400
0.200
0.000
0.600	0.700	0.800	0.900	1.000
HTU (1/2 GT)
Figure 7. HTUs were plotted against the mean salt elevations for the three buoys to derive the linear
regression formula that was used to calculate salt elevations for the seven sites.






• 		



y = 0.6436x + 0.5937
R* =r\ RT3Q
















18

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Table 5. Salt elevations for the seven sites.
Site
GT (m)
HTU
(GT/2)
Salt elevation
(m above MTL)1
Broadkill
1.42
0.71
1.052
Mispillion
1.81
0.91
1.182
StJones
1.81
0.91
1.18
Reeds
1.92
0.96
1.21
Dennis
1.92
0.96
1.21
Dividing
1.96
0.98
1.22
Maurice
1.96
0.98
1.22
1Salt elevations were derived from the following equation: 0.6436*HTU+0.5937 (based on Figure 8)
2During model calibration, salt elevations for the Broadkill and Mispillion were reduced to 1.04 and 1.10,
respectively, to account for moderating effects of barriers and dunes (Appendix A).
2.2.7 Wetland Elevation-Change Rate
We performed a literature search to collect relevant data on accretion rates and wetland-elevation
change rates. In addition, where appropriate, Surface Elevation Tables (SET) data from Site Specific
Intensive Monitoring (SSIM) sites6 were used to determine models of wetland elevation-change rates for
the study area. SETs are portable lightweight mechanical leveling devices with movable fiberglass or
metal pins that are lowered to the ground. They are used to measure millimeter-scale changes in
wetland surface elevation relative to a fixed benchmark. Repeated measurements of the same patch of
sediment surface are taken through time (Lynch et al. 2015). Changes in vertical elevation at the soil
surface can be highly variable over space and time. The changes occur due to a combination of surface
and subsurface processes such as accretion, erosion, soil organic matter accumulation, decomposition,
compaction, and groundwater flux. Having localized SET data enhances studies like ours and is
important for regional coastal wetland vulnerability assessments and predictive ecological models
(Osland et al. 2017).
2.2.7.1 Tidal Salt Marsh
The current SLAMM application attempts to account for what are potentially critical feedbacks between
tidal-marsh surface elevation change rates and SLR (Kirwan et al. 2010). In tidal marshes, increasing
inundation can lead to additional deposition of inorganic sediment that can help tidal wetlands keep
pace with rising sea levels (Reed 1995). In addition, salt marshes will often grow more rapidly at lower
elevations allowing for further inorganic sediment trapping (Morris et al. 2002). The extent to which
such feedbacks can offset SLR is subject to limits, however, based on habitat condition as well as on the
quantity of suspended sediments and the rate of SLR (Kirwan 2010).
6 The following organizations run the SSIM stations: (1) Delaware Department of Natural Resources and
Environmental Control (DNREC) Wetland Management and Assessment Program and PDE run the Broadkill; (2)
Delaware National Estuarine Research Reserve (DNERR) (also part of DNREC) funds/runs the St. Jones; and (3) PDE,
Barnegat Bay Partnership and the Academy of Natural Sciences run the stations at Dividing, Maurice, and Dennis.
19

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There are two primary coastal marsh types within our modeling area that are subject to these
feedbacks:
•	Regularly-Flooded Marsh (RFM) includes low to mid elevation marshes. Roughly speaking, these
are marshes that are inundated by tidal water at least once per day.
•	Irregularly-Flooded Marsh (IFM) includes high elevation marshes. These marshes are inundated
by tidal water once per day or less.
The persistence and conversion dynamics of RFM and IFM in SLAMM are summarized as follows:
-	SLAMM assumes that wetlands will occupy a range of vertical elevations that is a function of the tide
range and the mean-tide level (Titus and Wang 2008) (Figure 6).
-	When the IFM platform falls below the modeled minimum elevation, the land cover is converted to RFM.
-	When the RFM falls below the modeled minimum elevation, generally below mean-tide level, then the
land cover is converted to non-vegetated tidal flats.
-	The elevation intervals of existence (relative to tide ranges) can be adjusted by the user to reflect local
conditions.
Note: The upper elevation boundaries are not critical to the model; SLAMM does not project any
conversion to IFM or dry-land production above these elevations. However, examining these boundaries
is important to validate the consistency of model assumptions with regard to observed wetland
coverage, elevations, and tide data.
SET data were available for five of the seven sites (Table 6, Figures 8-9). We used these data to
determine models of elevation change in RFM and IFM in the study area (Figure 10). Due to the limited
amount of SET data, data from all sites were grouped together, and the following steps were performed
to derive accretion rate formulas (the same formulas were applied at all seven sites):
•	We calculated a linear relationship between marsh elevation-change rates and marsh platform
elevations, to derive the slope of this relationship (-1.634 mm/year per meter of elevation)
(Attachment 1, Excel worksheet).
•	As the majority of stations are within the IFM elevation range, this linear relationship is used for
IFM (Attachment 1).
•	We estimated a parabolic relationship for RFM using SLAMM accretion parameters and
knowledge of mechanistic modeling curves and the type of curves derived for other sites (e.g.,
Virginia's Eastern Shore (Warren Pinnacle Consulting, Inc. 2015), New York City (Clough et al.
2014)). Elevation-change rates are extremely limited for elevations below MHHW so best
professional judgment was used (Figure 10).
•	Parabolic curves for RFM were added to all model applications and tested within the model
interface.
•	In the absence of site-specific data, values for tidal fresh marsh accretion (the approximate
average of IFM rate in the absence of specific data) and inland fresh marsh (1 mm/yr) and
swamp accretion (3 mm/yr) were added based on model defaults and professional judgment.
We compared the curves to those used at other sites and found reasonable correspondence. Elevation
change tables and plots for each site with SET data can be found in Appendix C.
20

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Maurice! own
Dorchesli
Leesburg
MC1,
9.3 mm/yr
MC3,
4.3 mm/yr
Heislerville
"Nwin,,
DN3,
1.9 mm/yr
DN2,
•1.5 mm/yr
Figure 8. Locations of SET sites in the New Jersey marshes (Dividing (DIV), Maurice (MC) and Dennis (DN)). Values equal elevation change (mm/yr)
averaged across the period of record. All are located in irregularly-flooded marsh.
Marsh_type
¦	Irreg.-Flooded
¦	Regularly-flooded
~	Dennis
~	Dividing
~	Maurice_Lower
3 miles
J
£'
21

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Marsh_type
¦	Irreg.-Flooded
¦	Regularly-flooded
~	Broadkill
~	StJones_Lower
SJWC,
3.1 mm/yr
Highland
Acres
SJBW,
3 mm/yr
Magnolia
SJIP,
6.1 mm/yr
2 miles
BDK1,
BDK2, 6mm'yr
6.2 mm/yr
BDK3,
.1 mm/yr
0.9
1.8 mil
Figure 9. Locations of SET sites in the Delaware marshes (St. Jones (upper map) and Broadkill (lower map),
Values equal elevation change (mm/yr) averaged across the period of record.
22

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RFM UB
IFM UB
BOK
MR «
I SJ
RFM LB
DN
IFM LB
-0.5
-0.3
-0.1
0.1 0.3 0.5 0.7 0.9
RTK Elevation in half-tide units (0=MTL, 1.0=MHHW)
1.1
1.3
1.5
~ NJ ¦ DE A Station Averages
Figure 10. RFM and IFM SLAMM Elevation-Change Models. The blue shaded area defines the RFM boundaries
(LB = lower bound[ UB = upper bound) while the IFM boundaries are shown in orange. The dots represent SET
data from the five sites listed in Table 6. Measured elevation-change rates regularly exceeded 5 mm/year and
extended up to 9.3 mm/year while taken at elevations near MHHW. For this reason, assuming a potential
increase in mean accretion rates to 8 mm/year as flooding frequency increases provides a reasonable
estimate.
23

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Table 6. Locations of the SET stations, elevation change rate (averaged across the period of record) and first and last SET measurement dates (dates
cover the period of record available at the time of the analysis; more data may now be available).
Site
State
Longitude
Latitude
SET Station ID
Marsh type (SLAMM
category)
Mean elevation
change (mm/yr)
First date
Last date
Broadkill
DE
-75.1660
38.7873
BDK1
Regularly-flooded
6.0
2014-05-28
2016-08-24
-75.1698
38.7863
BDK2
Regularly-flooded
6.2
-75.1699
38.7811
BDK3
Regularly-flooded
4.1
St. Jones
DE
-75.4174
39.0707
SJIP
(Impoundment)
Regularly-flooded
6.1
2007-06-07
2015-03-19
-75.4375
39.0881
SJBW
(Boardwalk)
Regularly-flooded
3.0
2004-06-22
2015-09-01
-75.4975
39.1158
SJWC (Wildcat)
Irreg.-Flooded
3.1
2007-06-18
2015-03-18
Dennis
NJ
-74.8775
39.1697
DN1
Irreg.-Flooded
5.2
2011-05-13
2015-09-11
-74.8698
39.1734
DN2
Irreg.-Flooded
-1.5
-74.8496
39.1845
DN3
Irreg.-Flooded
1.9
Dividing
NJ
-75.1080
39.2273
DIV1
Irreg.-Flooded
2.2
2012-05-31
2015-10-21
-75.1168
39.2328
DIV2
Irreg.-Flooded
4.9
-75.1040
39.2398
DIV3
Irreg.-Flooded
6.7
Maurice
NJ
-75.0148
39.2442
MCI
Irreg.-Flooded
9.3
2011-04-18
2015-10-06
-75.0139
39.2438
MC2
Irreg.-Flooded
1.3
-75.0103
39.2420
MC3
Irreg.-Flooded
4.3
24

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2.2.7.2 Other Wetland Types
We lacked information on accretion rates and wetland-elevation change rates specific to the Lower
Delaware Bay for the other wetland habitat types. As a default, we used the values shown in Table 7 for
all sites. Table 7 cites the sources that the default values are based on. Average beach sedimentation
rates are assumed to be lower than marsh-accretion rates due to the lack of vegetation to trap
suspended sediment, though they are known to be highly spatially variable. In addition, it is worth
noting that future beach nourishment, should it occur within the study area, is not accounted for in
these SLAMM simulations.
Table 7. Accretion rate inputs that were used for the other marsh types (at all sites).
Parameter
Input
Source
Tidal-Fresh Marsh Acer (mm/yr)
5
Neubauer et al. 2002, Neubauer 2008
Inland-Fresh Marsh Acer (mm/yr)
1
Craft and Casey 2000, Graham et al. 2005
Tidal Swamp Acer (mm/yr)
1.1
Warren Pinnacle Consulting, Inc. 2015
(based on personal communications with Dr. Christopher Craft)
Swamp Accretion (mm/yr)
1.6
Beach Sed. Rate (mm/yr)
0.5
Warren Pinnacle Consulting, Inc. 2015
2.2.8 Erosion Rates
Average marsh erosion rates were calculated for each study site based on aerial photography using the
USGS ArcGIS tool Digital Shoreline Assessment System (Thieler et al. 2009). The calculations were
performed on shorelines that had fetch exposure greater than 30 m. Table 8 lists the erosion rates for
each site, which were provided by PDE.
SLAMM simulates erosion as additive to inundation. Horizontal wetland erosion is assumed to be the
effect of wave action, and marsh or swamp erosion is assumed to only occur when the wetland type in
question is directly exposed to open water with sufficient "fetch" (i.e., the open-water region over
which waves can set up). The SLAMM model default only triggers erosion in cells that have greater than
a 9-km fetch. In this case study, the 9-km default would have underestimated erosion because SLAMM
would have only applied erosion in the few areas that have a 9-km fetch across the open bay (compared
to a more common 30 m fetch within tidal creeks). To better match the shoreline erosion calculations
(that were based on data with a 30 m fetch), we reduced the fetch requirement to 0.1 km (100 m). We
used 0.1 km instead of 30 m due to uncertainty in our input wetland-layer rasters and concerns about
overestimating erosion in small rivers.
Swamp erosion was set to 1 m/yr, a rate commonly used in SLAMM when site-specific data are
unavailable. Within SLAMM, swamp erosion is only projected at a swamp to open water interface. As
swamps are rarely exposed to open wave action in this study area, this parameter is of limited
significance here.
25

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Table 8. Average marsh erosion rates for each site (Demberger et al. 2017).
State
Site
Erosion rates (m/yr)
NJ
Dividing Creek
-0.430
NJ
Maurice River
-0.340
NJ
Dennis Creek
-0.240
NJ
Reeds Beach
-0.240
DE
St. Jones
-0.309
DE
Broadkill
-0.116
DE
Mispillion
-0.564
2.3 SLAMM Model Setup and Calibration
The study area was divided into four blocks to reduce computer processing time: Dividing and Maurice
(NJ); Dennis and Reeds Beach (NJ); Broadkill and Mispillion (DE); and St. Jones (DE) (Appendix A, Figure
Al). Within several of the marsh areas, we had to create input subsites to account for differences in NWI
photo dates, DEM dates, tide range, salt elevation and marsh erosion rates. Maps of the input subsites
are included in Appendix A.
Before running the future SLR simulations, we performed "time zero" SLAMM runs in each block to
identify any initial problems with key SLAMM modeling inputs, such as NWI land cover, elevations,
modeled tidal ranges and hydraulic connectivity. In these "time zero" runs, the tides are applied to the
study area, but no SLR, accretion or erosion inputs are considered. Differences will arise between the
original NWI land cover layer and the SLAMM "time zero" land cover layer if cells are below the lowest
allowable elevation land cover categories (based on the SLAMM settings), which causes them to be
converted to a different land cover category (Table 9).
Where differences occurred, we generally found that the land cover re-categorization by SLAMM better
described the current coverage in these areas, which was not surprising given that some NWI images
date back to the 1990s. In some cases, the reason for initial land cover conversions of dry land is due to
differences in the horizontal resolutions of the input datasets. The native resolution of the impervious
cover layer (which is used to identify developed areas) is 30 m x 30 m, versus the higher horizontal
resolution elevation data (which, in this study, is 5 m x 5 m). The higher resolution elevation data allow
SLAMM to better define the wet to dry land interface at time zero.
For calibration, we checked the accuracy of the "time zero" SLAMM land cover layers by using GIS
software to overlay aerial photographs and GIS inundation files over the land cover layer (with particular
focus in areas where large conversions of wetlands were observed). In addition, two practitioners with
local knowledge reviewed the "time zero" maps. Appendix A contains results from the "time zero"
SLAMM runs and descriptions of corrections that were made to the land cover layers prior to running
the future simulations.
26

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Table 9. Inundation models for "Traditional SLAMM" Categories (when cells fall below their lower elevation
boundaries, these are generally what they convert to).
SLAMM category
General conversions (exceptions may occur)
Irreg.-Flooded Marsh
Regularly-Flooded Marsh
Trans. Salt Marsh
Regularly-Flooded Marsh
Regularly-Flooded Marsh
Tidal Flat
Tidal-Fresh Marsh
Tidal Flat
Tidal Flat
Estuarine Open Water
Developed Dry Land
Trans. Salt Marsh or Flooded Developed Dry Land
(depending on model settings)
Undeveloped Dry Land
Trans. Salt Marsh
Inland-Fresh Marsh
Trans. Salt Marsh
Swamp
Trans. Salt Marsh
Tidal Swamp
Irreg.-Flooded Marsh
Inland Open Water
Estuarine Open Water
Estuarine Beach
Estuarine Open Water
Riverine Tidal
Estuarine Open Water
Inland Shore
Estuarine Open Water
Ocean Beach
Open Ocean
2.3.1 Model Protection Scenarios
Human responses to losses of dry land in the face of SLR are uncertain. In cases for which land values are
high, land owners are likely to continue to erect dikes and seawalls to prevent increasing inundation. In
other locations, only developed land will be protected, or landowners will abandon property, thereby
allowing wetland conversion. To test the impacts of these responses, SLAMM has the capability to
model three different simplified protection scenarios:
•	"Protect None," where all cells are subject to inundation and can be converted to other habitat
types in the simulations
•	"Protect Developed Dry Land," where cells designated as developed dry land are protected from
inundation and cannot be converted to other habitat types in the simulations
•	"Protect All Dry Land," where cells designated as dry land (developed and undeveloped) are
protected from inundation and cannot be converted to other habitat types in the simulations.
Comparing these results can also help to assess wetland migration potential. For example, subtracting
results of the "Protect Developed Dry Land" scenario from the "Protect None" scenario quantifies the
potential marsh encroachment into developed dry land. Another option is to subtract results from the
"Protect All Dry Land" scenario from the "Protect Developed Dry Land" scenario, which allows for
assessment of possible marsh colonization in undeveloped dry land. For our study, we generated results
for all three protection scenarios.
27

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2.4 Sensitivity Analysis
We ran sensitivity analyses7 to better understand the influence of each input variable on the projected
changes in salt marsh acreage. SLAMM sensitivity analysis examines one variable at a time. We ran
analyses on the following parameters: GT, salt elevation, marsh erosion, and min/max accretion rates
for regularly- and irregularly-flooded marsh. Input parameters were varied by 15% (as per professional
judgment based on potential measurement error and uncertainty in model inputs). The analyses were
performed using the intermediate SLR scenario (1 m GMSL rise by 2100) (Sweet et al. 2017).
3 RESULTS
In this section, we present projected changes in habitat for the seven sites. Model projections are
reported from time zero forward so that the projected land cover changes are only due to SLR and not
due to initial model calibration. To bracket the most plausible range of sea level change projections
(given what we know at this time), we ran SLAMM model simulations for three SLR scenarios (low,
intermediate, and high per Sweet et al. 2017; Table 2)8. Along with the three model protection scenarios
(see Section 2.3.1), this resulted in a total of nine combinations of outputs. Due to the large quantity of
simulation and analysis results, here we only present results from the intermediate SLR scenario (1 m
GMSL rise by 2100), which is considered "very likely" (>90% probability) under future simulations of
moderate rates of warming (Sweet et al. 2017). For the model protection scenario, we limited the
results to the "Protect Developed Dry Land" scenario, which our work group felt was most likely. The full
set of results for each site (covering all nine combinations of outputs) are available in Appendices D-J.
While reporting the intermediate SLR scenario suits our purposes for this report, recent studies suggest
an even higher rate of SLR is possible (Sweet et al. 2017). This may be particularly important to consider
in the U.S. mid-Atlantic region, where rates of SLR have occurred at an accelerated rate compared to the
global mean (Sallenger et al. 2012, Callahan et al. 2017). Because upper end/low probability events carry
a disproportionate level of risk (with higher-consequence changes), it may be prudent for some
managers to focus on results from a higher risk (albeit less likely) scenario.
Results are divided into three sections. Section 3.1 describes general patterns across sites and includes
maps with projected changes in all SLAMM land cover categories, as well as a table showing percent
change at one example site. The full set of SLAMM outputs for each site can be found in Appendices D-J.
We have kept Section 3.1 short as our primary focus is on Section 3.2, which contains the cross-site
comparisons of high, low and total salt marsh habitats. These outputs diverge from traditional SLAMM
outputs to include scatterplots of response (mean percent change in acreage) versus exposure (mean
SLR) and site-specific gain/loss maps that highlight where vulnerabilities to changes in high and low
7SLAMM 6.7 includes a built-in nominal range sensitivity analysis based on Frey and Patil (2001).
8 The SLR scenario chosen has a large effect on projected changes in high and low marsh acreage, especially by
2100. This is due to differences in conversion rates. For example, at Dennis, under the low SLR scenario, large
tracts of high marsh remain by 2100; under the intermediate SLR scenario, these areas have converted from high
marsh to low marsh; under the high SLR scenario, these areas have converted from low marsh to tidal flat.
28

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marsh are projected to occur. Finally, Section 3.3 summarizes results from the sensitivity analysis (with
the full set of results available in Appendix K).
3.1 Projected changes in all SLAMM land cover categories
At time zero, undeveloped dry land, regularly and irregularly-flooded marsh, swamp, tidal swamp and
estuarine open water habitats generally comprise the largest areas. For salt marsh habitats specifically,
on average, at time zero, the irregularly-flooded marsh habitat (shown in orange in the maps) comprises
a higher percentage of acreage at the NJ sites than the DE sites (21% versus 4.2%, respectively). The
mean percent acreage of regularly-flooded marsh (shown in light blue in the maps) at time zero is higher
at the DE sites compared to the NJ sites (9% versus 4%, respectively). Sites in both states have similar
mean percentages of transitional salt marsh (2%). Table 10 is an example of one of the types of tables
typically found in SLAMM reports. It shows projected changes in acreage of SLAMM land use categories
from time zero to 2100. Figures 11-12 show the spatial distributions of the SLAMM land cover categories
across the DE and NJ sites, respectively, over three time periods (time zero, 2050 and 2100). Percent
change tables for each site and each SLAMM land cover category can be found in Appendices D-J.
While patterns of change vary across sites and time periods, some general themes are evident. For
example, the regularly-flooded marsh habitats are projected to gain acreage through late century at all
sites, primarily due to gains from inundation/conversion of irregularly-flooded marsh. By 2100, some
areas initially categorized as regularly-flooded marsh are lost. More specifically, two of the DE sites
(Broadkill and Mispillion) lose large areas of regularly-flooded marsh along the bay (through conversion
to tidal flat and estuarine open water) (Figure 11). Some areas in Dividing and Lower Maurice undergo a
similar transition (Figure 12). By 2100, all sites are projected to lose large percentages of irregularly-
flooded marsh acreage (on average 88%) due to inundation and conversion to regularly-flooded marsh.
The large-scale loss of irregularly-flooded marsh is particularly noticeable at Dennis, Reeds Beach and
the Lower St. Jones in the mid versus late-century maps (Figures 11-12). These are just a few of the
many interesting patterns in the SLAMM outputs. Detailed information on projected land cover changes
at each site can be found in Appendices D-J.
29

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Broadkill
Mispillion
Lower St. Jones
Time Zero
(2007)
Salt marsh habitats
I Regularly-Flooded Marsh
I Irreg.-Flooded Marsh
1 I Trans. Salt Marsh
Other SLAMM categories
| | Tidal Flat
| Estuarine Open Water & Riverine Tidal
| Undeveloped Dry Land
| Developed Dry Land
| Tidal Swamp
m Swamp
I 1 Inland Open Water
Inland-Fresh Marsh
| Tidal-Fresh Marsh
| Flooded Developed Dry
1 Estuarine Beach
I 1 Ocean Beach
| Inland Shore
Figure 11. SLAMM land use categories from early- to late-century for the Delaware sites (BroadkillMispillion and Lower St. Jones) under the
intermediate SLR scenario (based on Sweet et al. 2017) and "protect dry developed land" modeling scenario. Note that these maps have been
magnified to similar sizes for ease of pattern comparisons and are thus not to scale with each other; for relative scales please see Figure 1.
30

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Dennis
Reeds Beach
Dividing
Lower Maurice
Time Zero
(2014)
2050
2100
¦	Developed Dry Land
¦	Undeveloped Dry Land
¦	Swamp
B"l Inland Fresh Marsh
I I Tidal Fresh Marsh
¦	Trans. Salt Marsh
¦I Regularly-flooded Marsh
I I Estuarine Beach
I I Tidal Flat
l~~l Ocean Beach
I I Inland Open Water
¦	Riverine Tidal
¦	Estuarine Open Water
0 Irreg.-Flooded Marsh
¦	Inland Shore
¦	Tidal Swamp
¦	Flooded Dev. Dry Land
Figure 12. SLAMM land use categories from early- to late-century for the New Jersey sites (Dennis, Reeds Beach, Dividing and Lower Maurice) under the
intermediate SLR scenario (based on Sweet et al. 2017) and "protect dry developed land" modeling scenario. For Reeds Beach, the area above the
white dotted line is the area of overlap with Dennis. Note that these maps have been magnified to similar sizes for ease of pattern comparisons
and are thus not to scale with each other; for relative scales please see Figure 1.
31

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Table 10. Example of a percent change table typically found in SLAMM reports. This table, which is based on data from the Broadkill (DE) site, shows
projected changes in acreage of SLAMM land use categories from time zero to 2100. Salt marsh habitats are in bold print because they are the focus of
our larger project case study. Percent change calculations are based on change in acreage relative to time zero. The % change cells are color-coded
based on direction of change (loss in light red; gains in green). Results are based on the intermediate SLR scenario (Sweet et al. 2017) and "protect dry
developed land" modeling scenario (which prevents the developed dry land categories from changing, thus the hashes).
SLAMM category
Acres
% Change
2007
2025
2050
2075
2100
2025
2050
2075
2100
Irreg.-Flooded Marsh
1613.0
1301.0
547.8
832.2
348.4
-19.3
-66.0
-48.4
-78.4
Trans. Salt Marsh
1626.7
1583.3
1974.1
2220.6
1813.1
-2.7
21.4
36.5
11.5
Regularly-Flooded Marsh
3955.8
4678.8
5907.4
6931.7
5036.7
18.3
49.3
75.2
27.3
Tidal Flat
38.3
54.1
113.6
462.7
4458.9
41.4
196.8
1108.4
11545.8
Estuarine Open Water
8415.7
8509.8
8630.6
8804.5
9264.4
1.1
2.6
4.6
10.1
Undeveloped Dry Land
35813.8
35472.2
34774.4
33708.7
32557.3
-1.0
-2.9
-5.9
-9.1
Swamp
1802.6
1752.8
1669.4
1599.8
1531.2
-2.8
-7.4
-11.2
-15.1
Tidal Swamp
1445.9
1428.2
1254.8
426.4
107.2
-1.2
-13.2
-70.5
-92.6
Inland Open Water
727.1
718.3
708.7
698.4
682.5
-1.2
-2.5
-3.9
-6.1
Tidal-Fresh Marsh
159.3
157.3
147.2
108.4
20.3
-1.2
-7.6
-32.0
-87.3
Inland-Fresh Marsh
131.7
128.5
123.3
113.2
105.9
-2.5
-6.4
-14.0
-19.6
Estuarine Beach
114.0
92.7
67.3
43.5
28.3
-18.6
-41.0
-61.8
-75.2
Riverine Tidal
105.7
72.5
33.1
3.9
0.9
-31.4
-68.7
-96.4
-99.2
Inland Shore
37.0
37.0
37.0
37.0
36.7
0.0
0.0
0.0
-0.7
Ocean Beach
0.0
0.0
0.0
0.0
0.0
-
-
-
-
Open Ocean
0.0
0.0
0.0
0.0
0.0
-
-
-
-
Developed Dry Land
3232.2
-
-
-
-
-
-
-
-
Flooded Developed Dry Land
0.0
-
-
-
-
-
-
-
-
32

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3.2 Projected changes in high, low and total marsh acreage
In this section we present results from analyses in which we aggregated the salt marsh SLAMM land use
categories into two marsh types:
•	High marsh: irregularly-flooded marsh and transitional salt marsh9.
•	Low marsh: regularly-flooded marsh.
As discussed in Section 1.1, the two marsh types differ in that low marsh areas are flooded daily, while
high marshes are inundated by tidal water once per day or less. The SLAMM model assumes that low
marsh will be lost to inundation from SLR at a slower rate than high marsh due to positive feedbacks
between inundation and accretion (Section 2.2.7.1). For some managers, it is important to distinguish
between high and low marsh habitat because there are some differences in the types of ecosystem
services each provide (e.g., low marsh provides habitat for mussels and crabs, and high marsh provides
critical habitat for the salt marsh sparrow, which is a bird species of conservation concern).
The intent of these additional analyses is to:
•	Compare results across marsh types (high marsh, low marsh, total marsh [high plus low])
•	Compare results across sites
o Which sites are most and least vulnerable to long-term SLR rise and why?
o Where are changes in marsh type most likely to occur?
•	Compare results across time periods
o How much do results vary over time?
o Are "tipping points" evident?
Results and maps presented in this section are not typically included in SLAMM reports, and so are a
novel application of SLAMM outputs. Our intent is to illustrate the response of salt marshes to SLR so
that practitioners can detect important patterns across marsh types, sites and time periods.
9 Within SLAMM, initial conditions for irregularly-flooded marshes is that they are primarily composed of
"irregularly-flooded estuarine intertidal emergent marsh" based on the National Wetlands Inventory, while
"transitional marshes/scrub shrub" are primarily composed of "estuarine intertidal scrub-shrub and forested."
Because of this, the starting point of a transitional marsh is more of a woody plant than for high marsh. However,
these two classes significantly overlap in terms of their frequency of flooding (their elevation range in relation to
the tides.) When SLAMM finds dry land falling into this elevation range, there is significant uncertainty as to
whether the new wetland habitat will be an emergent marsh or a woody shrub type. SLAMM generally categorizes
these new wetlands as "transitional marsh" as this signifies a land category that has recently transitioned, and the
presence of an emergent marsh is undetermined.
33

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In the sections below, we highlight results for each marsh type. The accompanying scatterplots (Figures
13-15) allow for easy visualization of differences across sites and time periods. The full set of results for
each site (including the exact percent gain/loss numbers) can be found in Appendices D-J. This is
followed by a section where the results for each marsh type are visualized as gain/loss maps (Figures 16-
22), with brief examples of some spatial patterns that could have implications for management
decisions.
3.2.1 High Marsh
Despite variations in rates among sites and overtime, all sites are projected to experience high marsh
acreage loss overtime (Figure 13). General patterns include:
•	2025 - Broadkill and Mispillion have the highest percent loss (~10%); other sites have < 5%
•	2050 - Dividing and Broadkill have the highest percent loss (20-25%); other sites have < 6%
•	2075 - Dividing has the highest percent loss (56%) and Broadkill has the lowest (6%); the other
sites are grouped in-between (25-38%)
•	2100 - Lower Maurice and Dennis overtake Dividing with the highest percent loss (76 and 70%
loss, respectively); Dividing, Reeds Beach and St. Jones are close behind (> 60% loss); Broadkill
and Mispillion have the lowest percent loss (30-50%).
Note that at all sites but the Broadkill, the percent loss in high marsh acreage increases from 2050
onward. From 2050 to 2075, high marsh acreage is still being lost at the Broadkill, but at a lower rate.
This is because the Broadkill has large areas of tidal swamp and low-lying undeveloped dry land that
convert to high marsh during this time period. After 2075, these areas of high marsh convert to low
marsh, which causes the percent loss of high marsh acreage to increase again.
34

-------
High marsh
10
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a) -10
en
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Symbol
Site
Acres
Time zero
2050
2100
A
Broadkill
3239.7
2521.8
2161.5
O
Dennis
9152.5
9206.5
2716.0
•
Dividing
5026.6
3820.8
1665.4
O
Maurice
5225.4
4926.7
1241.0
A
Mispillion
4261.6
4152.6
2309.7
O
Reeds
3515.5
3528.3
1226.3
553.2
+
St Jones
1518.8
1563.2
0.0 0.2 0.4 0.6 0.8 1.0
Mean SLR
Figure 13. Scatterplot of mean percent change in high marsh acreage versus mean SLR (across four time steps - 2025, 2050, 2075, 2100), based on the
intermediate SLR scenario and "protect dry developed land" modeling scenario. Due to the partial overlap in the Dennis and Reeds monitoring and
management units, the acreages for these two sites are not fully independent. For more information about each site, see Appendices D-J.
35

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3.2.2 Low Marsh
Despite variations in rates among sites and overtime, all sites are projected to experience low marsh
acreage gains overtime (Figure 14). General patterns include:
•	2025 - Broadkill has the highest percent gain (18%); St. Jones has the lowest (3%)
•	2050 - Dennis and Reeds Beach have the highest percent increase (123% and 98%,
respectively); St. Jones has the lowest (13%)
•	2075 - Dennis and Reeds Beach continue to have the highest percent increase (1042% and
692%, respectively); Broadkill and Mispillion have the lowest (75% and 67%, respectively)
•	2100 - Dennis and Reeds Beach continue to have the highest percent increase (2339% and
1586%, respectively); Broadkill now has the lowest, dropping down to 27%.
Note that at all sites but the Broadkill, the percent gain in low marsh acreage increases over time. From
2075 to 2100, low marsh acreage is still being gained at the Broadkill, but at a lower rate. This is because
the Broadkill has large areas of low marsh near the bay that convert to tidal flats.
36

-------
Low marsh
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2500.0
750.0
500.0
250.0
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	1	


Symbol
Site
Acres
Time zero
2050
2100
A
Broadkill
3955.8
5907.4
5036.7
O
Dennis
421.6
939.3
10282.6
•
Dividing
1707.7
3121.5
5533.0
O
Maurice
1299.5
1899.9
5638.5
A
Mispillion
7165.8
9188.7
12114.5
O
Reeds
235.0
467.0
3961.4
+
St Jones
1865.2
2102.1
3933.5
0.0 0.2 0.4 0.6 0.8 1.0
Mean SLR
Figure 14. Scatterplot of mean percent change in low marsh acreage versus mean SLR (across four time steps - 2025, 2050l, 2075, 2100), based on the
intermediate SLR scenario arid "protect dry developed land" modeling scenario. The y-axis has been log-transformed. Due to the partial overlap in the
Dennis and Reeds monitoring and management units, the acreages for these two sites are not fully independent. For more information about each site,
see Appendices D-J.
37

-------
3.2.3 Total Marsh
Despite variations in rates among sites and overtime, all sites are projected to experience gains in total
salt marsh acreage (Figure 15). General patterns include:
•	2025 - Broadkill and Mispillion have the highest percent gain (5%); NJ sites have the lowest
percent gain (<2%)
•	2050 - Broadkill and Mispillion continue to have the highest percent gain (17%); NJ sites still
have the lowest (<7%)
•	2075 - Broadkill and Mispillion continue to have the highest percent gains (39% and 31%,
respectively); Dividing and Lower Maurice have the lowest (<7%)
•	2100 - Broadkill drops to around 0.04%; Dennis, Reeds Beach and Lower St. Jones have the
highest percent gains (>30%).
Note that from 2075 to 2100, the Broadkill goes from having the highest percent gains (39%) to the
lowest (0.04%; meaning the total marsh acreage at 2100 is about the same as at time zero). The high
rate of gains from 2050 to 2075 is driven in part by the conversion of large areas of tidal swamp and
undeveloped dry land to high marsh. The drop from 2075 to 2100 is due to the loss of these high marsh
areas in combination with the loss of large areas of low marsh near the bay (which convert to tidal flats).
38

-------
Total marsh
45
40
35
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CD ^
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Symbol
Site
Acres
Time zero
2050
2100
A
Broadkill
7195.6
8429.2
7198.2
O
Dennis
9574.1
10145.8
12998.6
•
Dividing
6734.3
6942.3
7198.4
O
Maurice
6524.9
6826.6
6879.5
A
Mispillion
11427.5
13341.3
14424.2
°
Reeds
3750.5
3995.3
5187.7
+
St Jones
3384.0
3665.2
4486.6
0.0 0.2 0.4 0.6 0.8 1.0
Mean SLR
Figure 15. Scatterplot of mean percent change in total marsh acreage versus mean SLR (across four time steps - 2025, 2050, 2075, 2100), based on the
intermediate SLR scenario and "protect dry developed land" modeling scenario. Due to the partial overlap in the Dennis and Reeds monitoring and
management units, the acreages for these two sites are not fully independent. For more information about each site, see Appendices D-J.
39

-------
3.2.4 Gain/Loss Patterns
Site-specific gain/loss maps (which highlight areas where changes are occurring) are shown in Figures
16-22. Spatial patterns of gains and losses of different marsh types, both within and among sites, could
inform management decisions. Such decisions might include where and when to prioritize conservation
or restoration efforts, plan for change, or establish long-term monitoring sites to detect whether
changes are occurring as projected.
For example, within the Broadkill (DE) site (Figure 16), a noticeable spatial pattern is that significant total
marsh losses are projected by 2100 in the southeast portion (due to conversions from low marsh to
open water and/or tidal flats); but in the northwest portion, both high and low marsh types are stable or
gaining, resulting in an overall increase in total marsh in that area. Depending on the management goal,
this pattern may be relevant for decisions about where (or whether) to engage in restoration or
conservation activities at different specific locations within the Broadkill site.
Meanwhile, in NJ, the partially overlapping sites of Dennis (Figure 21) and Reeds Beach (Figure 22) show
another pattern. They begin as predominantly high marsh at time zero but undergo a dramatic
changeover to mostly low marsh by 2100. It may be important to monitor here to determine whether
this shift occurs as projected; it is possible that the more frequent inundation of the high marsh habitats
will result in peat collapse and direct conversion of high marshes into tidal flats or open water (DeLaune
et al. 1994). Either way (successful conversion or not), it would mean major losses of critical habitat for
high marsh species. And while total marsh area shows acreage gains, there is some question as to
whether flood and erosion control services would be the same with such a different plant community
composition.
Finally, at the larger scale, if you compare patterns at the four NJ sites (Figures 19-22) versus the three
DE sites (Figures 16-18) (which together encompass a large portion of the Lower Delaware Bay), there
are differences that could affect large-scale management of high marsh habitat. In NJ, there is far more
high marsh habitat at time zero (20,184 acres) than in DE (9,020 acres) (Figure 13). However, by 2100,
losses of high marsh acreage are far more extensive in NJ (loss of 13,982 total acres, down to 6,202
acres) compared to DE (loss of 3,996 total acres, down to 5,025 acres)10. If these changes occur as
projected, the differential losses of high versus low marsh habitat types between the NJ and DE sides of
the bay would change the proportions of critical habitat available for different species, and potentially
affect what would be needed to manage target species and services at the regional scale.
Finally, it should be noted that when weighing these or any other potential management considerations,
there will be other important decision criteria to consider as well. The type of gain/loss information in
this report will need to be analyzed in combination with information on, e.g., the ownership status
(private or public) of the marshlands, impacts of projects and marsh loss/migration on property values,
and other factors. GIS files with information on land ownership and conservation status are available
and can be overlaid onto the SLAMM projections to help inform management decisions.
10 The Reeds Beach acreage that overlaps with Dennis was excluded from this calculation to avoid double counting.
40

-------
High marsh
Low marsh
Total marsh
Time Zero
(2007)
2050
m
\
„ ,	N.
r ,( Ai
*: '
J/M * »

Time zero
I I High
] Low
I I Total
Gain
I I High
Low
I I Total
Loss
High, low
or total
0	2.5 5 miles
	1	I	I
2100
Figure 16. Gain/loss maps for the Broadkill (DE) site, based on the intermediate SLR scenario and "protect dry developed land" modeling scenario.
41

-------
High marsh	Low marsh	Total marsh
Time zero
I I High
Low
I | Total
Gain
I I High
| Low
Total
Loss
High, low
or total
Time Zero
(2007)
2050
Figure 17. Gain/loss maps for the Mispillion (DEj site, based on the intermediate SLR scenario and "protect dry developed land" modeling scenario.
42
2100
0	3.5 7 miles
	1	I	I

-------
High marsh
Low marsh
Total marsh
Time Zero
(2007)
2050
2100
i
Time zero
I- I	High
I	Low
I I	Total
Gain
I I	High
|	Low
]	Total
Loss
High, low
or total
0	1.5 3 miles
	1	i	i
Figure 18. Gain/loss maps for the Lower St. Jones (DE) site, based on the intermediate SLR scenario and "protect dry developed land" modeling scenario.
43

-------
High marsh	Low marsh	Total marsh
Figure 19.
Time zero
~
~
High
Low
1 I	Total
Gain
I I	High
|	Low
I 1	Total
Loss
High, low
or total
Time Zero
(2014)
2050
Gairi/loss maps for Dividing (NJ) site, based on the intermediate SLR scenario and "protect dry developed land" modeling scenario.
2100
0	2.5 5 miles
	1	i	i

-------
High marsh
Low marsh
Total marsh
Time Zero
(2014)
2050
2100
Time zero
~ ~~
High
Low
Total
Gain
~
High
Low
Total
Loss
High, low
or total
0	2 4 miles
	1	I	I
Figure 20. Gain/loss maps for Lower Maurice (NJ) site, based on the intermediate SLR scenario and "protect dry developed land" modeling scenario.
45

-------
High marsh
Low marsh
Total marsh
Time Zero
(2014)
2050
2100
V
Figure 21. Gain/loss maps for Dennis (NJ) site, based on the intermediate SLR scenario and "protect dry developed land" modeling scenario.
* " -
Time zero
1	1
1 1
1 1
High
Low
Total
i
High
Low
Total
Loss
High, low
or total
3 6 miles
j	i

-------
High marsh
Low marsh
Total marsh
Time Zero
(2014)
2050
2100
Time zero
~ ~~
High
Low
Total
Gain
~
High
Low
Total
Loss
High, low
or total
2 4 miles
J	I
Figure 22. Gain/loss maps for Reeds Beach (NJ) site, based on the intermediate SLR scenario and "protect dry developed land" modeling scenario.
47

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3.3 Sensitivity Analysis
A sensitivity analysis is "the study of how uncertainty in the output of a model (numerical or otherwise)
can be apportioned to different sources of uncertainty in the model input" (Saltelli et al., 2004).
Sensitivity analysis therefore clarifies the relationship between model inputs and model outputs to
understand the potential impacts of uncertainties in model parameters.
As described in Section 2.5, the sensitivities of high marsh and low marsh to changes in GT, salt
elevation, marsh erosion, and regularly- and irregularly-flooded minimum and maximum accretion were
evaluated under the intermediate SLR scenario. Table 11 contains a summary of mean, minimum and
maximum percent acreage change across sites when each (individual) test variable is increased and
decreased by 15% (the mean percent change value represents the average of both directions). Results
are presented for three SLAMM land use categories: regularly-flooded marsh (low marsh), irregularly-
flooded marsh (high marsh) and transitional salt marsh (high marsh). Appendix K contains more detailed
results, including tornado diagrams with visual illustrations of the percentage changes for each site (and
each test variable).
Among the tested variables, GT is the dominant factor driving gain and loss of regularly- and irregularly-
flooded marshes (which is expected given how the tide range demarcates the boundary between high
marsh and low marsh). Salt elevation has greatest impact on transitional salt marsh, followed by GT. The
marsh erosion and accretion variables had a much smaller effect (<1%) (Table 11). While this general
pattern holds true across sites, the magnitude of change varies (e.g., percent change in acreage of
irregularly-flooded marsh driven by GT ranges from 1.4% at Broadkill to 74% at Dennis; Appendix K).
While the results help us better understand the influence of each input variable on the projected
changes in salt marsh acreage, they should be interpreted with caution due to limitations associated
with the sensitivity analysis, which only considers one variable at a time. In reality, responses are driven
by multiple interacting factors. It is also worth noting that, with regard to accretion rates, changing the
minimum and maximum accretion rates one at a time does not have a big impact on the generated
accretion rate curve (Table 11). The SLAMM model is generally sensitive to accretion rates (Chu-Agor et
al. 2011), so if an overall multiplier11 was available (across the range of accretion rates simulated, versus
only the minimum and maximum), this would have had a larger effect on model outputs.
In addition to performing the sensitivity analysis, we also did cross-site comparisons of key variables that
are known to affect the vulnerability of sites to SLR. Our intent was to gain a better understanding of
reasons behind the differences in projected changes across sites (which could potentially help inform
management strategies; for example, living shorelines could potentially be an effective tactic for sites
with high erosion rates, thin-layer sediment applications could potentially enhance low elevation sites,
etc.). We compared tide range, salt elevation, marsh erosion rate, elevation change rate (based on site-
specific SET data) and elevation data. Results suggest that the following characteristics may contribute
11 Multipliers, which are based on distribution values, may be used to assist with modifying variables that may be
spread out over multiple subsites. In other words, if accretion rates are assumed to increase by 10% they increase
by 10% over all subsites simultaneously.
48

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to differences in relative vulnerabilities across sites: Dividing - low mean elevation, higher tide range;
Broadkill - low mean elevation; Dennis - lowest elevation change rate; Reeds Beach - highest marsh
erosion rate; NJ sites - higher subsidence rates (Table 12).
Table 11. Summary of results from the sensitivity analysis. Values represent the mean, minimum and
maximum percent acreage change across sites when each (individual) test variable is increased and
decreased by 15%. Acer. = accretion. Rows highlighted in orange have the largest effect (darker = more,
lighter = less).
Marsh type
Variable
% Acreage change
Mean
Minimum
Maximum
Regularly
flooded marsh
GT Great Diurnal Tide Range
8.75
3.20
14.70
Salt Elevation
1.94
1.20
2.70
Marsh Erosion
0.35
0.10
0.90
Mean Reg Flood Max. Acer.
0.54
0.20
1.30
Mean Reg Flood Min. Acer.
0.30
0.10
0.90
Mean Irreg Flood Max. Acer.
0.43
0.20
1.10
Mean Irreg Flood Min. Acer.
0.48
0.20
1.50
Irregularly
flooded marsh
GT Great Diurnal Tide Range
32.47
1.40
74.25
Salt Elevation
2.96
0.20
8.81
Marsh Erosion
0.33
0.10
0.70
Mean Reg Flood Max. Acer.
0.53
0.00
0.86
Mean Reg Flood Min. Acer.
0.23
0.00
0.40
Mean Irreg Flood Max. Acer.
0.32
0.10
0.50
Mean Irreg Flood Min. Acer.
0.40
0.10
0.54
Transitional salt
marsh
GT Great Diurnal Tide Range
17.00
12.40
22.80
Salt Elevation
34.62
20.00
49.39
Marsh Erosion
0.06
0.00
0.10
Mean Reg Flood Max. Acer.
0.06
0.00
0.10
Mean Reg Flood Min. Acer.
0.02
0.00
0.10
Mean Irreg Flood Max. Acer.
0.06
0.00
0.20
Mean Irreg Flood Min. Acer.
0.08
0.00
0.20
49

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Table 12. Principal factors affecting vulnerability to SLR (tide range, salt elevation, erosion, accretion, elevation).
Sites
Historic
Relative
SLR
Trend*
(mm/yr)
Great
Diurnal
Tide
Range
(m)
Salt
elevation
(m
above
MTL)
Marsh
Erosion
(horz. m
/yr)
SET elevation change
(mm/yr) -
mean (min to max)
Elevation (m) -
mean ± st dev
High marsh
Low marsh
High marsh
Low marsh
Broadkill (DE)
3.4
1.42
1.04
0.12
-
5.42
(4.1 to 6.2)
0.405
±0.26
0.244
±0.21
Mispillion (DE)
3.4
1.81
1.1
0.56
-
-
0.672
±0.24
0.461
±0.27
St. Jones (DE)
3.4
1.81
1.18
0.31
3.13 (NA)
4.54
(3.0 to 6.1)
0.805
±0.23
0.972
±0.17
Dennis (NJ)
3.8
1.92
1.21
0.44
1.87
(-1.5 to 5.2)
-
0.810
±0.17
0.337
±0.38
Reeds (NJ)
3.8
1.92
1.21
1.34
-
-
0.809
±0.18
0.399
±0.40
Dividing (NJ)
3.8
1.96
1.22
0.43
4.61
(2.2 to 6.7)
-
0.689
±0.20
0.033
±0.39
Maurice (NJ)
3.8
1.96
1.22
0.42
4.97
(1.3 to 9.3)
-
0.833
±0.23
0.008
±0.41
*VLM rates were applied to the historic eustatic trend (1.7mm/yr) (Section 2.2.4)

-------
4 CONCLUSIONS
SLAMM was used to project potential responses of seven sites in the Lower Delaware Bay to accelerated
SLR. This study combined the best available elevation data with tidal data and site-specific accretion and
erosion rates. We examined three SLR scenarios (low = 0.3 m by 2100, intermediate = 1 m by 2100, and
high = 2 m by 2100) and found the salt marshes to be increasingly vulnerable to the effects of SLR as the
rate of SLR was increased, as evidenced by conversion to different habitat types.
The SLAMM simulations projected that all sites will experience loss of high marsh acreage by late
century. The high marsh habitats are projected to be lost at a faster rate than low marsh habitats,
largely because high marshes are assumed to have lower accretion rates (since they are inundated less
and collect less sediment). Additionally, high marsh plants (Spartina patens, Distichlis spicata) are less
tolerant of changes to inundation frequency compared to the low marsh dominant, Spartina alterniflora
(Naidoo et al. 1992), which suggests that high marshes will likely be disproportionately impacted by
more frequent inundation compared to low marsh habitats. The Broadkill and Mispillion sites in DE are
projected to experience the highest percent loss of high marsh acreage by 2025 (around 10%), likely due
in part to low elevations (on average, the elevation of high marsh habitats at these two sites is lower
than at other sites). By 2075, six of the seven sites are projected to lose over 20% of their high marsh
acreage. Projected losses in high marsh are even more extreme under the high SLR scenario. Under this
scenario, the large areas of high marsh in NJ (which has higher rates of vertical land movement and
subsidence than DE) are projected to convert to low marsh by 2050 or 2075 (and then tidal flats or open
water by 2100). Opportunities for landward migration of high marsh will likely be limited due to
development and terrain (steep slopes).
Low marsh acreage change shows a contrary pattern to high marsh, with projections of overall gains.
SLAMM assumes that low marsh has higher accretion rates than high marsh (and thus a higher
likelihood of building elevation and keeping pace with SLR) because low marsh habitats are inundated
more frequently and collect more sediment. While these overall gains may seem favorable, certain low
marsh areas are projected to be lost as early as mid-century (particularly near the bay and river
channels). Although these areas are relatively small compared to the areas of gains, some of these low
marshes may be highly valued for crab or mussel habitat, flood protection or recreation. Thus, it is
important to consider location and not just overall percent change.
The SLAMM simulations project varying rates of change across sites, time periods and SLR scenarios,
which is not unexpected due to the unique characteristics of each site. For example, the Broadkill (DE)
site has a large tidal swamp that is projected to convert to high marsh at a time when the other sites are
mostly projected to experience high marsh losses. The Broadkill also has a low marsh area along the bay
that is at a lower elevation than most other sites, which partly explains why it is projected to convert to
tidal flat sooner than low marsh areas at other sites. While patterns may vary in part due to these
unique characteristics, the sensitivity analysis shows that similar factors drive the patterns across sites
(the two most dominant driving factors being great diurnal tide range and salt elevation).
One of the factors that affects the outcome of the SLAMM simulations is the selection of the model
protection scenario (Section 2.3.1). In the main report, we only present results from the "Protect
51

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Developed Dry Land" scenario (which our work group felt was most likely), where cells designated as
developed dry land were protected from inundation and could not be converted to salt marsh habitats
in the simulations. If protections are extended to include undeveloped dry land as well (the "Protect All
Dry Land" scenario), there are substantial reductions in the percent of regularly-flooded and transitional
salt marsh acreage at certain sites by late century (Appendix L). The DE sites, which have large areas of
low-lying undeveloped dry land bordering the salt marshes, are particularly affected by this change. This
type of examination of marsh migration potential shows how management and human activities can
affect outcomes. The gain/loss maps in Section 3.2 highlight the areas in the Lower Delaware Bay that
are most likely to be affected and can help inform decisions about trade-offs between restricting marsh
movement and potential loss of ecosystem services.
It is possible that the projections for the Broadkill (DE) and Mispillion (DE) sites may over-predict
flooding frequency in certain areas. These two sites are difficult to accurately model because they are
influenced by barriers/dunes along the coast and have also been highly modified. In recent years, a large
restoration project has been underway in the Prime Hook National Wildlife Refuge (which covers
portions of both sites), where some of the marsh areas behind the barrier/dunes have significantly
subsided due to many years of impoundments. There may also be dikes or flow alterations affecting
these sites that are not currently accounted for in the GIS layers. All sites in the study area are affected
to some degree by anthropogenic landscape alterations, which are contributing to the ongoing loss of
coastal wetlands. In the Delaware Estuary, known sources of disturbance include conversion of wetlands
to agricultural and other land uses, mosquito control ditching, incremental filling, hydrological
alterations such as dredging, nutrient enrichment and spread of invasive species (Haaf et al. 2015,
USEPA 2015, Haaf et al. 2017).
In considering these results, it is important to bear in mind some limitations of the study. As discussed
earlier in the report, there are limitations with SLAMM itself, as well as with some of the input data. For
example, anthropogenic changes such as beach nourishment, shoreline armoring and levee construction
are not included in the simulations presented here. Another consideration is that SLAMM projects that
high marsh habitat that is regularly flooded will successfully convert into a viable low marsh habitat. In
some cases, it is possible that the more frequent inundation of the high marsh habitats will result in peat
collapse and direct conversion of high marshes into tidal flats or open water (DeLaune et al. 1994).
Given that changes in inundation frequency can be injurious to marsh habitats, the projections from this
model application can be considered optimistic.
Our study also exposed some data gaps and research needs. Tide range, salt elevation and wetland
elevation-change rates are critical input parameters for SLAMM. While tide range and Surface Elevation
Table (SET) data do exist for this project, it would be helpful to have more localized tide range data (as
the NOAA buoys were not located at any of the study sites), especially since tide range and salt elevation
emerged as very important input variables in the sensitivity analysis. It would also be helpful to have
more SET data, particularly in low marsh habitats. Strategically placed SET stations across the region
would help improve studies like ours and would also have importance for regional coastal wetland
vulnerability assessments and predictive ecological models (Osland et al. 2017).
The SET data that we analyzed in this study showed that accretion/elevation change data are highly
variable, sometimes even at the same site. Thus, it is fair to say that there is considerable uncertainty in
the precision of the accretion inputs that were used for this project. As a potential follow-up to this
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project, the confidence of model results could be evaluated and quantified using the built-in SLAMM
uncertainty-analysis module. Using Monte-Carlo simulations, the SLAMM model can be run iteratively,
with model inputs randomly drawn from distributions representing input uncertainty. Each model
realization represents one possible "future" for the studied area. All model realizations are then
assembled into probability distributions of wetland coverage reflecting the effect of input data/model
uncertainties on prediction results. When uncertainty-analysis is incorporated, the relative simplicity of
the SLAMM model becomes a useful compromise that allows for an efficient characterization of
uncertainties without excessive computational time. In addition, all model uncertainties can be
summarized in a single map such as the "percent likelihood of a coastal marsh" for each modeled cell at
a given date. In this manner, a complex uncertainty analysis can actually simplify the presentation of
model results.
Even taking these limitations into account, the results of this report have both immediate and longer-
term applications. The current modeling provides a set of maps and numerical results for examining
which dry lands and wetlands are expected to be most vulnerable to SLR and in what timeframe. As
demonstrated in Section 3.2.4, outputs like the gain/loss maps can be used to help facilitate the
evaluation of wetlands and land-management decisions given the likely threat of accelerated SLR in this
region. Results can also be used to inform monitoring strategies. For example, long-term monitoring
sites could be established in areas projected to be "transitional", which would enable researchers to
track whether changes are occurring as projected (e.g., is high marsh converting to low marsh as
projected, or is it converting directly to open water, and is high marsh migrating into freshwater swamp
areas as projected?). In addition, it will also be important for researchers to track how the changes in
high and low marsh dynamics are affecting ecosystem services and the protective capacity of the marsh
area. It is our hope that the vulnerabilities to SLR identified by the SLAMM projections in this report,
when considered in the context of management objectives for target services, can support robust
analysis and design of effective adaptation practices for protecting, restoring and/or enabling migration
of valued salt marsh ecosystems.
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