Developing a GIS based wetland restoration prioritization tool

for Minnesota

Jeremy Erickson, Lucinda B. Johnson, Terry Brown, Valerie Brady,

Natural Resources Research Institute, University of MN Duluth

Outline

Project objectives Background

• Available restorable wetland inventories (RWIs)• Supplementing RWIs

Project overview

Objectives

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 self-sustaining into the future.

What this tool is NOT…

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

How this tool could be used… 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

MN Restorable Wetland Inventory

Restorable Wetland Delineation (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.

SSURGO drainage data

National Wetlands Inventory (NWI) coverage

CTI >10.5Poorly or very poorly drained soils

NWI RWI

ESRI: http://arcscripts.esri.com/details.asp?dbid=11863)

Creating RWIs using ArcMap Model Builder

DEM to CTI

CTI to RWI

Statewide RWI using CTI/SSURGO method

Covers entire state

Can be easily adjusted stricter RWI estimates

• CTI threshold• Higher resolution

DEM Can supplement

areas without RWIs

Developing the decision tool:overview and vision

Web based tool

Utilizes readily available GIS data layers

Definitions 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.

Wetland restoration decision layers (data layer summary)

ViabilityFactors that predict the success (or failure) of restoration

StressFactors that predict the success (or failure) of restoration

BenefitsEnvironmental services that will be enhanced by restoration

ConditionEnvironmental data that acts as a potential modifier to the final output

Viability

Stressor

Benefits

Final output

Condition

ViabilityFactors that predict the success (or failure) of restoration

Topography (CTI)

Soil type

Network position

Ownership

Stress Land use

• Open development• Low density development• Medium density development• High density development• Pasture• Row crops

Twin Cities

Distance to Roads

Population Distance to Feedlots (MPCA)

Factors that predict the success (or failure) of restoration

Benefits

Environmental Benefits Index

Environmental services that will be enhanced by restoration

Soil erosion risk

Water quality risk

Wildlife habitat quality• Sites of biodiversity• Species of greatest conservation need• Bird potential habitat• Weighted habitat protection level

ConditionEnvironmental data that acts as a potential modifier to the final output

MPCA IBI data

MPCA Impaired waters designation (TMDL)

Biological, habitat, and water quality surveys

Surrounding landscape (buffers)

Google or Bing maps

Restorable wetlands inventories

Ownership

Network location

Topography

Viability score

Summarizing at the 30 m pixel level Watershed

boundary

Political boundary

Soil type

Weighting 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

Variable class

Unknown

All hydric

Partially hydric

1

2

3

4

43

21

Each pixel is assigned a score based on class weight

Data Layer

Soil type

Not hydric

Categorical layer weighting

High stress Gradual stress reduction

No stress

High stress Gradual stress reduction

No stress

Maximum effect threshold

No effect threshold

Continuous data: example 1

10050

Population tracts

150

Pixel population normalized

x’ = (x-xmin)/(xmax-xmin)

x’ = (100-50)/(150-50)

x’ = 0.5

Continuous data: example 2

Unknown

All hydric

Partially hydric

1

2

3

4

43

21

Soil typeNot hydric

Network

Ownership

Network

Ownership

Topography

Habitat

Water quality (N or P)

Viability

Soil type

Topography

Soil type

Land cover

Roads

Land cover

Roads

Population

Habitat

Water quality(N or P)

Stress

Feedlots

2

Population

Feedlots

Habitat suitability

Water quality

Soil erosion

Terrestrial value Habitat

Water quality(N or P)

Benefit

Final output

Condition

Class Data layers Focus areas Decision layers

2

1

1 1

1

Spatial tool schematic

2

1

1

1

Scenario A: low stress/high viability

Carver County

Low stress areas

High viability Restorable wetland locations

Scenario B: high stress

Locate highly stressed areas

Less concern about viability

Locate restorable wetlands

Carver CountyBluff Creek

Questions…..

Contact: Jeremy EricksonNatural Resources Research InstituteUniversity of Minnesota Duluth218-720-4320eric0792@d.umn.edu