Skip to main content

Advertisement

SpringerLink
Log in

Investigating the long-term response of plateau vegetation productivity to extreme climate: insights from a case study in Qinghai Province, China

  • Original Paper
  • Published:
International Journal of Biometeorology Aims and scope Submit manuscript

Abstract

Over the past three decades, there has been a significant global climate change characterized by an increase in the intensity and frequency of extreme climate events. The vegetation status in Qinghai Province has undergone substantial changes, which are more pronounced than other regions in the Qinghai-Tibet Plateau. However, a clear understanding of the response characteristics of plateau vegetation to extreme climate events is currently lacking. In this study, we investigated the response of net primary productivity (NPP) to different forms of extreme climate events across regions characterized by varying levels of aridity and elevation gradients. Specifically, we observed a significant increase in NPP in relatively arid regions. Our findings indicate that, in relatively arid regions, single episodes of high-intensity precipitation have a pronounced positive effect (higher correlation) on NPP. Furthermore, in high-elevation regions (4000–6000 m), both the intensity and frequency of precipitation events are crucial factors for the increase in regional NPP. However, continuous precipitation can have significant negative impacts on certain areas within relatively wet regions. Regarding temperature, a reduction in the number of frost days within a year has been shown to lead to a significant increase in NPP in arid regions. This reduction allows vegetation growth rate to increase in regions where it was limited by low temperatures. Vegetation conditions in drought-poor regions are expected to continue to improve as extreme precipitation intensifies and extreme low-temperature events decrease.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

Data will be made available on request.

References

  • Balducci L, Deslauriers A, Rossi S, Giovannelli A (2019) Stem cycle analyses help decipher the nonlinear response of trees to concurrent warming and drought. Ann for Sci 76:88. https://doi.org/10.1007/s13595-019-0870-7

  • Barickman TC, Simpson CR, Sams CE (2019) Waterlogging causes early modification in the physiological performance, carotenoids, chlorophylls, proline, and soluble sugars of cucumber plants. Plants-Basel 8:160. https://doi.org/10.3390/plants8060160

  • Bliss LC (1971) Arctic and alpine plant life cycles. Annu Rev Ecol Syst 2:405–438. https://doi.org/10.1146/annurev.es.02.110171.002201

  • Butt DK (1978) Solar and terrestrial radiation. J Arid Environ 1:99. https://doi.org/10.1016/S0140-1963(18)31761-0

  • Chen J, Tian Y, Zhang X, Zheng C, Song Z, Deng A, Zhang W (2014) Nighttime warming will increase winter wheat yield through improving plant development and grain growth in north China. J Plant Growth Regul 33:397–407. https://doi.org/10.1007/s00344-013-9390-0

  • Chen, H, Sun, J, Lin, W, Xu, H (2020) Comparison of CMIP6 and CMIP5 models for simulation of climate extremes (English). Sci Bull 65:1415–1418. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2020&filename=JXTW202017001&uniplatform=NZKPT&v=F6f02Z7PB1qZu6v6J4zJCmh29weNsQdOXFqZKRt_8cGSBVssxoUN5scF4lINttFX. Accessed 6 Dec 2022

  • Deng X, Wu L, He C, Shao H (2022) Study on spatiotemporal variation pattern of vegetation coverage on Qinghai-Tibet Plateau and the analysis of its climate driving factors. Int J Environ Res Public Health 19:8836. https://doi.org/10.3390/ijerph19148836

  • Ding Y, Yang S (2022) Surviving and thriving: How plants perceive and respond to temperature stress. Dev Cell 57:947–958. https://doi.org/10.1016/j.devcel.2022.03.010

  • Fan Y, Fang C (2020) Insight into carbon emissions related to residential consumption in Tibetan Plateau-case study of Qinghai. Sustain Cities Soc 61:102310. https://doi.org/10.1016/j.scs.2020.102310

  • Filazzola A, Matter SF, MacIvor JS (2021) The direct and indirect effects of extreme climate events on insects. Sci Total Environ 769:145161. https://doi.org/10.1016/j.scitotenv.2021.145161

  • Gehrmann F, Lehtimaki I-M, Hanninen H, Saarinen T (2020) Sub-Arctic alpine Vaccinium vitis-idaea exhibits resistance to strong variation in snowmelt timing and frost exposure, suggesting high resilience under climatic change. Polar Biol 43:1453–1467. https://doi.org/10.1007/s00300-020-02721-3

  • Gong J, Jia X, Zha T, Wang B, Kellomaki S, Peltola H (2016) Modeling the effects of plant-interspace heterogeneity on water-energy balances in a semiarid ecosystem. Agric for Meteorol 221:189–206. https://doi.org/10.1016/j.agrformet.2016.01.144

  • Guo B, Han B, Yang F, Chen S, Liu Y, Yang W (2020a) Determining the contributions of climate change and human activities to the vegetation NPP dynamics in the Qinghai-Tibet Plateau, China, from 2000 to 2015. Environ Monit Assess 192:663. https://doi.org/10.1007/s10661-020-08606-6

  • Guo B, Zang W, Yang F, Han B, Chen S, Liu Y, Yang X, He T, Chen X, Liu C, Gong R (2020b) Spatial and temporal change patterns of net primary productivity and its response to climate change in the Qinghai-Tibet Plateau of China from 2000 to 2015. J Arid Land 12:1–17. https://doi.org/10.1007/s40333-019-0070-1

  • He Y, Yan W, Cai Y, Deng F, Qu X, Cui X (2022) How does the Net primary productivity respond to the extreme climate under elevation constraints in mountainous areas of Yunnan, China? Ecol Indic 138:108817. https://doi.org/10.1016/j.ecolind.2022.108817

  • He T, Dai X, Li W, Zhou J, Zhang J, Li C, Dai T, Li W, Lu H, Ye Y (2023) Response of net primary productivity of vegetation to drought: a case study of Qinba Mountainous area, China (2001–2018). Ecol Ind 149:110148

  • Isaksen K, Benestad RE, Harris C, Sollid JL (2007) Recent extreme near-surface permafrost temperatures on Svalbard in relation to future climate scenarios. Geophys Res Lett 34:L17502. https://doi.org/10.1029/2007GL031002

  • Jiang L, Xing R, Chen X, Xue B (2021) A survey-based investigation of greenhouse gas and pollutant emissions from household energy consumption in the Qinghai-Tibet Plateau of China. Energy Build 235:110753. https://doi.org/10.1016/j.enbuild.2021.110753

  • Kang S, Kimball JS, Running SW (2006) Simulating effects of fire disturbance and climate change on boreal forest productivity and evapotranspiration. Sci Total Environ 362:85–102. https://doi.org/10.1016/j.scitotenv.2005.11.014

  • Kendall MG (1948) Rank correlation methods. Charles Griffin & Company Limited, London

  • Kennedy-Asser AT, Andrews O, Mitchell DM, Warren RF (2021) Evaluating heat extremes in the UK Climate Projections (UKCP18). Environ Res Lett 16:014039. https://doi.org/10.1088/1748-9326/abc4ad

  • Klemm T, Briske DD, Reeves MC (2020) Potential natural vegetation and NPP responses to future climates in the U.S. Great Plains. Ecosphere 11:e03264. https://doi.org/10.1002/ecs2.3264

  • Li C, Zhou M, Dou T, Zhu T, Yin H, Liu L (2021a) Convergence of global hydrothermal pattern leads to an increase in vegetation net primary productivity. Ecol Indic 132:108282. https://doi.org/10.1016/j.ecolind.2021.108282

  • Li H, Qu Y, Zeng X, Zhang H, Cui L, Luo C (2021b) Dynamic response of the vegetation carbon storage in the Sanjiang plain to changes in land use/cover and climate. Herit Sci 9:134. https://doi.org/10.1186/s40494-021-00605-1

  • Li M, Yan Q, Li G, Yi M, Li J (2022) Spatio-temporal changes of vegetation cover and its influencing factors in northeast China from 2000 to 2021. Remote Sens 14:5720. https://doi.org/10.3390/rs14225720

  • Liu D, Li Y, Wang T, Peylin P, MacBean N, Ciais P, Jia G, Ma M, Ma Y, Shen M, Zhang X, Piao S (2018) Contrasting responses of grassland water and carbon exchanges to climate change between Tibetan Plateau and Inner Mongolia. Agr Forest Meteorol 249:163–175. https://doi.org/10.1016/j.agrformet.2017.11.034

  • Liu, Z, Li, L, McVicar, TR, Van Niel, TG, Yang, Q, Li, R (2008) Introduction of the professional interpolation software for meteorology data: ANUSPLINN. Meteorological. 2:92–100. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFD2008&filename=QXXX200802014&v=. Accessed 21 Sept 2023

  • Liwen W, Yaxing W, Zheng N (2008) Spatial and temporal variations of vegetation in Qinghai Province based on satellite data. J Geogr Sci 18:73–84. https://doi.org/10.1007/s11442-008-0073-x

  • McMichael AJ (2012) Insights from past millennia into climatic impacts on human health and survival. Proc Natl Acad Sci U S A 109:4730–4737. https://doi.org/10.1073/pnas.1120177109

  • Niu F, Chen J, Xiong P, Wang Z, Zhang H, Xu B (2019) Responses of soil respiration to rainfall pulses in a natural grassland community on the semi-arid Loess Plateau of China. CATENA 178:199–208. https://doi.org/10.1016/j.catena.2019.03.020

  • Piao S, Tan K, Nan H, Ciais P, Fang J, Wang T, Vuichard N, Zhu B (2012) Impacts of climate and CO2 changes on the vegetation growth and carbon balance of Qinghai-Tibetan grasslands over the past five decades. Glob Planet Change 98–99:73–80. https://doi.org/10.1016/j.gloplacha.2012.08.009

  • Piao S, Tan K, Nan H, Ciais P, Fang J, Wang T, Vuichard N, Zhu B (2012) Impacts of climate and CO2 changes on the vegetation growth and carbon balance of Qinghai-Tibetan grasslands over the past five decades. Glob Planet Change 98–99:73–80. https://doi.org/10.1016/j.gloplacha.2012.08.009

  • Ratajczak Z, Churchill AC, Ladwig LM, Taylor JH, Collins SL (2019) The combined effects of an extreme heatwave and wildfire on tallgrass prairie vegetation. J Veg Sci 30:687–697. https://doi.org/10.1111/jvs.12750

  • Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389

    Article  Google Scholar 

  • Shen M, Wang S, Jiang N, Sun J, Cao R, Ling X, Fang B, Zhang L, Zhang L, Xu X, Lv W, Li B, Sun Q, Meng F, Jiang Y, Dorji T, Fu Y, Iler A, Vitasse Y, Steltzer H, Ji Z, Zhao W, Piao S, Fu B (2022a) Plant phenology changes and drivers on the Qinghai-Tibetan Plateau. Nat Rev Earth Environ 3:633–651. https://doi.org/10.1038/s43017-022-00317-5

  • Shen X, Liu Y, Zhang J, Wang Y, Ma R, Liu B, Lu X, Jiang M (2022) asymmetric impacts of diurnal warming on vegetation carbon sequestration of marshes in the Qinghai Tibet Plateau. Global Biogeochem Cycles 36:e2022GB007396. https://doi.org/10.1029/2022GB007396

  • Shi S, Wang P, Zhan X, Han J, Guo M, Wang F (2023) Warming and increasing precipitation induced greening on the northern Qinghai-Tibet Plateau. CATENA 233:107483

    Article  Google Scholar 

  • Sistla SA, Moore JC, Simpson RT, Gough L, Shaver GR, Schimel JP (2013) Long-term warming restructures Arctic tundra without changing net soil carbon storage. Nature 497:615. https://doi.org/10.1038/nature12129

  • Stott P (2016) How climate change affects extreme weather events Research can increasingly determine the contribution of climate change to extreme events such as droughts. Science 352:1517–1518. https://doi.org/10.1126/science.aaf7271

  • Sun G, Mu M (2013) Understanding variations and seasonal characteristics of net primary production under two types of climate change scenarios in China using the LPJ model. Clim Change 120:755–769. https://doi.org/10.1007/s10584-013-0833-1

  • Sun Y, Feng Y, Wang Y, Zhao X, Yang Y, Tang Z, Wang S, Su H, Zhu J, Chang J, Fang J (2021) Field-based estimation of net primary productivity and its above- and belowground partitioning in global grasslands. JGR Biogeosci 126:e2021JG006472. https://doi.org/10.1029/2021JG006472

  • Sun Y, Chang J, Fang J (2023) Above- and belowground net-primary productivity: a field-based global database of grasslands. Ecology 104:e3904. https://doi.org/10.1002/ecy.3904

  • Tan, K, Ciais, P, Piao, S, Wu, X, Tang, Y, Vuichard, N, Liang, S, Fang, J (2010) Application of the ORCHIDEE global vegetation model to evaluate biomass and soil carbon stocks of Qinghai-Tibetan grasslands: modeling C cycle in Qinghai-Tibetan grasslands. Glob. Biogeochem. Cycles, 24, n/a-n/a. https://doi.org/10.1029/2009GB003530

  • Tao J, Zhang Y, Dong J, Fu Y, Zhu J, Zhang G, Jiang Y, Tian L, Zhang X, Zhang T, Xi Y (2015) Elevation-dependent relationships between climate change and grassland vegetation variation across the Qinghai-Xizang Plateau. Int J Climatol 35:1638–1647. https://doi.org/10.1002/joc.4082

  • Theiler J (1986) Spurious dimension from correlation algorithms applied to limited time-series data. Phys Rev A 34:2427–2432. https://doi.org/10.1103/PhysRevA.34.2427

  • Wan Y, Yu P, Li X, Wang Y, Wang B, Yu Y, Zhang L, Liu X, Wang S (2022) Divergent seasonal patterns of Qinghai spruce growth with elevation in northwestern China. Forests 13:388. https://doi.org/10.3390/f13030388

  • Wang F, Pan X, Gerlein-Safdi C, Cao X, Wang S, Gu L, Wang D, Lu Q (2020) Vegetation restoration in Northern China: a contrasted picture. Land Degrad Dev 31:669–676. https://doi.org/10.1002/ldr.3314

  • Wang H, Wu J, Li G, Yan L, Wei X (2022a) Effects of rainfall frequency on soil labile carbon fractions in a wet meadow on the Qinghai-Tibet Plateau. J Soils Sediments 22:1489–1499. https://doi.org/10.1007/s11368-022-03170-7

  • Wang P, Wang J, Elberling B, Yang L, Chen W, Song L, Yan Y, Wang S, Pan J, He Y, Niu S (2022b) Increased annual methane uptake driven by warmer winters in an alpine meadow. Glob Change Biol 28:3246–3259. https://doi.org/10.1111/gcb.16120

  • Wang X, Zhu J, Peng S, Zheng T, Qi Z, Hu J, Ji C (2022c) Patterns of grassland community composition and structure along an elevational gradient on the Qinghai-Tibet Plateau. J Plant Ecol 15:808–817. https://doi.org/10.1093/jpe/rtab119

  • Wang Z, Zhang X, Niu B, Zheng Y, He Y, Cao Y, Feng Y, Wu J (2022d) Divergent climate sensitivities of the alpine grasslands to early growing season precipitation on the Tibetan Plateau. Remote Sens 14:2484. https://doi.org/10.3390/rs14102484

  • Weiler M, McDonnell JJ (2006) Testing nutrient flushing hypotheses at the hillslope scale: a virtual experiment approach. J Hydrol 319:339–356. https://doi.org/10.1016/j.jhydrol.2005.06.040

  • Weiler M, McDonnell JJ (2006) Testing nutrient flushing hypotheses at the hillslope scale: a virtual experiment approach. J Hydrol 319:339–356. https://doi.org/10.1016/j.jhydrol.2005.06.040

  • Wu C, Chen K, Chongyi E, You X, He D, Hu L, Liu B, Wang R, Shi Y, Li C, Liu F (2022) Improved CASA model based on satellite remote sensing data: simulating net primary productivity of Qinghai Lake basin alpine grassland. Geosci Model Dev 15:6919–6933. https://doi.org/10.5194/gmd-15-6919-2022

  • Xu X, Jiang H, Guan M, Wang L, Huang Y, Jiang Y, Wang A (2020) Vegetation responses to extreme climatic indices in coastal China from 1986 to 2015. Sci Total Environ 744:140784. https://doi.org/10.1016/j.scitotenv.2020.140784

  • Yan W, He Y, Cai Y, Qu X, Cui X (2021) Relationship between extreme climate indices and spatiotemporal changes of vegetation on Yunnan Plateau from 1982 to 2019. Glob Ecol Conserv 31:e01813. https://doi.org/10.1016/j.gecco.2021.e01813

  • Zhang Y, Hu Q, Zou F (2021) Spatio-temporal changes of vegetation net primary productivity and its driving factors on the Qinghai-Tibetan Plateau from 2001 to 2017. Remote Sens 13:1566. https://doi.org/10.3390/rs13081566

  • Zhang Y, Hong S, Liu D, Piao S (2023) Susceptibility of vegetation low-growth to climate extremes on Tibetan Plateau. Agric for Meteorol 331:109323. https://doi.org/10.1016/j.agrformet.2023.109323

  • Zhang, X, Yang, F (2004) RClimDex software 1.0. Available at https://acmad.net/rcc/procedure/RClimDexUserManual.pdf. Accessed 2 October 2023

  • Zhao L, Liu Z, Hu Y, Zhou W, Peng Y, Ma T, Liu L, Li S, Wang L, Mao X (2022) Evaluation of reasonable stocking rate based on the relative contribution of climate change and grazing activities to the productivity of alpine grasslands in Qinghai province. Remote Sens 14:1455. https://doi.org/10.3390/rs14061455

  • Zhou Y, Zhang Q, Singh VP, Xiao M (2015) General correlation analysis: a new algorithm and application. Stoch Environ Res Risk Assess 29:665–677. https://doi.org/10.1007/s00477-014-0970-8

  • Zhu X, Wu T, Li R, Xie C, Hu G, Qin Y, Wang W, Hao J, Yang S, Ni J, Yang C (2017) Impacts of summer extreme precipitation events on the hydrothermal dynamics of the active layer in the Tanggula permafrost region on the Qinghai-Tibetan Plateau. J Geophys Res: Atmos 122:11549–11567. https://doi.org/10.1002/2017JD026736

  • Zhu W, Pan Y, Zhang J (2007) Remote sensing estimation of net primary productivity of terrestrial vegetation in China. J Plant Ecol 3:413–424. https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKgchrJ08w1e7aLpFYbsPrqGce186coJoEzknmgMFP5RzXO9Lkb6eQy5_35uAQ60fpOblKomGNQxa&uniplatform=NZKPT. Accessed 21 Sept 2023

Download references

Acknowledgements

The authors would like to acknowledge X.P. Lei, J.Z. Hou, and H. Wu for their technical assistance in laboratory works.

Funding

This work was supported by the National Key Research and Development Program of China (grant numbers: 2022YFC3201702) and the National Natural Science Foundation of China (grant numbers: 42177328).

Author information

Authors and Affiliations

  1. Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A & F University, Weihui Road 23, Yangling, 712100, China

    Hexuan An, Xiaoyan Song, Ziyin Wang, Xubo Geng & Pingping Zhou

  2. Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment of the People’s Republic of China, Haidian District, Fengdedong Road 4, Beijing, 100094, China

    Jun Zhai

  3. State Key Lab Soil Eros & Dryland Farming Loess P, Northwest A&F University, Institute Soil & Water Conservat, Yangling, 712100, China

    Wenyi Sun

Authors
  1. Hexuan An
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoyan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ziyin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xubo Geng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pingping Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jun Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wenyi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xiaoyan Song or Jun Zhai.

Ethics declarations

Competing interests

The authors declare no competing intrests.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 24 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

An, H., Song, X., Wang, Z. et al. Investigating the long-term response of plateau vegetation productivity to extreme climate: insights from a case study in Qinghai Province, China. Int J Biometeorol 68, 333–349 (2024). https://doi.org/10.1007/s00484-023-02593-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00484-023-02593-2

Keywords

Search

Navigation