Skip to main content
SpringerLink
Log in

Spatial modeling of the Ulmus pumila growing season in China’s temperate zone

  • Research Paper
  • Published:
Science China Earth Sciences Aims and scope Submit manuscript

Abstract

To reveal the ecological mechanism of spatial patterns of plant phenology and spatial sensitivity of plant phenology responses to climate change, we used Ulmus pumila leaf unfolding and leaf fall data at 46 stations of China’s temperate zone during the period 1986–2005 to simulate 20-year mean and yearly spatial patterns of the beginning and end dates of the Ulmus pumila growing season by establishing air temperature-based spatial phenology models, and validate these models by extensive spatial extrapolation. Results show that the spatial patterns of 20-year mean and yearly February-April or September-November temperatures control the spatial patterns of 20-year mean and yearly beginning or end dates of the growing season. Spatial series of mean beginning dates shows a significantly negative correlation with spatial series of mean February-April temperatures at the 46 stations. The mean spring spatial phenology model explained 90% of beginning date variance (p<0.001) with a Root Mean Square Error (RMSE) of 4.7 days. In contrast, spatial series of mean end dates displays a significantly positive correlation with spatial series of mean September-November temperatures at the 46 stations. The mean autumn spatial phenology model explained 79% of end date variance (p<0.001) with a RMSE of 6 days. Similarly, spatial series of yearly beginning dates correlates negatively with spatial series of yearly February-April temperatures and the explained variances of yearly spring spatial phenology models to beginning date are between 72%–87% (p<0.001), whereas spatial series of yearly end dates correlates positively with spatial series of yearly September-November temperatures and the explained variances of yearly autumn spatial phenology models to end date are between 48%–76% (p<0.001). The overall RMSEs of yearly models in simulating beginning and end dates at all modeling stations are 7.3 days and 9 days, respectively. The spatial prediction accuracies of growing season’s beginning and end dates based on both 20-year mean and yearly models are close to the spatial simulation accuracies of these models, indicating that the models have a strong spatial extrapolation capability. Further analysis displays that the negative spatial response rate of growing season’s beginning date to air temperature was larger in warmer years with higher regional mean February-April temperatures than in colder years with lower regional mean February-April temperatures. This finding implies that climate warming in winter and spring may enhance sensitivity of the spatial response of growing season’s beginning date to air temperature.

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

Similar content being viewed by others

References

  1. Walther G R, Post E, Convey P, et al. Ecological responses to recent climate change. Nature, 2002, 416: 389–395

    Article  Google Scholar 

  2. Menzel A, Fabian P. Growing season extended in Europe. Nature, 1999, 397: 659

    Article  Google Scholar 

  3. Schwartz M D. Examining the spring discontinuity in daily temperature ranges. J Clim, 1996, 9: 803–808

    Article  Google Scholar 

  4. Peñuelas J, Rutishauser T, Filella I. Phenology feedbacks on climate change. Science, 2009, 324: 887–888

    Article  Google Scholar 

  5. Keeling C D, Chin J F S, Whorf T P. Increased activity of northern vegetation inferred from atmospheric CO2 measurements. Nature, 1996, 382: 146–149

    Article  Google Scholar 

  6. Piao S L, Ciais P, Friedlingstein P, et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 2008, 451: 49–52

    Article  Google Scholar 

  7. Cleland E E, Chiariello N R, Loarie S R, et al. Diverse responses of phenology to global changes in a grassland ecosystem. Proc Natl Acad Sci USA, 2006, 103:13740–13744

    Article  Google Scholar 

  8. Churkina G, Schimel D, Braswell B H, et al. Spatial analysis of growing season length control over net ecosystem exchange. Glob Change Biol, 2005, 11: 1777–1787

    Article  Google Scholar 

  9. Baldocchi D. Breathing of the terrestrial biosphere: Lessons learned from a global network of carbon dioxide flux measurement systems. Aust J Bot, 2008, 56: 1–26

    Article  Google Scholar 

  10. Wilson K B, Baldocchi D D. Seasonal and interannual variability of energy fluxes over a broadleaved temperate deciduous forest in North America. Agric For Meteorol, 2000, 100: 1–18

    Article  Google Scholar 

  11. Kljun N, Black T A, Griffis T J, et al. Response of net ecosystem productivity of three boreal forest stands to drought. Ecosystems, 2007, 10: 1039–1055

    Article  Google Scholar 

  12. Harding P H, Cochrane J, Smith L P. Forecasting the flowering stages of apple varieties in Kent, England, by the use of meteorological data. Agric Meteorol, 1976, 17: 49–54

    Article  Google Scholar 

  13. Schwartz M, Marotz G. An approach to examining regional atmosphere-plant interactions with phenological data. J Biogeogr, 1986, 13: 551–560

    Article  Google Scholar 

  14. Chmielewski F M, Rötzer T. Response of tree phenology to climate change across Europe. Agric For Meteorol, 2001, 108: 101–112

    Article  Google Scholar 

  15. Menzel A, Sparks T H, Estrella N, et al. European phenological response to climate change matches the warming pattern. Glob Change Biol, 2006, 12: 1969–1976

    Article  Google Scholar 

  16. Matsumoto K, Ohta T, Irasawa M, et al. Climate change and extension of the Ginkgo biloba L. growing season in Japan. Glob Change Biol, 2003, 9: 1634–1642

    Article  Google Scholar 

  17. Askeyev O V, Sparks T H, Askeyev I V, et al. East versus West: Contrasts in phenological patterns? Glob Ecol Biogeogr, 2010, 19: 783–793

    Article  Google Scholar 

  18. Robertson G A. Biometeorological time scale for a cereal crop involving day and night temperatures and photoperiod. Int J Biometeorol, 1968, 12: 191–223

    Article  Google Scholar 

  19. Sarvas R. Investigations on the annual cycle of development of forest trees: Autumn and winter dormancy. Comm Inst For Fenn, 1974, 84: 1–101

    Google Scholar 

  20. Cannell M G R, Smith R I. Thermal time, chill days and prediction of budburst in Picea sitchensis. J Appl Ecol, 1983, 20: 951–963

    Article  Google Scholar 

  21. Hänninen H. Modelling bud dormancy release in trees from cool and temperate regions. Acta For Fenn, 1990, 213: 1–47

    Google Scholar 

  22. Hunter A F, Lechowicz M J. Predicting the timing of budburst in temperate trees. J Appl Ecol, 1992, 29: 597–604

    Article  Google Scholar 

  23. Kramer K. A modeling analysis of the effects of climatic warming on the probability of spring frost damage to tree species in the Netherlands and Germany. Plant Cell Environ, 1994, 17: 367–377

    Article  Google Scholar 

  24. Chuine I, Cour P, Rousseau D D. Selecting models to predict the timing of flowering of temperate trees: Implications for tree phenology modelling. Plant Cell Environ, 1999, 22: 1–13

    Article  Google Scholar 

  25. Chen X Q, Hu B, Yu R. Spatial and temporal variation of phenological growing season and climate change impacts in temperate eastern China. Glob Change Biol, 2005, 11: 1118–1130

    Article  Google Scholar 

  26. Nakahara M. Phenology. Tokyo: Kawadesyobo Press, 1948

    Google Scholar 

  27. Gong G F, Jian W M. On the geographical distribution of phenodate in China (in Chinese). Acta Geog Sin, 1983, 38: 33–40

    Google Scholar 

  28. Zheng J Y, Ge Q S, Hao Z X. Climate change impacts on plant phenological changes in China in recent 40 years. Chin Sci Bull, 2002, 47: 1826–1831

    Article  Google Scholar 

  29. Park-Ono H S, Kawamura T, Yoshino M. Relationships between flowering date of cherry blossom (Prumus yedoensis) and air temperature in East Asia. In: Proceeding of the 13th International Congress of Biometerology 1993 September 12–18, Calgary, 1993. 207–220

  30. Rötzer T, Chmielewski F M. Phenological maps of Europe. Clim Res, 2001, 18: 249–257

    Article  Google Scholar 

  31. Hense A, Glowienka-Hense R, Müller M, et al. Spatial modelling of phenological observations to analyse their interannual variations in Germany. Agric For Meteorol, 2002, 112: 161–178

    Article  Google Scholar 

  32. Schwartz M D, Chen X Q. Examining the onset of spring in China. Clim Res, 2002, 21: 157–164

    Article  Google Scholar 

  33. Menzel A. Plant phenological anomalies in Germany and their relation to air temperature and NAO. Clim Change, 2003, 57: 243–263

    Article  Google Scholar 

  34. Gordo O, Sanz J. Impact of climate change on plant phenology in Mediterranean ecosystems. Glob Change Biol, 2010, 16: 1082–1106

    Article  Google Scholar 

  35. China Meteorological Administration. Atlas of the Climate of China (in Chinese). Beijing: Sinomaps Press, 1978

    Google Scholar 

  36. Chun W Y, Huang C C. Flora Reipublicae Popularis Sinicae (Tomus 22) (in Chinese). Beijing: Science Press, 1998

    Google Scholar 

  37. Ghelardini L, Santini A. Avoidance by early flushing: A new perspective on Dutch elm disease research. iForest, 2009, 2: 143–153

    Article  Google Scholar 

  38. Chen X Q. Phenological obseration in China. In: Hudson I L, Keatley M R, eds. Phenological Research: Methods for Environmental and Climate Change Analysis. Dordrecht Heidelberg London New York: Springer, 2009. 35–38

    Google Scholar 

  39. China Meteorological Administration. Observation Criterion of Agricultural Meteorology (in Chinese). Beijing: China Meteorological Press, 1993

    Google Scholar 

  40. Chen X Q. Untersuchung zur zeitlich-raeumlichen Aehnlichkeit von phaenologischen und klimatologischen Parametern in Westdeutschland und zum Einfluss geooekologischer Faktoren auf die phaenologische Entwicklung im Gebiet des Taunus. Offenbach am Main: Selbstverlag des Deutschen Wetterdienstes, 1994. 80–91

  41. Hutchinson M F. Anusplin Version 4.2 User Guide. 2002

  42. Menzel A, Sparks T H, Estrella N, et al. Altered geographic and temporal variability in phenology in response to climate change. Glob Ecol Biogeogr, 2006, 15: 498–504

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes of the Ministry of Education Peking University, Beijing, 100871, China

    Lin Xu & XiaoQiu Chen

Authors
  1. Lin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. XiaoQiu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to XiaoQiu Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xu, L., Chen, X. Spatial modeling of the Ulmus pumila growing season in China’s temperate zone. Sci. China Earth Sci. 55, 656–664 (2012). https://doi.org/10.1007/s11430-011-4299-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11430-011-4299-6

Keywords

Search

Navigation