HCRT`s SLAMM Report - Gulf of Mexico Alliance

Transcription

Application of the Sea-Level Affecting Marshes Model (SLAMM 6) to
Southeastern Louisiana
Prepared for:
The Gulf of Mexico Alliance Habitat Conservation and Restoration Team
December 20, 2013
Warren Pinnacle Consulting, Inc.
PO Box 315, Waitsfield VT, 05673
Application of the Sea-Level Affecting Marshes Model (SLAMM 6) to Southeastern Louisiana
Contents
Executive Summary ........................................................................................................................................... 1
Project Background ........................................................................................................................................... 3
Model Summary ................................................................................................................................................. 4
Methods and Data Sources............................................................................................................................... 7
Results and Discussion....................................................................................................................................27
Conclusions.......................................................................................................................................................34
References .........................................................................................................................................................36
Appendix A: Complete Tables of Model Parameters.................................................................................40
Appendix B: Study Area Maps .......................................................................................................................45
Funding for this project was provided through the Gulf of Mexico Foundation and Gulf of
Mexico Alliance under an award from the National Oceanic and Atmospheric
Administration (Award NA12NOS4730005)
Executive Summary
In 2010-2011, The SLAMM 6 model was applied to approximately 31,000 square kilometers of
southeastern Louisiana (Figure 1) under a project funded by the National Wildlife Federation
(NWF) and published in a special issue of the Journal of Coastal Research (Glick et al. 2013). The
analysis used 15 meter cells and incorporated high-quality digital elevation map and wetland
inventory datasets derived by examining wetting and drying using remotely sensed data (Couvillion
2010) as well as a spatial map of subsidence derived by two-dimensional interpolation (kriging) of
first-order leveling data and GPS observations from the National Geodetic Survey (NGS) and water
level (tide gauge) data from NOAA. The locations of dikes were determined through the use of
designations in the 1988 NWI data layer and supplemented with information from the Levees GIS
Database developed by the New Orleans District of the US Army Corps of Engineers (ACE). The
ACE data was considered to be the “Best Available Data” regarding dike and levee locations as of
mid-October, 2009. However, significant uncertainty in the extent and locations of dikes led to the
decision to protect all dry lands, i.e., no dry land was allowed to convert to wetland in the SLAMM
model. The model was first calibrated to historical data, closely matching trends in marsh and
swamp losses from 1956 to 2007. The calibrated model was then applied to predict effects of future
sea-level rise on coastal Louisiana given global sea-level rise (SLR) scenarios of 0.34-, 0.75-, 1.22-,
and 1.9-meters by 2100.
The SLAMM model has evolved to some degree since the NWF project was completed. For
example, the way levees and dikes are handled has become more complex and takes into account
water connectivity pathways. In addition, new LiDAR and levee-location data layers are now
available for Southern Louisiana.
This project builds on the previous SLAMM modeling of southeastern Louisiana in several ways:
1) Dry land is not assumed to be universally protected and new dike and levee height and
location data are incorporated;
2) New LiDAR data are used where available;
3) Simulations are now run using the same five scenarios of global sea-level rise by the year
2100 that were simulated in other SLAMM analyses conducted in the Gulf of Mexico: 0.39meters (2007 IPCC A1B Mean Scenario), 0.69-meters (2007 IPCC A1B Max Scenario), 1.0
meters, 1.5 meters, and 2.0 meters; and
4) Incorporation of VDATUM correction rasters.
Results of this SLAMM application suggest Southeastern Louisiana will be severely affected by
accelerated SLR, including many dry land areas that are not protected by closed dikes. For example,
SLAMM simulations predict that 1 meter of eustatic SLR by 2100 could lead to a loss of 43% of
undeveloped and 18% of developed dry land.
Prepared for GOMA HCRT
1
It is important to note that these results only predict the vulnerability of the initial-condition
landscape to the sea-level rise scenarios investigated as opposed to any additional anthropogenic
impacts. Moreover, results assume levees and dikes in the study area will remain in their current
configuration and be maintained against regular overtopping due to SLR. Finally, the modeling
approach taken herein does not include the restoration and protection projects proposed in the 2012
State Coastal Master Plan (“Louisiana’s 2012 Coastal Master Plan” 2012), which are predicted to
have a significant effect on the sustainability of the Louisiana coastal zone.
2
Project Background
Louisiana's wetlands today represent about 37 percent of the estuarine herbaceous marshes of the
conterminous United States, but sustain approximately 90 percent of the wetland losses observed
(Couvillion et al. 2011). The State's wetlands extend as much as 130 kilometers inland and along the
coast for about 300 kilometers (Williams 1995). These coastal wetlands provide many ecosystem
services, including their ability to protect landward areas from the effects of storms.
E st uar in e Open Wat er
Cypr ess S wamp
Un developed Dr y Lan d
Regular ly Flooded M ar sh
I r r egular ly Flooded M ar sh
S wamp
Open Water
Cypress Swamp
Undeveloped Dry Land
Regularly Flooded Marsh
Irregularly Flooded Marsh
Swamp
Td
i alFr esh M ar sh
I n lan d Fr esh M ar sh
Developed Dr y Lan d
Td
i alSwamp
Est uar in e B each
T r an sit ion alSalt M ar sh
Tidal Fresh Marsh
Inland Fresh Marsh
Developed Dry Land
Tidal Swamp
Estuarine Beach
Transitional Salt Marsh
Figure 1. Study area
The goal of this project is to build on the previous SLAMM analysis of Southeastern Louisiana by
incorporating more accurate levee locations and consequently including dry lands in the model
domain (which were previously assumed to be universally protected).
3
Model Summary
Changes in tidal marsh area and habitat type in response to sea-level rise were modeled using the Sea
Level Affecting Marshes Model (SLAMM 6), which accounts for the dominant processes involved in
wetland conversion and shoreline modifications during long-term sea level rise (Park et al. 1989);
www.warrenpinnacle.com/prof/SLAMM).
Successive versions of the model have been used to estimate the impacts of sea level rise on the
coasts of the U.S. (Craft et al. 2009; Galbraith et al. 2002; Glick et al. 2007; J. K Lee et al. 1992;
National Wildlife Federation and Florida Wildlife Federation 2006; Richard A. Park et al. 1993; Titus
et al. 1991, Glick et al. 2013).
Within SLAMM, there are five primary processes that affect wetland fate under different scenarios
of sea-level rise:





Inundation: The rise of water levels and the salt boundary are tracked by reducing
elevations of each cell as sea levels rise, thus keeping mean tide level (MTL) constant at zero.
The effects on each cell are calculated based on the minimum elevation and slope of that
cell.
Erosion: Erosion is triggered based on a threshold of maximum fetch and the proximity of
the marsh to estuarine water or open ocean. When these conditions are met, horizontal
erosion occurs at a rate based on site- specific data.
Overwash: Barrier islands of under 500 meters width are assumed to undergo overwash
during each specified interval for large storms. Beach migration and transport of sediments
are calculated.
Saturation: Coastal swamps and fresh marshes can migrate onto adjacent uplands as a
response of the fresh water table to rising sea level close to the coast.
Accretion: Sea level rise is offset by sedimentation and vertical accretion using average or
site-specific values for each wetland category. Accretion rates may be spatially variable
within a given model domain or can be specified to respond to feedbacks such as frequency
of flooding.
SLAMM Version 6.2 has been in development since 2008/2009 and provides several optional
capabilities:


Accretion Feedback Component: Feedbacks based on wetland elevation, distance to
channel, and salinity may be specified. This feedback was used in Louisiana simulations.
Salinity Model: Multiple time-variable freshwater flows may be specified. Salinity is
estimated and mapped at MLLW, MHHW, and MTL. Habitat switching may be specified as
4



a function of salinity. This optional sub-model was not employed in the simulation of the
Louisiana coast due to lack of sufficient data.
Integrated Elevation Analysis: SLAMM will summarize site-specific categorized elevation
ranges for wetlands as derived from LiDAR data or other high-resolution data sets. This
functionality is used to test the SLAMM conceptual model at each site. The causes of any
discrepancies are then tracked down and reported on within the model application report.
Flexible Elevation Ranges for land categories: If site-specific data indicate that wetland
elevation ranges are outside of SLAMM defaults, a different range may be specified within
the interface. If such a change is made, the change and the reason for it are fully
documented within the model application reports.
Integrated Uncertainty Analyses: Using a Monte-Carlo analysis, SLAMM is run hundreds of
times while sampling from input distributions rather than fixed parameters. Spatial
uncertainty in elevation data and uncertainty regarding the future rate of SLR are also
incorporated. Each model result then represents one possible “future” for the studied area.
Confidence intervals can be plotted on model projections on a site-by-site basis along with
other uncertainty-analysis products (e.g. maps of likelihood of land-cover change).
For a thorough accounting of SLAMM model processes and the underlying assumptions and
equations, please see the SLAMM 6 Technical Documentation (Clough et al. 2010). This document is
available at http://warrenpinnacle.com/prof/SLAMM.
All model results are subject to uncertainty due to limitations in input data, incomplete knowledge
about factors that control the behavior of the system being modeled, and simplifications of the
system (Council for Regulatory Environmental Modeling 2008). Site-specific factors that increase or
decrease model uncertainty are covered in the Results and Discussion section of this report.
Sea Level Rise Scenarios
Recent literature (J. L. Chen et al. 2006; Monaghan et al. 2006) indicates that the eustatic rise in sea
levels is progressing more rapidly than was previously assumed, likely due to the dynamic changes in
ice flow omitted within original IPCC calculations (IPCC 2007). Rahmstorf suggests that, taking
into account possible model error, a feasible range by 2100 of 50 to 140 cm (2007). This work was
recently updated and the ranges were increased to 75 to 190 cm (Vermeer and Rahmstorf 2009).
Pfeffer et al. (2008) suggests that 2 meters by 2100 is at the upper end of plausible scenarios due to
physical limitations on glaciological conditions. A recent US intergovernmental report states
"Although no ice-sheet model is currently capable of capturing the glacier speedups in Antarctica or
Greenland that have been observed over the last decade, including these processes in models will
very likely show that IPCC AR4 projected sea level rises for the end of the 21st century are too low."
(Clark 2009) A recent paper by Grinsted et al. (2009) states that “sea level 2090-2099 is projected to
5
be 0.9 to 1.3 m for the A1B scenario…” Grinsted also states that there is a “low probability” that
SLR will match the lower IPCC estimates.
The variability of SLR predictions presented in the scientific literature illustrates the significant
amount of uncertainty in estimating future SLR. Much of the uncertainty may be due to the
unknown future of the drivers climate change, such as fossil fuel consumption and the scale of
human enterprise. In order to account for these uncertainties, and to better reflect these
uncertainties as well as recently published peer-reviewed measurements and projections of SLR as
noted above, SLAMM was run not only assuming A1B-mean and A1B-maximum SLR scenarios,
but also for 1 m, 1.5 m, and 2 m of eustatic SLR by the year 2100 as shown in Figure 2.
200
180
A1B Mean
140
1 meter
1.5 meter
120
2 meters
100
80
60
Sea Level Rise (cm)
160
A1B max
40
20
0
1990
2015
2040
2065
2090
Figure 2. Summary of SLR scenarios examined
A SLAMM simulation is run starting from the wetland cover data photo date as the initial condition.
In this study the time-step was selected to be 25 years for forecasting resulting in model output for
years 2025, 2050, 2075, and 2100.
In order to conduct an initial model calibration SLAMM simulates a “time zero” step in which the
consistency of model assumptions for wetland elevations are validated with respect to available
wetland coverage information, elevation data and tidal frames. Due to simplifications within the
SLAMM conceptual model, DEM and wetland layer uncertainty, or other local factors, some cells
may fall below their lowest allowable elevation category and would be immediately converted by the
model to a different land cover category (e.g. an area categorized in the wetland layer as swamp
where water has a tidal regime according to its elevation and tidal information will be converted to a
tidal marsh). These cells represent outliers on the distribution of elevations for a given land-cover
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type. Large-scale conversions at “time zero” can also indicate that an area should be designated as
diked.
Methods and Data Sources
The current project is an adaptation of the original application of SLAMM to Southeast LA funded
by the NWF. This work was published in a special issue of the Journal of Coastal Research (Glick et
al. 2013).
The NWF analysis of Louisiana included a “hindcast” analysis to calibrate the SLAMM model to the
observed historical sea-level rise signal. The primary metric used to evaluate SLAMM hindcast
results in this study is the percent of the land cover lost during the model simulation for the primary
wetland/vegetation types. Hindcast results suggested the calibrated SLAMM model for Louisiana
closely predicts the amount of salt marsh loss observed, predicting 26% loss when 25% of marsh
loss was observed. The freshwater marsh loss model was stronger on the calibrated west side of the
simulation than the non-calibrated eastern side of the model. Fresh-marsh losses are under
predicted on the east side of the model. This may be partially due to the effects of hurricanes on the
east-side model domain.
The original digital elevation model (DEM) was provided by Brady Couvillion of USGS (Couvillion,
2010). Since a complete LiDAR dataset was not available, the USGS used a method which draws
upon patterns of wetting and drying as discerned in multiple dates of spectral imagery (Landsat band
5), correlated those patterns in areas for which topography data did exist, then applied those patterns
to gaps in the topography data to complete the data (Couvillion, 2010). Bare-earth LiDAR data
were used preferentially, where available, to produce a spatially continuous DEM. The original DEM
was updated with 2010 USACE LA and MS, 2010 USGS Atchafalaya, and 2011 ARRA Region 1
and Region 2 LiDAR data (shown in Figure 3) obtained from the NOAA Digital Coast database.
7
Figure 3. Elevation data sources. Grey areas reflect the original DEM of Couvillion(2010). The orange area
represents the 2010 USGS Atchafalaya LiDAR extent, green indicates the 2011 ARRA Region 1 LiDAR
extent, blue shows the 2011 ARRA Region 2 LiDAR extent, and red indicates the location of the 2010
USACE LA and MS LiDAR data.
USGS utilized a similar modeling process with spectral imagery to produce the wetlands cover layer
which was used in the NWF project and has also been utilized in this project. Aggregation of the 15
meter cells used for modeling suggests that the approximately 10 million acre study area (31,000
km2) was composed of the landcover types shown in Table 1.
8
Table 1. Landcover in LA study area using data from Couvillion (2010)
Wetland Category
Estuarine Open
Water
Cypress Swamp
Undeveloped Dry
Land
RegularlyFlooded Marsh
IrregularlyFlooded Marsh
Swamp
Tidal-Fresh
Marsh
Inland Fresh
Marsh
Inland Open
Water
Developed Dry
Land
Tidal Swamp
Estuarine Beach
Transitional Salt
Marsh
Estuarine Open Water
Cypress Swamp
Regularly-Flooded Marsh
Irregularly-Flooded Marsh
Swamp
Tidal-Fresh Marsh
Inland Fresh Marsh
Inland Open Water
Developed Dry Land
Tidal Swamp
Estuarine Beach
9
Initial
% of Study
Area
4,147,829
691,009
556,559
439,832
403,117
323,295
286,632
250,337
231,424
199,214
42,856
2,541
1,149
55%
9%
7%
6%
5%
4%
4%
3%
3%
3%
1%
< 1%
< 1%
Cypr ess Swamp
S wamp
Td
i al Fr esh M ar sh
Td
i al Swamp
E st uar in e Beach
Cypress Swamp
Swamp
Tidal Fresh Marsh
Inland Fresh Marsh
Developed Dry Land
Tidal Swamp
Estuarine Beach
Inland Open Water
Figure 4. SLAMM wetland classes converted from USGS coverage (West side of study area)
10
Est uar in e Open Wat er
Cypr ess S wamp
Swamp
Td
i alFr esh M ar sh
Td
i alS wamp
Est uar in e Beach
T r an sit ion al Salt M ar sh
Cypress Swamp
Swamp
Tidal Fresh Marsh
Inland Fresh Marsh
Developed Dry Land
Tidal Swamp
Estuarine Beach
Inland Open Water
Figure 5. SLAMM wetland classes converted from USGS coverage (East side of study area)
11
Extensive diked or impounded areas are present in the study area. The locations of dikes and levees
were determined from the following sources:




The U.S. Army Corps of Engineers, New Orleans District “Levees GIS - Topographic
Centerlines” and “Levees GIS - Topographic Centerline Points” shapefiles.
ftp.dnr.state.la.us/Large_Data_Requests/Levees_USACE/leveesgis_20130506.zip
Levee centerline and elevation points shapefiles obtained from Maurice Wolcott, Instructor
& Extension Specialist in the Biological and Agricultural Engineering Department at
Louisiana State University, for the following Parishes:
o LaFourche
o Plaquemines
o St. Charles
o St. James
o St. Tammany
o Terrebonne
United States Geological Survey Lafourche Parish Levee Mapping Project data obtained
from John Barras.
While calibrating the new SLAMM model configuration to the conceptual model using the
time-zero analysis, additional diked areas were specified based on the National Wetlands
Inventory data layer, which designates wetland polygons as impounded or diked, but does
not give this information for dry land.
SLAMM was run using the built-in connectivity algorithm to determine the potential paths of saltwater inundation for inland cells. This algorithm uses a four-sided search that determines whether a
cell is hydraulically connected to an adjoining cell (Clough et al. 2010). In this analysis, existing dikes
are assumed to be maintained against the effects of future sea-level rise. Dike heights are not
explicitly accounted for and tidal water is never assumed to overtop existing dikes. When dike
heights were explicitly included in the analysis, some diked areas showed potential for overtopping
by 2025. In most cases, these dikes will be maintained or built up. For example, in St. Charles
Parish, sandbagging low-elevation levees is already known to occur when tides are very high and
plans are in place to build up these levees in the immediate future (Wolcott 2013).
Connectivity maps of the project area, showing the inundation potential of the initial condition maps
as well as levees and areas designated as protected by dikes are presented in Figure 6 and Figure 7.
12
Figure 6. Connectivity map with locations of dikes (in yellow) in study area in the western portion of study
area.
13
Figure 7 Connectivity map with locations of dikes (in yellow) in study area in the eastern portion of study
area.
14
To enable proper model execution, all water within non-connected areas was converted to inland
open water, a non-tidal category. In addition, if dike layers suggested that a cell had a dike located
there, but the cell was characterized with an open-water wetland category, that cell was changed to a
dry land category.
Subsidence rates were estimated from point observations primarily derived from geodetic data
(Shinkle and Dokka 2004) as part of the NWF study. These data were added to information
obtained from the NOAA tide gauges at Grand Isle (gauge 8761724) and Eugene Island (gauge
8764311)1. The full set of points was interpolated to produce a continuous map via “kriging.”
Kriging is a method of interpolation that predicts unknown values from data observed at known
locations (Lang 2000). The final raster obtained, which has not changed from the published analysis
(Glick et al. 2013), is shown in Figure 8.
Figure 8. Kriged Subsidence raster from Shinkle and Dokka and long term NOAA tide gauge data
One of the unique features of the Southern Louisiana landscape is floating, or flotant, marsh. These
marshes are characterized by a buoyant, 1 to 2-foot thick organic mat of densely intertwined roots
that float, rising and falling in elevation with changing tides (Sasser et al. 2007). Floating marshes are
difficult to model effectively using SLAMM because they are subject to marsh succession based on
water quality (i.e., salinity and organic content) rather than land elevation (Sasser et al. 1996). Unless
1
Subsidence rates at the Grand Isle and Eugene Island tide gauges were calculated by subtracting the 1.7 mm/yr eustatic
SLR trend from the observed at each location, resulting in subsidence rates of 7.5 and 8.0 mm/yr, respectively.
15
linked to a hydrodynamic model, SLAMM either uses cell elevation as a surrogate for salinity or
employs a simple salt-wedge model. However, cell elevation is not an appropriate surrogate for
salinity within floating marshes given that the marsh floats atop the water. In the NWF project,
floating marsh areas were assigned relatively high rates of accretion (12 to 20 mm/yr) to allow them
to keep up with the SLR signal observed over the hindcast period. This approach has considerable
implications for model forecasts, as SLAMM predicts marsh losses when local SLR exceeds the rate
of marsh accretion. Given the difficulties in predicting historical and future salinity within the
floating marshes, the timing of the loss of these marshes is especially uncertain.
Figure 9. Extent of flotant marsh
The bayous of Southern Louisiana are largely populated by cypress swamps. These areas often occur
at elevations of 2m above mean sea level or less (Allen et al. 1996) and may be regularly inundated
16
with standing water. Bald cypress has been found to be highly tolerant of flooding, though
germination is not possible under permanent flooding conditions (Allen et al. 1996). In a study of
wetland tree growth-response to flooding, Keeland and coworkers found permanent shallow
flooding of approximately 25 cm occurred in the area of the Barataria basin swamp under
examination (1997).
For the NWF project, the SLAMM model was adjusted to predict that cypress swamps convert to
“flooded swamp” when their elevations falls to a level below which non-flooded land will rarely be
exposed (predicted to range from 0.3 to 0.5 meters below mean tide level). In addition, site-specific
data suggest that this elevation is the lowest elevation inhabited by this wetland type. In the very
long run, these swamps will likely convert to open water as germination is no longer possible (Allen
et al. 1996). Furthermore, in terms of forest management, if a flooded forest is cut then there will be
a permanent loss of that wetland habitat. The addition of “flooded swamp” is a bit of a departure
from SLAMM conventions. Generally, at each time step, the SLAMM model estimates what will
happen if a given habitat comes to equilibrium with the water levels predicted at that time.
However, given the length of time that cypress trees can remain alive within flooded swamps, it was
determined that assuming immediate conversion to open water was potentially misleading.
Several tide gauges were used to define the tide ranges for Southern Louisiana. A gradient of
decreasing tidal range from south to north was observed and applied to the SLAMM simulation.
Spatially variable tide range values were incorporated into the model through the use of subsites.
Figure 11 and Figure 12 show the location of each subsite for the west and east sides of the study
area, respectively.
The salt boundary parameter within SLAMM designates the boundary between wetlands and dry
lands or saline wetlands and fresh water wetlands. Based on regional frequency of inundation
analysis and also observed elevation ranges for these wetland categories, the lower elevation
boundaries for non-cypress swamp and inland fresh marsh was set to 170% of MHHW (mean
higher high water).
As part of the NWF project, accretion data for coastal Louisiana were collected from several studies
published in peer-reviewed journals (Bryant and Chabreck 1998; Cahoon and Turner 1989; Nyman
et al. 1993; Nyman et al. 1990; Nyman et al. 2006). A total of 40 averaged accretion rates were
combined to determine the accretion value used in the Louisiana SLAMM model. Each of these data
points were based on several cores. The average accretion value for coastal Louisiana SLAMM
modeling project was determined to be approximately 8.2 mm/year.
For comparison, the average elevation change calculated from data in the Coastwide Reference
Monitoring System (CRMS) database was 8.63 mm/year. From this extensive array of SET tables
placed throughout the study area, short term accretion rates were measured. However, this data set
had considerable variability (ranging from negative 114 mm/year to positive 60 mm/year). Analysis
17
of this data set revealed a statistically significant relationship between accretion rates and cell
elevations (with accretion rates tending to be higher in areas of lower elevation).
Based on the observed relationship between cell elevations and accretion rates within the CRMS
dataset, and also strong relationships between elevation and accretion encountered in other studies
of marsh accretion (e.g. Morris et al. 2002), a negative relationship between cell elevation and the
predicted accretion rate was utilized in this modeling analysis. The same elevation-to-accretion
relationship was used throughout the study area with the exception of floating marsh, fan-shaped
deltas, and the swamps of the Atchafalaya delta.
Based on model calibration, a maximum accretion rate of 11 mm/yr at the low elevation range and a
minimum accretion rate of 6 mm/yr at the top of the tidal frame for each tidal marsh category. This
simple relationship between accretion and marsh elevation is illustrated in Figure 10.
Figure 10. Relationship between predicted accretion rates and elevation used for regularly-flooded marsh
Accretion feedback relationships were not used for floating marshes. Since the accretion regime in
this marsh type was highly uncertain, accretion rates within floating marsh areas were used as a
calibration parameter as discussed above. In general, the accretion rates selected for application to
floating marshes were selected in order to offset marsh loss due to land subsidence.
The erosion rates applied were the default values for SLAMM. Marsh erosion was set to 1.8
horizontal meters per year, swamp erosion was set to 1 meter per year, and tidal flat erosion to 2
meters per year. It is also important to note that erosion only occurs in SLAMM if the land is (1) in
18
contact with open water (2) the maximum wave fetch requirement of 9 km is met (Clough et al.
2010).
Elevation data were available based on the NAVD88 vertical datum. These data were required to be
converted to a tidal datum for use within SLAMM, to estimate the frequency of inundation for each
model cell. The Louisiana study area is covered by VDATUM, the vertical datum transformation
product available from NOAA. Based on this product, a spatial grid of VDATUM corrections was
derived for the study area. Different from the previous NWF SLAMM application, correction
rasters were applied to the project (shown in Figure 13 and Figure 14). In the western portion of the
project, rasters were supplemented by the correction values used in the original project to avoid the
uncertainty added to the project by extrapolating values obtained from coastal areas to inland areas.
Figure 13 and Figure 14 present the VDATUM corrections applied. It is worth noticing that
although VDATUM corrections are provided by the NOAA product with a nominal precision of 1
mm, the overall elevation uncertainty of the transformation from NAVD88 to MTL is larger than
many other regions modeled by VDATUM. NOAA estimates the cumulative standard deviation for
this correction to be approximately 17 cm (NOS, US Department of Commerce, NOAA, 2012).
19
Maurepas
Subsite 7
Subsite 9b
Subsite 6
Subsite 9
Floating 6
Floating 8
Floating 2
Subsite 8
Subsite 2
Atchafalay
Floating 9
Subsite 5
Subsite 1
Subsite 4
Subsite 3
Floating 5
Subsite 1 and 3
Subsite 3 and 4
Figure 11. West side of study area subsites
20
Subsite 18
Subsite 20
Subsite 19
Floating
East
Subsite 16
Subsite 15
Subsite 17
Subsite 12
Subsite 11
Subsite 13
Subsite 14
Figure 12. East side of study area subsites
21
Figure 13. VDATUM-derived NAVD88 to MTL correction values (m) applied to the western portion of the
study area.
22
Figure 14. VDATUM-derived NAVD88 to MTL correction values (m) applied to the Eastern portion of the
study area.
23
Table 2. Selected parameters applied to model subsites. A full table of subsite parameters is located in
Appendix A
Subsite Name
SubSite 1
SubSite 2
SubSite 3
1 and 3
SubSite 4
3 and 4
SubSite 5/Atchafalaya
SubSite 6
SubSite 7
SubSite 8
SubSite 9
Maurepas
Floating 2
Floating 5
Floating 6
Floating 8
Floating 9
SubSite 10
SubSite 11
SubSite 12
SubSite 13
SubSite 14
SubSite 15
SubSite 16
SubSite 17
SubSite 18
SubSite 19
SubSite 20
Floating East
GT Great
MTLDirection
Diurnal
NAVD88
Offshore
Tide
(m)
Range (m)
South
0.3
0.39
South
0.3
0.31
South
0.3
0.39
South
0.3
0.39
South
0.3
0.321
South
0.3
0.36
South
0.3
0.48
South
0.3
0.06
0
0.15
East
South
0.3
0.32
South
0.3
0.32
0
0.20
East
South
0.3
0.24
South
0.3
0.48
South
0.3
0.06
South
0.3
0.32
South
0.3
0.23
South
0.3
0.26
South
0.3
0.26
South
0.3
0.31
South
0.3
0.36
South
0.36
0.42
East
0.3
0.42
East
0.3
0.36
East
0.36
0.36
East
0.3
0.40
East
0.3
0.45
East
0.3
0.45
South
0.3
0.31
24
Salt Elev.
(m above
MTL)
0.33
0.26
0.33
0.33
0.27
0.30
0.41
0.05
0.13
0.27
0.27
0.17
0.2
0.41
0.05
0.27
0.20
0.22
0.22
0.26
0.31
0.36
0.36
0.31
0.31
0.34
0.38
0.38
0.26
Elevation Analysis
The cell-size used for this analysis was 15 by 15 meters. SLAMM assumes that tidal wetlands inhabit
a range of elevations in relation to the local tide range. Observed relationships between wetland
types and measured elevations generally align with those in the SLAMM conceptual model; however,
there are occasional site specific differences, especially in microtidal regimes (Clough et al. 2010).
For this project, the SLAMM conceptual model was modified to more accurately reflect the unique
elevation characteristics of southern Louisiana. In particular, the lower bounds for tidal-fresh and
regularly-flooded marsh were decreased from the SLAMM default values to -0.05m and -0.1m,
respectively. Based on observed elevations and literature review, the tidal swamp and cypress swamp
categories were also adjusted to allow these landcover types to extend lower into the tidal frame than
suggested by the SLAMM default. The lower bound of tidal swamp was decreased to the mean tide
level and the cypress swamp was allowed to extend to 0.5 meters below mean tide level. These
modifications to the SLAMM elevation model did not change between the current model runs, and
the previous NWF-funded analysis.
The SLAMM conceptual model compared to site-specific elevation data are shown in Table 3 and
Table 4.
25
Table 3. Conceptual model and elevation analysis for the Western part of the study area
SLAMM Category
SLAMM
Conceptual Model
Min
Max
(m)
(m)
-0.27
0.5
0.27
0.5
0.27
0.5
-0.05
0.272
0.27
0.272
0.08
0.384
-0.1
0.384
0.27
0.5
0.27
3.048
0
3.048
-0.16
0.27
West Side
95th
Pct.(m)
0.74
5.23
3.53
0.57
1.41
0.66
0.62
5.49
0.93
0.69
4.20
mean
(m)
0.27
1.74
0.86
0.27
0.34
0.30
0.34
1.55
0.27
0.31
0.97
st.dev.
(m)
0.37
1.87
1.41
0.22
0.89
0.34
0.20
2.10
1.51
0.27
1.46
Table 4. Conceptual model and elevation analysis for the Eastern part of the study area
SLAMM
East Side
Conceptual Model
SLAMM Category
Max
5th
95th
Min (m)
n cells
(m)
Pct.(m) Pct.(m)
-0.1
0.312
5004637
-0.04
0.65
Irreg.-Flooded Marsh
0.065
0.312
4229304
-0.23
0.68
2
Developed Dry Land
0.221
0.5
1432796
-2.06
2.28
2
0.221
0.5
1301282
-1.61
2.27
Inland Open Water
0.221
0.5
1116921
-0.29
0.57
Tidal-Fresh Marsh
-0.05
0.221
894904
-0.44
1.02
2
Swamp
0.221
0.5
505883
-1.88
1.54
2
Inland-Fresh Marsh
0.221
0.5
298374
-1.36
1.28
Cypress Swamp
-0.5
0.5
253955
-1.35
1.23
Estuarine Beach
-0.13
0.221
21805
-0.26
1.15
mean
(m)
0.28
0.24
0.04
0.25
0.12
0.27
-0.26
0.14
0.13
0.16
st.dev.
(m)
0.25
0.34
1.42
1.60
0.46
0.51
1.07
0.95
0.79
0.62
Cypress Swamp
Swamp
Tidal-Fresh Marsh
Inland-Fresh Marsh
Irreg.-Flooded Marsh
Developed Dry Land
Inland Open Water
Tidal Swamp
Estuarine Beach
n cells
12199866
8719881
5310248
4326887
4167827
3182350
2994292
2468192
1084176
782654
22671
5th
Pct.(m)
-0.06
-1.04 2
-0.91 2
-0.06
-0.13 2
-0.09
0.03
-1.69 2
-0.30
-0.03
-0.11
2
This statistic includes areas protected by dikes and levees, so elevations below those expected by the SLAMM
conceptual model are considered acceptable.
26
Results and Discussion
SLAMM was applied using five different sea-level rise scenarios (see Sea Level Rise Scenarios
above): A1B-mean (0.39m) and A1B-maximum (0.69m), 1 m, 1.5 m, and 2 m of eustatic SLR by the
year 2100. Model results for the SLR scenarios examined suggest a vastly changed landscape within
southeastern Louisiana—a continuation of marsh losses that have been occurring for the past 50
years. In addition, including dry land in the model domain indicates that a significant portion of the
developed and undeveloped dry land is at risk of inundation, even under the lowest SLR scenarios.
Table 5 presents the acreage of land cover for each wetland type at 2100, as well as the initial and
“time zero” (presented as 2008) areal coverages, while Table 6 presents these data in terms of
percentage loss by 2100 (as compared to the “time zero” coverage). Both tables suggest an extreme
loss of habitat richness in the study area due to accelerated SLR. Nearly complete losses are
predicted in the cypress and tidal swamp categories for each SLR scenario examined; however, in the
case of the cypress swamp, these habitats may remain standing for many decades following
permanent flooding (shown by the large increase in flooded swamp coverage in Table 5).
Table 5. Projected coverage of wetland categories at 2100 for each accelerated SLR scenario (in Acres).
SLR by 2100 (m)
Initial
4147829
2008
4193872
Mean
4646697
Max
4983407
1 meter
5230058
1.5 meter
5437601
2 meter
5535750
Cypress Swamp
691009
688291
127955
60147
41463
31898
23927
556559
548509
374292
342591
314638
275157
244607
439832
515441
500594
295672
271694
236710
232592
403117
324861
148956
59854
35978
19487
18398
Swamp
323295
305673
166876
141373
121724
103975
91572
Tidal-Fresh Marsh
286632
266880
182031
117110
49669
4299
2902
Inland Fresh Marsh
250337
233907
150297
99603
57541
40439
37512
Inland Open Water
231424
185486
140097
135705
132755
131343
131014
Developed Dry Land
199214
198881
175522
168104
162571
155048
148984
Tidal Swamp
42856
40241
1393
498
293
203
181
Estuarine Beach
2541
2511
594
470
393
314
272
1149
43369
144220
170737
119583
109201
102351
Flooded Swamp
0
2718
563031
630843
649530
659096
667068
Tidal Flat
0
25154
253239
369681
387902
371022
338663
This model application focused on adding dry lands to the model domain of a previously calibrated
model and predicted losses in dry land categories are appreciable. Under the most conservative SLR
scenario examined, 32% of the undeveloped and 12% of the developed dry land are predicted to
flood by 2100. In comparison, 55% of the undeveloped and 25% of the developed dry land is
predicted to flood by 2100 under the 2m SLR scenario. It is important to note these results assume
27
levees and dikes in the study area will remain in their current configuration and be maintained
against regular overtopping due to SLR. However, if dikes do not completely encircle a plot of land,
water is often predicted to enter lands through existing openings – additional dike construction is
not assumed.
In addition to gains in flooded forest, major increases in transitional salt marsh and tidal flat are
predicted. Transitional salt marsh is a category used to represent the movement of dry land to
inundated marshland and therefore the large increases in this category are due to the loss of dry land.
Tidal flat is the final category a wetalnd may pass through before becoming open water and the large
increase in tidal flat is another indicator of the widespread habitat loss predicted by SLAMM for this
area.
Table 6. Percentage loss by 2100 for accelerated SLR scenarios examined (negative values indicate
gains). Calculations are based on “time zero” model results.
SLR by 2100 (m) Mean Maximum 1 meter 1.5 meter 2 meter
Cypress Swamp
81%
91%
94%
95%
97%
Undeveloped Dry Land 32%
38%
43%
50%
55%
Swamp
45%
54%
60%
66%
70%
Tidal-Fresh Marsh
32%
56%
81%
98%
99%
Inland Fresh Marsh
36%
57%
75%
83%
84%
Developed Dry Land
12%
15%
18%
22%
25%
Tidal Swamp
97%
99%
99%
99%
100%
Estuarine Beach
76%
81%
84%
87%
89%
Transitional Salt Marsh -233%
-294%
-176%
-152%
-136%
Tables of projections given individual SLR scenarios follow.
Results maps for the entire study area are available at the end of this document in Appendix B.
28
Mean eustatic by 2100
Results in Acres
Initial
2008
2025
2050
2075
2100
Estuarine
Open Water
4147828.7
4193872.5
4241929.4
4312347.3
4421722.7
4646696.9
Cypress
Swamp
Cypress Swamp
691009.0
688291.1
583916.2
401328.3
238426.6
127954.8
Undeveloped
Dry Land
556559.1
548508.8
530122.0
495184.6
431145.6
374291.7
RegularlyFlooded
Marsh
439832.1
515440.7
523286.8
566592.3
570779.0
500594.4
IrregularlyFlooded
Marsh
403117.3
324861.1
313458.6
276246.2
193407.7
148956.2
Swamp
Swamp
323295.2
305673.0
286316.5
248986.5
202050.4
166875.6
Tidal-Fresh
Marsh
Tidal-Fresh Marsh
286631.8
266880.1
261037.4
237915.9
205942.0
182031.0
Inland Fresh
Marsh
Inland Fresh Marsh
250337.0
233906.7
222836.9
203790.6
178923.5
150296.6
Inland Open
Water
Inland Open Water
231424.1
185485.8
163371.4
150029.1
144487.8
140097.1
Developed
Dry Land
Developed Dry Land
199213.7
198880.8
198177.4
194555.3
185680.6
175521.5
Tidal Swamp
Tidal Swamp
42856.3
40241.0
34018.2
13920.5
6350.6
1393.4
Estuarine
Beach
Estuarine Beach
2540.6
2511.0
1599.8
1077.6
794.4
594.5
Transitional
Salt Marsh
1148.7
43369.4
60173.5
106487.5
156889.6
144219.6
Flooded
Swamp
Flooded Swamp
0.0
2717.9
107075.4
289663.3
452560.0
563031.4
Tidal Flat
Tidal Flat
0.0
25153.9
48474.3
77668.7
186633.1
253239.1
7,575,794
7,575,794
7,575,794
7,575,794
7,575,794
7575793.8
Total (incl. water)
29
A1B Maximum
eustatic by 2100
Results in Acres
Initial
2008
2025
2050
2075
2100
4147828.7
4193872.5
4245789.6
4338406.9
4565213.3
4983407.3
Cypress
Swamp
Cypress Swamp
691009.0
688291.1
564140.7
333249.4
139946.3
60146.5
Undevel
oped
Dry Land
556559.1
548508.8
527295.5
464735.8
410693.8
342590.5
Regularl
yFlooded
Marsh
439832.1
515440.7
530416.5
568777.8
456488.7
295672.4
Irregular
lyFlooded
Marsh
403117.3
324861.1
304497.2
212601.8
106369.8
59854.0
Swamp
Swamp
323295.2
305673.0
282928.6
225944.7
181262.4
141373.1
TidalFresh
Marsh
Tidal-Fresh Marsh
286631.8
266880.1
257503.4
215344.1
161629.1
117109.5
Inland
Fresh
Marsh
Inland Fresh Marsh
250337.0
233906.7
217928.1
183001.1
144084.0
99603.1
Inland
Open
Water
Inland Open Water
231424.1
185485.8
161017.2
147080.6
140416.6
135705.2
Develop
ed Dry
Land
Developed Dry Land
199213.7
198880.8
197932.5
191571.0
181974.5
168103.7
Tidal
Swamp
Tidal Swamp
42856.3
40241.0
31956.3
10421.7
1697.8
498.1
Estuarin
e Beach
Estuarine Beach
2540.6
2511.0
1556.8
994.1
691.0
470.4
Transitio
nal Salt
Marsh
1148.7
43369.4
69330.4
168220.0
153198.0
170736.5
Flooded
Swamp
Flooded Swamp
0.0
2717.9
126851.5
357742.8
551042.8
630842.5
Tidal
Flat
Tidal Flat
0.0
25153.9
56649.5
157701.9
381085.7
369681.0
7,575,794
7,575,794
7,575,794
7,575,794
7,575,794
7,575,794
Estuarin
e Open
Water
Total (incl. water)
30
1 meter eustatic by
2100
Results in Acres
Initial
2008
2025
2050
2075
2100
4147828.7
4193872.5
4249958.0
4376696.3
4728996.8
5230057.7
Cypress Swamp
Cypress Swamp
691009.0
688291.1
543176.2
260726.2
75621.9
41462.5
556559.1
548508.8
524826.6
452178.1
373906.0
314638.0
439832.1
515440.7
537801.3
523665.6
385477.0
271693.9
Irregularly-Flooded
Marsh
Irregularly-Flooded
Marsh
403117.3
324861.1
291629.2
148812.9
57725.9
35978.5
Swamp
Swamp
323295.2
305673.0
279214.9
208670.7
153999.0
121724.4
Tidal-Fresh Marsh
Tidal-Fresh Marsh
286631.8
266880.1
252892.3
186956.5
99478.8
49669.5
Inland Fresh Marsh
Inland Fresh Marsh
250337.0
233906.7
213171.5
146044.3
71782.1
57541.2
Inland Open Water
Inland Open Water
231424.1
185485.8
158744.8
144003.3
136411.8
132755.2
Developed Dry Land
Developed Dry Land
199213.7
198880.8
197812.0
189411.0
176393.2
162571.2
Tidal Swamp
Tidal Swamp
42856.3
40241.0
29433.6
7490.1
740.3
292.8
Estuarine Beach
Estuarine Beach
2540.6
2511.0
1508.6
907.8
582.8
393.0
1148.7
43369.4
78161.5
220678.0
220316.5
119583.4
Flooded Swamp
Flooded Swamp
0.0
2717.9
147816.9
430266.9
615370.7
649530.1
Tidal Flat
Tidal Flat
0.0
25153.9
69646.4
279286.2
478990.9
387902.3
7,575,794
7,575,794
7,575,794
7,575,794
7,575,794
7,575,794
Total (incl. water)
31
1.5 meter eustatic
by 2100
Results in Acres
Initial
2008
2025
2050
2075
2100
4147828.7
4193872.5
4256656.3
4441347.9
4975187.6
5437601.0
Cypress Swamp
Cypress Swamp
691009.0
688291.1
509991.7
157109.6
45192.1
31898.3
556559.1
548508.8
520102.9
434207.4
339816.7
275157.2
Regularly-Flooded
Marsh
439832.1
515440.7
547391.1
413126.4
379773.1
236710.4
Irregularly-Flooded
Marsh
403117.3
324861.1
266945.5
89439.1
28404.7
19487.3
Swamp
Swamp
323295.2
305673.0
272099.1
189335.8
128687.0
103974.6
Tidal-Fresh Marsh
Tidal-Fresh Marsh
286631.8
266880.1
243422.2
119957.6
17900.2
4299.4
Inland Fresh Marsh
Inland Fresh Marsh
250337.0
233906.7
201934.0
86564.7
46795.7
40438.6
Inland Open Water
Inland Open Water
231424.1
185485.8
155751.5
141100.7
133285.6
131343.2
Developed Dry Land
Developed Dry Land
199213.7
198880.8
197422.3
186732.5
168546.7
155047.5
Tidal Swamp
Tidal Swamp
42856.3
40241.0
24992.7
2541.3
347.8
203.2
Estuarine Beach
Estuarine Beach
2540.6
2511.0
1429.1
794.9
461.1
313.9
1148.7
43369.4
97738.1
294656.2
212924.4
109201.5
Flooded Swamp
Flooded Swamp
0.0
2717.9
181002.5
533884.6
645802.0
659095.7
Tidal Flat
Tidal Flat
0.0
25153.9
98914.7
484994.9
452669.1
371021.8
7,575,794
7,575,794
7,575,794
7,575,794
7,575,794
7,575,794
Total (incl. water)
32
2 meter eustatic by
2100
Results in Acres
Initial
2008
2025
2050
2075
2100
4147828.7
4193872.5
4264158.2
4494989.4
5138332.1
5535750.3
Cypress Swamp
Cypress Swamp
691009.0
688291.1
466503.1
96084.4
35414.6
23926.9
556559.1
548508.8
510134.6
414015.4
308012.4
244607.5
Regularly-Flooded
Marsh
439832.1
515440.7
551762.3
392038.4
355175.7
232591.7
Irregularly-Flooded
Marsh
403117.3
324861.1
237935.8
57164.5
20668.3
18397.6
Swamp
Swamp
323295.2
305673.0
261068.7
172545.5
112615.2
91572.1
Tidal-Fresh Marsh
Tidal-Fresh Marsh
286631.8
266880.1
230211.0
45367.1
4539.7
2902.3
Inland Fresh Marsh
Inland Fresh Marsh
250337.0
233906.7
176207.4
71642.0
41953.7
37512.2
Inland Open Water
Inland Open Water
231424.1
185485.8
152948.3
138756.3
132360.6
131013.7
Developed Dry Land
Developed Dry Land
199213.7
198880.8
196883.8
183316.2
162461.5
148983.9
Tidal Swamp
Tidal Swamp
42856.3
40241.0
20717.3
1029.9
241.6
180.5
Estuarine Beach
Estuarine Beach
2540.6
2511.0
1355.9
708.8
388.0
271.9
1148.7
43369.4
143511.6
302671.6
216419.3
102351.5
Flooded Swamp
Flooded Swamp
0.0
2717.9
224492.4
594911.2
655581.0
667068.4
Tidal Flat
Tidal Flat
0.0
25153.9
137903.3
610553.2
391630.2
338663.4
7,575,794
7,575,794
7,575,794
7,575,794
7,575,794
7,575,794
Total (incl. water)
33
Conclusions
As observed in the previous application of SLAMM to Southeastern Louisiana, predictions indicate
this study area is likely to lose a significant amount of wetlands in the future, even under the most
conservative estimates of SLR.
The focus of this model application was to include dry land in the model domain by creating and
incorporating a comprehensive levee location input raster. Areas where dikes aren’t closed are
shown to flood in SLAMM projections, particularly in Plaquemines Parish. This is predicted to be
due to the combination of accelerated sea-level rise and land subsidence. While additional levees may
be planned, only existing structures were included in SLAMM simulations.
More than 80% of cypress swamp in the study area is predicted to convert to flooded swamp by
2100 under each SLR scenario. Cypress swamps in the Atchafalaya delta and the Lake Maurepas
area are both predicted to be affected. In fact, this trend has already been noted by scientists. In a
seven-year study of the Maurepas swamp, Shaffer and coworkers noted that nearly 20% of the trees
in their study plots suffered mortality, and recruitment of bald cypress and water tupelo saplings was
essentially absent (2009).
While the main difference between the model applications was the addition of a more complete
levee-location input raster, the SLAMM model itself has evolved, leading to minor differences
between model applications. In particular the approach to modeling tidal-fresh marsh has changed
since June 2011. Previously tidal-fresh marsh transitioned to other categories by first converting to
irregularly-flooded, then regularly-flooded marsh, then to tidal flats and to open water. The current
approach is for the tidal-fresh marsh to convert based on its elevation in the tidal frame, not directly
to irregularly-flooded marsh.3 Due to this change, when tidal-fresh marsh is lost in the current
model runs, it is more likely to convert directly to tidal flats or open water. As shown in Table 3 and
Table 4, there is a significant amount of tidal-fresh marsh found low in the tidal frame (at or below
mean-tide level) within Southeastern Louisiana. It is further worth noting that predictions of tidalfresh marsh in this study are especially uncertain since the majority is presumed to be flotant marsh
(Figure 9), a category that challenges standard SLAMM-model assumptions.
These results exclusively predict the vulnerability of the current landscape to the sea-level rise
scenarios investigated as opposed to other anthropogenic impacts. Moreover, results assume levees
and dikes in the study area will remain in their current configuration and be maintained against
regular overtopping due to SLR. The modeling approach taken herein does not include the
restoration and protection projects proposed in the 2012 State Coastal Master Plan (“Louisiana’s
3
Please see the SLAMM technical documentation for more detail. This was the only change in the SLAMM flow-chart
assumptions since the previous model application.
34
2012 Coastal Master Plan” 2012), which are predicted to have a significant effect on the
sustainability of the Louisiana coastal zone.
While the best available data have been included in this application of SLAMM, parameter inputs
and the conceptual model continue to have uncertainties that should be kept in mind when
interpreting these results. To account for some of these uncertainties, this study investigated effects
for a wide range of possible sea level rise scenarios, from a more conservative rise (0.39 m by 2100)
to a more accelerated process (2 m by 2100). To better support decision-makers, the results of this
project could be examined as a function of input-data uncertainty to provide a range of possible
outcomes and their likelihood.
35
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39
Appendix A: Complete Tables of Model Parameters
Subsite Name
NWI Photo Date (YYYY) Forecast
DEM Date (YYYY)
Direction Offshore [n,s,e,w]
MTL-NAVD88 (m)
GT Great Diurnal Tide Range (m)
Salt Elev. (m above MTL)
Marsh Erosion (horz. m /yr)
Swamp Erosion (horz. m /yr)
T.Flat Erosion (horz. m /yr)
Reg. Flood Marsh Accr (mm/yr)
Irreg. Flood Marsh Accr (mm/yr)
Tidal-Fresh Marsh Accr (mm/yr)
Beach Sed. Rate (mm/yr)
Freq. Overwash (years)
Reg Flood Use Model [True,False]
Reg Flood Max. Accr. (mm/year)
Reg Flood Min. Accr. (mm/year)
Reg Flood Elev a coeff. (cubic)
Reg Flood Elev b coeff. (square)
Reg Flood Elev c coeff. (linear)
Irreg Flood Use Model [True,False]
Irreg Flood Max. Accr. (mm/year)
Irreg Flood Min. Accr. (mm/year)
Irreg Flood Elev a coeff. (cubic)
Irreg Flood Elev b coeff. (square)
Irreg Flood Elev c coeff. (linear)
Tidal-Fresh Use Model [True,False]
Tidal-Fresh Max. Accr. (mm/year)
Tidal-Fresh Min. Accr. (mm/year)
Tidal-Fresh Elev a coeff. (cubic)
Tidal-Fresh Elev b coeff. (square)
Tidal-Fresh Elev c coeff. (linear)
SubSite
1
2008
2009
South
0.3
0.39
0.33
1.8
1
2
8.5
8.5
9.8
1
20
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
SubSite
2
2008
2009
South
0.3
0.31
0.26
1.8
1
2
8.5
8.5
9.8
1
20
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
SubSite
3
2008
2009
South
0.3
0.39
0.3315
1.8
1
2
8.5
8.5
9.8
1
20
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
1 and 3
2008
2009
South
0.3
0.39
0.3315
1.8
1
2
8.5
8.5
9.8
1
20
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
SubSite
4
2008
2011
South
0.3
0.321
0.273
1.8
1
2
8.5
8.5
9.8
1
20
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
Note: For all marsh accretion models, distance to channel and salinity effects are turned off.
40
3 and 4
2008
2009
South
0.3
0.3555
0.30225
1.8
1
2
8.5
8.5
9.8
1
20
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
Subsite Name
DEM Date (YYYY)
MTL-NAVD88 (m)
SubSite
5
2008
2011
South
0.3
0.48
0.408
1.8
1
2
8.5
8.5
9.8
1
20
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
41
SubSite
6
2008
2009
South
0.3
0.06
0.051
1.8
1
2
8.5
8.5
9.8
1
20
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
SubSite
7
2008
2009
East
0
0.153
0.13
1.8
1
2
8.5
8.5
9.8
1
20
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
SubSite
8
2008
2009
South
0.3
0.322
0.273
1.8
1
2
8.5
8.5
9.8
1
20
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
SubSite
9
2008
2009
South
0.3
0.32
0.27
1.8
1
2
8.5
8.5
9.8
1
20
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
Maurepas
2008
2009
East
0
0.2022
0.172
1.8
1
2
8.5
8.5
9.8
1
20
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
Subsite Name
DEM Date (YYYY)
MTL-NAVD88 (m)
Floating
2
2008
2009
South
0.3
0.24
0.2
1.8
1
2
15
15
15
1
20
FALSE
11
6
0
0
1
FALSE
11
6
0
0
1
FALSE
11
6
0
0
1
42
Floating
5
2008
2009
South
0.3
0.48
0.408
1.8
1
2
15
15
15
1
20
FALSE
11
6
0
0
1
FALSE
11
6
0
0
1
FALSE
11
6
0
0
1
Floating
6
2008
2009
South
0.3
0.06
0.051
1.8
1
2
15
15
15
1
20
FALSE
11
6
0
0
1
FALSE
11
6
0
0
1
FALSE
11
6
0
0
1
Floating
8
2008
2009
South
0.3
0.322
0.273
1.8
1
2
15
15
15
1
20
FALSE
11
6
0
0
1
FALSE
11
6
0
0
1
FALSE
11
6
0
0
1
Floating
9
2008
2009
South
0.3
0.2335
0.198
1.8
1
2
20
20
20
1
20
FALSE
11
6
0
0
1
FALSE
11
6
0
0
1
FALSE
11
6
0
0
1
SubSite
10
2008
2009
South
0.3
0.26
0.221
1.8
1
2
8.5
8.5
9.8
1
30
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
Subsite Name
DEM Date (YYYY)
MTL-NAVD88 (m)
SubSite
11
2008
2008
South
0.3
0.26
0.221
1.8
1
2
8.5
8.5
9.8
1
30
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
43
SubSite
12
2008
2009
South
0.3
0.31
0.2635
1.8
1
2
8.5
8.5
9.8
1
30
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
SubSite
13
2008
2010
South
0.3
0.36
0.306
1.8
1
2
8.5
8.5
9.8
1
30
TRUE
16
12
0
0
1
TRUE
16
12
0
0
1
TRUE
16
12
0
0
1
SubSite
14
2008
2011
South
0.36
0.42
0.357
1.8
1
2
8.5
8.5
9.8
1
30
TRUE
16
12
0
0
1
TRUE
16
12
0
0
1
TRUE
16
12
0
0
1
SubSite
15
2008
2010
East
0.3
0.42
0.357
1.8
1
2
8.5
8.5
9.8
1
30
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
SubSite
16
2008
2010
East
0.3
0.36
0.306
1.8
1
2
8.5
8.5
9.8
1
30
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
TRUE
11
6
0
0
1
Subsite Name
DEM Date (YYYY)
MTL-NAVD88 (m)
Floating
SubSite 17 SubSite 18 SubSite 19 SubSite 20 East
2008
2008
2008
2008
2008
2011
2010
2011
2011
2009
East
East
East
East
South
0.36
0.3
0.3
0.3
0.3
0.36
0.4
0.45
0.45
0.31
0.306
0.34
0.3825
0.3825
0.2635
1.8
1.8
1.8
1.8
1.8
1
1
1
1
1
2
2
2
2
2
8.5
8.5
8.5
8.5
10
8.5
8.5
8.5
8.5
10
9.8
9.8
9.8
9.8
10
1
1
1
1
1
30
30
30
30
30
TRUE
TRUE
TRUE
TRUE
FALSE
11
11
11
11
11
6
6
6
6
6
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
TRUE
TRUE
TRUE
TRUE
FALSE
11
11
11
11
11
6
6
6
6
6
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
TRUE
TRUE
TRUE
TRUE
FALSE
11
11
11
11
11
6
6
6
6
6
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
44
Appendix B: Study Area Maps
Es tua ri ne Open Wa ter
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
T idal Fr esh M ar sh
Ti da l Fres h Mars h
Tra ns iti onal Sa lt Ma rs h
Undeveloped Dry La nd
Inl and Fres h Mars h
I n lan d Open Wat er
Inla nd Open Wa ter
Regul arly Fl ooded Ma rs h
Developed Dry La nd
Flooded Swamp
T idal Swamp
Ti da l Swamp
T idalFlat
Cypr ess Swamp
Irregul arly Fl ooded Marsh
Initial Condition
Fl ooded Swamp
Ti dal Fl at
45
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
T idal Swamp
Ti da l Swamp
T idalFlat
Cypr ess Swamp
“Time zero” or 2008 in tables
Fl ooded Swamp
Ti dal Fl at
46
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
T idal Swamp
Ti da l Swamp
T idalFlat
Cypr ess Swamp
Fl ooded Swamp
2025, Scenario A1B Mean, 0.39 m SLR by 2100
Ti dal Fl at
47
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
T idal Swamp
Ti da l Swamp
T idalFlat
Cypr ess Swamp
Fl ooded Swamp
Ti dal Fl at
48
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
T idal Swamp
Ti da l Swamp
T idalFlat
Cypr ess Swamp
Fl ooded Swamp
Ti dal Fl at
49
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
T idal Swamp
Ti da l Swamp
T idalFlat
Cypr ess Swamp
Fl ooded Swamp
Ti dal Fl at
50
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
“Time zero”
Ti dal Fl at
51
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
2025, Scenario A1B Maximum, 0.69 m SLR by 2100
Ti dal Fl at
52
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
Ti dal Fl at
53
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
Ti dal Fl at
54
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
Ti dal Fl at
55
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
West study area, “time zero”
Ti dal Fl at
56
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
2025, 1 meter SLR by 2100
Ti dal Fl at
57
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
Ti dal Fl at
58
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
Ti dal Fl at
59
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
Ti dal Fl at
60
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
“time zero”
Ti dal Fl at
61
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
2025, 1.5 m SLR by 2100
Ti dal Fl at
62
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
2050, 1.5 m SLR by 2100
Ti dal Fl at
63
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
2075, 1.5 m SLR by 2100
Ti dal Fl at
64
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
2100, 1.5 m SLR by 2100
Ti dal Fl at
65
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
“time zero”
Ti dal Fl at
66
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
2025, 2 m SLR by 2100
Ti dal Fl at
67
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
2050, 2 m SLR by 2100
Ti dal Fl at
68
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
2075, 2 m SLR by 2100
Ti dal Fl at
69
Swamp
Swamp
Est uar in e Beach
Es tua ri ne Beach
Cypres s Swa mp
Td
i al Fr esh M ar sh
Inla nd Open Wa ter
Developed Dry La nd
Flooded Swamp
Td
i al Swamp
Ti da l Swamp
Td
i alFlat
Cypr ess Swamp
Fl ooded Swamp
2100, 2 m SLR by 2100
Ti dal Fl at
70

HCRT`s SLAMM Report - Gulf of Mexico Alliance

Transcription

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August 21, 2010