摘要
采用深度学习长短时记忆模型LSTM,基于中国重要冬小麦种植区河南省的99个气象站点1981-2020年的气象、作物、灾情等多源数据,识别河南冬小麦春季冻害发生情况,探讨冬小麦春季冻害时空分布规律和年代际变化特征。结果表明:(1)优化后的LSTM模型可较好实现冬小麦春季冻害的识别。2017-2020年LSTM模型模拟日最低气温结果与实际最低气温相比,平均绝对百分比误差MAPE为8.73%,拟合优度R^(2)为0.90。结合冬小麦春季冻害指标和灾情资料,2017-2020年实际受灾情况与本研究模型模拟结果相符。(2)1981-2020年河南冬小麦春季轻度冻害危害在逐渐减弱,轻度冻害发生频率高值区由东北部南移至东部。1981-2020年河南冬小麦春季轻度冻害的整体分布情况呈东北部高,西南部和东南部低,40a平均发生频率为0~1.75次·10a^(-1)。轻度冻害发生频率由0.843次·10a^(-1)逐步降至0.157次·10a^(-1),其高值区由东北部南移至东部。(3)1981-2020年河南冬小麦春季重度冻害发生频率呈先增后减趋势,其发生频率高值区由东北部南移至东部。重度冻害40a整体分布表现为东部地区高于西部地区,北部地区高于南部地区,40a平均发生频率为0~2.75次·10a^(-1)。冬小麦春季重度冻害的发生频率由0.508次·10a^(-1)增至0.857次·10a^(-1),后减至0.289次·10a^(-1),重度冻害发生频率高值区由东北部南移至东部。河南冬小麦春季冻害整体呈减少趋势,但气候变暖下极端天气事件发生频率呈增加态势,加强气候变化背景下中国重大农业气象灾害对各地区农业生产影响的研究仍然是今后研究重点之一。本研究提出的基于深度学习LSTM的冬小麦春季冻害识别模型,对各精度评价指标均有提升,冻害识别结果与实际发生情况基本一致,可为全球气候变化背景下大面积冬小麦冻害识别提供思路和方法,对其他农业气象灾害识别有一定参考价值。
Freezing damage in spring is one of the serious agricultural meteorological disasters for winter wheat production in the Huang-Huai-Hai plain.In order to effectively identify freezing damages of winter wheat in spring,this paper used a deep learning long short-term memory model LSTM to identify the occurrences of freezing damages of winter wheat in spring in Henan province,which was an important winter wheat planting area in China.The spatiotemporal distributions and decadal changes of freezing damages of winter wheat in spring based on the multi-source meteorological,crop,and disaster data from 99 meteorological stations from 1981 to 2020 were explored.The results indicated that:(1)the optimized LSTM model effectively identified freezing damages of winter wheat in spring.The daily minimum temperature simulated by LSTM model from 2017 to 2020 showed an average absolute percentage error(MAPE)of 8.73%and a goodness of fit(R^(2))with 0.90 compared to the actual minimum temperature.Based on the disaster data and freezing damage index for winter wheat in spring,the actual disaster situations from 2017 to 2020 were found to be consistent with the simulated results by the optimized LSTM model.(2)From 1981 to 2020,the harm of mild freezing damage to winter wheat in spring in Henan province had gradually weakened,and the high frequency area of mild freezing damage moved from the Northeast to the East.The overall distribution of mild freezing damage of winter wheat in spring in Henan province between 1981 and 2020 was higher in the northeastern Henan,and lower in the southwestern and southeastern Henan,with an average frequency of 0-1.75 times·10y^(-1) during the past 40 years.The frequency of mild freezing damage of winter wheat in spring decreased per decade,gradually decreasing from 0.843 times per decade to 0.157 times per decade.The high frequency area of mild freezing damage of winter wheat in spring moved from the Northeast to the East.(3)From 1981 to 2020,the frequency of severe freezing damage of winter wheat in spring in Henan province showed a trend of first increasing and then decreasing.The high frequency area of severe freezing damage of winter wheat in spring moved from the Northeast to the East.The overall distribution of severe freezing damage during the 40a period was higher in the East than in the West,and higher in the North than in the South.The average frequency of severe freezing damage in the 40a period was 0-2.75times·10y^(-1).The frequency of severe freezing damage of winter wheat in spring increased from 0.508 times per decade to 0.857 times per decade and then decreased to 0.289 times per decade.The high frequency area of severe freezing damage moved from the Northeast to the East.The overall trend of severe freezing damage of winter wheat in spring in Henan Province was decreasing,but the frequency of extreme weather events under climate warming was increasing.Strengthening the researches on the impacts of major agricultural meteorological disasters on agricultural production in various regions in China under climate change was still one of the future research focuses.Various accuracy evaluation indicators of the deep learning LSTM model for identifying freezing damage of winter wheat in spring proposed in this study were improved,and the simulation results of freezing damage by LSTM model were basically consistent with the actual occurrences.Our results can provide ideas and methods for large-scale freezing damage identification of winter wheat under global climate change,and also have certain reference values for other agricultural meteorological disaster identification.
作者
黄睿茜
赵俊芳
杨嘉琪
彭慧文
秦曦
HUANG Rui-xi;ZHAO Jun-fang;YANG Jia-qi;PENG Hui-wen;QIN Xi(State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100081,China;College of Agronomy,Inner Mongolia Agricultural University,Hohhot 010019;Beijing Huayun Shinetek Science and Technology Co.,Ltd,Beijing 100081)
出处
《中国农业气象》
CSCD
2024年第9期1041-1052,共12页
Chinese Journal of Agrometeorology
基金
中国气象局气候变化专题项目(CMA-CCSP202407)。
关键词
深度学习
冬小麦
春季冻害
年代际变化
Deep learning
Winter wheat
Freezing damage in spring
Interdecadal change
