Spatial variability of topsoil δ13C across Qinghai-Tibet Plateau

Soil carbon isotopes (δ¹³C) provide reliable insights at the long-term scale for the study of soil carbon turnover and topsoil δ¹³C could well reflect organic matter input from the current vegetation. Qinghai-Tibet Plateau (QTP) is called “the third pole of the earth” because of its high elevation, and it is one of the most sensitive and critical regions to global climate change worldwide. Previous studies focused on variability of soil δ¹³C at in-site scale. However, a knowledge gap still exists in the spatial pattern of topsoil δ¹³C in QTP. In this study, we first established a database of topsoil δ¹³C with 396 observations from published literature and applied a Random Forest (RF) algorithm (a machine learning approach) to predict the spatial pattern of topsoil δ¹³C using environmental variables. Results showed that topsoil δ¹³C significantly varied across different ecosystem types (p < 0.05).  Topsoil δ¹³C was -26.3 ± 1.60 ‰ for forest, 24.3 ± 2.00 ‰ for shrubland, -23.9 ± 1.84 ‰ for grassland, -18.9 ± 2.37 ‰ for desert, respectively. RF could well predict the spatial variability of topsoil δ¹³C with a model efficiency (pseudo R²) of 0.65 and root mean square error of 1.42. The gridded product of topsoil δ¹³C and topsoil β (indicating the decomposition rate of soil organic carbon, calculated by δ¹³C divided by logarithmically converted SOC) with a spatial resolution of 1000 m were developed. Strong spatial variability of topsoil δ¹³C was observed, which increased gradually from the southeast to the northwest in QTP. Furthermore, a large variation was found in β, ranging from -7.87 to -81.8, with a decreasing trend from southeast to northwest, indicating that carbon turnover rate was faster in northwest QTP compared to that of southeast. This study was the first attempt to develop a fine resolution product of topsoil δ¹³C for QTP using a machine learning approach, which could provide an independent benchmark for biogeochemical models to study soil carbon turnover and terrestrial carbon-climate feedbacks under ongoing climate change.