1. The SLAMM Model (Sea Level Affecting Marshes Model)
Jonathan Clough 1

2. 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

3. 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
2009
2100, 1 m SLR

4. “Uncertainty Cloud” for Selected Region
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
“Uncertainty Cloud” for Selected Region
The Range Between 5th and 95th percentiles is graphed along with mean and deterministic results

5. Model Development Overview
Intermittently under development since 1985, Park & Titus
Three Year EPA STAR grant ( ) provided funds for significant model development.
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
Point out the STAR grant gave us a chance to take a step back and reexamine the model predictions given modern data and the most recent literature. 5

6. Latest Version SLAMM 6.2 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 6

7. 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 7

8. Ongoing Work on SLAMM Model
Gulf Coast Prairie LCC
“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 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.

9. Model Process Overview
Addresses Six Primary Processes (Inundation, Erosion, Saturation, Overwash, Accretion, Salinity)
These have been determined to be the dominant processes.
Each of these processes are modeled rather simply, though the accretion model is currently undergoing a transformation into a more complex formulation.
Underline the fact that SLAMM is a relatively simple model which provides strengths of relative ease of application and capability to apply the model at large scales.
Titus and Wang 2008

10. Model Process Overview
Inundation: Calculated based on the minimum elevation and slope of the cell.
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.

11. 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
The point here is that SLAMM utilizes a raster grid or “bitmap grid” of output that is projected into three dimensions.
Land elevations are tracked at height above MTL (mean tide level).
This grid is then adjusted vertically for rises in sea level and projected accretion rates.
When wetlands fall below their natural elevation range, they are converted to a different class.
(Or if erosion, soil saturation, or overwash of barrier islands is predicted, conversion occurs)
Various Wetlands
Open Water
2D Representation 3D Representation

12. SLAMM Inundation Model
Equilibrium Approach
Elevation
Land Elevation
Salt Elev.
(30 day inundation)
Salt Boundary
MHHW
MTL
MLW
Tidal Flat
Regularly- Flooded Marsh (Often Salt Marsh)
Transitional or Irregularly- Flooded Marsh
Inland Fresh and Dry Land
Distance Inland

13. SLAMM Inundation Model (Migration of Wetlands Boundaries due to Sea Level Rise)
Elevation
Old Land Elevation
New Land Elevation
Salt Elev.
(30 day inundation)
Salt Boundary
MHHW
MTL
Discuss implications of Dikes and Developed land on marsh migration as well as land elevation.
MLW
Tidal Flat
Regularly- Flooded Marsh (Often Salt Marsh)
Inland Fresh and Dry Land
Water
Irregularly- Flooded Marsh
Distance Inland

14. Feedbacks to Accretion
SLAMM 6 Allows for Elevation Feedbacks to Accretion as shown by Morris et al. (2002)
Linkage to Morris MEM model
“Unstable Zone”
High-elevation marsh subject to less flooding

15. Linkage to Marsh Equilibrium Model
Explicitly accounts for physical and biological processes affecting marsh accretion

16. 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

17. 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 or spatial subsidence
Grinsted, 2009 Clim. Dyn.
Vermeer and Rahmstorf, 2009, Proceedings of the National Academy of Sciences

18. Powerful SLAMM Interface – Main Interface
(Illustrates 3-D Graphing Capabilities)

19. Powerful SLAMM Interface – Execution Options

20. Powerful SLAMM Interface – Uncertainty Analyses

21. Powerful SLAMM Interface – Landcover Maps

22. Powerful SLAMM Interface – Elevation Maps

23. Powerful SLAMM Interface – Depth Profiles

24. Powerful SLAMM Interface – Elevation Histograms

25. 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
Must account for culverts and tide gates
Subject to small scale DEM error

26. 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.

27. Uncertainty Module Addresses Two Primary Criticisms
Accretion Model
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

28. Model Output Distributions
Parametric Model Input Distributions
Model Output Distributions
“Uncertainty Cloud” for Selected Region
Examining SLAMM results as distributions can improve the decision making process
Results account for parametric uncertainties
Range of possible outcomes and their likelihood
Robustness of deterministic results may be evaluated

29. 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

30. “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

31. 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

32. Original Inundation (No Roads)

33. Inundation with Roads

34. Vulnerability of Alligator River Roads to 1M of SLR by 2100

35. Initial Condition

36. 2025 under 1M by 2100

37. 2050 under 1M by 2100

38. 2075 under 1M by 2100

39. 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

40. 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

41. Water Table Rise Near Shore, Based on Carter et al., 1973
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

42. 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

43. SLAMM 5 Salinity Component
(For cells defined as “in-estuary”)
Elevation
Salinity calculated as a function of estuary width,
tide range, fresh-water flows, and bathymetry
FWH, fn of River Discharge
Fresh Water
Tide Range + SLR
Salt Water
SaltMarsh
Brackish
Tidal Fresh
Tidal Swamp
Estuary Area, Moving Inland
salinity decreasing, but not linearly

44. 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

45. Next Steps in Model Development
Make “flow-chart” of habitat switching and land-categories modeled completely flexible (international applications)
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

46. 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

47. Complicating Factor: Subsidence
Spatial maps used in hindcasting
Used to convert eustatic to local SLR
Held constant over simulation
Gabrysch and Coplin 1990

48. Accretion Marsh Type Study Location Accretion Rate Measured (mm/y)
Average Accretion (applied to model, mm/yr)
Freshwater
Yeager et al, 2007
North of Trinity River Dam
1.3
2.9
Williams, 2003
2.5
White et al., 2002
4.9
Saltwater
Ravens et al., 2009
West Bay
2.0
4.7 or
7.75
Trinity Bay, South of Dam
10.2
5.3
Slide showing areas of high and low sediment supply is located at end of slideshow

49. 1979 2009 Different Footprint Fresh Marsh Expansion
Conversion from reg flooded (salt) to fresh marsh – poses a problem for hindcast metrics – is this really happening?
Salt to fresh marsh conversion is not happening due to SLR and therefore SLAMM won’t predict it.
Different Footprint
Fresh Marsh Expansion

50. 2009
Pred.
2009
Obs.
Fresh Marsh Expansion
Anthropogenic Actions

51. 2100, Scenario A1B Mean Irreg. Flooded Marsh Streaks – soil saturation
Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irregularly Flooded Marsh
Irreg. Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water
Streaks – soil saturation

52. 2100, Scenario A1B Maximum Irreg. Flooded Marsh Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irregularly Flooded Marsh
Irreg. Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water

53. 2100, 1 Meter Irreg. Flooded Marsh Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irregularly Flooded Marsh
Irreg. Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water

54. 2100, 1.5 Meters Irreg. Flooded Marsh Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irregularly Flooded Marsh
Irreg. Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water

55. 2100, 2 Meters Irreg. Flooded Marsh Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irregularly Flooded Marsh
Irreg. Flooded Marsh
Regularly Flooded Marsh
Swamp
Tidal Swamp
Tidal Fresh Marsh
Inland Shore
Open Ocean
Estuarine Open Water
Riverine Tidal
Inland Open Water

56. SLAMM Strengths 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

57. 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

58. 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

59. 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

60. Mcleod, Poulter, et al., 2010 SLAMM 5 Advantages
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.”
“elevation, habitat type, slope, sedimentation, accretion, erosion rates, existing seawalls”

61. 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

62. 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

63. To Stay In Touch with Future Model Developments
SLAMM webpage
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
me --