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    This study presents a cost-efficient method for calibrating the APEX hydrologic model, improving simulations of runoff, sediment, and nutrient transport in agricultural watersheds. The stepwise optimization significantly enhances model accuracy for key environmental pollutants.

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    Area of Science:

    • Environmental science
    • Hydrology
    • Agricultural engineering

    Background:

    • Hydrologic models are crucial for assessing agricultural nonpoint-source pollution.
    • Automatic calibration is computationally intensive, limiting widespread use.
    • The Agricultural Environmental Policy eXtender (APEX) model requires efficient calibration for accurate environmental assessment.

    Purpose of the Study:

    • Develop and evaluate a stepwise, multiobjective, multivariable automatic calibration method for the APEX model.
    • Improve simulations of runoff, sediment, total phosphorus (TP), and total nitrogen (TN).
    • Enhance the cost-efficiency of hydrologic model calibration.

    Main Methods:

    • Grouped sensitive APEX model parameters by process (runoff, sediment, soil biology, TP, TN) and optimized them sequentially.
    • Utilized two multiobjective functions (combinations of R², slope, and Nash-Sutcliffe Coefficient - NSC) and Generalized Likelihood Uncertainty Estimation (GLUE) for parameter selection.
    • Employed a previously calibrated APEX model for three Missouri watersheds as a baseline.

    Main Results:

    • Runoff parameter optimization was critical for improving sediment, TP, and TN simulations, with R² values increasing from 0.59-0.87 to 0.77-0.94.
    • NSC values for TP improved after soil biological activity and TP parameter optimizations.
    • The objective function combining R², slope, and NSC demonstrated superior performance over other tested functions.

    Conclusions:

    • The developed stepwise, multiobjective calibration method is a cost-efficient technique for APEX model optimization.
    • This approach significantly enhances the accuracy of simulating sediment and nutrient transport in agricultural watersheds.
    • Modelers can benefit from this computationally efficient optimization strategy, requiring only 2570 runs for 23 parameters.