Abstract
Globally,soil is the largest terrestrial carbon(C)reservoir.Robust quantification of soil organic C(SOC)stocks in existing global observation-based estimates avails accurate predictions in carbon-climate feedbacks and future climate trends.We investigated the magnitudes and distributions of global and regional SOC estimates(i.e.,density and stocks)based on five widely used global gridded SOC datasets,a regional permafrost dataset developed in 2021(UM2021),and a global-scale soil profile database(World Soil Information Service)reporting measurements of a series of physical and chemical edaphic attributes.The five global gridded SOC datasets were the Harmonized World Soil Database(HWSD),World Inventory of Soil Emission Potentials at 30 arc-second resolution(WISE30sec),Global Soil Dataset for Earth System Models(GSDE),Global Gridded Soil Information at 250-m resolution(SoilGrids250m),and Global Soil Organic Carbon Map(GSOCmap).Our analyses showed that the magnitude and distribution of SOC varied widely among datasets,with certain datasets showing region-specific robustness.At the global scale,SOC stocks at the top 30 and 100 cm were estimated to be 828(range:577-1 171)and 1 873(range:1 086-2 678)Pg C,respectively.The estimates from GSDE,GSOCmap,and WISE30sec were comparable,and those of SoilGrids250m and HWSD were at the upper and lower ends.The spatial SOC distribution varied greatly among datasets,especially in the northern circumpolar and Tibetan Plateau permafrost regions.Regionally,UM2021 and WISE30sec performed well in the northern circumpolar permafrost regions,and GSDE performed well in China.The estimates of SOC by different datasets also showed large variabilities across different soil layers and biomes.The discrepancies were generally smaller for the 0-30 cm soil than the 0-100 cm soil.The datasets demonstrated relatively higher agreement in grasslands,croplands,and shrublands/savannas than in other biomes(e.g.,wetlands).The users should be mindful of the gaps between regions and biomes while choosing the most appropriate SOC dataset for specific uses.Large uncertainties in existing global gridded SOC estimates were generally derived from soil sampling density,different sources,and various mapping methods for soil datasets.We call for future efforts for standardizing soil sampling efforts,cross-dataset comparison,proper validation,and overall global collaboration to improve SOC estimates.
基金项目
National Natural Science Foundation of China(U21A6001)
National Natural Science Foundation of China(41975113)
Guangdong Provincial Department of Science and Technology,China(2019ZT08G090)
United States Department of Energy grant to the Sandia National Laboratories()
NETL
NSTL
万方数据