基于EnKS和SWAT模型的闽江流域径流数据同化

地表水文过程中观测变量对状态变量的响应存在时间滞后性，为提高径流数据同化的精度，以闽江流域为研究区，基于集合卡尔曼平滑器（EnKS）和SWAT模型，构建径流数据同化方案，并与集合卡尔曼滤波（EnKF）方法进行对比，评价不同同化模型的精度，分析数据同化对不同径流分量的影响。结果表明：EnKS最优时间窗口长度在不同水文周期、流域存在差异；考虑水文模型的时间滞后性可以有效提高模型的同化精度，对比EnKF方法，EnKS方法的纳什效率系数(NSE)在七里街、沙县、竹岐3个站点上分别提升了0.03、0.12、0.03，均方根误差(RMSE)分别减小了7.43%、26.81%、4.25%；数据同化方法对不同径流分量的改进程度存在空间异质性和时间异质性，在高渗透率土壤和陡坡区域EnKS方法能使壤中流获得更显著的改进，丰水期EnKS方法对地表径流的改进较枯水期更明显。

Stremflow data assimilation of the Minjiang River Basin based on EnKS approach and SWAT model

A time lag exists in the response of observed variables to state variables in surface hydrological process. To improve the accuracy of streamflow data assimilation, a streamflow data assimilation scheme was constructed based on the ensemble Kalman smoother (EnKS) and SWAT model for the Minjiang River Basin, which was then compared with the ensemble Kalman filter (EnKF) method, so as to evaluate the accuracy of different assimilation methods and to analyze the effect of data assimilation on different streamflow components. The results indicate that the optimal time window length of EnKS differs in each hydrological period and basin, and the consideration of the time lag of the hydrological model can effectively improve the accuracy of model assimilation. Compared with the EnKF method, the NSE of the EnKS method increased by 0.03,0.12,0.03 at the Qilijie station, Shaxian station and Zhuqi station, respectively; whereas the RMSE of the three stations decreased by 7.43%, 26.81% and 4.25%, respectively. There is spatial and temporal heterogeneity in the improvement effect of data assimilation method on different streamflow components. The EnKS method can significantly improve the accuracy of the lateral flow in regions with high-permeability soil and steep slopes, and it has a better performance in wet season than dry season for the improvement of surface flow prediction.