Hydrological Modeling of Mahi Basin Using SWAT

In general, hydrological models such as SWAT incorporate many parameters (both statistical and of physical significance). Most of these parameters obtain their values via extensive field surveys and experiments; the resulting values are then used to calibrate the model. And few of these parameters have a significant impact on the modeling results, and so they are referred to as “sensitive parameters” while others don’t have much impact or very little impact. The other parameters (“insensitive parameters”) can be ignored in the mathematical model, leading to a simplification of the model’s structure. Sensitivity analysis of parameters aims to explore the sensitivity of prediction variables to parameter variability. Sensitivity analysis helps reduce the number of parameters in the calibration process, while an automatic calibration technique allows the user to avoid tedious, time$consuming, manual calibration. The re sult is a computationally efficient calibration [19]. By including uncertainty analysis in the model parameters and output variable, more information can be conveyed about the degree of risk associated with a specific action. Uncertainty and sensitivity analyses are critical for decision$maki ng. While calibrating the model, two concepts must be kept in mind: (1) parameter non-uniqueness , which states that there are many other solutions (different parameter values) that produce equally good results; and (2) parameter conditionality , which means that a calibrated model is only locally conditional and cannot be applied globally [2]. This paper’s objective is to check the SWAT model’s