Temporal and spatial variation of vegetation photosynthetic phenology in Dongting Lake basin and its response to climate change

doi:10.17521/cjpe.2022.0170

Abstract

Abstract:

Aims This study investigated the spatial and temporal variation of spring and autumn photosynthetic phenology of vegetation in the Dongting Lake basin and revealed its response to climate change, and provides a useful reference for the establishment of model of subtropical vegetation phenology and the evaluation of carbon budget.

Methods Using solar-induced chlorophyll fluorescence (SIF) data, we extracted spring photosynthetic phenology (the start date of photosynthesis) and autumn photosynthetic phenology (the end date of photosynthesis) of vegetation in Dongting Lake basin, and evaluated temporal and spatial patterns of vegetation spring and autumn photosynthetic phenology and its response to climate change.

Important findings (1) From 2000 to 2018, the vegetation spring photosynthetic phenology was significantly advanced at the rate of 0.75 d·a^-1, the autumn photosynthetic phenology was delayed at the rate of 0.17 d·a^-1, and the vegetation growing season length was significantly prolonged at the rate of 0.90 d·a^-1. (2) The preseason maximum air temperature and minimum air temperature were the main factors affecting the advance of spring photosynthetic phenology. The autumn photosynthetic phenology of vegetation was positively correlated with preseason precipitation, minimum air temperature and radiation intensity, but negatively correlated with preseason maximum air temperature. (3) In addition, we found that the spring photosynthetic phenology of vegetation in the study area was more sensitive to climate change, especially the increase of preseason minimum air temperature led to the significant advance of spring photosynthetic phenology of evergreen needleleaf forest, evergreen broadleaf forest, bush and grassland. In conclusion, the advance of vegetation spring photosynthetic phenology in Dongting Lake basin played a dominant role in prolonging the growth season, indicating that spring photosynthetic phenology plays a more important role in enhancing the carbon sink function than the autumn photosynthetic phenology in the context of global warming. The vegetation spring photosynthetic phenology was more sensitive to climate change and the air temperature was the main factor controlling the vegetation spring photosynthetic phenology, which provides a scientific basis for the simulation and prediction of evergreen vegetation phenology.

Key words: vegetation phenology, solar-induced chlorophyll fluorescence, climate change, subtropics, carbon sink

REN Pei-Xin, LI Peng, PENG Chang-Hui, ZHOU Xiao-Lu, YANG Ming-Xia. Temporal and spatial variation of vegetation photosynthetic phenology in Dongting Lake basin and its response to climate change[J]. Chin J Plant Ecol, 2023, 47(3): 319-330.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2022.0170

https://www.plant-ecology.com/EN/Y2023/V47/I3/319

Figures/Tables 7

Fig. 1 Location and vegetation types of Dongting Lake basin.

Fig. 2 Spatial pattern and frequency distribution of vegetation photosynthetic phenology and growing season length in Dongting Lake basin from 2000 to 2018. EOP, the end date of photosynthesis; LOP, the length of photosynthesis; SOP, the start date of photosynthesis.

Fig. 3 Spatial distribution patterns of the linear trend and annual variation of vegetation photosynthetic phenology and growing season length in Dongting Lake basin from 2000 to 2018. EOP, the end date of photosynthesis; LOP, the length of photosynthesis; SOP, the start date of photosynthesis. P indicated the percentage of positive coefficients, indicating that the photosynthetic phenology tends to delay (prolong); N indicated the percentage of negative coefficients, indicating that the photosynthetic phenology tends to advance (shorten); percentage of significant correlations in parentheses (p < 0.05) are provided.

Fig. 4 Linear trends of vegetation photosynthetic phenology across the biomes in Dongting Lake basin from 2000 to 2018. EOP, the end date of photosynthesis; SOP, the start date of photosynthesis. DBF, deciduous broadleaf forest; EBF, evergreen broadleaf forest; ENF, evergreen needleleaf forest. Positive values represented the delay of phenological indicators, and negative values represented the advance of phenological indicators. *, p < 0.05.

Fig. 5 Spatial pattern and frequency distribution of partial correlation coefficient between the spring and autumn photosynthetic phenology of vegetation and preseason climatic factors. Pre, precipitation; Srad, radiation intensity; Tmax, maximum air temperature; Tmin, minimum air temperature. P means the proportion of positive correlation coefficients, indicating that the increase of climate factors led to the delay of phenology; N means the proportion of negative correlation coefficients, indicating that the increase of climate factors led to the advance of phenology; percentage of significant correlations in parentheses (p < 0.05) are provided. EOP, the end date of photosynthesis; LOP, the length of photosynthesis; SOP, the start date of photosynthesis.

Fig. 6 Partial correlation coefficient between the photosynthetic phenology of different vegetation and preseason climatic factors. EOP, the end date of photosynthesis; SOP, the start date of photosynthesis. DBF, deciduous broadleaf forest; EBF, evergreen broadleaf forest; ENF, evergreen needleleaf forest. Pre, precipitation; Srad, radiation intensity; Tmax, maximum air temperature; Tmin, minimum air temperature. *, p < 0.05; **, p < 0.01.

Table 1 Interannual variation of climatic factors in deciduous broadleaf forest region

气候因子 Climate factor	变化速率 Rate of change	p
降水 Precipitation	-1.321 2 (mm·a^-1)	0.756 1
最高气温 Maximum air temperature	-0.080 8 (℃·a^-1)	0.603 5
最低气温 Minimum air temperature	-0.052 3 (℃·a^-1)	0.595 7
辐射强度 Radiation intensity	-1.211 8 (W·m^-2·a^-1)	0.477 0

References 49

[1]	Bai Y, Liang SL, Yuan WP (2021). Estimating global gross primary production from sun-induced chlorophyll fluorescence data and auxiliary information using machine learning methods. Remote Sensing, 13, 963. DOI: 10.3390/rs13050963. DOI
[2]	Beier C, Emmett B, Gundersen P, Tietema A, Peñuelas J, Estiarte M, Gordon C, Gorissen A, Llorens L, Roda F, Williams D (2004). Novel approaches to study climate change effects on terrestrial ecosystems in the field: drought and passive nighttime warming. Ecosystems, 7, 583-597.
[3]	Cao B, Zhang B, Ma B, Tang M, Wang GQ, Wu QH, Jia YQ (2018). Spatial and temporal characteristics analysis of drought based on SPEI in the Middle and Lower Yangtze Basin. Acta Ecologica Sinica, 38, 6258-6267.
	[曹博, 张勃, 马彬, 唐敏, 王国强, 吴乾慧, 贾艳青 (2018). 基于SPEI指数的长江中下游流域干旱时空特征分析. 生态学报, 38, 6258-6267.]
[4]	Chen J, Jönsson P, Tamura M, Gu ZH, Matsushita B, Eklundh L (2004). A simple method for reconstructing a high- quality NDVI time-series data set based on the Savitzky- Golay filter. Remote Sensing of Environment, 91, 332-344. DOI URL
[5]	Cheng LL, Li YH, Sun HY, Zhang Y, Zhan JQ, Liu M (2019). Applicability of fitting and reconstruction method of MODIS long-time enhanced vegetation index in Beijing- Tianjin-Hebei. Transactions of the Chinese Society of Agricultural Engineering, 35, 148-158.
	[程琳琳, 李玉虎, 孙海元, 张也, 詹佳琪, 刘梅 (2019). 京津冀MODIS长时序增强型植被指数拟合重建方法适用性研究. 农业工程学报, 35, 148-158.]
[6]	Estiarte M, Peñuelas J (2015). Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency. Global Change Biology, 21, 1005-1017. DOI PMID
[7]	Fu YH, Li XX, Zhou XX, Geng XJ, Guo YH, Zhang YR (2020). Progress in plant phenology modeling under global climate change. Science China Earth Sciences, 50, 1206-1218.
	[付永硕, 李昕熹, 周轩成, 耿晓君, 郭亚会, 张雅茹 (2020). 全球变化背景下的植物物候模型研究进展与展望. 中国科学: 地球科学, 50, 1206-1218.]
[8]	He J, Yang K, Tang WJ, Lu H, Qin J, Chen YY, Li X (2020). The first high-resolution meteorological forcing dataset for land process studies over China. Scientific Data, 7, 25. DOI: 10.1038/s41597-020-0369-y. DOI
[9]	Hu Z, Wang HJ, Dai JH, Ge QS (2021). Using controlled experiments to investigate plant phenology in response to climate change: progress and prospects. Acta Ecologica Sinica, 41, 9119-9129.
	[胡植, 王焕炯, 戴君虎, 葛全胜 (2021). 利用控制实验研究植物物候对气候变化的响应综述. 生态学报, 41, 9119-9129.]
[10]	Jeong SJ, Ho CH, Gim HJ, Brown ME (2011). Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982-2008. Global Change Biology, 17, 2385-2399. DOI URL
[11]	Ji ZX, Pei TT, Chen Y, Qin GX, Hou QQ, Xie BP, Wu HW (2021). Vegetation phenology change and its response to seasonal climate changes on the Loess Plateau. Acta Ecologica Sinica, 41, 6600-6612.
	[吉珍霞, 裴婷婷, 陈英, 秦格霞, 侯青青, 谢保鹏, 吴华武 (2021). 黄土高原植被物候变化及其对季节性气候变化的响应. 生态学报, 41, 6600-6612.]
[12]	Kong DD, Zhang Q, Huang WL, Gu XH (2017). Vegetation phenology change in Tibetan Plateau from 1982 to 2013 and its related meteorological factors. Acta Geographica Sinica, 72, 39-52.
	[孔冬冬, 张强, 黄文琳, 顾西辉 (2017). 1982-2013年青藏高原植被物候变化及气象因素影响. 地理学报, 72, 39-52.] DOI
[13]	Li C, Zhuang DF, He JF, Wen KG (2021). Spatiotemporal variations in remote sensing phenology of vegetation and its responses to temperature change of boreal forest in tundra-taiga transitional zone in the Eastern Siberia. Acta Geographica Sinica, 76, 1634-1648. DOI
	[李程, 庄大方, 何剑锋, 文可戈 (2021). 东西伯利亚苔原—泰加林过渡带植被遥感物候时空特征及其对气温变化的响应. 地理学报, 76, 1634-1648.] DOI
[14]	Li P, Peng CH, Wang M, Luo YP, Li MX, Zhang KR, Zhang DL, Zhu QA (2018). Dynamics of vegetation autumn phenology and its response to multiple environmental factors from 1982 to 2012 on Qinghai-Tibetan Plateau in China. Science of the Total Environment, 637- 638, 855-864.
[15]	Li X, Fu YH, Chen S, Xiao J, Yin G, Li X, Zhang X, Geng X, Wu Z, Zhou X, Tang J, Hao F (2021). Increasing importance of precipitation in spring phenology with decreasing latitudes in subtropical forest area in China. Agricultural and Forest Meteorology, 304-305, 108427. DOI: 10.1016/j.agrformet.2021.108427. DOI
[16]	Li X, Xiao JF (2019). A global, 0.05-degree product of solar- induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data. Remote Sensing, 11, 517. DOI URL
[17]	Li X, Xiao JF (2020). Global climatic controls on interannual variability of ecosystem productivity: similarities and differences inferred from solar-induced chlorophyll fluorescence and enhanced vegetation index. Agricultural and Forest Meteorology, 288-289, 108018. DOI: 10.1016/j.agrformet.2020.108018. DOI
[18]	Li XT, Guo W, Ni XN, Wei XY (2019). Plant phenological responses to temperature variation in an alpine meadow. Acta Ecologica Sinica, 39, 6670-6680.
	[李晓婷, 郭伟, 倪向南, 卫晓依 (2019). 高寒草甸植物物候对温度变化的响应. 生态学报, 39, 6670-6680.]
[19]	Li Y, Zhang CC, Luo WR, Gao WJ (2019). Summer maize phenology monitoring based on normalized difference vegetation index reconstructed with improved maximum value composite. Transactions of the Chinese Society of Agricultural Engineering, 35(14), 159-165.
	[李艳, 张成才, 罗蔚然, 郜文江 (2019). 基于改进最大值法合成NDVI的夏玉米物候期遥感监测. 农业工程学报, 35(14), 159-165.]
[20]	Li YB, Zhang YD, Gu FX, Liu SR (2019). Changes of spring phenology and sensitivity analysis in temperate grassland and desert zones of China. Forest Research, 32(4), 1-10.
	[李耀斌, 张远东, 顾峰雪, 刘世荣 (2019). 中国温带草原和荒漠区域春季物候的变化及其敏感性分析. 林业科学研究, 32(4), 1-10.]
[21]	Liang HX, Huang JG, Ma QQ, Li JY, Wang Z, Guo XL, Zhu HX, Jiang SW, Zhou P, Yu BY, Luo DW (2019). Contributions of competition and climate on radial growth of Pinus massoniana in subtropics of China. Agricultural and Forest Meteorology, 274, 7-17. DOI URL
[22]	Lin SZ, Ge QS, Wang HJ (2021). Spatiotemporal variations in leaf-out phenology of typical European tree species and their responses to climate change. Chinese Journal of Applied Ecology, 32, 788-798.
	[林少植, 葛全胜, 王焕炯 (2021). 欧洲典型树种展叶始期的时空变化及其对气候变化的响应. 应用生态学报, 32, 788-798.] DOI
[23]	Liu Q, Fu YH, Zeng Z, Huang M, Li X, Piao S (2016a). Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China. Global Change Biology, 22, 644-655. DOI URL
[24]	Liu Q, Fu YH, Zhu Z, Liu Y, Liu Z, Huang M, Janssens IA, Piao S (2016b). Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology. Global Change Biology, 22, 3702-3711. DOI URL
[25]	Liu Q, Piao S, Janssens IA, Fu Y, Peng S, Lian X, Ciais P, Myneni RB, Peñuelas J, Wang T (2018). Extension of the growing season increases vegetation exposure to frost. Nature Communications, 9, 426. DOI: 10.1038/s41467-017-02690-y. DOI
[26]	Liu XT, Zhou L, Shi H, Wang SQ, Chi YG (2018). Phenological characteristics of temperate coniferous and broad-leaved mixed forests based on multiple remote sensing vegetation indices, chlorophyll fluorescence and CO₂ flux data. Acta Ecologica Sinica, 38, 3482-3494.
	[刘啸添, 周蕾, 石浩, 王绍强, 迟永刚 (2018). 基于多种遥感植被指数、叶绿素荧光与CO₂通量数据的温带针阔混交林物候特征对比分析. 生态学报, 38, 3482-3494.]
[27]	Liu ZL, Zhang XP, Li ZX, He XG, Guan HD (2021). Relationship between droughts/floods throughout a year over the Dongting Lake Basin and atmospheric circulation and sea surface temperature over key sea areas. Tropical Geography, 41, 987-999. DOI
	[刘仲藜, 章新平, 黎祖贤, 贺新光, 关华德 (2021). 洞庭湖流域各季节旱涝及其与大气环流和关键区海温的关系. 热带地理, 41, 987-999.] DOI
[28]	Magney TS, Bowling DR, Logan BA, Grossmann K, Stutz J, Blanken PD, Burns SP, Cheng R, Garcia MA, Kӧhler P, Lopez S, Parazoo NC, Raczka B, Schimel D, Frankenberg C (2019). Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proceedings of the National Academy of Sciences of the United States of America, 116, 11640-11645. DOI PMID
[29]	Piao S, Fang J, Zhou L, Ciais P, Zhu B (2006). Variations in satellite-derived phenology in Chinaʼs temperate vegetation. Global Change Biology, 12, 672-685. DOI URL
[30]	Piao S, Tan J, Chen A, Fu YH, Ciais P, Liu Q, Janssens IA, Vicca S, Zeng Z, Jeong SJ, Li Y, Myneni RB, Peng S, Shen M, Peñuelas J (2015). Leaf onset in the northern hemisphere triggered by daytime temperature. Nature Communications, 6, 6911. DOI: 10.1038/ncomms7911. DOI
[31]	Ren PX, Liu ZL, Zhou XL, Peng CH, Xiao JF, Wang SH, Li X, Li P (2021). Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China. Forest Ecosystems, 8, 31. DOI: 10.1186/s40663-021-00309-9. DOI
[32]	Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M (2013). Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology, 169, 156-173. DOI URL
[33]	Rossi S, Burgess P, Jespersen D, Huang B (2017). Heat- induced leaf senescence associated with chlorophyll metabolism in bentgrass lines differing in heat tolerance. Crop Science, 57, S169-S178.
[34]	Sa RG, Bao G, Bao YH, Hu RC, Jiang K (2020). Variation in the vegetation fade stage and its relationships with climate and vegetation productivity in Inner Mongolia, China. Chinese Journal of Applied Ecology, 31, 1898-1908.
	[萨日盖, 包刚, 包玉海, 胡日查, 姜康 (2020). 内蒙古植被枯黄期变化及其与气候和植被生产力的关系. 应用生态学报, 31, 1898-1908.] DOI
[35]	Wang J, Liu DS, Ciais P, Peñuelas J (2022). Decreasing rainfall frequency contributes to earlier leaf onset in northern ecosystems. Nature Climate Change, 12, 386-392. DOI
[36]	Wareing PF (1956). Photoperiodism in woody plants. Annual Review of Plant Physiology, 7, 191-214. DOI URL
[37]	Wu CY, Wang XY, Wang HJ, Ciais P, Peñuelas J, Myneni RB, Desai AR, Gough CM, Gonsamo A, Black AT, Jassal RS, Ju WM, Yuan WP, Fu YS, Shen MG, et al. (2018). Contrasting responses of autumn-leaf senescence to daytime and night-time warming. Nature Climate Change, 8, 1092-1096. DOI
[38]	Xu WT, Wu BF, Yan CZ, Huang HP (2005). China land cover 2000 using SPOT VGT S10 data. Journal of Remote Sensing, 9, 204-214.
	[徐文婷, 吴炳方, 颜长珍, 黄慧萍 (2005). 用SPOT-VGT数据制作中国2000年度土地覆盖数据. 遥感学报, 9, 204-214.]
[39]	Yang ZY, Shen MG, Jia SG, Guo L, Yang W, Wang C, Chen XH, Chen J (2017). Asymmetric responses of the end of growing season to daily maximum and minimum temperatures on the Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 122, 13278-13287. DOI URL
[40]	Zhang JR, Tong XJ, Zhang JS, Meng P, Li, J, Liu PR (2021). Dynamics of phenology and its response to climatic variables in a warm-temperate mixed plantation. Forest Ecology and Management, 483, 118785. DOI: 10.1016/j.foreco.2020.118785. DOI
[41]	Zhang Y, Commane R, Zhou S, Williams AP, Gentine P (2020). Light limitation regulates the response of autumn terrestrial carbon uptake to warming. Nature Climate Change, 10, 739-743. DOI
[42]	Zhang Y, Joiner J, Alemohammad SH, Zhou S, Gentine P (2018). A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences, 15, 5779-5800. DOI URL
[43]	Zhang Y, Xiao XM, Jin C, Dong JW, Zhou S, Wagle P, Joiner J, Guanter L, Zhang YG, Zhang GL, Qin YW, Wang J, Moore III B (2016). Consistency between sun-induced chlorophyll fluorescence and gross primary production of vegetation in North America. Remote Sensing of Environment, 183, 154-169. DOI URL
[44]	Zhang ZY, Wang SH, Qiu B, Song L, Zhang YG (2019). Retrieval of sun-induced chlorophyll fluorescence and advancements in carbon cycle application. Journal of Remote Sensing, 23, 37-52.
	[章钊颖, 王松寒, 邱博, 宋练, 张永光 (2019). 日光诱导叶绿素荧光遥感反演及碳循环应用进展. 遥感学报, 23, 37-52.]
[45]	Zhou L, Chi YG, Liu XT, Dai XQ, Yang FT (2020). Land surface phenology tracked by remotely sensed sun-induced chlorophyll fluorescence in subtropical evergreen coniferous forests. Acta Ecologica Sinica, 40, 4114-4125.
	[周蕾, 迟永刚, 刘啸添, 戴晓琴, 杨风亭 (2020). 日光诱导叶绿素荧光对亚热带常绿针叶林物候的追踪. 生态学报, 40, 4114-4125.]
[46]	Zhou LM, Tucker CJ, Kaufmann RK, Slayback D, Shabanov NV, Myneni RB (2001). Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research: Atmospheres, 106, 20069-20083.
[47]	Zhou W, Chi YG, Zhou L (2021). Vegetation phenology in the Northern Hemisphere based on the solar-induced chlorophyll fluorescence. Chinese Journal of Plant Ecology, 45, 345-354. DOI URL
	[周稳, 迟永刚, 周蕾 (2021). 基于日光诱导叶绿素荧光的北半球森林物候研究. 植物生态学报, 45, 345-354.]
[48]	Zhu KZ, Wan MW (1999). Phenology. Hunan Education Publishing House, Changsha.
	[竺可桢, 宛敏渭 (1999). 物候学. 湖南教育出版社, 长沙.]
[49]	Zhu WQ, Tian HQ, Xu XF, Pan YZ, Chen GS, Lin WP (2012). Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982-2006. Global Ecology and Biogeography, 21, 260-271. DOI URL