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.

[Periodic fluctuation features of air temperature, precipitation, and aboveground net primary production of alpine meadow ecosystem on Qinghai-Tibetan Plateau].

With Mexican Hat function as mother function, a wavelet analysis was conducted on the periodic fluctuation features of air temperature, precipitation, and aboveground net primary production (ANPP) in the Alpine Meadow Ecosystem Research Station, Chinese Academy of Sciences from 1980 to 2007. The results showed that there was a main period of 13 years for the annual fluctuations of air temperature, precipitation, and ANPP. A secondary period of 2 years for the annual fluctuations of air temperature and ANPP had lesser influence, whereas that of 4 years for the annual fluctuation of precipitation had greater effect. Lagged correlation analysis indicated that the annual fluctuation of ANNP was mainly controlled by the air temperature in a 20 years scale and had a weak 5-9 years lag effect, but there was a less correlation between ANPP and precipitation.