Jeremy Erickson, Lucinda B. Johnson, Terry
Brown, Valerie Brady,
Natural Resources Research Institute,
University of MN Duluth
Project objectives
 Background

• Available restorable wetland inventories (RWIs)
• Supplementing RWIs

Project overview
 Prioritize
areas where wetland restoration will
result in the improvement of water quality (N
and P) and habitat.
 Identify
areas that will most likely result in
high quality wetlands that will be selfsustaining into the future.
A
site-specific model to identify individual
wetlands for restoration;
Does not replace:
• Wildlife Habitat Evaluation Procedure
• Water quality assessments
• Local knowledge
• Soil loss equations

To identify stressed areas that would benefit from wetland
restoration

To identify areas with the greatest chance for successful
restoration

To recognize areas where current wetlands should be
protected or restored

To allow managers and researchers see what types of broad
conditions wetlands are being restored in.
 MNBWSR
- GIS analysis
 Ducks Unlimited
- photo interpretation
 Incomplete areas
- CTI/SSURGO method
Required data:
 Compound topographic index (CTI): a wetness index
estimated from slope and flow accumulation (estimation of
soil moisture content). Requires a DEM.
CTI = ln (As / (tan(beta))
where As = (flow accumulation + 1 ) *(pixel area m2)
beta = slope expressed in radians.
ESRI: http://arcscripts.esri.com/details.asp?dbid=11863)


SSURGO drainage data
National Wetlands Inventory (NWI) coverage
CTI >10.5
Poorly or very
poorly
drained soils
NWI
RWI
DEM to CTI
CTI to RWI

Covers entire state

Can be easily
adjusted stricter
RWI estimates
•
•
 Can
CTI threshold
Higher resolution DEM
supplement
areas without RWIs
Web
based tool
Utilizes
layers
readily available GIS data
 Decision
Layer- one of three primary groups of
data which will form the basis of our model, e.g.,
Stress, Viability, Benefits.
 Focus Area- distinct ecosystem services that are
affected by wetland restoration, e.g. water quality
in the form of N and P inputs and habitat.
 Data Layer- thematic layers representing distinct
spatial data inputs, e.g., Land use.
 Class- distinct classification units for a given data
layer, e.g., row crops, high density development.

Viability
Factors that predict the success
(or failure) of restoration

Stress
Factors that predict the success
(or failure) of restoration

Stressor
Final output
Benefits
Environmental services that will
be enhanced by restoration

Viability
Benefits
Condition
Environmental data that acts as
a potential modifier to the final
output
Condition

Ownership

Topography (CTI)

Soil type

Network position

Land use
• Open development
• Low density development
• Medium density development
• High density development
• Pasture
• Row crops

Distance to Roads

Population
Distance to Feedlots (MPCA)

Twin Cities
Environmental Benefits Index

Soil erosion risk

Water quality risk

Wildlife habitat quality
•
•
•
•
Sites of biodiversity
Species of greatest conservation need
Bird potential habitat
Weighted habitat protection level

MPCA IBI data

MPCA Impaired waters designation (TMDL)

Biological, habitat, and water quality surveys

Surrounding landscape (buffers)

Google or Bing maps

Restorable wetlands inventories
Topography
Soil type
Political
boundary
Network
location
Viability
score
Ownership
Summarizing at the
30 m pixel level
Watershed
boundary
 Expert
panel
• Comprised of wetland, hydrology, GIS, and landscape
experts
• Survey Monkey (http://www.surveymonkey.com )
 N and P
 Habitat
• Weighting discussion
• Additional data layer discussion
 Literature review
Not hydric
1
Soil type
Unknown
Partially
hydric
2
1
2
3
3
4
4
All hydric
Variable
class
Each pixel is assigned
a score based on
class weight
Data Layer
Maximum
effect
threshold
High stress
High stress
Gradual
stress
reduction
Gradual
stress
reduction
No effect
threshold
No stress
No stress
Pixel population normalized
100
50
150
Population tracts
x’ = (x-xmin)/(xmax-xmin)
x’ = (100-50)/(150-50)
x’ = 0.5
Habitat suitability
Spatial tool
schematic
Habitat
Terrestrial value
Soil erosion
Water quality
(N or P)
Water quality
Network
1
2
Ownership
1
Topography
Soil type
Not
hydric
1
Soil type
Network
1
Ownership
2
Viability
1
1
Water quality
(N or P)
3
2
4
2
Condition
Soil type
Land cover
4
Roads
All
hydric
Population
Habitat
Feedlots
Stress
Land cover
Roads
Water quality
(N or P)
Population
Feedlots
Class
Final output
1
Topography
1
3
Partially
hydric
Habitat
1
2
Unknown
Benefit
Data layers
Focus areas
Decision layers
Scenario A: low stress/high viability

Low stress areas

High viability

Restorable wetland
locations
Carver County

Locate highly stressed areas

Less concern about viability

Locate restorable wetlands
Carver County
Bluff Creek
Contact:
Jeremy Erickson
Natural Resources Research Institute
University of Minnesota Duluth
218-720-4320
eric0792@d.umn.edu