| BMA集合预报在淮河流域应用及参数规律初探 |
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| 引用本文： | 刘开磊,胡友兵,汪跃军,王秀庆.BMA集合预报在淮河流域应用及参数规律初探[J].湖泊科学,2017,29(6):1520-1527. |
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| 作者姓名： | 刘开磊 胡友兵 汪跃军 王秀庆 |
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| 作者单位： | 淮河水利委员会水文局(信息中心), 蚌埠 233000,淮河水利委员会水文局(信息中心), 蚌埠 233000,淮河水利委员会水文局(信息中心), 蚌埠 233000,沂沭泗水利管理局水文局(信息中心), 徐州 221000 |
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| 基金项目： | 国家重点基础研究发展计划项目（2016YFC0402703，2016YFC0400909）资助. |
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| 摘 要： | 以淮河流域吴家渡水文站作为试验站点,采用基于贝叶斯平均法(BMA)的集合预报模型处理来源于马斯京根法、一维水动力学方法、BPNN(Back Propagation Neural Network)的预报流量序列,通过分析BMA的参数以及其预报结果,对各方法在淮河典型站点流量预报中的适用性进行验证与分析.经2003—2016年19场洪水模拟检验可知,BMA模型能够有效避免模型选择带来的洪水预报误差放大效应,可以提供高精度、鲁棒性强的洪水预报结果.通过进一步比较各模型统计最优的频率与BMA权重值之间的相关性,发现权重值不适用于对单场洪水预报精度评定,而适用于描述多场洪水预报中,模型为最优的统计频率;基于大量先验信息,提前获取BMA的权重等参数,将是指导模型选择、降低洪水预报不确定性、改进洪水预报技术的有效手段.
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| 关 键 词： | 集合预报 洪水预报 不确定性 权重 淮河流域 贝叶斯平均法 |
| 收稿时间： | 2017/1/18 0:00:00 |
| 修稿时间： | 2017/3/23 0:00:00 |
Performance and parameterization of the BMA model applied in the Huaihe River Basin |
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| LIU Kailei,HU Youbing,WANG Yuejun and WANG Xiuqing.Performance and parameterization of the BMA model applied in the Huaihe River Basin[J].Journal of Lake Science,2017,29(6):1520-1527. |
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| Authors: | LIU Kailei HU Youbing WANG Yuejun and WANG Xiuqing |
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| Institution: | Huaihe River Commission of the Ministry of Water Resources, Bengbu 233000, P. R. China,Huaihe River Commission of the Ministry of Water Resources, Bengbu 233000, P. R. China,Huaihe River Commission of the Ministry of Water Resources, Bengbu 233000, P. R. China and Water Conservancy Administration of the Yi-Shu-Si River Basin, Xuzhou 221000, P. R. China |
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| Abstract: | In this study, the BMA (Bayesian Model Averaging) method is used to deal with forecasts derived from three different flood routing models, i.e. the Muskingum, the one-dimensional hydrodynamic routing model and the BPNN (Back Propagation Neural Network). The numerical experiments are processed at the Wujiadu hydrological station in the Huaihe River Basin as a test site. By studying the BMA method parameters and ensemble forecast results the applicability of each method in the Huaihe River station flow forecast is verified and analyzed. According to the results from our experimental tests among 19 flood events, from 2003 to 2016, it is concluded that the BMA ensemble forecasting method can effectively avoid the flood forecast error amplification caused by uncertainty underlying in model selection and can provide high accuracy and robust flood forecasting result. After comparing the statistically optimal frequency and BMA weight value of each model, it is concluded that the BMA weight parameter reflects the probability that the model is optimal in the mean sense and is not suitable for evaluating the technicity of the model in one single flood event. It is an effective method to guide the model selection, reduce the uncertainties of flood forecasting and improve the flood forecasting techniques. |
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| Keywords: | Ensemble forecast flood forecast forecast uncertainty weight Huaihe River Basin Bayesian Model Averaging |
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