Multi-spatiotemporal heterogeneous legacy effects of climate on terrestrial vegetation dynamics in China

Lian, Xihong; Jiao, Limin; Liu, Zejin; Jia, Qiqi; Zhong, Jing; Fang, Miao; Wang, Weilin

doi:10.1080/15481603.2021.2022426

Cited by 22 publications

(10 citation statements)

References 100 publications

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“…This is mainly due to the time interval between precipitation events and water uptake by plant roots and leaf growth (Kuzyakov & Gavrichkova, 2010). Consistent with our findings, previous studies in arid and semiarid regions have also reported a time lag of about 1 month between vegetation and precipitation (Lian et al., 2022; Wu et al., 2015), particularly in summer (Zhao et al., 2020). In comparison to summer precipitation, the positive correlation phase relationship between spring temperature and NDVI at different time‐frequency domains was nonconform, but dominated by arrow parallelism.…”

Section: Discussionsupporting

confidence: 92%

“…Understanding how vegetation responds to climate change is a complex dynamic process that involves not only seasonal variations but also multitime scale characteristics and legacy effects (Lian et al, 2021(Lian et al, , 2022. In this study, we not only quantified the seasonal differences in the effect of climatic factors on vegetation growth, but also obtained coherence coefficients with phase differences in the timescales domain using PWC to describe how these effects vary over time.…”

Section: Response Of Grassland Growth To Driving Factors At Different...mentioning

confidence: 99%

“…The heterogeneity of vegetation growth at temporal and spatial scales results from variations in their preferences for hydrothermal conditions, constituting a complex, and dynamic process (Lian et al., 2022). Previous studies have shown that although temperature and precipitation drive grassland growth, their contributions have different temporal scales (Y. H. Fu et al., 2021; Hao et al., 2021; P. Li et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Wavelet transform is a widely used method for analyzing the multiscale features of time series (Jia et al., 2018; Nourani et al., 2021), and binary wavelet transform coherence (WTC) provides coherence and phase angle of bivariate at multiple time scales. This method has been employed to explore the legacy effects of NDVI and phenological changes in response to climatic factors (Detto et al., 2018; Lian et al., 2022). Hu and Si (2021) proposed an improved partial wavelet coherency method based on WTC, which effectively solves the problem of interference from multiple coherent variables.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, wavelet coherence can provide information about the correlations and time lags between factors at different timescales. As a result, these methods have gradually been applied to explore the response of vegetation changes to climate variations at different timescales (Detto et al., 2018; Lian et al., 2022; Sebastian et al., 2019; Wei et al., 2022). Therefore, in the investigation of the dynamic relationship between vegetation growth and its driving factors, wavelet transformation and partial wavelet coherence (PWC) can offer novel insights at various timescales compared to traditional analytical methods, such as univariate linear regression, partial correlation analysis, and multiple linear regression.…”

Section: Introductionmentioning

confidence: 99%

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Effects of Spring Phenology on Grassland Growth in Qilian Mountains Across Multiple Spatiotemporal Scales

Sun,

Guan,

et al. 2023

JGR Biogeosciences

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Investigating the differences in grassland growth and its response to advancing spring phenology and climate change across various spatiotemporal scales is crucial for the sustainable development of alpine mountain regions under a warming and wetting climate. This study analyzed the growth dynamics of grassland in the Qilian Mountains from 2000 to 2019 at seasonal and periodic scales. Using partial wavelet coherency and partial correlation analysis, we investigated the influence of temperature, precipitation, and spring phenology on grassland growth. Additionally, we quantified the dominant factors affecting grassland growth in different ecological zones and elevational gradient. The results indicated that the mean normalized difference vegetation index increased at an average rate of 1.1 and 2.8 × 10−3/year for grassland in spring and summer, respectively. Furthermore, the start of vegetation growing season (SOS) has advanced 6.0 days during 2000–2019. The dominant factor for grassland growth shifted from spring temperature to summer precipitation. The importance of SOS ranked second, significantly influencing 29.3% and 25.1% of grassland during spring and summer, respectively. Grasslands situated above 4,000 m above sea level (ASL) were more sensitive to climate change, with SOS exerting the greatest influence within the elevation range of 2,800–4,000 m ASL. Additionally, the phase difference in the time‐frequency scale of partial wavelet coherency revealed a lag effect of approximately 1 month for summer grassland growth to respond to precipitation. These findings can provide a scientific basis for optimizing global vegetation models and predicting the productivity of alpine mountain grasslands.

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