基于蒸散发数据同化的径流过程模拟

采用集合卡尔曼滤波算法，以遥感反演的蒸散发作为观测数据，构建一种基于新安江模型的蒸散发同化系统；根据同化后的蒸散发，采用粒子群算法估计新安江模型的土壤张力水蓄量，进而改进模型的径流模拟效果。选取地表能量平衡系统模型进行汉江流域蒸散发（ET_SEBS）反演，采用基于GRACE水储量距平数据的水量平衡蒸散发（ET_GRACE）进行验证，结果显示ET_SEBS总体表现好于蒸散发产品ET_GLDAS、ET_Zhang、ET_MODIS，且相关系数（R）、均方根误差（E_RMS）、偏差（B）为0.93、11.93 mm/月、-3.47 mm/月，表明SEBS模型能够较好地估算蒸散发。将同化方案在旬河流域进行应用，结果显示同化后径流的纳什效率系数（E_NS）为0.85，较同化前0.81明显提高，且模型对枯水期径流的低估问题有一定改善，对径流峰值模拟效果提高明显。

Runoff simulation by hydrological model based on the assimilated evapotranspiration

A new evapotranspiration data assimilation system was proposed by using the ensemble Kalman filter to assimilate the remote sensing evapotranspiration and simulated evapotranspiration by the Xin'anjiang model. The assimilated evapotranspiration was then used to estimate the soil moisture content in Xin'anjiang model via Particle Swarm Optimization (PSO) to improve the accuracy of runoff simulation. The Hanjiang River basin in China was used as a case study. The remote sensing evapotranspiration (ET_SEBS) based on the surface energy balance system model (SEBS) was validated by the water balance evapotranspiration (ET_GRACE) calculated through the water balance equation based on the GRACE water storage anomaly data, and compared with the other evapotranspiration productions (i.e., ET_GLDAS, ET_Zhang and ET_MODIS). The results showed that ET_SEBS outperformed the other evapotranspiration productions when ET_GRACE was used as the reference evapotranspiration, with three statistical criterion (R, E_RMSand B values of 0.93, 11.93 mm/month and -3.47 mm/month, respectively. Furthermore, the proposed assimilation system was applied to the Xunhe River basin, a tributary of Hanjiang River. The results indicated that during the period 2005-2007, the Nash-Sutcliffe efficiency coefficient (E_NS) was 0.85, which was higher than the E_NS value of 0.81 without assimilation, and the evapotranspiration assimilation system improved the accuracy for runoff simulation with a slightly improvement during the drought period and a remarkable improvement during wet period, particularly for the peak values.