Abstract
GOAL, SCOPE, BACKGROUND: Sheet erosion from agricultural, forest and urban lands may increase stream sediment loads as well as transport other pollutants that adversely affect water quality, reduce agricultural and forest production, and increase infrastructure maintenance costs. This study uses spatial analysis techniques and a numerical modeling approach to predict areas with the greatest sheet erosion potential given different soils disturbance scenarios. A Geographic Information System (GIS) and the Universal Soil Loss Equation (USLE) were used to estimate sheet erosion from 0.64 ha parcels of land within the watershed. The Soil Survey of St. Tammany Parish, Louisiana was digitized, required soil attributes entered into the GIS database, and slope factors determined for each 80 x 80 meter parcel in the watershed. The GIS/USLE model used series-specific erosion K factors, a rainfall factor of 89, and a GIS database of scenario-driven cropping and erosion control practice factors to estimate potential soil loss due to sheet erosion. A general trend of increased potential sheet erosion occurred for all land use categories (urban, agriculture/grasslands, forests) as soil disturbance increases from cropping, logging and construction activities. Modeling indicated that rapidly growing urban areas have the greatest potential for sheet erosion. Evergreen and mixed forests (production forest) had lower sheet erosion potentials; with deciduous forests (mostly riparian) having the least sheet erosion potential. Erosion estimates from construction activities may be overestimated because of the value chosen for the erosion control practice factor. This study illustrates the ease with which GIS can be integrated with the Universal Soil Loss Equation to identify areas with high sheet erosion potential for large scale management and policy decision making. The GIS/USLE modeling approach used in this study offers a quick and inexpensive tool for estimating sheet erosion within watersheds using publicly available information. This method can quickly identify discrete locations with relatively precise spatial boundaries (approximately 80 meter resolution) that have a high sheet erosion potential as well as areas where management interventions might be appropriate to prevent or ameliorate erosion.
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