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| 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 | |
| 引用本文： | 曹飞凤,张世强,许月萍,楼章华.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[J].中山大学学报(自然科学版),2011,50(2). |
| 作者姓名： | 曹飞凤 张世强 许月萍 楼章华 |
| 作者单位： | 1. 浙江省水利水电工程局,浙江杭州,310020;浙江大学建筑工程学院水文与水资源工程研究所,浙江杭州,310058 2. 中国科学院寒区旱区环境与工程研究所,甘肃兰州,730000 3. 浙江大学建筑工程学院水文与水资源工程研究所,浙江杭州,310058 |
| 基金项目： | 国家自然科学基金资助项目，科技部国际合作资助项目 |
| 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。 |
| 关 键 词： | Markov链蒙特卡罗法 参数优选 SCEM-UA 敏感性分析 不确定性分析 降雨径流概念模型 |
| 收稿时间： | 2010-03-29; |
Parameter Optimization of Hydrologic Model Parameters based on Regional Sensitivity Analysis and SCEM-UA Algorithm |
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| CAO Feifeng,ZHANG Shiqiang,XU Yueping,LOU Zhanghua.Parameter Optimization of Hydrologic Model Parameters based on Regional Sensitivity Analysis and SCEM-UA Algorithm[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2011,50(2). | |
| Authors: | CAO Feifeng ZHANG Shiqiang XU Yueping LOU Zhanghua |
| Institution: | (1.Bureau of Water Resources &; Hydropower Engineering of Zhejiang Province,Hangzhou 310020,China;2Institute of Hydrology and Water Resources, Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China;3Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Science, Lanzhou 730000,China) |
| Abstract: | 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. |
| Keywords: | SCEM-UA |
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