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森林生态系统持续的碳吸收能力在很大程度上取决于净初级生产力(NPP)的增长趋势及生态系统碳周转的时间,因此,获取生态系统碳周转时间的空间分布格局是有效评估生态系统碳汇潜力的基础.本研究采用数据-模型融合方法,基于区域生态系统碳循环过程模型(TECO-R),结合生态系统观测数据(NPP、生物量及土壤有机碳)、遥感数据(NDVI)及气象、植被与土壤等辅助空间数据,利用遗传算法(Genetic Algorithm)反演了中国森林生态系统各碳库的周转时间及分配系数,并在此基础上估算了平衡状态下森林生态系统碳周转时间的空间分布格局.研究结果表明:数据-模型融合技术能有效地反演中国森林生态系统碳循环过程模型中的关键参数,从而很好地模拟中国森林生态系统的碳循环过程;反演的中国森林生态系统的碳周转时间在空间上存在很大的异质性,其值大多介于24～70年之间;不同森林类型的统计结果表明,落叶针叶林与常绿针叶林的平均周转时间最大(分别为73.8与71.3年),其次是混交林与落叶阔叶林(38.1与37.3年),而常绿阔叶林的值最小(31.7年);从全国尺度看,中国各种森林生态系统总的碳周转时间的均值为57.8年.
The continued carbon sequestration of forest ecosystems depends largely on the growth of net primary productivity (NPP) and on the timing of carbon cycle in ecosystems. Therefore, the spatial distribution of access times for carbon sequestration in ecosystems is an effective assessment of ecosystem carbon Based on the TECO-R model and the data of ecosystem observations (NPP, biomass and soil organic carbon), remote sensing data (NDVI) and Weather, vegetation and soils. The genetic algorithm was used to invert the turnover time and distribution coefficient of carbon pools in China’s forest ecosystem. Based on this, the carbon cycle time of forest ecosystem under the equilibrium condition The results show that the data-model fusion technique can effectively invert the key parameters in the process model of carbon cycling in forest ecosystems in China so as to simulate the carbon cycle of forest ecosystems in China. The carbon cycle time of forest ecosystems is highly heterogeneous in space, mostly between 24 and 70 years ; The statistical results of different forest types show that the average turnover time of deciduous coniferous forest and evergreen coniferous forest is the highest (73.8 and 71.3 years respectively), followed by the mixed forest and deciduous broad-leaved forest (38.1 and 37.3 years) The value of evergreen broad-leaved forests is the smallest (31.7 years). On a national scale, the mean carbon turnover time of various forest ecosystems in China is 57.8 years.