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首页> 外文期刊>Journal of Geophysical Research. Biogeosciences >Constrained partitioning of autotrophic and heterotrophic respiration reduces model uncertainties of forest ecosystem carbon fluxes but not stocks
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Constrained partitioning of autotrophic and heterotrophic respiration reduces model uncertainties of forest ecosystem carbon fluxes but not stocks

机译：自养和异养呼吸的受限分区减少了森林生态系统碳通量的模型不确定性，但没有减少种群

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We partitioned the soil carbon dioxide flux (Rs) into its respective autotrophic and heterotrophic components in a mature temperate-boreal forest (Howland Forest in Maine, USA). We combined automated chamber measurements of Rs with two different partitioning methods: (1) a classic root trenching experiment and (2) a radiocarbon (~(14)C) mass balance approach. With a model-data fusion approach, we used these data to constrain a parsimonious ecosystem model (F?BAAR), andweinvestigated differences in modeled C fluxes and pools under both current and future climate scenarios. The trenching experiment indicated that heterotrophic respiration accounted for 53 ± 11% of total Rs. In comparison, using the ~(14)C method, the heterotrophic contribution was 42 ± 9%. For both current and future model runs, incorporating the partitioning data as constraints substantially reduced the uncertainties of autotrophic and heterotrophic respiration fluxes. Moreover, with best fit model parameters, the two partitioning methods yielded fundamentally different estimates of the relative contributions of autotrophic and heterotrophic respiration to total Rs, especially at the annual time scale. Surprisingly, however, modeled soil C and biomass C pool size trajectories did not differ significantly between model runs based on the different methods. Instead, model differences in partitioning were compensated for by changes in C allocation, resulting in similar, but still highly uncertain, soil C pool trajectories. Our findings show that incorporating constraints on the partitioning of Rs can reduce model uncertainties of fluxes but not pools, and the results are sensitive to the partitioning method used.
机译：在成熟的温带-北方森林（美国缅因州的霍兰森林）中，我们将土壤二氧化碳通量（Rs）分为各自的自养和异养成分。我们将Rs的自动腔室测量与两种不同的分区方法相结合：（1）经典的根部挖沟实验和（2）放射性碳（〜（14）C）质量平衡方法。通过模型-数据融合方法，我们使用这些数据来约束简约的生态系统模型（F？BAAR），并研究了当前和未来气候情景下模拟C流量和库的差异。挖沟实验表明，异养呼吸占总Rs的53±11％。相比之下，使用〜（14）C方法，异养贡献为42±9％。对于当前和将来的模型运行，合并分区数据作为约束条件，可以大大减少自养和异养呼吸通量的不确定性。此外，使用最佳拟合模型参数，这两种分区方法得出自养和异养呼吸对总Rs的相对贡献的根本不同的估计，尤其是在年度时间尺度上。但是，令人惊讶的是，基于不同方法的模型运行之间，模型化的土壤C和生物量C池径轨迹没有显着差异。取而代之的是，分配模型的差异可以通过碳分配的变化得到补偿，从而产生相似但仍然高度不确定的土壤碳库轨迹。我们的研究结果表明，在Rs的分区中合并约束可以减少通量的模型不确定性，但不能减少池的不确定性，并且结果对所使用的分区方法很敏感。

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