| 基于贝叶斯模型平均的径流模拟及不确定性分析 |
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| 引用本文： | 杜新忠,李叙勇,王慧亮,李文赞.基于贝叶斯模型平均的径流模拟及不确定性分析[J].水文,2014,34(3):6-10. |
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| 作者姓名： | 杜新忠 李叙勇 王慧亮 李文赞 |
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| 作者单位： | 1.中国科学院生态环境研究中心,城市与区域生态国家重点实验室2.中国科学院大学 |
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| 基金项目： | 国家自然科学基金(41071323)；国家水体污染控制与治理科技重大专项(2012ZX07203003,2012ZX07029002)； |
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| 摘 要： | 水文模型是模拟水循环过程重要手段,依靠单个模型进行模拟往往存在很大的不确定性,使通过多模型进行组合模拟成为必然趋势。选取3个集总式水文模型应用贝叶斯模型平均(BMA)进行流域月径流量的多模型模拟,采用期望最大化算法推求BMA分布参数以得到BMA均值模拟序列和90%不确定性区间。以武烈河实测数据为例进行分析,结果表明:BMA方法既能通过均值模拟提供更高精度的模拟效果,还可通过不确定性置信区间定量评价模型结构不确定性,为径流模拟提供丰富信息。
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| 关 键 词： | 水文模型 贝叶斯平均 多模型 不确定性 |
| 收稿时间： | 2013/2/4 0:00:00 |
Multi-model Ensemble Runoff Simulation Based on Bayesian Model Averaging Method and Model Structure Uncertainty Analysis |
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| DU Xinzhong,LI Xuyong,WANG Huiliang,LI Wenzan.Multi-model Ensemble Runoff Simulation Based on Bayesian Model Averaging Method and Model Structure Uncertainty Analysis[J].Hydrology,2014,34(3):6-10. |
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| Authors: | DU Xinzhong LI Xuyong WANG Huiliang LI Wenzan |
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| Institution: | 1.State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | Hydrologic model is a significant tool to simulate the water cycle, while there is great uncertainty depending on single model simulation which makes ensemble hydrologic model simulation become inevitable trend. Three lumped hydrological models were selected to conduct watershed monthly runoff ensemble simulation, and the expectation maximum algorithm was used to estimate the parameters of the BMA in order to obtain BMA mean simulation and 90% uncertainty interval. The result using the observed data of the Wuliehe watershed indicates that the BMA method can not only provide simulation with higher precision through mean simulation, but also provide quantitative evaluation of the model structure through confidence interval which could offer rich information of the runoff simulation. |
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| Keywords: | hydrological model Bayesian model averaging (BMA) multi-model uncertainty |
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