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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Autores principales:	Wenxi Tang, Shuguang Liu, Peng Kang, Xi Peng, Yuanyuan Li, Rui Guo, Jingni Jia, Maochou Liu, Liangjun Zhu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Climate change Climate factors Time-lag effect Vegetation growth Enhanced vegetation index Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/eb273f5229e04f9a9da9be37b8bd9b7a
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spelling	oai:doaj.org-article:eb273f5229e04f9a9da9be37b8bd9b7a2021-12-01T05:01:57ZQuantifying the lagged effects of climate factors on vegetation growth in 32 major cities of China1470-160X10.1016/j.ecolind.2021.108290https://doaj.org/article/eb273f5229e04f9a9da9be37b8bd9b7a2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21009559https://doaj.org/toc/1470-160XClimate 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.Wenxi TangShuguang LiuPeng KangXi PengYuanyuan LiRui GuoJingni JiaMaochou LiuLiangjun ZhuElsevierarticleClimate changeClimate factorsTime-lag effectVegetation growthEnhanced vegetation indexEcologyQH540-549.5ENEcological Indicators, Vol 132, Iss , Pp 108290- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Climate change Climate factors Time-lag effect Vegetation growth Enhanced vegetation index Ecology QH540-549.5
spellingShingle	Climate change Climate factors Time-lag effect Vegetation growth Enhanced vegetation index Ecology QH540-549.5 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
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.
format	article
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
title	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_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_sort	quantifying the lagged effects of climate factors on vegetation growth in 32 major cities of china
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/eb273f5229e04f9a9da9be37b8bd9b7a
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Quantifying the lagged effects of climate factors on vegetation growth in 32 major cities of China

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