Can We Derive Climate-Forest-Soil Dependent Proxies for Litter Carbon?

IF 4 2区 农林科学 Q2 SOIL SCIENCE
Mathias Neumann, Edwin Herzberger, Michael Englisch, Hubert Hasenauer
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引用次数: 0

Abstract

Organic soil layers, including litter, fermentation and humus layers depending on humus form, are a large carbon pool in forests. Soil carbon in organic and mineral layers can be quantified using (i) direct field observations using profiles or cores, (ii) pedo-transfer functions using simple-to-measure proxies for soil carbon, or (iii) biogeochemical modelling considering soil carbon input and output. Despite large amounts of soil data available for researchers, there is little knowledge available on suitable proxies and estimation concepts for carbon in soil layers of predominantly organic origin (here called litter carbon), compared to carbon in mineral soil layers. Here, we test models using litter carbon measurements from Austria. We consider forest and site information as well as litter depth measurements as input data in a machine learning approach for covariate selection and fit multivariate models with remaining significant covariates. We validate the developed models versus independent validation data sets. Our results show a clear relationship between litter carbon and litter depth, with the latter being linked to different humus forms. Models using forest and site parameters in addition to litter depth reach explained variation up to +60%, while models solely using forest and site parameters were clearly inferior in estimating litter carbon (< 30% explained variation). Validation with German, Swedish and Austrian data confirms that litter depth, key forest and site parameters (i.e., air temperature, soil pH, share of broadleaves, soil carbon) are needed for predicting litter carbon with bias < 1 tC/ha and root mean square error < 15 tC/ha. A model estimating litter carbon by first estimating litter bulk density and then multiplying litter bulk density with measured litter depth best explained the observed increase in litter carbon of Austrian forests, with lowest bias, plausible results, and 64% explained variation. Measured litter depth is thus a potent proxy for litter carbon without invasive, time-demanding measurements. We discuss potential research topics (including soil fauna, role of pH in litter decay, using large-scale litter depth surveys such as National Forest Inventories) to explore the still large unexplained variation of litter carbon.

我们能得出气候-森林-土壤相关的凋落物碳代用物吗?
有机土层,包括凋落物、发酵和腐殖质层,取决于腐殖质的形式,是森林中一个巨大的碳库。有机层和矿物层的土壤碳可以通过以下方法进行量化:(i)使用剖面或岩心进行直接现场观测,(ii)使用土壤碳的简单测量代用物进行土壤土壤传递函数,或(iii)考虑土壤碳输入和输出的生物地球化学模型。尽管研究人员可以获得大量的土壤数据,但与矿物土壤层中的碳相比,有机土壤层中的碳(这里称为凋落物碳)的合适代用物和估算概念知之甚少。在这里,我们使用奥地利的凋落物碳测量来测试模型。我们将森林和站点信息以及凋落物深度测量作为输入数据,采用机器学习方法进行协变量选择,并用剩余的显著协变量拟合多变量模型。我们将开发的模型与独立的验证数据集进行验证。我们的研究结果表明凋落物碳与凋落物深度之间存在明确的关系,后者与不同的腐殖质形式有关。利用森林和立地参数外加凋落物深度达的模型对变化的解释可达+60%,而仅利用森林和立地参数的模型在估计凋落物碳方面明显较差(解释变化为<; 30%)。德国、瑞典和奥地利的数据验证证实,预测凋落物碳需要凋落物深度、关键森林和立地参数(即空气温度、土壤pH值、阔叶占比、土壤碳),偏差为1 tC/ha,均方根误差为15 tC/ha。首先估算凋落物容重,然后将凋落物容重与实测凋落物深度相乘的模型最能解释观测到的奥地利森林凋落物碳的增加,其偏差最小,结果可信,解释了64%的变化。因此,测量凋落物深度是凋落物碳的有效代表,而不需要侵入性的、耗时的测量。我们讨论了潜在的研究课题(包括土壤动物,pH在凋落物腐烂中的作用,使用大规模凋落物深度调查,如国家森林调查)来探索仍未解释的凋落物碳的巨大变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
European Journal of Soil Science
European Journal of Soil Science 农林科学-土壤科学
CiteScore
8.20
自引率
4.80%
发文量
117
审稿时长
5 months
期刊介绍: The EJSS is an international journal that publishes outstanding papers in soil science that advance the theoretical and mechanistic understanding of physical, chemical and biological processes and their interactions in soils acting from molecular to continental scales in natural and managed environments.
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