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Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China’s forest ecosystem

China’s forests play a vital role in the global carbon cycle through the absorption of atmospheric CO(2) to mitigate climate change caused by the increase of anthropogenic CO(2). It is essential to evaluate the carbon sink potential (CSP) of China’s forest ecosystem. Combining NDVI, field-investigat...

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Autores principales: Wang, Zhaosheng, Li, Renqaing, Guo, Qingchun, Wang, Zhaojun, Huang, Mei, Cai, Changjun, Chen, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10333463/
https://www.ncbi.nlm.nih.gov/pubmed/37441384
http://dx.doi.org/10.1016/j.heliyon.2023.e17243
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author Wang, Zhaosheng
Li, Renqaing
Guo, Qingchun
Wang, Zhaojun
Huang, Mei
Cai, Changjun
Chen, Bin
author_facet Wang, Zhaosheng
Li, Renqaing
Guo, Qingchun
Wang, Zhaojun
Huang, Mei
Cai, Changjun
Chen, Bin
author_sort Wang, Zhaosheng
collection PubMed
description China’s forests play a vital role in the global carbon cycle through the absorption of atmospheric CO(2) to mitigate climate change caused by the increase of anthropogenic CO(2). It is essential to evaluate the carbon sink potential (CSP) of China’s forest ecosystem. Combining NDVI, field-investigated, and vegetation and soil carbon density data modeled by process-based models, we developed the state-of-the-art learning ensembles model of process-based models (the multi-model random forest ensemble (MMRFE) model) to evaluate the carbon stocks of China’s forest ecosystem in historical (1982–2021) and future (2022–2081, without NDVI-driven data) periods. Meanwhile, we proposed a new carbon sink index ([Formula: see text]) to scientifically and accurately evaluate carbon sink status and identify carbon sink intensity zones, reducing the probability of random misjudgments as a carbon sink. The new MMRFE models showed good simulation results in simulating forest vegetation and soil carbon density in China (significant positive correlation with the observed values, r = 0.94, P < 0.001). The modeled results show that a cumulative increase of 1.33 Pg C in historical carbon stocks of forest ecosystem is equivalent to 48.62 Bt CO(2), which is approximately 52.03% of the cumulative increased CO(2) emissions in China from 1959 to 2018. In the next 60 years, China’s forest ecosystem will absorb annually 1.69 (RCP45 scenario) to 1.85 (RCP85 scenario) Bt CO(2). Compared with the carbon stock in the historical period, the cumulative absorption of CO(2) by China’s forest ecosystem in 2032–2036, 2062–2066, and 2077–2081 are approximately 11.25–39.68, 110.66–121.49 and 101.31–111.11 Bt CO(2,) respectively. In historical and future periods, the medium and strong carbon sink intensity regions identified by the historical [Formula: see text] covered 65% of the total forest area, cumulative absorbing approximately 31.60 and 65.83–72.22 Bt CO(2), respectively. In the future, China’s forest ecosystem has a large CSP with a non-continuous increasing trend. However, the CSP should not be underestimated. Notably, the medium carbon sink intensity region should be the priority for natural carbon sequestration action. This study not only provides an important methodological basis for accurately estimating the future CSP of forest ecosystem but also provides important decision support for future forest ecosystem carbon sequestration action.
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spelling pubmed-103334632023-07-12 Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China’s forest ecosystem Wang, Zhaosheng Li, Renqaing Guo, Qingchun Wang, Zhaojun Huang, Mei Cai, Changjun Chen, Bin Heliyon Research Article China’s forests play a vital role in the global carbon cycle through the absorption of atmospheric CO(2) to mitigate climate change caused by the increase of anthropogenic CO(2). It is essential to evaluate the carbon sink potential (CSP) of China’s forest ecosystem. Combining NDVI, field-investigated, and vegetation and soil carbon density data modeled by process-based models, we developed the state-of-the-art learning ensembles model of process-based models (the multi-model random forest ensemble (MMRFE) model) to evaluate the carbon stocks of China’s forest ecosystem in historical (1982–2021) and future (2022–2081, without NDVI-driven data) periods. Meanwhile, we proposed a new carbon sink index ([Formula: see text]) to scientifically and accurately evaluate carbon sink status and identify carbon sink intensity zones, reducing the probability of random misjudgments as a carbon sink. The new MMRFE models showed good simulation results in simulating forest vegetation and soil carbon density in China (significant positive correlation with the observed values, r = 0.94, P < 0.001). The modeled results show that a cumulative increase of 1.33 Pg C in historical carbon stocks of forest ecosystem is equivalent to 48.62 Bt CO(2), which is approximately 52.03% of the cumulative increased CO(2) emissions in China from 1959 to 2018. In the next 60 years, China’s forest ecosystem will absorb annually 1.69 (RCP45 scenario) to 1.85 (RCP85 scenario) Bt CO(2). Compared with the carbon stock in the historical period, the cumulative absorption of CO(2) by China’s forest ecosystem in 2032–2036, 2062–2066, and 2077–2081 are approximately 11.25–39.68, 110.66–121.49 and 101.31–111.11 Bt CO(2,) respectively. In historical and future periods, the medium and strong carbon sink intensity regions identified by the historical [Formula: see text] covered 65% of the total forest area, cumulative absorbing approximately 31.60 and 65.83–72.22 Bt CO(2), respectively. In the future, China’s forest ecosystem has a large CSP with a non-continuous increasing trend. However, the CSP should not be underestimated. Notably, the medium carbon sink intensity region should be the priority for natural carbon sequestration action. This study not only provides an important methodological basis for accurately estimating the future CSP of forest ecosystem but also provides important decision support for future forest ecosystem carbon sequestration action. Elsevier 2023-06-17 /pmc/articles/PMC10333463/ /pubmed/37441384 http://dx.doi.org/10.1016/j.heliyon.2023.e17243 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Wang, Zhaosheng
Li, Renqaing
Guo, Qingchun
Wang, Zhaojun
Huang, Mei
Cai, Changjun
Chen, Bin
Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China’s forest ecosystem
title Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China’s forest ecosystem
title_full Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China’s forest ecosystem
title_fullStr Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China’s forest ecosystem
title_full_unstemmed Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China’s forest ecosystem
title_short Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China’s forest ecosystem
title_sort learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of china’s forest ecosystem
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10333463/
https://www.ncbi.nlm.nih.gov/pubmed/37441384
http://dx.doi.org/10.1016/j.heliyon.2023.e17243
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