The spatiotemporal evolution characteristics of vegetation in China’s ten first-level water resources areas and their sensitivity to extreme climate responses

ZHOU Haiyan; LIN Jiquan; LIU Wenjie; SUN Zhongyi; ZHAN Huasi; ZHANG Jie

doi:10.15886/j.cnki.rdswxb.20240016

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Volume 16 Issue 1

Mar. 2025

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Article Navigation > Journal of Tropical Biology > 2025 > 16(1): 43-57

ZHOU Haiyan, LIN Jiquan, LIU Wenjie, SUN Zhongyi, ZHAN Huasi, ZHANG Jie. The spatiotemporal evolution characteristics of vegetation in China’s ten first-level water resources areas and their sensitivity to extreme climate responses[J]. Journal of Tropical Biology, 2025, 16(1): 43-57. doi: 10.15886/j.cnki.rdswxb.20240016

Citation:

ZHOU Haiyan, LIN Jiquan, LIU Wenjie, SUN Zhongyi, ZHAN Huasi, ZHANG Jie. The spatiotemporal evolution characteristics of vegetation in China’s ten first-level water resources areas and their sensitivity to extreme climate responses[J]. Journal of Tropical Biology, 2025, 16(1): 43-57. doi: 10.15886/j.cnki.rdswxb.20240016

The spatiotemporal evolution characteristics of vegetation in China’s ten first-level water resources areas and their sensitivity to extreme climate responses

doi: 10.15886/j.cnki.rdswxb.20240016

ZHOU Haiyan¹,
LIN Jiquan²,
LIU Wenjie^1,3,
SUN Zhongyi¹,
ZHAN Huasi^4,5,
ZHANG Jie^{1,3,4
,
,}

1. College of Ecology and Environment, Hainan University, Haikou, Hainan 570228, China;
2. Hainan Monitoring Center for Ecological Environment, Haikou, Hainan 571126, China;
3. Hainan Key Lab for Agroforestry Environmental Processes and Ecological Regulation, Haikou, Hainan 570100, China;
4. Sanya Observation Center of Source and Sink of Carbon in Tropical China, Sanya, Hainan 572022, China;
5. Haikou Marine Geological Survey Center, China Geological Survey, Haikou, Hainan 571127, China

Received Date: 2024-01-24
Rev Recd Date: 2024-07-08
Publish Date: 2025-03-15

Abstract

With the continued impact of global warming, extreme climate risks have increased significantly, directly affecting different stages of vegetation growth. Data from ten first-level water resources areas in China were collected and organized, including daily temperature（maximum, minimum, average temperature） and precipitation data sets of 443 stations in China from 1960 to 2020 as well as monthly/yearly GIMMS-NDVI data from 1982 to 2015 to quantify 27 extreme climate indices and characterize vegetation growth conditions. The data were analyzed by using trend analysis, geostatistical analysis and other methods to clarify the differences in extreme climate change trends and vegetation response sensitivities between regions. The results showed that except for the Songhua River Basin, the vegetation NDVI in all the water resource area under study in China from 1982 to 2015 tended to show an upward trend, with the most obvious increase trend in the Huang-Huai-Hai region. The extreme high temperature index mostly shows an upward trend. The extreme precipitation index shows an upward trend in the eastern and central parts of the study area and a downward trend in the northeast. The impact of extreme temperature on NDVI is stronger than that of extreme precipitation. The extreme temperature index that has the greatest impact on NDVI is different in different water resource areas. Among them, the index with the greatest influence is the maximum temperature（TMAXmean） in the Songhua River Basin and Liao River Basin. The index with the greatest influence in the Northwest River Basin,Yellow River Basin and Southeast River Basin is the number of warm night days（TN90P）. The index with the greatest influence in the Haihe River Basin, Huaihe River Basin, Yangtze River Basin and Pearl River Basin is minimum temperature（TMINmean）, and the index with the greatest impact in the Southwest River Basin is the extremely high daily minimum temperature（TNx）. NDVI responds to extreme climate with a 1-3 month response Time, in which vegetation in the southwest and southeast regions has a response time of 2-3 months to extreme climate.
- water resource zone,
- extreme temperatures and precipitation,
- NDVI,
- response research

References

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[22]	王鑫,王明田,冯勇,等. 2001-2020年川西北高原归一化植被指数演变特征及其对极端气候的响应[J]. 应用生态学报, 2022, 33(7):1957-1965.
[23]	王晓利,侯西勇. 1982-2014年中国沿海地区归一化植被指数(NDVI) 变化及其对极端气候的响应[J]. 地理研究, 2019, 38(4):807-821.
[24]	LUO M, SA C, MENG F, et al. Assessing extreme climatic changes on a monthly scale and their implications for vegetation in Central Asia[J]. Journal of Cleaner Production, 2020, 271:122396.
[25]	ISLAM A R M T, TOUHIDUL ISLAM H M, SHAHID S,et al. Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change[J]. Journal of Environmental Management, 2021,289:112505.
[26]	刘宪锋,朱秀芳,潘耀忠,等. 1982-2012年中国植被覆盖时空变化特征[J]. 生态学报, 2015, 35(16):5331-5342.
[27]	雷茜,胡忠文,王敬哲,等. 1985-2015年中国不同生态系统NDVI时空变化及其对气候因子的响应[J]. 生态学报, 2023, 43(15):6378-6391.
[28]	姜萍. 1982-2015年中国植被覆盖变化及其对气候变化的敏感性分析[D]. 兰州:兰州大学, 2022.
[29]	王茜,陈莹,阮玺睿,等. 1982-2012年中国NDVI变化及其与气候因子的关系[J]. 草地学报, 2017, 25(4):691-700.
[30]	PENG S, CHEN A, XU L, et al. Recent change of vegetation growth trend in China[J]. Environmental Research Letters, 2011, 6(4):044027.
[31]	LIU Y, LI Y, LI S, et al. Spatial and temporal patterns of global NDVI trends:correlations with climate and human factors[J]. Remote Sensing, 2015, 7(10):13233-13250.
[32]	PIAO S, YIN G, TAN J, et al. Detection and attribution of vegetation greening trend in China over the last 30 years[J]. Global Change Biology, 2015, 21(4):1601-1609.
[33]	韩佶兴,王宗明,毛德华,等. 1982-2010年松花江流域植被动态变化及其与气候因子的相关分析[J]. 中国农业气象, 2011, 32(3):430-436.
[34]	CHU H, VENEVSKY S, WU C, et al. NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015[J]. The Science of the Total Environment, 2019, 650(Pt 2):2051-2062.
[35]	毛德华,王宗明,罗玲,等.基于MODIS和AVHRR数据源的东北地区植被NDVI变化及其与气温和降水间的相关分析[J]. 遥感技术与应用, 2012, 27(1):77-85.
[36]	刘国彬,上官周平,姚文艺,等.黄土高原生态工程的生态成效[J]. 中国科学院院刊, 2017, 32(1):11-19.
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The spatiotemporal evolution characteristics of vegetation in China’s ten first-level water resources areas and their sensitivity to extreme climate responses

1. College of Ecology and Environment, Hainan University, Haikou, Hainan 570228, China;

2. Hainan Monitoring Center for Ecological Environment, Haikou, Hainan 571126, China;

3. Hainan Key Lab for Agroforestry Environmental Processes and Ecological Regulation, Haikou, Hainan 570100, China;

4. Sanya Observation Center of Source and Sink of Carbon in Tropical China, Sanya, Hainan 572022, China;

5. Haikou Marine Geological Survey Center, China Geological Survey, Haikou, Hainan 571127, China

Received Date: 2024-01-24

Rev Recd Date: 2024-07-08

Publish Date: 2025-03-15

Key words:

water resource zone /
extreme temperatures and precipitation /
NDVI /
response research

Abstract: With the continued impact of global warming, extreme climate risks have increased significantly, directly affecting different stages of vegetation growth. Data from ten first-level water resources areas in China were collected and organized, including daily temperature（maximum, minimum, average temperature） and precipitation data sets of 443 stations in China from 1960 to 2020 as well as monthly/yearly GIMMS-NDVI data from 1982 to 2015 to quantify 27 extreme climate indices and characterize vegetation growth conditions. The data were analyzed by using trend analysis, geostatistical analysis and other methods to clarify the differences in extreme climate change trends and vegetation response sensitivities between regions. The results showed that except for the Songhua River Basin, the vegetation NDVI in all the water resource area under study in China from 1982 to 2015 tended to show an upward trend, with the most obvious increase trend in the Huang-Huai-Hai region. The extreme high temperature index mostly shows an upward trend. The extreme precipitation index shows an upward trend in the eastern and central parts of the study area and a downward trend in the northeast. The impact of extreme temperature on NDVI is stronger than that of extreme precipitation. The extreme temperature index that has the greatest impact on NDVI is different in different water resource areas. Among them, the index with the greatest influence is the maximum temperature（TMAXmean） in the Songhua River Basin and Liao River Basin. The index with the greatest influence in the Northwest River Basin,Yellow River Basin and Southeast River Basin is the number of warm night days（TN90P）. The index with the greatest influence in the Haihe River Basin, Huaihe River Basin, Yangtze River Basin and Pearl River Basin is minimum temperature（TMINmean）, and the index with the greatest impact in the Southwest River Basin is the extremely high daily minimum temperature（TNx）. NDVI responds to extreme climate with a 1-3 month response Time, in which vegetation in the southwest and southeast regions has a response time of 2-3 months to extreme climate.

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Reference (52)

[1]	ALLAN R P, ACHUTARAO K M. Climate change 2021:the physical science basis:Working Group I contribution to the sixth assessment report of the Intergovernmental Panel on climate change[M]. Cambridge:Cambridge University Press. 2021.
[2]	MALHI Y, FRANKLIN J, SEDDON N, et al. Climate change and ecosystems:threats, opportunities and solutions[J]. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 2020, 375(1794):20190104.
[3]	MEARNS L O, ROSENZWEIG C, GOLDBERG R. Mean and variance change in climate scenarios:methods, agricultural applications, and measures of uncertainty[J]. Climatic Change, 1997, 35(4):367-396.
[4]	黄浩,张勃,黄涛,等.近30 a甘肃省河东地区极端气温指数时空变化特征及趋势预测[J]. 干旱区地理, 2020,43(2):319-328.
[5]	GHIL M, LUCARINI V. The physics of climate variability and climate change[J]. Reviews of Modern Physics, 2020,92(3):035002.
[6]	吴润琦.全球增暖1.5℃/2.0℃下中国极端气候事件响应的精细结构及高敏感区的甄别[D]. 南京:南京大学,2019.
[7]	SIEGMUND J F, WIEDERMANN M, DONGES J F, et al.Impact of temperature and precipitation extremes on the flowering dates of four German wildlife shrub species[J]. Biogeosciences, 2016, 13(19):5541-5555.
[8]	封国林,龚志强,支蓉.气候变化检测与诊断技术的若干新进展[J]. 气象学报, 2008, 66(6):892-905.
[9]	任国玉,封国林,严中伟.中国极端气候变化观测研究回顾与展望[J]. 气候与环境研究, 2010, 15(4):337-353.
[10]	COUMOU D, RAHMSTORF S. A decade of weather extremes[J]. Nature Climate Change, 2012, 2:491-496.
[11]	周贵尧,周灵燕,邵钧炯,等.极端干旱对陆地生态系统的影响:进展与展望[J]. 植物生态学报, 2020, 44(5):515-525.
[12]	刘应帅,余瑞,郑彬彬,等.海南岛森林植被NEP季节性时空变化规律及气候驱动因素分析[J]. 热带生物学报, 2022, 13(2):166-176.
[13]	RICHARDSON A D, BLACK T A, CIAIS P, et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity[J]. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 2010, 365(1555):3227-3246.
[14]	BARICHIVICH J, BRIFFA K R, OSBORN T J, et al.Thermal growing season and timing of biospheric carbon uptake across the Northern Hemisphere[J]. Global Biogeochemical Cycles, 2012, 26(4):1-13.
[15]	KEENAN T F, GRAY J, FRIEDL M A, et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology[J]. Nature Climate Change, 2014, 4:598-604.
[16]	ALI S, HENCHIRI M, YAO F, et al. Analysis of vegetation dynamics, drought in relation with climate over South Asia from 1990 to 2011[J]. Environmental Science and Pollution Research, 2019, 26(11):11470-11481.
[17]	ZHUMANOVA M, MÖNNIG C, HERGARTEN C, et al.Assessment of vegetation degradation in mountainous pastures of the Western Tien-Shan, Kyrgyzstan, using eMODIS NDVI[J]. Ecological Indicators, 2018, 95:527-543.
[18]	GUAN Q, YANG L, PAN N, et al. Greening and browning of the Hexi corridor in Northwest China:spatial patterns and responses to climatic variability and anthropogenic drivers[J]. Remote Sensing, 2018, 10(8):1270.
[19]	PEDDLE D R, BRUNKE S P, HALL F G. A comparison of spectral mixture analysis and ten vegetation indices for estimating boreal forest biophysical information from airborne data[J]. Canadian Journal of Remote Sensing,2001, 27(6):627-635.
[20]	杨静,张亚杰,张京红,等.基于MODIS NDVI的海南岛植被覆盖面积的反演技术[J]. 热带生物学报, 2022,13(4):397-403.
[21]	郭铌.植被指数及其研究进展[J]. 干旱气象, 2003(4):71-75.
[22]	王鑫,王明田,冯勇,等. 2001-2020年川西北高原归一化植被指数演变特征及其对极端气候的响应[J]. 应用生态学报, 2022, 33(7):1957-1965.
[23]	王晓利,侯西勇. 1982-2014年中国沿海地区归一化植被指数(NDVI) 变化及其对极端气候的响应[J]. 地理研究, 2019, 38(4):807-821.
[24]	LUO M, SA C, MENG F, et al. Assessing extreme climatic changes on a monthly scale and their implications for vegetation in Central Asia[J]. Journal of Cleaner Production, 2020, 271:122396.
[25]	ISLAM A R M T, TOUHIDUL ISLAM H M, SHAHID S,et al. Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change[J]. Journal of Environmental Management, 2021,289:112505.
[26]	刘宪锋,朱秀芳,潘耀忠,等. 1982-2012年中国植被覆盖时空变化特征[J]. 生态学报, 2015, 35(16):5331-5342.
[27]	雷茜,胡忠文,王敬哲,等. 1985-2015年中国不同生态系统NDVI时空变化及其对气候因子的响应[J]. 生态学报, 2023, 43(15):6378-6391.
[28]	姜萍. 1982-2015年中国植被覆盖变化及其对气候变化的敏感性分析[D]. 兰州:兰州大学, 2022.
[29]	王茜,陈莹,阮玺睿,等. 1982-2012年中国NDVI变化及其与气候因子的关系[J]. 草地学报, 2017, 25(4):691-700.
[30]	PENG S, CHEN A, XU L, et al. Recent change of vegetation growth trend in China[J]. Environmental Research Letters, 2011, 6(4):044027.
[31]	LIU Y, LI Y, LI S, et al. Spatial and temporal patterns of global NDVI trends:correlations with climate and human factors[J]. Remote Sensing, 2015, 7(10):13233-13250.
[32]	PIAO S, YIN G, TAN J, et al. Detection and attribution of vegetation greening trend in China over the last 30 years[J]. Global Change Biology, 2015, 21(4):1601-1609.
[33]	韩佶兴,王宗明,毛德华,等. 1982-2010年松花江流域植被动态变化及其与气候因子的相关分析[J]. 中国农业气象, 2011, 32(3):430-436.
[34]	CHU H, VENEVSKY S, WU C, et al. NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015[J]. The Science of the Total Environment, 2019, 650(Pt 2):2051-2062.
[35]	毛德华,王宗明,罗玲,等.基于MODIS和AVHRR数据源的东北地区植被NDVI变化及其与气温和降水间的相关分析[J]. 遥感技术与应用, 2012, 27(1):77-85.
[36]	刘国彬,上官周平,姚文艺,等.黄土高原生态工程的生态成效[J]. 中国科学院院刊, 2017, 32(1):11-19.
[37]	SONG W, FENG Y, WANG Z. Ecological restoration programs dominate vegetation greening in China[J]. The Science of the Total Environment, 2022, 848:157729.
[38]	CAI Y, ZHANG F, DUAN P, et al. Vegetation cover changes in China induced by ecological restorationprotection projects and land-use changes from 2000 to 2020[J]. CATENA, 2022, 217:106530.
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The spatiotemporal evolution characteristics of vegetation in China’s ten first-level water resources areas and their sensitivity to extreme climate responses

doi: 10.15886/j.cnki.rdswxb.20240016

Abstract

References

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通讯作者: 陈斌, bchen63@163.com

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