Diagnose the dominant climate factors and periods of spring phenology in Qinling Mountains, China

The important effect of climate factors on spring phenology has been confirmed by numerous studies, but its temporal variation remains unclear. Based on the daily meteorological observation data and annual vegetation phenology data from 1987 to 2016, this study proposes a Time Window Sliding Fitting...

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Autores principales:	Cong Yin, Yaping Yang, Fei Yang, Xiaona Chen, Ying Xin, Peixian Luo
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Spring phenology Climate change Vegetation Land cover Qinling Mountains Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/62660bba401d44639b159d737cc9c529
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spelling	oai:doaj.org-article:62660bba401d44639b159d737cc9c5292021-12-01T05:00:43ZDiagnose the dominant climate factors and periods of spring phenology in Qinling Mountains, China1470-160X10.1016/j.ecolind.2021.108211https://doaj.org/article/62660bba401d44639b159d737cc9c5292021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21008761https://doaj.org/toc/1470-160XThe important effect of climate factors on spring phenology has been confirmed by numerous studies, but its temporal variation remains unclear. Based on the daily meteorological observation data and annual vegetation phenology data from 1987 to 2016, this study proposes a Time Window Sliding Fitting (TWSF) method. Partial Least Squares (PLS) regression is used to investigate the relationship between spring phenology and five climate factors, including mean temperature, total precipitation, mean photoperiod, chilling index and forcing index, in all time windows from December to next May with 7-day interval in Qinling Mountains (QLMs) of China. The results show that: (1) Temperature in QLMs has an increasing trend with 0.41 °C/10a, and precipitation decreases with 400 mm/a. Moreover, spring phenology in QLMs is advancing with 4 days/10a. (2) Temperature is found having dominant effect on spring phenology in QLMs, and photoperiod has a significant effect on spring phenology in grassland area. (3) Temperature’s impact increases in cultivated land and grassland after March 25 but a consecutive decrease trend in forest, while photoperiod’s explanatory ability on grassland spring phenology is highlighted in early January, early February and early April. Chilling has the best explanatory ability on crop spring phenology in early March and early May. Additionally, forcing can significantly influence spring phenology in early March for cultivated land and late May for forest. The results of this study are beneficial to understand the mechanism of plant-climate interaction.Cong YinYaping YangFei YangXiaona ChenYing XinPeixian LuoElsevierarticleSpring phenologyClimate changeVegetationLand coverQinling MountainsEcologyQH540-549.5ENEcological Indicators, Vol 131, Iss , Pp 108211- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Spring phenology Climate change Vegetation Land cover Qinling Mountains Ecology QH540-549.5
spellingShingle	Spring phenology Climate change Vegetation Land cover Qinling Mountains Ecology QH540-549.5 Cong Yin Yaping Yang Fei Yang Xiaona Chen Ying Xin Peixian Luo Diagnose the dominant climate factors and periods of spring phenology in Qinling Mountains, China
description	The important effect of climate factors on spring phenology has been confirmed by numerous studies, but its temporal variation remains unclear. Based on the daily meteorological observation data and annual vegetation phenology data from 1987 to 2016, this study proposes a Time Window Sliding Fitting (TWSF) method. Partial Least Squares (PLS) regression is used to investigate the relationship between spring phenology and five climate factors, including mean temperature, total precipitation, mean photoperiod, chilling index and forcing index, in all time windows from December to next May with 7-day interval in Qinling Mountains (QLMs) of China. The results show that: (1) Temperature in QLMs has an increasing trend with 0.41 °C/10a, and precipitation decreases with 400 mm/a. Moreover, spring phenology in QLMs is advancing with 4 days/10a. (2) Temperature is found having dominant effect on spring phenology in QLMs, and photoperiod has a significant effect on spring phenology in grassland area. (3) Temperature’s impact increases in cultivated land and grassland after March 25 but a consecutive decrease trend in forest, while photoperiod’s explanatory ability on grassland spring phenology is highlighted in early January, early February and early April. Chilling has the best explanatory ability on crop spring phenology in early March and early May. Additionally, forcing can significantly influence spring phenology in early March for cultivated land and late May for forest. The results of this study are beneficial to understand the mechanism of plant-climate interaction.
format	article
author	Cong Yin Yaping Yang Fei Yang Xiaona Chen Ying Xin Peixian Luo
author_facet	Cong Yin Yaping Yang Fei Yang Xiaona Chen Ying Xin Peixian Luo
author_sort	Cong Yin
title	Diagnose the dominant climate factors and periods of spring phenology in Qinling Mountains, China
title_short	Diagnose the dominant climate factors and periods of spring phenology in Qinling Mountains, China
title_full	Diagnose the dominant climate factors and periods of spring phenology in Qinling Mountains, China
title_fullStr	Diagnose the dominant climate factors and periods of spring phenology in Qinling Mountains, China
title_full_unstemmed	Diagnose the dominant climate factors and periods of spring phenology in Qinling Mountains, China
title_sort	diagnose the dominant climate factors and periods of spring phenology in qinling mountains, china
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/62660bba401d44639b159d737cc9c529
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Diagnose the dominant climate factors and periods of spring phenology in Qinling Mountains, China

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