1. Jeremy Erickson, Lucinda B. Johnson, Terry Brown, Valerie Brady, Natural Resources Research Institute, University of MN Duluth

2.  Project objectives  Background Available restorable wetland inventories (RWIs) Supplementing RWIs  Project overview

3.  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.

4.  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

5.  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.

6.  MNBWSR - GIS analysis  Ducks Unlimited - photo interpretation  Incomplete areas - CTI/SSURGO method

7. 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 (A s / (tan(beta)) CTI = ln (A s / (tan(beta)) where A s = (flow accumulation + 1 ) *(pixel area m 2 ) beta = slope expressed in radians. beta = slope expressed in radians.  SSURGO drainage data  National Wetlands Inventory (NWI) coverage CTI >10.5 Poorly or very poorly drained soils NWIRWI ESRI: http://arcscripts.esri.com/details.asp?dbid=11863)

8. DEM to CTI CTI to RWI

9.  Covers entire state  Can be easily adjusted stricter adjusted stricter RWI estimates RWI estimates CTI threshold CTI threshold Higher resolution DEM Higher resolution DEM  Can supplement areas without RWIs

10.  Web based tool  Utilizes readily available GIS data layers

11.  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.

12.  Viability Factors that predict the success (or failure) of restoration  Stress Factors that predict the success (or failure) of restoration  Benefits Environmental services that will be enhanced by restoration  Condition Environmental data that acts as a potential modifier to the final output Viability Stressor Benefits Final output Condition

13.  Topography (CTI)  Soil type  Network position   Ownership

14.  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)

15. Environmental Benefits Index  Soil erosion risk  Water quality risk  Wildlife habitat quality Sites of biodiversity Sites of biodiversity Species of greatest conservation need Species of greatest conservation need Bird potential habitat Bird potential habitat Weighted habitat protection level Weighted habitat protection level

16.  MPCA IBI data  MPCA Impaired waters designation (TMDL)  Biological, habitat, and water quality surveys  Surrounding landscape (buffers)  Google or Bing maps  Restorable wetlands inventories

17. Ownership Network location Topography Viability score Summarizing at the 30 m pixel level Watershed risk score Watershed boundary Political boundary Soil type

18.  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

19. 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

20. High stress Gradual stress reduction No stress High stress Gradual stress reduction No stress Maximum effect threshold No effect threshold

21. 100 50 Population tracts 150 Pixel population normalized x’ = (x-x min )/(x max -x min ) x’ = (100-50)/(150-50) x’ = 0.5 Continuous data: example 2

22. Unknown All hydric Partially hydric 1 2 3 4 43 21 Soil type Not hydric Network Ownership Network Ownership Topography Habitat Water quality (N or P) Viability Soil type Topography Land cover Roads 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 11 1 Spatial tool schematic 2 1 1 1

23. Scenario A: low stress/high viability Carver County  Low stress areas  High viability  Restorable wetland locations locations

24.  Locate highly stressed areas  Less concern about viability  Locate restorable wetlands Carver County Bluff Creek

25. Contact: Jeremy Erickson Natural Resources Research Institute University of Minnesota Duluth 218-720-4320eric0792@d.umn.edu