Precise estimation of soil organic carbon stocks in the northeast Tibetan Plateau.

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.

Estimating soil organic carbon stocks and spatial patterns with statistical and GIS-based methods.

Accurately quantifying soil organic carbon (SOC) is considered fundamental to studying soil quality, modeling the global carbon cycle, and assessing global climate change. This study evaluated the uncertainties caused by up-scaling of soil properties from the county scale to the provincial scale and from lower-level classification of Soil Species to Soil Group, using four methods: the mean, median, Soil Profile Statistics (SPS), and pedological professional knowledge based (PKB) methods. For the SPS method, SOC stock is calculated at the county scale by multiplying the mean SOC density value of each soil type in a county by its corresponding area. For the mean or median method, SOC density value of each soil type is calculated using provincial arithmetic mean or median. For the PKB method, SOC density value of each soil type is calculated at the county scale considering soil parent materials and spatial locations of all soil profiles. A newly constructed 1∶50,000 soil survey geographic database of Zhejiang Province, China, was used for evaluation. Results indicated that with soil classification levels up-scaling from Soil Species to Soil Group, the variation of estimated SOC stocks among different soil classification levels was obviously lower than that among different methods. The difference in the estimated SOC stocks among the four methods was lowest at the Soil Species level. The differences in SOC stocks among the mean, median, and PKB methods for different Soil Groups resulted from the differences in the procedure of aggregating soil profile properties to represent the attributes of one soil type. Compared with the other three estimation methods (i.e., the SPS, mean and median methods), the PKB method holds significant promise for characterizing spatial differences in SOC distribution because spatial locations of all soil profiles are considered during the aggregation procedure.

[Estimation of soil organic carbon density and storage in Zhejiang Province of East China by using 1:50000 soil database].

As an important component of the carbon pool of terrestrial ecosystem, soil carbon pool plays a key role in the studies of greenhouse effect and global change. By using a 1:50000 soil database, the organic carbon density in the 0-100 cm layer of 277 soil species in Zhejiang Province was estimated, and the soil organic carbon (SOC) density and storage in the whole Province as well as the spatial distribution of the SOC density and storage in the main soil types of the Province were analyzed. In the whole Province, the SOC density ranged from 5 kg.m-2 to 10 kg.m-2. Among the main soil types in the Province, humic mountain yellow soil had the highest SOC density (52.80 kg.m-2), whereas fluvio-sand ridge soil had the lowest one (1.82 kg.m-2). Red soil and paddy soil had the largest SOC storages, with the sum accounting for 63.8% of the total SOC storage in the Province. The total area of the soils in the Province was 100784.19 km2, the estimated SOC storage was 875. 42 x 10(6) t, and the estimated SOC density was averagely 8.69 kg.m-2. The analysis with the superposition digital elevation model showed that the SOC density presented an obvious variation trend with the changes of elevation, slope gradient, and aspect.