结合糙率校正的河网水情数据同化  

Research on data-assimilation combined with roughness correction for dynamic river system

作　　者：陈一帆[1,2] 程海洋[1] 万晓丽[1,2] 方华建[1,2] 

机构地区：[1]浙江省水利河口研究院,浙江杭州310020 [2]浙江省水利防灾减灾重点实验室,浙江杭州310020

出　　处：《水科学进展》2015年第5期731-738,共8页Advances in Water Science

基　　金：浙江省博士后科研资助项目(BSH1401035);浙江省重点科技创新团队项目(2010R50035)~~

摘　　要：为解决河网水动力模型重要参数糙率与水力状态量水位、流量的同步校正问题,以糙率和水力状态量作为河网非线性动态系统变量,采用扩展卡尔曼滤波,构建结合糙率动态校正的河网水情数据同化模型。通过算例计算,系统分析了水位动态噪声水平、糙率动态噪声水平、糙率初始值及测站个数对模型校正的影响。结果表明:模型能够有效用于水位状态量的实时校正;靠近测站的糙率校正值趋于真值,远离测站的糙率校正值趋于初始值;通过调整糙率动态噪声水平,可以有效控制糙率的修正量,防止糙率修正过大而引起计算失效问题。In order to synchronously correct river roughness parameters and hydraulic state variables （including discharge, stage or water depth） of river network hydrodynamic model, extended Kalman filter method is used to develop a data assimilation model. This model takes roughness parameters and hydraulic state variables as model state variables for synchronization correction. In a simulation example, it systematically examined and analyzed the factors including roughness dynamic noise level, water level dynamic noise level, initial value of roughness and station number, which have important influence on model correction capability. The results reveal that the present model is able to carry out data assimilation of dynamic river system efficaciously. Corrected roughness values near stations tend to be true values, and those far away from stations tend to be initial values. By adjusting roughness dynamic noise level, it can effectively control roughness correction degree, avoiding calculating failure.

关 键 词：河网 糙率校正 扩展卡尔曼滤波 数据同化 

分 类 号：TV122[水利工程—水文学及水资源]