| 河道洪水实时概率预报模型与应用 |
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| 引用本文： | 徐兴亚,方红卫,张岳峰,赖瑞勋,黄磊,刘晓波. 河道洪水实时概率预报模型与应用[J]. 水科学进展, 2015, 26(3): 356-364. DOI: 10.14042/j.cnki.32.1309.2015.03.007 |
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| 作者姓名： | 徐兴亚 方红卫 张岳峰 赖瑞勋 黄磊 刘晓波 |
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| 作者单位： | 1.清华大学水沙科学与水利水电工程国家重点实验室, 北京 100084; |
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| 基金项目： | 国家自然科学基金资助项目(51209230;11372161) |
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| 摘 要： | 通过数据同化方法合理地将实时水文观测数据融入到洪水预报模型中,可提高洪水预报模型的实时性和精确度。选取沿程断面流量、水位和糙率系数作为代表水流状态的基本粒子,以监测断面实测水位数据作为观测信息,建立了基于粒子滤波数据同化算法的河道洪水实时概率预报模型。模型应用于黄河中下游河道洪水预报计算的结果表明,采用粒子滤波方法同化观测水位后,不仅可以直接校正水位,同时也可以有效地校正流量和糙率,为未来时刻模型预报计算提供更准确的水流初始条件和糙率取值区间,进而有效地提高模型预报结果的精度,给出合理的概率预报区间。不同预报期的预报结果表明,随着预报期的增长,同化效果减弱,模型预报结果的精度会有所降低,水位概率预报结果受粒子间糙率不同的影响不确定性增加,而流量概率预报结果受给定模型边界条件的影响不确定性降低。所提出模型可以有效同化真实水位观测数据,适合应用于实际的洪水预报工作中。
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| 关 键 词： | 洪水预报 概率预报 粒子滤波 数据同化 实时校正 |
| 收稿时间： | 2014-09-10 |
A real-time probabilistic channel flood forecasting model and application based on particle filters |
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| XU Xingya,FANG Hongwei,ZHANG Yuefeng,LAI Ruixun,HUANG Lei,LIU Xiaobo. A real-time probabilistic channel flood forecasting model and application based on particle filters[J]. Advances in Water Science, 2015, 26(3): 356-364. DOI: 10.14042/j.cnki.32.1309.2015.03.007 |
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| Authors: | XU Xingya FANG Hongwei ZHANG Yuefeng LAI Ruixun HUANG Lei LIU Xiaobo |
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| Affiliation: | 1.State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China;2.Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China;3.Department of Water Environment, China Institute of Water Resources and Hydropower Research, Beijing 100038, China |
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| Abstract: | We can improve the accuracy of real-time flood forecasting models using data assimilation, which integrates hydrological observation data with the flood forecasting model. We have developed a real-time, probabilistic channel flood forecasting model based on a particle filter. It takes the discharge, stage, and roughness coefficient of cross sections along the river as the basic particles of the flow state, and stage observations at hydrological stations as the required observations. We applied the model to a real flood event, downstream from the Yellow River. Our results show that particle filter algorithm effectively corrected the flow state particles. Additionally, we produced more accurate intervals for the flow's initial condition and roughness coefficient, which can be used in future flood forecasting calculations. These will improve the accuracy of the model's predictions, because the probabilistic intervals are more appropriate. Moreover, the forecasts for different lead times indicate that, as the lead time increases, the positive effect of the data assimilation weakens, reducing the accuracy of the forecasts. The uncertainties of the stage prediction increase over time, because different particles have different roughness coefficients. Additionally, the uncertainties of the discharge predictions decrease over time, because of the given deterministic model boundary conditions. The model can successfully assimilate the original historical stage observation data, which shows that it is practical and can be applied to real flood forecasting tasks. |
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| Keywords: | flood forecasting probabilistic forecast particle filter data assimilation real-time correction |
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