广西师范大学学报（自然科学版） ›› 2025, Vol. 43 ›› Issue (5): 218-232.doi: 10.16088/j.issn.1001-6600.2024101702

• 生态环境科学研究 • 上一篇    下一篇

定量评估气候变化对云南省植被GPPGS变化的影响

万爱玲1, 廖朝莲1, 张天祥1, 陈宇霖1, 叶江霞2, 周汝良1*   

  1. 1.西南林业大学 水土保持学院,云南 昆明 650224;
    2.西南林业大学 林学院,云南 昆明 650224
  • 收稿日期:2024-10-17 修回日期:2024-12-24 出版日期:2025-09-05 发布日期:2025-08-05
  • 通讯作者: 周汝良(1963—),男,云南祥云人,西南林业大学教授。E-mail:zhou_ruliang@163.com
  • 基金资助:
    国家自然科学基金(32360392);云南省科技计划项目(202301BD070001-245)

Quantitative Assessment of Climate Change Impacts on Vegetation GPPGS Changes in Yunnan Province

WAN Ailing1, LIAO Chaolian1, ZHANG Tianxiang1, CHEN Yulin1, YE Jiangxia2, ZHOU Ruliang1*   

  1. 1. College of Soil and Water Conservation, Southwest Forestry University, Kunming Yunnan 650224, China;
    2. College of Forestry, Southwest Forestry University, Kunming Yunnan 650224, China
  • Received:2024-10-17 Revised:2024-12-24 Online:2025-09-05 Published:2025-08-05

PDF (PC)

5

摘要： 植被总初级生产力(GPP)是衡量陆地生态系统碳循环的关键参数,对云南省植被生长季总初级生产力(GPPGS)进行研究,有助于理解陆地生态系统植被动态和碳循环模式,对区域生态系统的可持续发展具有重要意义。本文利用Theil-Sen Median趋势分析和Mann-Kendall显著性检验,分析云南省植被GPPGS时空变化特征,并通过通径分析揭示气候因子对植被GPPGS变化的直接、间接和综合影响。结果表明:①2001—2020年云南省植被GPPGS呈波动上升趋势,上升速率为1.216 g·(m2·a)-1;大部分植被GPPGS呈现上升趋势,其中草甸上升速率最高,为1.674 g·(m2·a)-1。②云南省植被GPPGS呈上升趋势的面积占比为73.83%,高山植被、草甸、针叶林、灌丛、草丛、栽培植物和阔叶林GPPGS呈上升趋势的面积占比分别为85.21%、84.64%、79.11%、75.85%、73.58%、71.76%和58.67%。③通径分析显示,平均气温是导致草甸和针叶林GPPGS变化的主要因子,降水是造成草丛和栽培植物GPPGS变化的主要因子,太阳辐射是影响高山植被、灌丛和阔叶林GPPGS变化的主要因子。④对云南省植被GPPGS产生直接影响的主导因子占比为平均气温(54.87%)、降水(7.86%)和太阳辐射(9.08%)。

关键词: 植被, 总初级生产力, 通径分析, Sen Median趋势分析, Mann-Kendall显著性检验, 植被类型, 云南

Abstract: Vegetation gross primary productivity (GPP) is a key parameter for measuring the carbon cycle in terrestrial ecosystems. The study of growing season gross primary productivity (GPPGS) of vegetation in Yunnan Province can help to understand the vegetation dynamics and carbon cycling pattern of terrestrial ecosystems, which is of great significance to the sustainable development of regional ecosystems. Using Theil-Sen Median trend analysis and Mann-Kendall significance test, the spatial and temporal characteristics of vegetation GPPGS changes in Yunnan Province were analysed, and then the direct, indirect and combined effects of climatic factors on the changes of vegetation GPPGS were revealed by using the pathway analysis. The results showed that: ① the vegetation GPPGS in Yunnan Province from 2001 to 2020 showed a fluctuating upward trend, with an increasing rate of 1.216 g·(m2·a)-1; most of the vegetation GPPGS showed an upward trend, among which the meadows had the highest rate of increase, which was 1.674 g·(m2·a)-1. ② The area with an upward trend in vegetation GPPGS in Yunnan Province accounted for 73.83%, with the area with an upward trend in alpine vegetation, meadow, coniferous forest, shrubland, herbaceous vegetation, cultivated plants, and broad-leaved forest being 85.21%, 84.64%, 79.11%, 75.85%, 73.58%, 71.76%, and 58.67%, respectively. ③ As a result of the through-trail results, changes in the GPPGS of both meadow and coniferous forests were most strongly influenced by temperature fluctuations, with average temperature playing a central role, precipitation was the main factor causing the change in vegetation GPPGS of herbaceous vegetation and cultivated plants, and the primary influence on the variation of vegetation GPPGS in alpine vegetation, shrublands, and broadleaf forests was solar radiation. ④ The dominant factors that directly influenced vegetation GPPGS in Yunnan Province accounted for average temperature (54.87%), precipitation (7.86%), and solar radiation (9.08%).

Key words: vegetation, gross primary productivity(GPP), pathway analysis, Sen Median trend analysis, Mann-Kendall significance test, vegetation types, Yunnan, China

中图分类号:  Q948

[1] XU C G, MCDOWELL N G, FISHER R A, et al. Increasing impacts of extreme droughts on vegetation productivity under climate change[J]. Nature Climate Change, 2019, 9(12): 948-953. DOI: 10.1038/s41558-019-0630-6.
[2] ZHANG Y C, PIAO S L, SUN Y, et al. Future reversal of warming-enhanced vegetation productivity in the Northern Hemisphere[J]. Nature Climate Change, 2022, 12(6): 581-586. DOI: 10.1038/s41558-022-01374-w.
[3] HUANG K, XIA J Y, WANG Y P, et al. Enhanced peak growth of global vegetation and its key mechanisms[J]. Nature Ecology & Evolution, 2018, 2(12): 1897-1905. DOI: 10.1038/s41559-018-0714-0.
[4] YAO J Y, LIU H P, HUANG J P, et al. Accelerated dryland expansion regulates future variability in dryland gross primary production[J]. Nature Communications, 2020, 11(1): 1665. DOI: 10.1038/s41467-020-15515-2.
[5] SCHENUIT F. Staging science: dramaturgical politics of the IPCC's special report on 1.5 ℃[J]. Environmental Science & Policy, 2023, 139: 166-176. DOI: 10.1016/j.envsci.2022.10.014.
[6] DING J Z, WANG T, WANG Y Y, et al. New understanding of the response of permafrost carbon cycling to climate warming[J]. Science Bulletin, 2022, 67(13): 1322-1325. DOI: 10.1016/j.scib.2022.05.022.
[7] 徐勇, 盘钰春, 邹滨, 等. 定量评估气候变化对长江中下游地区植被GPPGS变化的影响[J]. 环境科学, 2024, 45(3): 1615-1628. DOI: 10.13227/j.hjkx.202304152.
[8] 徐勇, 赵纯, 郭振东, 等. 中国八大经济区GPP变化及影响因子协同机制[J]. 中国环境科学, 2023, 43(1): 477-487. DOI: 10.19674/j.cnki.issn1000-6923.2023.0009.
[9] 邱博, 郭维栋. 叶绿素荧光遥感在陆地生态系统碳循环和陆气相互作用中的应用研究进展[J]. 大气科学学报, 2022, 45(6): 801-814. DOI: 10.13878/j.cnki.dqkxxb.20220507005.
[10] PIAO S L, SITCH S, CIAIS P, et al. Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends[J]. Global Change Biology, 2013, 19(7): 2117-2132. DOI: 10.1111/gcb.12187.
[11] WANG Y H, XIAO J F, LI X, et al. Global evidence on the asymmetric response of gross primary productivity to interannual precipitation changes[J]. Science of the Total Environment, 2022, 814: 152786. DOI: 10.1016/j.scitotenv.2021.152786.
[12] LIU Y C, YU G R, WANG Q F, et al. How temperature, precipitation and stand age control the biomass carbon density of global mature forests[J]. Global Ecology and Biogeography, 2014, 23(3): 323-333. DOI: 10.1111/geb.12113.
[13] 曹玉娟, 宋振华, 武志涛, 等. 不同数据集的1982—2017年中国总初级生产力的时空动态[J]. 应用生态学报, 2022, 33(10): 2644-2652. DOI: 10.13287/j.1001-9332.202210.027.
[14] 姚炳楠, 陈报章, 车明亮. 鄱阳湖流域植被总初级生产力时空变化特征及其气候驱动因子分析[J]. 植物学报, 2016, 51(5): 639-649.
[15] 张世喆, 朱秀芳, 刘婷婷, 等. 气候变化下中国不同植被区总初级生产力对干旱的响应[J]. 生态学报, 2022, 42(8): 3429-3440. DOI: 10.5846/stxb202010232707.
[16] 平晓莹, 马俊, 刘淼, 等. 基于VPM模型的长白山自然保护区植被总初级生产力动态变化[J]. 应用生态学报, 2019, 30(5): 1589-1598. DOI: 10.13287/j.1001-9332.201905.029.
[17] 冯思齐. 云南省森林GPP遥感估算及时空特征变化分析[D]. 昆明: 昆明理工大学, 2023. DOI: 10.27200/d.cnki.gkmlu.2023.001792.
[18] 黄立成, 周远洋, 周起超, 等. 云南程海浮游植物初级生产力的时空变化及其影响因子[J]. 湖泊科学, 2019, 31(5): 1424-1436. DOI: 10.18307/2019.0508.
[19] YAO J Y, LIU H P, HUANG J P, et al. Accelerated dryland expansion regulates future variability in dryland gross primary production[J]. Nature Communications, 2020, 11(1): 1665. DOI: 10.1038/s41467-020-15515-2.
[20] LIU Y, LIU H H, CHEN Y, et al. Quantifying the contributions of climate change and human activities to vegetation dynamic in China based on multiple indices[J]. Science of the Total Environment, 2022, 838: 156553. DOI: 10.1016/j.scitotenv.2022.156553.
[21] CHEN X J, MO X G, HU S, et al. Contributions of climate change and human activities to ET and GPP trends over North China Plain from 2000 to 2014[J]. Journal of Geographical Sciences, 2017, 27(6): 661-680.. DOI: 10.1007/s11442-017-1399-z.
[22] SEN P K. Estimates of the regression coefficient based on Kendall's tau[J]. Journal of the American Statistical Association, 1968, 63(324): 1379-1389. DOI: 10.1080/01621459.1968.10480934.
[23] 徐勇, 黄雯婷, 窦世卿, 等. 2000—2020年西南地区植被NDVI对气候变化和人类活动响应特征[J]. 环境科学, 2022, 43(6): 3230-3240. DOI: 10.13227/j.hjkx.202108107.
[24] 熊小菊, 廖春贵, 胡宝清, 等. 基于MODIS的广西植被NPP时空分异及驱动力分析[J]. 广西师范大学学报(自然科学版), 2019, 37(3): 187-195. DOI: 10.16088/j.issn.1001-6600.2019.03.022.
[25] 崔利芳, 王伦澈, 屈赛, 等. 气温、降水量和人类活动对长江流域植被NDVI的影响[J]. 地球科学, 2020, 45(6): 1905-1917.
[26] ZHOU H, LIU Y B. SPI based meteorological drought assessment over a humid basin: effects of processing schemes[J]. Water, 2016, 8(9): 373. DOI: 10.3390/w8090373.
[27] LI J Y. Responses of vegetation NDVI to climate change and land use in Ordos City, North China[J]. Applied Sciences, 2022, 12(14): 7288. DOI: 10.3390/app12147288.
[28] 谢舒笛, 莫兴国, 胡实, 等. 三北防护林工程区植被绿度对温度和降水的响应[J]. 地理研究, 2020, 39(1):152-165. DOI: 10.11821/dlyj020181071.
[29] 吴玉鸣, 李建霞. 通径分析在我国粮食生产相关研究中的应用[J]. 广西师范大学学报(自然科学版), 2003, 21(3): 95-99. DOI: 10.3969/j.issn.1001-6600.2003.03.023.
[30] 李双成, 蔡运龙. 地理尺度转换若干问题的初步探讨[J]. 地理研究, 2005, 24(1): 11-18. DOI: 10.3321/j.issn:1000-0585.2005.01.002.
[31] 杜家菊, 陈志伟. 使用SPSS线性回归实现通径分析的方法[J]. 生物学通报, 2010, 45(2): 4-6. DOI: 10.3969/j.issn.0006-3193.2010.02.002.
[32] 朱燕琴, 赵志斌, 齐广平, 等. 黄土丘陵区坡面产流产沙的影响因素分析[J]. 干旱区资源与环境, 2020, 34(8): 173-178. DOI: 10.13448/j.cnki.jalre.2020.227.
[33] 徐勇, 郑志威, 戴强玉, 等. 顾及时滞效应的西南地区植被NPP变化归因分析[J]. 农业工程学报, 2022, 38(9): 297-305, 339. DOI: 10.11975/j.issn.1002-6819.2022.09.033.
[34] 程建刚, 解明恩. 近50年云南区域气候变化特征分析[J]. 地理科学进展, 2008(5): 19-26. DOI: 10.1007/s10499-007-9164-4.
[35] 牛剑龙. 云南省植被覆盖动态变化与生态环境效应分析[D]. 昆明: 昆明理工大学, 2023. DOI: 10.27200/d.cnki.gkmlu.2023.001643.
[36] 黄中艳. 2005年云南农业气候及其对农业影响评价[J]. 西南农业学报, 2006, 19(S1): 277-280.
[37] 李美丽, 尹礼昌, 张园, 等. 基于MODIS-EVI的西南地区植被覆盖时空变化及驱动因素研究[J]. 生态学报, 2021, 41(3): 1138-1147. DOI: 10.5846/stxb201907101451.
[38] 徐虹, 程晋昕, 何雨芩, 等. 气候变化和人类活动对云南省植被净初级生产力的影响[J]. 高原气象, 2024, 43(4): 1064-1075. DOI: 10.7522/j.issn.1000-0534.2023.00047.
[39] 斐文明,葛峰,张慧,等.基于净初级生产力的黑龙江生物多样性维护功能重要性评价[J].东北师大学报(自然科学版), 2024, 56(2): 140-149. DOI: 10.1613/j.cnki.dslkxb202304090001.
[40] 刘涛, 寇卫利. 基于遥感的西双版纳近20年植被覆盖率时空变化分析[J]. 南方农业, 2024, 18(15): 126-131. DOI: 10.19415/j.cnki.1673-890x.2024.15.026.
[41] CONGALTON R G. A review of assessing the accuracy of classifications of remotely sensed data[J]. Remote Sensing of Environment, 1991, 37(1): 35-46. DOI: 10.1016/0034-4257(91)90048-B.
[42] 王永红, 鲁恒. 2001-2018年云南省植被变化及驱动力[J]. 山地学报, 2022, 40(4): 531-541. DOI: 10.16089/j.cnki.1008-2786.000691.
[43] WEN Z F, WU S J, CHEN J L, et al. NDVI indicated long-term interannual changes in vegetation activities and their responses to climatic and anthropogenic factors in the Three Gorges Reservoir Region, China[J]. Science of the Total Environment, 2017, 574: 947-959. DOI: 10.1016/j.scitotenv.2016.09.049.
[44] 罗健梅, 阿布都热合曼·哈力克, 段越帆, 等. 艾比湖流域植被NPP时空演变特征及其驱动因素分析[J]. 生态学报, 2025, 45(1): 182-196. DOI: 10.20103/j.stxb.202401240224.
[45] 曾觉民. 云南自然森林分类系统及地理分布研究[J]. 西南林业大学学报(自然科学), 2018, 38(6): 1-18, 231. DOI: 10.11929/j.issn.2095-1914.2018.06.001.
[46] 姜炎彬, 范苗, 张扬建. 短期增温对藏北高寒草甸植物群落特征的影响[J]. 生态学杂志, 2017, 36(3): 616-622. DOI: 10.13292/j.1000-4890.201703.033.
[47] 林伟山, 德科加, 钱诗祎, 等. 高寒草甸植被-土壤养分含量与气象因子相关关系研究[J]. 草原与草坪, 2023, 43(6): 109-120. DOI: 10.13817/j.cnki.cyycp.2023.06.015.
[48] KATTGE J, KNORR W. Temperature acclimation in a biochemical model of photosynthesis: a reanalysis of data from 36 species[J]. Plant, Cell & Environment, 2007, 30(9): 1176-1190. DOI: 10.1111/j.1365-3040.2007.01690.x.
[49] 徐雨晴, 肖风劲, 於琍. 中国森林生态系统净初级生产力时空分布及其对气候变化的响应研究综述[J]. 生态学报, 2020, 40(14): 4710-4723. DOI: 10.5846/stxb201905080928.
[50] PIAO S L, CIAIS P, HUANG Y, et al. The impacts of climate change on water resources and agriculture in China[J]. Nature, 2010, 467(7311): 43-51. DOI: 10.1038/nature09364.
[51] NIPPERT J B, KNAPP A K. Soil water partitioning contributes to species coexistence in tallgrass prairie[J]. Oikos, 2007, 116(6): 1017-1029. DOI: 10.1111/j.0030-1299.2007.15630.x.
[52] LOBELL D B, GOURDJI S M. The influence of climate change on global crop productivity[J]. Plant Physiology, 2012, 160(4): 1686-1697. DOI: 10.1104/pp.112.208298.
[53] FAY P A, CARLISLE J D, KNAPP A K, et al. Productivity responses to altered rainfall patterns in a C4-dominated grassland[J].Oecologia, 2003, 137(2): 245-251. DOI: 10.1007/s00442-003-1331-3.
[54] ZHA X J, NIU B, LI M, et al. Increasing impact of precipitation on alpine-grassland productivity over last two decades on the Tibetan Plateau[J]. Remote Sensing, 2022, 14(14): 3430. DOI: 10.3390/rs14143430.
[55] TAO Z X, HUANG W J, WANG H J. Soil moisture outweighs temperature for triggering the green-up date in temperate grasslands[J]. Theoretical and Applied Climatology, 2020, 140(3): 1093-1105.. DOI: 10.1007/s00704-020-03145-z.
[56] SUN H Z, WANG J Y, XIONG J N, et al. Vegetationchange and its response to climate change in Yunnan Province, China[J]. Advances in Meteorology, 2021, 2021(1): 8857589. DOI: 10.1155/2021/8857589.
[57] 李蒙, 黄玮, 周建琴, 等. 云南不同气候带农业气候资源变化特征研究[J]. 中国农学通报, 2021, 37(34): 103-111. DOI: 10.11924/j.issn.1000-6850.casb2020-0561.
[58] DU G M, YAN S H, CHEN H, et al. Intra-annual cumulative effects and mechanisms of climatic factors on global vegetation biomes' growth[J]. Remote Sensing, 2024, 16(5): 779. DOI: 10.3390/rs16050779.
[59] TONG R, CAO Y N, ZHU Z H, et al. Solar radiation effects on leaf nitrogen and phosphorus stoichiometry of Chinese fir across subtropical China[J]. Forest Ecosystems, 2021, 8(1): 62. DOI: 10.1186/s40663-021-00344-6.
[1] 左晓东, 汪星星, 许祖元, 郑宏, 曹光球, 曹世江. 不同林分密度对杉木林土壤特性及林下植被的影响[J]. 广西师范大学学报（自然科学版）, 2025, 43(4): 201-212.
[2] 唐利, 李梦霞, 黄慧欣, 潘心茹, 姜雪芳, 杨淑君, 潘于, 覃云斌. 桂北喀斯特植被恢复对球囊霉素相关土壤蛋白的影响[J]. 广西师范大学学报（自然科学版）, 2025, 43(1): 9-19.
[3] 张仕艳, 谢强, 黄丽娟, 黄庆, 冯学宇, 苏华龙. 广西灵渠流域青冈栎群落主要种群生态位分析[J]. 广西师范大学学报（自然科学版）, 2021, 39(6): 162-173.
[4] 潘友粮, 黄焜, 刘兴平, 张江涛. 中国绵蚧科一新记录属及一新记录种(半翅目:蚧次目)[J]. 广西师范大学学报（自然科学版）, 2021, 39(1): 102-106.
[5] 张晓晓, 王苗苗, 冯书珍, 邱虎森, 盖爽爽, 赵蕾, 胡亚军, 何寻阳, 陆祖军. 岩性与植被类型对喀斯特土壤AM真菌群落的影响[J]. 广西师范大学学报（自然科学版）, 2019, 37(2): 158-167.
[6] 廖春贵,陈月连,熊小菊,胡宝清. 2007—2016年广西植被覆盖时空分布特征及其驱动因素[J]. 广西师范大学学报（自然科学版）, 2018, 36(2): 118-127.
[7] 梁士楚, 田华丽, 田丰, 夏艺, 覃盈盈. 漓江湿地植被类型及其分布特点[J]. 广西师范大学学报（自然科学版）, 2015, 33(4): 115-119.
[8] 梁保平, 李艺, 刘庆业. 典型植被指数与地表温度空间特征分析——以桂林市为例[J]. 广西师范大学学报（自然科学版）, 2012, 30(2): 132-137.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 钟俏, 陈生龙, 唐聪聪. 水凝胶技术在微藻采收中的应用:现状、挑战与发展分析[J]. 广西师范大学学报（自然科学版）, 2024, 42(6): 16 -29 .
[2] 施慧露, 莫燕华, 骆海玉, 马姜明. 檵木乙酸乙酯萃取物抑菌活性研究[J]. 广西师范大学学报（自然科学版）, 2025, 43(1): 1 -8 .
[3] 贺青, 李栋, 罗思源, 贺寓东, 李彪, 王强. 超宽带里德堡原子天线技术研究进展[J]. 广西师范大学学报（自然科学版）, 2025, 43(2): 1 -19 .
[4] 黄仁慧, 张锐锋, 文晓浩, 闭金杰, 黄守麟, 李廷会. 基于复数协方差卷积神经网络的运动想象脑电信号解码方法[J]. 广西师范大学学报（自然科学版）, 2025, 43(3): 43 -56 .
[5] 田晟, 熊辰崟, 龙安洋. 基于改进PointNet++的城市道路点云分类方法[J]. 广西师范大学学报（自然科学版）, 2025, 43(4): 1 -14 .
[6] 黎宗孝, 张健, 罗鑫悦, 赵嶷飞, 卢飞. 基于K-means和Adam-LSTM的机场进场航迹预测研究[J]. 广西师范大学学报（自然科学版）, 2025, 43(4): 15 -23 .
[7] 宋铭楷, 朱成杰. 基于H-WOA-GWO和区段修正策略的配电网故障定位研究[J]. 广西师范大学学报（自然科学版）, 2025, 43(4): 24 -37 .
[8] 韩烁, 江林峰, 杨建斌. 基于注意力机制PINNs方法求解圣维南方程[J]. 广西师范大学学报（自然科学版）, 2025, 43(4): 58 -68 .
[9] 李志欣, 匡文兰. 结合互注意力空间自适应和特征对集成判别的细粒度图像分类[J]. 广西师范大学学报（自然科学版）, 2025, 43(4): 69 -82 .
[10] 石天怡, 南新元, 郭翔羽, 赵濮, 蔡鑫. 基于改进ConvNeXt的苹果叶片病害分类算法[J]. 广西师范大学学报（自然科学版）, 2025, 43(4): 83 -96 .
版权所有 © 广西师范大学学报（自然科学版）编辑部
地址：广西桂林市三里店育才路15号　邮编：541004
电话：0773-5857325　E-mail: gxsdzkb@mailbox.gxnu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发