scholarly journals Consolidating soil carbon turnover models by improved estimates of belowground carbon input

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Arezoo Taghizadeh-Toosi ◽  
Bent T. Christensen ◽  
Margaret Glendining ◽  
Jørgen E. Olesen
Keyword(s):  
Soil Carbon ◽  
Carbon Turnover ◽  
Belowground Carbon ◽  
Consolidating Soil ◽  
Soil Carbon Turnover ◽  
Turnover Models

Bacterial and enchytraeid abundance accelerate soil carbon turnover along a lowland vegetation gradient in interior Alaska

2012 ◽  
Vol 50 ◽  
pp. 188-198 ◽  
Author(s):  
M.P. Waldrop ◽  
J.W. Harden ◽  
M.R. Turetsky ◽  
D.G. Petersen ◽  
A.D. McGuire ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Carbon Turnover ◽  
Interior Alaska ◽  
Vegetation Gradient ◽  
Soil Carbon Turnover

Spatial variability of topsoil δ13C across Qinghai-Tibet Plateau

2021 ◽  
Author(s):  
Yunsen Lai ◽  
Shaoda Li ◽  
Xiaolu Tang ◽  
Xinrui Luo ◽  
Liang Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Spatial Variability ◽  
Soil Carbon ◽  
Tibet Plateau ◽  
Learning Approach ◽  
Carbon Turnover ◽  
Machine Learning Approach ◽  
Qinghai Tibet Plateau ◽  
Soil Carbon Turnover

<p>Soil carbon isotopes (δ<sup>13</sup>C) provide reliable insights at the long-term scale for the study of soil carbon turnover and topsoil δ<sup>13</sup>C could well reflect organic matter input from the current vegetation. Qinghai-Tibet Plateau (QTP) is called “the third pole of the earth” because of its high elevation, and it is one of the most sensitive and critical regions to global climate change worldwide. Previous studies focused on variability of soil δ<sup>13</sup>C at in-site scale. However, a knowledge gap still exists in the spatial pattern of topsoil δ<sup>13</sup>C in QTP. In this study, we first established a database of topsoil δ<sup>13</sup>C with 396 observations from published literature and applied a Random Forest (RF) algorithm (a machine learning approach) to predict the spatial pattern of topsoil δ<sup>13</sup>C using environmental variables. Results showed that topsoil δ<sup>13</sup>C significantly varied across different ecosystem types (p < 0.05).  Topsoil δ<sup>13</sup>C was -26.3 ± 1.60 ‰ for forest, 24.3 ± 2.00 ‰ for shrubland, -23.9 ± 1.84 ‰ for grassland, -18.9 ± 2.37 ‰ for desert, respectively. RF could well predict the spatial variability of topsoil δ<sup>13</sup>C with a model efficiency (pseudo R<sup>2</sup>) of 0.65 and root mean square error of 1.42. The gridded product of topsoil δ<sup>13</sup>C and topsoil β (indicating the decomposition rate of soil organic carbon, calculated by δ<sup>13</sup>C divided by logarithmically converted SOC) with a spatial resolution of 1000 m were developed. Strong spatial variability of topsoil δ<sup>13</sup>C was observed, which increased gradually from the southeast to the northwest in QTP. Furthermore, a large variation was found in β, ranging from -7.87 to -81.8, with a decreasing trend from southeast to northwest, indicating that carbon turnover rate was faster in northwest QTP compared to that of southeast. This study was the first attempt to develop a fine resolution product of topsoil δ<sup>13</sup>C for QTP using a machine learning approach, which could provide an independent benchmark for biogeochemical models to study soil carbon turnover and terrestrial carbon-climate feedbacks under ongoing climate change.</p>

scholarly journals A dual isotope approach to isolate soil carbon pools of different turnover times

2013 ◽  
Vol 10 (12) ◽  
pp. 8067-8081 ◽  
Author(s):  
M. S. Torn ◽  
M. Kleber ◽  
E. S. Zavaleta ◽  
B. Zhu ◽  
C. B. Field ◽  
...  
Keyword(s):  
Environmental Change ◽  
Soil Carbon ◽  
Carbon Storage ◽  
Co2 Efflux ◽  
Carbon Turnover ◽  
Soil Carbon Storage ◽  
Density Fraction ◽  
Dual Isotope ◽  
Slow Cycling ◽  
Soil Carbon Turnover

Abstract. Soils are globally significant sources and sinks of atmospheric CO2. Increasing the resolution of soil carbon turnover estimates is important for predicting the response of soil carbon cycling to environmental change. We show that soil carbon turnover times can be more finely resolved using a dual isotope label like the one provided by elevated CO2 experiments that use fossil CO2. We modeled each soil physical fraction as two pools with different turnover times using the atmospheric 14C bomb spike in combination with the label in 14C and 13C provided by an elevated CO2 experiment in a California annual grassland. In sandstone and serpentine soils, the light fraction carbon was 21–54% fast cycling with 2–9 yr turnover, and 36–79% slow cycling with turnover slower than 100 yr. This validates model treatment of the light fraction as active and intermediate cycling carbon. The dense, mineral-associated fraction also had a very dynamic component, consisting of ∼7% fast-cycling carbon and ∼93% very slow cycling carbon. Similarly, half the microbial biomass carbon in the sandstone soil was more than 5 yr old, and 40% of the carbon respired by microbes had been fixed more than 5 yr ago. Resolving each density fraction into two pools revealed that only a small component of total soil carbon is responsible for most CO2 efflux from these soils. In the sandstone soil, 11% of soil carbon contributes more than 90% of the annual CO2 efflux. The fact that soil physical fractions, designed to isolate organic material of roughly homogeneous physico-chemical state, contain material of dramatically different turnover times is consistent with recent observations of rapid isotope incorporation into seemingly stable fractions and with emerging evidence for hot spots or micro-site variation of decomposition within the soil matrix. Predictions of soil carbon storage using a turnover time estimated with the assumption of a single pool per density fraction would greatly overestimate the near-term response to changes in productivity or decomposition rates. Therefore, these results suggest a slower initial change in soil carbon storage due to environmental change than has been assumed by simpler (one-pool) mass balance calculations.

scholarly journals Climate exerts stronger control on topsoil carbon persistence than plant input in alpine grasslands

2021 ◽  
Author(s):  
Donghai Wu ◽  
Xiangtao Xu ◽  
Haicheng Zhang
Keyword(s):  
Soil Carbon ◽  
Direct Effect ◽  
Carbon Turnover ◽  
Alpine Grasslands ◽  
Soil Carbon Turnover

Chen et al. (2021) concluded that plant input governs topsoil carbon persistence in alpine grasslands. We demonstrated that the excluded direct effect of precipitation on topsoil Δ14C in their analysis was in fact significant and strong. Our results provide an alternative viewpoint on the drivers of soil carbon turnover.

Long-term sensitivity of soil carbon turnover to warming

Nature ◽  
2005 ◽  
Vol 433 (7023) ◽  
pp. 298-301 ◽  
Author(s):  
W. Knorr ◽  
I. C. Prentice ◽  
J. I. House ◽  
E. A. Holland
Keyword(s):  
Soil Carbon ◽  
Carbon Turnover ◽  
Soil Carbon Turnover

The decline in plant biodiversity slows down soil carbon turnover under increasing nitrogen deposition in a temperate steppe

2019 ◽  
Vol 33 (7) ◽  
pp. 1362-1372 ◽  
Author(s):  
Sen Yang ◽  
Weixing Liu ◽  
Chunlian Qiao ◽  
Jing Wang ◽  
Meifeng Deng ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Nitrogen Deposition ◽  
Plant Biodiversity ◽  
Carbon Turnover ◽  
Temperate Steppe ◽  
Soil Carbon Turnover

Unexpected control of soil carbon turnover by soil carbon concentration

2013 ◽  
Vol 11 (4) ◽  
pp. 407-413 ◽  
Author(s):  
Axel Don ◽  
Christian Rödenbeck ◽  
Gerd Gleixner
Keyword(s):  
Soil Carbon ◽  
Carbon Concentration ◽  
Carbon Turnover ◽  
Soil Carbon Turnover

A spatial emergent constraint on the sensitivity of soil carbon turnover time to global warming

2020 ◽  
Author(s):  
Rebecca Varney ◽  
Peter Cox ◽  
Sarah Chadburn ◽  
Pierre Friedlingstein ◽  
Eleanor Burke ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Soil Carbon ◽  
Turnover Time ◽  
Carbon Turnover ◽  
Climate Change Projections ◽  
Emergent Constraint ◽  
Earth System Models ◽  
Soil Carbon Turnover ◽  
Effective Sensitivity

<p>Carbon cycle feedbacks represent large uncertainties on climate change projections, and the response<br>of soil carbon to climate change contributes the greatest uncertainty to this. Future changes in soil<br>carbon depend on changes in litter and root inputs from plants, and especially on reductions in the<br>turnover time of soil carbon (τ<sub>s</sub>) with warming. The latter represents the change in soil carbon<br>due to the response of soil turnover time (∆C<sub>s,τ</sub>), and can be diagnosed from projections made with<br>Earth System Models (ESMs). It is found to span a large range even at the Paris Agreement Target<br>of 2<sup>◦</sup>C global warming. We use the spatial variability of τ<sub>s</sub> inferred from observations to obtain a<br>constraint on ∆C<sub>s,τ</sub> . This spatial emergent constraint allows us to greatly reduce the uncertainty in<br>∆C<sub>s,τ</sub> at 2<sup>◦</sup>C global warming. We do likewise for other levels of global warming to derive a best<br>estimate for the effective sensitivity of τ<sub>s</sub> to global warming, and derive a q10 equivalent value for<br>heterotrophic respiration.</p>

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