• 方法与技术 •

基于Biome-BGC模型和集合卡尔曼滤波方法的阔叶红松林生态系统水碳通量模拟

1. (1中国科学院新疆生态与地理研究所荒漠与绿洲生态国家重点实验室， 乌鲁木齐 830011； 2中国科学院大学， 北京 100049)
• 出版日期:2017-06-10 发布日期:2017-06-10

Simulation of water and carbon fluxes in a broad-leaved Korean pine forest in Changbai Mountains based on Biome-BGC model and Ensemble Kalman Filter method.

ZHENG Lei1,2, SONG Shi-kai1,2, YUAN Xiu-liang1,2, DONG Jia-qi1,2, LI Long-hui1*#br#

1. (1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; 2University of Chinese Academy of Sciences, Beijing 100049, China).
• Online:2017-06-10 Published:2017-06-10

Abstract: Data assimilation provides an effective way to integrate the model simulation and remote sensing observation, through the integration of remote sensing data in the run of the model, adjusting the model trajectory to reduce model error and improve simulation accuracy. This paper uses the ensemble Kalman filter (EnKF) assimilated MODIS LAI into the BiomeBGC model in growing season to simulate the water and carbon fluxes in a broadleaved Korean pine forest in Changbai Mountains. At the same time, the simulated snow sublimation and the parameters of the calculation method of soil temperature are improved, which can effectively reduce the error of the ecological respiration in winter. The result shows that as compared with the original model simulated without data assimilation, the improved Biome-BGC model with the assimilation of the MODIS LAI makes the correlation coefficient between the simulated values and the observed values of the gross ecosystem primary productivity (GPP) increased by 0.06, and reduced the centered rootmeansquare error (RMSE) by 0.48 g C·m-2·d-1, ecosystem respiration (RE) correlation coefficient increased by 0.02, centered rootmeansquare error decreased by 0.20 g C·m-2·d-1; the correlation coefficient of net ecosystem exchange of carbon (NEE) increased by 0.35, centered rootmeansquare error decreased by 0.50 g C·m-2·d-1. Meanwhile, data assimilation has no significant effect on the simulation precision of evapotranspiration (ET), but the improved model improves the correlation coefficient of ET. The data assimilation based on EnKF algorithm improves the accuracy of the carbon flux simulation in the broadleaved Korean pine forest in Changbai Mountains, and has an important significance on more accurate estimation of carbon flux at regional scale.