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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. [ABSTRACT FROM AUTHOR]

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.

At the ecosystem level, Gross Primary Productivity (GPP) was defined as the organic compounds formed by plants that absorb atmospheric CO2 through photosynthesis and sequester carbon in plant bodies. How to accurately assess the spatiotemporal evolution of ecosystem carbon indicators has become a critical issue to be addressed. Chinese grasslands are located in a typical arid and semi-arid climate zone and are sensitive to global changes, which will inevitably have serious impacts on the function and structure of Chinese grasslands. Based on this, our paper takes Chinese grassland ecosystems as the research area, and used multi-source GPP dataset from terrestrial ecosystem model simulations and remote sensing satellite observations to quantitatively analyze the spatiotemporal evolution patterns of GPP in Chinese grasslands over the past 40 years. We combined trend analysis and breakpoint test, and then to analyze the mechanism components of GPP space–time variability. The main results are as follows: (1) The model and remote sensing estimated GPP results showed that more than 80% of Chinese grasslands show a significant increasing trend over the past 40 years, with growth rates ranging from 0.68 to 3.13 g C/m−2 year−1. (2) GPPmax also shows that more than 80% of Chinese grasslands are growing rapidly, with an overall growth rate of more than 0.1 g C/m−2 year−1 in each region. The overall long-term trends and interannual variability of multi-source GPP and GPPmax are generally consistent, yet vegetation dynamics in local areas are still uncertain. (3) The breakpoint test showed that ‘monotonically increasing’ was the largest breakpoint type of GPP in Chinese grasslands (33.09%), and the direction of change of GPP in Chinese grasslands before and after the breakpoint was also increasing. (4) GPPmax × CUP explained 91% of the temporal variability of annual-scale GPP in Chinese grasslands from a mechanistic view, and we found that the peak photosynthetic growth and the length of the phenological period synergistically controlled the interannual variability of GPP in Chinese grasslands. Under the scenario of rapid global change, our study accurately assessed the long-term dynamics of GPP and its mechanism-driven of grassland ecosystems in China, which is helpful for estimating and predicting the carbon budget of grassland ecosystems in China, and has important guiding significance for policy formulation to mitigate climate change.

Rainfall erosivity is regarded as one of the main factors affecting soil erosion. Based on 117-year monthly precipitation data of Beijing from 1901 to 2017, the spatiotemporal evolutionary analysis of rainfall erosivity in Beijing were analyzed by using Theil-Sen median analysis (Sen), the Mann–Kendall (MK) trend test, R/S analysis method, cumulative anomaly method, MK mutation test method, Pettitt test, and wavelet analysis. The results showed that the average annual rainfall erosivity in Beijing ranged from 1080.6 to 6432.78 MJ • mm/(hm2 • h • a), with an average value of 3465.06 MJ • mm/(hm2 • h • a), showing a gradual decrease from the southeast to northwest. Regarding seasonal distribution, 86% of rainfall erosivity was mainly concentrated in summer. In the past 117 years, the annual rainfall erosivity in most areas of Beijing showed a downward trend, but its future trend also showed an increasing trend, indicating that Beijing, especially the northern part, was facing greater potential pressure from soil erosion. Through cross-validation of various methods, the abrupt change interval of rainfall erosivity in Beijing from 1901 to 2017 was from 1994 to 1997. The change in rainfall erosivity in Beijing had a strong oscillation in 32 years and a small periodic change in 15 and 7 years. The results will provide a decision-making basis for soil erosion control and water/soil conservation planning. Additionally, they will benefit and ensure national agricultural and food security. [ABSTRACT FROM AUTHOR]