Distinct calibration for wet and dry periods of the simulation period has resulted in fixed discretization of the simulation periods throughout the year with a repetitive pattern for all the simulation years ignoring the variability of rainfall and soil moisture conditions along time. This may induce anomalies in the water balance of the basin. This study focused on developing a discretization methodology using clustering methods that help eliminate the existing limitations. Subsequently, the clusters are calibrated independently, as well as simultaneous with multiple objectives. The procedure is illustrated through two models viz. SWAT and a grid based model; set up for the US watersheds Cedar Creek, Indiana and Riesel Y2, Texas respectively. The proposed approach resulted in improved stream flow simulations with higher NSE (0.83 in multi-objective; 0.68 in traditional calibration) in Cedar Creek. The simulation of water balance components was found to be more effective in both the basins.

对模拟期的干湿期进行了不同的校准，从而使全年的模拟期固定离散，并且在所有模拟年中都具有重复模式，而忽略了降雨和土壤湿度条件随时间的变化。这可能会导致流域水平衡异常。这项研究的重点是使用聚类方法开发离散化方法，以帮助消除现有限制。随后，对群集进行独立校准，并同时校准多个目标。该过程通过两个模型来说明。特警和基于网格的模型；分别为美国印第安纳州锡达克里克（Cedar Creek）和德克萨斯州Riesel Y2建立分水岭。拟议的方法导致了更高的NSE（0。多目标83分；在锡达克里克（Cedar Creek）中，传统校准值为0.68）。发现在两个盆地中水平衡分量的模拟都更有效。