Date of Award


Degree Name

Master of Science


Geography and Environmental Resources

First Advisor

Oyana, Tonny


Pesticides have been recognized as one major agricultural non-point source (NPS) pollution to the environment and surface water in United States. Numerous mathematical models have been developed over the last decades to simulate the fate and transport of NPS at watershed scale. Geographic Information System (GIS) combined with models extends the spatial and temporal scopes of the research by integrating a variety of climates, soils, land covers, and management practices. The Annualized Agricultural Nonpoint Source model (AnnAGNPS) has received considerable attention in the United States for estimating runoff, sediment yield, pesticide and nutrients transport from ungauged agricultural watershed. However, few studies have been conducted on pesticide loading prediction in surface water using AnnAGNPS. In this study, the AnnAGNPS model was calibrated and validated for prediction of stream flow and chlorpyrifos loading for an agricultural dominated watershed of Orestimba Creek, in Central Valley, California. Large amounts of chlorpyrifos are applied to almonds, walnuts and other stone-fruit orchards in this area every year, which caused significant concern regarding their contamination to the San Joaquin River. Variety of data obtained from multiple sources were utilized as model input, including climate, land use, topology, soil, crop management and schedule, non-crop data, and pesticide. The model's performance was quantitatively analyzed using mean, standard deviation, coefficient of determination (r2), coefficient of efficiency (NSE), and root mean square error (RMSE). Model's prediction was considered to be unsatisfactory if NSE < 0.36, satisfactory if 0.36 < NSE < 0.75 and good if NSE > 0.75. Monthly stream flow discharge prediction was satisfactory and fit the observed data during model calibration mode. The prediction had major improvement in validation mode with modified curve number and rainfall interception values (r2 = 0.78 and NSE = 0.77). The AnnAGNPS predictions of chlorpyrifos concentrations in runoff water were unsatisfactory in both calibration and validation modes. Predicted chlorpyrifos concentrations at rainfall events were 1/1000 of observed data and it was impossible to improve the results through any type of calibration. The overall results suggested the model's poor performance was most likely a result of coarse sampling resolution of observed chlorpyrifos concentrations and lack of irrigation data.




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