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机构地区:[1]东北林业大学,哈尔滨150040 [2]黑龙江省水文水资源勘测局
出 处:《东北林业大学学报》2016年第5期15-19,共5页Journal of Northeast Forestry University
基 金:"十二五"国家科技支撑项目(2011BAD37B01);国家自然科学基金项目(31300533)
摘 要:以2011年帽儿山生态站通量塔观测的二氧化碳通量(总初级生产力和显热通量)数据为基础,使用集合卡尔曼滤波的顺序同化技术,对北方生态系统生产力(BEPS)模型的关键参数进行优化。结果表明:参数在季节尺度上变化显著,通常在展叶期迅速增大,夏季达到稳定,秋季落叶期降低。根据优化的参数,模型的总初级生产力、生态系统呼吸的模拟值精度显著提高,精度分别达到91%、96%,比优化之前的模拟值精度提高了8%和11%。说明集合卡尔曼滤波的参数优化可以明显改善模型模拟碳水通量的能力。With CO_2 flux observation data( gross primary productivity( GPP) and sensible heat flux( LE)) from Maoershan Ecosystem Station flux tower in 2011,we used sequential data assimilation and ensemble Kalman filter technique to optimize some of the key parameters of the Boreal Ecosystem Productivity Simulator( BEPS) model by taking into account the errors in inputs,parameters,and observations. We optimized the parameters by data assimilation including maximum photosynthetic carboxylation rate( Vcmax),and the slope of stomatal conductance and net photosynthetic rate( M). Parameters were optimized in daily steps. The parameters varied significantly at seasonal scales,was with usually rapid increase in leaf expansion period,and in summer would reach a steady and decline in senescence of leaves. According to optimized parameters,model simulated value of GPP and RE flux were significantly increased. GPP and RE simulated accuracy reached91% and 96%,and the precision values of the simulated values before optimization were increased by 8% and 11%,respectively. Ensemble Kalman filter parameter optimization could significantly improve model capacity in simulating the carbon and water fluxes.
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