Vol.22, NO.4, 2011
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Article Navigation >  Journal of Applied Meteorological Science  > 2011  >  22(4): 445-452
Wang Shanshan, Han Lijuan, Cui Hengjian, et al. The soil moisture predictive model based on the precipitation in North China. J Appl Meteor Sci, 2011, 22(4): 445-452.
Citation: Wang Shanshan, Han Lijuan, Cui Hengjian, et al. The soil moisture predictive model based on the precipitation in North China. J Appl Meteor Sci, 2011, 22(4): 445-452.
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The Soil Moisture Predictive Model Based on the Precipitation in North China

  • 1.

    School of Mathematics, Beijing Normal University, Beijing 100875

  • 2.

    National Meteorological Center, Beijing 100081

  • 3.

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the IRSA, CAS, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, School of Geography and Remote Sensing Science, Beijing Normal University, Beijing 100875

  • Received Date: 2010-09-27
  • Rev Recd Date: 2011-04-26
  • Publish Date: 2011-08-31
    Abstract
  • A better soil moisture predictive model for North China can enhance the accuracy of drought forecast, which will have great significance in predicting the extent and distribution of drought, playing an important role in taking positive and active measures for drought in time. It is well acknowledged that one of the key points and difficult points in research on dry farming is the change of soil moisture, which is influenced by various factors, such as rainfall, evaporation, the time of sunshine, the kind and texture of soil and so on. Of all the factors, the amount of rainfall is one of the most important factors, which contributes a lot to the change of the soil moisture, deserves more and more attentions to study their relationships.A random variable's mechanism-rational model is therefore introduced, and the meteorological data in North China during 2000 to 2008 is analyzed, taking the atmosphere precipitation as random variable as well as its delay effect, employing the regression method to set up the soil relative moisture predictive model, which is effective for ten days. 10-cm and 20-cm soil relative moisture predictive models for the 55 stations in the north of China are established, but only the fitting results of 10 stations are given, and their fitness accuracy (the Pearson correlation coefficient) is all above 60%. Meanwhile, the efficiency of this model is verified with soil moisture data of 2009 by means of the two indexes, indicating that all of the forecasting rates are above 90% and most of the drought prediction rates are above 70%, therefore a series of objective, dynamic soil moisture predictive method is established. The fitting graphs and the graphs of 95% confidence interval are also given for the 10 stations, from which the feasibility of this method can be verified. The method provides a new support for the prediction of the soil moisture in the field.The method can extend to every station in the country to set up local unique models. Though this approach is only tentative experiment, it is a success according to the results of fitting and prediction. Moreover, the model involves 10-day rainfall delay effect, whose prediction is effective for 10 days. With the extension of rainfall stations in the country, it is easier to get the daily observations of the rainfall, and it is possible to conduct continuous prediction for the soil moisture with the method, which will have far-reaching influence on the dry farming research.
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  • Fig. 1  The delay effects of 10 cm and 20 cm soil moisture at Zhuozhou Station

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    Fig. 2  The delay effects of 10 cm and 20 cm soil moisture at Wendeng Station

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    Fig. 3  The fitting figures of 10 cm and 20 cm soil moisture at Zhuozhou Station

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    Fig. 4  The fitting figures of 10 cm and 20 cm soil moisture at Wendeng Station

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    Fig. 5  The forecasting figures of 10 cm and 20 cm soil moisture at Zhuozhou Station in 2009

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    Fig. 6  The forecasting figures of 10 cm and 20 cm soil moisture at Wendeng Station in 2009

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    Table  1  The rating criteria of drought distribution

    土壤相对湿度/% 干旱程度
    50~60 轻旱
    40~50 中旱
    30~40 重旱
    ＜30 特旱
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    Table  2  The results of parameter fitting of ten stations in North China

    站名 土壤相对湿度 γ τ α0 α1 β1 β2 相关系数
    密云 w1
    w2    		 0.60
    0.70    		 50 32.9
    28.9    		 0.44
    0.56    		 1.43
    0.52    		 -0.45
    -0.21    		 0.70
    0.70
    宝坻 w1
    w2    		 0.60
    0.80    		 70 42.8
    43.5    		 0.27
    0.41    		 2.51
    0.28    		 -0.57
    -0.12    		 0.62
    0.56
    涿州 w1
    w2    		 0.80
    0.95    		 80 30.3
    31.2    		 0.34
    0.38    		 1.39
    1.55    		 -0.19
    -0.07    		 0.67
    0.65
    南阳 w1
    w2    		 0.35
    0.70    		 70 26.0
    19.4    		 0.37
    0.60    		 2.08
    0.56    		 0.22
    0.00    		 0.74
    0.80
    商丘 w1
    w2    		 0.55
    0.60    		 100 30.7
    34.0    		 0.25
    0.29    		 2.36
    1.68    		 0.55
    0.49    		 0.65
    0.70
    文登 w1
    w2    		 0.45
    0.60    		 100 26.1
    27.0    		 0.18
    0.28    		 6.00
    2.61    		 1.06
    0.63    		 0.67
    0.71
    胶州 w1
    w2    		 0.65
    0.80    		 70 33.0
    30.5    		 0.36
    0.45    		 1.77
    0.74    		 0.51
    0.25    		 0.76
    0.78
    安泽 w1
    w2    		 0.60
    0.70    		 100 20.3
    19.7    		 0.47
    0.55    		 2.65
    1.29    		 0.02
    0.03    		 0.79
    0.77
    长治 w1
    w2    		 0.60
    0.90    		 100 14.8
    21.1    		 0.39
    0.49    		 3.42
    0.66    		 0.33
    0.20    		 0.70
    0.64
    万荣 w1
    w2    		 0.60
    0.70    		 100 33.8
    34.5    		 0.34
    0.42    		 2.05
    0.98    		 0.51
    0.35    		 0.75
    0.72
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    Table  3  The soil moisture forecasting results of 10 stations in North China

    站点 w1预测率 w2预测率 w1等级预测率 w2等级预测率
    密云 0.98(0.97) 0.95(0.90) 0.80(0.70) 0.81(0.87)
    宝坻 0.95(0.90) 0.95(0.93) 0.95(0.73) 0.93(0.93)
    涿州 1.00(0.98) 0.98(0.95) 0.90(0.62) 0.95(0.62)
    南阳 1.00(1.00) 1.00(1.00) 0.64(0.55) 0.67(0.62)
    商丘 0.92(0.90) 0.93(0.89) 0.70(0.60) 0.84(0.56)
    文登 1.00(0.98) 1.00(0.95) 0.95(0.48) 0.98(0.55)
    胶州 1.00(1.00) 1.00(0.94) 1.00(0.97) 1.00(0.60)
    安泽 1.00(0.94) 1.00(0.93) 0.65(0.53) 0.63(0.44)
    长治 0.98(0.93) 1.00(0.95) 0.67(0.35) 0.67(0.48)
    万荣 0.98(0.93) 1.00(0.95) 0.74(0.38) 0.84(0.23)
    注：表 3为华北地区10个站点预测时效分别为1旬和2旬结果比较，其中括号内的数值表示对应站点预测时效为2旬的预测率或者干旱等级预报率。
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    • Received : 2010-09-27
    • Accepted : 2011-04-26
    • Published : 2011-08-31
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