应用新安江模型进行水文模拟时，由于模型本身的不足及参数多、信息量少等原因，会出现率定的最优参数组不唯一、不稳定等问题。考虑到以往的参数优选，都只得出一个参数组，不能反映出其不确定性状况。提出应用基于马尔可夫链蒙特卡罗（MCMC）理论的SCEM-UA算法，通过双牌流域以1 h为时段间隔的36场典型洪水数据对新安江模型参数进行优选和不确定性评估。结果表明，该算法能很好地推出新安江模型参数的后验概率分布；率定和检验结果分析也表明，应用SCEM-UA算法对新安江模型进行优选和不确定评估是有效和可行的。

While Xin'anjiang model is applied to simulate hydrograph, the "best"  parameter set calibrated may be not unique and uncertain because of model limitation, more parameters and limited information. Considering previously parameter optimization of Xin'anjiang model, there is only a unique "best"  parameter set to be found and it doesn't describe uncertainty of parameter. This paper presents using SCEM-UA algorithm based Markov Chain Monte Carlo (MCMC) methods for optimization and uncertainty assessment of Xin'anjiang model parameters by means of 36 historical floods data with one hour interval. The results demonstrate that SCEM-UA algorithm is well suited to infer the posterior distribution of Xin'anjiang model parameters. The results of calibration and validation indicate that it is feasible and effective for optimization and uncertainty assessment of Xin'anjiang model parameters.

王文川（1976－），男，河南鹿邑县人，博士，主要从事水电系统不确定因素分析及优化建模