Spatiotemporal evolutionary and mechanism analysis of grassland GPP in China

• Published in: Ecological indicators Vol. 143; p. 109323
• Main Authors: He, Panxing, Ma, Xiaoliang, Meng, Xiaoyu, Han, Zhiming, Liu, Huixia, Sun, Zongjiu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2022; Elsevier
• Subjects: carbon; carbon dioxide; carbon sequestration; China; Chinese grasslands; climate change; data collection; ecological models; global carbon budget; GPPmax × CUP; grasslands; Gross primary productivity; Interannual variability; issues and policy; phenology; photosynthesis; satellites; semiarid zones; Spatiotemporal evolutionary; temporal variation; terrestrial ecosystems; China; Chinese grasslands; GPPmax × CUP; Interannual variability; Spatiotemporal evolutionary; Gross primary productivity
• ISSN: 1470-160X
• DOI: 10.1016/j.ecolind.2022.109323

•Both GPP and GPPmax in Chinese grasslands showed increases at different space–time scales.•Trend analysis and breakpoint tests were used for the first time to monitor GPP change simultaneously in Chinese grasslands.•GPPmax × CUP is a robust indicator to explain annual-scale variation in GPP. 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.