The SLAMM Model (Sea Level Affecting Marshes Model) Jonathan Clough 3-18-2014

The SLAMM Model(Sea Level Affecting Marshes Model)

Jonathan Clough

3-18-2014

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Warren Pinnacle Consulting, Inc. Founded in 2001Located in Central VT, Environmental Modeling ExpertsJonathan S. Clough, Founder, Environmental Consultant since 1994Dr. Amy Polaczyk, SLAMM modeler, with us since 2010Dr. Marco Propato, Accretion modeling expert, joined in 2011

warrenpinnacle.com/quals.pdfwarrenpinnacle.com

SLAMMSea 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 Open Ocean

Estuarine Open Water

Estuarine Open Water Undeveloped Dry Land

Undeveloped Dry Land Inland Fresh Marsh

Inland Fresh Marsh Developed Dry Land

Developed Dry Land Irregularly Flooded Marsh

Irregularly Flooded Marsh Estuarine Open

Inland Open Water

Swamp Swamp

Regularly Flooded Marsh


Tidal Swamp Tidal Swamp

Tidal Fresh Marsh

Tidal Fresh Marsh

Inland Shore Inland Shore

Estuarine Beach

Estuarine Beach Riverine Tidal

Riverine Tidal

Ocean Beach Ocean Beach

Transitional Salt Marsh

Transitional Salt Marsh Cypress Swamp

Cypress Swamp

Tidal Flat Tidal Flat

2009 2100, 1 m SLR

• 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

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. – 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

http://www.jcronline.org/doi/abs/10.2112/SI63-0017.1

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

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

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• 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 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.

Ongoing Work on SLAMM Model

Model Process Overview

Addresses Six Primary Processes (Inundation, Erosion, Saturation, Overwash, Accretion, Salinity)

Titus and Wang 2008

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.

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

2D Representation 3D Representation

Dry Land

Open Water

Various Wetlands

SLAMM Inundation Model

Distance Inland

Elev

ation

Tidal Flat Regularly- Flooded Marsh (Often Salt Marsh)

Transitional or Irregularly- Flooded Marsh

Inland Fresh and Dry LandMLW

MTL

MHHW

Salt Elev.(30 day inundation)

Salt Boundary

Land Elevation

Equilibrium Approach

SLAMM Inundation Model(Migration of Wetlands Boundaries due to Sea Level Rise)

Distance Inland

Elev

ation

Tidal Flat

Salt BoundaryNew Land Elevation

Water

Old Land Elevation

MLW

MTL

MHHW

Salt Elev.(30 day inundation)

Regularly- Flooded Marsh (Often Salt Marsh)

Irregularly- Flooded Marsh

Inland Fresh and Dry Land

• SLAMM 6 Allows for Elevation Feedbacks to Accretion as shown by Morris et al. (2002)

• Linkage to Morris MEM model

Feedbacks to Accretion

0

1

2

3

4

5

6

7

8

0.00 0.50 1.00 1.50 2.00 2.50

Elevation above MTL

Accretion (mm/yr)

“Unstable Zone”

High-elevation marsh subject to less

flooding

http://129.252.139.114/model/marsh/mem2.asp

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 correctedfor local effects using long-term tide gauge trendsor spatial subsidence

Vermeer and Rahmstorf, 2009, Proceedings of the National Academy of Sciences

Grinsted, 2009Clim. Dyn.

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.

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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

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Model Output DistributionsParametric Model Input Distributions

"Eustatic SLR by 2100 (m) "

"Eustatic SLR by 2100 (m) "

Fre

qu

en

cy

0.5 1.0 1.5 2.0

01

02

03

04

05

06

0

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

“Uncertainty Cloud” for Selected Region

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 DaysRegularly Flooded Marsh

Flooded Every 60 DaysI rregularly Flooded Marsh

Flooded Every 90 DaysI n land Open Water

Not FloodedSwamp

Open WaterEstuarine Open Water

Flooded Every 30 DaysRegularly Flooded Marsh



Not FloodedSwamp

Open WaterEstuarine Open Water

Inundation with Roads

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

0

50

100

150

200

19832025

20502075

2100

KM o

f Roa

ds fr

om U

SFW

S Ro

ad In

vent

ory

Prog

ram

No Regular Inundation 60-90 d inundation 30-60 d inundation 0-30 d inundation

Initial ConditionFlooded Every 30 Days




Not Flooded

2025 under 1M by 2100Flooded Every 30 Days




Not Flooded





Not Flooded





Not Flooded

• 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

SLAMM Erosion Model

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(For cells defined as “in-estuary”)

Estuary Area, Moving Inlandsalinity decreasing, but not linearly

SaltMarsh Brackish Tidal Fresh Tidal Swamp

Salt Water

Fresh Water

Elev

ation

Salinity calculated as a function of estuary width, tide range, fresh-water flows, and bathymetry

Tide

Ran

ge +

SLR

FWH

, fn

of R

iver

D

isch

arge

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 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

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– http://www.slammview.org/slammview2/reports/Galveston_Report_6_30_2011_w_GBEP_reduc.pdf

Complicating Factor: Subsidence

• Spatial maps used in hindcasting

• Used to convert eustatic to local SLR

• Held constant over simulation

Gabrysch and Coplin 1990

AccretionMarsh 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

1.32.9Williams, 2003 2.5

White et al., 2002 4.9

Saltwater

Ravens et al., 2009 West Bay 2.04.7 or7.75

Ravens et al., 2009 1.3Williams, 2003 Trinity Bay, South

of Dam10.2

Williams, 2003 5.3

0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.600

2

4

6

8

10

12

Accretion - LowAccretion - High

Elevation above MTL

Accr

etion

mm

/yr)

• Different Footprint• Fresh Marsh Expansion

1979 2009

2009 Obs.

2009 Pred.

• Fresh Marsh Expansion• Anthropogenic Actions

2100, Scenario A1B MeanUndeveloped Dry Land

Undeveloped Dry LandInland Fresh Marsh

Inland Fresh MarshDeveloped Dry Land

Developed Dry LandIrregularly Flooded Marsh

Irreg. Flooded MarshRegularly Flooded Marsh

Regularly Flooded MarshSwamp

SwampTidal Swamp

Tidal SwampTidal Fresh Marsh

Tidal Fresh MarshInland Shore

Inland ShoreOpen Ocean

Open Ocean Estuarine Open Water

Estuarine Open WaterRiverine Tidal

Riverine TidalInland Open Water

Inland Open Water

2100, Scenario A1B Maximum

Undeveloped Dry LandUndeveloped Dry Land

Inland Fresh MarshInland Fresh Marsh

Developed Dry LandDeveloped Dry Land

Irregularly Flooded MarshIrreg. Flooded Marsh

Regularly Flooded MarshRegularly Flooded Marsh

SwampSwamp

Tidal SwampTidal Swamp

Tidal Fresh MarshTidal Fresh Marsh

Inland ShoreInland Shore

Open Ocean Open Ocean

Estuarine Open WaterEstuarine Open Water

Riverine TidalRiverine Tidal

Inland Open WaterInland Open Water

2100, 1 MeterUndeveloped Dry Land






SwampTidal Swamp







Inland Open Water

2100, 1.5 MetersUndeveloped Dry Land






SwampTidal Swamp







Inland Open Water

2100, 2 MetersUndeveloped Dry Land






SwampTidal Swamp







Inland Open Water

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

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

– 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]


Documents

The SLAMM Model (Sea Level Affecting Marshes Model) Jonathan Clough 3-18-2014