|Senaviratne, Anomaa - University Of Missouri|
|Nelson, Nathan O - Kansas State University|
|Van Liew, Mike - University Of Nebraska|
|Udawatta, Ranjith - University Of Missouri|
|Bhandari, Ammar - Kansas State University|
|Lory, John - University Of Missouri|
Submitted to: American Society of Agronomy Meetings
Publication Type: Abstract Only
Publication Acceptance Date: 8/26/2014
Publication Date: 11/2/2014
Citation: Senaviratne, A., Baffaut, C., Nelson, N., Van Liew, M., Udawatta, R.P., Bhandari, A.B., Lory, J.A. 2014. Impact of APEX parameterization and soil data on runoff, sediment, and nutrients transport assessment. American Society of Agronomy Meetings. Paper No. 262-7.
Technical Abstract: Hydrological models have become essential tools for environmental assessments. This study’s objective was to evaluate a best professional judgment (BPJ) parameterization of the Agricultural Policy and Environmental eXtender (APEX) model with soil-survey data against the calibrated model with either soil-survey or measured soil data. The BPJ parameterization was derived from the CEAP parameterization, the User’s manual, discussions with model developers, and our own understanding of the parameters. Long-term monitored data from a watershed with claypan soils and a corn (Zea mays L)-soybean [Glycine max (L.) Merr.] rotation in Northeast Missouri was used for the model performance assessment. Overall, the coefficient of determination (r2), Nash-Sutcliffe Coefficient (NSC), and Percent bias (Pbias) were always better for the calibrated model and with site-specific soil data. The calibrated model with soil-survey data showed satisfactory predictions for runoff. The sediment simulation performance was very poor (NSC less than 0) with the BPJ parameterization and best with measured soil data and a calibrated model (NSC 0.25 -0.53). Using soil survey or measured data has inconsistent effects on model performance. Average annual runoff, TP, and TN with the BPJ parameterization were 12%, 19%, and 26% lower, respectively than with the calibrated model and site-specific soil data while sediment losses were 113% higher. This indicates the need for calibration before running scenario analyses of sediment or nutrient transport.