Carbon storage in China's forest ecosystems: estimation by different integrative methods.
Carbon (C) storage for all the components, especially dead mass and soil organic carbon, was rarely reported and remained uncertainty in China's forest ecosystems. This study used field-measured data published between 2004 and 2014 to estimate C storage by three forest type classifications and three spatial interpolations and assessed the uncertainty in C storage resulting from different integrative methods in China's forest ecosystems. The results showed that C storage in China's forest ecosystems ranged from 30.99 to 34.96 Pg C by the six integrative methods. We detected 5.0% variation (coefficient of variation, CV, %) among the six methods, which was influenced mainly by soil C estimates. Soil C density and storage in the 0-100 cm soil layer were estimated to be 136.11-153.16 Mg C·ha(-1) and 20.63-23.21 Pg C, respectively. Dead mass C density and storage were estimated to be 3.66-5.41 Mg C·ha(-1) and 0.68-0.82 Pg C, respectively. Mean C storage in China's forest ecosystems estimated by the six integrative methods was 8.557 Pg C (25.8%) for aboveground biomass, 1.950 Pg C (5.9%) for belowground biomass, 0.697 Pg C (2.1%) for dead mass, and 21.958 Pg C (66.2%) for soil organic C in the 0-100 cm soil layer. The R:S ratio was 0.23, and C storage in the soil was 2.1 times greater than in the vegetation. Carbon storage estimates with respect to forest type classification (38 forest subtypes) were closer to the average value than those calculated using the spatial interpolation methods. Variance among different methods and data sources may partially explain the high uncertainty of C storage detected by different studies. This study demonstrates the importance of using multimethodological approaches to estimate C storage accurately in the large-scale forest ecosystems.
Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research CAS Beijing 100101 China; Key laboratory of Ecological Restoration in the Hilly Area Pingdingshan University Pingdingshan He'nan Province 467000 China.
Shunlei
S
Peng
Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research CAS Beijing 100101 China.
Ding
D
Wen
Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research CAS Beijing 100101 China.
Nianpeng
N
He
Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research CAS Beijing 100101 China.
Guirui
G
Yu
Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research CAS Beijing 100101 China.
Anna
A
Ma
Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research CAS Beijing 100101 China.
Qiufeng
Q
Wang
Carbon storage; coefficient variation; forest type classification; multimethodology; spatial interpolation; uncertain