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Predicting maize production in Northeastern China: Unraveling the influence of summer compound heat-drought events through physical mechanisms

文献类型： 外文期刊

作者： Zhou, Yeran 1 ; Li, Huixin 1 ; Sun, Bo 1 ; Wang, Huijun 1 ; Ju, Hui 4 ; Yuan, Yuan 1 ; Zeng, Jiani 1 ;

作者机构： 1.Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteorol, State Key Lab Climate Syst Predict & Risk Manageme, Minist Educ,Key Lab Meteorol Disaster, Nanjing, Peoples R China

2.Southern Marine Sci & Engn Guangdong Lab, Zhuhai, Peoples R China

3.Chinese Acad Sci, Inst Atmospher Phys, Nansen Zhu Int Res Ctr, Beijing, Peoples R China

4.Chinese Acad Agr Sci, Inst Environm & Sustainable Dev Agr, Beijing, Peoples R China

5.Minist Agr & Rural Affaire PR China, Key Lab Agr Environm, Beijing, Peoples R China

关键词： Northeastern China; Maize production; Compound heat-drought events; Atmospheric circulation; Prediction models

期刊名称：AGRICULTURAL AND FOREST METEOROLOGY （ 影响因子：5.7； 五年影响因子：6.6 ）

ISSN： 0168-1923

年卷期： 2025 年 372 卷

页码：

收录情况： SCI

摘要： Northeastern China (NEC), known as the granary of China, is significantly affected by compound heat-drought events (CHDEs), which have detrimental impacts on maize production. This study aims to investigate the physical mechanisms underlying the occurrences of CHDEs on maize production in NEC. Our findings indicate that CHDEs are associated with anomalous positive geopotential height at 500 hPa, the presence of anticyclone at 850 hPa and a uniform downward motion in NEC, all of which are adverse to maize production. Using a year-to-year increment method, we reveal that several key factors collectively influence CHDEs and maize production in NEC, including sea ice concentration in the Barents Sea in May, sea surface temperature (SST) in the equatorial East Pacific in February and March, soil water over northwestern Siberia in April, and the North Atlantic Oscillation (NAO) in February. To differentiate the diverse influences of these key factors on CHDEs and maize production, we developed two distinct prediction models (Prediction Model #1 and #2). Both Prediction Model #1 (r = 0.90, p < 0.01) and #2 (r = 0.91, p < 0.01) demonstrate high correlation coefficients between predicted and observed values, as validated through leave-one-out cross-validation (Prediction Model #1: r = 0.90, p < 0.01; Prediction Model #2: r = 0.90, p < 0.01) and independent hindcasts (Prediction Model #1: r = 0.72, p < 0.01; Prediction Model #2: r = 0.79, p < 0.01). This study provides precise predictions of maize production in eastern China, offering significant safeguards for national food security.

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