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The integration of geographic information systems (GIS) and hydrologic models provides the user with the ability to simulate watershed-scale processes within a spatially digitized computer-based environment. Soil type and land use data are essential GIS data layers used in a wide array of government and private sector activities, including resource inventory, land management, landscape ecology, and hydrologic modeling. This investigation was conducted to evaluate the use of different combinations of Soil Survey Geographic (SSUR.GO) and State Soil Geographic (STATSGO) soil classification systems and the USDA National Agricultural Statistics Service (NASS) and national Gap Analysis Project (GAP) land use data sets and their effects on modeled stream flow using the Soil and later Assessment Tool (SWAT2005). Performance of the model was tested on the Cedar Creek Watershed in northeastern Indiana, one of 14 benchmark watersheds in the USDA Agricultural Research Service Conservation Effects Assessment Project (CEAP) watershed assessment component. CEAP comprises two main components: (1) a national assessment that provides model estimates of conservation benefits for annual reporting and (2) a watershed assessment component aimed at quantifying the environmental benefits from specific conservation practices at the watershed scale. Model performance for daily, monthly, and annual uncalibrated stream flow responses in SWAT was assessed using the Nash-Sutcliffe efficiency coefficient (ENS), coefficient of determination (R2). root mean square error (RMSE), ratio of RMSE to the standard deviation of measured data (RSR), and percent bias (PBIAS). We found that the range of relative error (e.g., PBIAS) and ENS values for uncalibrated stream flow predictions in this study were similar to others that have been reported in the literature. Simulated stream flow values ranged from slight overestimations of approximately 5%, to underestimating stream flow by 25% to 41% depending on the combination of soil and land use input data sets. Overall, the NASS SSURGO data sets gave the best model performance for monthly stream flow having an ENS value of 0.58, R2 of 0.66, RSR of 0.65, and PBIAS equal to 21 .93. The poorest model performance results were obtained using the GAP SSURGO data sets that had an ENS value of -2.58, R2 of 0.49, RSR of 1.89, and a PBIAS value of 27.92. The results of this study indicate that in using the SWAT model, several factors regarding GIS input data sets may affect stream flow simulations and, consequently, water quality assessment studies. In addition to the effect of GIS source data on model output (e.g., SSURGO, STATSGO, NASS, GAP), there is evidence shown in this study that the interaction, pre-processing, and aggregation of unique combinations of GIS input layers within SWAT also influence simulated stream flow output. Overall, results of the study indicate that the use of different land use GIS layers has a greater effect on stream flow estimates than different soil data layers. [ABSTRACT FROM AUTHOR]

This study evaluated the performance of two water quality models in accordance to specific tasks designated in the USDA Agricultural Research Service Conservation Effects Assessment Project. The Soil and Water Assessment Tool (SWAT) and the Annualized Agricultural Non-Point Source (AnnAGNPS) models were applied uncalibrated to the Cedar Creek watershed within the St. Joseph River watershed in northeastern Indiana to predict streamflow and atrazine losses. In order to ultimately assess the benefits of conservation practices in agricultural watersheds (which is one of the major goals of the Conservation Effects Assessment Project), proper application of the SWAT and AnnAGNPS models is essential including baseline comparisons made in an uncalibrated mode aimed at eliminating bias due to parameter optimization. Streamflow prediction results show that SWAT model performance was superior to AnnAGNPS, with SWAT model efficiency values ranging from 0.66 to 0.25 and AnnAGNPS model efficiency values ranging from 0.13 to -2.06 for monthly and annual streamflow respectively. For uncalibrated conditions, neither model was able to adequately simulate atrazine loss concentrations. Overall results suggest that for Conservation Effects Assessment Project modeling applications at the Cedar Creek watershed scale in this study, the use of the SWAT model would be preferable to AnnAGNPS in terms of overall model performance and model support technology (e.g., model interface and documentation). [ABSTRACT FROM AUTHOR]

Agricultural nutrient losses contribute to hypoxia in the Gulf of Mexico and eutrophication in the Great Lakes. Our objective was to assess effects of topography, geomorphology, climate, cropping systems and land use and conservation practices on hydrology and nutrient fate and transport in the St. Joseph River watershed. We monitored five sites (298 to 4,300 ha [736 to 10,600 ac]) on two drainage ditches within the St. Joseph River watershed in northeastern Indiana. Row crop agriculture, primarily corn (Zea mays L.) and soybean (Glycine max [L.] Merr.), is the dominant land use (~60%) in this pothole or closed depression landscape. The hydrology is dominated by subsurface tile drainage supplemented with surface drainage of remote potholes. Vegetative buffer strips have been implemented along >60% of the agricultural drainage ditches. The vegetative buffer strips play an invaluable role protecting water quality though by acting as natural setbacks during fertilizer and pesticide applications. Multiple regressions indicated land cropped to corn and areas with direct drainage or potholes are highly sensitive to nutrient losses. Future conservation assessment efforts in this and similar watersheds should focus on management of potholes in cropped fields and the subsequent effect of those practices on tile drainage water. [ABSTRACT FROM AUTHOR]