Effects of topography on soil organic carbon stocks in grasslands of a semiarid alpine region,northwestern China

Purpose

Soil organic carbon (SOC) in mountainous regions is characterized by strong topography-induced heterogeneity, which may contribute to large uncertainties in regional SOC stock estimation. However, the quantitative effects of topography on SOC stocks in semiarid alpine grasslands are currently not well understood. Therefore, the purpose of this research study is to determine the role of topography in shaping the spatial patterns of SOC stocks.

Materials and methods

Soils from the summit, shoulder, backslope, footslope, and toeslope positions along nine toposequences within three elevation-dependent grassland types (i.e., montane desert steppe at ~?2450 m, montane steppe at ~?2900 m, and subalpine meadow at ~?3350 m) are sampled at four depths (0–10, 10–20, 20–40, and 40–60 cm). SOC content, bulk density, soil texture, soil water content, and grassland biomass are determined. The general linear model (GLM) is employed to quantify the effects of topography on the SOC stocks. Ordinary least squares regressions are performed to explore the underlying relationships between SOC stocks and the other edaphic factors.

Results and discussion

In accordance with the present results, the SOC stocks at 0–60 cm show an increasing trend in respect to the elevation zone, with the highest stock being approximately 37.70 g m^?2 in the subalpine meadow, about 2.07 and 3.41 times larger than that in the montane steppe and montane desert steppe, respectively. Along the toposequences, it is revealed the SOC stocks are maximal at toeslope, reaching to 14.98, 31.76, and 49.52 kg m^?2, which are also significantly larger than those at the shoulder by a factor of 1.38, 2.31, and 1.44, in montane desert steppe, montane steppe, and subalpine meadow, respectively. Topography totally is seen to explain about 84% of the overall variation in SOC stocks, of which 70.61 and 9.74% are attributed to elevation zone and slope position, while the slope aspect and slope gradient are seen to plausibly explain only about 1.84 and 0.01%, respectively.

Conclusions

The elevation zone and the slope position are seen to markedly shape the spatial patterns of the SOC stocks, and thus, they may be considered as key indicating factors in constructing the optimal SOC estimation model in such semiarid alpine grasslands.