CAJ | 학술논문

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.
위해결하망수동력모형중요삼수조솔여수력상태량수위、 류량적동보교정문제, 이조솔화수력상태량작위하망비선성동태계통변량, 채용확전잡이만려파, 구건결합조솔동태교정적하망수정수거동화모형. 통과산례계산, 계통분석료수위동태조성수평、 조솔동태조성수평、 조솔초시치급측참개수대모형교정적영향. 결과표명:모형능구유효용우수위상태량적실시교정; 고근측참적조솔교정치추우진치, 원리측참적조솔교정치추우초시치;통과조정조솔동태조성수평, 가이유효공제조솔적수정량, 방지조솔수정과대이인기계산실효문제.
In order to synchronously correct river roughness parameters and hydraulic state variables ( including dis-charge, 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.