WATERSHED–SCALE SIMULATION OF SEDIMENT ANDNUTRIENT LOADS IN GEORGIA COASTAL PLAIN STREAMS USING THE ANNUALIZED AGNPS MODEL

Abstract

Sediment and nutrient loadings in the Little River Research Watershed in south central Georgia were modeledusing the continuous simulation Annualized Agricultural NonpointSource Pollution (AnnAGNPS) model, part of the AGNPSsuite of modeling components. Specifically, nitrogen, phosphorus, sediment, and runoff were predicted over a sevenyearperiod. Land under cultivation makes up approximately 25% of the 333 km2 watershed. Livestock facilities include swine,poultry, dairy cows, and beef cattle. Results from the simulation were compared to seven years of monitoring data at the outletof five nested subwatersheds and at the outlet of the Little River Research Watershed (LRRW). The average annual predictedrunoff in the upper part of the watershed was onethird to half of observed runoff. In contrast, predicted runoff in the lowerpart of the watershed was close to observed, and was 100% of observed at the outlet of the watershed. Runoff underpredictionwas attributed to the method of landcover discretization. The extent of forest land in the upper watershed (55% to 63%) andthe fragmented landscape that has relatively small fields surrounded by riparian forests and tracts of forest resulted inoverestimation of forested area in the watershed. In addition to runoff, sediment and nutrient loads were also underpredictedin the upper part of the LRRW. Two factors are most likely responsible for underprediction. Runoff is underpredicted at thesesites, which reduces the carrying capacity of sediment loads. In addition, the overestimation of forested areas at these sitescoincides with underestimation of sedimentproducing areas, such as cropland. In contrast to the upper part of the watershed,sediment and nutrient loads were overpredicted in the lower part of the watershed. This may have resulted from inadequatelysimulating nonpointsource pollution attenuation by the extensive riparian forests and forested instream wetland areasfound in these watersheds. Prediction results can be improved through better input into the model, as well as modificationof the processes within the model to account for forest and riparian conditions.