引用本文:  何亮,吴门新,侯英雨,赵刚,靳宁,于强.基于极值概率分布函数的中国早稻高温热害时空分布统计特征[J].中国生态农业学报,2018,26(11):16011612 
 HE Liang,WU Menxin,HOU Yingyu,ZHAO Gang,JIN Ning,YU Qiang.Statistical characteristics of heat stress in early rice based on extreme value distribution in China[J].Chinese Journal of EcoAgriculture,2018,26(11):16011612 
DOI：  10.13930/j.cnki.cjea.180269 



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基于极值概率分布函数的中国早稻高温热害时空分布统计特征 
何亮^{1}, 吴门新^{1}, 侯英雨^{1}, 赵刚^{2}, 靳宁^{3}, 于强^{3,4}

1.国家气象中心 北京 100081;2.拜耳公司数字农业部 朗根费尔德 40764;3.西北农林科技大学黄土高原土壤侵蚀与旱地农业国家重点实验室 杨凌 712100;4.澳大利亚悉尼科技大学生命科学学院 悉尼 2007


摘要: 揭示水稻高温热害风险特征对农业适应气候变化具有重要意义。本研究以中国早稻种植区为研究区域，基于早稻种植区214个气象站19712015年的数据，利用MannKendall非参数趋势检验方法和极值概率分布理论，探究中国早稻高温热害的时空变化趋势和极值概率分布规律。研究发现：1）反映早稻高温热害的两个指标即高温热害累计天数（ADHS，accumulated days of heat stress）和热害有害积温（HDD，heat stress degree days）的均值在湖南中南部、江西中部、浙江和福建中部较大，表明这些区域的早稻遭受高温热害的风险较大；从MannKenall趋势检验看，两个指标在超过1/3的站点都呈显著增加的趋势，说明高温热害风险在这些站点显著增加，尤其20世纪90年代以后超过1/2的站点两个指标都呈显著增加的趋势。2）超过1/2以上的站点的高温热害累计天数和高温有害积温都满足极值概率函数分布。对于高温热害累计天数，56个站点满足耿贝尔分布（Gumbel），82个站点满足广义极值分布（GEV）；对于热害有害积温，61个站点满足耿贝尔分布，58个站点满足广义极值分布。3）两个高温热害指标的10年、50年、100年重现期的空间分布规律和2个指标的均值空间分布类似，即均值较大的区域，其10年、50年、100年重现期对应的重现期水平（return level）也较大；重现期水平与经度、纬度和海拔无明显相关关系。研究结果有助提升对早稻高温热害时空趋势和概率分布规律的认识，可为农业适应气候变化和农业天气指数保险设计等方面提供理论参考。 
关键词: 早稻 高温热害累计天数 热害有害积温 时空分布 极值概率分布 
中图分类号:S161.2 
基金项目:国家自然科学基金项目（41705095）资助 

Statistical characteristics of heat stress in early rice based on extreme value distribution in China 
HE Liang^{1}, WU Menxin^{1}, HOU Yingyu^{1}, ZHAO Gang^{2}, JIN Ning^{3}, YU Qiang^{3,4}

1.National Meteorological Center, Beijing 100081, China;2.Bayer AG, Digital Farming, Langenfeld 40764, Germany;3.State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A & F University, Yangling 712100, China;4.School of Life Sciences, University of Technology Sydney, Sydney 2007, Australia

Abstract: Rice is one of the most important staple foods globally, eaten by more than half the world population. China is the largest producer of rice, accounting for 18.5% of the rice planted area globally and 28% of the global rice production. Rice is easily exposed to heat stress because of highly frequent heatstress events in recent climate warming. Heatstress is one of the main meteorological disasters causing yield loss in agriculture. Thus, it is essential to explore spatial and temporal characteristics along with extreme heatwave distribution in early rice so as to develop measures for agricultural adaptation to climate change and to prevent and reduce natural disasters. Studies on heatstress in rice have mainly focused on spatial and/or temporal distributions of heatstress at provincial or catchment scales and on the relationship between heatstress and yield production. However, spatial and temporal distributions of heatstress at national scale and extreme heatwave distribution have remained rarely explored. Extremevalue (outlier) theory is a branch of statistical deviation of median probability distribution, which is widely used in structural engineering, hydrology and traffic prediction. Here, we introduced extremevalue theory to analyze heatstress in early rice and hypothesized that heatstress in rice obeyed specific outlier distribution. Thus, using 214 meteorological data on early rice region in China, we studied spatial and temporal characteristics along with extremevalue distribution of heatstress in early rice. Nonparametric methods (such as the MannKendal trend test and extremevalue distribution) were used in this study. We found that:1) mean values of two heatstress indicesADHS (cumulative heatstress days) and HDD (heatstress degree days)used to determine the extent of heatstress were larger in the south and central Hunan Province, central Jiangxi Province, central Zhejiang and Fujian Provinces than that in other areas. This indicated that there were more severe heatstress events in these areas. The two heatstress indices significantly increased in more than a third of the investigated site (more than half of the sites in 19902015). This further indicated that early rice at these sites suffered from worsening heatstress. 2) ADHS and HDD at more than half of the sites satisfied the extremevalue (outlier) distribution. ADHS at 56 sites obeyed the Gumbel distribution and at 82 sites satisfied the General extremevalue (outlier) distribution. HDD at 61 sites obeyed Gumbel distribution and at 58 sites satisfied the general extremevalue distribution. 3) The spatial distributions of the 10, 50and 100year return periods of the two heat indices were similar to their mean values. It then meant that regions with larger mean values of the two heatstress indices also had larger return periods. Furthermore, the return periods of the two heatstress indices were not significantly correlated with longitude, latitude and altitude. The results improved our understanding of spatial and temporal distributions along with extremevalue (outlier) distributions of heatstress in rice. It provided the scientific basis for adaptation to climate change and agricultural weather index insurance. 
Keyword: Early rice Accumulated days of heat stress Heat stress degree days Spatial and temporal distribution Extremevalue distribution 



