## 3 Results and Discussion
### Sensitivity Analysis
However, their uncertainty impacts winter drainage flow and, so, their impact on water quality might be significant. TKA and TKB represent soil thermal conductivity function and had little impact on both model outputs. Figures 6 and 7 summarize the LH-OAT sensitivity index value of DRAINMOD hydrologic parameters for CS-3 and CS-4, respectively. RMSE and PBIAS are represented by the absolute sensitivity index, while the relative sensitivity index characterizes NSE, RSQ relative yield, and cumulative flow. It is because the values of RMSE and PBIAS are zero at base simulation, which cannot represent the actual relative sensitivity of each parameter since the ratio of change in model output per change in parameters input would always be constant, whatever the parameters be. In both figures, the sensitivity of PBIAS appears more dominating than RMSE. The dominating magnitude is because of the higher absolute values of PBIAS than RMSE. However, the relative sensitivity characteristics can depict the comparative evaluation of the model output's behavior. Most RSI values were higher for daily flow indicators (NSE and RSQ) than the water balance indicator (cumulative flow) in both fields. It represents the lower agreement of the daily flow between the base and simulated values than the long-term water balance by varying parameters in each model run. Figure 5: DRAINMOD output sensitivity with parametric variation for field CS-4. Figure 6: LH-OAT sensitivity index for CS-3. Figure 7: LH-OAT sensitivity index for CS-4. For the CS-3 field, daily flow was most sensitive to drain spacing (ranked first for NSE and RSQ sensitivity), followed by drain depth and lateral hydraulic conductivity of the third layer from the surface (Table 2). Similarly, LH-OAT ranked CICE and DC as the fourth and fifth sensitive parameters for daily flow. Nevertheless, RSQ and NSE did not behave similarly in field CS-4 (Table 3). RSI for NSE was highest for drain depth, followed by drain spacing, depth of impermeable layer, TKA, ZA, and Kirkham depth, respectively. However, drain spacing ranked first, followed by the depth of impermeable layer, drain spacing, TKA, and ZA. Slope and surface storage had negligible effects on the model outputs in both fields. In general, the slope affects the runoff and, hence, the infiltration capacity of the field, but the slopes in both fields were less than 2%, which is comparatively flat, and the length of fields was also short (7.8 m for CS-3 and 8.6 m for CS-4) in the direction of drain tiles layout. Thus, the variation of slopes within the set ranges did not affect the model outputs significantly. The lateral conductivity of the second and third layers also had an insignificant contribution to flow outputs for CS-4. In CS-4, these were layers with comparatively lower thicknesses than the other layers. Moreover, the soil layers' composition (clay, silt, and sand percentage) and water-holding capacity also impacted the layer properties. For the CS-3 field, the long-term water balance was most sensitive to soil temperature parameters with the first ranking of CICE (Table 2). Similarly, other soil temperature parameters ZA, TKA, and TKB were ranked third, fourth, and fifth for CS-3. This result was quite unusual compared to other flow indices and the water balance index for field CS-4. A possible reason could be the winter flow observed from the model output compared to the no-flow condition from the observed data (Figure 3). This result also depicts an uncertainty of soil temperature parameters affecting the behavior of sub-surface hydrology in cold regions such as Illinois. For CS-4, however, water balance was most affected by the drainage system parameters drain depth and spacing with first and second ranking, respectively. This result corresponds to the daily flow indicator (NSE) for CS-4. Kirkham depth, LK5, and CDEG were ranked third, fourth, and fifth, respectively.