## 4 Conclusions and Limitations  
Calibration, validation, and sensitivity analysis of 17 hydrologic parameters for DRAINMOD were performed in two fields with distinct soil characteristics, and drainage design was conducted. The multi-objective sensitivity analysis of the fields using daily drainage flow, water balance, and relative yield depicts the varying results for these fields. The results indicated that both daily and long-term flow, as well as the relative yield of the corn, were most sensitive to the drainage design parameters of the tile drain spacing and drain depth. Parameters related to the soil properties influencing sub-surface hydrology, such as the lateral hydraulic conductivity of dominant layers, impacted the flow and yield results. Soil temperature-related parameters mainly impacted the long-term water balance but did not affect the relative yield of corn. These findings, however, may differ in other regions since this study was conducted in specific climatic regions.  
Since sensitivity analysis reflects the parameter uncertainty of the model, careful calibration of the most sensitive parameters needs to be conducted to reduce prediction errors. The study also suggests that drainage design variables such as depth and spacing must be considered cautiously depending on field settings, as they highly impact flow and  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{8}{c}{**Relative Sensitivity**} & \multicolumn{8}{c}{**Absolute Sensitivity**} \\ \cline{2-11}  & \multicolumn{2}{c}{**NSE**} & \multicolumn{2}{c}{**RSQ**} & \multicolumn{2}{c}{**Yield**} & \multicolumn{2}{c}{**Cum flow**} & \multicolumn{2}{c}{**PBIAS**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{2-11}  & **Sx** & **Rank** & **Sx** & **Rank** & **Sx** & **Rank** & **Sx** & **Rank** & **Sx** & **Rank** & **Sx** & **Rank** \\ \hline  
A** & 0.352 & 1 & 0.286 & 1 & 0.307 & 1 & 0.096 & 1 & 7.332 & 6 & 0.172 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Compiled LH-OAT sensitivity ranking for CS-4.  
crop productivity. Some parameters were affected during the calibration of the model in combination with other parameters but not in the LH-OAT analysis, whose actual impacts might be realized by global sensitivity analysis. Moreover, we did not consider the nutrient parameters that could influence the relative crop yield, which we acknowledge was a limitation in the current study.  
Conceptualization, H.T., S.H. and R.B.; methodology, S.H.; software, H.T. and S.H.; validation, S.H. and R.B.; formal analysis, H.T.; resources, R.A.C. and R.B.; data curation, S.H.; writing--original draft preparation, H.T.; writing--review and editing, S.H. and R.B.; visualization, H.T.; supervision, S.H.; project administration, R.A.C. and R.B.; funding acquisition, R.A.C. and R.B. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Illinois Nutrient Research & Education Council (project # 2018-3-360624-356). A partial support was provided by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch project (No. ILLU-741-337).  
Not applicable.  
Data Availability StatementData are available upon request from the corresponding author.  
Gratitude is extended to the University of Illinois at Urbana Champaign, Department of Agricultural and Biological Engineering. We gratefully acknowledge the comments and suggestions of the anonymous reviewers.  
The authors declare no conflict of interest.