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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #301498
Title: Evaluation of a stepwise, multi-objective, multi-variable parameter optimization method for the APEX model

Author
item SENAVIRATINE, G.M.M.M ANOMAA - University Of Missouri
item UDAWATTA, RANJITH - University Of Missouri
item Baffaut, Claire
item ANDERSON, SREPHEN - University Of Missouri

Submitted to: Journal of Environmental Quality
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/12/2014
Publication Date: 6/30/2014
Citation: Senaviratine, G., Udawatta, R.P., Baffaut, C., Anderson, S.H. 2014. Evaluation of a stepwise, multi-objective, multi-variable parameter optimization method for the APEX model. Journal of Environmental Quality. 43(4):1381-1391. DOI:10.2134/jeq2013.12.0509.

Interpretive Summary: Hydrologic models can help assess agricultural non-point source pollution. However, they have numerous input parameters that need to be adjusted based on the comparison of simulated results and observed data. The automatic adjustment of these parameters, though efficient, demands significant computational power. The objective of this study was to investigate a cost efficient parameter optimization technique for the Agricultural Environmental Policy eXtender model. Selected parameters were grouped according to the output variable they most affect: runoff, sediment, soil biological properties, total phosphorous, and total nitrogen and within each group, the parameters were optimized for runoff, sediment, and nutrients at the same time. Several multi-objective functions were considered to select the optimal parameter combination. The greatest improvements in model performance for all output variables but runoff were found after optimization of the runoff parameters, which shows that runoff parameter optimization was crucial for good simulation of sediment and nutrients. Phosphorus simulation further improved after optimization of the soil biological activity and phosphorus parameters. Subsequent optimizations did not improve sediment or nitrogen simulations. The objective function that was based on multiple model performance indicators outperformed the other ones. Modelers can benefit from this cost efficient optimization technique that should improve model performance for water quality constituents.

Technical Abstract: Hydrologic models are essential tools for environmental assessment of agricultural non-point source pollution. The automatic calibration of hydrologic models, though efficient, demands significant computational power, which can limit its application. The study objective was to investigate a cost efficient parameter optimization technique for the Agricultural Environmental Policy eXtender (APEX) model. The most sensitive parameters were grouped according to the output variable they primarily affect: runoff, sediment, soil biological properties, total phosphorous (TP), and total nitrogen (TN). Within each group separately and consecutively, parameters were optimized simultaneously for runoff, sediment, TP, and TN. Two multi-objective functions comprising combinations of coefficient of determination (r2), regression slope, and Nash-Sutcliffe coefficient (NSC), and the Generalized Likelihood Uncertainty Estimation, were considered to select the optimal parameter combination. A previously manually calibrated and validated APEX model for three adjacent row-crop field-size watersheds in Northeast Missouri was used as the baseline. The greatest improvements in model performance for sediment, TP, and TN, but not for runoff, were found after runoff parameter optimization, showing that runoff parameter optimization was crucial for good simulation of sediment and nutrients. The r2 values for sediment, TP, and TN improved from 0.59-0.87 to 0.77-0.94. The NSC values for TP also improved after soil biological activity and TP parameter optimizations but subsequent optimizations did not improve sediment or TN simulations. The objective function based on r2, slope and NSC outperformed the other ones. This cost efficient optimization technique (2,570 runs for 23 parameters) will help modelers improve model performance for water quality constituents.