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应用生态学报 ›› 2024, Vol. 35 ›› Issue (3): 731-738.doi: 10.13287/j.1001-9332.202403.035

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东北地区水稻开花期冷涡型光-温-水复合逆境产量损失评估指标构建

吕佳佳1,2, 初征1,2, 李百超3, 宫丽娟1,2, 周宝才1,2, 刘丹1,2, 王冬妮4, 姜丽霞1,2*   

  1. 1中国气象局东北地区生态气象创新开放实验室, 哈尔滨 150030;
    2黑龙江省气象科学研究所, 哈尔滨 150030;
    3黑龙江省生态气象中心, 哈尔滨 150030;
    4吉林省气象科学研究所, 长春 130062
  • 收稿日期:2023-11-02 修回日期:2024-02-01 出版日期:2024-03-18 发布日期:2024-06-18
  • 通讯作者: *E-mail: hljjlx@163.com
  • 作者简介:吕佳佳, 女, 1983年生, 高级工程师。主要从事农业气象灾害研究。E-mail: wflljj@163.com
  • 基金资助:
    国家重点研发计划项目(2022YFD2300201)、黑龙江省自然科学基金项目(LH2021D018)、中国气象局沈阳大气环境研究所联合开放基金课题(2022SYIAEKFZD04-02)和黑龙江省气象局科技创新发展项目(HQSD2022009)

Construction of yield loss indicators for cold vortex, light-temperature-water combined stress during the flowering period of rice in Northeast China

LYU Jiajia1,2, CHU Zheng1,2, LI Baichao3, GONG Lijuan1,2, ZHOU Baocai1,2, LIU Dan1,2, WANG Dongni4, JIANG Lixia1,2*   

  1. 1Innovation and Opening Laboratory of Eco-Meteorology in Northeast China, China Meteorological Administration, Harbin 150030, China;
    2Heilongjiang Province Institute of Meteorological Sciences, Harbin 150030, China;
    3Heilongjiang Ecometeorological Center, Harbin 150030, China;
    4Jilin Institute of Meteorological Sciences, Changchun 130062, China
  • Received:2023-11-02 Revised:2024-02-01 Online:2024-03-18 Published:2024-06-18

摘要: 构建东北地区水稻开花期冷涡型光-温-水复合逆境产量损失评估等级指标,对阐明中高纬度地区冷涡型复合灾害叠加效应对水稻产量损失的影响机制具有参考意义,可为保障中国优质粳稻安全生产、区域减灾增效提供气象支撑。本研究利用生育期资料、气象资料、产量资料,界定并构建寒稻开花期冷涡型光-温-水复合逆境发生指数,采用BP神经网络法分层次分析致灾因子与产量结构的关系度、不同产量结构与产量的关系度,解析复合致灾过程,基于K-均值聚类方法及历史典型灾害年确定灾害临界值和等级,建立冷涡型光-温-水复合逆境产量损失评估指标及评估模型,并分析东北三省水稻开花期低温多雨寡照的时空分布特征。结果表明: 研究区水稻开花期轻度、中度、重度冷涡型光-温-水复合逆境产量损失评估指标临界阈值分别为[0,0.21)、[0.21,0.32)、[0.32,0.64],产量损失率分别为[0, 0.03)、[0.03, 0.08)、[0.08, 0.096]。基于1961—2020年随机11年总计751条样本的验证结果显示,利用本研究构建复合指数计算的减产等级与实际减产等级一致的站点比例为63.7%,各年均超过58.0%;一致或相差1级的站点比例为88.3%,各年均超过85.0%。该指标能够很好地评估冷涡型复合灾害造成的东北地区水稻产量损失率。

关键词: 水稻, 开花期, 冷涡型复合灾害, 复合指数, 产量损失

Abstract: The construction of a yield loss evaluation index for the cold vortex type light-temperature-water composite adversity during rice flowering period in Northeast China is important for elucidating the impacts of cold vortex type composite disasters on rice yield loss in middle and high latitude areas. Moreover, it can provide meteorological support to ensure safe production of high-quality japonica rice in China and contribute to regional disaster reduction and efficiency improvement. By combining growth period data, meteorological data, and yield data, we delineated and constructed the composite stress occurrence index of cold vortex type light-temperature-water at the flowering stage of japonica. We analyzed the relationship between factors causing disasters and yield structure, as well as the relationship between different yield structures and yield by employing BP neural network method. We further dissected the processes involved in the causation of combined disasters. Based on the K-means clustering method and historical typical disaster years, we quantified the critical thresholds and disaster grades, and established an evaluation index and model for assessing yield loss caused by combined stress from cold vortex type light-temperature-water. Finally, we examined the spatial and temporal variations of low temperature, abundant rainfall, and reduced sunlight during the flowering period in the three provinces of Northeast China. Results showed that the critical thresholds for light, temperature, and water stress index during the flowering stage of mild, moderate, and severe cold vortex types were [0, 0.21), [0.21, 0.32), and [0.32, 0.64], respectively. The rates of yield loss were [0, 0.03), [0.03, 0.08), and [0.08, 0.096], respectively. Based on the verification results of a total of 751 samples in 11 random years from 1961 to 2020, the percentage of stations for which the production reduction grade, as calculated by the composite index developed in this study, aligning with the actual production reduction grade was 63.7%, consistently exceeding 58.0% annually. Moreover, the proportion of sites with a similarity or difference level of 1 stood at 88.3%, surpassing 85.0% in each year. The index could effectively assess the extent of rice yield loss caused by cold vortex disasters in Northeast China.

Key words: rice, flowering period, cold vortex composite disaster, composite index, yield loss