Transcript SLAMM Overview Powerpoint - Warren Pinnacle Consulting, Inc.

The SLAMM Model
(Sea Level Affecting Marshes Model)
Jonathan Clough
3-18-2014
Warren Pinnacle Consulting, Inc. Founded in 2001
Located in Central VT, Environmental Modeling Experts
Jonathan S. Clough, Founder, Environmental Consultant since 1994
Dr. Amy Polaczyk, SLAMM modeler, with us since 2010
Dr. Marco Propato, Accretion modeling expert, joined in 2011
warrenpinnacle.com
warrenpinnacle.com/quals.pdf
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SLAMM
Sea Level Affecting Marshes Model
• Simulates the dominant processes involved in wetland conversions under
different scenarios of sea level rise
– inundation, erosion, accretion, soil saturation and barrier island overwash
• Uses a complex decision tree incorporating geometric and qualitative
relationships to represent transfers among coastal classes
• Can provide numerical and map-based output with minimal computational
time
Open Ocean
Estuarine
Open Water
Undeveloped
Dry Land
Inland Fresh
Marsh
Developed
Dry Land
Irregularly
Flooded
Marsh
Estuarine
Open
Swamp
Regularly
Flooded
Marsh
Tidal Swamp
Tidal Fresh
Marsh
Inland Shore
Estuarine
Beach
Riverine
Tidal
Ocean Beach
Transitional
Salt Marsh
Cypress
Swamp
2009
2100, 1 m SLR
Tidal Flat
Open Ocean
Estuarine Open Water
Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irregularly Flooded Marsh
Inland Open Water
Swamp
Regularly Flooded Marsh
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Estuarine Beach
Riverine Tidal
Ocean Beach
Transitional Salt Marsh
Cypress Swamp
Tidal Flat
SLAMM
• Modest data requirements allow
application to many sites at a
reasonable cost
• Integrated stochastic uncertainty and
parameter sensitivity analyses
• Provides a range of possible outcomes and
their likelihood
• Users have included US EPA, USGS,
The Nature Conservancy, National
Wildlife Federation, and the U.S. Fish
& Wildlife Service, among others
• Calibrated to historic SLR in Louisiana
• The model has been applied to more
than 100 National Wildlife Refuges in
the US
http://warrenpinnacle.com/prof/SLAMM
“Uncertainty Cloud” for Selected Region
The Range Between 5th
and 95th percentiles is
graphed along with
mean and deterministic
results
Model Development Overview
• Intermittently under development since 1985, Park & Titus
• Three Year EPA STAR grant (2005-2008) provided funds for
significant model development.
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Simulations of entire Georgia and South Carolina Coastline
Model assumptions closely re-examined by team of scientists
Survey of recent literature
Model tested using LIDAR data
Model results linked to ecosystem services
• National Wildlife Federation Funded Simulations
– Florida, Puget Sound, Chesapeake Bay, Louisiana (Glick et al, 2013)
• USFWS funding of refuge simulations
– Over 100 refuges completed to date in USFWS Regions 4,5,8
• TNC / GOMA funding of Gulf of Mexico Simulations
Latest Version SLAMM 6.2
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64-bit (parallel processing in-house)
Dynamic Accretion
Open Source
Elevation Analyses with histograms
Increased Flexibility in Parameterization.
Upgrade of Salinity Component (Bathymetry)
Users Manual & Technical Documentation
Update
Coming Soon – SLAMM 6.3 and 6.4
• SLAMM 6.3 – USGS Sponsored
– Salinity linkages
– SAV model
• SLAMM 6.4 – USFWS Sponsored
– Roads and Infrastructure module
Ongoing Work on SLAMM Model
• Gulf Coast Prairie LCC
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– “Gap Analysis:” filling in all holes in the Gulf of Mexico
NY State – Application to Long Island, NY City, Hudson River
– Examine the effects of DEM processing and “hydro enforcement”
– All CT coasts
USGS:OR – SLAMM 6.3. Linkages created to EPA salinity
models. SAV predictions
– Habitat switching based on salinity, model testing and
documentation
• Ducks Unlimited – Pacific Northwest
– Application of uncertainty analysis in WA & OR, evaluating land
parcels for restoration
• TNC TX – Examine effects on infrastructure given
development and restoration scenarios
– Dike model refined to assess likelihood of overtopping
– Alternative green/grey infrastructure design.
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Model Process Overview
Addresses Six Primary Processes
(Inundation, Erosion, Saturation,
Overwash, Accretion, Salinity)
Titus and Wang 2008
Model Process Overview
• Inundation:
of the cell.
Calculated based on the minimum elevation and slope
• Erosion:
Triggered given a maximum fetch threshold and proximity of
the marsh to estuarine water or open ocean.
• Accretion: Vertical rise of marsh due to buildup of organic and
inorganic matter on the marsh surface. Rate differs by marsh-type.
• Salinity: Optional model or linkage to existing model.
Salinity affects
habitat switching in areas with significant freshwater flows
• Overwash:
Barrier islands undergo overwash at a fixed storm interval.
Beach migration and transport of sediments are calculated.
• Saturation:
Migration of coastal swamps and fresh marshes onto
adjacent uplands-- response of the water table to rising sea level.
Conceptual Model
• Square “raster” cells with elevation, slope, aspect,
estimated salinity, wetland type
– Cells may contain multiple land-types
– Cell size flexible given size of study area
Dry Land
Various Wetlands
Open Water
2D Representation
3D Representation
SLAMM Inundation Model
Elevation
Equilibrium Approach
Salt Elev.
Salt Boundary
(30 day inundation)
MHHW
MTL
MLW
Tidal Flat
RegularlyFlooded
Marsh
(Often Salt
Marsh)
Transitional or
IrregularlyFlooded Marsh
Distance Inland
Inland Fresh and Dry Land
SLAMM Inundation Model
Elevation
(Migration of Wetlands Boundaries due to Sea Level Rise)
Salt Elev.
Salt Boundary
(30 day inundation)
MHHW
MTL
MLW
Water
Tidal Flat
RegularlyFlooded
Marsh
(Often Salt
Marsh)
Distance Inland
IrregularlyFlooded
Marsh
Inland Fresh and Dry Land
Feedbacks to Accretion
• SLAMM 6 Allows for Elevation Feedbacks to
Accretion as shown by Morris et al. (2002)
• Linkage to Morris MEM model
Accretion (mm/yr)
“Unstable
Zone”
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High-elevation
marsh subject to less
flooding
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5
4
3
2
1
0
0.00
0.50
1.00
1.50
Elevation above MTL
2.00
2.50
Linkage to Marsh Equilibrium Model
• Explicitly accounts for physical and biological processes affecting marsh accretion
http://129.252.139.114/model/marsh/mem2.asp
Detailed SLAMM Land Categories
• 26 Categories, often derived from NWI (National Wetlands Inventory)
• May be specified as “protected by dikes or seawalls”
Dry Land: Developed and Undeveloped
Swamp: General, Cypress, & Tidal
Transitional Marsh: Occasionally Inundated, Scrub Shrub
Marsh:
Salt, Brackish, Tidal Fresh, Inland Fresh, Tall Spartina
Mangrove: Tropical Settings Only
Beach: Estuarine, Marine, Rocky Intertidal
Flats:
Tidal Flats & Ocean Flats
Open Water: Ocean, Inland, Riverine, Estuarine, Tidal Creek
Sea Level Rise Scenarios
• Model incorporates IPCC Projections as well as fixed
rates of SLR
• Global (Eustatic)
Rates of SLR
are corrected
for local effects
using long-term
tide gauge trends
Grinsted,
2009
Clim. Dyn.
or spatial subsidence
Vermeer and Rahmstorf, 2009, Proceedings of the National
Academy of Sciences
Powerful SLAMM Interface – Main Interface
(Illustrates 3-D Graphing Capabilities)
Powerful SLAMM Interface – Execution Options
Powerful SLAMM Interface – Uncertainty Analyses
Powerful SLAMM Interface – Landcover Maps
Powerful SLAMM Interface – Elevation Maps
Powerful SLAMM Interface – Depth Profiles
Powerful SLAMM Interface – Elevation Histograms
Connectivity Component
• Method of Poulter & Halpin 2007
• Assesses whether land barriers or roads prevent
saline inundation
• Can be used for levee overtop model with fine-scale
DEM
Built-in Sensitivity Analyses
• Marshes most sensitive to accretion rates
• Beaches and Tidal flats most sensitive to parameters
that affect SLR rates, tide ranges, and initial condition
elevations
• Dry land most sensitive to SLR rates.
Uncertainty Module Addresses Two
Primary Criticisms
• Accretion Model
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Doesn’t account for feedbacks (not true in SLAMM 6)
Manner in which feedbacks are accounted for is
uncertain
• Lack of uncertainty evaluation
How confident are you of the results?
Interpretation of deterministic results difficult
What to do if available input parameters are not very
good?
 Decision making difficult since likelihood and outcome
variability are unknown
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Parametric Model
Input Distributions
Model Output Distributions
30
10
20
Frequency
40
50
60
"Eustatic SLR by 2100 (m) "
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“Uncertainty Cloud” for Selected Region
0.5
1.0
1.5
2.0
"Eustatic SLR by 2100 (m) "
Examining SLAMM results as distributions can improve the
decision making process
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Results account for parametric uncertainties
Range of possible outcomes and their likelihood
Robustness of deterministic results may be evaluated
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Dike Considerations
• Traditional SLAMM has “on-off” dike layer
– Option to model dike elevations is included
• New: Dike elevations may be input at fine
scale
– Connectivity can be used to calculate dike overtop
• Dike Removal
– Dynamic accretion processes following dike
removal are not represented
“Hindcasting” Capability
• Run the model with historical data for validation and
calibration
• Results will be imperfect
– Historical elevation data with high vertical resolution unavailable
– Historical land-cover data are spotty and changes in NWI classification
have occurred
– Model will not predict land-use changes, beach nourishment or
shoreline armoring
• For many sites, hindcasting is not possible due to insignificant
RSLR “signal”
• In GOM, land subsidence amplifies SLR signal enough to make
hindcasting possible
SLAMM Infrastructure Module
• Grant funded by US Fish and Wildlife Service
• Integrates predicted SLR and tide-ranges with
roads and infrastructure databases
• Predicts effects on infrastructure
• Better captures infrastructure effects on
surrounding wetlands
Original Inundation (No Roads)
Flooded Every 30 Days
Flooded Every 60 Days
Flooded Every 90 Days
Not Flooded
Open Water
Regular ly Flooded M ar sh
I r r egular ly Flooded M ar sh
I n lan d Open Wat er
Swamp
Est uar in e Open Wat er
Inundation with Roads
Flooded Every 30 Days
Flooded Every 60 Days
Flooded Every 90 Days
Not Flooded
Open Water
Regular ly Flooded M ar sh
I r r egular ly Flooded M ar sh
I n lan d Open Wat er
Swamp
Est uar in e Open Wat er
KM of Roads from USFWS Road Inventory Program
Vulnerability of Alligator River
Roads to 1M of SLR by 2100
200
150
100
50
0
1983
2025
2050
2075
2100
No Regular Inundation
60-90 d inundation
30-60 d inundation
0-30 d inundation
Initial Condition
Flooded Every 30 Days
Flooded Every 60 Days
Flooded Every 90 Days
Not Flooded
Regular ly Flooded M ar sh
I r r egular ly Flooded M ar sh
I n lan d Open Wat er
2025 under 1M by 2100
Flooded Every 30 Days
Flooded Every 60 Days
Flooded Every 90 Days
Not Flooded
Regular ly Flooded M ar sh
I r r egular ly Flooded M ar sh
I n lan d Open Wat er
2050 under 1M by 2100
Flooded Every 30 Days
Flooded Every 60 Days
Flooded Every 90 Days
Not Flooded
Regular ly Flooded M ar sh
I r r egular ly Flooded M ar sh
I n lan d Open Wat er
2075 under 1M by 2100
Flooded Every 30 Days
Flooded Every 60 Days
Flooded Every 90 Days
Not Flooded
Regular ly Flooded M ar sh
I r r egular ly Flooded M ar sh
I n lan d Open Wat er
SLAMM Erosion Model
• Erosion assumed a function of wave action
• Maximum Fetch calculated at each cell based on
previous land-changes.
• When threshold of 9km is exceeded horizontal
erosion rates are implemented.
– 9 km threshold based on visual inspection of maps
– Value verified within literature (Knutson et al., 1981)
• Tidal Flats have different assumptions
Overwash Assumptions
• Barrier islands of under 500 meter width are
identified and assumed to be affected
• Frequency of “large storms” is user input
• Assumed effects are professional judgment based on
observations of existing overwash areas (Leatherman
and Zaremba, 1986).
• Effects editable in SLAMM 6
Soil Saturation
• SLAMM estimates (fresh) water table from the
elevation nearby swamps or fresh-water wetlands
• As sea levels rise, this applies pressure to fresh water
table (within 4km of open salt water)
• Model results could include “streaking” as a result of
soil saturation predictions.
Water Table Rise
Near Shore, Based
on Carter et al.,
1973
SLAMM Salinity Model
• Required as marsh-type is more highly correlated to
salinity than elevation when fresh-water flow is
significant (Higinbotham et. al, 2004)
• Simple steady-state salinity model; not
hydrodynamic
• Adds complexity to model development
• Requires additional model specifications
– Estuary Geometry
– Freshwater flow and projections
• Linkages to external salinity models are already built
in to SLAMM 6.3
SLAMM 5 Salinity Component
Fresh Water
Salt Water
SaltMarsh
Brackish
Tidal Fresh
Estuary Area, Moving Inland
salinity decreasing, but not linearly
Tidal Swamp
FWH, fn of River
Discharge
Salinity calculated as a function of estuary width,
tide range, fresh-water flows, and bathymetry
Tide Range + SLR
Elevation
(For cells defined as “in-estuary”)
Salinity Calibration
• Successfully calibrated to 5 GA estuaries
• Good match of salinity to river mile vs LMER
data
• Publication pending
• Spatially calibrated to salinity data in Port
Susan Bay, WA
Next Steps in Model Development
• Make “flow-chart” of habitat switching and landcategories modeled completely flexible (international
applications)
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Linkage to hydrodynamic, sediment transport models
More salinity testing
Wider testing of SAV module
Model evaluation and refinement – erosion,
overwash, soil saturation
Seeking collaborative partners
Galveston Bay
• Hindcast and Initial Forecast Results
• Meeting with Stakeholders in TX
• Incorporation of & Response to Stakeholder
Comments
• Final results available at GOMA and
SLAMMView website
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http://www.slammview.org/slammview2/reports/Galveston_Report_6_30_2011_w_GBEP_reduc.pdf
Complicating Factor: Subsidence
Gabrysch and Coplin 1990
• Spatial maps used in
hindcasting
• Used to convert
eustatic to local SLR
• Held constant over
simulation
Accretion
Marsh Type
Study
Yeager et al, 2007
Williams, 2003
White et al., 2002
Ravens et al., 2009
Ravens et al., 2009
Williams, 2003
Williams, 2003
Freshwater
Saltwater
Accretion Rate
Measured (mm/y)
Location
1.3
2.5
4.9
2.0
1.3
10.2
5.3
North of Trinity
River Dam
West Bay
Trinity Bay, South
of Dam
Average Accretion
(applied to model,
mm/yr)
2.9
4.7 or
7.75
12
Accretion mm/yr)
10
8
6
Accretion - Low
Accretion - High
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2
0
0.00
0.20
0.40
0.60
0.80
1.00
Elevation above MTL
1.20
1.40
1.60
1979
• Different Footprint
• Fresh Marsh Expansion
2009
2009
Pred.
• Fresh Marsh Expansion
• Anthropogenic Actions
2009
Obs.
2100, Scenario A1B Mean
Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irregularly Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water
Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irreg. Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water
2100, Scenario A1B Maximum
Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irregularly Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water
Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irreg. Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water
2100, 1 Meter
Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irregularly Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water
Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irreg. Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water
2100, 1.5 Meters
Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irregularly Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water
Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irreg. Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water
2100, 2 Meters
Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irregularly Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water
Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irreg. Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water
SLAMM Strengths
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Open source
Relatively simple model
Ease and cost of application
Relatively quick to run (enables uncertainty
analysis)
• Contains all major processes pertinent to
wetland fate
• Provides information needed by policymakers
Strengths (cont.)
• Detail oriented flow chart
• Relatively minimal data requirements
• Designed in poor data environment -- has
assumptions to work through those
conditions.
• Internal uncertainty & sensitivity analyses
SLAMM Model Limitations
• Not a Hydrodynamic Model
– Conceptual model captures these sites initial
conditions well; future changes in hydrodynamics may
not be properly represented.
• Spatially Simple Erosion Model
– Could be modified or replaced with more sophisticated
model
– Beach erosion is ephemeral and difficult to quantify
anyway
Model Limitations
• No Mass Balance of Solids
– i.e. accretion rates not affected by bank sloughing
– Storms do not mobilize sediment
• Overwash component is subject to
considerable uncertainty
– Timing and size of storms is unknown
– Based on observations of barrier islands after large
storms
Mcleod, Poulter, et al., 2010
• Ocean & Coastal Management “SLR impact models and
environmental conservation, a review of models and their
applications”
• SLAMM 5 Advantages
– Can be applied at wide range of scales
– Provides detailed information about coastal habitats and
shift in response to SLR
– Can be used to identify potential future land-use conflicts
– Integrates numerous driving variables
– “Provides useful, high-resolution, insights regarding how
SLR may impact coastal habitats.”
Mcleod, Poulter, et al., 2010
• SLAMM 5 Disadvantages
– Lacks feedback mechanisms between hydrodynamic and
ecological systems
– Changes in wave regime from erosion not modeled
• Note wave setup is recalculated on basis of land loss
– Lacks feedback between salinity and accretion rates in
fresh marshes
• SLAMM 6 does include feedbacks between frequency of
inundation and accretion rates and links to mechanistic modeling.
– Does not include a socioeconomic component to estimate
costs; not useful for adaptation policies
Considerations and Costs of
Implementation
• GIS expertise required to produce raster inputs
• Tidally-coordinated LiDAR elevation data highly
beneficial
• NWI data often out-of-date
– Alternative data sources have often been used
– Crosswalk process time consuming
• Salinity model requires additional support
• Model QA tests can be time-consuming
To Stay In Touch with Future Model
Developments
• SLAMM webpage
http://warrenpinnacle.com/prof/SLAMM
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Includes brief model overview, bibliography
Updated with latest projects and results
Technical documentation with full model specs
Model executable available at this site
Model code is “open source” available for review or
modification
• Email me -- [email protected]