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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #308793

Title: A stepwise, multi-objective, multi-variable parameter optimization method for the APEX model

Author
item Senaviratne, Anomaa - University Of Missouri
item Udawatta, Ranjith - University Of Missouri
item Baffaut, Claire
item Anderson, Stephen - 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., Udawatta, R.P., Baffaut, C., Anderson, S.H. 2014. A stepwise, multi-objective, multi-variable parameter optimization method for the APEX model. American Society of Agronomy Meetings. Paper No. 143-4.

Interpretive Summary:

Technical Abstract: Proper parameterization enables hydrological models to make reliable estimates of non-point source pollution for effective control measures. The automatic calibration of hydrologic models requires significant computational power limiting its application. The study objective was to develop and evaluate a stepwise, multi-objective, multi-variable parameter optimization method for the Agricultural Environmental Policy eXtender (APEX) model for simulating runoff, sediment, total phosphorus (TP), and total nitrogen (TN). The most sensitive parameters were grouped based on the process they primarily affect: runoff, sediment transport, soil biological activity, TP transport, and TN transport, and were used separately and consecutively to optimize the model. A previous manually calibrated and validated APEX model for three adjacent row-crop field-size watersheds in Northeast Missouri was used as the baseline. Two multi-objective functions comprising combinations of coefficient of determination (r2), regression slope, and Nash-Sutcliffe coefficient (NSC), and a global objective function, the Generalized Likelihood Uncertainty Estimation (GLUE) were used for model evaluation. The optimization of runoff related parameters gave little improvement of model performance for runoff but greatly improved model performance for sediment, TP, and TN. The r2 values for sediment, TP, and TN improved from 0.59-0.87 to 0.77-0.94. The NSC values for TP improved after soil biological activity and TP parameter optimizations. The runoff parameter optimization was crucial for reliable estimation of sediment and nutrients. The objective functions based on r2, slope and NSC and on GLUE led to the best model performance. The step-wise optimization offers a cost efficient technique (2,570 runs for 23 parameters) for model calibration.