基于SCEM-UA算法和全局敏感性分析的水文模型参数优选不确定性研究

 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。

Parameter Optimization of Hydrologic Model Parameters based on Regional Sensitivity Analysis and SCEM-UA Algorithm

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.