Demonstration of Uncertainty Analysis
using the Sea Level Affecting Marshes
Model (SLAMM)

Jonathan Clough

Marco Propato, Ph.D

Ecosystem-Based Management (EBM) Tools Network Webinar
May 9, 20121

Sea Level Affecting Marshes Model (SLAMM)• 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
• Modest data requirements allow application to many sites at areasonable cost
• Applied to more than 120 National Wildlife Refuges, SouthernLouisiana, Coastal Georgia, South Carolina Puget Sound, Oregon
Coast, etc.
Open Ocean

Estuarine
Open Water

Undeveloped
Dry LandInland Fresh
MarshDevelopedDry Land
Irregularly
FloodedMarsh

Estuarine
OpenSwamp
Regularly
FloodedMarshTidal Swamp

Tidal Fresh
Marsh

Inland Shore

Estuarine
BeachRiverine
Tidal

Ocean BeachTransitional
Salt Marsh2009

2100, 1 m SLR

Cypress
Swamp

Tidal Flat

Open Ocean
Estuarine Open Water
Undeveloped Dry Land
Inland Fresh Marsh
Developed Dry Land
Irregularly Flooded MarshInland Open Water
Swamp
Regularly Flooded MarshTidal Swamp
Tidal Fresh MarshInland Shore
Estuarine BeachRiverine Tidal
Ocean BeachTransitional Salt Marsh
Cypress Swamp
Tidal Flat2

Model Process Overview• Simulates the dominant processes involved in wetland
conversions under different scenarios of sea level riseinundation, erosion, accretion, soil saturation and barrier
island overwash

Titus and Wang 2008

SLAMM Strengths• Open source• Relatively simple model• Ease and cost of application• Relatively quick to run (enables uncertaintyanalysis)• Contains all major processes pertinent towetland fate
• Provides information needed by policymakers

Strengths (cont.)• Detail oriented flow chart• Relatively minimal data requirements• Designed in poor data environment -- hasassumptions to work through thoseconditions.
• Internal uncertainty analysis

SLAMM Model Limitations• Not a Hydrodynamic Model– Conceptual model captures these sites initialconditions well; future changes in hydrodynamics may
not be properly represented.

• Spatially Simple Erosion Model– Could be modified or replaced with more sophisticatedmodel– 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• Accretion Rates Based on EmpiricalRelationships– Not a mechanistic model• Overwash component is subject toconsiderable uncertainty– Timing and size of storms is unknown– Based on observations of barrier islands after largestorms

Two Primary Criticisms Addressed• Accretion Model Doesn’t account for feedbacks (not true in SLAMM 6)
 Manner in which feedbacks are accounted for isuncertain• 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 verygood?
 Decision making difficult since likelihood and outcome
variability are unknown8Model prediction uncertainties• SLAMM predictions are always affected byuncertainties
Inputs affected by uncertainty and data errors:
••
•
•
•

Sea Level Rise
Uplift / Subsidence
Tide ranges
Height of salt-water
Overwash Parameters• Elevations– LiDAR and NAVD88 Corr.

• Accretion Rates– Extent of Feedbacks• Erosion Rates• Therefore, there is not one prediction that is right, butrather a distribution of possible future wetland coverages
9Parametric ModelInput Distributions

Model Output Distributions

403001020Frequency5060"Eustatic SLR by 2100 (m) "

0.5

1.0

1.5

2.0"Eustatic SLR by 2100 (m) "Examining SLAMM results as distributions can improve thedecision making process

Results account for parametric uncertainties
Range of possible outcomes and their likelihood
Robustness of deterministic results may be evaluated10SLAMM Uncertainty Analysis Module• Initial development of SLAMM
Uncertainty Analysis funded byDucks Unlimited• SLAMM 6.1 beta complete,technical documentation underdevelopment• Monte Carlo Uncertainty Analysis
fully built into SLAMM interface• Latin-Hypercube stratified
sampling designed for efficiency(reduces iterations toconvergence)11

Parameter uncertainty input distributions
All parameters may
be represented as
distributions

Multipliers used
to retain subsite
individuality

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SLAMM Elevation Uncertainty• Elevation, MTL correction• Creates a set of unique, equally likely input maps for eachsimulated scenario on the basis of elevation data
uncertainty statistics– Root mean squared error (RMSE) from LiDAR metadata• Spatial Autocorrelation– high error zones tend to cluster together

13Accretion feedback• Accretion feedback can be modeled in SLAMM as a general
cubic polynomial function of elevation with respect to sea level𝑟 = 𝑎𝑎 3 + 𝑏𝑥 2 + 𝑐𝑐 + 𝑑• All parameters can be entered as distributions reflecting theconfidence (or lack of it) of the rate value and accretion
response14

Accretion feedback• Accretion feedback can be modeled in SLAMM as a general
cubic polynomial function of elevation with respect to sea level𝑟 = 𝑎𝑎 3 + 𝑏𝑥 2 + 𝑐𝑐 + 𝑑• All parameters can be entered as distributions reflecting theconfidence (or lack of it) of the rate value and accretion
response15

SLAMM Uncertainty Analysis Procedure
Monte-Carlo simulations with Latin Hypercube Sampling

1. User defines uncertainty statistics regarding model inputs
Collected from metadata, NOAA, journal articles, professional judgment

2. SLAMM randomly selects input parameters from theassigned uncertainty distributions
3. A model simulation is run with selected input parameters
4. Wetland coverage prediction results are collected for thedesired output years (e.g. Initial date, 2025, 2050, 2075, 2100)
5. Steps 2 to 4 are automatically repeated for many scenarios(as necessary for statistical quantity convergence)
6. All model realizations are assembled into distributions ofwetland coverage• Raw data and distribution statistics directly from SLAMM• Spatial uncertainty evaluation using Uncertainty Viewer
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SLAMM Uncertainty Viewer• Designed by Ducks
Unlimited• Processes
completed SLAMM
uncertainty runs
• Graphicalrepresentation ofresults• Integration with GISLayers

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SLAMM Uncertainty Viewer• SLAMM Uncertainty
results are integrated withGIS layers• User can process theentire study area as well
as a user specified areafor:• Land acquisition• local interests or
projects• Polygons defining the areaof interest can beimported from GIS ordrawn by the user

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SLAMM Uncertainty Viewer
Input parameter uncertainties can lead over time to different wetland coverage“Uncertainty Cloud” for Selected Region

The Range Between 5th and95th percentiles is graphedalong with mean anddeterministic results

SLAMM Uncertainty Viewer
A pie-chart is also available topresent uncertainty in thedominant land cover type (forthe selected region) over time

2025
Salt MarshOpen Water

2050

2075

In this example, in 2025 the major land coverof the area is predicted to be saltmarsh nomatter the input parameters.

2100

In 2050, 60% of the simulated scenarios still
predict saltmarsh as the major land covertype but 40% simulations predict open water
as the major type.
By 2100 95% of the simulations
predict open water the major land
cover no matter the input
parameters

20Interface Demonstration with Marco Propato

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Summary and Perspectives• Uncertainty analysis combined with the viewer simplifies

uncertainty output for end-users, analysts and decision makers‒ The effects of input uncertainties, some model uncertainties‒ Robustness of deterministic results‒ Potential outcomes in particular areas that may be interest forland acquisition, etc.
‒ Quantitative framework to assist educated decision making• Development of the tool is an ongoing project– Open to feedback from end-users– Will soon include other existing analysis tools and calculations:
For example, cell-by-cell maps showing likelihood of land typechange– Addition of correlations
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Ongoing Work on SLAMM Model• Ducks Unlimited – Pacific Northwest– Application of uncertainty analysis in WA & OR, evaluating land parcels
for restoration• NWF – Louisiana application with Hindcasting; In Review– Used model to evaluate potential effects of river diversions• USGS:OR – Linkage created to EPA salinity models– Habitat switching based on salinity, model testing and documentation• Indiana U. – Refinement of all-GA run– Accretion feedbacks based on linkage to Morris’ MEM2 model• TNC TX – Examine effects on infrastructure given development
and restoration scenarios– Dike model refined to assess likelihood of overtopping• Looking Forward– Make “flow-chart” of habitat switching and land-categories modeled

completely flexible (international applications)– Linkage to hydrodynamic, sediment transport models?
– Model evaluation and refinement – erosion, overwash, soil saturation– Seeking collaborative partners
23For MoreInformation• SLAMM Website (Google “SLAMM”)– Includes brief model overview, bibliography– Updated with latest projects and results– SLAMM Forum for Q/A– Technical documentation with full model specs– Model executable / source code available through the forum– SLAMM 6.1 available upon request; Tech Doc. not yet complete.

• Email -- jclough@warrenpinnacle.com
mpropato@warrenpinnacle.com

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