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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

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Presentation Transcript
slide2

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

slamm sea level affecting marshes model
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

2009

2100, 1 m SLR

slamm
SLAMM

“Uncertainty Cloud” for Selected Region

    • Modest data requirements allowapplication 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

The Range Between 5th and 95th percentiles is graphed along with mean and deterministic results

model development overview
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

latest version slamm 6 2
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
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
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 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.
model process overview
Model Process Overview

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

Titus and Wang 2008

model process overview1
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
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
SLAMM Inundation Model

Equilibrium Approach

Elevation

Land Elevation

Salt Elev.

(30 day inundation)

Salt Boundary

MHHW

MTL

MLW

Regularly- Flooded Marsh (Often Salt Marsh)

Inland Fresh and Dry Land

Tidal Flat

Transitional or Irregularly- Flooded Marsh

Distance Inland

slamm inundation model migration of wetlands boundaries due to sea level rise
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

MLW

Tidal Flat

Regularly- Flooded Marsh (Often Salt Marsh)

Inland Fresh and Dry Land

Irregularly- Flooded Marsh

Water

Distance Inland

slide14

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

slide15

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
Detailed SLAMM Land Categories
  • 26Categories, 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
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

Grinsted, 2009Clim. Dyn.

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

slide18

Powerful SLAMM Interface – Main Interface

(Illustrates 3-D Graphing Capabilities)

connectivity component
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
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.
slide27

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
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
dike considerations
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
“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
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
slamm erosion model
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
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
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
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
SLAMM 5 Salinity Component

(For cells defined as “in-estuary”)

Salinity calculated as a function of estuary width,

tide range, fresh-water flows, and bathymetry

Elevation

FWH, fn of River Discharge

Fresh Water

Tide Range + SLR

Salt Water

Brackish

Tidal Fresh

Tidal Swamp

SaltMarsh

Estuary Area, Moving Inland

salinity decreasing, but not linearly

salinity calibration
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
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
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
Complicating Factor: Subsidence
  • Spatial maps used in hindcasting
  • Used to convert eustatic to local SLR
  • Held constant over simulation

Gabrysch and Coplin 1990

slide49

Different Footprint

  • Fresh Marsh Expansion

2009

1979

slide50

Fresh Marsh Expansion

  • Anthropogenic Actions

2009

Pred.

2009

Obs.

slamm strengths
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
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
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
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
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 20101
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
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
To Stay In Touch with Future Model Developments
  • SLAMM webpagehttp://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]
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