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1971-2015年的数据，利用Mann-Kendall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Mann-Kenall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
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 heat-stress events in recent climate warming. Heat-stress 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 heat-wave distribution in early rice so as to develop measures for agricultural adaptation to climate change and to prevent and reduce natural disasters. Studies on heat-stress in rice have mainly focused on spatial and/or temporal distributions of heat-stress at provincial or catchment scales and on the relationship between heat-stress and yield production. However, spatial and temporal distributions of heat-stress at national scale and extreme heat-wave distribution have remained rarely explored. Extreme-value (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 extreme-value theory to analyze heat-stress in early rice and hypothesized that heat-stress 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 extreme-value distribution of heat-stress in early rice. Non-parametric methods (such as the Mann-Kendal trend test and extreme-value distribution) were used in this study. We found that:1) mean values of two heat-stress indices-ADHS (cumulative heat-stress days) and HDD (heat-stress degree days)-used to determine the extent of heat-stress 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 heat-stress events in these areas. The two heat-stress indices significantly increased in more than a third of the investigated site (more than half of the sites in 1990-2015). This further indicated that early rice at these sites suffered from worsening heat-stress. 2) ADHS and HDD at more than half of the sites satisfied the extreme-value (outlier) distribution. ADHS at 56 sites obeyed the Gumbel distribution and at 82 sites satisfied the General extreme-value (outlier) distribution. HDD at 61 sites obeyed Gumbel distribution and at 58 sites satisfied the general extreme-value distribution. 3) The spatial distributions of the 10-, 50-and 100-year 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 heat-stress indices also had larger return periods. Furthermore, the return periods of the two heat-stress indices were not significantly correlated with longitude, latitude and altitude. The results improved our understanding of spatial and temporal distributions along with extreme-value (outlier) distributions of heat-stress in rice. It provided the scientific basis for adaptation to climate change and agricultural weather index insurance.
Key words:Early rice/
Accumulated days of heat stress/
Heat stress degree days/
Spatial and temporal distribution/
Extreme-value distribution
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