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Soil Conservation on the Lower Wisconsin
                  Riverway:
 GIS Modeling to Determine Prospective CRP
Candidates Based on Soil Erosion Susceptibility
       in the Fennimore Fork Watershed
The Fennimore Fork
Watershed exhibits
characteristics that make
it prone to soil erosion
 Highly variable
   topography
 Farming throughout
   region
 Soil loss as threat to
   soil quality (Govers et
   al., 2004)
                             http://www.nrcs.usda.gov/news/images/erosion.jpg
Study Area:
                              Fennimore Fork
                              Watershed



• Fennimore Fork Watershed is a
subwatershed within one of
eighteen watersheds draining into
the Lower Wisconsin Riverway
•   Agriculture is the economic
    mainstay of the region
•   The Conservation Reserve
    Program (CRP) is a
    nationwide, voluntary
    program that works in part
    to enhance the environment
    by reducing sedimentation
    in streams and lakes
•   Use GIS modeling
    (WetSpa) to find land
    most susceptible to
    erosion in the Fennimore
    Fork Watershed of Grant
    County
•   Provide results to CRP to
    determine which land
    should be targeted for
    enrollment in the
    program.

                                http://www.vub.ac.be/WetSpa/
 Process-based
 GIS based hydrologic model
 Watershed/catchment scale
 Simulate hydrograph (flood), water
  balance
 Hydrological attributes over watershed
 WetSpa      can provide:
    • Spatial distribution of runoff velocity
    • Spatial distribution of runoff volume
    Define areas with high runoff velocity and
    volume as highly susceptible to soil
    erosion
    • Velocity is calculated over entire time period
    • Volume is calculated for specific storm events
LAND                30 Meter
Layers     SOIL            DEM
                    USE                Resolution




         PARAMETERS DERIVED FROM
                                   +      CLIMATE INPUTS
             EACH LAYER ABOVE




                              WETSPA
PERMATERS DERIVED FROM
                          EACH LAYER



Soil                 Land Use                Topography
                                             Flow Direction
Conductivity         Root Depth              Stream Link
                                             Fill Sink
Residual Moisture    Manning’s Coefficient   Mask
                                             Flow Length
Porosity             Vegetated Fraction      Stream Order
                                             Stream Network
Pore distribution    Interception Capacity   Slope of land/river
                                             Subwatershed
Index                Leaf Area Index         Stream width
                                             Hydraulic Radius
Wilting point

Field Capacity
Simulates basic hydrologic process in each cell




                                                  Yongbo Liu, Ping Wang
LAND
 SOIL    USE   DEM



 PARAMETERS DERIVED
   FROM EACH LAYER      CLIMATE INPUTS
       ABOVE


               WETSPA




 Precipitation,
               Temperature, Potential
  Evapotranspiration
 Vary Temporally, Not Spatially
Parameterize with DEM, WISCLand, and soils layer




                                                   Yongbo Liu, Ping Wang
 Quartile   classification:
         • Four classes with even
           distribution
        Bottom  left corner
         chunk is the town of
         Fennimore
         (impervious area)
High
        Dark red considered

Low
         highest
High Velocity                   Farmland




         Farmland with high velocity
Farmland with                High Runoff
High Velocity               Volume in 11
                            Storm Events



                Intersect




             Farmland
           susceptible to
              erosion
 The  red areas show
  up as high velocity
  and volume in all
  eleven storm events
 Therefore, these
  areas, while taking
  into account land
  ownership parcels,
  should be considered
  for enrollment in CRP
POTENTIAL ERROR                         FUTURE RESEARCH

   Bad news: Climate data not within      Run comparison of our
    study site                              results to soil classes most
    Good news: Within 5 miles               susceptible to erosion to find
   No model is perfect                     even more conclusive
   6-8 year difference in data from
    today
                                            results
   30 meter raster pixel size – not       Assign addresses to land
    very precise                            with most susceptible areas
    TROUBLE ENCOUNTERED
   Our first time using this
    model so roadblocks required
    help from more experienced
    users – even a fix from the
    creator of the model
A special thank you to Ping Wang for all of her help,

                   A-Xing Zhu for the guidance and

                 WetSpa Model Author: Yongbo Liu
 Classification based results
 Calibration gives more accurate numeric
  results, not the objective of this study

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Soil Conservation-GIS-Fennimore Fork

  • 1. Soil Conservation on the Lower Wisconsin Riverway: GIS Modeling to Determine Prospective CRP Candidates Based on Soil Erosion Susceptibility in the Fennimore Fork Watershed
  • 2. The Fennimore Fork Watershed exhibits characteristics that make it prone to soil erosion  Highly variable topography  Farming throughout region  Soil loss as threat to soil quality (Govers et al., 2004) http://www.nrcs.usda.gov/news/images/erosion.jpg
  • 3. Study Area: Fennimore Fork Watershed • Fennimore Fork Watershed is a subwatershed within one of eighteen watersheds draining into the Lower Wisconsin Riverway
  • 4. Agriculture is the economic mainstay of the region • The Conservation Reserve Program (CRP) is a nationwide, voluntary program that works in part to enhance the environment by reducing sedimentation in streams and lakes
  • 5. Use GIS modeling (WetSpa) to find land most susceptible to erosion in the Fennimore Fork Watershed of Grant County • Provide results to CRP to determine which land should be targeted for enrollment in the program. http://www.vub.ac.be/WetSpa/
  • 6.  Process-based  GIS based hydrologic model  Watershed/catchment scale  Simulate hydrograph (flood), water balance  Hydrological attributes over watershed
  • 7.  WetSpa can provide: • Spatial distribution of runoff velocity • Spatial distribution of runoff volume  Define areas with high runoff velocity and volume as highly susceptible to soil erosion • Velocity is calculated over entire time period • Volume is calculated for specific storm events
  • 8. LAND 30 Meter Layers SOIL DEM USE Resolution PARAMETERS DERIVED FROM + CLIMATE INPUTS EACH LAYER ABOVE WETSPA
  • 9. PERMATERS DERIVED FROM EACH LAYER Soil Land Use Topography Flow Direction Conductivity Root Depth Stream Link Fill Sink Residual Moisture Manning’s Coefficient Mask Flow Length Porosity Vegetated Fraction Stream Order Stream Network Pore distribution Interception Capacity Slope of land/river Subwatershed Index Leaf Area Index Stream width Hydraulic Radius Wilting point Field Capacity
  • 10. Simulates basic hydrologic process in each cell Yongbo Liu, Ping Wang
  • 11. LAND SOIL USE DEM PARAMETERS DERIVED FROM EACH LAYER CLIMATE INPUTS ABOVE WETSPA  Precipitation, Temperature, Potential Evapotranspiration  Vary Temporally, Not Spatially
  • 12. Parameterize with DEM, WISCLand, and soils layer Yongbo Liu, Ping Wang
  • 13.  Quartile classification: • Four classes with even distribution  Bottom left corner chunk is the town of Fennimore (impervious area) High  Dark red considered Low highest
  • 14. High Velocity Farmland Farmland with high velocity
  • 15. Farmland with High Runoff High Velocity Volume in 11 Storm Events Intersect Farmland susceptible to erosion
  • 16.
  • 17.
  • 18.
  • 19.  The red areas show up as high velocity and volume in all eleven storm events  Therefore, these areas, while taking into account land ownership parcels, should be considered for enrollment in CRP
  • 20. POTENTIAL ERROR FUTURE RESEARCH  Bad news: Climate data not within  Run comparison of our study site results to soil classes most Good news: Within 5 miles susceptible to erosion to find  No model is perfect even more conclusive  6-8 year difference in data from today results  30 meter raster pixel size – not  Assign addresses to land very precise with most susceptible areas TROUBLE ENCOUNTERED  Our first time using this model so roadblocks required help from more experienced users – even a fix from the creator of the model
  • 21. A special thank you to Ping Wang for all of her help, A-Xing Zhu for the guidance and WetSpa Model Author: Yongbo Liu
  • 22.  Classification based results  Calibration gives more accurate numeric results, not the objective of this study

Editor's Notes

  1. Introduce ourselves
  2. CHELSEA
  3. CHELSEA-More specifically, our study area is along the Blue River, a tributary of Fennimore Creek which drains directly into the lower Wisconsin Riverway.-The resulting GIS model of areas prone to erosion will be used in conjunction with: land ownership maps- to determine specific parcels of interest-The Conservation Researve Program, from here on out refered to as the CRP, is a nationwide, voluntary program that works to enhance the environment by aiming to reduce sedimentation in streams and lakes, improve water quality, and protect wildlife habitat.
  4. CHELSEA
  5. CHELSEA-We used the WETSPA Model-WETSPA is a process based model-We determined the best way to model areas most prone to soil erosion, using the available data, was to use a hydrologic model as opposed to a sediment loss model-Primary reason for this is that discharge data was not available for our study area-WETSPA actually predicts runoff. This, coupled with discharge data allowed us to calculate soil erosion susceptibility-WETSPA extension allowed us to spatially reference data
  6. JOHN-Two types of models: 1) empirically based models -earlier models -based only on direct observation 2)process based models (or physically based models) -simulate actual processes -can make predictions -conceptual models are a hybrid of these two
  7. CHELSEAStep 1 Process…
  8. ChelseaParameters used in step 1…
  9. TIM
  10. Tim
  11. TIM
  12. JOHN
  13. Tim
  14. Chelsea
  15. JOHN