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Modelling Soil δ13C across the Tibetan Plateau Using Deep-Learning

T. Zhou1,2 *, Y. S. Lai1 *, Z. H. Yang1, Y. H. Shi1, X. R. Luo1, L. Liu1, P. Yu1, G. Chen2, L. X. Cao2, S. H. Fan3, C. J. Cai3, J. Sun4, S. H. Chen5, H. Y. Lu6, 7, 8, X. L. Ma9, S. D. Li1, and X. L. Tang2, 10, 11 **

  1. College of Earth Science, Chengdu University of Technology, Chengdu 610059, China
  2. College of Ecology and Environment, Chengdu University of Technology, Chengdu 610059, China
  3. Key Laboratory of Bamboo and Rattan Science and Technology of National Forestry and Grassland Administration, International Centre for Bamboo and Rattan, Beijing 100102, China
  4. Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
  5. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  6. Center for Excellence in Tibetan Plateau Earth Science, Chinese Academy of Sciences, Beijing 100101, China
  7. University of Chinese Academy of Sciences, Beijing 100049, China
  8. Key Laboratory of Cenozoic Geology and Environment, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
  9. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
  10. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
  11. Tianfu Yongxing Laboratory, Chengdu 610059, China

* Co-first authors
** Corresponding author. Tel.: 176-1115-9509, fax: 86-028-84073434 E-mail address: (X. L. Tang).


Soil carbon isotopes (δ13C) 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 δ13C variability at site scales, there is still a knowledge gap existing in the spatial pattern of soil δ13C across the TP. In this study, we compiled a database of 198 topsoil δ13C observations from published literatures and used a modified multi-layer perceptron (MLP) neural network algorithm to predict the spatial pattern of topsoil δ13C and β (indicating the decomposition rate of soil organic carbon (SOC), calculated as δ13C divided by logarithmically converted SOC) at 500m resolution. Results showed that MLP model effectively predicted topsoil δ13C with a model efficiency of 0.72 and a root mean square error of 1.16‰. Topsoil δ13C varied significantly across different ecosystem types (p < 0.001) with a mean δ13C of –25.89 ± 1.15‰ (mean ± standard deviation) for forests, –24.91 ± 1.03‰ for shrublands, –22.95 ± 1.44‰ for grasslands, and –18.88 ± 2.37‰ for deserts. Furthermore, there was an increasing trend of predicted δ13C from the southeastern to the northwestern TP, likely linked to vegetation type and climatic conditions. β 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 δ13C and β across the TP, which could provide an independent, data-driven benchmark for biogeochemical cycling models to study SOC turnover and terrestrial carbon-climate feedback over the TP under climate change.

Keywords: soil δ13C, spatial variability, multi-layer perceptron neural network, soil carbon turnover, Tibetan Plateau, biogeochemical cycles

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