Land (Aug 2024)

Evolution and Mechanism Analysis of Terrestrial Ecosystems in China with Respect to Gross Primary Productivity

  • Hanshi Sun,
  • Yongming Cheng,
  • Qiang An,
  • Liu Liu

DOI
https://doi.org/10.3390/land13091346
Journal volume & issue
Vol. 13, no. 9
p. 1346

Abstract

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The gross primary productivity (GPP) of vegetation stores atmospheric carbon dioxide as organic compounds through photosynthesis. Its spatial heterogeneity is primarily influenced by the carbon uptake period (CUP) and maximum photosynthetic productivity (GPPmax). Grassland, cropland, and forest are crucial components of China’s terrestrial ecosystems and are strongly influenced by the seasonal climate. However, it remains unclear whether the evolutionary characteristics of GPP are attributable to physiology or phenology. In this study, terrestrial ecosystem models and remote sensing observations of multi-source GPP data were utilized to quantitatively analyze the spatio-temporal dynamics from 1982 to 2018. We found that GPP exhibited a significant upward trend in most areas of China’s terrestrial ecosystems over the past four decades. Over 60% of Chinese grassland and over 50% of its cropland and forest exhibited a positive growth trend. The average annual GPP growth rates were 0.23 to 3.16 g C m−2 year−1 for grassland, 0.40 to 7.32 g C m−2 year−1 for cropland, and 0.67 to 7.81 g C m−2 year−1 for forest. GPPmax also indicated that the overall growth rate was above 1 g C m−2 year−1 in most regions of China. The spatial trend pattern of GPPmax closely mirrored that of GPP, although local vegetation dynamics remain uncertain. The partial correlation analysis results indicated that GPPmax controlled the interannual GPP changes in most of the terrestrial ecosystems in China. This is particularly evident in grassland, where more than 99% of the interannual variation in GPP is controlled by GPPmax. In the context of rapid global change, our study provides an accurate assessment of the long-term dynamics of GPP and the factors that regulate interannual variability across China’s terrestrial ecosystems. This is helpful for estimating and predicting the carbon budget of China’s terrestrial ecosystems.

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