Unifying soil organic matter formation and persistence frameworks: the MEMS model
Unifying soil organic matter formation and persistence frameworks: the MEMS model
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DOI：
10.5194/bg-16-1225-2019
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发表时间：
2018-10
期刊：
影响因子：
4.9
通讯作者：
Andy D. Robertson;K. Paustian;S. Ogle;M. Wallenstein;E. Lugato;M. F. Cotrufo
中科院分区：
文献类型：
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作者：
Andy D. Robertson;K. Paustian;S. Ogle;M. Wallenstein;E. Lugato;M. F. Cotrufo
抽象的。生态系统尺度土壤地球化学模型中的土壤有机质（SOM）动态传统上被模拟为概念上定义的库之间不可测量的通量。这极大地限制了如何使用经验数据来提高模型性能并降低与碳（C）循环预测相关的不确定性。最近的进展，我们了解的土壤地球化学过程，支配SOM的形成和持久性需要一个新的数学模型的结构周围的关键机制和土壤地球化学相关的池。在这里，我们提出了一种旨在满足这一需求的方法。我们的新模型（MEMS v1.0）是从微生物效率矩阵稳定框架，它强调了有机物输入的化学与微生物处理的效率，并最终与土壤矿物基质，在研究SOM的形成和稳定的重要性。在此框架的基础上，MEMS v1.0还能够模拟碳饱和度的概念，并代表分解过程和物理化学稳定机制，将SOM形成定义为四个主要部分。在详细描述了模型之后，我们通过基于方差的敏感性分析优化了四个关键参数。优化采用土壤分馏数据从154个站点不同的环境条件下，直接等同于矿物相关的有机质和颗粒有机质组分与相应的模型池。最后，模型的性能进行了评估，使用总表土（0-20厘米）C数据从8192森林和草原网站在欧洲。尽管该模型相对简单，但它能够准确地捕捉土壤碳储量在温度、降水、年度碳输入和土壤质地等广泛梯度上的总体趋势。MEMS v1.0模拟SOM动态的新方法有可能改善我们对土壤如何响应管理和环境扰动的预测。确保这些预测准确是有效制定政策的关键，这些政策可以解决生态系统服务的可持续性问题，并有助于减缓气候变化。
Abstract. Soil organic matter (SOM) dynamics in ecosystem-scale biogeochemical models have traditionally been simulated as immeasurable fluxes between conceptually defined pools. This greatly limits how empirical data can be used to improve model performance and reduce the uncertainty associated with their predictions of carbon (C) cycling. Recent advances in our understanding of the biogeochemical processes that govern SOM formation and persistence demand a new mathematical model with a structure built around key mechanisms and biogeochemically relevant pools. Here, we present one approach that aims to address this need. Our new model (MEMS v1.0) is developed from the Microbial Efficiency-Matrix Stabilization framework, which emphasizes the importance of linking the chemistry of organic matter inputs with efficiency of microbial processing and ultimately with the soil mineral matrix, when studying SOM formation and stabilization. Building on this framework, MEMS v1.0 is also capable of simulating the concept of C saturation and represents decomposition processes and mechanisms of physico-chemical stabilization to define SOM formation into four primary fractions. After describing the model in detail, we optimize four key parameters identified through a variance-based sensitivity analysis. Optimization employed soil fractionation data from 154 sites with diverse environmental conditions, directly equating mineral-associated organic matter and particulate organic matter fractions with corresponding model pools. Finally, model performance was evaluated using total topsoil (0–20 cm) C data from 8192 forest and grassland sites across Europe. Despite the relative simplicity of the model, it was able to accurately capture general trends in soil C stocks across extensive gradients of temperature, precipitation, annual C inputs and soil texture. The novel approach that MEMS v1.0 takes to simulate SOM dynamics has the potential to improve our forecasts of how soils respond to management and environmental perturbation. Ensuring these forecasts are accurate is key to effectively informing policy that can address the sustainability of ecosystem services and help mitigate climate change.