农田土壤入渗不确定性组成分析
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摘要
依据大田四个测点的测试数据,基于三种土壤入渗模型(Kostiakov-lewis、Philip和Horton模型),对土壤入渗过程进行模拟和分析。探究模型结构、入渗时间和空间变异等不确定性因素对入渗过程的影响,以模型模拟残差及均方根误差随时间的变化为指标,比较分析得出三种不确定性因素对农田入渗规律的影响程度。结果表明,三种模型的相关系数均达到0.99以上,平均相对误差1.60%～11.77%;其中Horton模型的拟合精度最高(平均R~2=0.998 3),模型参数偏差系数较小,稳定性强;时间不确定性围绕一个固定值随时间上下波动,空间和模型结构不确定性均随时间呈现线性增加趋势,其中时间、空间和模型结构不确定性的变化区间分别为0.021 8～16.228、5.674～107.532和0.004～5.504 mm。分析认为空间不确定性对土壤入渗过程产生的影响最大,时间不确定性影响次之,模型结构的不确定性影响最小。该研究可为畦田灌溉过程中土壤入渗规律的研究和分析提供重要依据。
The soil infiltration process was simulated and analyzed based on the test data of four field measurement points and three soil infiltration models(kostiakov-lewis, Philip and Horton models).To explore the influence of uncertain factors such as model structure, infiltration time and spatial variation on the infiltration process, the residual error and root-mean-square error of the model simulation over time are taken as indicators to compare and analyze the influence degree of three uncertain factors on the infiltration law of farmland. The results show that the correlation coefficients of the three models are above 0.99, and the average relative error is between 1.60% and 11.77%. Among them, the fitting accuracy of Horton model is the highest(average R~2=0.998 3), the deviation coefficient of model parameters is small, and the stability is strong. Time uncertainty fluctuates up and down with time around a fixed value, and spatial and model structural uncertainty both show a linear increase trend with time, among which time, space and model structural uncertainty vary between 0.021 8～16.228, 5.674～107.532 and 0.004～5.504 mm, respectively. The analysis shows that spatial uncertainty has the greatest impact on soil infiltration, followed by temporal uncertainty, and the model structure has the least impact. This study can provide an important basis for the study and analysis of soil infiltration in border irrigation.
The soil infiltration process was simulated and analyzed based on the test data of four field measurement points and three soil infiltration models(kostiakov-lewis, Philip and Horton models).To explore the influence of uncertain factors such as model structure, infiltration time and spatial variation on the infiltration process, the residual error and root-mean-square error of the model simulation over time are taken as indicators to compare and analyze the influence degree of three uncertain factors on the infiltration law of farmland. The results show that the correlation coefficients of the three models are above 0.99, and the average relative error is between 1.60% and 11.77%. Among them, the fitting accuracy of Horton model is the highest(average R~2=0.998 3), the deviation coefficient of model parameters is small, and the stability is strong. Time uncertainty fluctuates up and down with time around a fixed value, and spatial and model structural uncertainty both show a linear increase trend with time, among which time, space and model structural uncertainty vary between 0.021 8～16.228, 5.674～107.532 and 0.004～5.504 mm, respectively. The analysis shows that spatial uncertainty has the greatest impact on soil infiltration, followed by temporal uncertainty, and the model structure has the least impact. This study can provide an important basis for the study and analysis of soil infiltration in border irrigation.
引文
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[15] 牛文全,邹小阳,刘晶晶,等.残膜对土壤水分入渗和蒸发的影响及不确定性分析[J].农业工程学报,2016,(32):110-119.
[16] 王仰仁.灌溉排水工程[M]. 北京:中国水利水电出版社,2014.
[17] 沈洪政,王仰仁,韩娜娜.非线性入渗模型求参中参数初始值确定方法研究[J].中国农村水利水电,2017,(10):183-187.
[2] 黄俊,金平伟,李敏,等.径流小区尺度土壤入渗率影响因子与估算模型研究[J]. 农业机械学报,2016,47(8):171-178.
[3] 王文圣,张翔,金菊良等.水文学不确定性分析方法[M]. 北京:科学出版社,2011.
[4] 黄振平,陈元芳.水文统计学[M]. 北京:中国水利水电出版社,2011.
[5] 寇小华,王文,郑国权.土壤水分入渗模型的研究方法综述[J].亚热带水土保持,2013,(25):53-55.
[6] 王维汉,陈晓东,缴锡云.土壤入渗参数的估算方法及其变异性研究进展[J].中国农学通报,2011,27(6):272-275.
[7] Saltelli A, Tarantola S, Campolongo F. Sensitivity analysis as an ingredient of modeling[J]. Statistical Science, 2000,15(4):377-395.
[8] Yao Fengmei,Qin pengcheng,Zhang jiahua,et al.Uncertainties in assessing the effect of climate change on agriculture using model simulation and uncertainty processing methods[J].Atmospheric Science,2011,56(8):729-737.
[9] 徐海芳,刘恩民,郑丽萍.农田土壤入渗参数的空间统计特征[J].中国农村水利水电,2004,(11):11-13.
[10] Machiwal D, Madan K.J., Mal B.C. Modeling infiltration and quantifying spatial soil variability in a Wasteland of Kharagpur, India [J]. Biosystems Engineering. 2006,95(4):568- 582.
[11] 刘泓志,肖长来,张岩祥,等.基于地统计学的降水入渗补给系数的空间变异特征分析[J].节水灌溉,2014,(6):54-59.
[12] 何锦,付雷,刘元晴,等.区域尺度下土壤入渗特征参数的空间变异性研究[J]. 节水灌溉,2015,(9):23-29.
[13] 房全孝.根系水质模型中土壤与作物参数优化及其不确定性评价[J].农业工程学报,2012,(28):118-123.
[14] 梁小丛,董青青.考虑模型和失效准则不确定性的自平衡桩基测试可靠度分析[J].农业工程学报,2013,(1):115-118.
[15] 牛文全,邹小阳,刘晶晶,等.残膜对土壤水分入渗和蒸发的影响及不确定性分析[J].农业工程学报,2016,(32):110-119.
[16] 王仰仁.灌溉排水工程[M]. 北京:中国水利水电出版社,2014.
[17] 沈洪政,王仰仁,韩娜娜.非线性入渗模型求参中参数初始值确定方法研究[J].中国农村水利水电,2017,(10):183-187.