Vol.32, NO.1, 2021
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Article Navigation >  Journal of Applied Meteorological Science  > 2021  >  32(1): 38-51
Li Ying, Zhao Guoqiang, Chen Huailiang, et al. WOFOST model parameter calibration based on agro-climatic division of winter wheat. J Appl Meteor Sci, 2021, 32(1): 38-51. DOI:  10.11898/1001-7313.20210104.
Citation: Li Ying, Zhao Guoqiang, Chen Huailiang, et al. WOFOST model parameter calibration based on agro-climatic division of winter wheat. J Appl Meteor Sci, 2021, 32(1): 38-51. DOI:  10.11898/1001-7313.20210104.
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WOFOST Model Parameter Calibration Based on Agro-climatic Division of Winter Wheat

DOI: 10.11898/1001-7313.20210104
  • 1.

    CMA·Henan Key Laboratory of Agrometeorological Support and Applied Technique, Zhengzhou 450003

  • 2.

    Henan Institute of Meteorological Sciences, Zhengzhou 450003

  • 3.

    Henan Meteorological Service, Zhengzhou 450003

  • 4.

    Harbin Meteorological Bureau, Harbin 150028

  • 5.

    College of Land Science and Technology, China Agricultural University, Beijing 100083

  • 6.

    School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001

  • Received Date: 2020-09-30
  • Rev Recd Date: 2020-11-02
  • Publish Date: 2021-01-31
    Abstract
  • Crop model parameter calibration is an important work of extending point-scale crop model to regional application.Using K-means method with the main meteorological factors affecting the growth and yield formation of winter wheat obtained from 113 meteorological stations from 1981 to 2010 as zoning indicators, Henan Province is divided into five different agro-climatic ecological zones and the cumulative temperature parameters are calculated for each zone. Based on the observations during 2013-2017, nine sensitive parameters are obtained by using Sobol global sensitivity analysis method to analyze and select crop parameters with total sensitivity index greater than 0.01. The sensitive parameters selected from different agro-climatic ecological zones of different winter wheat varieties are highly consistent. A cost function is constructed with yield and leaf area index(LAI), and each partition is calibrated for sensitive parameters using Differential Evolution Markov Chain method. The results show that the simulated leaf area index in the different agro-climatic ecological zones are in good agreement with the observed values, the root mean square error (RMSE) using the posterior mean value of regional parameters adjustment to simulate the LAI of key growth periods is 0.655, which is obviously higher than that of using default parameters or using the same set of optimized parameters in the whole study area. Results show that the WOFOST model based on agro-climatic division can accurately simulate the growth process of crops. In terms of yield estimation accuracy, the yield simulation accuracy of regional parameter adjustment is also significantly improved. The best accuracy of simulated yield is achieved by using the posterior mean of regional parameters and RMSE is 672.016 kg·hm-2, 70.55% reduction than the yield simulation error when using the default parameters, or 48.75% reduction than the yield simulation error when the same set of optimized parameters (posterior mean) are used for the entire area. The method takes advantage of the knowledge of agro-climatology with the scientific and efficient Differential Evolution Markov Chain optimization algorithm to provide a scientific and theoretical basis for the application of crop models and optimization of regional model parameters through rational and efficient zonal calibration of the study area.
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  • Fig. 1  Agro-climatic division of winter wheat in Henan Province

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    Fig. 2  Total sensitivity index of parameters in five agro-climatic ecological zones

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    Fig. 3  Verification results of yield and leaf area index in 2019

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    Table  1  Accumulated temperature parameters of winter wheat in different zones(unit:℃·d)

    生育阶段 Ⅰ区 Ⅱ区 Ⅲ区 Ⅳ区 Ⅴ区
    播种至出苗 1170 1180 1100 1150 1250
    出苗至开花 600 620 610 600 610
    开花至成熟 110 120 120 130 120
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    Table  2  The proportion of changes of parameters to be analyzed around the default value

    参数 定义 最小比例 最大比例
    AM00 发育期为0时CO2最大同化速率 0.7 1.5
    AM10 发育期为1时CO2最大同化速率 0.7 1.5
    AM13 发育期为1.3时CO2最大同化速率 0.7 1.5
    AM20 发育期为2时CO2最大同化速率 0.7 1.5
    SL00 发育期为0时比叶面积 0.7 1.5
    SL05 发育期为0.5时比叶面积 0.7 1.5
    SL20 发育期为2时比叶面积 0.7 1.5
    FL 总物质分配到叶片的比例 0.5 2.0
    FO 总物质分配到储存器官的比例 0.8 1.2
    FR 总物质分配到根的比例 0.5 2.0
    SP 35℃时叶片的生命周期 0.7 1.5
    TB 出苗最低温度 0.5 1.5
    TD 初始总干物质重量 0.9 1.3
    TE 出苗最高有效温度 0.9 1.3
    TM00 日平均温度为0℃时CO2最大同化速率减小因子 0.7 1.5
    TM10 日平均温度为10℃时CO2最大同化速率减小因子 0.7 1.3
    TM15 日平均温度为15℃时CO2最大同化速率减小因子 0.7 1.5
    TM25 日平均温度为25℃时CO2最大同化速率减小因子 0.7 1.5
    TM35 日平均温度为35℃时CO2最大同化速率减小因子 0.7 1.5
    RD 根的相对死亡速率 0.9 1.0
    RG 叶面积指数最大日增量 0.9 1.3
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    Table  3  Modeling data and verification data used for parameter calibration

    研究区 优化数据 验证数据
    年份 站点 年份 站点
    Ⅰ区 2013 林州
    2014 汤阴
    2015 安阳 2018—2019 汤阴
    2016 濮阳
    2017 范县
    Ⅱ区 2013 郑州
    2014 商丘
    2015 伊川 2018—2019 郑州
    2016 济源
    2017 郑州
    Ⅲ区 2013 许昌
    2014 许昌
    2015 黄泛区 2018—2019 黄泛区
    2016 驻马店
    2017 南阳
    Ⅳ区 2013 信阳
    2014 正阳
    2015 新野 2018—2019 潢川
    2016 潢川
    2017 固始
    Ⅴ区 2013 卢氏
    2014 三门峡
    2015 三门峡 2018—2019 卢氏
    2016 卢氏
    2017 卢氏
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    Table  4  The posteriori distribution of sensitive parameters in Zone Ⅰ of Henan Province

    参数 平均值 中值 最大似然值 均方根误差 95%置信区间
    AM00 1.369 1.414 1.470 0.00396 [1.361, 1.377]
    AM10 1.351 1.374 1.426 0.00301 [1.345, 1.357]
    AM13 0.729 0.722 0.704 0.00070 [0.728, 0.731]
    SL00 1.443 1.455 1.500 0.00130 [1.440, 1.445]
    SL05 0.774 0.770 0.707 0.00127 [0.771, 0.776]
    FL 1.904 1.932 1.994 0.00220 [1.900, 1.909]
    FO 0.857 0.856 0.860 0.00023 [0.856, 0.857]
    FR 1.423 1.429 1.487 0.00131 [1.421, 1.426]
    SP 0.831 0.829 0.826 0.00026 [0.831, 0.832]
    DownLoad: Download CSV

    Table  5  The posteriori distribution of sensitive parameters in Zone Ⅱ of Henan Province

    参数 平均值 中值 最大似然值 均方根误差 95%置信区间
    AM00 0.761 0.750 0.704 0.00176 [0.757, 0.764]
    AM10 1.397 1.410 1.412 0.00274 [1.392, 1.403]
    AM13 0.793 0.785 0.760 0.00235 [0.788, 0.798]
    SL00 1.350 1.361 1.458 0.00385 [1.342, 1.357]
    SL05 0.772 0.759 0.737 0.00195 [0.768, 0.775]
    FL 1.823 1.848 1.973 0.00496 [1.813, 1.833]
    FO 0.921 0.920 0.901 0.00055 [0.919, 0.922]
    FR 0.946 0.964 1.112 0.00573 [0.934, 0.957]
    SP 0.822 0.824 0.824 0.00034 [0.822, 0.823]
    DownLoad: Download CSV

    Table  6  The posteriori distribution of sensitive parameters in Zone Ⅲ of Henan Province

    参数 平均值 中值 最大似然值 均方根误差 95%置信区间
    AM00 0.715 0.711 0.702 0.00040 [0.714, 0.716]
    AM10 0.891 0.872 0.869 0.00319 [0.885, 0.897]
    AM13 0.738 0.730 0.700 0.00089 [0.737, 0.740]
    SL00 1.352 1.376 1.498 0.00296 [1.346, 1.358]
    SL05 1.082 1.067 1.144 0.00383 [1.075, 1.090]
    FL 1.326 1.334 1.215 0.00426 [1.317, 1.334]
    FO 0.809 0.807 0.802 0.00023 [0.809, 0.810]
    FR 0.790 0.787 0.794 0.00397 [0.782, 0.798]
    SP 0.932 0.938 0.940 0.00042 [0.932, 0.933]
    DownLoad: Download CSV

    Table  7  The posteriori distribution of sensitive parameters in Zone Ⅳ of Henan Province

    参数 平均值 中值 最大似然值 均方根误差 95%置信区间
    AM00 1.446 1.460 1.496 0.00094 [1.444, 1.447]
    AM10 1.118 1.127 0.744 0.00472 [1.109, 1.128]
    AM13 1.418 1.441 1.497 0.00151 [1.415, 1.421]
    SL00 1.174 1.188 0.918 0.00304 [1.168, 1.180]
    SL05 0.799 0.777 0.711 0.00164 [0.796, 0.802]
    FL 1.090 1.076 1.297 0.00250 [1.085, 1.095]
    FO 1.033 1.021 1.124 0.00076 [1.031, 1.034]
    FR 0.625 0.595 0.602 0.00198 [0.621, 0.629]
    SP 0.834 0.829 0.828 0.00027 [0.833, 0.834]
    DownLoad: Download CSV

    Table  8  The posteriori distribution of sensitive parameters in Zone Ⅴ of Henan Province

    参数 平均值 中值 最大似然值 均方根误差 95%置信区间
    AM00 1.082 1.070 1.333 0.00562 [1.071, 1.093]
    AM10 0.732 0.724 0.705 0.00068 [0.730, 0.732]
    AM13 0.728 0.721 0.708 0.00057 [0.727, 0.729]
    SL00 1.481 1.484 1.492 0.00036 [1.480, 1.481]
    SL05 0.718 0.715 0.708 0.00026 [0.717, 0.718]
    FL 1.880 1.885 1.792 0.00203 [1.876, 1.884]
    FO 0.937 0.935 0.915 0.00046 [0.936, 0.938]
    FR 0.532 0.527 0.502 0.00059 [0.531, 0.533]
    SP 0.738 0.741 0.748 0.00030 [0.738, 0.739]
    DownLoad: Download CSV

    Table  9  The posteriori distribution of sensitive parameters for the whole Henan Province

    参数 平均值 中值 最大似然值 均方根误差 95%置信区间
    AM00 0.758 0.747 0.756 0.00170 [0.755, 0.761]
    AM10 0.724 0.717 0.709 0.00072 [0.722, 0.725]
    AM13 0.714 0.711 0.702 0.00044 [0.714, 0.715]
    SL00 1.483 1.485 1.499 0.00048 [1.482, 1.484]
    SL05 0.712 0.709 0.701 0.00032 [0.711, 0.712]
    FL 1.855 1.853 1.856 0.00178 [1.851, 1.858]
    FO 0.901 0.899 0.892 0.00046 [0.900, 0.902]
    FR 0.553 0.544 0.538 0.00149 [0.550, 0.556]
    SP 0.896 0.896 0.906 0.00015 [0.895, 0.896]
    DownLoad: Download CSV
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    • Received : 2020-09-30
    • Accepted : 2020-11-02
    • Published : 2021-01-31
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