Advertisement

Climatic determinants impacting the distribution of greenness in China: regional differentiation and spatial variability

  • Kewei Jiao
  • Jiangbo GaoEmail author
  • Shaohong Wu
Original Paper

Abstract

This study investigated climatic determinants for regional greenness in China and spatially variable correlations between climatic determinants and vegetation in specific regions using the geographical detector and geographically weighted regression (GWR) methodologies. The analyses were based on normalized difference vegetation index (NDVI) and interpolations of climatic determinants from 652 Chinese meteorological stations. The study period (1982–2013) was divided into two stages (T1–T2) before and after the inflection year identified by the accumulative anomaly of NDVI. Three typical regions (R1–R3) were then selected according to the same NDVI variation trend as China in the two periods. Precipitation was the dominant climatic factor of NDVI in China, and the effect of temperature on greenness increased with warming from T1 to T2. In a relatively arid region (R1), the effect of precipitation in T2 was further strengthened compared to T1. Meanwhile, the effect of minimum temperature in T2 also increased compared with T1 in a relatively humid region (R2), becoming the major climatic determinant. In addition to the regional differentiation, spatial variability was investigated by comparing normalized coefficients of GWR for climatic determinants; this showed significant spatial heterogeneity within each region. Temperature impact areas also existed within precipitation-dominated regions (R1 and R3), where areas of precipitation impact expanded from T1 to T2. Furthermore, regression coefficients between NDVI dynamics and climate variability revealed relationships between regional differentiation and spatial variability. For example, the increasing precipitation rate could mediate the adverse impacts on greenness caused by the higher warming rate in relatively arid regions (R1).

Keywords

Greenness NDVI Climatic determinants Spatial variability Regional variations China 

Notes

Funding information

This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 41671098 and 41530749), the National Key R&D Program of China (2018YFC1508801), and the “Strategic Priority Research Program” of the Chinese Academy of Sciences (Grant Nos. XDA20020202 and XDA19040304).

References

  1. Baez S, Collins SL, Pockman WT, Johnson JE, Small EE (2013) Effects of experimental rainfall manipulations on Chihuahuan Desert grassland and shrubland plant communities. Oecologia 172(4):1117–1127.  https://doi.org/10.1007/s00442-012-2552-0 CrossRefGoogle Scholar
  2. Brown S, Versace VL, Laurenson L, Ierodiaconou D, Fawcett J, Salzman S (2012) Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression. Environ Model Assess 17(3):241–254.  https://doi.org/10.1007/s10666-011-9289-8 CrossRefGoogle Scholar
  3. Brunsdon C, Fotheringham AS, Charlton ME (1996) Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal 28(4):281–298.  https://doi.org/10.1111/j.1538-4632.1996.tb00936.x CrossRefGoogle Scholar
  4. Chang NB, Vasquez MV, Chen CF, Imen S, Mullon L (2015) Global nonlinear and nonstationary climate change effects on regional precipitation and forest phenology in Panama, Central America. Hydrol Process 29(3):339–355.  https://doi.org/10.1002/hyp.10151 CrossRefGoogle Scholar
  5. Duo A, Zhao WJ, Qu XY, Jing R, Xiong K (2016) Spatio-temporal variation of vegetation coverage and its response to climate change in North China plain in the last 33 years. Int J Appl Earth Obs 53:103–117.  https://doi.org/10.1016/j.jag.2016.08.008 CrossRefGoogle Scholar
  6. Fang JY, Piao SL, He JS, Ma WH (2004) Increasing terrestrial vegetation activity in China, 1982-1999. Sci China Life Sci 47(3):229–240.  https://doi.org/10.1360/03yc0068 Google Scholar
  7. Fang JY, Tang YH, Son Y (2010) Why are East Asian ecosystems important for carbon cycle research? Sci China Life Sci 53(7):753–756.  https://doi.org/10.1007/s11427-010-4032-2 CrossRefGoogle Scholar
  8. Feng XM, Fu BJ, Piao SL, Wang S, Ciais P, Zeng ZZ, Lü YH, Zeng Y, Li Y, Jiang XH, Wu BF (2016) Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat Clim Chang 6(11):1019–1022.  https://doi.org/10.1038/nclimate3092 CrossRefGoogle Scholar
  9. Franzke CLE (2014) Warming trends: nonlinear climate change. Nat Clim Chang 4(6):423–424.  https://doi.org/10.1038/nclimate2245 CrossRefGoogle Scholar
  10. Gao JB, Jiao KW, Wu SH, Ma DY, Zhao DS, Yin YH, Dai EF (2017) Past and future effects of climate change on spatially heterogeneous vegetation activity in China. Earth’s Future 5(7):679–692.  https://doi.org/10.1002/2017EF000573 CrossRefGoogle Scholar
  11. Hoover DL, Knapp AK, Smith MD (2014) Resistance and resilience of a grassland ecosystem to climate extremes. Ecology 95(9):2646–2656.  https://doi.org/10.1890/13-2186.1 CrossRefGoogle Scholar
  12. Huete A (2016) Ecology: Vegetation's responses to climate variability. Nature 531(7593):181–182.  https://doi.org/10.1038/nature17301 CrossRefGoogle Scholar
  13. Ji F, Wu ZH, Huang JP, Chassignet EP (2014) Evolution of land surface air temperature trend. Nat Clim Chang 4(6):462–466.  https://doi.org/10.1038/nclimate2223 CrossRefGoogle Scholar
  14. Jiang LL, Jiapaer G, Bao AM, Guo H, Ndayisaba F (2017) Vegetation dynamics and responses to climate change and human activities in Central Asia. Sci Total Environ 599-600:967–980.  https://doi.org/10.1016/j.scitotenv.2017.05.012 CrossRefGoogle Scholar
  15. Krishnaswamy J, John R, Joseph S (2014) Consistent response of vegetation dynamics to recent climate change in tropical mountain regions. Glob Chang Biol 20(1):203–215.  https://doi.org/10.1111/gcb.12362 CrossRefGoogle Scholar
  16. Levine JM (2015) Ecology: a trail map for trait-based studies. Nature 529(7585):163–164.  https://doi.org/10.1038/nature16862 CrossRefGoogle Scholar
  17. Li HF, Calder CA, Cressie N (2007) Beyond Moran’s I: testing for spatial dependence based on the spatial autoregressive model. Geogr Anal 39(4):357–375.  https://doi.org/10.1111/j.1538-4632.2007.00708.x CrossRefGoogle Scholar
  18. Lü YH, Zhang LW, Feng XM, Zeng Y, Fu BJ, Yao XL, Li JR, Wu BF (2015) Recent ecological transitions in China: greening, browning, and influential factors. Sci Rep 5:8732.  https://doi.org/10.1038/srep08732 CrossRefGoogle Scholar
  19. Mao DH, Wang ZM, Luo L, Ren CY (2012) Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China. Int J Appl Earth Obs 18(1):528–536.  https://doi.org/10.1016/j.jag.2011.10.007 CrossRefGoogle Scholar
  20. Peng J, Liu YH, Shen H, Han YN, Pan YJ (2012) Vegetation coverage change and associated driving forces in mountain areas of Northwestern Yunnan, China using RS and GIS. Environ Monit Assess 184(8):4787–4798.  https://doi.org/10.1007/s10661-011-2302-5 CrossRefGoogle Scholar
  21. Peng SS, Piao SL, Ciais P, Myneni RB, Chen AP, Chevallier F, Dolman AJ, Janssens IA, Peñuelas J, Zhang GX, Vicca S, Wan SQ, Wang SP, Zeng H (2013) Asymmetric effects of daytime and night-time warming on northern hemisphere vegetation. Nature 501(7465):88–92.  https://doi.org/10.1038/nature12434 CrossRefGoogle Scholar
  22. Piao SL, Nan HJ, Huntingford C, Ciais P, Friedlingstein P, Sitch S, Peng SS, Ahlström A, Canadell JG, Cong N, Levis S, Levy PE, Liu LL, Lomas MR, Mao JF, Myneni RB, Peylin P, Poulter B, Shi XY, Yin GD, Viovy N, Wang T, Wang XH, Zaehle S, Zeng N, Zeng ZZ, Chen AP (2014) Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nat Commun 5:5018.  https://doi.org/10.1038/ncomms6018 CrossRefGoogle Scholar
  23. Reyer CPO, Leuzinger S, Rammig A, Wolf A, Bartholomeus RP, Bonfante A, De Lorenzi F, Dury M, Gloning P, Abou Jaoude R, Klein T, Kuster TM, Martins M, Niedrist G, Riccardi M, Wohlfahrt G, De Angelis P, De Dato G, Francois L, Menzel A, Pereira M (2013) A plant's perspective of extremes: terrestrial plant responses to changing climatic variability. Glob Chang Biol 19(1):75–89.  https://doi.org/10.1111/gcb.12023 CrossRefGoogle Scholar
  24. Seddon AWR, Macias-Fauria M, Long PR, Benz D, Willis KJ (2016) Sensitivity of global terrestrial ecosystems to climate variability. Nature 531(7593):229–243.  https://doi.org/10.1038/nature16986 CrossRefGoogle Scholar
  25. Sun J, Cheng GW, Li WP, Sha YK, Yang YC (2013) On the variation of NDVI with the principal climatic elements in the Tibetan plateau. Remote Sens 5(4):1894–1911.  https://doi.org/10.3390/rs5041894 CrossRefGoogle Scholar
  26. Tucker CJ, Pinzon JE, Brown ME, Slayback DA, Pak EW, Mahoney R, Vermote EF, El Saleous N (2005) An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens 26(20):4485–4498.  https://doi.org/10.1080/01431160500168686 CrossRefGoogle Scholar
  27. Wan SQ, Norby RJ, Ledford J, Weltzin JF (2007) Responses of soil respiration to elevated CO2, air warming, and changing soil water availability in a model old-field grassland. Glob Chang Biol 13(11):2411–2424.  https://doi.org/10.1111/j.1365-2486.2007.01433.x CrossRefGoogle Scholar
  28. Wang JF, Li XH, Christakos G, Liao YL, Zhang T, Gu X, Zheng XY (2010) Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. Int J Geogr Inf Sci 24(1):107–127.  https://doi.org/10.1080/13658810802443457 CrossRefGoogle Scholar
  29. Wang JF, Zhang TL, Fu BJ (2016) A measure of spatial stratified heterogeneity. Ecol Indic 67:250–256.  https://doi.org/10.1016/j.ecolind.2016.02.052 CrossRefGoogle Scholar
  30. Wang SJ, Yan YX, Yan M, Zhao XK (2012) Quantitative estimation of the impact of precipitation and human activities on runoff change of the Huangfuchuan River Basin. J Geogr Sci 22(5):906–918.  https://doi.org/10.1007/s11442-012-0972-8 CrossRefGoogle Scholar
  31. Wang YH, Zhou GS, Wang YH (2007) Modeling responses of the meadow steppe dominated by Leymus chinensis to climate change. Clim Chang 82(3/4):437–452.  https://doi.org/10.1007/s10584-006-9145-z CrossRefGoogle Scholar
  32. Wright CK, De Beurs KM, Henebry GM (2012) Combined analysis of land cover change and NDVI trends in the Northern Eurasian grain belt. Front Earth Sci 6(2):177–187.  https://doi.org/10.1007/s11707-012-0327-x CrossRefGoogle Scholar
  33. Wu SH, Liu WZ, Pan T, Deng HY, Jiao KW, Yin YH (2016) Amplitude and velocity of the shifts in the Chinese terrestrial surface regions from 1960 to 2011. Chin Sci Bull 61(19):2187–2197.  https://doi.org/10.1360/N972016-0005 CrossRefGoogle Scholar
  34. Zeppel MJB, Wilks JV, Lewis JD (2014) Impacts of extreme precipitation and seasonal changes in precipitation on plants. Biogeosciences 11(11):3083–3093.  https://doi.org/10.5194/bg-11-3083-2014 CrossRefGoogle Scholar
  35. Zhao MS, Running SW (2010) Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329(5994):940–943.  https://doi.org/10.1126/science.1192666 CrossRefGoogle Scholar

Copyright information

© ISB 2019

Authors and Affiliations

  1. 1.Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  2. 2.Key Laboratory of Forest Ecology and Management, Institute of Applied EcologyChinese Academy of SciencesShenyangChina
  3. 3.College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijingChina

Personalised recommendations