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

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...

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Main Authors: Wenxi Tang, Shuguang Liu, Peng Kang, Xi Peng, Yuanyuan Li, Rui Guo, Jingni Jia, Maochou Liu, Liangjun Zhu
Format: Article
Language:English
Published: Elsevier 2021-12-01
Series:Ecological Indicators
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1470160X21009559
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author Wenxi Tang
Shuguang Liu
Peng Kang
Xi Peng
Yuanyuan Li
Rui Guo
Jingni Jia
Maochou Liu
Liangjun Zhu
author_facet Wenxi Tang
Shuguang Liu
Peng Kang
Xi Peng
Yuanyuan Li
Rui Guo
Jingni Jia
Maochou Liu
Liangjun Zhu
author_sort Wenxi Tang
collection DOAJ
description 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.
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spelling doaj.art-eb273f5229e04f9a9da9be37b8bd9b7a2022-12-21T21:24:21ZengElsevierEcological Indicators1470-160X2021-12-01132108290Quantifying the lagged effects of climate factors on vegetation growth in 32 major cities of ChinaWenxi Tang0Shuguang Liu1Peng Kang2Xi Peng3Yuanyuan Li4Rui Guo5Jingni Jia6Maochou Liu7Liangjun Zhu8National Engineering Laboratory for Applied Technology of Forestry &amp; Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, ChinaNational Engineering Laboratory for Applied Technology of Forestry &amp; 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.National Engineering Laboratory for Applied Technology of Forestry &amp; Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, ChinaNational Engineering Laboratory for Applied Technology of Forestry &amp; Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, ChinaNational Engineering Laboratory for Applied Technology of Forestry &amp; Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, ChinaNational Engineering Laboratory for Applied Technology of Forestry &amp; Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, ChinaNational Engineering Laboratory for Applied Technology of Forestry &amp; Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, ChinaNational Engineering Laboratory for Applied Technology of Forestry &amp; Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, ChinaNational Engineering Laboratory for Applied Technology of Forestry &amp; Ecology in South China, Central South University of Forestry and Technology (CSUFT), Changsha 410004, China; College of Life Science and Technology, CSUFT, Changsha 410004, ChinaClimate 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.http://www.sciencedirect.com/science/article/pii/S1470160X21009559Climate changeClimate factorsTime-lag effectVegetation growthEnhanced vegetation index
spellingShingle Wenxi Tang
Shuguang Liu
Peng Kang
Xi Peng
Yuanyuan Li
Rui Guo
Jingni Jia
Maochou Liu
Liangjun Zhu
Quantifying the lagged effects of climate factors on vegetation growth in 32 major cities of China
Ecological Indicators
Climate change
Climate factors
Time-lag effect
Vegetation growth
Enhanced vegetation index
title Quantifying the lagged effects of climate factors on vegetation growth in 32 major cities of China
title_full Quantifying the lagged effects of climate factors on vegetation growth in 32 major cities of China
title_fullStr Quantifying the lagged effects of climate factors on vegetation growth in 32 major cities of China
title_full_unstemmed Quantifying the lagged effects of climate factors on vegetation growth in 32 major cities of China
title_short Quantifying the lagged effects of climate factors on vegetation growth in 32 major cities of China
title_sort quantifying the lagged effects of climate factors on vegetation growth in 32 major cities of china
topic Climate change
Climate factors
Time-lag effect
Vegetation growth
Enhanced vegetation index
url http://www.sciencedirect.com/science/article/pii/S1470160X21009559
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