Pre-seasonal temperature trend break dominating the trend break in autumn grassland phenology in China
Interannual variations in the end of the growing season (EOS) play a crucial role in assessing carbon and energy cycling within grassland ecosystems. Previous studies have often fixed the trend breakpoint in autumn phenology around the year 2000 to examine the response of the vegetation EOS to long-...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223004144 |
| _version_ | 1797389469143269376 |
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| author | Ning Qi Yanzheng Yang Guijun Yang Weizhong Li Chunjiang Zhao Jun Zhao Boheng Wang Shaofeng Su Pengxiang Zhao |
| author_facet | Ning Qi Yanzheng Yang Guijun Yang Weizhong Li Chunjiang Zhao Jun Zhao Boheng Wang Shaofeng Su Pengxiang Zhao |
| author_sort | Ning Qi |
| collection | DOAJ |
| description | Interannual variations in the end of the growing season (EOS) play a crucial role in assessing carbon and energy cycling within grassland ecosystems. Previous studies have often fixed the trend breakpoint in autumn phenology around the year 2000 to examine the response of the vegetation EOS to long-term climate change. However, the asymmetry of climate change and the diversity of grass species may lead to spatial disparities in EOS trend breakpoints, but little research has been done to quantify their characteristics and underlying climatic driving mechanisms. Focusing on the period from 1982 to 2015, this study extracts EOS data from six different grassland subregions in China to identify EOS trend breakpoints, and then investigates the associated climatic driving mechanisms. The results highlight the presence of significant breakpoints in the EOS trend within 54.1% of China's grasslands. Prior to 1997, the grassland EOS trend exhibited a pronounced delay, with a rate of 0.29 days per year (P < 0.01), which subsequently shifted to 0.10 days per year. In addition, pre-seasonal climate factors emerged as the dominant driver, contributing a remarkable 98.8% to the timing of the grassland EOS, with pre-seasonal temperature and solar radiation standing out as the dominant climate variables influencing the grassland EOS. Furthermore, the main driver of the trend break in the grassland EOS was the trend break in pre-seasonal temperature, which contributed to 52.2% of the trend break in the grassland EOS. These results confirm the presence of breakpoints in the autumn phenological trend across Chinese grasslands, and elucidate the intrinsic climate-driven mechanism responsible for the autumn phenological trend break at the pixel scale. These findings provide valuable insights to better understand and model the complex interactions between ecosystems and the climate system. |
| first_indexed | 2024-03-08T22:57:24Z |
| format | Article |
| id | doaj.art-50eb54a952f3444eb4f48d7655760c52 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-03-08T22:57:24Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-50eb54a952f3444eb4f48d7655760c522023-12-16T06:06:36ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-12-01125103590Pre-seasonal temperature trend break dominating the trend break in autumn grassland phenology in ChinaNing Qi0Yanzheng Yang1Guijun Yang2Weizhong Li3Chunjiang Zhao4Jun Zhao5Boheng Wang6Shaofeng Su7Pengxiang Zhao8School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; College of Forestry, Northwest A&F University, Yangling, Shaanxi 712100, China; Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaState Key Laboratory of Urban and Regional Ecology, Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, Beijing 100085, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, ChinaCollege of Forestry, Northwest A&F University, Yangling, Shaanxi 712100, China; Corresponding authors at: School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China (C. Zhao). College of Forestry, Northwest A&F University, Yangling, Shaanxi 712100, China (W. Li).School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Corresponding authors at: School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China (C. Zhao). College of Forestry, Northwest A&F University, Yangling, Shaanxi 712100, China (W. Li).State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, Beijing 100085, ChinaCollege of Forestry, Northwest A&F University, Yangling, Shaanxi 712100, ChinaCollege of Forestry, Northwest A&F University, Yangling, Shaanxi 712100, ChinaCollege of Forestry, Northwest A&F University, Yangling, Shaanxi 712100, ChinaInterannual variations in the end of the growing season (EOS) play a crucial role in assessing carbon and energy cycling within grassland ecosystems. Previous studies have often fixed the trend breakpoint in autumn phenology around the year 2000 to examine the response of the vegetation EOS to long-term climate change. However, the asymmetry of climate change and the diversity of grass species may lead to spatial disparities in EOS trend breakpoints, but little research has been done to quantify their characteristics and underlying climatic driving mechanisms. Focusing on the period from 1982 to 2015, this study extracts EOS data from six different grassland subregions in China to identify EOS trend breakpoints, and then investigates the associated climatic driving mechanisms. The results highlight the presence of significant breakpoints in the EOS trend within 54.1% of China's grasslands. Prior to 1997, the grassland EOS trend exhibited a pronounced delay, with a rate of 0.29 days per year (P < 0.01), which subsequently shifted to 0.10 days per year. In addition, pre-seasonal climate factors emerged as the dominant driver, contributing a remarkable 98.8% to the timing of the grassland EOS, with pre-seasonal temperature and solar radiation standing out as the dominant climate variables influencing the grassland EOS. Furthermore, the main driver of the trend break in the grassland EOS was the trend break in pre-seasonal temperature, which contributed to 52.2% of the trend break in the grassland EOS. These results confirm the presence of breakpoints in the autumn phenological trend across Chinese grasslands, and elucidate the intrinsic climate-driven mechanism responsible for the autumn phenological trend break at the pixel scale. These findings provide valuable insights to better understand and model the complex interactions between ecosystems and the climate system.http://www.sciencedirect.com/science/article/pii/S1569843223004144Autumn phenologyTrend breakPre-season climateChinese grasslandsContribution |
| spellingShingle | Ning Qi Yanzheng Yang Guijun Yang Weizhong Li Chunjiang Zhao Jun Zhao Boheng Wang Shaofeng Su Pengxiang Zhao Pre-seasonal temperature trend break dominating the trend break in autumn grassland phenology in China International Journal of Applied Earth Observations and Geoinformation Autumn phenology Trend break Pre-season climate Chinese grasslands Contribution |
| title | Pre-seasonal temperature trend break dominating the trend break in autumn grassland phenology in China |
| title_full | Pre-seasonal temperature trend break dominating the trend break in autumn grassland phenology in China |
| title_fullStr | Pre-seasonal temperature trend break dominating the trend break in autumn grassland phenology in China |
| title_full_unstemmed | Pre-seasonal temperature trend break dominating the trend break in autumn grassland phenology in China |
| title_short | Pre-seasonal temperature trend break dominating the trend break in autumn grassland phenology in China |
| title_sort | pre seasonal temperature trend break dominating the trend break in autumn grassland phenology in china |
| topic | Autumn phenology Trend break Pre-season climate Chinese grasslands Contribution |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223004144 |
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