Guo Jianping, Luan Qing, Wang Jingxuan, et al. Model construction of rainfall interception by maize canopy. J Appl Meteor Sci, 2020, 31(4): 397-404. DOI:  10.11898/1001-7313.20200402.
Citation: Guo Jianping, Luan Qing, Wang Jingxuan, et al. Model construction of rainfall interception by maize canopy. J Appl Meteor Sci, 2020, 31(4): 397-404. DOI:  10.11898/1001-7313.20200402.

Model Construction of Rainfall Interception by Maize Canopy

DOI: 10.11898/1001-7313.20200402
  • Received Date: 2020-03-19
  • Rev Recd Date: 2020-04-30
  • Publish Date: 2020-07-31
  • Rainfall is the main water source of crops. Crops maintain their normal growth and development by absorbing water from the soil. However, the role of rainfall is often overestimated in water resource evaluation and farmland water balance research because the rainfall interception effect of crop canopy is not considered. It is difficult to calculate crop interception quantitatively, which seriously restricts the impact assessment of rainfall on crops. Therefore, in order to determine the interception effect in different growth stages of maize under different rainfall, the rainfall interception experiment of maize is carried out at Jinzhou Agricultural Meteorological Experimental Station of Liaoning Province in 2018. A total of 10 rainfall levels (rainfall over 20 mm is designed for the measurement of saturated interception) and 8 leaf area indexes (representing 8 different growth periods) are examined in the experiment. The rainfall interception effect of maize canopy is systematically analyzed by simulation. Results show that under certain rainfall, the relationship between the interception of maize canopy and leaf area index conforms to the relationship of quadratic polynomial, exponential function and power function, among which the quadratic polynomial has the highest explanation rate. Under the assumption of fixed leaf area index, the rainfall interception of maize canopy is in accordance with the quadratic polynomial, exponential function, power function and logarithmic function. When leaf area index is above 3, the explanation rate of power function is the highest, and the relationship between saturated interception of maize and leaf area index is in accordance with the quadratic polynomial, exponential function and power function, among which the explanation rate of quadratic polynomial is the highest. The comprehensive leaf area index and rainfall analysis indicate a positive correlation between canopy interception and the square of leaf area index and the logarithm of rainfall. According to the traditional planting mode of maize in China, the maximum leaf area index of high-yield maize is generally about 5-6. Therefore, the maximum interception of a rainfall process is usually 1.5-2.3 mm. When leaf area index is less than 1, the rainfall interception of maize can be ignored. Results are of practical significance for the evaluation of the effectiveness of rainfall resources and the study of farmland water balance.
  • Fig. 1  The relationship between saturated interception and leaf area index

    Fig. 2  The relationship between interception and leaf area index under different rainfall levels

    Table  1  The fitting model for saturated interception and leaf area index

    类型 拟合模型 决定系数 样本量
    二次多项式模型 y=0.0744x2-0.0701x+0.0416 0.9609** 8
    指数函数模型 y=0.0188e1.1107x 0.9531** 8
    幂函数模型 y=0.1134x1.0511 0.9463** 8
    注:y表示饱和截留量,单位:mm;x表示叶面积指数。**表示拟合模型达到0.01显著性水平。
    DownLoad: Download CSV

    Table  2  The fitting model for interception and leaf area index

    降水量/mm 拟合模型 样本量 决定系数
    y=0.0038x2+0.027x+0.0064 8 0.9794**
    0.3 y=0.0119e0.7383x 8 0.8983**
    y=0.0475x0.7716 8 0.9699**
    y=0.0045x2+0.0407x+0.004 8 0.9934**
    0.7 y=0.0125e0.7393x 8 0.7900**
    y=0.0555x1.0226 8 0.9481**
    y=0.0197x2+0.0144x+0.0096 8 0.9809**
    1.2 y=0.0147e0.895x 8 0.9286**
    y=0.0637x1.0568 8 0.9479**
    y=0.0455x2-0.0631x+0.0324 8 0.9534**
    2.1 y=0.0162e0.8868x 8 0.9486**
    y=0.0911x0.8753 8 0.9384**
    y=0.0147x2+0.0436x+0.0072 8 0.966**
    2.7 y=0.0203e0.7453x 8 0.7447**
    y=0.0771x1.1306 8 0.9357**
    y=0.0306x2+0.0003x+0.0207 8 0.9648**
    3.0 y=0.0166e0.9196x 8 0.9148**
    y=0.1017x0.8736 8 0.9636**
    y=0.0322x2+0.0391x+0.0097 8 0.9921**
    7.8 y=0.0272e0.7986x 8 0.8618**
    y=0.119x1.0613 8 0.9450**
    注:y表示截留量,单位:mm;x表示叶面积指数。**表示拟合模型达到0.01显著性水平。
    DownLoad: Download CSV

    Table  3  The fitting model for interception and rainfall

    叶面积指数 拟合模型 样本量 决定系数
    1 y=0.0005x2+0.0017x+0.0389 7 0.4967
    y=0.0338e0.1046x 7 0.2349
    y=0.0405x0.1716 7 0.1124
    y=0.0107lnx+0.0436 7 0.2944
    2 y=-0.0007x2+0.0235x+0.077 7 0.8623**
    y=0.0887e0.1229x 7 0.7496*
    y=0.1047x0.2927 7 0.7567**
    y=0.0387lnx+0.1089 7 0.7403*
    3 y=-0.0046x2+0.0744x+0.1168 7 0.9738**
    y=0.1657e0.1371x 7 0.7489*
    y=0.1948x0.373 7 0.9864**
    y=0.0873lnx+0.207 7 0.9557**
    4 y=-0.0113x2+0.1553x+0.1542 7 0.9196**
    y=0.2595e0.1495x 7 0.6399*
    y=0.306x0.4298 7 0.9405**
    y=0.157lnx+0.3355 7 0.9345**
    5 y=-0.0206x2+0.2635x+0.201 7 0.836**
    y=0.3764e0.157x 7 0.554
    y=0.4436x0.4685 7 0.8779**
    y=0.2458lnx+0.5018 7 0.8668**
    注:y表示截留量,单位:mm;x表示降水量,单位:mm。*和**分别表示拟合模型达到0.05和0.01显著性水平。
    DownLoad: Download CSV

    Table  4  Interception rate of different leaf areas under some rainfall levels(unit:%)

    叶面积指数 降水量
    2 mm 5 mm 10 mm 20 mm
    1 2.7 1.8 1.3 0.8
    2 6.4 3.4 2.0 1.2
    3 12.7 5.7 3.3 1.8
    4 21.4 9.4 5.0 2.7
    5 32.7 13.9 7.3 3.8
    6 46.4 19.4 10.0 5.2
    DownLoad: Download CSV

    Table  5  Independent sample verification of fitting model

    降水量/mm 叶面积指数 截留量
    实测/mm 模拟/mm 误差/%
    1.4 0.0378 0.0051 0.0128 151.0
    0.1647 0.0140 0.0135 -3.6
    0.2647 0.0260 0.0146 -43.8
    0.7221 0.0307 0.0258 -16.0
    1.6381 0.0674 0.0799 18.5
    1.6928 0.0847 0.0844 -0.3
    2.5363 0.1883 0.1736 -7.8
    4.1837 0.5383 0.4504 -16.3
    2.0 0.1349 0.0113 0.0290 156.6
    0.1586 0.0133 0.0291 118.8
    0.8439 0.0420 0.0463 10.2
    1.3391 0.0567 0.0733 29.3
    1.9770 0.1107 0.1262 14.0
    2.1979 0.1427 0.1493 4.5
    2.6974 0.2701 0.2104 -22.1
    3.1516 0.3449 0.2768 -19.7
    DownLoad: Download CSV
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    • Received : 2020-03-19
    • Accepted : 2020-04-30
    • Published : 2020-07-31

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