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Sponsored by the National Geospatial Intelligence Agency and the National Science Foundation In cooperation with Oglala Lakota College. Watershed Classification “A GIS Approach” Don Belile, Jason Tinant Oglala Lakota College. Table 1 – Classification Parameters.

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  1. Sponsored by the National Geospatial Intelligence Agency and the National Science Foundation In cooperation with Oglala Lakota College Watershed Classification “A GIS Approach”Don Belile, Jason TinantOglala Lakota College Table 1 – Classification Parameters Overview Reproduction of the native Great Plains Cottonwood (Populous deltoides) may be significantly declining within the boundaries of the Pine Ridge Reservation in southwestern South Dakota. Cottonwood is culturally significant to the Lakota people, and is ecologically important to Great Plains ecosystems. Oglala Lakota College has initiated a project to identify the distribution of cottonwood and other woody riparian species across the Pine Ridge reservation. The Great Riparian Protection Project (GRIPP) incorporates GIS remote sensing, dendrology and geomorphology. We will apply ArcGIS and ERDAS Imagine software to analyze and model GIS remotely sensed and field data to better understand the life history of cottonwoods and other woody riparian species. Watershed classification is a part of our larger study. We hypothesize we can identify potential cottonwood recruitment sites by integrating hydrologic models and available soils data using ArcGIS. We have selected 15 - 20 physical, chemical and habitatl parameters . These parameters will be used to group small catchments on the Pine Ridge reservation into broader physiographic regions. Methodology We generated 3,884 watersheds in our study area. First, we generated flow direction and flow accumulation rasters from a depressionless 10-m digital elevation model (DEM) with Spatial Analyst and calibrated a final flow accumulation model with 1:24,000 vector stream data. Next, we created a Strahler stream order vector shapefile from the calibrated rasters. The Strahler model allowed us to identify pourpoint locations needed to model an initial watershed layer. We iteratively added and manipulated the locations of the pourpoints to generate a final watershed model that resembled the “tear drop” shape of actual watersheds. ec (electrical conductivity) cec7 (cation exchange rate) SAR or Na ratio to Ca/Mg Gypsum CaCO3-Calcium Carbonate Biological: Range Productivity Grain habitat Grass habitat Herb habitat Shrub habitat Hardwood habitat Conifer habitat Wetlands habitat Water habitat Climate Data: Max Temperature Min Temperature Total Precipitation Precipitation Intensity Humidity Degree Growing Days Curvature Terrain Data: Watershed Area Mean Slope Std Dev Slope Elevation Relief Flow Length Physical: Rock 3-10 in. #4 Sieve/Gravel #10/Very Course Sand #40/Course Sand #200/Sand Total Sand Total Silt Total Clay kw or erodibility factor Albedo ksat (permeability) % Organic Matter Chemical: Soil pH • Sources of Uncertainty in our Model • Flow direction was derived from 10 meter DEM data. • Pour points were manipulated to form “tear drop” shaped catchments within close proximity of other catchments. • Discussion • GIS appears to be an effective tool for watershed modeling in the complex and varied terrain of the Pine Ridge Reservation. The Strahler flow model in Figure 1 (colored lines) is very close to the USGS 1:24,000 digital line graph of streams (black lines) for both perennial and intermittent streams. • The classification rasters show sharp distinctions between physiographic regions. For example, the percent sand raster (Figure 2) reveals the three major physiographic regions of the reservation; White River Badlands, Keya Paha Tablelands, and Nebraska Sandhills. • Future Work • 1, Summarize the classification parameters for individual catchments with zonal statistics in ArcMap. • 2. Group the catchments on the reservation into 10 – 15 physiographic regions using an isomeans clustering algorithm available in ArcGIS and ERDAS Imagine. Clusters form around nodes (peaks) in the data and data that is most similar to a certain node is grouped into a class. Figure 1: shows the projection area of Medicine Root Creek’s confluence with the White River. A watershed layer of the entire reservation is the projection base. Parameters for Classification We created continuous raster files of selected soils, and DEM data that may affect vegetation distribution. First, we downloaded and joined the SSURGO soil databases to soils polygon shapefiles. Next, we generated and mosaiced rasters of the parameters shown in Table 1. We displayed each of the rasters to determine whether or not the raster would be significant in our final physiographic classification (figure 2). For example, rocks greater than 10 inches do not commonly occur in Pine Ridge reservation soils and therefore this raster was removed from our final parameter list. Acknowledgements Jim Sanovia for helping with creation of the initial watershed; deriving flow direction and a depressionless DEM, and 3rd order pourpoint manipulation. Resources Bulley, H. N., J. W. Merchant, D. B. Marx, J. C. Holz, and A. A. Holz. 2007. A GIS approach to watershed classification for Nebraska reservoirs. Journal of the American Water Resources Association 43(3):607-612. Environmental Systems Research Institute, Inc.2004. Arc GIS Desktop. Version 9.2. ESRI Inc.,Redlands, California Tinant, C. T. (2007) [Great Riparian Protection Project]. Unpublished raw data. USDA NRCS (U.S. Department of Agriculture, Natural Resources Conservation Service), 2005. Soil Survey Geographic (SSURGO) database. Bennet County, South Dakota. http://soildatamart.nrcs.usda.gov. Accessed October, 2007 USDA NRCS (U.S. Department of Agriculture, Natural Resources Conservation Service), 2006. Soil Survey Geographic (SSURGO) database. Jackson County, South Dakota. http://soildatamart.nrcs.usda.gov. Accessed October, 2007 USDA NRCS (U.S. Department of Agriculture, Natural Resources Conservation Service), 2006. Soil Survey Geographic (SSURGO) database. Shannon County, South Dakota. http://soildatamart.nrcs.usda.gov. Accessed October, 2007 USGS (United States Geological Survey), 2006. National Elevation Dataset. Digital elevation model, Bennett County, South Dakota. http://ned.usgs.gov/. Accessed October, 2007 USGS (United States Geological Survey), 2006. National Elevation Dataset. Digital elevation model, Jackson County, South Dakota. http://ned.usgs.gov/. Accessed October, 2007 USGS (United States Geological Survey), 2006. National Elevation Dataset. Digital elevation model, Shannon County, South Dakota. http://ned.usgs.gov/. Accessed October, 2007 USGS (United States Geological Survey), South Dakota Geological Survey. Digital line graph, Bennett County, South Dakota. http://www.sdgs.usd.edu/data-access/index.html . Accessed October, 2007 USGS (United States Geological Survey), South Dakota Geological Survey. Digital line graph, Jackson County, South Dakota. http://www.sdgs.usd.edu/data-access/index.html . Accessed October, 2007 USGS (United States Geological Survey), South Dakota Geological Survey. Digital line graph, Shannon County, South Dakota. http://www.sdgs.usd.edu/data-access/index.html . Accessed October, 2007 Figure 2: a parameter raster for sand distribution.

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