中文摘要：

为减少非恒定水流计算中的不确定性, 基于集合卡尔曼滤？波提出多变量交替校正的方法.该 方法通过交替校正水位和流量, 避开了滤波过程中的大矩阵计算, 实现了利用观测信息直接校正非 恒定流状态的目的; 同时, 应用尺度转换方法提高水位滤波精度.数值试验重点考察了观测误差和 水位变换系数对模型计算精度的影响.结果表明： 观测误差越小, 模型的计算精度越高; 水位尺度 变换系数能显著增强多变量交替校正方法的效果, 变换系数越大, 计算精度越高; 基于集合卡尔曼 滤波的多变量交替校正方法具有良好的校正性能, 能显著提高河道水流的预报精度.

英文摘要：

To reduce the uncertainty in calculation of unsteady flows,a multivariate alternate updating method is proposed based on the ensemble Kalman filter. This method updates water stage and discharge data alternately to calibrate unsteady flow,using the obsered inlornation without the large matrix calculating; meanwhile,scaling transformation is used in order to improve the water level filter precision. Numerical experiments emphatically investigate the effects of measurement accuracy and water level transformation coefficient on forecast precision of the method. The results show that the forecast error increases as the measurement accuracy decreases; the water level transformation coefficient can obviously improve the effect of the multivariate alternate updating method, the larger the water level transformation coefficient i s, the higher the forecast precision will be; the multivariate alternate updating method has good calibrating performance and can improve forecast accuracy of unsteady flows in open channel.