Mortality as a key driver of the spatial distribution of aboveground biomass in Amazonian forest: results from a dynamic vegetation model
Mortality as a key driver of the spatial distribution of aboveground biomass in Amazonian forest: results from a dynamic vegetation model
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DOI：
10.5194/bg-7-3027-2010
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发表时间：
2010-01-01
期刊：
影响因子：
4.9
通讯作者：
Le Toan, T.
中科院分区：
文献类型：
--
作者：
Delbart, N.;Ciais, P.;Le Toan, T.
动态植被模型 (DVM) 模拟生态系统与大气之间、植被与土壤之间以及植物器官之间的能量、水和碳通量。他们还估计了在给定气候和大气二氧化碳水平下生长的平衡森林的潜在生物量。在本研究中，我们通过将模拟结果与大量地面测量结果（220 个生物量站点，104 个 NPPAGW 站点）进行比较，评估了 DVM ORCHIDEE 在亚马逊森林中模拟的地上木本生物量 (AGWB) 和地上木本净初级生产力 (NPPAGW)。我们发现 NPPAGW 平均高估了 63%。我们还发现，因死亡而损失的生物量比例高达 85%。这些模型偏差几乎相互补偿，使模拟 AGWB 的平均值接近地面测量平均值。然而，模拟的 AGWB 空间分布与观测结果存在显着差异。然后，我们分析生物量的差异与 NPPAGW 和死亡率的差异。当我们校正 NPPAGW 的误差时，AGWB 空间变化的误差加剧，这清楚地表明生物量的误报很大一部分来自于死亡率过程的错误模型。 先前的研究表明，生产力高的亚马逊森林比生产力低的森林具有更高的死亡率。我们引入了这种关系，从而大大改进了生物量及其空间变化的建模。我们讨论了修改 ORCHIDEE 死亡率模型的可能性，以及通过整合生物量测量（特别是遥感测量）来改进森林生产力模型的机会。
Dynamic Vegetation Models (DVMs) simulate energy, water and carbon fluxes between the ecosystem and the atmosphere, between the vegetation and the soil, and between plant organs. They also estimate the potential biomass of a forest in equilibrium having grown under a given climate and atmospheric CO2 level. In this study, we evaluate the Above Ground Woody Biomass (AGWB) and the above ground woody Net Primary Productivity (NPPAGW) simulated by the DVM ORCHIDEE across Amazonian forests, by comparing the simulation results to a large set of ground measurements (220 sites for biomass, 104 sites for NPPAGW). We found that the NPPAGW is on average overestimated by 63%. We also found that the fraction of biomass that is lost through mortality is 85% too high. These model biases nearly compensate each other to give an average simulated AGWB close to the ground measurement average. Nevertheless, the simulated AGWB spatial distribution differs significantly from the observations. Then, we analyse the discrepancies in biomass with regards to discrepancies in NPPAGW and those in the rate of mortality. When we correct for the error in NPPAGW, the errors on the spatial variations in AGWB are exacerbated, showing clearly that a large part of the misrepresentation of biomass comes from a wrong modelling of mortality processes.Previous studies showed that Amazonian forests with high productivity have a higher mortality rate than forests with lower productivity. We introduce this relationship, which results in strongly improved modelling of biomass and of its spatial variations. We discuss the possibility of modifying the mortality modelling in ORCHIDEE, and the opportunity to improve forest productivity modelling through the integration of biomass measurements, in particular from remote sensing.