A data-driven topsoil δ 13 C dataset and the drivers of spatial variability across the Tibetan Plateau

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 https://doi.org/10.6084/m9.figshare.16641292.v2 (Tang, 2021).