Scale effect of climate factors on soil organic carbon stock in natural grasslands of northern China

Abstract

Changes in grassland soil organic carbon stock (SOCS) may significantly affect the regional climate and carbon cycle of terrestrial ecosystems. However, how the impact of climate factors on SOCS and the dominant climate factors are regulated by the area scale of grasslands remains unclear. To understand the scale effects of climate on SOCS and how to accurately estimate SOCS at different scales, three area scales were defined by extending grassland types on the basis of meadow, typical and desert grasslands (Scale I (average area 37.22 × 104 km2) included each of these three types of grasslands, Scale II (average area 74.45 × 104 km2) was achieved by a pairwise combination of these three types of grasslands. Scale III (area 111.67 × 104 km2) was an aggregate of these three types of grasslands), the relationship between climate factors (i.e., mean annual precipitation, mean annual temperature, annual maximum temperature, annual minimum temperature, mean annual ground temperature, mean annual humidity, annual sunshine duration, annual maximum depth of accumulated snow, and the number of snow-covered days) and SOCS at the three scales were explored in the grasslands of northern China. Our results indicated that the total SOCS in grasslands at the three scales was ordered as desert grassland < meadow grassland < typical grassland. Of the nine climate factors, mean annual precipitation, positively correlated with SOCS, was the most significant climatic factor for all three scales. The dominant climatic factors of the SOCS differed across grassland area scales (i.e., MAP and MAH for meadow grassland, AMAT, MAP, NSD, and MAH for typical grassland, MAP, NSD, MAH, AMAT, and ASD for meadow-typical grassland scale, MAP, MAT, and MAGT for typical-desert grassland scale, MAP and MAT for meadow-typical-desert grassland scale). The impact of climate factors on the SOCS decreased as the scale increased. It is essential to screen appropriate climate predictors according to a given area scale when assessing regional SOCS. Multiple climate factors are better predictors for accessing SOCS at a small scale. At a large scale, however, dominant climatic factors are predictors that are more efficient.