水科学进展2018，Vol.29Issue(2)：159-168,10.DOI:10.14042/j.cnki.32.1309.2018.02.002
基于蒸散发数据同化的径流过程模拟
Runoff simulation by hydrological model based on the assimilated evapotranspiration
摘要
Abstract
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 (ETsEBs) based on the surface energy balance system model (SEBS) was validated by the water balance evapotranspiration (ETGRACE) calculated through the water balance equation based on the GRACE water storage anomaly data,and compared with the other evapotranspiration productions (i.e.,ETGLDAS,ETzhang and ETMODIS).The results showed that ETSEBS outperformed the other evapotranspiration productions when ETGRACE was used as the reference evapotranspiration,with three statistical criterion (R,ERMSand 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 (ENS) was 0.85,which was higher than the ENS 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.关键词
蒸散发/SEBS模型/新安江模型/集合卡尔曼滤波/径流模拟Key words
evapotranspiration/SEBS model/Xin'anjiang model/ensemble Kalman filter/runoff simulation分类
建筑与水利引用本文复制引用
王卫光,李进兴,魏建德,邵全喜,邓超,余钟波..基于蒸散发数据同化的径流过程模拟[J].水科学进展,2018,29(2):159-168,10.基金项目
国家自然科学基金资助项目(51779073)The study is financially supported by the National Natural Science Foundation of China (No.51779073). （51779073）
