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The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial

BACKGROUND: The Qinghai-Tibet Plateau is the “sensitive area” of climate change, and also the “driver” and “amplifier” of global change. The response and feedback of its carbon dynamics to climate change will significantly affect the content of greenhouse gases in the atmosphere. However, due to the...

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Autores principales: Zeng, Jingyu, Zhou, Tao, Xu, Yixin, Lin, Qiaoyu, Tan, E., Zhang, Yajie, Wu, Xuemei, Zhang, Jingzhou, Liu, Xia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10494389/
https://www.ncbi.nlm.nih.gov/pubmed/37695559
http://dx.doi.org/10.1186/s13021-023-00239-9
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author Zeng, Jingyu
Zhou, Tao
Xu, Yixin
Lin, Qiaoyu
Tan, E.
Zhang, Yajie
Wu, Xuemei
Zhang, Jingzhou
Liu, Xia
author_facet Zeng, Jingyu
Zhou, Tao
Xu, Yixin
Lin, Qiaoyu
Tan, E.
Zhang, Yajie
Wu, Xuemei
Zhang, Jingzhou
Liu, Xia
author_sort Zeng, Jingyu
collection PubMed
description BACKGROUND: The Qinghai-Tibet Plateau is the “sensitive area” of climate change, and also the “driver” and “amplifier” of global change. The response and feedback of its carbon dynamics to climate change will significantly affect the content of greenhouse gases in the atmosphere. However, due to the unique geographical environment characteristics of the Qinghai-Tibet Plateau, there is still much controversy about its carbon source and sink estimation results. This study designed a new algorithm based on machine learning to improve the accuracy of carbon source and sink estimation by integrating multiple scale carbon input (net primary productivity, NPP) and output (soil heterotrophic respiration, Rh) information from remote sensing and ground observations. Then, we compared spatial patterns of NPP and Rh derived from the fusion of multiple scale data with other widely used products and tried to quantify the differences and uncertainties of carbon sink simulation at a regional scale. RESULTS: Our results indicate that although global warming has potentially increased the Rh of the Qinghai-Tibet Plateau, it will also increase its NPP, and its current performance is a net carbon sink area (carbon sink amount is 22.3 Tg C/year). Comparative analysis with other data products shows that CASA, GLOPEM, and MODIS products based on remote sensing underestimate the carbon input of the Qinghai-Tibet Plateau (30–70%), which is the main reason for the severe underestimation of the carbon sink level of the Qinghai-Tibet Plateau (even considered as a carbon source). CONCLUSIONS: The estimation of the carbon sink in the Qinghai-Tibet Plateau is of great significance for ensuring its ecological barrier function. It can deepen the community’s understanding of the response to climate change in sensitive areas of the plateau. This study can provide an essential basis for assessing the uncertainty of carbon sources and sinks in the Qinghai-Tibet Plateau, and also provide a scientific reference for helping China achieve “carbon neutrality” by 2060. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13021-023-00239-9.
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spelling pubmed-104943892023-09-12 The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial Zeng, Jingyu Zhou, Tao Xu, Yixin Lin, Qiaoyu Tan, E. Zhang, Yajie Wu, Xuemei Zhang, Jingzhou Liu, Xia Carbon Balance Manag Research BACKGROUND: The Qinghai-Tibet Plateau is the “sensitive area” of climate change, and also the “driver” and “amplifier” of global change. The response and feedback of its carbon dynamics to climate change will significantly affect the content of greenhouse gases in the atmosphere. However, due to the unique geographical environment characteristics of the Qinghai-Tibet Plateau, there is still much controversy about its carbon source and sink estimation results. This study designed a new algorithm based on machine learning to improve the accuracy of carbon source and sink estimation by integrating multiple scale carbon input (net primary productivity, NPP) and output (soil heterotrophic respiration, Rh) information from remote sensing and ground observations. Then, we compared spatial patterns of NPP and Rh derived from the fusion of multiple scale data with other widely used products and tried to quantify the differences and uncertainties of carbon sink simulation at a regional scale. RESULTS: Our results indicate that although global warming has potentially increased the Rh of the Qinghai-Tibet Plateau, it will also increase its NPP, and its current performance is a net carbon sink area (carbon sink amount is 22.3 Tg C/year). Comparative analysis with other data products shows that CASA, GLOPEM, and MODIS products based on remote sensing underestimate the carbon input of the Qinghai-Tibet Plateau (30–70%), which is the main reason for the severe underestimation of the carbon sink level of the Qinghai-Tibet Plateau (even considered as a carbon source). CONCLUSIONS: The estimation of the carbon sink in the Qinghai-Tibet Plateau is of great significance for ensuring its ecological barrier function. It can deepen the community’s understanding of the response to climate change in sensitive areas of the plateau. This study can provide an essential basis for assessing the uncertainty of carbon sources and sinks in the Qinghai-Tibet Plateau, and also provide a scientific reference for helping China achieve “carbon neutrality” by 2060. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13021-023-00239-9. Springer International Publishing 2023-09-11 /pmc/articles/PMC10494389/ /pubmed/37695559 http://dx.doi.org/10.1186/s13021-023-00239-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zeng, Jingyu
Zhou, Tao
Xu, Yixin
Lin, Qiaoyu
Tan, E.
Zhang, Yajie
Wu, Xuemei
Zhang, Jingzhou
Liu, Xia
The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial
title The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial
title_full The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial
title_fullStr The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial
title_full_unstemmed The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial
title_short The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial
title_sort fusion of multiple scale data indicates that the carbon sink function of the qinghai-tibet plateau is substantial
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10494389/
https://www.ncbi.nlm.nih.gov/pubmed/37695559
http://dx.doi.org/10.1186/s13021-023-00239-9
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