Date of Award

12-2010

Type

Thesis

Major

Earth and Space Science - Environmental Science Track

Department

Earth & Space Science

First Advisor

Roger Brown

Abstract

Sixteen watersheds located on Fort Benning Military Installation in Georgia were analyzed using both physically collected data and computer modeling data. Physical data collected included total suspended solids (TSS) and grain size analysis using the Wolman Pebble Count method. Computer modeling analyzed the watersheds using ArcGIS 9.3 for comparison to physical data. Land use, slope, and soil data were used in a modified revised universal soil loss equation (RUSLE) to create a soil erodibility index map. Wolman Pebble Count data showed that in half of the watersheds, 84% of the sampled grains were less than half a millimeter in size. Watersheds studied were dominated by Nankin sandy clay loam soils, Troup loamy sand soils and Cowarts & Ailey soils types. Results showed that baseflow TSS was greatest in disturbed and urbanized catchments. The soil erodibility index maps produced in ArcGIS using the modified RUSLE equation indicate areas with the potential for high erosion rates. Watersheds that had the highest potential for erosion contained less than 55% forest coverage. Correlation analysis indicated relationships between the D10 and D50 grain size and the slopes of the watersheds. Relationships were also established between TSS, soil loss erodibility index, and land use classification factor. The results suggest that the GIS/RUSLE model could be used for estimating soil loss; however, other factors like unique land disturbance need to be included to improve its accuracy. Additionally, a land use classification image would be sufficient in determining areas with potential water quality issues within a watershed. However, this method would not provide a physical measurement of soil loss within the watershed. Creating an additional index for proposed military land use in each of the watersheds would refine the GIS modeling and provide a better output for the identification of best management practices to improve water quality.

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