中文摘要：

针对复杂非线性水文模型参数识别及不确定性分析问题,引入基于马尔可夫链蒙特卡罗思想的SCEM-UA算法,以岷江流域为研究实例,对降雨径流模型的参数优选问题进行了分析,并探讨了该算法在推求参数后验分布的搜索性能和效率。结果发现,SCEM-UA算法能快速有效地推求出参数后验概率分布。同时,开展基于SCEM-UA算法取样的参数全局敏感性分析,对比参数敏感性和后验分布,表明两者密切相关,敏感性强的参数其边缘后验概率密度分布存在明显峰值,相反,敏感性弱的参数其后验概率分布较为平坦且无规律可循,从而导致模型参数的不确定性大大增强。

英文摘要：

Shuffled Complex Evolution Metropolis Algorithm（SCEM-UA） is an adaptive Markov Chain Monte Carlo sampler,which can be applied to parameter optimization of nonlinear hydrologic model and uncertainty analysis.The efficiency and effectiveness of SCEM-UA for sampling the posterior distribution of model parameters are discussed based on the case study of the Min River catchment.The results show that SCEM-UA algorithm is consistent,effective and efficient in inferring the parameter posterior distribution.Moreover,the results of regional sensitivity analysis using samples from SCEM-UA algorithm sampler show that sensitivity and posterior distribution of parameters are highly interdependent.High sensitive parameters correspond with distinct peak in posterior distribution,while low sensitive parameters correspond with flat posterior distribution which could highlight the uncertainty of model parameters.