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).
Highlights
Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems, containing about 1500 Pg (1 Pg = 1015 g) carbon within the first meter, which is two-fold higher than that of the atmosphere (Scharlemann et al, 2014)
Moderate-resolution Imaging Spectroradiometer (MODIS) products, including normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) with a spatial resolution of 1 km, leaf area index (LAI) and the fraction of photosynthetically active radiation (FPAR), with a spatial resolution of 500 m, gross primary productivity (GPP) with a spatial resolution of 500 m, evapotranspiration (ET) and potential evapotranspiration (PET) with a spatial resolution of 500 m, land cover type (LCT), with a spatial resolution of 500 m were from https://lpdaac.usgs.gov/. 110 Due to data availability, NDVI, EVI and GPP covered from 2000 to 2010, while ET and PET covered from 2001 to 2010, and LAI and FPAR covered from 2002 to 2010
3.1 Soil δ13C among different ecosystems 155 Large variabilities of soil δ13C values were observed among different ecosystem types (Fig. 2 and Table S1). δ13C ranges from -29.71 ‰ in forests to -15.08 ‰ in deserts, and mean δ13C was -24.41 ± 2.38 ‰
Summary
Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems, containing about 1500 Pg (1 Pg = 1015 g) carbon within the first meter, which is two-fold higher than that of the atmosphere (Scharlemann et al, 2014). Understanding SOC dynamics is of great importance to assess ecosystem carbon balance and its feedbacks to climate change (Averill et al, 2014; Campbell et al, 2009; Wang et al, 2012). Previous studies focused on spatial variability of soil δ13C at in-site scale (Acton et al, 2013; Lu et al, 2004; Wang et al, 2012). A better understanding of the spatial variability of soil δ13C and its controlling factors at the regional scale is important to understand soil carbon dynamics and potential feedbacks to climate change (Li et al, 2020; Rao et al, 2017; Zhao et al, 2019)
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