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>

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>

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

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Rebecca M. Varney ◽  
Sarah E. Chadburn ◽  
Pierre Friedlingstein ◽  
Eleanor J. Burke ◽  
Charles D. Koven ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Soil Carbon ◽  
Large Range ◽  
Turnover Time ◽  
Carbon Turnover ◽  
Climate Change Projections ◽  
Emergent Constraint ◽  
Earth System Models ◽  
Soil Carbon Turnover

Abstract Carbon cycle feedbacks represent large uncertainties in climate change projections, and the response of soil carbon to climate change contributes the greatest uncertainty to this. Future changes in soil carbon depend on changes in litter and root inputs from plants and especially on reductions in the turnover time of soil carbon (τs) with warming. An approximation to the latter term for the top one metre of soil (ΔCs,τ) can be diagnosed from projections made with the CMIP6 and CMIP5 Earth System Models (ESMs), and is found to span a large range even at 2 °C of global warming (−196 ± 117 PgC). Here, we present a constraint on ΔCs,τ, which makes use of current heterotrophic respiration and the spatial variability of τs inferred from observations. This spatial emergent constraint allows us to halve the uncertainty in ΔCs,τ at 2 °C to −232 ± 52 PgC.

scholarly journals Species Ecological Envelopes under Climate Change Scenarios: A Case Study for the Main Two Wood-Production Forest Species in Portugal

Forests ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 880 ◽  
Author(s):  
Cristina Alegria ◽  
Natália Roque ◽  
Teresa Albuquerque ◽  
Saki Gerassis ◽  
Paulo Fernandez ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Species Distribution ◽  
Environmental Variables ◽  
Species Distributions ◽  
Learning Approach ◽  
Climate Change Scenarios ◽  
Wood Production ◽  
Machine Learning Approach ◽  
Species Productivity

Species ecological envelope maps were obtained for the two main Portuguese wood-production species (Eucalyptus globulus Labill. and Pinus pinaster Aiton) and projected future climate change scenarios. A machine learning approach was used to understand the most influential environmental variables that may explain current species distribution and productivity. Background and Objectives: The aims of the study were: (1) to map species potential suitability areas using ecological envelopes in the present and to project them in the future under climate change scenarios; (2) to map species current distributions; (3) to map species current productivity; and (4) to explore the most influential environmental variables on species current distribution and productivity. Materials and Methods: Climate, elevation data, and soil data sets were used to obtain present and future species ecological envelopes under two climate change scenarios. The official land cover maps were used to map species distributions. Forest inventory data were used to map the species productivity by geostatistical techniques. A Bayesian machine learning approach, supported by species distributions and productivity data, was used to explore the most influential environmental variables on species distribution and productivity and to validate species ecological envelopes. Results: The species ecological envelope methodology was found to be robust. Species’ ecological envelopes showed a high potential for both species’ afforestation. In the future, a decrease in the country’s area potentiality was forecasted for both species. The distribution of maritime pine was found to be mainly determined by precipitation-related variables, but the elevation and temperature-related variables were very important to differentiate species productivity. For eucalypts, species distribution was mainly explained by temperature-related variables, as well as the species productivity. Conclusions: These findings are key to support recommendations for future afforestation and will bring value to policy-makers and environmental authorities in policy formulation under climate change scenarios.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use
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