Watershed-scale simulation of sediment and nutrient loads in Georgia Coastal Plain streams using the annualized AGNPS model

Sediment and nutrient loadings in the Little River Research Watershed in south central Georgia were modeled using the continuous simulation Annualized Agricultural Nonpoint–Source Pollution (AnnAGNPS) model, part of the AGNPS suite of modeling components. 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 seven years of monitoring data at the outlet of five nested subwatersheds and at the outlet of the Little River Research Watershed (LRRW). The average annual 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 method of landcover discretization. The extent of forest land in the upper watershed (55% to 63%) and the fragmented landscape that has relatively small fields surrounded by riparian forests and tracts of forest resulted in overestimation of forested area in the watershed. In addition to runoff, sediment and nutrient loads were also underpredicted in the upper part of the LRRW. Two factors are most likely responsible for underprediction. Runoff is underpredicted 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 were overpredicted in the lower part of the watershed. This may have resulted from inadequately simulating nonpoint–source pollution attenuation by the extensive riparian forests and forested in–stream wetland areas found in these watersheds. 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.

[1]  G. R. Foster,et al.  Predicting soil erosion by water : a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE) , 1997 .

[2]  Yongping Yuan,et al.  EVALUATION OF ANNAGNPS ON MISSISSIPPI DELTA MSEA WATERSHEDS , 2000 .

[3]  David D. Bosch,et al.  Management effects on runoff and sediment transport in riparian forest buffers , 1999 .

[4]  L. E. Asmussen,et al.  Waterborne nutrient budgets for the riparian zone of an agricultural watershed , 1983 .

[5]  Awwa,et al.  Standard Methods for the examination of water and wastewater , 1999 .

[6]  John R. Williams,et al.  A modeling approach to determining the relationship between erosion and soil productivity [EPIC, Erosion-Productivity Impact Calculator, mathematical models] , 1984 .

[7]  L. M. Safley,et al.  Loss of nitrogen during sprinkler irrigation of swine lagoon liquid , 1992 .

[8]  R. Lowrance.  Nitrogen Outputs from a Field-Size Agricultural Watershed , 1992 .

[9]  H. H. V. Horn.  Achieving Environmental Balance of Nutrient Flow Through Animal Production Systems1 , 1991 .

[10]  R. Young,et al.  AGNPS: A nonpoint-source pollution model for evaluating agricultural watersheds , 1989 .

[11]  W. G. Knisel,et al.  GLEAMS: Groundwater Loading Effects of Agricultural Management Systems , 1987 .

[12]  L. E. Asmussen,et al.  Interflow or shallow phreatic flow in the coastal plain of Georgia , 1969 .

[13]  S. Leibowitz,et al.  Prioritizing Wetland Restoration for Sediment Yield Reduction: A Conceptual Model , 2003, Environmental management.

[14]  E. Blood,et al.  MODELING DEVELOPED COASTAL WATERSHEDS WITH THE AGRICULTURAL NON‐POINT SOURCE MODEL 1 , 1999 .

[15]  Richard A C Cooke,et al.  Assessment of management alternatives on a small agricultural watershed , 1997 .

[16]  D. F. Heermann,et al.  Empirical estimation of daily clear sky solar radiation , 1985 .

[17]  Debbie Whitall,et al.  WETLANDS , 1995, Restoration & Management Notes.

[18]  P. Ambus,et al.  Comparison of Denitrification in Two Riparian Soils , 1991 .

[19]  D. L. Thomas,et al.  Watershed-Scale Water Quality Impacts of Riparian Forest Management , 1999 .

[20]  G. Hoogenboom.  The Georgia Automated Environmental Monitoring Network , 1993 .

[21]  W. G. Knisel,et al.  An approximate method for partitioning daily streamflow data , 1984 .

[22]  R. Lowrance,et al.  A conceptual model for assessing ecological risk to water quality function of bottomland hardwood forests , 1995 .

[23]  C. A. Madramootoo,et al.  Use of AGNPS for watershed modeling in Quebec , 1997 .

[24]  R. Leonard,et al.  Streamflow Nutrient Dynamics on Coastal Plain Watersheds , 1988 .

[25]  R. K. Hubbard,et al.  Nutrient transport in a restored riparian wetland. , 2003, Journal of environmental quality.

[26]  L. E. Asmussen,et al.  Riparian Forests as Nutrient Filters in Agricultural Watersheds , 1984 .

[27]  A. Holmes,et al.  Physical Geography , 1872, Nature.

[28]  S. F. Actory,et al.  Personal correspondence , 1997 .

[29]  C. K. Mutchler,et al.  Comparison of Sediment Yield Models on Watersheds in Mississippi , 1989 .