Abstract
There is a need for accurate estimate of soil organic carbon (SOC) stocks for understanding the role of alpine soils in the global carbon cycle. We tested a method for mapping digitally the continuous distribution of the SOC stock in three dimensions in the northeast of the Tibetan Plateau. The approach integrated the spatial distribution of the mattic epipedon which is a special surface horizon widespread and rich in organic matter in Tibetan grasslands. Prediction models resulted in high prediction accuracy. An average SOC stock in the mattic epipedon was estimated to be 4.99 kg m−2 in a mean depth of 14 cm. The amounts of SOC in the mattic epipedon, the upper 30 cm and 50 cm accounted for about 21%, 80% and 89%, respectively, of the total SOC stock in the upper 1 m depth. Compared with previous estimates, our approach resulted in more reliable predictions. The mattic epipedon was proven to be an important factor for modelling the realistic distribution of the SOC stock in Tibetan grasslands. Vegetation-related covariates have the most important influence on the distribution of the mattic epipedon and the SOC stock in the alpine grassland soils of northeast Tibetan Plateau.
Highlights
The soil organic carbon (SOC) pool is one of the most important reservoirs in the global C cycle[1]
Solved by predicting the parameters of the function using available environmental covariates. This is more suitable than data-driven methods in Tibetan regions, because it is difficult to conduct high density sampling at a regional scale in such areas
The methodology presented here addressed the mattic epipedon in modelling the SOC depth distribution, which is in contrast with the method based on a simple decay function[8,21]
Summary
The soil organic carbon (SOC) pool is one of the most important reservoirs in the global C cycle[1]. In order to produce spatially explicit distribution of SOC stocks in three dimensions in this region, a combined prediction model of DSM and soil depth functions may be applicable based on available environmental covariates and a small number of soil samples. We mapped SOC stocks within the upper 1 m depth in the northeast Tibetan Plateau (Fig. 1) using soil depth functions and random forest.
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