水动力学模型实时校正方法对比
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摘要
选择典型的实时校正方法:传统的误差自回归、基于K最邻近算法(KNN)的非参数校正及基于Kalman滤波的多断面校正法,并以Kalman滤波与KNN结合构造综合方法,以淮河流域吴家渡—小柳巷区间作为试验河段,构建一维水动力学模型并与实时校正方法联合应用。简要介绍这4种方法的原理与模型构建方法,然后对比分析各种方法的模拟结果,尤其对模拟洪峰稳定性、峰现时间、峰现误差等进行比较,认为前3种基本方法均能在相当长的预见期内提高洪水的预报精度,而综合法实时校正法对洪峰部位的模拟更为稳定可靠、总体效果更好,更适合预报校正工作的需要。
Three typical real-time correction methods,including the traditional error autoregressive method,the nonparametric correction method based on the K-nearest neighbor( KNN),and the multi-cross section correction method based on Kalman filtering,as well as a combination of the KNN method and the Kalman filtering method were used in combination with a one-dimensional hydrodynamic model for flood forecasting and real-time correction in the Wujiadu-Xiaoliuxiang reach of the Huaihe Basin. The principles and construction methods of the four methods are briefly introduced,and a comparative analysis of their simulation results,especially of the stability of the flood peak,the time of occurrence of the flood peak,and the error of occurrence of the flood peak,is conducted. The conclusions are as follows: the three basic methods can improve the accuracy of flood forecasting over quite a long forecast period,and the combination method is more effective and reliable for flood peak simulation,and is more applicable to forecast correction.
Three typical real-time correction methods,including the traditional error autoregressive method,the nonparametric correction method based on the K-nearest neighbor( KNN),and the multi-cross section correction method based on Kalman filtering,as well as a combination of the KNN method and the Kalman filtering method were used in combination with a one-dimensional hydrodynamic model for flood forecasting and real-time correction in the Wujiadu-Xiaoliuxiang reach of the Huaihe Basin. The principles and construction methods of the four methods are briefly introduced,and a comparative analysis of their simulation results,especially of the stability of the flood peak,the time of occurrence of the flood peak,and the error of occurrence of the flood peak,is conducted. The conclusions are as follows: the three basic methods can improve the accuracy of flood forecasting over quite a long forecast period,and the combination method is more effective and reliable for flood peak simulation,and is more applicable to forecast correction.
引文
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[3]王船海,白耀玲.基于卡尔曼滤波技术的河道汇流实时校正[J].河海大学学报:自然科学版,2007,35(2):181-185.(WANG Chuanhai,BAI Yaoling.Real-time correction of flow concentration of river channels based on Kalman filter technique[J].Journal of Hohai University:Natural Sciences,2007,35(2):181-185.(in Chinese))
[4]REFSGAARD J C.Validation and intercomparison of different updating procedures for real-time forecasting[J].Nordic Hydrology,1997,28:65-84.
[5]杨敏.水文时间序列相似性模型的研究与应用[D].南京:河海大学,2002.
[6]KARLSSON M,YAKOWITZ S.Nearest-neighbor methods for nonparametric rainfall-runoff forecasting[J].Water Resource Research,1987,23(7):1300-1308.
[7]葛守西.现代洪水预报技术[M].北京:中国水利水电出版社,1999:72-129.
[8]李致家,孔凡哲,王栋,等.现代水文模拟与预报技术[M].南京:河海大学出版社,2010:125-127.
[9]DAVID R M.Handbook of Hydrology[M].New York:McGraw-Hill,1993:960-986.
[10]夏自强.多汊道串联感潮河段洪水演算方法探讨[D].南京:河海大学,1988.
[11]CLOKE H L,PAPPERGER F.Ensemble flood forecasting:a review[J].Journal of Hydrology,2009,375(3/4):613-626.
[12]HENRIK M,CLAUS S.Adaptive state updating in real-time river flow forecasting:a combined filtering and error forecasting procedure[J].Journal of Hydrology,2005,308(1/4):302-312.
[13]VAN S N,RONSYN J,WILLEMS P.A non-parametric data-based approach for probabilistic flood forecasting in support of uncertainty communication[J].Environmental Modeling&Software,2012,33:92-105.
[14]赖锡军,李纪人.洪水淹没范围数据与动力学模型的融合[J].河海大学学报:自然科学版,2009,37(5):529-533.(LAI Xijun,LI Jiren.Assimilation of flood extent data into hydrodynamic model[J].Journal of Hohai University:Natural Sciences,2009,37(5):529-533.(in Chinese))