Mapping Chinese annual gross primary productivity with eddy covariance measurements and machine learning.

Annual gross primary productivity (AGPP) is the basis for grain production and terrestrial carbon sequestration. Mapping regional AGPP from site measurements provides methodological support for analysing AGPP spatiotemporal variations thereby ensures regional food security and mitigates climate change. Based on 641 site-year eddy covariance measuring AGPP from China, we built an AGPP mapping scheme based on its formation and selected the optimal mapping way, which was conducted through analysing the predicting performances of divergent mapping tools, variable combinations, and mapping approaches in predicting observed AGPP variations. The reasonability of the selected optimal scheme was confirmed by assessing the consistency between its generating AGPP and previous products in spatiotemporal variations and total amount. Random forest regression tree explained 85 % of observed AGPP variations, outperforming other machine learning algorithms and classical statistical methods. Variable combinations containing climate, soil, and biological factors showed superior performance to other variable combinations. Mapping AGPP through predicting AGPP per leaf area (PAGPP) explained 86 % of AGPP variations, which was superior to other approaches. The optimal scheme was thus using a random forest regression tree, combining climate, soil, and biological variables, and predicting PAGPP. The optimal scheme generating AGPP of Chinese terrestrial ecosystems decreased from southeast to northwest, which was highly consistent with previous products. The interannual trend and interannual variation of our generating AGPP showed a decreasing trend from east to west and from southeast to northwest, respectively, which was consistent with data-oriented products. The mean total amount of generated AGPP was 7.03 ± 0.45 PgC yr-1 falling into the range of previous works. Considering the consistency between the generated AGPP and previous products, our optimal mapping way was suitable for mapping AGPP from site measurements. Our results provided a methodological support for mapping regional AGPP and other fluxes.

Spatial-temporal variations of carbon storage and carbon sequestration rate in China's national forest parks.

Forests play an important role in regulating climate change and maintaining carbon balance. To explore the carbon storage and carbon sequestration rate of national forest parks is of great significance for carbon sequestration capacity assessment and sustainable forest management. A process-based ecosystem model (CEVSA2 model) was used to simulate the spatial distribution of carbon density, carbon storage and carbon sequestration rate of 881 national forest parks in China during 1982-2017. The results showed that the average carbon density of national forest parks was 255.18 t C·hm-2, being higher than the average carbon density of forest ecosystem in China. In 2017, the total carbon storage of national forest parks increased to 3.56 Pg C, accounting for 11.0%-12.2% of the total carbon storage in national forest ecosystems. During 1982-2017, the average carbon sequestration rate of national forest parks reached 0.45 t C·hm-2·a-1, and the carbon sequestration rate of all national forest parks was above 0.30 t C·hm-2·a-1. National forest parks in the northeast and southwest of China had the highest total carbon storage. The national forest parks in northeast of China had the highest soil organic carbon sequestration rate, while those in eastern China and central southern China had the highest biomass carbon sequestration rate. The area of national forest parks accounted for 5.8% of the total forest area of China, playing an important role in forest carbon sink management of China. Accurate assessment of the growth status, carbon sequestration potential and carbon absorption characteristics of national forest parks could provide reference for the comprehensive assessment of ecosystem service of forest parks in China.