Science China Earth Sciences

, Volume 55, Issue 4, pp 656–664

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

Research Paper

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.

Keywords

phenology Ulmus pumila air temperature spatial response spatial simulation sensitivity 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Walther G R, Post E, Convey P, et al. Ecological responses to recent climate change. Nature, 2002, 416: 389–395CrossRefGoogle Scholar
  2. 2.
    Menzel A, Fabian P. Growing season extended in Europe. Nature, 1999, 397: 659CrossRefGoogle Scholar
  3. 3.
    Schwartz M D. Examining the spring discontinuity in daily temperature ranges. J Clim, 1996, 9: 803–808CrossRefGoogle Scholar
  4. 4.
    Peñuelas J, Rutishauser T, Filella I. Phenology feedbacks on climate change. Science, 2009, 324: 887–888CrossRefGoogle Scholar
  5. 5.
    Keeling C D, Chin J F S, Whorf T P. Increased activity of northern vegetation inferred from atmospheric CO2 measurements. Nature, 1996, 382: 146–149CrossRefGoogle Scholar
  6. 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: 49–52CrossRefGoogle Scholar
  7. 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, 103:13740–13744CrossRefGoogle Scholar
  8. 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: 1777–1787CrossRefGoogle Scholar
  9. 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: 1–26CrossRefGoogle Scholar
  10. 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: 1–18CrossRefGoogle Scholar
  11. 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: 1039–1055CrossRefGoogle Scholar
  12. 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: 49–54CrossRefGoogle Scholar
  13. 13.
    Schwartz M, Marotz G. An approach to examining regional atmosphere-plant interactions with phenological data. J Biogeogr, 1986, 13: 551–560CrossRefGoogle Scholar
  14. 14.
    Chmielewski F M, Rötzer T. Response of tree phenology to climate change across Europe. Agric For Meteorol, 2001, 108: 101–112CrossRefGoogle Scholar
  15. 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: 1969–1976CrossRefGoogle Scholar
  16. 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: 1634–1642CrossRefGoogle Scholar
  17. 17.
    Askeyev O V, Sparks T H, Askeyev I V, et al. East versus West: Contrasts in phenological patterns? Glob Ecol Biogeogr, 2010, 19: 783–793CrossRefGoogle Scholar
  18. 18.
    Robertson G A. Biometeorological time scale for a cereal crop involving day and night temperatures and photoperiod. Int J Biometeorol, 1968, 12: 191–223CrossRefGoogle Scholar
  19. 19.
    Sarvas R. Investigations on the annual cycle of development of forest trees: Autumn and winter dormancy. Comm Inst For Fenn, 1974, 84: 1–101Google Scholar
  20. 20.
    Cannell M G R, Smith R I. Thermal time, chill days and prediction of budburst in Picea sitchensis. J Appl Ecol, 1983, 20: 951–963CrossRefGoogle Scholar
  21. 21.
    Hänninen H. Modelling bud dormancy release in trees from cool and temperate regions. Acta For Fenn, 1990, 213: 1–47Google Scholar
  22. 22.
    Hunter A F, Lechowicz M J. Predicting the timing of budburst in temperate trees. J Appl Ecol, 1992, 29: 597–604CrossRefGoogle Scholar
  23. 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: 367–377CrossRefGoogle Scholar
  24. 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: 1–13CrossRefGoogle Scholar
  25. 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: 1118–1130CrossRefGoogle Scholar
  26. 26.
    Nakahara M. Phenology. Tokyo: Kawadesyobo Press, 1948Google Scholar
  27. 27.
    Gong G F, Jian W M. On the geographical distribution of phenodate in China (in Chinese). Acta Geog Sin, 1983, 38: 33–40Google Scholar
  28. 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: 1826–1831CrossRefGoogle Scholar
  29. 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. 207–220Google Scholar
  30. 30.
    Rötzer T, Chmielewski F M. Phenological maps of Europe. Clim Res, 2001, 18: 249–257CrossRefGoogle Scholar
  31. 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: 161–178CrossRefGoogle Scholar
  32. 32.
    Schwartz M D, Chen X Q. Examining the onset of spring in China. Clim Res, 2002, 21: 157–164CrossRefGoogle Scholar
  33. 33.
    Menzel A. Plant phenological anomalies in Germany and their relation to air temperature and NAO. Clim Change, 2003, 57: 243–263CrossRefGoogle Scholar
  34. 34.
    Gordo O, Sanz J. Impact of climate change on plant phenology in Mediterranean ecosystems. Glob Change Biol, 2010, 16: 1082–1106CrossRefGoogle Scholar
  35. 35.
    China Meteorological Administration. Atlas of the Climate of China (in Chinese). Beijing: Sinomaps Press, 1978Google Scholar
  36. 36.
    Chun W Y, Huang C C. Flora Reipublicae Popularis Sinicae (Tomus 22) (in Chinese). Beijing: Science Press, 1998Google Scholar
  37. 37.
    Ghelardini L, Santini A. Avoidance by early flushing: A new perspective on Dutch elm disease research. iForest, 2009, 2: 143–153CrossRefGoogle Scholar
  38. 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. 35–38Google Scholar
  39. 39.
    China Meteorological Administration. Observation Criterion of Agricultural Meteorology (in Chinese). Beijing: China Meteorological Press, 1993Google Scholar
  40. 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. 80–91Google Scholar
  41. 41.
    Hutchinson M F. Anusplin Version 4.2 User Guide. 2002Google Scholar
  42. 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: 498–504Google Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.College of Urban and Environmental SciencesLaboratory for Earth Surface Processes of the Ministry of Education Peking UniversityBeijingChina

Personalised recommendations