Optimizing and uncertainty evaluation of soil and crop parameters in root zone water quality model

Reducing uncertainty in optimizing agricultural system model parameters is the key issue for model applications. An automated parameter estimation software (PEST) was used to calibrate the soil parameters and crop genetic parameters in the root zone water quality model (RZWQM). The simulation results optimized by PEST were better than the calibration results via manual trial and error method, and showed higher efficiency. Parameterization uncertainty analysis of the model by PEST showed that the calibration data selection, initial parameter value, soil hydraulic parameter estimation method, interactions between these different parameter types and objective functions (error resources) had significant influences on PEST optimization results. Similar optimized soil hydraulic parameters were obtained from above processes, but produced different soil water retention curve. By above assessment, the uncertainty in RZWQM parameter optimization was reduced with improved soil water and crop yield predictions. This result can help optimize parameters of other similar models by PEST.