The aims of this study are capability assessment of the SWAT model and SWAT-CUP software in hydrological simulation and evaluation of uncertainty of SWAT model in estimating runoff. In the modeling process, the basin was divided into 12 sub-basins and 294 hydrological units (HRUs). Model calibration and uncertainty analysis were performed using the sequential uncertainty fitting (SUFI2) algorithm for 2000–2006 and 2007–2010, respectively. Based on the sensitivity analysis results, the parameters are the USLE_P soil protection factor, wet soil density (SOL_BD), and CN among the most important parameters in determining the amount of output runoff. Among these factors, SCS-CN was recognized as the most sensitive parameter. Based on the results, the coefficients R², bR², and Nash–Sutcliffe index (NS) were 0.75, 0.59, and 0.67 for calibration period and 0.46, 0.24, and 0.42 for validation period. The results of the model showed the model performance is weak in the stage of calibration. This is due to the lack of accuracy and precision in the statistics available in the region, the lack of statistics on the amount of water collected from the upstream gardens of the area, as well as the lack of statistics on the existing springs. The model is therefore recommended for applications in arid and semiarid catchments within Iran with similar data. Due to the limited availability of hydrological data in Iran, this study has not assessed and compared the uncertainty related to the SWAT model of future runoff.

本研究的目的是评估 SWAT 模型和 SWAT-CUP 软件在水文模拟中的能力，并评估 SWAT 模型在估计径流中的不确定性。在建模过程中，该流域被划分为 12 个子流域和 294 个水文单元 （HRU）。分别使用 2000-2006 年和 2007-2010 年的顺序不确定性拟合 （SUFI2） 算法进行模型校准和不确定性分析。根据敏感性分析结果，参数是USLE_P土壤保护因子、湿土壤密度 （SOL_BD） 和 CN 是确定输出径流量的最重要参数。在这些因素中，SCS-CN 被认为是最敏感的参数。根据结果，校准期的系数 R²、bR² 和 Nash-Sutcliffe 指数 （NS） 分别为 0.75、0.59 和 0.67，验证期的系数为 0.46、0.24 和 0.42。模型结果表明，模型在标定阶段性能较弱。这是由于该地区可用的统计数据缺乏准确性和精确性，缺乏从该地区上游花园收集的水量的统计数据，以及缺乏现有泉水的统计数据。因此，建议将该模型用于伊朗境内具有类似数据的干旱和半干旱集水区的应用。由于伊朗水文数据的可用性有限，本研究尚未评估和比较与未来径流的 SWAT 模型相关的不确定性。