The Evolution Of Vegetation Phenology Change Trend And Its Nonlinear Response To Climate Change

Vegetation is the primary producer of ecosystem.With the continuous global warming,the growth of vegetation has undergone tremendous changes,and it has a profound effect on ecosystem.Phenology is an important indicator for assessing the growth of vegetation.Nowadays,the research of vegetation phenology changes has become one of the hotspots in the field of vegetation research and global change.This research used three phenological events,namely the start(SOS),end(EOS)and length(LOS)of growing season,in China during the period of 1981 to 2016 and the Ensemble empirical mode decomposition(EEMD)method to reveal the trend evolution of vegetation phenology.Meanwhile,the nonlinear response of vegetation phenology to climate change was also analyzed using Generalized additive model(GAM)combined with bioclimatic variables.The results were as following:1.The vegetation phenology at national scale showed an advanced trend of 0.22 days per year in SOS,a delayed trend of 0.20 days per year in EOS,and a prolonged trend of 0.42 days per year in LOS.The EEMD results showed that the rate of SOS gradually raised and exceeded the linear rate in the early 21 st century;the rate of EOS gradually shrank and dropped below the linear rate in the later-1980 s,and the rate of LOS also gradually raised and exceeded the linear rate in the early 21 st century.2.The results showed that traditional linear methods overestimated the proportion of significant decrease in SOS,significant increase in EOS and LOS.Furthermore,the EEMD results showed that the shifted trends of vegetation phenology were widely distributed throughout the whole country.The trend evolution of vegetation phenology showed obvious longitudinal and latitudinal gradient changes.Meanwhile,the longitudinal gradient changes were more obvious,especially in EOS.In each vegetation type,the phenology trends of needleleaf forest were different from the overall trend,or even opposite,especially deciduous needleleaf forest.The trends amplitude of evergreen broadleaf forest and grasslands in EOS,and SOS and LOS was the largest one in the corresponding phenological event,and the trends amplitude of croplands was the smallest one in each phenological event.3.Mean Diurnal Range(bio02),Mean Temperature of Coldest Quarter(bio11),and Precipitation of Coldest Quarter(bio19)were the significant driving factors of SOS changes.Moveover,there was a linear relationship between bio02 and SOS,and a nonlinear relationship between bio11 and SOS,and bio19 and SOS.The pairwise interaction terms among the three variables were also significant driving factors of SOS changes.In addition,the interaction between bio02 and bio11 made SOS advanced;the interaction between bio02 and bio19 also made SOS advanced;however,the interaction between bio11 and bio19 not only made SOS advanced but also made SOS delayed.Mean Diurnal Range(bio02),Mean Temperature of Wettest Quarter(bio08),and Precipitation of Warmest Quarter(bio18)were the significant driving factors of EOS changes.Moveover,there is a nonlinear relationship between bio02 and EOS,and bio08 and EOS,while the nonlinear relationship between bio18 and EOS changes was relatively weak and even there was a little linear relationship between bio18 and EOS.Only the interaction between bio02 and bio08 and the interaction between bio08 and bio18 had a significant effect on EOS.And both interaction made EOS delayed.Annual Mean Temperature(bio01),Mean Temperature of Wettest Quarter(bio08),Mean Temperature of Coldest Quarter(bio11),and Annual Precipitation(bio12)were significant driving factors of LOS changes.And they all had nonlinear relationships with LOS.Only the interaction between bio01 and bio08,the interaction between bio01 and bio11,the interaction between bio01 and bio12,and the interaction between bio11 and bio12 had significant effect on LOS.The interaction between bio01 and bio08 was relatively weak,which made LOS shortened.The interaction between bio01 and bio11 made LOS prolonged.The interaction between bio01 and bio12 and the interaction between bio11 and bio12 both made LOS shortened.This research found that the EEMD method can well reveal the trend evolution of vegetation phenology,and the GAM model can not only reveal the effect of single factor on vegetation phenology,but also the effect of multiple factors and the interaction between the two factors on vegetation phenology.This research is of great theoretical significance for understanding the trend of vegetation phenology and its nonlinear response to climate change,and predicting future vegetation phenology changes.Moreover,this research will provide a scientific and theoretical basis for China to respond to climate change challenges and vegetation protection.