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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(En KF)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En KF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。
Data assimilation provides an effective way to combine the model with remote sensing observation. By integrating the remote sensing data and the trajectory of the model during the model operation, the model error can be reduced and the simulation accuracy can be improved. In this paper, the water carbon fluxes of the broad-leaved Korean pine forest in Changbai Mountains were simulated by the EnKF (Ensemble Kalman Filter) algorithm with assimilating Leaf Area Index (LAI) of MODIS and Biome-BGC models. At the same time, the simulation error of winter ecological respiration is reduced by improving the parameters of simulated snow sublimation and soil temperature calculation. The results show that, compared with the original model, the data assimilation and model improvement improve the correlation coefficient between the simulated and observed values of ecosystem total primary productivity (GPP) by 0.06 and reduce the central root mean square error (RMSE) by 0.48 g C · M ~ (-2) · d ~ (-1); the correlation coefficient of ecosystem respiration (RE) increased by 0.02, the central root mean square error decreased by 0.20 g C · m -2 · d -1 ). The correlation coefficient of net ecosystem carbon exchange (NEE) increased by 0.35 and the central root mean square error decreased by 0.50 g C m -2 d -1. At the same time, data assimilation has no significant effect on the simulation accuracy of evapotranspiration (ET), and the improved model improves its correlation coefficient. Data assimilation based on En KF algorithm improves the simulation accuracy of carbon flux in broad-leaved Korean pine forest in Changbai Mountain, which is of great significance for accurate estimation of regional carbon flux.