Impact of future climate warming on soil organic carbon stocks in Inner Mongolia, China

Accurate assessment on temporal and spatial changes in soil organic carbon stocks (SOCS) is essential for evaluating the potential of soil carbon sequestration and formulating effective strategies to mitigate climate change. While most SOCS studies have focused on topsoil, there remains a lack of comprehensive understanding regarding the vertical distribution of SOCS within soil profiles. Furthermore, the key environmental variables influencing deep SOCS are still not fully comprehended. In this study, we employed an integrated approach combining equal-area spline function, boosted regression trees model (BRT), and the space-for-time substitution method to accurately model the three-dimensional distribution of SOCS in response to climate warming in Inner Mongolia, China. A total of 12 environmental variables (generated from climate, topography, biological, and soil property factor) and 208 soil profile data were selected to construct the model. The 10-fold cross-validation technique was employed to assess the predictive performance of the BRT model for soil organic carbon stocks (SOCS) at various depths, using four accuracy validation indicators: root mean square error (RMSE), mean ab-solute error (MAE), coefficient of determination (R2), and Lin's concordance correlation coefficient (LCCC). The results demonstrated the BRT model accurately predicted SOCS with higher LCCC and R2 values, as well as lower RMSE and MAE values. The predicted map revealed higher SOCS concentration in the northeast and lower concentration in the western area. Grassland and forest land were found to store a majority of SOCS, with over 46 % located the topsoil depth of 30 cm. Furthermore, under different climate warming scenarios with temperature increase of 1.5 degrees C, 2 degrees C and 4 degrees C, there was a corresponding decreased in SOCS by 7 %, 9 % and 17 % respectively at a soil depth. Additionally, this study identified MAT and NDVI as primary environmental variables influencing the spatial distribution of three-dimensional scale SOCS. We believed that accurately predicting and mapping three-dimensional SOCS under different climate warming scenarios will contribute to the development of scientifically sound land management policies aimed at enhancing soil carbon sequestration in the region.

Automated summary

Accurate assessment of temporal and spatial changes in soil organic carbon stocks (SOCS) is crucial for mitigating climate change. This study successfully modeled the three-dimensional distribution of SOCS in Inner Mongolia, China and identified grassland and forest land as the primary storage areas for SOCS.