Yang RM Yang F Yang F Huang LM Liu F Yang JL Zhao YG Li DC Zhang GL. Pedogenic knowledge-aided modelling of soil inorganic carbon stocks in an alpine environment. Science of The Total Environment 2017 599-600: 1445-1453
Abstract
Accurate estimation of soil carbon is essential for accounting carbon cycling on the background of global environment change. However previous studies made little contribution to the patterns and stocks of soil inorganic carbon (SIC) in large scales. In this study we defined the structure of the soil depth function to fit vertical distribution of SIC based on pedogenic knowledge across various landscapes. Soil depth functions were constructed from a dataset of 99 soil profiles in the alpine area of the northeastern Tibetan Plateau. The parameters of depth functions were mapped from environmental covariates using random forest. Finally SIC stocks at three depth intervals in the upper 1 m depth were mapped across the entire study area by applying predicted soil depth functions at each location. The results showed that the soil depth functions were able to improve accuracy for fitting the vertical distribution of the SIC content with a mean determination coefficient of R2 = 0.93. Overall accuracy for predicted SIC stocks was assessed on training samples. High Lin’s concordance correlation coefficient values (0.84–0.86) indicate that predicted and observed values were in good agreement (RMSE: 1.52–1.67 kg m− 2 and ME: − 0.33 to − 0.29 kg m− 2). Variable importance showed that geographic position predictors (longitude latitude) were key factors predicting the distribution of SIC. Terrain covariates were important variables influencing the three-dimensional distribution of SIC in mountain areas. By applying the proposed approach the total SIC stock in this area is estimated at 75.41 Tg in the upper 30 cm 113.15 Tg in the upper 50 cm and 190.30 Tg in the upper 1 m. We concluded that the methodology would be applicable for further prediction of SIC stocks in the Tibetan Plateau or other similar areas.
