格点预报数据与ORYZA模型耦合的吉林水稻花期障碍型冷害评估

doi:10.3969/j.issn.1000-6362.2025.04.012

中国农业气象 ›› 2025, Vol. 46 ›› Issue (4): 569-579.doi: 10.3969/j.issn.1000-6362.2025.04.012

格点预报数据与ORYZA模型耦合的吉林水稻花期障碍型冷害评估

刘子琪，纪玲玲，云天，宋爽，李依瞳，杨晓光

1.中国气象局沈阳大气环境研究所，沈阳 110166；2.吉林省气象台，长春 130062；3.中国农业大学资源与环境学院，北京 100193

收稿日期:2024-05-13 出版日期:2025-04-20 发布日期:2025-04-14
作者简介:刘子琪，E-mail：liuzq5789@163.com
基金资助:
中国气象局沈阳大气环境研究所和辽宁省农业气象灾害重点实验室联合开放基金项目（2023SYIAEKFMS26）；中国气象局决策服务专项项目（JCZX2020006）

Chilling Injury Assessment During the Flowering Stage of Rice in Jinlin Province by Coupling the Grid Weather Forecast Data and ORYZA Model

LIU Zi-qi, JI Ling-ling, YUN Tian, SONG Shuang, LI Yi-tong, YANG Xiao-guang

1.Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China; 2.Meteorological Observatory of Jilin Province, Changchun 130062; 3.College of Resources and Environmental Science, China Agricultural University, Beijing 100193

Received:2024-05-13 Online:2025-04-20 Published:2025-04-14

摘要/Abstract

摘要：

基于格点预报数据和作物模型耦合建立水稻花期冷害产量损失评估模型，以提升水稻花期冷害评估能力。利用1993−2000年吉林省通化农业气象试验站水稻观测资料，确定ORYZA.v3模型中水稻生长发育关键参数；基于校准后模型，利用1961−2021年气象数据资料和冷害灾情统计数据分析水稻障碍型冷害的敏感性；结合未来10d的0.125°×0.125°欧洲中期天气预报中心（ECMWF）的格点预报数据和天气要素实况，建立水稻花期冷害评估方法，定量评估2022−2023年冷害对水稻产量的影响。结果表明：（1）基于ORYZA.v3模型水稻开花期、成熟期和产量模拟值与实测值的MAE分别为1.2d、2.6d和345.7kg·hm⁻²，模型可较好地模拟水稻关键生育期和产量。（2）水稻花期障碍型冷害强度（降温幅度、持续日数）与水稻产量减损呈显著相关，冷害发生时日平均气温每降低1.0℃，水稻平均减产2.13%；低温持续日数每增加1d，水稻平均减产2.66%。典型障碍型冷害发生年份内模型模拟误差在±10.0个百分点内，模拟精度较高，可定量模拟障碍型冷害对水稻产量减损的影响。（3）2022−2023年轻、中和重度冷害导致的水稻减产率分别为3.0%、3.7%和5.1%；轻、中和重度障碍型冷害评估TS评分为0.76，0.50和0.63。评估与实际发生的冷害等级空间范围基本一致，TS综合评分为0.63，效果较好。

关键词: 水稻, 冷害评估, 格点预报, ORYZA.v3模型, 吉林省

Abstract:

This study established a method for evaluating the yield loss due to chilling injury during the flowering stage of rice to improve the ability to assess the chilling injury. Key parameters for rice growth by the ORYZA.v3 model were determined using the experimental observation data from Tonghua agricultural meteorological experimental station in Jilin from 1993 to 2000. The sensitivity of the model to simulate the chilling injury of rice was analyzed based on the calibrated model and meteorological data from 1961 to 2021. The method for evaluating chilling injury during the flowering stage of rice was established by combining the 0.125°×0.125° European Centre for Medium-range Weather Forecasts (ECMWF) grid data with real-time weather data. The method was used to quantitatively assess the loss of rice yield due to chilling injury from 2022 to 2023. The results showed that: (1) the ORYZA.v3 model could effectively reproduce the rice development and growth, for the reason that the MAE values were 1.2d, 2.6d and 345.7kg·ha⁻¹ with the rice flowering stage, maturing stage and yield. (2) There was a significant correlation between the intensity of chilling injury (chilling daily mean temperature, duration) and the yield reduction of rice. When the chilling injury occurred during the flowering stage of rice, the yield reduction increased by 2.13% along with the daily mean temperature decreasing 1℃. In addition, the reduction in yield increased by 2.66% with the duration of 1d chilling injury. The simulation error of the model was less than 10.0% when compared to the historical disaster data, indicating high simulation accuracy. Thus, the ORYZA.v3 model was able to quantitatively simulate the effect of sterile−type chilling injury on rice yield loss. (3) The yield reduction of rice under the light, moderate, and serious chilling injury were estimated to be 3.0%, 3.7% and 5.1%, respectively, the TS scores were 0.76, 0.50, and 0.63 from 2022 to 2023. The distribution of chilling injury was in good agreement with the measured values, while the comprehensive TS score was 0.63, which indicated a good estimation.

Key words: Rice, Chilling injury assessment, Grid weather forecast, ORYZA.v3 model, Jilin province

刘子琪, 纪玲玲, 云天, 宋爽, 李依瞳, 杨晓光. 格点预报数据与ORYZA模型耦合的吉林水稻花期障碍型冷害评估[J]. 中国农业气象, 2025, 46(4): 569-579.

LIU Zi-qi, JI Ling-ling, YUN Tian, SONG Shuang, LI Yi-tong, YANG Xiao-guang. Chilling Injury Assessment During the Flowering Stage of Rice in Jinlin Province by Coupling the Grid Weather Forecast Data and ORYZA Model[J]. Chinese Journal of Agrometeorology, 2025, 46(4): 569-579.

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格点预报数据与ORYZA模型耦合的吉林水稻花期障碍型冷害评估

Chilling Injury Assessment During the Flowering Stage of Rice in Jinlin Province by Coupling the Grid Weather Forecast Data and ORYZA Model

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