Runoff simulation is highly significant for hydrological monitoring, flood peak simulation, water resource management, and basin protection. Runoff simulation by distributed hydrological models, such as the soil and water assessment tool (SWAT) model which is the most widely used, is becoming a hotspot for hydrological forecasting research. However, parameter calibration is inefficient and inaccurate for the SWAT model. An automatic parameter calibration (APC) method of the SWAT model was developed by hybrid of the genetic algorithm (GA) and particle swarm optimization (PSO). Multi‐station and multi‐period runoff simulation and accuracy analysis were conducted in the basin of the Zhangjiang River on the basis of this hybrid algorithm. For example, in the Yaoxiaba Station, the calibration results produced an R² of 0.87 and Nash Sutcliffe efficiency (NSE) index of 0.85, while verification results revealed an R² of 0.83 and NSE of 0.83. Results of this study show that the proposed method can effectively improve the efficiency and simulation accuracy of the model parameters. It can be concluded that the feasibility and applicability of GA‐PSO as an APC method for the SWAT model were confirmed via case studies. The proposed method can provide theoretical guidance for many hydrological research fields, such as hydrological simulation, flood prevention, and forecasting.

中文翻译：

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径流模拟中SWAT模型的参数自动校正方法

径流模拟对于水文监测，洪峰模拟，水资源管理和流域保护具有重要意义。利用分布式水文模型进行径流模拟，例如使用最广泛的土壤和水评估工具（SWAT）模型，正在成为水文预报研究的热点。但是，参数校准对于SWAT模型而言效率低下且不准确。通过遗传算法（GA）和粒子群优化算法（PSO）的混合，开发了SWAT模型的自动参数校准（APC）方法。基于这种混合算法，在张江流域进行了多站，多期径流模拟和精度分析。例如，在姚下坝站，校准结果产生了R ²系数为0.87，纳什苏特克利夫效率（NSE）指数为0.85，而验证结果显示R ²为0.83，NSE为0.83。研究结果表明，该方法可以有效提高模型参数的效率和仿真精度。可以得出结论，通过案例研究证实了GA-PSO作为SWAT模型的APC方法的可行性和适用性。该方法可以为水文模拟，防洪，预报等许多水文研究领域提供理论指导。