本研究从当前数值大气模式应用于水文预报的现状出发，分析了将数据同化技术融入到陆气耦合水文预报中的重要性，针对时空分辨率较高的天气雷达测雨数据，在WRF模式耦合WRF-Hydro陆面水文模式的基础上，构建了逐小时快速更新的WRF-3DVAR同化系统，并进一步验证了其在华北地区降雨径流预报中的应用效果。结果表明，同化天气雷达结合传统气象监测数据可以有效提高WRF模式的预报降雨精度，特别是针对时空分布均匀型的降雨，同化后预报降雨的CSI/RMSE指标提高了23.24%~50.00%。在径流预报中，经过数据同化后，更准确的预报降雨使径流预报结果也一定程度上得到了改善，洪峰流量和洪量误差均有所降低，Nash系数也所提高，整体上数据同化对预报水文过程的改进效果也较为明显。但对于时空分布不均匀型的降雨，洪峰流量和峰现时间的预报结果仍不理想，后续需要从陆面水文参数的精准率定、预报误差的实时校正等方面进行改进。

Due to the influence of climate change and human activities, the frequency of extreme rainfall and flood in North China has intensified, and in summer, there are convective and strong precipitation processes. Under the influence of mixed flow generation mechanism in semi humid and semi-arid areas, flood burst is strong and difficult to forecast. Based on the Weather Research Forecast (WRF) model coupled WRF-Hydro, this paper constructs an hourly rapid update WRF-3DVAR assimilation system by using three-dimensional variational data assimilation (3DVAR) to assimilate high spatial and temporal resolution radar reflectivity data combined with Global Telecommunication System (GTS) traditional meteorological observed data. Taking the typical rainfall processes of the north and south branches of the Daqinghe River Basin as the research object, the study of rainfall runoff prediction based on land atmosphere coupling is carried out, and the performance on the method in rainfall-runoff prediction in North China is further verified. The research results will have some theoretical and practical value for the construction of the data assimilation system of atmospheric model and flood forecast practice in northern China. We employed three nested domains, and adopted the GFS data for driving the WRF model. By assimilating radar reflectivity and GTS data, this paper evaluates the improvement effect of WRF forecasting rainfall and WRF-Hydro forecasting runoff. Since the GTS data is released every six hours, in the hourly assimilation scheme, GTS is only assimilated at the 6th, 12th, 18th, and 24th hour from the start of the storm, while radar reflectivity is set to assimilate once every hour. The rainfall evaluation indexes include RMSE (root mean square error), MBE (mean bias error) and CSI (critical success index). CSI / RMSE is used as a comprehensive index to evaluate the rainfall forecast results. RMSE, MBE and Nash (Nash-Sutcliffe efficiency coefficient) were used to evaluate runoff. The results show that precipitation forecasted by the WRF model is always lower than observed rainfall, but assimilation systems can increase rainfall. The improved initial conditions in WRF-3DVAR system via radar data assimilation and GTS data achieved better short-term and convective strong precipitation. The high assimilation frequency significantly helps to trigger and maintain the convective activities in the 3DVAR framework as well as the storm case applied. The assimilation weather radar combined with the traditional meteorological observed data can effectively improve the rainfall prediction accuracy of WRF model, especially for the rainfall with uniform spatial and temporal distribution, and the CSI/RMSE index of the forecast rainfall after assimilation is increased by 23.24%~50.00%. Whether data assimilation is carried out or not, the CSI index results show different degrees of rainfall false alarm frequency. In the runoff forecast, after data assimilation, the more accurate rainfall forecast also improves the runoff forecast results to a certain extent. The peak flow error is reduced by 15.05%, 38.07%, 18.53% and 6.99% respectively, the flood volume error is reduced by 25.99%, 29.32%, 26.02% and 23.95% respectively, and Nash efficiency coefficient is increased by 0.25, 0.25, 0.29 and 0.48 respectively. However, for the rainfall with uneven spatial and temporal distribution and large magnitude and slow water retreat, the forecast results of flood peak discharge and peak occurrence time are still not ideal, and subsequent improvements should be made in terms of accurate calibration of hydrological parameters and real-time correction of forecast errors. The accuracy of WRF-Hydro runoff forecast in the mixed runoff generation areas of northern China mainly depends on two aspects: one is the accuracy of rainfall forecast of WRF model, which is related to the driving data and rainfall distribution type. For the rainfall with uneven spatial-temporal distribution, the poor rainfall forecast indirectly affects the runoff forecast effect; On the other hand, it is related to different runoff processes, such as the complexity of runoff process, the magnitude of runoff, the presence or absence of base flow in the early stage, and the soil water content. On the basis of data assimilation to improve rainfall forecast, the runoff forecast results of WRF-Hydro will be better improved to a certain extent by reasonably using basic flow module, improving land surface initial conditions such as soil water content, and combining with effective real-time correction technology.