Document Type

Thesis

Date of Degree Completion

Spring 2020

Degree Name

Master of Science (MS)

Department

Cultural and Environmental Resource Management

Committee Chair

Robert Hickey

Second Committee Member

Sterling Quinn

Third Committee Member

Michael Pease

Abstract

Soil erosion is a global problem that reduces land productivity and causes environmental degradation. Soil erosion models, such as the Revised Universal Soil Loss Equation (RUSLE), are used to estimate the severity and distribution of erosion. The topographic factor (LS), which combines slope length and angle, is an important part of RUSLE. This work compared two methods of L calculation, the grid cumulation (GC) and the contributing area (CA) methods, and two methods of S calculation, the neighborhood (NBR) and maximum downhill slope (MDS) methods. These were compared across digital elevation models (DEMs) of 1, 5, 10, and 30m resolutions. This study rectifies the lack of direct and consistent testing OF these methods across multiple sites and DEM resolutions.

The CA method produces higher mean, median, and max values of L than the GC method across all landscapes, especially along drainage channels where the greatest area accumulates to produce extremely high L values. The GC method, unlike the CA method, accounts for decreases in slope steepness that initiate deposition and reset accumulated values. Differences between these methods occur most from different treatments of convergence. The CA method combines flow paths but the GC method only continues the one longest flow path.

The NBR and MDS method produced similar mean and median S values. However, maximum values using the NBR method are more sensitive to DEM resolution and decrease more with coarse resolutions. The NBR method produces lower S values along ridge lines and higher S values along drainage channels and concave depressions and slopes. This is due to the averaging of calculating slope angle in the NBR method. The neighborhood method smooths landscapes and reduces the ability to capture erosion variability related to S.

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