Responses of Vegetation Autumn Phenology to Climatic Factors in Northern China

Zhaozhe Li, Ranghui Wang, Bo Liu, Zhonghua Qian, Yongping Wu and Cheng Li
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Zhaozhe Li: School of Horticulture and Plant Protection, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education, Yangzhou University, Yangzhou 225009, China
Ranghui Wang: Jiangsu Key Laboratory of Agricultural Meteorology, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
Bo Liu: College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China
Zhonghua Qian: College of Physical Science and Technology, Yangzhou University, Yangzhou 225002, China
Yongping Wu: College of Physical Science and Technology, Yangzhou University, Yangzhou 225002, China
Cheng Li: School of Horticulture and Plant Protection, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education, Yangzhou University, Yangzhou 225009, China

Sustainability, 2022, vol. 14, issue 14, 1-13

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.

Keywords: end of growing season (EOS); climatic factors; sensitivities; enhanced vegetation index (EVI) data; northern China (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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