Evaluation of AnnAGNPS on Mississippi Delta MSEA Watersheds

Sediment and its associated pollutants entering a water body can be very destructive to the health of that system. Best Management Practices (BMPs) can be used to reduce these pollutants, but understanding the most effective practices is very difficult. Watershed models are the most cost-effective tools to aid in the decision-making process of selecting the BMP that is most effective in reducing the pollutant loadings. The Annualized Agricultural Non-Point Source Pollutant Loading model (AnnAGNPS) is one such tool. The objectives of this study were to assemble all necessary data from the Mississippi Delta Management System Evaluation Area (MDMSEA) Deep Hollow watershed to validate AnnAGNPS, and to use the validated AnnAGNPS to evaluate the effectiveness of BMPs for sediment reduction. In this study, AnnAGNPS predictions were compared with three years of field observations from the MDMSEA Deep Hollow watershed. Using no calibrated parameters, AnnAGNPS underestimated observed runoff for extreme events, but the relationship between simulated and observed runoff on an event basis was significant (R2 = 0.9). In contrast, the lower R2 of 0.5 for event comparison of predicted and observed sediment yields demonstrated that the model was not best suited for short-term individual event sediment prediction. This may be due to the use of Revised Universal Soil Loss Equation (RUSLE) within AnnAGNPS, and parameters associated with determining soil loss were derived from long-term average annual soil loss estimates. The agreement between monthly average predicted sediment yield and monthly average observed sediment yield had an R2 of 0.7. Three-year predicted total runoff was 89% of observed total runoff, and three-year predicted total sediment yield was 104% of observed total sediment yield. Alternative scenario simulations showed that winter cover crops and impoundments are promising BMPs for sediment reduction.