Journal of Meteorological Research

, Volume 33, Issue 2, pp 363–374 | Cite as

Projection of Heat Injury to Single-Cropping Rice in the Middle and Lower Reaches of the Yangtze River, China under Future Global Warming Scenarios

  • Xiaomin Lyu
  • Guangsheng ZhouEmail author
  • Mengzi Zhou
  • Li Zhou
  • Yuhe Ji
Regular Articles


Based on simulation results from the 16 CMIP5 model runs under three Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5) in combination with the recent five years of growth-stage data from agrometeorological observation stations in the middle and lower reaches of the Yangtze River, changes in heat injury and spatial distribution patterns of single-cropping rice in China during the early (2016–35), middle (2046–65), and late (2080–99) 21st century were projected by using quantitative estimations. Relative to the reference period (1986–2005), the occurrence probabilities of heat injury to single-cropping rice under different RCP scenarios increased significantly, showing a trend of mild > moderate > severe. The occurrence probabilities increased with time and predicted emissions, especially the average and maximum occurrence probabilities, which were ~48% and ~80%, respectively, in the late 21st century under the RCP8.5 scenario. The spatial patterns of the occurrence probabilities at each level of heat injury to single-cropping rice did not change, remaining high in the middle planting region and low in the east. The high-value areas were mainly in central Anhui and southeastern Hubei provinces, and the areas extended to the northwest and northeast of the cultivation area over time. Under the RCP2.6, RCP4.5, and RCP8.5 scenarios, the total area of heat injury to single-cropping rice showed a significant linear increasing trend of 7.4 × 103, 19.9 × 103, and 35.3 × 103 ha yr−1, respectively, from 2016 to 2099, and the areas of heat injury were greatest in the late 21 st century, accounting for ~25%, ~40%, and ~59% of the cultivation area.

Key words

projection single-cropping rice heat injury climate change China 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



We thank Lesley Benyon and Alex Boon from Liwen Bianji, Edanz Group China (, for editing the English text of a draft of this manuscript.


  1. Anav, A., P. Friedlingstein, M. Kidston, et al., 2013: Evaluating the land and ocean components of the global carbon cycle in the CMIP5 Earth System Models. J. Climate, 26, 6801–6843, doi: 10.1175/JCLI-D-12-00417.1.CrossRefGoogle Scholar
  2. Battisti, D.S., and R. L. Naylor, 2009: Historical warnings of future food insecurity with unprecedented seasonal heat. Science, 323, 240–244, doi: 10.1126/science.ll64363.CrossRefGoogle Scholar
  3. Betts, R.A., L. Alfieri, C. Bradshaw, et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philos. Trans. Roy. Soc. A: Math., Phys. Eng. Sci., 376, 20160452, doi: 10.1098/rsta.2016.0452.CrossRefGoogle Scholar
  4. Chen, M.P., and E. D. Lin, 2010: Global greenhouse gas emission mitigation under representative concentration pathways scenarios and challenges to China. Adv. Climate Change Res., 6, 436–442, doi: 10.3969/j.issn.l673-1719.2010.06.008. (in Chinese)Google Scholar
  5. Chen, X.L., and T. J. Zhou, 2016: Uncertainty in crossing time of 2°C warming threshold over China. Sci. Bull., 61, 1451–1459, doi: 10.1007/s11434-016-1166-z.CrossRefGoogle Scholar
  6. Dong, S.Y., Y. Xu, B. T. Zhou, et al., 2014: Projected risk of extreme heat in China based on CMTP5 models. Adv. Climate Change Res., 10, 365–369, doi: 10.3969/j.issn.1673-1719. 2014.05.008. (in Chinese)Google Scholar
  7. Duan, J.Q., and G. S. Zhou, 2012: Climatic suitability of single cropping rice planting region in China. Chinese J. Appl. Ecol., 23, 426–432. (in Chinese)Google Scholar
  8. General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Standardization Administration of China, 2008: GB/T 21985-2008 Temperature index of high temperature harm for main crops. Standards Press of China, Beijing, 4 pp. (in Chinese)Google Scholar
  9. Granderson, A.A., 2014: Making sense of climate change risks and responses at the community level: A cultural-political lens. Climate Risk Manag., 3, 55–64, doi: 10.1016/j.crm. 2014.05.003.CrossRefGoogle Scholar
  10. Guo, J.M., Y. Y. Wang, S. T. Li, et al., 2018: Calculation of rice field temperature based on station temperature and its evaluation on heat injury of rice. J. Nat. Disasters, 27, 162–171, doi: 10.13577/j.jnd.2018.0319. (in Chinese)Google Scholar
  11. Guo, Y., W. J. Dong, F. M. Ren, et al., 2013: Assessment of CMTP5 simulations for China annual average surface temperature and its comparison with CMLP3 simulations. Adv. Climate Change Res., 9, 181–186, doi: 10.3969/j.issn.l673-1719.2013.03.004. (in Chinese)Google Scholar
  12. Han, B., S. H. Lyu, Y. H. Gao, et al., 2015: Response of atmospheric energy to historical climate change in CMIP5. J. Meteor. Res., 29, 93–105, doi: 10.1007/sl3351-014-4016-4.CrossRefGoogle Scholar
  13. He, B., Z. J. Liu, X. G. Yang, et al., 2017: Temporal and spatial variations of agro-meteorological disasters of main crops in China in a changing climate (II): Drought of cereal crops in Northwest China. Chinese J. Agrometeorol., 38, 31–41, doi: 10.3969/j.issn.1000-6362.2017.01.004. (in Chinese)Google Scholar
  14. Hiwasaki, L., E. Luna, Syamsidik, et al., 2014: Process for integrating local and indigenous knowledge with science for hydro-meteorological disaster risk reduction and climate change adaptation in coastal and small island communities. Int. J. Disast.RiskRe., 10, 15–27, doi: 10.1016/j.ijdrr.2014.07.007.CrossRefGoogle Scholar
  15. Hou, W. J., T. Geng, Q. Chen, et al., 2015: Impacts of climate warming on growth period and yield of rice in Northeast China during recent two decades. Chinese J. Appl. Ecol, 26, 249–259, doi: 10.13287/j.1001-9332.2015.0002. (in Chinese)Google Scholar
  16. Hu, X. Y., Y. Huang, W. J. Sun, et al., 2017: Shifts in cultivar and planting date have regulated rice growth duration under climate warming in China since the early 1980s. Agric. Forest Meteor, 247, 34–41, doi: 10.1016/j.agrformet.2017.07.014.CrossRefGoogle Scholar
  17. IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, C. B. Field, V. R. Barros, D. J. Dokken, et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1–32.Google Scholar
  18. Jiang, Y.M., and H. M. Wu, 2013: Simulation capabilities of 20 CMTP5 models for annual mean air temperatures in central Asia. Progressus Inquisitiones de Mutatione Climatis, 9, 110–116, doi: 10.3969/j.issn.l673-1719.2013.02.005. (in Chinese)Google Scholar
  19. Jiao, H.Y., G. S. Zhou, and Z. Q. Zhang, 2017: Blue Book of Agriculture for Addressing Climate Change: Assessment Report of Agro-meteorological Disasters and Yield Losses in China (No. 2). Social Sciences Academic Press, Beijing, 1–33. (in Chinese)Google Scholar
  20. Lesk, C., P. Rowhani, and N. Ramankutty, 2016: Influence of extreme weather disasters on global crop production. Nature, 529, 84–87, doi: 10.1038/naturel6467.CrossRefGoogle Scholar
  21. Li, X.T., J. Chen, and W. Guo, 2018: A review of the influence factors of plant phenology under different climate types. J. Earth Environ., 9, 16–27, doi: 10.7515/JEE181002. (in Chinese)Google Scholar
  22. Li, Y., Y. H. Ding, and W. J. Li, 2017: Observed trends in various aspects of compound heat waves across China from 1961 to 2015. J. Meteor. Res., 31, 455–467, doi: 10.1007/sl3351-017-6150-2.CrossRefGoogle Scholar
  23. Lin, Z.H., X. Y. Yang, C. L. Wu, et al., 2018: Capability assessment of CMLP5 models in reproducing observed climatology and decadal changes in summer rainfall with different intensities over eastern China. Climatic Environ. Res., 23, 1–25. (in Chinese)Google Scholar
  24. Liu, J., C. Chen, Y. F. Zhang, et al., 2018: Space-time distribution of high temperature disasters on single-cropping rice during heading-flowering stage and filling-harvest stage in Sichuan Province. Chinese J. Agrometeorol., 39, 46–58, doi: 10.3969/j.issn.1000-6362.2018.01.006. (in Chinese)Google Scholar
  25. Liu, X.C., Q. H. Tang, X. J. Zhang, et al., 2018: Projected changes in extreme high temperature and heat stress in China. J. Meteor. Res., 32, 351–366, doi: 10.1007/sl3351-018-7120-z.CrossRefGoogle Scholar
  26. Liu, Y.H., J. M. Feng, and Z. G. Ma, 2014: An analysis of historical and future temperature fluctuations over China based on CMPP5 simulations. Adv. Atmos. Sci., 31, 457–467, doi: 10.1007/s00376-013-3093-0.CrossRefGoogle Scholar
  27. Meng, L., C. Y. Wang, and J. Q. Zhang, 2016: Heat injury risk assessment for single-cropping rice in the middle and lower reaches of the Yangtze River under climate change. J. Meteor. Res., 30, 426–443, doi: 10.1007/sl3351-016-5186-z.CrossRefGoogle Scholar
  28. Qin, D. H., Z. L. Chen, Y. Luo, et al., 2007: Updated understanding of climate change science. Adv. Climate Change Res., 3, 63–73, doi: 10.3969/j.issn.l673-1719.2007.02.001. (in Chinese)Google Scholar
  29. Palerme, C., C. Genthon, C. Claud, et al., 2017: Evaluation of current and projected Antarctic precipitation in CMPP5 models. ClimateDyn., 48, 225–239, doi: 10.1007/s00382-016-3071-1.Google Scholar
  30. Sun, Q. H., C. Y. Miao, A. AghaKouchak, et al., 2017: Unraveling anthropogenic influence on the changing risk of heat waves in China. Geophys. Res. Lett., 44, 5078–5085, doi: 10.1002/2017GP073531.CrossRefGoogle Scholar
  31. Pao, F.P., and Z. Zhang, 2013: Climate change, high-temperature stress, rice productivity, and water use in eastern China: A new superensemble-based probabilistic projection. J. Appl. Meteor. Climatol, 52, 531–551, doi: 10.1175/JAMC-D-12-0100.1.CrossRefGoogle Scholar
  32. Pao, F.P., Z. Zhang, W. J. Shi, et al., 2013: Single rice growth period was prolonged by cultivars shifts, but yield was damaged by climate change during 1981–2009 in China, and late rice was just opposite. Glob. Chang. Biol, 19, 3200–3209, doi: 10.1111/gcb.l2250.CrossRefGoogle Scholar
  33. Paylor, K.E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMPP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485–498, doi: 10.1175/BAMS-D-11-00094.1.CrossRefGoogle Scholar
  34. Pian, D., W. J. Dong, H. Zhang, et al., 2017: Future changes in coverage of 1.5°C and 2°C warming thresholds. Sci. Bull., 62, 1455–1463, doi: 10.1016/j.scib.2017.09.023.CrossRefGoogle Scholar
  35. Pokarska, K.B., and N. P. Gillett, 2018: Cumulative carbon emissions budgets consistent with 1.5°C global warming. Nat. Clim. Change, 8, 296–299, doi: 10.1038/s41558-018-0118-9.CrossRefGoogle Scholar
  36. Pramblay, Y., W. Badi, F. Driouech, et al., 2012: Climate change impacts on extreme precipitation in Morocco. Glob. Planet. Change, 82–83, 104–114, doi: 10.1016/j.gloplacha.2011.12. 002.CrossRefGoogle Scholar
  37. Wang, X.H., S. P. Piao, X. P. Xu, et al., 2015: Has the advancing onset of spring vegetation green-up slowed down or changed abruptly over the last three decades? Glob. Ecol. Biogeogr., 24, 621–631, doi: 10.1111/geb.12289.CrossRefGoogle Scholar
  38. Wang, Z.Y., 2011: Study of effects of future climate change on rice production in the middle and lower reaches of the Yangtze River. Master dissertation, Nanjing University of Information Science & Pechnology, Nanjing, 52 pp. (in Chinese)Google Scholar
  39. Xie, Z.Q., Y. Du, P. Gao, et al., 2013: Impact of high-temperature on single cropping rice over Yangtze-Huaihe River valley and response measures. Meteor. Mon., 39, 774–781, doi: 10.7519/j.issn.l000-0526.2013.06.014. (in Chinese)Google Scholar
  40. Xiong, W., P. Z. Feng, H. Ju, et al., 2016: Possible impacts of high temperatures on China’s rice yield under climate change. Adv. Earth Sci., 31, 515–528. (in Chinese)Google Scholar
  41. Xu, Y., X. J. Gao, and F. Giorgi, 2010: Upgrades to the reliability ensemble averaging method for producing probabilistic climate-change projections. Climate Res., 41, 61–81, doi: 10.3354/cr00835.CrossRefGoogle Scholar
  42. Yang, S.C., S. H. Shen, and S. P. Pao, 2016: Spatiotemporal variation and risk assessment of single-harvest rice heat injury along the middle and lower reaches of Yangtze River. J. Nat. Disasters, 25, 78–85, doi: 10.13577/j.jnd.2016.0209. (in Chinese)Google Scholar
  43. Zhan, M.J., X. C. Pi, H. M. Sun, et al., 2018: Changes in extreme maximum temperature events and population exposure in China under global warming scenarios of 1.5 and 2.0°C: Analysis using the regional climate model COSMO-CPM. J. Meteor. Res., 32, 99–112, doi: 10.1007/sl3351-018-7016-y.CrossRefGoogle Scholar
  44. Zhang, Q., Y. X. Zhao, and C. Y. Wang, 2011: Study on the impact of high temperature damage to rice in the lower and middle reaches of the Yangtze River. J. Catastrophol, 26, 57–62, doi: 10.3969/j.issn.1000-811X.2011.04.011. (in Chinese)Google Scholar
  45. Zhang, X.F., D. Y. Wang, F. P. Fang, et al., 2005: Food safety and rice production in China. Research of Agricultural Modernization, 26, 85–88, doi: 10.3969/j.issn.1000-0275.2005. 02.002. (in Chinese)Google Scholar
  46. Zhou, G.S., Q. J. He, and Y. H. Ji, 2016: Advances in the international action and agricultural measurements of adaptation to climate change. J. Appl. Meteor. Sci., 27, 527–533, doi: 10.11898/1001-7313.20160502. (in Chinese)Google Scholar
  47. Zhu, D.F., Y. P. Zhang, H. Z. Chen, et al., 2015: Innovation and practice of high-yield rice cultivation technology in China. Scientia Agricultura Sinica, 48, 3404–3414, doi: 10.3864/j.issn.0578-1752.2015.17.008. (in Chinese)Google Scholar
  48. Zuo, Q.J., S. P. Gao, and X. G. Sun, 2016: Effects of the upstream temperature anomaly on freezing rain and snowstorms over southern China in early 2008. J. Meteor. Res., 30, 694–705, doi: 10.1007/s13351-016-5253-5.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2019

Authors and Affiliations

  • Xiaomin Lyu
    • 1
  • Guangsheng Zhou
    • 1
    • 2
    Email author
  • Mengzi Zhou
    • 1
  • Li Zhou
    • 1
  • Yuhe Ji
    • 1
  1. 1.State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesBeijingChina
  2. 2.Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science & TechnologyNanjingChina

Personalised recommendations