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Title: WATERSHED-SCALE SIMULATION OF SEDIMENT AND NUTRIENT LOADS IN GEORGIA COASTAL PLAIN STREAMS USING THE ANNUALIZED AGNPS MODEL

Author
item SUTTLES, JENNIFER - UNIVERSITY OF GEORGIA
item VELLIDIS, GEORGE - UNIVERSITY OF GEORGIA
item Bosch, David - Dave
item Lowrance, Robert
item USERY, E. - UNIVERSITY OF GEORGIA
item Sheridan, Joseph

Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/20/2002
Publication Date: 5/1/2003
Citation: Suttles, J.B., Vellidis, G., Bosch, D.D., Lowrance, R.R., Usery, E.L., Sheridan, J.M. 2003. Watershed scale simulation of sediment and nutrient loads in Georgia coastal plain streams using the annualized AGNPS model. Transactions of the American Society of Agricultural Engineers. 46(5):1325-1335.

Interpretive Summary: Over the past 20 years the United States has made considerable progress in cleaning up it's water. However, there continue to be problems with the quality of our nation's rivers and lakes. In order to make additional progress in the clean up effort, we must reduce the sources of pollution which originate from distributed, harder to define locations, commonly referred to as nonpoint source pollution. Computer simulation models are commonly used to rapidly assess different management practices designed to reduce nonpoint source pollution. This manuscript describes the testing of one such model for a Georgia Coastal Plain Watershed. Results from the simulation were compared to 7 years of monitoring data in 6 locations of varying drainage areas throughout the watershed. While the trends predicted by the model for simulated runoff and simulated nutrient transport generally followed actual observed trends, considerable differences were observed in the absolute predictions. Our results indicate that in order to improve confidence in the model output and it's overall utility, improvements are required in the way the model represents variations in land use. Specifically, prediction results can be improved through better input into the model, as well as modification of the processes within the model that account for forest and riparian conditions.

Technical Abstract: Sediment and nutrient loadings in the Little River watershed in south central Georgia were modeled using the continuous simulation annualized Agricultural Nonpoint Source Pollution (AnnAGNPS) model. Specifically, nitrogen, phosphorus, sediment and runoff were predicted over a seven-year period. 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 7 years of monitoring data at the outlet of 5 nested subwatersheds and at the outlet of the Little River watershed. Annual average predicted runoff in the upper part of the watershed was one-third to half of observed runoff. In contrast, predicted runoff in the lower part of the watershed was close to observed and was 100% of observed at the outlet of the watershed. Runoff underprediction was attributed to the extent of forest land in the upper watershed (55-63%), the fragmented landscape which has relatively small fields surrounded by riparian forests and tracts of forest, and the landcover discretization method used, in which dominant landcover was assigned to AGNPS cells and was unable to adequately represent the landscape. In addition to runoff, sediment and nutrient loads are also under-predicted in the upper part of the Little River watershed. Two factors are most likely responsible for under-prediction. Runoff is under-predicted at these sites, which reduces the carrying capacity of sediment loads. In addition, the overestimation of forested areas at these sites coincides with underestimation of sediment-producing areas, such as cropland. In contrast to the upper part of the watershed, sediment and nutrient loads are over-predicted in the lower part of the watershed. This may result from not adequately simulating nonpoint source pollution attenuation by the extensive riparian forests and forested in-stream wetland areas found in these subwatersheds. Prediction results can be improved through better input into the model, as well as modification of the processes within the model to account for forest and riparian conditions.