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Received：02 July 2021，
Published：07 July 2023
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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。
The frequency of extreme rainfall and flooding in North China has increased because of the influence of climate change and human activities. Convective and strong precipitation processes occur in summer. Under the influence of the mixed flow generation mechanism in semihumid and semiarid areas
the flood burst is strong and difficult to forecast. Based on the Weather Research Forecast (WRF) model
namely
coupled WRF-Hydro
this study uses three-dimensional variational data assimilation (3DVAR) in constructing the WRF-3DVAR assimilation system for a rapid hourly update to assimilate high spatial and temporal resolution radar reflectivity data with the traditional meteorological observed data from the Global Telecommunication System (GTS). The study of rainfall-runoff prediction based on the land-atmosphere coupling is conducted by taking the typical rainfall processes of the north and south branches of the Daqinghe River Basin as the research object. Moreover
the performance of the rainfall-runoff prediction method in North China is further verified. The research results have some theoretical and practical values for constructing the data assimilation system of the atmospheric model and flood forecast practice in northern China.
We employ three nested domains and adopt the GFS data for driving the WRF model. This study evaluates the improvement effect of WRF on forecasting rainfall and WRF-Hydro forecasting runoff by assimilating radar reflectivity and GTS data. The GTS data are released every 6 h. Thus
in the hourly assimilation scheme
GTS is only assimilated at the 6th
12th
18th
and 24th h from the start of the storm. However
radar reflectivity is set to assimilate once every hour. The rainfall evaluation indexes include Root Mean Square Error (RMSE)
Mean Bias Error (MBE)
and Critical Success Index (CSI). CSI/RMSE is a comprehensive index for evaluating rainfall forecast results. RMSE
MBE
and Nash (Nash-Sutcliffe efficiency coefficient) are used to evaluate runoff.
The results show that the precipitation forecasted by the WRF model is always lower than the observed rainfall. However
assimilation systems can increase rainfall. The improved initial conditions in the WRF-3DVAR system via radar data assimilation and GTS data achieve good short-term and convectively strong precipitation. The high assimilation frequency significantly helps trigger and maintain the convective activities in the 3DVAR framework and the storm case applied. The assimilation weather radar combined with the traditional meteorological observed data can effectively improve the rainfall prediction accuracy of the WRF model
particularly for the rainfall with uniform spatial and temporal distribution. 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
the accurate rainfall forecast after data assimilation 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%
the flood volume error is reduced by 25.99%
29.32%
26.02% and 23.95%
and the Nash efficiency coefficient is increased by 0.25
0.25
0.29 and 0.48
respectively. However
the forecast results of flood peak discharge and the peak occurrence time for the rainfall with uneven spatial and temporal distribution
large magnitude
and slow water retreat are still not ideal. Moreover
subsequent improvements should be made in terms of accurate calibration of hydrological parameters and real-time correction of forecast errors. The accuracy of the WRF-Hydro runoff forecast in the mixed runoff generation areas of northern China mainly depends on two aspects. One is the accuracy of the 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 contrary
it is related to different runoff characteristics
such as the complexity of the runoff process
the magnitude of runoff
the presence or absence of base flow in the early stage
and the soil water content. Data assimilation improves rainfall forecast. Thus
the runoff forecast results of WRF-Hydro are improved to a certain extent by reasonably using a basic flow module
improving land surface initial conditions
such as soil water content
and combining with effective real-time correction technology.
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