the slamm model (sea level affecting marshes model) jonathan clough 3-18-2014
TRANSCRIPT
The SLAMM Model(Sea Level Affecting Marshes Model)
Jonathan Clough
3-18-2014
2
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
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
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
8
• 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
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.
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 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
Flooded Every 60 DaysI rregularly Flooded Marsh
Flooded Every 90 DaysI n land Open Water
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
Regularly Flooded Marsh
Flooded Every 60 DaysI rregularly Flooded Marsh
Flooded Every 90 DaysI n land Open Water
Not Flooded
2025 under 1M by 2100Flooded Every 30 Days
Regularly Flooded Marsh
Flooded Every 60 DaysI rregularly Flooded Marsh
Flooded Every 90 DaysI n land Open Water
Not Flooded
2050 under 1M by 2100Flooded Every 30 Days
Regularly Flooded Marsh
Flooded Every 60 DaysI rregularly Flooded Marsh
Flooded Every 90 DaysI n land Open Water
Not Flooded
2075 under 1M by 2100Flooded Every 30 Days
Regularly Flooded Marsh
Flooded Every 60 DaysI rregularly Flooded Marsh
Flooded Every 90 DaysI n land Open Water
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
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, 1.5 MetersUndeveloped 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, 2 MetersUndeveloped 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
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]