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	Historical Attributions and Future Projections of Gross Primary Productivity in the Yangtze River Basin under Climate Change Based on a Novel Coupled LUE-RE Model
	Du, Hong1; Wu, Jian2,3; Zeng, Sidong1,2,3; Xia, Jun2,4
	2023-09-01
摘要	Attributions and predictions of gross primary productivity (GPP) under climate change is of great significance for facilitating a deeper understanding of the global and regional terrestrial carbon cycle and assessing ecosystem health. In this study, we have designed a novel approach to simulate GPP based on the satellite and meteorological data compiling the advantages of the light use efficiency model with regression methods (LUE-RE model), which overcomes the limitation of the satellite-based method in GPP simulation and projection in the future time without satellite data. Based on the proposed method, results show that GPP in the Yangtze River Basin shows a significant increase trend in the historical period. Elevated CO2 dominates the changes of GPP in the Yangtze River Basin. In the future, with the increase in elevated CO2 and climate change, the trend of GPP growth is more obvious. The growth slopes under different scenarios are 2.65 gCm-2year-1a-1, 12.34 gCm-2year-1a-1, 24.91 gCm-2year-1a-1, and 39.62 gCm-2year-1a-1. There are obvious seasonal differences in the future changes of GPP in the Yangtze River Basin, of which the GPP changes mostly in spring. The spatial patterns show that higher GPP is concentrated in the upper stream, while the low values are mainly concentrated in the middle reaches. This study contributes a new method to project GPP and highlights that stakeholders should pay more attention to the significant GPP increases in spring in the future.
关键词	gross primary productivity LUE-RE model attribution analysis future projection
DOI	10.3390/rs15184489
发表期刊	REMOTE SENSING
卷号	15期号:18页码:25
通讯作者	Zeng, Sidong(zengsidong@cigit.ac.cn)
收录类别	SCI
WOS记录号	WOS:001078171000001
语种	英语