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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  • [1]
    申双和, 周英.农田土壤水分预测模型应用研究.南京气象学院学报, 1992, 15(4):540-548. http://www.cnki.com.cn/Article/CJFDTOTAL-XBNY200908027.htm
    [2]
    华北平原作物水分胁迫与干旱研究课题组.作物水分胁迫与干旱研究.郑州:河南科学技术出版社, 1990:1-235.
    [3]
    郑剑飞, 范嘉泉.土壤水分系统动态模拟.北京农业大学学报, 1984(4):493-500. http://www.cnki.com.cn/Article/CJFDTOTAL-NYDX198704020.htm
    [4]
    李保国, 龚元石, 左强, 等.农田土壤水的动态模型及应用.北京:科学出版社, 2000.
    [5]
    申双和, 周英.旱地农田土壤水分动态平衡的模拟.南京气象学院学报, 1994, 17(4):462-469. http://www.cnki.com.cn/Article/CJFDTOTAL-NJQX404.010.htm
    [6]
    申双和, 李胜利.一种改进的土壤水分平衡模式.南京气象学院学报, 1998, 24(6):17-21. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXX806.002.htm
    [7]
    龚元石.冬小麦和夏玉米农田土壤分层水分平衡模型.北京农业大学学报, 1995, 21(1):61-67. http://www.cnki.com.cn/Article/CJFDTOTAL-NYDX199501013.htm
    [8]
    申双和, 周英.农田土壤水分的随机模拟和预测.南京气象学院学报, 1993, 16(3):324-328. http://www.cnki.com.cn/Article/CJFDTOTAL-NJQX199303010.htm
    [9]
    左志燕.我国东部春季土壤湿度影响夏季降水.中国气象科学研究院年报, 1997:20-21. http://cdmd.cnki.com.cn/Article/CDMD-10730-1016723643.htm
    [10]
    刘可群, 刘志雄, 梁益同, 等.基于前期有效降水推算耕作层土壤湿度的方法.中国农业气象, 2009, 30(3):365-369. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY200903017.htm
    [11]
    陈斌, 丁裕国, 刘晶淼.土壤湿度的一种统计预报模型初步试验.气象科学, 2005, 25(3):232-236. http://www.cnki.com.cn/Article/CJFDTOTAL-QXKX200503001.htm
    [12]
    李新辉, 宋小宁, 周霞.半干旱区土壤湿度遥感监测方法研究.地理与地理信息科学, 2010, 26(1):90-93. http://www.cnki.com.cn/Article/CJFDTOTAL-DLGT201001023.htm
    [13]
    Eiko N, Mark A S, Jan K S, et al. Resistance modeling of ammonia exchange over oilseed rape. Agricultural and Forest Meteorology, 2000, 105:405-425. doi:  10.1016/S0168-1923(00)00206-9
    [14]
    Nemitz E, Sutton M A, Gut A, et al. Sources and sinks of ammonia within an oilseed rape canopy. Agricultural and Forest Meteorology, 2000, 105:385-404. doi:  10.1016/S0168-1923(00)00205-7
    [15]
    赵艳霞, 王馥棠, 裘国旺.冬小麦干旱识别和预测模型研究.应用气象学报, 2001, 12(2):234-241. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20010231&flag=1
    [16]
    魏凤英.华北干旱的多时间尺度组合预测模型.应用气象学报, 2003, 14(5):585-592. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20030573&flag=1
    [17]
    刘建栋, 王馥棠, 于强, 等.华北地区农业干旱预测模型及其应用研究.应用气象学报, 2003, 14(5):593-604. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20030574&flag=1
    [18]
    孙家民, 黄朝迎.中国农业气候年景的评估及预测.应用气象学报, 2005, 16(增刊):111-115. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=yyqx2005s1014&dbname=CJFD&dbcode=CJFQ
    [19]
    李巧萍, 丁一江, 董文杰.土壤湿度异常对区域短期气候影响的数值模拟试验.应用气象学报, 2007, 18(1):1-11. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20070102&flag=1
    [20]
    周秉荣, 李凤霞, 申双和, 等.从MODIS资料提取土壤湿度信息的主成分分析方法.应用气象学报, 2009, 20(1):114-118. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20090115&flag=1
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    • Received : 2010-09-27
    • Accepted : 2011-04-26
    • Published : 2011-08-31
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