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农业涝渍灾害评估中不同气象产量分离方法的比较研究

蒙华月 王兆林 姚佩 钱龙 陈诚 罗云英 鞠学良

蒙华月, 王兆林, 姚佩, 钱龙, 陈诚, 罗云英, 鞠学良. 农业涝渍灾害评估中不同气象产量分离方法的比较研究[J]. 中国生态农业学报 (中英文), 2022, 30(6): 976−989 doi: 10.12357/cjea.20210770
引用本文: 蒙华月, 王兆林, 姚佩, 钱龙, 陈诚, 罗云英, 鞠学良. 农业涝渍灾害评估中不同气象产量分离方法的比较研究[J]. 中国生态农业学报 (中英文), 2022, 30(6): 976−989 doi: 10.12357/cjea.20210770
MENG H Y, WANG Z L, YAO P, QIAN L, CHEN C, LUO Y Y, JU X L. Comparative study of different meteorological yield separation methods in waterlogging disaster assessment[J]. Chinese Journal of Eco-Agriculture, 2022, 30(6): 976−989 doi: 10.12357/cjea.20210770
Citation: MENG H Y, WANG Z L, YAO P, QIAN L, CHEN C, LUO Y Y, JU X L. Comparative study of different meteorological yield separation methods in waterlogging disaster assessment[J]. Chinese Journal of Eco-Agriculture, 2022, 30(6): 976−989 doi: 10.12357/cjea.20210770

农业涝渍灾害评估中不同气象产量分离方法的比较研究

doi: 10.12357/cjea.20210770
基金项目: 国家自然科学基金项目(51909286)和中央高校基本科研业务费(2021qntd15)资助
详细信息
    作者简介:

    蒙华月, 主要从事农业水利工程研究。E-mail: menghuayue@whu.edu.cn

    通讯作者:

    钱龙, 主要从事农业旱涝灾害研究。E-mail: qianlong@mail.sysu.edu.cn

  • 中图分类号: S271

Comparative study of different meteorological yield separation methods in waterlogging disaster assessment

Funds: This study was supported by the National Natural Science Foundation of China (51909286) and the Fundamental Research Funds for the Central Universities of China (2021qntd15)
More Information
  • 摘要: 农业旱涝灾害评估中常采用不同方法计算作物气象产量, 但其中鲜见关于不同方法的比较研究。长江中下游棉区因涝减产现象严重, 因此本文以该地区6省为研究区, 利用标准化降水蒸散指数量化棉花生长期的涝渍强度, 选用7种常见气象产量分离方法(线性拟合、二次多项式拟合、三次多项式拟合、HP滤波法、3年滑动平均法、5年滑动平均法及五点二次平滑法)计算棉花气象产量, 并通过涝渍强度与气象产量的相关分析结果评估不同方法的表现。此外, 结合历史受涝面积对不同方法的涝渍灾害刻画能力进行对比。结果表明: 不同方法计算的棉花气象产量具有相似的长期趋势, 但在短期波动上存在较大差异。就气象产量和涝渍程度的相关性而言, HP滤波法、线性拟合和二次多项式效果更好; 而就气象产量结果与历史受涝面积的相符程度而言, HP滤波最优, 其次是二次多项式和三次多项式。此外, 基于7种方法的相关分析结果均判定湖北省和安徽省是棉花因涝减产最严重的省份, 但对于其余省份的判定结果会存在差异。湖北省和安徽省虽然因涝减产严重, 但两省棉花涝渍的发生倾向低于其余省份。因此, 在农业涝渍评估工作中推荐使用HP滤波法, 而长江中下游棉区的涝渍防治工作应重点关注湖北省和安徽省。
  • 图  1  研究区及其国家基准气象站分布

    Figure  1.  Study region and distribution of the national-level meteorological stations

    图  2  不同气象产量分离方法拟合的1990—2019年长江中下游6省棉花趋势产量

    Figure  2.  Cotton trend yields fitted with different meteorological yield separation methods of the six provinces in the middle-and-lower reach of the Yangtze River from 1990 to 2019

    AY is actual cotton yield. LF, QP, CP, HP, FMA, FPQS, and TMA indicate linear fitting method, quadratic polynomial method, cubic polynomial method, HP filter method, five-year moving average method, five point quadratic smoothing method, and three-year moving average method, respectively.

    图  3  1990—2019年不同气象产量分离方法分离的长江中下游6省棉花气象产量

    Figure  3.  Climatic cotton yields fitted by different methods of the six provinces in the middle-and-lower reach of the Yangtze River from 1990 to 2019

    LF, QP, CP, HP, FMA, FPQS, and TMA indicate linear fitting method, quadratic polynomial method, cubic polynomial method, HP filter method, five-year moving average method, five point quadratic smoothing method, and three-year moving average method, respectively.

    图  4  基于7种气象产量方法分离的长江中下游6省棉花气象产量与涝渍指标的相关系数

    Figure  4.  Correlation coefficients between cotton climatic yield fitted by different methods and cotton waterlogging indicator of the six provinces in the middle-and-lower reach of the Yangtze River

    LF, QP, CP, HP, FMA, FPQS, and TMA indicate linear fitting method, quadratic polynomial method, cubic polynomial method, HP filter method, five-year moving average method, five point quadratic smoothing method, and three-year moving average method, respectively.

    图  5  基于7种气象产量方法分离的长江中下游6省棉花气象产量与涝渍指标的相关系数的空间分布

    Figure  5.  Spatial distribution of correlation coefficients between cotton climatic yield fitted by different methods and cotton waterlogging indicator of the six provinces in the middle-and-lower reach of the Yangtze River

    图中点标记表示该地区存在显著负相关结果。黄山市和湖州市棉花种植面积和产量均很小, 且个别年份出现了极端气象产量, 因此未纳入计算范畴。The points indicate negatively significant correlation. Cotton planting areas and cotton yields in Huangshan and Huzhou were very few, and their cotton climatic yields were abnormally high in a few years; thus, they were excluded in the calculation processes. LF, QP, CP, HP, FMA, FPQS, and TMA indicate linear fitting method, quadratic polynomial method, cubic polynomial method, HP filter method, five-year moving average method, five point quadratic smoothing method, and three-year moving average method, respectively.

    图  6  长江中下游6省典型涝渍年与对应的气象产量

    Figure  6.  Cotton climatic yields during the typical waterlogging years in the six provinces in the middle-and-lower reach of Yangtze River Plain

    LF, QP, CP, HP, FMA, FPQS, and TMA indicate linear fitting method, quadratic polynomial method, cubic polynomial method, HP filter method, five-year moving average method, five point quadratic smoothing method, and three-year moving average method, respectively.

    图  7  长江中下游6省1990—2019年棉花涝渍指标均值比较(a)及空间分布特征(b)

    Figure  7.  Averaged cotton waterlogging intensity (a) and its spatial distribution (b) in the six provinces in the middle-and-lower reach of the Yangtze River Plain from 1990 to 2019

    表  1  7种气象产量分离方法拟合的长江中下游6省棉花趋势产量序列与对应方法所得的研究区域平均趋势产量序列间的相关系数

    Table  1.   Correlation coefficients between the cotton trend yield series fitted by seven meteorological yield separation methods and the averaged trend yield series obtained by the corresponding method in the six provinces in the middle-and-lower reach of the Yangtze River

    方法 Method湖北 Hubei湖南 Hunan安徽 Anhui江苏 Jiangsu江西 Jiangxi浙江 Zhejiang
    线性拟合 LF1.000**1.000**1.000**1.000**1.000**1.000**
    二次多项式 QP0.398*0.947**0.999**0.936**0.998**0.993**
    三次多项式 CP0.479**0.932**0.999**0.739**0.999**0.976**
    HP滤波法 HP0.411*0.923**0.998**0.858**0.996**0.983**
    5年滑动平均 FMA0.462*0.638**0.938**0.719**0.967**0.957**
    五点二次平滑 FPQS0.453*0.643**0.952**0.733**0.975**0.965**
    3年滑动平均 TMA0.489**0.623**0.894**0.700**0.940**0.918**
      *和**分别表示P<0.05和P<0.01显著性水平。计算样本量为6。* and ** indicate significance levels of P<0.05 and P<0.01, respectively. The calculation sample size is 6. LF, QP, CP, HP, FMA, FPQS, and TMA indicate linear fitting method, quadratic polynomial method, cubic polynomial method, HP filter method, five-year moving average method, five point quadratic smoothing method, and three-year moving average method, respectively.