A data-driven topsoil δ13C dataset and the drivers of spatial variability across the Tibetan Plateau
- 1College of Earth Science, Chengdu University of Technology, Chengdu 610059, China
- 2College of Ecology and Environment, Chengdu University of Technology, Chengdu 610059, China
- 3Key Laboratory of Bamboo and Rattan Science and Technology of National Forestry and Grassland Administration, International Centre for Bamboo and Rattan, Beijing 100102, China
- 4Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
- 5Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- 6Center for Excellence in Tibetan Plateau Earth Science, Chinese Academy of Sciences, Beijing 100101, China
- 7University of Chinese Academy of Sciences, Beijing 100049, China
- 8Key Laboratory of Cenozoic Geology and Environment, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
- 9College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
- 10State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu 610059, Chengdu, China
Abstract. Soil carbon isotopes (δ13C) provide reliable insights at a long-term scale for studying soil carbon turnover. The Tibetan Plateau (TP), called “the third pole of the earth” is one of the most sensitive areas to global climate change and exhibits an early warning signal of global warming. Although many studies detected the variability of soil δ13C at site scales, a knowledge gap still exists in the spatial pattern of topsoil δ13C across the TP. To fill the substantial knowledge gap, we first compiled a database of topsoil δ13C with 396 observations from published literatures. Then we applied a Random Forest (RF) algorithm – a machine learning approach, to predict the spatial pattern of topsoil δ13C and β (indicating the decomposition rate of soil organic carbon (SOC), calculated by δ13C divided by logarithmically converted SOC). Finally, two datasets – topsoil δ13C and β with a fine spatial resolution of 1 km across the TP were developed. Results showed that topsoil δ13C varied significantly among different ecosystem types (p < 0.001). Topsoil δ13C was −26.3 ± 1.60 ‰ (mean ± standard deviation) for forests, 24.3 ± 2.00 ‰ for shrublands, −23.9 ± 1.84 ‰ for grasslands, −18.9 ± 2.37 ‰ for deserts, respectively. RF could well predict the spatial variability of topsoil δ13C with a model efficiency of 0.62 and root mean square error of 1.12 ‰, enabling to derive data-driven δ13C and β products. Data-driven topsoil δ13C varied from −28.26 ‰ to −16.95 ‰, with the highest topsoil δ13C in the north and northwest TP and the lowest δ13C in Southeast or South TP, indicating strong spatial variabilities in topsoil δ13C. Similarly, there were strong spatial variabilities in data-driven β, with the lowest β values at the east and middle TP, indicating a higher SOC turnover in the east and middle TP compared that of other regions in the TP. This study was the first attempt 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 feedbacks over the TP under climate change. The data-driven δ13C and β datasets are public available at https://doi.org/10.6084/m9.figshare.16641292.v2 (Tang, 2021).
Yunsen Lai et al.
Yunsen Lai et al.
A data-driven estimate of topsoil (0-5 cm) isotope carbon across the Tibetan Plateau https://doi.org/10.6084/m9.figshare.16641292.v2
Yunsen Lai et al.
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