Nghiên cứu phản ứng lâu dài của năng suất thực vật trên cao nguyên đối với biến đổi khí hậu cực đoan: những hiểu biết từ nghiên cứu trường hợp tại tỉnh Qinghai, Trung Quốc

Hexuan An1, Xiaoyan Song1, Ziyin Wang1, Xubo Geng1, Pingping Zhou1, Jun Zhai2, Wenyi Sun3
1Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling, China
2Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment of the People’s Republic of China, Haidian District, Beijing, China
3State Key Lab Soil Eros & Dryland Farming Loess P, Northwest A&F University, Institute Soil & Water Conservat, Yangling, China

Tóm tắt

Trong ba thập kỷ qua, đã có những thay đổi khí hậu toàn cầu đáng kể, đặc trưng bởi sự gia tăng cường độ và tần suất của các sự kiện khí hậu cực đoan. Tình trạng thực vật tại tỉnh Qinghai đã trải qua những biến đổi lớn, rõ rệt hơn so với các khu vực khác trên cao nguyên Qinghai-Tây Tạng. Tuy nhiên, hiện tại vẫn thiếu hiểu biết rõ ràng về các đặc điểm phản ứng của thực vật trên cao nguyên đối với các sự kiện khí hậu cực đoan. Trong nghiên cứu này, chúng tôi đã điều tra phản ứng của năng suất sơ cấp ròng (NPP) đối với các hình thức khác nhau của các sự kiện khí hậu cực đoan ở các khu vực có mức độ khô hạn và độ cao khác nhau. Cụ thể, chúng tôi đã quan sát thấy sự gia tăng đáng kể NPP ở các khu vực tương đối khô hạn. Kết quả của chúng tôi chỉ ra rằng, ở các khu vực tương đối khô hạn, các đợt mưa với cường độ cao đơn lẻ có tác động tích cực rõ rệt (hệ số tương quan cao hơn) đến NPP. Hơn nữa, ở các khu vực có độ cao lớn (4000–6000 m), cả cường độ và tần suất của các sự kiện mưa là những yếu tố quan trọng cho sự gia tăng NPP khu vực. Tuy nhiên, mưa liên tục có thể có tác động tiêu cực đáng kể đến một số khu vực trong các vùng tương đối ẩm ướt. Về nhiệt độ, việc giảm số ngày băng trong một năm đã cho thấy dẫn đến sự gia tăng đáng kể NPP ở các khu vực khô hạn. Việc giảm này cho phép tốc độ sinh trưởng của thực vật gia tăng ở những khu vực mà sự phát triển bị hạn chế bởi nhiệt độ thấp. Điều kiện thực vật ở các vùng ít hạn hán được kỳ vọng sẽ tiếp tục cải thiện khi mưa cực đoan gia tăng và các sự kiện nhiệt độ thấp cực đoan giảm.

Từ khóa

#biến đổi khí hậu #năng suất sơ cấp ròng #thực vật cao nguyên #sự kiện khí hậu cực đoan #tỉnh Qinghai

Tài liệu tham khảo

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 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 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