Modelling Soil δ13C across the Tibetan Plateau Using Deep-Learning
文献类型：期刊论文
| 作者 | Zhou, T.10,11; Lai, Y. S.11; Yang, Z. H.11; Shi, Y. H.11; Luo, X. R.11; Liu, L.11; Yu, P.11; Chen, G.10; Cao, L. X.10; Fan, S. H.9 |
| 刊名 | JOURNAL OF ENVIRONMENTAL INFORMATICS
 |
| 出版日期 | 2024-09-01 |
| 卷号 | 44期号:1页码:48-60 |
| 关键词 | soil delta C-13 spatial variability multi-layer perceptron neural network soil carbon turnover Tibetan Plateau biogeochemical cycles biogeochemical cycles |
| ISSN号 | 1726-2135 |
| DOI | 10.3808/jei.202400519 |
| 英文摘要 | Soil carbon isotopes (delta C-13) provide reliable insights for studying soil carbon turnover at a long-term scale.The Tibetan Plateau (TP), often referred as "the third pole of the earth", is highly sensitive to global climate change, and exhibits an early warning signal of global warming. Although many studies detected soil delta C-13 variability at site scales, there is still a knowledge gap existing in the spatial pattern of soil delta C-13 across the TP. In this study, we compiled a database of 198 topsoil delta C-13 observations from published literatures and used a modified multi-layer perceptron (MLP) neural network algorithm topredict the spatial pattern of topsoil delta C-13 and beta(indicating the decomposition rate of soil organic carbon (SOC), calculated as delta C-13 divided by logarithmically converted SOC) at 500 m resolution. Results showed that MLP model effectively predicted topsoil delta C-13 with a model efficiency of 0.72 and a root mean square error of 1.16 parts per thousand. Topsoil delta C-13 varied significantly across different ecosystem types (p< 0.001) with a mean delta C-13 of -25.89 +/- 1.15 parts per thousand (mean +/- standard deviation)for forests, -24.91 +/- 1.03 parts per thousand for shrublands, -22.95 +/- 1.44 parts per thousand for grasslands, and -18.88 +/- 2.37 parts per thousand for deserts. Furthermore, there was an increasing trend of predicted delta C-13 from the southeastern to the northwestern TP, likely linked to vegetation type and climatic conditions. beta values were low in the eastern TP and higher in the northern and northwestern TP, indicating faster SOC turnover rate in the east TP compared to the north and northwest. This study represents the first effort to develop a fine resolution product of topsoil delta C-13 and beta acrossthe TP, which could provide an independent, data-driven benchmark for biogeochemical cycling models to study SOC turnover and terrestrial carbon-climate feedbackover the TP under climate change |
| WOS关键词 | CARBON ISOTOPIC COMPOSITION ; ORGANIC-CARBON ; DEPTH PROFILES ; PINUS-SYLVESTRIS ; C-13 ENRICHMENT ; TEMPERATURE ; MATTER ; CLIMATE ; DECOMPOSITION ; RESPIRATION |
| 资助项目 | Na-tional Natural Science Fund Program[32271856] ; Chengdu University of Technology[80000-2023-ZF11410] ; Sichuan Science and Technology Program[2019YFG0460] ; Sichuan Science and Technology Program[2021YJ0377] ; State Key Laboratory of Geohazard Prevention and Geoen-vironment Protection Independent Research Project[SKLGP2018Z004] ; State Key Laboratory of Geohazard Prevention and Geoen-vironment Protection Independent Research Project[SKLGP2021K024] ; State Key Laboratory of Geohazard Prevention and Geoen-vironment Protection Independent Research Project[JC2022/D01] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001267541200004 |
| 出版者 | INT SOC ENVIRON INFORM SCI |
| 资助机构 | Na-tional Natural Science Fund Program ; Chengdu University of Technology ; Sichuan Science and Technology Program ; State Key Laboratory of Geohazard Prevention and Geoen-vironment Protection Independent Research Project |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/207381]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Tang, X. L. |
| 作者单位 | 1.Tianfu Yongxing Lab, Chengdu 610059, Peoples R China 2.Chengdu Univ Technol, State Key Lab Geohazard Prevent & Geoenvironm Prot, Chengdu 610059, Peoples R China 3.Lanzhou Univ, Coll Earth & Environm Sci, Lanzhou 730000, Peoples R China 4.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Cenozo Geol & Environm, Beijing 100029, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 6.Chinese Acad Sci, Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China 7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 8.Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Alpine Ecol, Beijing 100101, Peoples R China 9.Natl Forestry & Grassland Adm, Int Ctr Bamboo & Rattan, Key Lab Bamboo & Rattan Sci & Technol, Beijing 100102, Peoples R China 10.Chengdu Univ Technol, Coll Ecol & Environm, Chengdu 610059, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, T.,Lai, Y. S.,Yang, Z. H.,et al. Modelling Soil δ13C across the Tibetan Plateau Using Deep-Learning[J]. JOURNAL OF ENVIRONMENTAL INFORMATICS,2024,44(1):48-60. |
| APA | Zhou, T..,Lai, Y. S..,Yang, Z. H..,Shi, Y. H..,Luo, X. R..,...&Tang, X. L..(2024).Modelling Soil δ13C across the Tibetan Plateau Using Deep-Learning.JOURNAL OF ENVIRONMENTAL INFORMATICS,44(1),48-60. |
| MLA | Zhou, T.,et al."Modelling Soil δ13C across the Tibetan Plateau Using Deep-Learning".JOURNAL OF ENVIRONMENTAL INFORMATICS 44.1(2024):48-60. |
入库方式： OAI收割
来源：地理科学与资源研究所
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