Detecting the Turning Points of Grassland Autumn Phenology on the Qinghai-Tibetan Plateau: Spatial Heterogeneity and Controls

Abstract

Autumn phenology, commonly represented by the end of season (EOS), is considered to be the most sensitive and crucial productivity indicator of alpine and cold grassland in the Qinghai-Tibetan Plateau. Previous studies typically assumed that the rates of EOS changes remain unchanged over long time periods. However, pixel-scale analysis indicates the existence of turning points and differing EOS change rates before and after these points. The spatial heterogeneity and controls of these turning points remain unclear. In this study, the EOS turning point changes are extracted and their controls are explored by integrating long time-series remote sensing images and piecewise regression methods. The results indicate that the EOS changed over time with a delay rate of 0.08 days/year during 1982–2015. The rates of change are not consistent over different time periods, which clearly highlights the existence of turning points. The results show that temperature contributed most strongly to the EOS changes, followed by precipitation and insolation. Furthermore, the turning points of climate, human activities (e.g., grazing, economic development), and their intersections are found to jointly control the EOS turning points. This study is the first quantitative investigation into the spatial heterogeneity and controls of the EOS turning points on the Qinghai-Tibetan Plateau, and provides important insight into the growth mechanism of alpine and cold grassland.

Critical role of water conditions in the responses of autumn phenology of marsh wetlands to climate change on the Tibetan Plateau

“…Previous studies have shown that in the grassland vegetation of the Tibetan Plateau, increased precipitation significantly enhances water use efficiency (Lin et al., 2020; Zhou et al., 2020), delaying the EOS. In these relatively arid systems, the higher maximum temperatures increase evaporation and reduce water use efficiency, thus advancing the EOS (Dorji et al., 2013; Yang et al., 2021). In contrast, the marshes examined in the present study contained far more water (Shen, Liu, et al., 2022), making it less likely that preseason precipitation would affect the regionally averaged EOS for their vegetation, and explain how the abundance of water allowed the EOS to be delayed by the increased preseason temperatures on the Tibetan Plateau.…”

“…Therefore, in the polyfit‐maximum approach, the EOS date is set to correspond to the time of the largest decrease in NDVI at the end of the growth period (Piao et al., 2006). The polyfit‐maximum method has been widely used to extract vegetation phenology owing to its excellent performance (e.g., Cong et al., 2013; Fu et al., 2014; Jeong et al., 2011; Kafaki et al., 2009; Li et al., 2023; Liu et al., 2016, 2023; Ma et al., 2022; Piao et al., 2015; Shen et al., 2018, 2019, 2023; Su et al., 2022; Wang et al., 2016, 2018; Wu & Liu, 2013; Yang et al., 2015, 2021; Zhang et al., 2013; Zhou et al., 2020) and consists in a number of steps.…”

“…This preprocessing of data has been validated and included in numerous previous studies (Ganguly et al, 2010;Shen et al, 2015a;Wang et al, 2021). Based on previous research (Cong et al, 2013;Liu et al, 2016;Ma et al, 2021;Piao et al, 2006b;Shen et al, 2016;Su et al, 2022), we used the poly fit-maximum approach to extract phenological information (Piao et al, 2006b). In this method, the growth curve of the growing season is fitted with a daily temporal resolution in order to remove abnormal NDVI values (Piao et al, 2006b(Piao et al, , 2011, and the EOS date is set to correspond to the time of the largest decrease in NDVI at the end of the growth period.…”

“…Global climate change has significantly changed the autumn phenology worldwide, affecting the regional and global energy balance, water flux, and carbon budget (Che et al., 2014; Estiarte & Peñuelas, 2015; Kelsey et al., 2021; Richardson et al., 2013; Yang et al., 2021). Although the variations in the autumn phenology of vegetation and its response to regional and global climate changes have been extensively analyzed, most studies have focused on grassland or forest ecosystems, with few investigations of wetland ecosystems (Coleman et al., 2022; Ge et al., 2015; Ma et al., 2022; Rice et al., 2018; Yang et al., 2015).…”

Response of Vegetation Phenology to the Interaction of Temperature and Precipitation Changes in Qilian Mountains

“…In the past, researchers have usually used linear regression analysis to study the effect of a single element on vegetation phenology change, or to compare the impact of multiple factors on phenology change [21,22]. Yang et al [23] used the redundancy analysis method to detect the impact of the interaction of climate and human activities on the vegetation phenology of the Qinghai-Tibet Plateau, which broke through the previous studies that only focused on the correlation of a single element and comprehensively considered the interaction of multiple elements. However, the essence of this method is still based on linear regression analysis.…”