OverviewOverviewReproduction of the native Great Plains Cottonwood Reproduction of the native Great Plains Cottonwood ((Populous deltoidesPopulous deltoides) may be significantly declining within the ) may be significantly declining within the boundaries of the Pine Ridge Reservation in southwestern boundaries of the Pine Ridge Reservation in southwestern South Dakota. Cottonwood is culturally significant to the South Dakota. Cottonwood is culturally significant to the Lakota people, and is ecologically important to Great Plains Lakota people, and is ecologically important to Great Plains ecosystems. ecosystems.

Oglala Lakota College has initiated a project to identify the Oglala Lakota College has initiated a project to identify the distribution of cottonwood and other woody riparian species distribution of cottonwood and other woody riparian species across the Pine Ridge reservation. The Great Riparian across the Pine Ridge reservation. The Great Riparian Protection Project (GRIPP) incorporates GIS remote sensing, Protection Project (GRIPP) incorporates GIS remote sensing, dendrology and geomorphology. We will apply ArcGIS and dendrology and geomorphology. We will apply ArcGIS and ERDAS Imagine software to analyze and model GIS remotely ERDAS Imagine software to analyze and model GIS remotely sensed and field data to better understand the life history of sensed and field data to better understand the life history of cottonwoods and other woody riparian species. cottonwoods and other woody riparian species.

Watershed classification is a part of our larger study. We Watershed classification is a part of our larger study. We hypothesize we can identify potential cottonwood recruitment hypothesize we can identify potential cottonwood recruitment sites by integrating hydrologic models and available soils sites by integrating hydrologic models and available soils data using ArcGIS. We have selected 15 - 20 physical, data using ArcGIS. We have selected 15 - 20 physical, chemical and habitatl parameters . These parameters will be chemical and habitatl parameters . These parameters will be used to used to group small catchments on the Pine Ridge group small catchments on the Pine Ridge reservation into broader physiographic regions. reservation into broader physiographic regions.

MethodologyMethodology We generated 3,884 watersheds in our study area. First, we We generated 3,884 watersheds in our study area. First, we generated flow direction and flow accumulation rasters from a generated flow direction and flow accumulation rasters from a depressionless 10-m digital elevation model (DEM) with depressionless 10-m digital elevation model (DEM) with Spatial Analyst and calibrated a final flow accumulation Spatial Analyst and calibrated a final flow accumulation model with 1:24,000 vector stream data. Next, we created a model with 1:24,000 vector stream data. Next, we created a Strahler stream order vector shapefile from the calibrated Strahler stream order vector shapefile from the calibrated rasters. The Strahler model allowed us to identify pourpoint rasters. The Strahler model allowed us to identify pourpoint locations needed to model an initial watershed layer. We locations needed to model an initial watershed layer. We iteratively added and manipulated the locations of the iteratively added and manipulated the locations of the pourpoints to generate a final watershed model that pourpoints to generate a final watershed model that resembled the “tear drop” shape of actual watersheds.resembled the “tear drop” shape of actual watersheds.

Sources of Uncertainty in our ModelSources of Uncertainty in our Model1.1.Flow direction was derived from 10 meter DEM data. Flow direction was derived from 10 meter DEM data. 2.2.Pour points were manipulated to form “tear drop” shaped Pour points were manipulated to form “tear drop” shaped catchments within close proximity of other catchments.catchments within close proximity of other catchments.

DiscussionDiscussionGIS appears to be an effective tool for watershed modeling in the GIS appears to be an effective tool for watershed modeling in the complex and varied terrain of the Pine Ridge Reservation. The complex and varied terrain of the Pine Ridge Reservation. The Strahler flow model in Figure 1 (colored lines) is very close to 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 USGS 1:24,000 digital line graph of streams (black lines) for both perennial and intermittent streams. perennial and intermittent streams.

The classification rasters show sharp distinctions between The classification rasters show sharp distinctions between physiographic regions. For example, the percent sand raster (Figure physiographic regions. For example, the percent sand raster (Figure 2) reveals the three major physiographic regions of the reservation; 2) reveals the three major physiographic regions of the reservation; White River Badlands, Keya Paha Tablelands, and Nebraska White River Badlands, Keya Paha Tablelands, and Nebraska Sandhills. Sandhills.

Future WorkFuture Work1, 1, Summarize the classification parameters for individual catchments Summarize the classification parameters for individual catchments with with zonal statistics in ArcMap. zonal statistics in ArcMap. 2. 2. Group the catchments on the reservation into 10 – 15 Group the catchments on the reservation into 10 – 15 physiographic regions using an isomeans clustering algorithm physiographic regions using an isomeans clustering algorithm available in ArcGIS and available in ArcGIS and ERDAS Imagine. Clusters form around nodes ERDAS Imagine. Clusters form around nodes (peaks) in the data and data that is most similar to a certain node is (peaks) in the data and data that is most similar to a certain node is grouped into a class.grouped into a class.

Sponsored by theNational Geospatial Intelligence Agencyand the National Science FoundationIn cooperation with Oglala Lakota College

Figure 1: shows the projection area of Medicine Root Creek’s confluence with the White shows the projection area of Medicine Root Creek’s confluence with the White River. River. A watershed layer of the entire reservation is the projection base.A watershed layer of the entire reservation is the projection base.

We created continuous raster files of selected soils, and DEM We created continuous raster files of selected soils, and DEM data that may affect vegetation distribution. First, we data that may affect vegetation distribution. First, we downloaded and joined the SSURGO soil databases to soils downloaded and joined the SSURGO soil databases to soils polygon shapefiles. Next, we generated and mosaiced rasters polygon shapefiles. Next, we generated and mosaiced rasters of the parameters shown in Table 1. We displayed each of of the parameters shown in Table 1. We displayed each of the rasters to determine whether or not the raster would be the rasters to determine whether or not the raster would be significant in our final physiographic classification (figure 2). significant in our final physiographic classification (figure 2). For example, rocks greater than 10 inches do not commonly For example, rocks greater than 10 inches do not commonly occur in Pine Ridge reservation soils and therefore this raster occur in Pine Ridge reservation soils and therefore this raster was removed from our final parameter list.was removed from our final parameter list.

Climate Data:Climate Data:Max TemperatureMax TemperatureMin TemperatureMin TemperatureTotal PrecipitationTotal PrecipitationPrecipitation IntensityPrecipitation IntensityHumidityHumidityDegree Growing DaysDegree Growing DaysCurvatureCurvature

Terrain Data:Terrain Data:Watershed AreaWatershed AreaMean SlopeMean SlopeStd Dev Slope Std Dev Slope ElevationElevationRelief Relief Flow LengthFlow Length

Table 1 – Classification ParametersTable 1 – Classification Parameters

Parameters for Classification Parameters for Classification

Acknowledgements Jim Sanovia for helping with creation of the initial watershed; deriving flow direction and a depressionless DEM, and 3 rd order pourpoint manipulation. ResourcesBulley, 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, 2007USDA 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, 2007USDA 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, 2007USGS (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, 2007USGS (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, 2007USGS (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

Physical: Physical: Rock 3-10 in. Rock 3-10 in. #4 Sieve/Gravel #4 Sieve/Gravel #10/Very Course Sand #10/Very Course Sand #40/Course Sand #40/Course Sand #200/Sand #200/Sand Total Sand Total Sand Total Silt Total Silt Total ClayTotal Claykw or erodibility factorkw or erodibility factorAlbedoAlbedoksat (permeability)ksat (permeability)% Organic Matter% Organic Matter

Chemical:Chemical:Soil pHSoil pH

ec (electrical conductivity) ec (electrical conductivity) cec7 (cation exchange rate)cec7 (cation exchange rate)SAR or Na ratio to Ca/Mg SAR or Na ratio to Ca/Mg Gypsum Gypsum CaCO3- CaCO3-Calcium CarbonateCalcium Carbonate

Biological:Biological:Range ProductivityRange ProductivityGrain habitatGrain habitatGrass habitatGrass habitatHerb habitatHerb habitatShrub habitatShrub habitatHardwood habitatHardwood habitatConifer habitatConifer habitatWetlands habitatWetlands habitatWater habitatWater habitat

Figure 2: a parameter raster for sand distributiona parameter raster for sand distribution.