Assessing multi-risk characteristics of heat and cold stress for rice across the southern parts of China

  • Lei Zhang
  • Bingyun Yang
  • Sen Li
  • Dapeng Huang
  • Zhiguo HuoEmail author
Original Paper


Rice (Oryza sativa) growth is always threatened by heat as well as cold stress, when it is exposed to natural environment. Heat growing degree hours (HGDH) and cold growing degree hours (CGDH1 and CGDH2) were firstly proposed to quantify heat and cold stress occurred during different growing stages. Information diffusion method was effectively used to fit the distribution and estimate probability of single stress at each station, with an advantage of no limitation in data series. In terms of single stress, highest probability was seen in HGDH, followed by CGDH1 and CGDH2. Seven copula functions, i.e., normal and t, Gumbel-Hougaard, Clayton, Frank, Joe, and Ali-Mikhail-Haq, were applied to fit the distribution of multi-stress with significant dependence, and simple calculation based on single stress was used to quantify the probability for multi-stress with independence. In these copulas, t was the most chosen one in the fitting of HGDH-CGDH1, HGDH-CGDH2, CGDH1-CGDH2, and HGDH-CGDH1-CGDH2, selected by the statistic of Akaike information criterion. Regarding bi-stress, higher joint probability was in HGDH-CGDH1, relative to HGDH-CGDH2 and CGDH1-CGDH2. As expected, the co-occurrence probability of tri-stress was lower than that of bi-stress in the magnitude and spatial extent, while joint probability of tri-stress was larger. Given the condition of occurrence of HGDH or CGDH1, the joint probability of HGDH-CGDH1 was higher than other pairs of bi-stress and tri-stress. It was special that higher joint probability of CGDH1-CGDH2 was detected under the condition of CGDH2 relative to CGDH1. Joint probability of tri-stress was lower under the condition of HGDH-CGDH1, in comparison with HGDH-CGDH2 and CGDH1-CGDH2. Hazards of single stress and multi-stress were expressed by the integration of intensity of stress index and corresponding probability. Most consistent conclusions agreed that central Fujian, Zhejiang, and northeastern Jiangxi were exposed to higher hazard, derived from not only single stress but also multi-stress. These results can be helpful in provision of information regarding prevention and adaptation strategies for rice cultivation as a response to extreme temperature stress.


Heat stress Cold stress Information diffusion Copula Risk assessment 



This study was co-funded by the National Key R&D Program, Ministry of Science and Technology, China [2017YFD0300101]; Meteorological Public Welfare Profession of China [GYHY201306045, GYHY201506001].

Supplementary material

484_2019_1772_MOESM1_ESM.docx (741 kb)
ESM 1 (DOCX 741 kb)


  1. Ariff NM, Jemain AA, Ibrahim K, Zin WZW (2012) IDF relationships using bivariate copula for storm events in peninsular Malaysia. J Hydrol 470:158–171CrossRefGoogle Scholar
  2. Ahmed O, Serra T (2015) Economic analysis of the introduction of agricultural revenue insurance contracts in Spain using statistical copulas. Agric Econ 46:69–79CrossRefGoogle Scholar
  3. Bheemanahalli R, Sathishraj R, Tack J, Nalley LL, Muthurajan R (2016) Temperature thresholds for spikelet sterility and associated warming impacts for sub-tropical rice. Agric For Meteorol 221:122–130CrossRefGoogle Scholar
  4. Bouman B (2001) ORYZA2000: modeling lowland rice international rice research institute/Wageningen University and Research Centre, Los Banos. Philippines/ Wageningen, NetherlandsGoogle Scholar
  5. Bonnecarrere V, Borsani O, Diaz P, Capdevielle F, Blanco P, Monza J (2011) Response to photoxidative stress induced by cold in japonica rice is genotype dependent. Plant Sci 180:726–732CrossRefGoogle Scholar
  6. Challinor AJ, Wheeler TR, Craufurd PQ, Slingo JM (2005) Simulation of the impact of high temperature stress on annual crop yields. Agric For Meteorol 135:180–189CrossRefGoogle Scholar
  7. Chen FB, Sushil P, Ding SJ (2013) Changing rice cropping patterns: evidence from the Yangtze River valley, China. Outlook Agr 42:109–115CrossRefGoogle Scholar
  8. Chen Y, Zhang Z, Tao FL (2018) Impacts of climate change and climate extremes on major crops productivity in China at a global warming of 1.5 and 2.0 °C. Earth Syst Dynam 9:543–556CrossRefGoogle Scholar
  9. Cheng YX, Huang JF, Han ZL, Guo JP, Zhao YX, Wang XZ, Guo RF (2013) Cold damage risk assessment of double cropping rice in Hunan, China. J Integr Agr 12:352–363CrossRefGoogle Scholar
  10. Endo M, Tsuchiya T, Hamada K, Kawamura S, Yano K, Ohshima M, Higashitani A, Watanabe M, Kobayashi MK (2009) High temperatures cause male sterility in rice plants with transcriptional alterations during pollen development. Plant and Cell Physiol 50:1911–1922CrossRefGoogle Scholar
  11. GB/T 21985–2008 (2008) Temperature index of high temperature harm for main crops. China Standards Press, Beijing [in Chinese]Google Scholar
  12. GB/T 27949–2011 (2012) Low temperature disaster of southern rice, rapeseed and orange. China Standards Press, Beijing [in Chinese]Google Scholar
  13. Genz A, Bretz F (2010) Computation of multivariate Normal and t probabilities. J Stat Softw 33:1641Google Scholar
  14. Gu S (2015) Growing degree hours - a simple, accurate, and precise protocol to approximate growing heat summation for grapevines. Int J Biometeorol 60:1–12Google Scholar
  15. Guo L, Xu J, Dai J, Cheng J, Luedeling E (2015) Statistical identification of chilling and heat requirements for apricot flower buds in Beijing, China. Sci Hortic-Amsterdam 195:138–144CrossRefGoogle Scholar
  16. Hangshing L, Dabral PP (2018) Multivariate frequency analysis of meteorological drought using copula. Water Resour Manage,,
  17. Hao L, Zhang XY, Liu SD (2012) Risk assessment to China’s agricultural drought disaster in county unit. Nat Hazards 61:785–801CrossRefGoogle Scholar
  18. Hopper JL, Mathews JD (2012) Extensions to multivariate normal models for pedigree analysis. Ann Hum Genet 46:373–383CrossRefGoogle Scholar
  19. Houghton JT, Ding Y, Griggs DJ (2001) Climate change 2001: scientific basis Cambridge University press, New YorkGoogle Scholar
  20. Huang CF, Moraga C (2005) Extracting fuzzy if-then rules by using the information matrix technique. J Comput Syst Sci 70:26–52CrossRefGoogle Scholar
  21. Huang M, Jiang JG, Zou YB, Zhang WX (2013a) On-farm assessment of effect of low temperature at seedling stage on early-season rice quality. Field Crop Res 141:63–68CrossRefGoogle Scholar
  22. Huang M, Zhang WX, Jiang LG, Zou YB (2013b) Impact of temperature changes on early-rice productivity in a subtropical environment of China. Field Crop Res 146:10–15CrossRefGoogle Scholar
  23. Huang J, Zhang FM, Xue Y, Lin J (2017) Recent changes of rice heat stress in Jiangxi province, Southeast China. Int J Biometeorol 61:623–633CrossRefGoogle Scholar
  24. Jagadish SVK, Craufurd P, Shi W, Oane R (2014) A phenotypic marker for quantifying heat stress impact during microsporogenesis in rice (Oryza sativa L). Funct Plant Biol 41:48–55CrossRefGoogle Scholar
  25. Julia C, Dingkuhn M (2012) Variation in time of day of anthesis in rice in different climatic environments. Eur J Agron 43:166–174CrossRefGoogle Scholar
  26. Kappes MS, Keiler M, Elverfeldt K, Glade T (2012) Challenges of analyzing multi-hazard risk: a review. Nat Hazards 64:1925–1958CrossRefGoogle Scholar
  27. Kong L, Ashraf U, Cheng S, Rao G, Mo Z (2017) Short-term water management at early filling stage improves early-season rice performance under high temperature stress in South China. Eur J Agron 90:117–126CrossRefGoogle Scholar
  28. Li Y, Gu W, Cui W, Chang Z, Xu Y (2015) Exploration of copula function use in crop meteorological drought risk analysis: a case study of winter wheat in Beijing, China. Nat Hazards 77:1289–1303CrossRefGoogle Scholar
  29. Liu B, Liu LL, Tian LY, Cao W, Zhu Y, Asseng S (2014) Post-heading heat stress and yield impact in winter wheat of China. Glob Chang Biol 20:372–381CrossRefGoogle Scholar
  30. Liu CT, Wang W, Mao BG, Chu CC (2018) Cold stress tolerance in rice: physiological changes, molecular mechanism, and future prospects. Hereditas 40:171Google Scholar
  31. Liu XF, Wang SX, Zhou Y, Wang FT, Yang G, Liu WL (2016) Spatial analysis of meteorological drought return periods in China using copulas. Nat Hazards 80:367–388CrossRefGoogle Scholar
  32. Madadgar S, AghaKouchak A, Farahmand A, Davis SJ (2017) Probabilistic estimates of drought impacts on agricultural production. Geophys Res Lett.
  33. Ming XD, Xu W, Li Y, Du J, Liu BY, Shi PJ (2015) Quantitative multi-hazard risk assessment with vulnerability surface and hazard joint return period. Stoch Environ Res Risk Assess 29:35–44CrossRefGoogle Scholar
  34. Mosquera-Machado S, Dilley M (2009) A comparison of selected global disaster risk assessment results. Nat Hazards 48:439–456CrossRefGoogle Scholar
  35. Nguyen DN, Lee KJ, Kim DI, Anh NT, Lee BW (2014) Modeling and validation of high-temperature induced spikelet sterility in rice. Field Crop Res 156:293–302CrossRefGoogle Scholar
  36. NY/T 2915–2016 (2016) Identification and classification of heat injury of rice. China Agriculture Press, Beijing [in Chinese]Google Scholar
  37. QX/T 98–2008 (2008) Grade of cold and rainy weather during the seeding-raising stage of early rice. China Meteorological Press, Beijing [in Chinese]Google Scholar
  38. Sebastian JSV, Somayanda IM, Chiluwal A, Perumal R, Prasad PVV, Jagadish K (2017) Resilience of pollen and post-flowering response in diverse sorghum genotypes exposed to heat stress under field conditions. Crop Sci 57:1658–1669CrossRefGoogle Scholar
  39. Sraj M, Bezak N, Brilly M (2015) Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River. Hydrol Process 29:225–238CrossRefGoogle Scholar
  40. Shi PH, Tang L, Wang LH, Sun T, Liu LL, Cao WX, Zhu Y (2015) Post-heading heat stress in rice of South China during 1981-2010. Plos One.
  41. Snider JL, Oosterhuis DM, Loka DA, Kawakami EM (2011) High temperature limits in vivo pollen tube growth rates by altering diurnal carbohydrate balance in field-grown Gossypium hirsutum pistils. J Plant Physiol 168:1168–1175CrossRefGoogle Scholar
  42. Srivastava (2003) Singular wishart and multivariate Beta distributions. Ann Stat 31:1537–1560CrossRefGoogle Scholar
  43. Tao FL, Hayashi Y, Zhang Z, Sakamoto T, Yokozawa M (2008) Global warming, rice production, and water use in China: developing a probabilistic assessment. Agric For Meteorol 148:94–110CrossRefGoogle Scholar
  44. Tao FL, Zhang S, Zhang Z (2013) Changes in rice disasters across China in recent decades and the meteorological and agronomic causes. Reg Environ Chang 13:743–759CrossRefGoogle Scholar
  45. Tao FL, Zhang Z (2013) Climate change, high-temperature stress, rice productivity, and water use in eastern China: a new superensemble-based probabilistic projection. J Appl Meteorol Clim 52:531–551CrossRefGoogle Scholar
  46. Teixeira EI, Fischer G, van Velthuizen H, Walter C, Ewert F (2013) Global hot-spots of heat stress on agricultural crops due to climate change. Agric For Meteorol 170:206–215CrossRefGoogle Scholar
  47. Tosunoglu F, Can I (2016) Application of copulas for regional bivariate frequency analysis of meteorological droughts in Turkey. Nat Hazards 82:1457–1477CrossRefGoogle Scholar
  48. Van Oort PAJ, Saito K, Zwart SJ, Shrestha S (2014) A simple model for simulating heat induced sterility in rice as a function of flowering time and transpirational cooling. Field Crops Res 156:303–312CrossRefGoogle Scholar
  49. Van Oort PAJ, de Vries ME, Yoshida H, Saito K (2015) Improved climate risk simulations for rice in arid environments. PLoS One 10:e0118114CrossRefGoogle Scholar
  50. Vergni L, Todisco F, Mannocchi F (2015) Analysis of agricultural drought characteristics through a two-dimensional copula. Water Resour Manag 29:2819–2835CrossRefGoogle Scholar
  51. Vittal H, Singh J, Kumar P, Karmakar S (2015) A framework for multivariate data-based at-site flood frequency analysis: essentiality of the conjugal application of parametric and nonparametric approaches. J Hydrol 525:658–675CrossRefGoogle Scholar
  52. Wang P, Zhang Z, Song X, Chen Y, Wei X, Shi PJ, Tao FL (2014) Temperature variations and rice yields in China: historical contributions and future trends. Clim Chang 124:777–789CrossRefGoogle Scholar
  53. Wang P, Zhang Z, Chen Y, Wei X, Feng BY, Tao FL (2016) How much yield loss has been caused by extreme temperature stress to the irrigated rice production in China? Clim Chang 134:1–16CrossRefGoogle Scholar
  54. Wu MH, Chen YN, Xu CC (2015a) Assessment of meteorological disasters based on information diffusion theory in Xinjiang, Northwest China. J Geogr Sci 25:69–84CrossRefGoogle Scholar
  55. Wu Z, Lin Q, Lu G, He H, Qu JJ (2015b) Analysis of hydrological drought frequency for the Xijiang River basin in South China using observed streamflow data. Nat Hazards 77:1655–1677CrossRefGoogle Scholar
  56. Ye Q, Yang XG, Dai S, Cheng GS, Li Y, Zhang CX (2015) Effects of climate change on suitable rice cropping areas, cropping systems and crop water requirements in southern China. Agr Water Manage 159:35–44CrossRefGoogle Scholar
  57. Yoshida R, Fukui S, Shimada T, Hasegawa T, Ishigooka Y, Takayabu I, Iwasaki T (2015) Adaptation of rice to climate change through an ultivar-based simulation: a possible cultivar shift in eastern Japan. Clim Res 64:275–290CrossRefGoogle Scholar
  58. Yuan J, Meng J, Liang X, Yang E, Yang X, Chen WF (2017) Organic molecules from biochar leacheates have a positive effect on rice seedling cold tolerance. Front Plant Sci 8:1624CrossRefGoogle Scholar
  59. Zhang L, Singh VP (2007) Bivariate rainfall frequency distributions using Archimedean copulas. J Hydrol 332:93–109CrossRefGoogle Scholar
  60. Zhang Q, Li JF, Singh VP, Xu CF (2013) Copula-based spatio-temporal patterns of precipitation extremes in China. Int J Climatol 5:1140–1152CrossRefGoogle Scholar
  61. Zhang Z, Wang P, Chen Y, Song X, Wei X, Shi PJ (2014) Global warming over 1960-2009 did increase heat stress and reduce cold stress in the major rice-planting areas across China. Eur J Agron 59:49–56CrossRefGoogle Scholar
  62. Zhang RR, Chen X, Cheng QB, Zhang ZC, Shi P (2016a) Joint probability of precipitation and reservoir storage for drought estimation in the headwater basin of the Huaihe River, China. Stoch Environ Res Risk Assess 30:1641–1657CrossRefGoogle Scholar
  63. Zhang S, Tao FL, Zhang Z (2016b) Changes in extreme temperatures and their impacts on rice yields in southern China from 1981 to 2009. Field Crop Res 189:43–50CrossRefGoogle Scholar
  64. Zhang L, Huo ZG, Zhang LZ, Huang DP (2017a) Integrated risk assessment of major meteorological disasters in paprika pepper in Hainan Province. J Trop Meteorol 23:334–344Google Scholar
  65. Zhang Z, Chen Y, Wang CZ, Wang P, Tao FL (2017b) Future extreme temperature and its impact on rice yield in China. Int J Climatol.
  66. Zhang L, Yang BY, Li S, Hou YY, Huang DP (2018a) Potential rice exposure to heat stress along the Yangtze River in China under RCP85 scenario. Agric For Meteorol 248:185–196CrossRefGoogle Scholar
  67. Zhang CX, Li GY, Chen TT, Feng BH, Fu WM, Yan JX, Islam RM, Jin QQ, Tao LX, Fu GF (2018b) Heat stress induces spikelet sterility in rice at anthesis through inhibition of pollen tube elongation interfering with auxin homeostasis in pollinated pistils. Rice 11:14CrossRefGoogle Scholar

Copyright information

© ISB 2019

Authors and Affiliations

  1. 1.National Meteorological CenterBeijingChina
  2. 2.National Satellite Meteorological CenterBeijingChina
  3. 3.National Climate CenterBeijingChina
  4. 4.Chinese Academy of Meteorological SciencesBeijingChina
  5. 5.Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science & TechnologyNanjingChina

Personalised recommendations