首页|Forest carbon storage in Guizhou Province based on field measurement dataset
Forest carbon storage in Guizhou Province based on field measurement dataset
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国家科技期刊平台
NETL
NSTL
万方数据
维普
Accurate estimation of forest carbon storage is crucial in understanding global and regional carbon cycles and projecting future ecological and economic scenarios.Guizhou is the largest karst landform province in China;61.9％ of its land area is characterized as karst.However,monitoring its field biomass and carbon storage is difficult.This study synthesized and analyzed a comprehensive database of direct field observations of forest vegetation and soil carbon storage in Guizhou Province by using data from existing literature.The total vegetation carbon storage in Guizhou Province was 488.170 TgC,the average vegetation carbon density (VCD) was 27.866 MgC hm-2,the total amount of soil organic carbon (SOC) (20 cm) was 1017.364 TgC,and the average SOC density was 58.074 MgC hm-2.Among all vegetation types,needleleaf forest had the highest vegetation carbon stocks,and scrub presented the highest SOC storage.The vegetation and SOC storage values of the karst landform were 282.352 and 614.825 TgC,respectively,which were higher than those of the non-karst landform.VCD was concentrated at 10-40 MgC hm-2,and SOC density was concentrated at 40-60,60-80,and 80-100 MgC hm-2.This comprehensive regional data synthesis and analysis based on direct field measurement of vegetation and soil will improve our understanding of the forest carbon cycle in karst landforms under a changing climate.
State Key Laboratory of Environmental Geochemistry,Institute of Geochemistry, Chinese Academy of Sciences,Guiyang 550081, China
University of Chinese Academy of Sciences, Beijing 100049,China
Puding Karst Ecosystem Research Station, Chinese Academy of Sciences, Puding 562100, China
College of Chemistry and Life Sciences, Zhejiang Normal University, Jinhua 321004, China
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This study was financially supported by National Natural Science Foundation of ChinaThis study was financially supported by National Natural Science Foundation of China
国家科技期刊平台
NSTL
万方数据
维普
