在SCE－UA算法的基础上，结合Pareto排序和模糊多目标优选的优点，提出了水文模型模糊多目标 SCE－UA（FMOSCE－UA）参数率定方法。目标函数综合考虑了洪峰流量、水量平衡、峰现时间以及流量过程均 方差等水文过程的不同要素，使得优选的参数更能反映流域水文特征。双牌水库实例研究结果表明FMOSCE－ UA优于标准SCE－UA算法，优选参数完全可以用于实际洪水预报。

Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristic of the observed data deemed to be important. In this paper, a fuzzy multi-objective SCE-UA(FMOSCE-UA) optimization method for rainfall-runoff models which combines advantages of Pareto ranking and fuzzy multi-objective optimization is developed to solve the multi-objective optimization problem for hydrologic models. The FMOSCE-UA considers different aspects of the hydrograph, such as water balance, overall shape of the hydrograph, peak flow and its corresponding time, which makes the model's behavior match the observed hydrograph more closely. The features and capabilities of FMOSCE-UA are illustrated by means of a simple calibration study for Shuangpai Reservior with Xinanjiang model.