Spatially distributed watershed models are commonly utilized to address a wide range of water-related issues. However, setting up a reliable watershed model is a difficult task involving several essential decisions making. Choice of calibration method is one of the most important decisions that has been sparsely investigated in semi-distributed watershed models. In this study, therefore, we used the Soil and Water Assessment Tool (SWAT) model to investigate the impact of three calibration methods: sequential (SQN), simultaneous (SML) and sequential-simultaneous (SQN_SML) on model performance and parameter uncertainty in the Kantamal catchment of the Mahanadi basin, India. The findings across the calibration methods; evaluated fit scores of streamflow for respective calibration and validation period; showed that SQN_SML calibration has the least amount of bias (PBAIS = 1.7, −4.2), the highest NSE (0.91, 0.92), KGE (0.95, 0.94) and R² (0.91, 0.92). Furthermore, SQN_SML outperformed the other two methods in all three streamflow regimes (low, medium and high) of flow duration curve analysis. Suspended sediment load (SSL) analyses of partitioned sediment duration curve showed the best performance of SQN_SML for mid and low SSL regimes while all three calibration methods performed similarly in the high SSL regime. SML calibration approach showed the least parameter uncertainty followed by SQN_SML and SQN. The P-factor for sediment simulation was better for the SQN_SML approach, indicating the minimal model error for sediment simulation. The SQN_SML produced the least equifinal solution, while the SQN approach produced the highest equifinal solution. Overall, the findings of this study may help the watershed modelling communities for selecting suitable calibration strategies when dealing with integrated water resources management.

空间分布的流域模型通常用于解决广泛的与水有关的问题。然而，建立一个可靠的流域模型是一项艰巨的任务，涉及到几个重要的决策。校准方法的选择是在半分布式流域模型中很少研究的最重要的决定之一。因此，在本研究中，我们使用土壤和水评估工具 (SWAT) 模型来研究三种校准方法的影响：顺序 (SQN)、同步 (SML) 和顺序同步 (SQN_SML) 对模型性能和参数不确定性的影响印度马哈纳迪盆地的坎塔马尔流域。校准方法的结果；评估各个校准和验证期间的流量拟合分数；²(0.91, 0.92)。此外，SQN_SML 在流量持续时间曲线分析的所有三种流态（低、中和高）中都优于其他两种方法。分区沉积物持续时间曲线的悬浮沉积物载荷 (SSL) 分析显示，SQN_SML 在中低 SSL 状态下的性能最佳，而所有三种校准方法在高 SSL 状态下的性能相似。SML 校准方法显示出最小的参数不确定性，其次是 SQN_SML 和 SQN。SQN_SML 方法的沉积物模拟 P 因子更好，表明沉积物模拟的模型误差最小。SQN_SML 产生了最小等终解，而 SQN 方法产生了最高等终解。全面的，