Inverse Determination of the Influence of Fire on Vegetation Carbon Turnover in the Pantropics

Inverse Determination of the Influence of Fire on Vegetation Carbon Turnover in the Pantropics
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DOI:
10.1029/2018gb005925
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发表时间:
2018-12
影响因子:
5.2
通讯作者:
J. Exbrayat;T. Smallman;A. Bloom;L. Hutley;M. Williams
J. Exbrayat;T. Smallman;A. Bloom;L. Hutley;M. Williams
中科院分区:
地球科学1区
文献类型:
--
作者:
J. Exbrayat;T. Smallman;A. Bloom;L. Hutley;M. Williams

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火灾是陆地碳循环的一个主要组成部分,目前大多数全球陆地生态系统模型都已采用这一循环。在这里,我们使用陆地碳循环观测来表征火情梯度在生态系统功能特性(例如热带地区的碳分配、通量和周转时间)空间分布中的重要性。将贝叶斯模型数据融合方法应用于生态系统碳模型,以推导 2000 年至 2015 年热带地区相应参数的后验分布。我们执行模型数据融合过程两次,即有火和没有火的情况。这些实验之间出现了模型参数和生态系统特性对火灾响应的差异梯度。例如,年平均燃烧分数与碳利用效率的提高和碳周转时间的减少相关。此外,我们的分析表明,在最常燃烧的区域,更多的耐火组织的分配增加。随着火灾模块越来越多地在全球陆地生态系统模型中实施,我们建议模型开发包括火灾对生态系统特性影响的表示,因为它们可能会导致气候变化预测下的巨大差异。
Fire is a major component of the terrestrial carbon cycle that has been implemented in most current global terrestrial ecosystem models. Here we use terrestrial carbon cycle observations to characterize the importance of fire regime gradients in the spatial distribution of ecosystem functional properties such as carbon allocation, fluxes, and turnover times in the tropics. A Bayesian model‐data fusion approach is applied to an ecosystem carbon model to derive the posterior distribution of corresponding parameters for the tropics from 2000 to 2015. We perform the model‐data fusion procedure twice, that is, with and without imposing fire. Gradient of differences in model parameters and ecosystem properties in response to fire emerge between these experiments. For example, mean annual burned fraction correlates with an increase in carbon use efficiency and reductions in carbon turnover times. Further, our analyses reveal an increased allocation to more fire‐resistant tissues in the most frequently burned regions. As fire modules are increasingly implemented in global terrestrial ecosystem models, we recommend that model development includes a representation of the impact of fire on ecosystem properties as they may lead to large differences under climate change projections.