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

+ Author Affiliations + Find other works by these authors
  • Corresponding author: Guangsheng ZHOU, zhougs@cma.gov.cn
  • Funds:

    Supported by the Special Climate Change Project of China Meteorological Administration (CCSF201801) and National Natural Science Foundation of China (41330531 and 41501047)

  • doi: 10.1007/s13351-019-8105-2

PDF

  • 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 scena-rios, 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 21st century, accounting for ~25%, ~40%, and ~59% of the cultivation area.
  • 加载中
  • Fig. 1.  Planting areas of single-cropping rice in the middle and lower reaches of the Yangtze River, China.

    Fig. 2.  Occurrence probability of heat injury to single-cropping rice under different RCP climate scenarios (RCP2.6, RCP4.5, and RCP8.5). The inset plot in the upper left corner shows the occurrence probability for the reference period (P0, 1986–2005). Mil, Mod, Sev, and Total indicate the mild, moderate, severe, and total occurrence probability of heat injury, respectively; P1: early period (2016–35); P2: middle period (2046–65); P3: late period (2080–99). Black lines from top to bottom of box-whisker plot indicate the occurrence probabilities of 95%, 75%, 50%, 25%, and 5% quantiles; black dots indicate the average values.

    Fig. 3.  Spatial distributions of the occurrence probability of total heat injury to single-cropping rice under RCP climate scenarios (RCP2.6, RCP4.5, and RCP8.5). P0: the reference period (1986–2005); P1: early period (2016–35); P2: middle period (2046–65); P3: late period (2080–99).

    Fig. 4.  As in Fig. 3, but for the mild heat injury .

    Fig. 5.  As in Fig. 3, but for the moderate heat injury.

    Fig. 6.  As in Fig. 3, but for the severe heat injury.

    Fig. 7.  Occurrence area of different levels of heat injury to single-cropping rice under RCP climate scenarios (RCP2.6, RCP4.5, and RCP8.5) for (a) mild, (b) moderate, (c) severe, and (d) total heat injury.

    Fig. 8.  Areas with heat injury occurrence probability ≥ 50% and their proportion of the main planting area in single-cropping rice under different RCP climate scenarios (RCP2.6, RCP4.5, and RCP8.5). Notes see Fig. 3.

    Table 1.  Characteristics of the 16 CMIP5 models used in this study

    No.ModelCountryResolution (grids)
    1CanESM2Canada 64 × 128
    2GFDL-CM3America 90 × 144
    3GFDL-ESM2GAmerica 90 × 144
    4GFDL-ESM2MAmerica 90 × 144
    5HadGEM2-ESEngland192 × 145
    6IPSL-CM5A-MRFrance143 × 144
    7MIROC-ESM-CHEMJapan160 × 320
    8NorESM1-MNorway 96 × 144
    9MIROC5Japan128 × 256
    10MIROC-ESMJapan 64 × 128
    11MPI-ESM-MRJapan 64 × 128
    12MPI-ESM-LRGermany 96 × 192
    13MRI-CGCM3Germany 96 × 192
    14BCC-CSM1-1China 64 × 128
    15CCSM4America192 × 288
    16CSIRO-MK3-6-0Australia 96 × 192
    Download: Download as CSV

    Table 2.  Grades of heat injury and heat injury indices in single-cropping rice

    GradeReference of yield reduction rate (%)Heat injury index
    Maximum daily temperature (Tmax; °C)Duration (D; day)
    Mild5% < yield reduction rate ≤10%≥ 35.03–4
    Moderate10% < yield reduction rate ≤ 15%≥ 35.05–7
    Severeyield reduction rate >15% ≥ 35.0≥ 8
    Notes: In the classification of heat injury grades, “severe” was given priority over “moderate”, and “moderate” priority was over “mild”.
    Download: Download as CSV

    Table 3.  Percentages of occurrence area at different levels of heat injury to planting area in single-cropping rice under different RCP climate scenarios (RCP2.6, RCP4.5, and RCP8.5)

    Level of heat injuryClimate scenarioPercentage (%)
    P0 (1986–2005)P1 (2006–25)P2 (2046–65)P3 (2080–99)
    RCP2.6 6.59 7.45 7.4210.47
    MildRCP4.5 9.5010.5815.42
    RCP8.5 8.8811.3719.87
    RCP2.6 4.29 5.15 5.31 7.48
    ModerateRCP4.5 6.43 7.4512.20
    RCP8.5 6.38 8.9916.39
    RCP2.6 3.56 4.62 3.64 6.55
    SevereRCP4.5 5.48 6.7312.30
    RCP8.5 5.56 8.8722.35
    RCP2.617.2216.3624.50
    TotalRCP4.514.4321.4124.7539.92
    RCP8.520.8229.2458.60
    Download: Download as CSV
  • [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.
    [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.1164363.
    [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.
    [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.1673-1719.2010.06.008. (in Chinese)
    [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.
    [6]

    Dong, S. Y., Y. Xu, B. T. Zhou, et al., 2014: Projected risk of extreme heat in China based on CMIP5 models. Adv. Climate Change Res., 10, 365–369. doi: 10.3969/j.issn.1673-1719.2014.05.008. (in Chinese)
    [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)
    [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)
    [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.
    [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)
    [11]

    Guo, Y., W. J. Dong, F. M. Ren, et al., 2013: Assessment of CMIP5 simulations for China annual average surface temperature and its comparison with CMIP3 simulations. Adv. Climate Change Res., 9, 181–186. doi: 10.3969/j.issn.1673-1719.2013.03.004. (in Chinese)
    [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/s13351-014-4016-4.
    [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)
    [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. Risk Re., 10, 15–27. doi: 10.1016/j.ijdrr.2014.07.007.
    [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)
    [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.
    [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.
    [18]

    Jiang, Y. M., and H. M. Wu, 2013: Simulation capabilities of 20 CMIP5 models for annual mean air temperatures in central Asia. Progressus Inquisitiones de Mutatione Climatis, 9, 110–116. doi: 10.3969/j.issn.1673-1719.2013.02.005. (in Chinese)
    [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)
    [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/nature16467.
    [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)
    [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/s13351-017-6150-2.
    [23]

    Lin, Z. H., X. Y. Yang, C. L. Wu, et al., 2018: Capability assessment of CMIP5 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)
    [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)
    [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/s13351-018-7120-z.
    [26]

    Liu, Y. H., J. M. Feng, and Z. G. Ma, 2014: An analysis of historical and future temperature fluctuations over China based on CMIP5 simulations. Adv. Atmos. Sci., 31, 457–467. doi: 10.1007/s00376-013-3093-0.
    [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/s13351-016-5186-z.
    [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.1673-1719.2007.02.001. (in Chinese)
    [29]

    Palerme, C., C. Genthon, C. Claud, et al., 2017: Evaluation of current and projected Antarctic precipitation in CMIP5 models. Climate Dyn., 48, 225–239. doi: 10.1007/s00382-016-3071-1.
    [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: 10.1002/2017GL073531.
    [31]

    Tao, F. L., 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.
    [32]

    Tao, F. L., 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.12250.
    [33]

    Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485–498. doi: 10.1175/BAMS-D-11-00094.1.
    [34]

    Tian, 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.
    [35]

    Tokarska, 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.
    [36]

    Tramblay, 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.
    [37]

    Wang, X. H., S. L. Piao, X. T. 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.
    [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 & Technology, Nanjing, 52 pp. (in Chinese)
    [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.1000-0526.2013.06.014. (in Chinese)
    [40]

    Xiong, W., L. 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)
    [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.
    [42]

    Yang, S. C., S. H. Shen, and S. L. Tao, 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)
    [43]

    Zhan, M. J., X. C. Li, 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-CLM. J. Meteor. Res., 32, 99–112. doi: 10.1007/s13351-018-7016-y.
    [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)
    [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)
    [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)
    [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)
    [48]

    Zuo, Q. J., S. T. 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.
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

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

    Corresponding author: Guangsheng ZHOU, zhougs@cma.gov.cn
  • 1. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
  • 2. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044
Funds: Supported by the Special Climate Change Project of China Meteorological Administration (CCSF201801) and National Natural Science Foundation of China (41330531 and 41501047)

Abstract: 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 scena-rios, 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 21st century, accounting for ~25%, ~40%, and ~59% of the cultivation area.

1.   Introduction
  • Global warming is an indisputable reality that will continue into the foreseeable future (Qin et al., 2007; Zhou et al., 2016; Sun et al., 2017). The global surface temperature increased linearly by 0.85°C from 1880 to 2012 and is expected to increase by 1.0–3.7°C by the end of the 21st century (IPCC, 2014), with high-temperature events increasing globally (Betts et al., 2018). In the future, global warming will lead to the multiple and frequent occurrences of extreme meteorological disasters (Battisti and Naylor, 2009; Tramblay et al., 2012). Extremely high temperatures may also lead to large fluctuations in global and Chinese agricultural production (Lesk et al., 2016), and climate change puts global agricultural production, and even food security, at risk (Granderson, 2014; Hiwasaki et al., 2014; Jiao et al., 2017). China, which is a major global agricultural producer, is affected by a complex and variable climate, including its monsoon climate. Thus, China is one of the countries, which are most greatly affected by meteorological disasters (Zuo et al., 2016; He et al., 2017). The climate change that experienced in China is basically consistent with the trends in the global climate change, but its warming rate is greater than the global average (Wang et al., 2015; Li et al., 2017). In the future, China’s warming rate is predicted to reach 0.09–0.61°C (10 yr)–1 (Taylor et al., 2012; Liu et al., 2014), and extreme high-temperature events, such as heat waves, may also increase (Dong et al., 2014; Liu J. et al., 2018; Zhan et al., 2018).

    Rice is an important food crop in China, which plays an important role in the world rice production. The rice cultivation area in China accounts for ~18.5% of the global rice-cropping area, and the total output ranks first in the world, accounting for ~27.7% (Zhang et al., 2005; Zhu et al., 2015). According to the rice cultivation system, Chinese rice can be divided into single- and double-cropping rice, the cultivation areas of which are determined by agroclimatic resources (Duan and Zhou, 2012). The middle and lower reaches of the Yangtze River represent the largest rice-growing belt in China, with a rice planting area of ~20 million hm2, which accounts for ~67% of the national yield (Meng et al., 2016). Statistics have shown that the planting area of single-cropping rice has continuously increased since the mid-1970s, with an adjustment to the planting structure, reaching more than 40% of the total rice-planted area (Wang, 2011). Under subtropical high-temperature conditions, the middle and lower reaches of the Yangtze River are prone to the persistent hot weather from July to August and have experienced strong, wide-ranging extreme high-temperatures lasting more than 10 days every year since 1999 (Liu X. C. et al., 2018). The duration of the high-temperature periods has increased, especially in plain areas (Yang et al., 2016; Meng et al., 2016). Usually, single-cropping rice in the middle and lower reaches of the Yangtze River is in its earliest flowering period in July and August, and it is most sensitive to high temperatures during this time. Continuous maximum daily temperatures exceeding 35°C result in decreased rice production and reduced quality (Hou et al., 2015). Previous studies mostly assessed the effects of heat-damage to single-cropping rice in the past, and there are currently few reports of future projections based on coupled climate models. Therefore, the projection of heat injury to single-cropping rice production in the middle and lower reaches of the Yangtze River, China, under climate warming scenarios, is of great importance for the early adoption of heat injury defense measures to protect rice yield and quality.

    The development of numerical models provides a means for studying the future climate change and resulting extreme climate events (Betts et al., 2018; Tokarska et al., 2018). The latest Coupled Mode Intercomparison Phase 5 (CMIP5) earth system model has a high resolution and provides a global climate change database of nearly 30 climate models for current simulations and future projections. It has been widely applied to evaluate and estimate temperature, precipitation, sea surface temperature, wind speed, and other climatic factors (Han et al., 2015; Palerme et al., 2017; Lin et al., 2018). This study will project and reveal the occurrence probability of heat injury to single-cropping rice in China and its spatial distribution in the early, middle, and late 21st century by using CMIP5-coupled climate models under different Representative Concentration Pathway (RCP) emission scenarios (RCP2.6, RCP4.5, and RCP8.5) as well as observational data from agrometeorological stations. These findings will provide a scientific basis to enhance the disaster prevention and reduction and manage the single-cropping rice cultivation system.

2.   Data and methods
  • Sixteen global climate models were used in this study, for which the maximum daily temperature data from CMIP5 for 1861–2099 were simultaneously provided in historical and RCP2.6, RCP4.5, and RCP8.5 scenario simulations. Owing to the different spatial resolutions of the output variables of the global climate models (Table 1), the model data were interpolated to a resolution of 0.1° × 0.1° by using a bilinear interpolation method. To project the heat injury to single-cropping rice, we used 1986–2005 (P0) as a reference period for the current climate, which is consistent with the definition of IPCC AR5, and divided the 21st century into three periods: P1, the early period (2016–35); P2, the middle period (2046–65); and P3, the late period (2080–99).

    No.ModelCountryResolution (grids)
    1CanESM2Canada 64 × 128
    2GFDL-CM3America 90 × 144
    3GFDL-ESM2GAmerica 90 × 144
    4GFDL-ESM2MAmerica 90 × 144
    5HadGEM2-ESEngland192 × 145
    6IPSL-CM5A-MRFrance143 × 144
    7MIROC-ESM-CHEMJapan160 × 320
    8NorESM1-MNorway 96 × 144
    9MIROC5Japan128 × 256
    10MIROC-ESMJapan 64 × 128
    11MPI-ESM-MRJapan 64 × 128
    12MPI-ESM-LRGermany 96 × 192
    13MRI-CGCM3Germany 96 × 192
    14BCC-CSM1-1China 64 × 128
    15CCSM4America192 × 288
    16CSIRO-MK3-6-0Australia 96 × 192

    Table 1.  Characteristics of the 16 CMIP5 models used in this study

  • Heat injury to single-cropping rice in China typically occurs in the middle and lower reaches of the Yangtze River. The study region was thus located in the middle and lower reaches of the Yangtze River, including the entire territories of Jiangsu, Hubei, and Zhejiang, central and southern Anhui, northwestern Hunan, and northeastern Jiangxi provinces (Fig. 1). Based on site information from the national agrometeorological observation stations, we extracted the phenological data on single-cropping rice from the past five years and then calculated the average daily sequence of phenological phases at each station. Spatial interpolation was performed by inverse-distance weighting, and latitudinal and longitudinal information from future climate scenario data were used to extract the daily sequence of crop developmental phases.

    Figure 1.  Planting areas of single-cropping rice in the middle and lower reaches of the Yangtze River, China.

  • Heat injury of single-cropping rice in the middle and lower reaches of the Yangtze River mainly occurred at the heading–flowering stage. High temperature at the heading–flowering stage affected the cracking rate and pollen fertility of anthers, which in turn reduced the seed-setting rate. Based on previous research and the actual conditions in the middle and lower reaches of the Yangtze River, a maximum daily temperature of ≥ 35.0°C was used as the index of heat injury at the heading–flowering stage of single-cropping rice, and its duration (day) was used to determine the level of heat injury (Table 2) (General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China and Standardization Administration of China, 2008; Zhang et al., 2011). When performing heat injury grading, we exclude mild from moderate and exclude moderate from severe.

    GradeReference of yield reduction rate (%)Heat injury index
    Maximum daily temperature (Tmax; °C)Duration (D; day)
    Mild5% < yield reduction rate ≤10%≥ 35.03–4
    Moderate10% < yield reduction rate ≤ 15%≥ 35.05–7
    Severeyield reduction rate >15% ≥ 35.0≥ 8
    Notes: In the classification of heat injury grades, “severe” was given priority over “moderate”, and “moderate” priority was over “mild”.

    Table 2.  Grades of heat injury and heat injury indices in single-cropping rice

  • When projecting the heat injury of single-cropping rice, the occurrence probability of mild heat injury was based on the number of occurrences $ F_m^n$ of mild heat injury under the n-th global climate model in the m-th year at a certain grid. The total number of occurences of mild heat injury that occurred in a certain period (M years) of the grid was defined as below:

    $$F_{{\rm{Period}}}^n = \mathop \sum \nolimits_{m = 1}^M F_m^n.$$ (1)

    The total number of occurrences of mild heat injury that occurred in the N climate models during a certain period of the grid was calculated by

    $$F_{{\rm{Period}}}^{{\rm{Model}}} = \mathop \sum \nolimits_{n = 1}^N F_{{\rm{Period}}}^n.$$ (2)

    The occurrence probability of mild heat injury during a certain period at a grid (Pmil) was expressed as

    $${P_{{\rm{mil}}}} = F_{{\rm{Period}}}^{{\rm{Model}}}/\left({M \times N} \right), $$ (3)

    where M is the research period [with P0, P1, P2, and P3 referring to the reference (1986–2005), early (2016–35), middle (2046–65) and late (2080–99) periods respectively, and M = 20]; N is the number of climate models used.

    The occurrence probability of moderate (Pmod) and severe (Psev) heat injury during a certain period at a grid-based location was calculated as Pmil. Thus, the occurrence probability of total heat injury in single-cropping rice during a certain period at a grid-based location was calculated as following:

    $${P_{{\rm{total}}}} = 1 - (1 - {P_{{\rm{mil}}}}) \times (1 - {P_{{\rm{mod}}}}) \times (1 - {P_{{\rm{sev}}}}),$$ (4)

    where Ptotal, Pmil, Pmod, and Psev indicate the occurrence probability of the total, mild, moderate and severe heat-damage levels in single-cropping rice during a certain period at a grid-based location.

3.   Results
  • Single-cropping rice in the middle and lower reaches of the Yangtze River was often affected by heat injury at the heading and flowering stages, which resulted in a significant decline in yield. The changes in the occurrence probability of heat injury to single-cropping rice are shown in Fig. 2. In the reference period P0 (1986–2005), the occurrence probability of different levels of heat injury to single-cropping rice was ranked as mild > moderate > severe, with the average occurrence probabilities of 11.3%, 6.1%, and 5.2%, respectively. Under RCP2.6, RCP4.5, and RCP8.5 scenarios, the occurrence probabilities of heat injury to single-cropping rice showed increasing trends in the early, middle, and late periods of the 21st century, which were greater than that in P0. The occurrence probability of heat injury increased as emission scenarios increased during the same period. In particular, the occurrence probability of heat injury reached its highest levels, with an average of ~48% in the late 21st century under the RCP8.5 scenario.

    Figure 2.  Occurrence probability of heat injury to single-cropping rice under different RCP climate scenarios (RCP2.6, RCP4.5, and RCP8.5). The inset plot in the upper left corner shows the occurrence probability for the reference period (P0, 1986–2005). Mil, Mod, Sev, and Total indicate the mild, moderate, severe, and total occurrence probability of heat injury, respectively; P1: early period (2016–35); P2: middle period (2046–65); P3: late period (2080–99). Black lines from top to bottom of box-whisker plot indicate the occurrence probabilities of 95%, 75%, 50%, 25%, and 5% quantiles; black dots indicate the average values.

  • The spatial distribution of the occurrence probability of total heat injury to single-cropping rice under different climate scenarios is shown in Fig. 3. In P0, the occurrence probability of heat injury was high in the middle planting area and low in the east (Fig. 3). The average occurrence probabilities of heat injury in northern Jiangsu, central Anhui, and southeastern Hubei provinces were 30%–50%, while the probabilities in the other regions were less than 30%. Compared with P0, the occurrence probabilities of heat injury all increased, but the distribution patterns did not change under the RCP2.6, RCP4.5, and RCP8.5 scenarios. In addition, with the increasing emission levels and time, the distribution range of the occurrence probability of heat injury to single-cropping rice exceeded 50%. In particular, in the late 21st century under the RCP8.5 scenario, the occurrence probability of heat injury to single-cropping rice in the planting area (in addition to Zhejiang and northern Jiangxi provinces) was greater than 50%.

    Figure 3.  Spatial distributions of the occurrence probability of total heat injury to single-cropping rice under RCP climate scenarios (RCP2.6, RCP4.5, and RCP8.5). P0: the reference period (1986–2005); P1: early period (2016–35); P2: middle period (2046–65); P3: late period (2080–99).

  • The spatial distributions of the occurrence probabilities of mild (Fig. 4), moderate (Fig. 5), and severe (Fig. 6) heat injury to single-cropping rice show that the mild level of heat injury to single-cropping rice was the most prevalent, followed by the moderate and severe levels during P0. The probabilities of mild and moderate heat injury were both less than 20%, whereas that of severe heat injury in central Anhui and southern Hubei provinces was greater than 20%. In the early, middle, and late 21st century, under the RCP scenarios, the distribution of the occurrence probability of heat injury was mainly at the mild level, with low occurrences of moderate and severe heat injury levels. The overall distribution pattern was high in the middle planting area and low in the east. In addition, the spatial distribution range of the high occurrence probability of heat injury at each level increased with time and increasing emissions in the late 21st century.

    Figure 4.  As in Fig. 3, but for the mild heat injury .

    Figure 5.  As in Fig. 3, but for the moderate heat injury.

    Figure 6.  As in Fig. 3, but for the severe heat injury.

  • According to the projections of Aheat to single-cropping rice in the 21st century under the RCP scenarios by using a linear regression method (Fig. 7), the occurrence areas of mild (Amil), moderate (Amod), severe (Asev), and total (Atol) heat injury showed significant linear increases over time during the 21st century, and Aheat values at each level were the greatest under the RCP8.5 scenario, followed by the RCP4.5 and RCP2.6 scenarios. In P0, the Aheat values at different levels were ranked as: mild > moderate > severe, accounting for 6.6%, 4.3%, and 3.6%, respectively, of the total planting areas in the middle and lower reaches of the Yangtze River (Table 3). In the early, middle, and late 21st century under the RCP scenarios, Aheat was still ranked as mild > moderate > severe (Table 3), but there were differences in the linear increase rates among the different levels of heat injury over time (Fig. 7). Under the RCP2.6 scenario, the increase rates of Amil, Amod, and Asev to single-cropping rice were 2.93 × 103 (R2 = 0.82), 2.44 × 103 (R2 = 0.89), and 2.04 × 103 (R2 = 0.59) ha yr–1, respectively. Under the RCP4.5 scenario, there was no significant difference in the increase rates of Amil and Asev (~6.9 × 103 ha yr–1), and the rates were slightly lower when a moderate level of heat injury occurred. Under the RCP8.5 scenario, the increase rate of Asev was the greatest, at 15.02 × 103 ha yr–1 (R2 = 0.87), and those of Amil and Amod were ~10 × 103 ha yr–1.

    Figure 7.  Occurrence area of different levels of heat injury to single-cropping rice under RCP climate scenarios (RCP2.6, RCP4.5, and RCP8.5) for (a) mild, (b) moderate, (c) severe, and (d) total heat injury.

    Level of heat injuryClimate scenarioPercentage (%)
    P0 (1986–2005)P1 (2006–25)P2 (2046–65)P3 (2080–99)
    RCP2.6 6.59 7.45 7.4210.47
    MildRCP4.5 9.5010.5815.42
    RCP8.5 8.8811.3719.87
    RCP2.6 4.29 5.15 5.31 7.48
    ModerateRCP4.5 6.43 7.4512.20
    RCP8.5 6.38 8.9916.39
    RCP2.6 3.56 4.62 3.64 6.55
    SevereRCP4.5 5.48 6.7312.30
    RCP8.5 5.56 8.8722.35
    RCP2.617.2216.3624.50
    TotalRCP4.514.4321.4124.7539.92
    RCP8.520.8229.2458.60

    Table 3.  Percentages of occurrence area at different levels of heat injury to planting area in single-cropping rice under different RCP climate scenarios (RCP2.6, RCP4.5, and RCP8.5)

    The variable Atol to single-cropping rice was significantly larger than that during the reference period under the RCP2.6, RCP4.5, and RCP8.5 scenarios, and ranked as RCP8.5 > RCP4.5 > RCP 2.6 in all the three periods of the 21st century (Fig. 7d). Atol showed a significant linear increase over time during the 21st century, and the increase rates of Atol were 35.33 × 103 (R2 = 0.90), 19.89 × 103 (R2 = 0.94), and 7.41 × 103 (R2 = 0.78) ha yr–1 under the RCP8.5, RCP4.5, and RCP 2.6 scenarios, respectively. Thus, Atol reached its maximum value in the late 21st century, accounting for about 24.5% (RCP2.6), 39.9% (RCP4.5), and 58.6% (RCP8.5) of the total planting areas (Table 3).

  • The average occurrence probability of each level (mild, moderate, and severe) of heat injury to single-cropping rice in the 21st century was less than 50%, but the average occurrence probability of total heat injury was approximately 19%–53% under the RCP scenarios. Here, incidents with Ptotal ≥ 50% were defined as high-probability events, fewer of which occurred in single-cropping rice during the early 21st century, but increased significantly in the middle and late 21st century (Fig. 8). The maximum Atol value occurred in the late 21st century under the RCP8.5 scenario and involved ~ 4.3 × 106 ha, which constituted ~ 55% of the planting area (Fig. 8).

    Figure 8.  Areas with heat injury occurrence probability ≥ 50% and their proportion of the main planting area in single-cropping rice under different RCP climate scenarios (RCP2.6, RCP4.5, and RCP8.5). Notes see Fig. 3.

4.   Conclusions and discussion
  • Heat injury of single-cropping rice mainly occurs in the middle and lower reaches of the Yangtze River, China, from July to August when single-cropping rice is in the reproductive growth stage. Heat injury at this stage leads to a reduced seed setting rate, which seriously affects the production and quality of the rice crop. This study projected and analyzed the future occurrence probability, spatial distribution pattern, and changes in the occurrence area of different levels of heat injury to single-cropping rice in China based on CMIP5-coupled climate models under different emission scenarios (RCP2.6, RCP4.5, and RCP8.5).

    In P0 (1986–2005), the average occurrence probability of heat injury to single-cropping rice in the middle and lower reaches of the Yangtze River was 20%, with the highest probability of mild, followed by moderate and then severe heat injury. This was consistent with previous findings of heat injury to single-cropping rice based on the observational data of meteorological and agrometeorological stations from 1961 to 2012 in the middle and lower reaches of the Yangtze River (Yang et al., 2016). Thus, the results of multi-model collection produced highly accurate predictions of heat injury to single-cropping rice in China (Xu et al., 2010). Throughout the 21st century, the occurrence probability of heat injury to single-cropping rice was significantly greater than that during P0 under the RCP scenarios, and it increased as the emissions increased over time. In particular, during the late 21st century under the RCP8.5 scenario, the average and maximum occurrence probabilities were ~ 48% and 80%, respectively. Based on the grades of heat injury and its corresponding reference (yield reduction rate), the severe heat injury was calculated to correspond to a yield reduction rate of > 15%. For single-cropping rice in China, the increase rates of the occurrence area affected by severe heat injury, which may result in the yield reduction rate of over 15%, were about 2.04 × 103, 6.9 × 103, and 15.02 × 103 ha yr–1 in 2006–99 under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively (Fig. 7). Consequently, there is a high future risk of heat injury to single-cropping rice in the middle and lower reaches of Yangtze River, which may seriously impact the rice yield. The reduction and prevention of heat injury to single-cropping rice in this region are therefore still important for the future rice production (Xiong et al., 2016). In this study, heat injury of the single-cropping rice mainly occurred from late July to mid-August (heading–flowering stage). Moreover, an advanced planting date prolongs the vegetative growth duration and delays the reproductive growth phrase, which may lead to a shift towards higher temperatures for the heading–maturity phase and effectively avoid heat injury (Xie et al., 2013; Hu et al., 2017). In addition, screening and cultivation of high-temperature rice cultivars are also effective methods to deal with the changes of heat injury (Xie et al., 2013). Previous studies have shown that when the planting date remained the same, a switch to new cultivars could also prolong the growth duration for single-cropping rice. The prolongation of the growth duration associated with cultivar renewal not only compensates for the shortening of the growth duration induced by climate warming, but may also make full use of the improvement of local heat resources in single-cropping rice production (Tao et al., 2013; Hu et al., 2017). Hence, advances in the planting date of single-cropping rice and high-temperature-tolerant cultivars should be developed and popularized to deal with the increasing heat injury risk in the middle and lower reaches of the Yangtze River, China (Tao et al., 2013; Tao and Zhang, 2013; Guo et al., 2018).

    The study of the distribution of heat injury to rice in China based on the observational data for 1961–2000 showed that the occurrence probability of heat injury was the greatest in the central part of in the Yangtze River basin, which is a major planting area of single-cropping rice (Xiong et al., 2016). In addition, the increased extreme high-temperature stress under the climate warming would increase the probability of single-cropping rice yield decrease in the Yangtze River basin (Tao and Zhang, 2013), which is consistent with our results. Compared with P0 (1986–2005), the spatial distribution patterns of the occurrence probabilities of mild, moderate, and severe heat injury in the middle and lower reaches of the Yangtze River were also high in the central planting area and low in the east. The high Aheat values expanded in the northwest and northeast directions over time under the RCP scenarios. Moreover, the areas with Ptotal ≥ 50% increased significantly in the middle and late 21st century, with the maximum Aheat value occurring in the late 21st century under the RCP8.5 scenario, accounting for ~ 55% of the planting area (Fig. 8). Under all the RCP scenarios, Amil, Amod, and Asev of single-cropping rice significantly increased with the increase in emissions over time. The variable Atol linearly increased at rates of ~ 7 × 103, ~ 20 × 103, and 35 × 103 ha yr–1 and reached the maximum values in the late 21st century, accounting for 24.5%, 39.9%, and 58.6% of the total planting area under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively.

    This study combined the simulation results of 16 global climate models to predict the heat injury to single-cropping rice in China in the future and, to a certain extent, improved the accuracy compared with single-model predictions (Xu et al., 2010; Tian et al., 2017). However, although the global climate models have improved considerably in recent years, there are still large uncertainties in the simulation of climate factors (Guo et al., 2013; Jiang and Wu, 2013), which may lead to uncertainty in projecting the heat-damage hazards to single-cropping rice. It should be pointed out that the results of this study were based on the results of CMIP5 model simulations, which had a large pattern dependence. The previous study pointed out that the uncertainty of the increasing temperature of the model prediction was mainly due to the difference in the climate sensitivity. Different climate models had significant differences in the warming response forced by the same CO2 concentration because of their structural framework, especially the different model-driven data sets and different expressions of physical processes of the climate system (Anav et al., 2013; Chen and Zhou, 2016). Therefore, future climate change research needs to strengthen the coupling study of climate and CO2, while accumulating observation data, and improve the coupling global climate model, to provide more accurate support for projecting agrometeorological disasters.

    In terms of assessments of the changes in plant phenology under the future global warming scenarios, most previous studies focused on the relationship between plant phenological changes and environmental factors. However, less research has been conducted on mechanisms of the phenological influence factors regulating plant growth and reproduction (Wang et al., 2015; Li et al., 2018), which reduces the accuracy of phenological prediction. Moreover, little research has been conducted on the changes in phenology of crops, especially in rice, because the changes in crop phenology are not only affected by climatic factors, but are also strongly influenced by human factors. Moreover, variations in planting patterns and varieties of rice may increase the uncertainty of rice phenology prediction, which makes it difficult to accurately predict changes in the future crop phenology. In the present study, we predicted and analyzed the occurrence probabilities and spatial distributions of heat injury to single-cropping rice for 1961–2000 based on the assumption that the varieties, sowing dates, and growth periods in this region were constant, which may lead to some deviation in the research results. As a result, further study should focus on the development of more objective and reasonable technical methods (Taylor et al., 2012; Dong et al., 2014), examination of the mechanism of changes associated with the heat injury to single-cropping rice, and refining evaluation indicators to improve the accuracy of future predictions.

    Acknowledgments. We thank Lesley Benyon and Alex Boon from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

Reference (48)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return