Quantifying the lagged effects of climate factors on vegetation growth in 32 major cities of China

Wenxi Tang; Shuguang Liu; Peng Kang; Xi Peng; Yuanyuan Li; Rui Guo; Jingni Jia; Maochou Liu; Liangjun Zhu

Ecological Indicators (Dec 2021)

Quantifying the lagged effects of climate factors on vegetation growth in 32 major cities of China

Wenxi Tang,
Shuguang Liu,
Peng Kang,
Xi Peng,
Yuanyuan Li,
Rui Guo,
Jingni Jia,
Maochou Liu,
Liangjun Zhu

Affiliations

Wenxi Tang: National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, China
Shuguang Liu: National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, China; Corresponding author at: College of Life Science and Technology, Central South University of Forestry and Technology, 498 South Shaoshan Road, Changsha 410004, China.
Peng Kang: National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, China
Xi Peng: National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, China
Yuanyuan Li: National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, China
Rui Guo: National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, China
Jingni Jia: National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, China
Maochou Liu: National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, China
Liangjun Zhu: National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, China

Journal volume & issue: Vol. 132
p. 108290

Abstract

Read online

Climate change affects vegetation growth around the world. It has been recognized that the effect of climate change on vegetation growth exhibits hysteresis. However, the duration and intensity of time-lag effect of climate factors on vegetation growth is still difficult to quantify. We analyzed the impacts of climate on vegetation growth in 32 major cities of China from 2010 to 2016. Vegetation growth conditions were characterized using enhanced vegetation index (EVI) datasets from Moderate Resolution Imaging Spectrometer (MODIS). The climate data were extracted from the Daily Value Data Set of China Surface Climate Data (V3.0), including precipitation (PRE; mm), air temperature (TEM; oC), sunshine duration (SSD; h), humidity (RHU; %), and evapotranspiration (EVP; mm). We used the vector autoregressive model (VAR) to analyze the lagged effects of climate factors on EVI, predict vegetation responses to future global changes, and validate its accuracy. Results showed that RHU had the longest (6.13 ± 1.96 months) and strongest (median 0.34 EVI per unit RHU in the first lag period) time-lag effect on EVI, while EVP had the shortest (3.45 ± 1.09 months) and weakest (median −0.02 EVI per unit EVP in the first lag period) time-lag effect on EVI. The time-lag effects of PRE and SSD on EVI were stronger in the south than in the north. Meanwhile, the EVI predicted by the VAR model was highly consistent with the observed EVI (root mean squared error, RMSE < 0.08), and the prediction accuracy generally improved by 23.43% compared with the EVI predicted by the multiple linear regression model (MLR). Our study highlights the necessity of considering time-lag effects when exploring vegetation-climate interaction. The methods developed in this study can be used to reveal the lagged effects of climatic factors on vegetation growth and improve prediction of EVI dynamics under climate change.

Published in Ecological Indicators

ISSN: 1470-160X (Print); 1872-7034 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General): Ecology
Website: https://www.sciencedirect.com/journal/ecological-indicators

About the journal

Abstract

Keywords