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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

郑磊1,2,宋世凯1,2,袁秀亮1,2,董嘉琪1,2,李龙辉1*   

  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

摘要: 数据同化为模型与遥感观测结合提供了一条有效的途径,通过在模型运行过程中融入遥感观测数据,调整模型运行轨迹从而降低模型误差,提高模拟精度。本文利用集合卡尔曼滤波(EnKF)算法同化生长季中分辨率成像光谱仪(MODIS)叶面积指数(LAI)与BiomeBGC模型模拟的LAI模拟长白山阔叶红松林的水碳通量。同时,通过改进模拟的雪面升华与土壤温度计算方法的参数,旨在降低冬季生态呼吸的模拟误差。结果表明,相对于原始模型,数据同化与模型改进后使得生态系统总初级生产力(GPP)的模拟值与观测值之间的相关系数提高0.06,中心化均方根误差(RMSE)降低0.48 g C·m-2·d-1;生态系统呼吸(RE)的相关系数提高0.02,中心化均方根误差降低0.20 g C·m-2·d-1;净生态系统碳交换量(NEE)相关系数提高0.35,中心化均方根误差降低0.50 g C·m-2·d-1。同时,数据同化对蒸散发(ET)的模拟精度没有显著影响,改进的模型提高了其相关系数。基于EnKF算法的数据同化提高了长白山阔叶红松林碳通量模拟精度,对于精确估算区域碳通量有着重要的意义。

关键词: 透光抚育, 温室气体, 非生长季, 主控因子, 温带红松林

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.

Key words: non-growing season, greenhouse gas, temperate Korean pine plantation, main controlling factor, light-felling