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Evaluation of BMP sediment load reductions in southeastern US forest regions using WEPP

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Surface runoff from undisturbed forested landscapes has little adverse impact on water quality to receiving waters compared to runoff from agriculture or urban lands. However, during forest operations such as clearcutting and road construction stream sediment concentrations increase, deteriorating water quality and reducing storage capacity of reservoirs. The objective of this project is to use the Water Erosion Prediction Project (WEPP) 2008 model to estimate percent sediment load reductions in southeastern US forest regions using selected BMPs. Clearcut operations are simulated with and without Streamside Management Zones (SMZs). Six Major Land Resource Area (MLRA) regions are selected across the southeastern US for this study. Five-year sediment yield simulations on a hillslope were performed using CLIGEN (a stochastic weather generator), forest soil series, and two forested surface management files. Modeling simulations were performed in each region using undisturbed forest and clearcut with and without an SMZ. To model clearcut, rotation files were developed in WEPP to emulate fifty recurring 5-year harvest rotations for each site. Simulation results indicated sediment load reductions from 9 to 27 percent using SMZs. Modeling simulations of forest roads with and without BMPs were conducted in five MLRA regions. BMPs included water structures diverting to an SMZ and an improved gravel road surface across a stream. Forest road simulations represented regional state guidelines for water diversion on forest roads. Forest road structures resulted in sediment yield reductions from 31 to 53 percent. When gravel road crossings were added, comparable sediment yield reductions were 43 to 60 percent.
Dougherty, Mark , Srivastava, Anurag , Grace, Jonny III , American Society of Agricultural and Biological Engineers. Meeting.
Paper presented at the ASABE Annual International Meeting, held June 21-24, 2009, Reno, Nevada.
Paper 2009, no. 096737
Journal Articles, USDA Authors, Peer-Reviewed
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