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生态学杂志 ›› 2026, Vol. 45 ›› Issue (2): 644-652.doi: 10.13292/j.1000-4890.202602.015

• 技术与方法 • 上一篇    下一篇

河南省冬小麦关键阶段降水致灾的解析与预判

黄进,张方敏*   

  1. (南京信息工程大学农业与生态气象江苏省高校重点实验室/生态与应用气象学院, 南京 210044)

  • 出版日期:2026-02-10 发布日期:2026-08-01

Analysis and prediction of precipitationinduced disasters during the critical stage of winter wheat in Henan Province.

HUANG Jin, ZHANG Fangmin*#br#

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  1. (Jiangsu Provincial University Key Laboratory of Agricultural and Ecological Meteorology/School of Ecology and Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China).

  • Online:2026-02-10 Published:2026-08-01

摘要: 评估生育期内降水异常对产量的影响有利于降低冬小麦种植的气候风险。基于1980—2019年河南省的逐日降水数据及省级单产记录,通过一阶差分处理识别了冬小麦单产对不同生育阶段极端降水的响应特征。在此基础上,运用TOPSIS法构建了关键期雨灾强度指数(RDII);通过K-means聚类、M-K检验、集合经验模态分解诊断了不同区域RDII的趋势及周期特征;结合时滞Spearman相关分析与分类判别模型评估了环流因子的预警效果。结果表明:气候导致的单产变化对灌浆成熟期的极端降水更为敏感,多因子构建的RDII解释了其28%的波动信息;关键期RDII呈现出显著的由南向北递减的空间分布格局;各子区域关键期RDII总体呈现非显著减弱趋势和主周期为2~3年的高频振荡;前期12个月环流因子驱动的线性判别模型对豫中南等区域的雨灾年景有较高的预判准确率。


关键词: 产量, 冬小麦, 极端降水, 雨灾强度指数, 环流因子

Abstract: Assessing the impact of precipitation anomalies during the growing season on crop yields helps mitigate climatic risks in the cultivation of winter wheat. Based on daily precipitation data and provincial yield records in Henan Province from 1980 to 2019, the responses of winter wheat yield to precipitation anomaly at different growth stages were identified using first-order differencing. TOPSIS method was then employed to construct a rainfall disaster intensity index (RDII) for critical periods. The trend and periodic characteristics of RDII in different regions were diagnosed by K-means clustering, M-K test, and ensemble empirical mode decomposition. The early-warning effectiveness of circulation factors was evaluated by combining time-lagged Spearman correlation analysis with a classification discriminant model. The results showed that climate-induced yield variations were more sensitive to extreme precipitation during the milking ripening period, with the RDII constructed by multi-factor explaining 28% of its fluctuation. Spatially, RDII during critical periods exhibited a significant decreasing trend from south to north. Sub-regional RDII generally showed a non-significant weakening trend and high-frequency oscillations with a primary cycle of 2-3 years. A linear discriminant model driven by circulation factors from the preceding 12 months demonstrated relatively high predictive accuracy for rainfall disaster years in regions such as south-central Henan.

Key words: yield, winter wheat, extreme precipitation, rainfall disaster intensity index, circulation factor