Publications

Gang, CC; Shi, H; Tian, HQ; Pan, SF; Pan, NQ; Xu, RT; Wang, ZA; Bian, ZH; You, YF; Yao, YZ (2023). Uncertainty in land use obscures global soil organic carbon stock estimates. AGRICULTURAL AND FOREST METEOROLOGY, 339, 109585.

Abstract
The impact of land use and land cover change (LULCC) on soil organic carbon (SOC) stock is one of the most uncertain items in estimating the global C budget. Despite the improvements in satellite monitoring techniques and inventory data in recent decades, the uncertainty in modeled LULCC-induced SOC changes stemming from the choice of land use datasets remains largely unknown. Using a process-based model, the Dynamic Land Ecosystem Model (DLEM), we investigated global SOC changes during 1900-2018 driven by six LULCC datasets (i.e., LUH2-GCB2019, ESA CCI-LC, MODIS, GLASS-GLC, HH, and RF), which were generated by varied data sources and methodologies. The simulated global SOC stock was negatively affected by land conversions in all the LULCC datasets; however, the corresponding SOC loss was highly different ranging from 33.11 Pg C to 106.20 Pg C. Such significant differences in the global SOC stock estimates due to LULCC uncertainty were mainly located in boreal and temperate forests of the northern high latitudes and were most likely attributed to the LULCC-induced changes in vegetation net primary production. Meanwhile, regions exhibiting large divergence in relative changes of LULCC-induced SOC loss were mainly located in the low latitudes. When considering the interactive effects of LULCC with other environmental factors, the simulated SOC showed divergent trends, increasing in MODIS-, ESA CCI-LC-, and GLASS-GLC-based estimations, but decreasing in LUH2-GCB2019-, HH-, and RF-based estimations. These results highlight the importance of the accuracy of LULCC data in determining the global carbon budget. Future efforts are required for harmonizing satellite observations and inventory data both spatially and temporally to better represent the land conversion processes in terrestrial ecosystem modeling.

DOI:
10.1016/j.agrformet.2023.109585

ISSN:
1873-2240