Merging a mechanistic enzymatic model of soil heterotrophic respiration into an ecosystem model in two AmeriFlux sites of northeastern USA
Merging a mechanistic enzymatic model of soil heterotrophic respiration into an ecosystem model in two AmeriFlux sites of northeastern USA
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DOI：
10.1016/j.agrformet.2018.01.026
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发表时间：
2018-04
影响因子：
6.2
通讯作者：
D. Sihi;E. Davidson;Min Chen;K. Savage;A. Richardson;T. Keenan;D. Hollinger
中科院分区：
文献类型：
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作者：
D. Sihi;E. Davidson;Min Chen;K. Savage;A. Richardson;T. Keenan;D. Hollinger
异养呼吸（Rh）是微生物将土壤有机质转化为二氧化碳（CO2）的过程，是陆地系统向大气排放碳的主要途径，但其不确定性很高。在生态系统模式和地球系统模式中，Rh的温度敏感性通常用一个简单的Q 10函数来表示，有时还伴随着一个经验的土壤水分修正因子。更明确的表示土壤水分，基质供应，以及它们与温度的相互作用的影响已被提出作为一种方法来解开的混淆因素的表观温度敏感性的Rh和提高生态系统模型和ESM的性能。这项工作的目的是插入到一个生态系统模型的一个更机械的，但仍然吝啬，环境因素控制Rh模型和评估模型的性能在土壤和生态系统呼吸。Dual Arrhenius and Michaelis-Menten（DAMM）模型使用Michaelis-Menten、Arrhenius和扩散函数来模拟Rh。土壤水分影响Rh和它的表观温度敏感性DAMM通过调节扩散的氧气，可溶性C底物，和胞外酶的酶反应位点。在这里，我们将DAMM土壤通量模型与一个简约的生态系统通量模型FöBAAR（森林生物量，同化，分配和呼吸）合并。我们使用高频土壤通量数据从自动土壤室和涡度协方差塔在美国东北部的AmeriFlux网站（哈佛森林，MA和豪兰森林，ME）估计参数，验证合并后的模型，并量化的不确定性在多约束方法。优化的DAMM-FöBAAR模型更好地捕捉到土壤呼吸（土壤R）的季节性和年际动态相比，FöBAAR只有模型的哈佛森林，更高的频率和持续时间的干燥事件显着调节基质供应异养。然而，DAMM-FöBAAR仅在北半球过渡的豪兰森林中表现出比FöBAAR更好的表现，只有在异常干旱的年份。天气尺度的干燥期的频率较低，在豪兰，导致只有短暂的水限制的Rh在某些年份。在这两个网站，土壤R在干燥事件的下降趋势被捕获的DAMM-FöBAAR模型，但是，模型的性能也取决于现场条件，气候和时间尺度的利益。虽然DAMM函数比简单的Q10函数需要更多的参数，但我们已经证明它们可以包含在生态系统模型中，并减少模型数据不匹配。此外，使用DAMM函数的土壤水分效应的机制结构应该比常用的各种经验函数更具有普遍性，这些DAMM函数可以很容易地纳入其他生态系统模型和ESM。
Heterotrophic respiration (Rh), microbial processing of soil organic matter to carbon dioxide (CO2), is a major, yet highly uncertain, carbon (C) flux from terrestrial systems to the atmosphere. Temperature sensitivity of Rh is often represented with a simple Q10function in ecosystem models and earth system models (ESMs), sometimes accompanied by an empirical soil moisture modifier. More explicit representation of the effects of soil moisture, substrate supply, and their interactions with temperature has been proposed as a way to disentangle the confounding factors of apparent temperature sensitivity of Rh and improve the performance of ecosystem models and ESMs. The objective of this work was to insert into an ecosystem model a more mechanistic, but still parsimonious, model of environmental factors controlling Rh and evaluate the model performance in terms of soil and ecosystem respiration. The Dual Arrhenius and Michaelis-Menten (DAMM) model simulates Rh using Michaelis-Menten, Arrhenius, and diffusion functions. Soil moisture affects Rh and its apparent temperature sensitivity in DAMM by regulating the diffusion of oxygen, soluble C substrates, and extracellular enzymes to the enzymatic reaction site. Here, we merged the DAMM soil flux model with a parsimonious ecosystem flux model, FöBAAR (Forest Biomass, Assimilation, Allocation and Respiration). We used high-frequency soil flux data from automated soil chambers and landscape-scale ecosystem fluxes from eddy covariance towers at two AmeriFlux sites (Harvard Forest, MA and Howland Forest, ME) in the northeastern USA to estimate parameters, validate the merged model, and to quantify the uncertainties in a multiple constraints approach. The optimized DAMM-FöBAAR model better captured the seasonal and inter-annual dynamics of soil respiration (Soil R) compared to the FöBAAR-only model for the Harvard Forest, where higher frequency and duration of drying events significantly regulate substrate supply to heterotrophs. However, DAMM-FöBAAR showed improvement over FöBAAR-only at the boreal transition Howland Forest only in unusually dry years. The frequency of synoptic-scale dry periods is lower at Howland, resulting in only brief water limitation of Rh in some years. At both sites, the declining trend of soil R during drying events was captured by the DAMM-FöBAAR model; however, model performance was also contingent on site conditions, climate, and the temporal scale of interest. While the DAMM functions require a few more parameters than a simple Q10function, we have demonstrated that they can be included in an ecosystem model and reduce the model-data mismatch. Moreover, the mechanistic structure of the soil moisture effects using DAMM functions should be more generalizable than the wide variety of empirical functions that are commonly used, and these DAMM functions could be readily incorporated into other ecosystem models and ESMs.