中国农业气象 ›› 2026, Vol. 47 ›› Issue (9): 1457-1472.doi: 10.3969/j.issn.1000-6362.2026.09.008

• 农业生物气象栏目 • 上一篇    下一篇

佛山市南海区早稻气候风险评估与最优播期识别

王广伦,杨建莹,简修发,郭峻泓,江绮珊,温四妹   

  1. 1. 佛山市气象局,佛山 528300;2. 中国气象科学研究院,北京 100081;3. 广东省始兴县气象局,始兴 512500
  • 收稿日期:2026-02-12 出版日期:2026-09-20 发布日期:2026-09-18
  • 作者简介:王广伦,E-mail:llmwgl@163.com
  • 基金资助:
    中国气象局创新发展专项项目(CXFZ2022J051;CXFZ2023J053;CXFZ2024J049);中国气象科学研究院基本业务费专项资金项目(2023Z014);中国气象局农业气象重点创新团队项目(2024Z001)

Climate Risk Assessment and Optimal Sowing Period Identification for Early Rice in Nanhai District, Foshan City

WANG Guang-lun, YANG Jian-ying, JIAN Xiu-fa, GUO Jun-hong, JIANG Qi-shan, WEN Si-mei   

  1. 1. Foshan Meteorological Bureau, Foshan 528300, China; 2. Chinese Academy of Meteorological Sciences, Beijing 100081; 3. Shixing County Meteorological Bureau, Shixing 512500
  • Received:2026-02-12 Online:2026-09-20 Published:2026-09-18

摘要: 基于1981−2025年广东农业气象试验站早稻逐年观测资料和南海国家气象站逐日气象数据,运用滑动窗口相关分析识别5种早稻农业气象灾害的关键影响时段与敏感响应参数,通过K−Means聚类厘定各灾害等级阈值,结合发生频率概率估计与累计偏离度法构建灾害危险性和脆弱性指数,基于加权期望模型计算不同播期组下早稻的气候风险综合指数,结合4种权重情景检验指数的稳健性,以期定量评估气候变化背景下佛山市南海区双季早稻多灾种综合气候风险,明确风险最小化下的最适播期。结果表明:(1)寒害、冷害、寡照、暴雨和高温5种农业气象灾害对佛山市南海区早稻的关键影响时段分别为播种−移栽期、移栽−分蘖期、抽穗前5d−抽穗后15d、抽穗前15d−抽穗后20d和抽穗前10d−抽穗后20d,敏感响应参数分别为播种−移栽天数、移栽−分蘖天数、实际产量、结实率和千粒重。(2)K−Means聚类将历史播期划分为早播(2月下旬)、中播(3月上旬)和晚播(3月中旬)三组,随早稻播期的推迟,寒害、冷害、寡照危险性指数呈下降趋势,其中寒害危险性指数从1.268降至0.152,降幅达88.0%;高温危险性指数明显上升,从1.044升至1.231,增幅为17.9%;暴雨危险性指数的变化相对较小。(3)南海早稻晚播组气候风险综合指数为1.086,较中播组、早播组分别降低2.25%、17.29%;4种脆弱性权重情景下,不同播期下早稻的气候风险综合指数排序一致,结论稳健性强。佛山市南海区早稻适宜播期调整至3月中旬,可有效降低早春低温风险,实现综合气候风险最小化。

关键词: 早稻, 气候风险综合指数, 播期优化, 气象灾害, 佛山市南海区

Abstract:

This study quantified the integrated climate risk posed by multiple hazards to double−cropping early rice in Nanhai district of Foshan, under climate change and identified the sowing period that minimize this risk. Based on annual observations of early rice from the Guangdong agrometeorological experiment station and the daily meteorological data of the Nanhai national meteorological station from 1981 to 2025, the sliding−window correlation analysis identified the critical exposure periods and response variables for the five hazards. K−Means clustering was used to define the threshold of each disaster level, frequency−based probability estimates and cumulative deviation were used to derive hazard and vulnerability indices, and a weighted expectation model was used to calculate an integrated climate risk index for each sowing period group. Robustness was evaluated under four vulnerability weighting scenarios. The results showed that: (1) the critical periods for cold damage, chilling injury, low sunshine, rainstorm and high temperature were sowing to transplanting, transplanting to tillering, 5d before heading to 15d after heading, 15d before heading to 20d after heading and 10d before heading to 20d after heading, respectively. Their corresponding response variables were the duration from sowing to transplanting, the duration from transplanting to tillering, actual yield, seed−setting rate and 1000−grain weight, respectively. (2) Historical sowing dates clustered into early (late February), intermediate (early March) and late (mid−March) groups by K−Means. As the early rice sowing period was delayed, the risk indices for cold damage, chilling injury and low light conditions showed a declining trend, with cold damage decreasing from 1.268 to 0.152, a reduction of 88.0%. In contrast, the high−temperature risk index increases significantly, rising from 1.044 to 1.231, an increase of 17.9%, while the heavy rainfall risk index changed relatively little. (3) The integrated climate risk index for the late−sown early rice group in Nanhai was 1.086, which was 2.25% and 17.29% lower than those of the mid−sown and early−sown groups, respectively. Under four vulnerability weighting scenarios, the ranking of the integrated climate risk indices for early rice across different sowing periods remained consistent, indicating strong robustness of the conclusions. Adjusting the optimal sowing period for early rice in Nanhai district of Foshan, to mid−March can effectively reduce the risk of low spring temperatures and minimize overall climate risk. The findings provide valuable references for optimizing early rice sowing schedules and climate adaptation decisions in similar regions.

Key words: Early rice, Integrated climate?risk index, Sowing period optimization, Agrometeorological hazards, Nanhai district of Foshan