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土壤异养呼吸的水分功能,纳入微尺度过程。

A moisture function of soil heterotrophic respiration that incorporates microscale processes.

机构信息

Institute of Surface-Earth System Science, Tianjin University, 300072, Tianjin, China.

Pacific Northwest National Laboratory-University of Maryland Joint Global Climate Change Research Institute, College Park, MD, 20740, USA.

出版信息

Nat Commun. 2018 Jul 2;9(1):2562. doi: 10.1038/s41467-018-04971-6.

Abstract

Soil heterotrophic respiration (HR) is an important source of soil-to-atmosphere CO flux, but its response to changes in soil water content (θ) is poorly understood. Earth system models commonly use empirical moisture functions to describe the HR-θ relationship, introducing significant uncertainty in predicting CO flux from soils. Generalized, mechanistic models that address this uncertainty are thus urgently needed. Here we derive, test, and calibrate a novel moisture function, f, that encapsulates primary physicochemical and biological processes controlling soil HR. We validated f using simulation results and published experimental data, and established the quantitative relationships between parameters of f and measurable soil properties, which enables f to predict the HR-θ relationships for different soils across spatial scales. The f function predicted comparable HR-θ relationships with laboratory and field measurements, and may reduce the uncertainty in predicting the response of soil organic carbon stocks to climate change compared with the empirical moisture functions currently used in Earth system models.

摘要

土壤异养呼吸(HR)是土壤向大气 CO 通量的重要来源,但人们对其对土壤含水量(θ)变化的响应知之甚少。地球系统模型通常使用经验性水分函数来描述 HR-θ 关系,这在预测土壤 CO 通量方面引入了很大的不确定性。因此,迫切需要一种能够解决这一不确定性的通用、机制模型。在这里,我们推导出、测试和校准了一种新的水分函数 f,它包含了控制土壤 HR 的主要物理化学和生物学过程。我们使用模拟结果和已发表的实验数据对 f 进行了验证,并建立了 f 的参数与可测量土壤特性之间的定量关系,这使得 f 能够预测不同土壤在空间尺度上的 HR-θ 关系。f 函数预测的 HR-θ 关系与实验室和现场测量结果相当,与地球系统模型中目前使用的经验性水分函数相比,它可能会降低预测土壤有机碳储量对气候变化响应的不确定性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcfc/6028431/8478bbbaa927/41467_2018_4971_Fig1_HTML.jpg

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