Citation:

Liu, Qiang, Fu, Yongshuo H., Zeng, Zhenzhong, Huang, Mengtian, Li, Xiran, Piao, Shilong. Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China. Global Change Biology, 2016, 22(2):644-655. doi:10.1111/gcb.13081

Literature information
Title Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China
Year 2016
Publisher

Global Change Biology

Description

Autumn phenology plays a critical role in regulating climate–biosphere interactions. However, the climatic drivers of autumn phenology remain unclear. In this study, we applied four methods to estimate the date of the end of the growing season (EOS) across China's temperate biomes based on a 30-year normalized difference vegetation index (NDVI) dataset from Global Inventory Modeling and Mapping Studies (GIMMS). We investigated the relationships of EOS with temperature, precipitation sum, and insolation sum over the preseason periods by computing temporal partial correlation coefficients. The results showed that the EOS date was delayed in temperate China by an average rate at 0.12 ± 0.01 days per year over the time period of 1982–2011. EOS of dry grassland in Inner Mongolia was advanced. Temporal trends of EOS determined across the four methods were similar in sign, but different in magnitude. Consistent with previous studies, we observed positive correlations between temperature and EOS. Interestingly, the sum of precipitation and insolation during the preseason was also associated with EOS, but their effects were biome dependent. For the forest biomes, except for evergreen needle-leaf forests, the EOS dates were positively associated with insolation sum over the preseason, whereas for dry grassland, the precipitation over the preseason was more dominant. Our results confirmed the importance of temperature on phenological processes in autumn, and further suggested that both precipitation and insolation should be considered to improve the performance of autumn phenology models.

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