Responses of Vegetation Autumn Phenology to Climatic Factors in Northern China

Abstract

Understanding the dynamics of vegetation autumn phenology (i.e., the end of growing season, EOS) is crucial for evaluating impacts of climate change on vegetation growth. Nevertheless, responses of the EOS to climatic factors were unclear at the regional scale. In this study, northern China was chosen for our analysis, which is a typical ecologically fragile area. Using the Enhanced Vegetation Index (EVI) and climatic data from 1982 to 2016, we extracted the EOS and analyzed its trends in northern China by using the linear least-squares regression and the Bayesian change-point detection method. Furthermore, the partial correlation analysis and multivariate regression analysis were used to determine which climatic factor was more influential on EOS. The main findings were as follows: (1) multi-year average of EOS mainly varied between 275 and 305 day of year (DOY) and had complicated spatial differences for different vegetation types; (2) the percentage of the pixel showing delaying EOS (65.50%) was larger than that showing advancing EOS (34.50%), with a significant delaying trend of 0.21 days/year at the regional scale during the study period. As for different vegetation types, their EOS trends were similar in sign but different in magnitude; (3) temperature showed a dominant role in governing EOS trends from 1982 to 2016. The increase in minimum temperature led to the delayed EOS, whereas the increase in maximum temperature reversed the EOS trends. In addition to temperature, the impacts of precipitation and radiation on EOS trends were more complex and largely depended on the vegetation types. These findings can provide a crucial support for developing vegetation dynamics models in northern China.

Spatial–Temporal Pattern and Influencing Factors of Vegetation Phenology and Net Primary Productivity in the Qilian Mountains of Northwest China

“…Figure 6 showed the characteristics of temporal changes in the study area over a 20-year period, with a rate of SOS advanced by −0.4158 d/year; a rate of EOS delayed by 0.2009 d/year, and a rate of LOS increased by 0.374 d/year. In most terrestrial ecosystems, changes in vegetation phenology are clearly attributable to climate change [58], while influenced by terrain (high in the northwest and low in the southeast). The variation of temperature and precipitation in the study area showed spatial heterogeneity from northwest to southeast (Figure 2).…”

Understanding vegetation phenology responses to easily ignored climate factors in china's mid-high latitudes

“…These variables engage in complex interactions that either facilitate or constrain natural vegetation growth 13 , 17 – 21 . While temperature's primary role in influencing vegetation phenology is widely recognized, the distinct impacts of relative humidity and solar radiation remain less clear 3 , 22 – 24 . Notably, relative humidity significantly influences nutrient phenology in specific vegetation types 25 , 26 , while solar radiation, specifically shortwave radiation, exerts considerable influence by modulating photosynthetically active radiation.…”

Analysis of Factors Driving Subtropical Forest Phenology Differentiation, Considering Temperature and Precipitation Time-Lag Effects: A Case Study of Fujian Province

“…In addition, there is a dormant phase preceding leaf germination, during which low temperatures are required to transition the dormant bud from the ecodormancy to endodormancy period, just as in deciduous forests [24,26]. Previous studies have usually used traditional statistical methods, such as correlation, regression, distributed lag regression models, or vector autoregressive models, to analyze the time-lag effects [22,[27][28][29], and these results have indicated the sensitivity of the vegetation growth to a single preseason meteorological factor. However, the explanation of phenological spatial differentiation by use of multiple climatic factors is stronger than that by a single climatic factor [30].…”