Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France.
Gamma Remote Sensing, Gümligen, Switzerland.
Glob Chang Biol. 2020 Jul;26(7):3997-4012. doi: 10.1111/gcb.15117. Epub 2020 May 19.
Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above-ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365 ± 66 Pg C, the spread corresponding to the standard deviation between models, compared to 275 Pg C with an uncertainty of ~13.5% from GlobBiomass. Model-data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000 km. The 95th percentile of AGB density (AGB ) in tropics can be considered as the potential maximum of AGB density which can be reached for a given annual precipitation. GlobBiomass data show local deficits of AGB density compared to the AGB , particularly in transitional and/or wet regions in tropics. We hypothesize that local human disturbances cause more AGB density deficits from GlobBiomass than from DGVMs, which rarely represent human disturbances. We then analyse empirical relationships between AGB density deficits and forest cover changes, population density, burned areas and livestock density. Regression analysis indicated that more than 40% of the spatial variance of AGB density deficits in South America and Africa can be explained; in Southeast Asia, these factors explain only ~25%. This result suggests TRENDY v6 DGVMs tend to underestimate biomass loss from diverse and widespread anthropogenic disturbances, and as a result overestimate turnover time in AGB.
我们目前对生物量碳储量的理解和量化存在差距，特别是在热带地区，这导致对陆地碳平衡的未来预测存在很大不确定性。我们利用最近发表的 2010 年森林地上生物量（AGB）密度的 GlobBiomass 数据集，该数据集由多个遥感和原地观测在 100m 空间分辨率下获得，用于评估九个动态全球植被模型（DGVM）估计的 AGB。九个 DGVM 的全球总森林 AGB 为 365±66PgC，这是模型之间的标准偏差，相比之下，GlobBiomass 的 AGB 为 275PgC，不确定性约为 13.5%。总森林 AGB 的模型数据差异可归因于它们在 AGB 密度和/或森林面积上的差异。尽管 DGVM 合理地代表了全球 AGB 密度的空间梯度，但它们在低于 1000km 的尺度上复制 AGB 密度的区域空间梯度的能力有限。热带地区 AGB 密度的 95 百分位数（AGB ）可被视为给定年降水量下 AGB 密度的潜在最大值。与 AGB 相比，GlobBiomass 数据显示热带地区的 AGB 密度存在局部亏缺，特别是在过渡区和/或湿润地区。我们假设与 DGVM 相比，局部人为干扰导致 AGB 密度亏缺更多，而 DGVM 很少代表人为干扰。然后，我们分析了 AGB 密度亏缺与森林覆盖变化、人口密度、燃烧面积和牲畜密度之间的经验关系。回归分析表明，南美洲和非洲 AGB 密度亏缺的空间方差有超过 40%可以得到解释；在东南亚，这些因素仅能解释~25%。这一结果表明，TRENDY v6 DGVM 往往低估了来自各种广泛的人为干扰的生物量损失，因此高估了 AGB 的周转时间。
