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The sensitivity of soil respiration to soil temperature, moisture, and carbon supply at the global scale

机译:土壤呼吸对土壤温度,水分和碳源的敏感性

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摘要

Soil respiration (Rs) is a major pathway by which fixed carbon in the biosphere is returned to the atmosphere, yet there are limits to our ability to predict respiration rates using environmental drivers at the global scale. While temperature, moisture, carbon supply, and other site characteristics are known to regulate soil respiration rates at plot scales within certain biomes, quantitative frameworks for evaluating the relative importance of these factors across different biomes and at the global scale require tests of the relationships between field estimates and global climatic data. This study evaluates the factors driving Rs at the global scale by linking global datasets of soil moisture, soil temperature, primary productivity, and soil carbon estimates with observations of annual Rs from the Global Soil Respiration Database (SRDB). We find that calibrating models with parabolic soil moisture functions can improve predictive power over similar models with asymptotic functions of mean annual precipitation. Soil temperature is comparable with previously reported air temperature observations used in predicting Rs and is the dominant driver of Rs in global models; however, within certain biomes soil moisture and soil carbon emerge as dominant predictors of Rs. We identify regions where typical temperature-driven responses are further mediated by soil moisture, precipitation, and carbon supply and regions in which environmental controls on high Rs values are difficult to ascertain due to limited field data. Because soil moisture integrates temperature and precipitation dynamics, it can more directly constrain the heterotrophic component of Rs, but global-scale models tend to smooth its spatial heterogeneity by aggregating factors that increase moisture variability within and across biomes. We compare statistical and mechanistic models that provide independent estimates of global Rs ranging from 83 to 108 Pg yr(-1), but also highlight regions of uncertainty where more observations are required or environmental controls are hard to constrain.
机译:土壤呼吸(RS)是一种主要的途径,通过该主要碳,生物圈中的固定碳被返回到大气中,但我们在全球范围内使用环境司机预测呼吸率的能力限制。虽然已知温度,水分,碳供应和其他位点特征在某些生物群系中调节绘制鳞片的土壤呼吸速率,但用于评估不同生物群体和全球范围内这些因素的相对重要性的定量框架需要测试之间的关系现场估计和全球气候数据。本研究通过将全球土壤水分,土壤温度,初级生产率和土壤碳估算联系起来,评估在全球范围内驾驶卢比的因素。从全球土壤呼吸数据库(SRDB)的年度卢比观察。我们发现具有抛物线土壤湿度函数的校准模型可以提高与平均年降水的渐近功能相似模型的预测力。土壤温度与先前报道的空气温度观测相当,用于预测RS,是全球模型中卢比的主要驱动因素;然而,在某些生物体土壤水分和土壤中出现,作为卢比的主要预测因子。我们识别典型的温度驱动的反应进一步通过土壤水分,沉淀和碳供应和区域进一步介导的区域,其中难以确定高级RS值的环境控制因现场数据有限而难以确定。由于土壤湿度整合了温度和降水动态,因此可以更直接限制卢比的异质组分,但通过聚集在生物群和跨越生物群系内和跨越的因素来倾向于平滑其空间异质性。我们比较统计和机械模型,提供从83到108 pg Yr(-1)的全局Rs的独立估计,但也强调了不确定的区域,在需要更多观察或环境控制难以约束。

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