## 1 Introduction  
For facilitating crop production and improved yield in areas with inadequate agricultural drainage, subsurface (tile) drainage helps assure reliable and successful crop production by removing extra water from the agricultural field. By enhancing traffacability, drainage makes it easier to access fields in time for activities like tillage, planting, and harvesting. Plant stress can be also reduced by removing the extra water from the root zone and boosting crop output [1]. In addition, subsurface drainage lessens surface runoff, sediment losses, and the flow of contaminants adhering to the sediment, including phosphorus, nitrogen, and pesticides, into surface waters [2]. It enhances soil aeration at the root depth, which assists in organic matter decomposition in the soil. Due to the interconnected dynamics of soil, water, and plant systems with multiple processes and variables that influence the behavior, monitoring studies on a large scale to capture this complexity can be challenging. Hydrological models have proliferated in the past few decades as a tool to evaluate the problems associated with water management primarily for two reasons. First, using models enhances the current understanding of cause-effect dynamics in the aquatic ecosystem. Second, models offer a synthesis of essential insights in the policy arena [3]. The process-based hydrological models conceptualize the scientific understanding of the watershed's and field's hydrological, plant physiological, and biogeochemical processes, providing an edge over empirical models for simulating these associated processes [4]. No hydrological models are an ideal representation of the actual process involved, and it is also challenging to provide the initial and boundary conditions required by a model with absolute precision. Thus, all the model calibrations and future predictions/projections are subjected to uncertainty which arises primarily due to input parameters, observed data, and uncertainties in the structure of model [5]. On the other hand, different sets of model parameters lead to a similar performance index (for example, NSE or RSQ), which leads to equifinality [6]. The bias in model predictions and the likelihood of errors increases with the increase in uncertainty of the input parameters. Due to the incompatibility between the model complexity and observed data, determining the model input parameters during the calibration phase is one of the critical tasks regardless of the hydrological model used. For example, the model parameters in hydrological models can be estimated using manual and autocalibration approaches using discharge-related measures. However, if the hydrological model has lots of parameters, calibrating numerous parameters by manual is a time-consuming and cumbersome process. Parameter sensitivity analysis can be considered to effectively minimize adjustment workload and achieve optimum simulation quickly and efficiently direct field data collection and monitoring [7,8]. Sensitivity analysis is widely used to quantify the impact of change in model parameters in the variance in predicting model outputs. It provides insights into the parameters that have the most bearing on the model outputs, which can be utilized to refine calibration and improve the model structure to lower model complexity and uncertainty [4]. Primarily, two methods, global sensitivity and local sensitivity, are applied to study the sensitivity of the models. In global sensitivity, all parameters are simultaneously altered in each model run. In contrast to global methods, in the local sensitivity analysis (also called the one-at-a-time (OAT) method), one parameter is altered at a time, keeping all other parameters as base values. Due to high computational efficiency and simplicity, OAT methods have been extensively utilized for sensitivity analysis of hydrological models [9, 10, 11, 12].