摘要
实时校正作为提升洪水预报精度的最后一道屏障,是洪水预报的重要组成部分。针对洪水过程与洪水要素校正效果较差的问题,以横江屯溪以上流域为例,在对流域历史洪水数据的降雨径流序列与洪水特征值遴选的基础上,构建了暴雨洪水特征库,提出了一种结合卡尔曼滤波和K最邻近结点算法的联合实时校正方法。结果表明:相较于模型未校正及单一校正方法,联合实时校正方法在减少洪水预报过程中的洪峰预报误差、洪量预报误差及峰现时间误差上更为有效,预见期在6 h及以下时仍可以保持较好的稳定性与准确性。此方法对于提高中小河流洪水预报精度、有效预警和防灾减灾等方面都具有重要作用,同时对研究区域洪水特征具有一定参考价值。
Real-time correction,as the last barrier to improving forecast accuracy,is an important part of flood forecasting.To address the problem of poor correction of flood process and flood elements,a joint real-time correction method combining a Kalman filter and a K-nearest neighbor algorithm is developed through selecting the rainfall runoff series of the basin and the eigenvalues of its historical flood data and constructing a storm flood feature database,in combination with a case study of the watershed above Tunxi of Hengjiang River.The results show that relative to the uncorrected model or single correction method,this new method is more effective in reducing forecast errors of flood peak,volume,and flood peak time,while it can maintain stability and accuracy for a forecast period of six hours or shorter.It could play an important role in improving flood forecast accuracy,effective early warning,and disaster prevention and mitigation for small-and medium-sized rivers or similar regional flood forecasting.
作者
陈鑫
刘艳丽
张建云
曹孟
贺瑞敏
金君良
王国庆
鲍振鑫
CHEN Xin;LIU Yanli;ZHANG Jianyun;CAO Meng;HE Ruimin;JIN Junliang;WANG Guoqing;BAO Zhenxin(School of Civil Engineering,Tianjin University,Tianjin 300072,China;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Nanjing Hydraulic Research Institute,Nanjing 210098,China;Research Center for Climate Change of Ministry of Water Resources,Nanjing 210029,China;Yangtze Institute for Conservation&Development,Nanjing 210098,China)
出处
《水力发电学报》
CSCD
北大核心
2022年第8期54-62,共9页
Journal of Hydroelectric Engineering
基金
国家重点研发计划资助项目(2018YFC1508104)
国家自然科学基金资助项目(52079079)。
关键词
洪水预报
实时校正
K最邻近算法
集合卡尔曼滤波
暴雨洪水数据库
屯溪流域
flood forecast
real-time correction
K-nearest neighbor algorithm
ensemble Kalman filter
storm and flood database
Tunxi River basin
