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畦田灌溉模拟中田面微地形空间分布插值方法改进

刘姗姗, 白美健, 许 迪, 章少辉

刘姗姗, 白美健, 许 迪, 章少辉. 畦田灌溉模拟中田面微地形空间分布插值方法改进[J]. 农业工程学报, 2015, 31(17): 108-114. DOI: 10.11975/j.issn.1002-6819.2015.17.014
引用本文: 刘姗姗, 白美健, 许 迪, 章少辉. 畦田灌溉模拟中田面微地形空间分布插值方法改进[J]. 农业工程学报, 2015, 31(17): 108-114. DOI: 10.11975/j.issn.1002-6819.2015.17.014
Liu Shanshan, Bai Meijian, Xu Di, Zhang shaohui. Improvement of interpolation methods for surface micro-topography spatial distribution in border irrigation simulation[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(17): 108-114. DOI: 10.11975/j.issn.1002-6819.2015.17.014
Citation: Liu Shanshan, Bai Meijian, Xu Di, Zhang shaohui. Improvement of interpolation methods for surface micro-topography spatial distribution in border irrigation simulation[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(17): 108-114. DOI: 10.11975/j.issn.1002-6819.2015.17.014

畦田灌溉模拟中田面微地形空间分布插值方法改进

基金项目: 国家自然科学基金项目(51279225);国家支撑课题(2012BAD08B01);中国水利水电科学研究院博士生学位论文创新研究资助课题

Improvement of interpolation methods for surface micro-topography spatial distribution in border irrigation simulation

  • 摘要: 田面微地形是影响灌溉过程及灌溉性能的重要因素之一。针对灌溉模型中田面微地形的插值问题,该文在比较Kriging插值、三次样条插值和反距离加权插值的插值精度和程序运行时间的基础上,进一步分析了不同规格畦田下Kriging插值程序运行时间随插值网格数的变化趋势,提出了利用Kriging插值和三次样条插值及反距离加权插值进行组合插值的方法,并将不同中间插值网格数经组合插值所得田面高程数据用于灌溉模拟。结果表明,Kriging插值所得田面高程的精度最高,程序所需运行时间最长;而三次样条插值和反距离加权插值所需运行时间非常短,但精度较低。为了同时满足田面高程的插值精度和模型计算效率的要求,选用Kriging插值和三次样条插值组合的方式进行灌溉模型中田面高程的插值计算最为适宜。中间插值网格数为1/4及1/8灌溉模型剖分网格数经组合插值的田面高程及Kriging插值数据用于灌溉模型所得水流推进过程与实测水流推进过程基本一致。因此,在实际应用中,中间插值网格数的确定,可根据对插值精度和运行时间的不同要求在灌溉模型剖分网格数的1/4或1/8之间选取。该研究可为灌溉模型的应用提供依据。
    Abstract: Abstract: Surface micro-topography is one of the most important factors affecting irrigation process and performances. In view of the interpolation problem of surface micro-topography in irrigation model, using observed data on surface relative elevation of 4 different size ridge field, this study firstly compared the interpolation accuracy and program running time of Kriging interpolation, cubic spline interpolation and inverse distance weighted interpolation (IDW), and then analyzed the trend of Kriging interpolation program running time with the change of interpolation grid numbers under different sizes of ridged field. Furthermore, the combination interpolation methods of Kriging interpolation and cubic spline interpolation, and Kriging interpolation and inverse distance weighted interpolation were proposed, and the surface flow advance processes simulated based on data on surface relative elevation interpolated by the combination method and kriging interpolation were compared with observed surface flow advance process. The results showed that compared to the cubic spline interpolation and IDW, the interpolation accuracy of Kriging interpolation was the highest and Kriging interpolation could truthfully reflect the spatial distribution of micro-topography but the program running time was the longest, which had a great impact on the computational efficiency of irrigation model, while the program running time of cubic spline interpolation and IDW was relatively short, but their accuracies were low, which affected greatly the simulation accuracy of irrigation model. The running time of Kriging interpolating program increased with the size of ridge field and the number of interpolation grid. The reason of that might be there was an increase trend of equations and equations number in Kriging interpolation with the increase of ridge field size and interpolation gird numbers. The program running time of the combination interpolation method of Kriging interpolation and cubic spline interpolation and that of combination interpolation method Kriging interpolation and inverse distance weighted interpolation was the same, but the interpolation accuracy of the former was higher. In order to both meet the requirements of interpolation accuracy and computational efficiency of irrigation model, it was feasible to use the combination method of Kriging interpolation and cubic spline interpolation for surface elevation interpolation in irrigation model. For those ridge fields with their area less than 2700 m2, the combination interpolation could satisfy the requirements of mean absolute error less than 0.5 mm and program running time less than 10 s. For those ridge fields with their area more than 2700 m2, the combination interpolation could satisfy the requirements of mean absolute error less than 0.5mm and program running time less than 20s when the intermediate interpolation grid number was 1/4 of irrigation model grid number and meet the requirements of mean absolute error less than 1 mm and program running time less than 10s when the intermediate interpolation grid number was 1/8 of irrigation model grid number. The surface flow advance processes simulated based on the surface elevation data interpolated by the combination method with the 1/4 and 1/8 irrigation model grid numbers and Kriging interpolation were similar with the observed data. Therefore, the intermediate interpolation grid number could be selected between 1/4 and 1/8 of irrigation model grid number depending on the requirements of interpolation accuracy and running time.
  • [1] Walker W R, Skogerboe G V. Surface Irrigation Theory and Practice[M]. Englewood Cliffs, NJ: Prentice-Hall, Inc, 1987.
    [2] Zapata N, Playan E. Simulating elevation and infiltration in level-basin irrigation[J]. Journal of Irrigation and Drainage Engineering, 2000, 126(2): 78-84.
    [3] Zapata N, Playan E. Elevation and infiltration in a level basin. I. Characterizing variability[J]. Irrigation Science, 2000, 19(4): 155-164.
    [4] Zapata N, Playan E, Faci J M. Elevation and infiltration in a level basin. II. Impact on soil water and corn yield[J]. Irrigation Science, 2000, 19(4): 165-173.
    [5] 白美健. 微地形和入渗空间变异及其对畦灌系统影响的二维模拟评价[D]. 北京:中国水利水电科学研究院,2007.Bai Meijian. The Spatial and Temporal Variability of Microtopography and Infiltration and Evaluation on their Effects on the Basin Irrigation Performance by Two Dimensional Simulation[D]. Beijing: China Institute of Water Resources and Hydropower Research, 2007. (in Chinese with English abstract)
    [6] 胡克林,李保国,林启美,等. 农田土壤养分的空间变异性特性[J]. 农业工程学报,1999,15(3):33-38.Hu Kelin, Li Baoguo, Lin Qimei, et al. Spatial variability of soil nutrient in wheat field[J]. Transactions of the Chinese Society and Agricultural Engineering(Transactions of the CSAE), 1999, 15(3): 33-38. (in Chinese with English abstract)
    [7] Robinson T P, Metternicht G. Testing the performance of spatial interpolation techniques for mapping soil properties[J]. Computers and Electronics in Agriculture , 2006, 50(2): 97-108.
    [8] 谢恒星,张振华,刘继龙,等. 苹果园土壤含水量测定取样点数目及插值方法研究[J]. 莱阳农学院学报,2005,22(4):298-302.Xie Hengxing, Zhang Zhenhua, Liu Jilong, et al. Research on soil water sampling number and interpolating method in apple orchard[J]. Journal of Laiyang Agricultural College, 2005, 22(4): 298-302. (in Chinese with English abstract )
    [9] 沈掌泉,施洁斌,王珂,等. 应用集成BP神经网络进行田间土壤空间变异研究[J]. 农业工程学报,2004,20(3):35-39.Shen Zhangquan, Shi Jiebin, Wang Ke, et al. Spatial variety of soil properties by BP neural network ensemble[J]. Transactions of the Chinese Society and Agricultural Engineering(Transactions of the CSAE), 2004, 20(3): 35-39. (in Chinese with English abstract )
    [10] Schloeder C A, Zimmerman N E, Jacobs M J .Comparison of methods for interpolating soil properties using limited data[J]. Soil Science Society of America Journal, 2001, 65(2): 470-479.
    [11] 王春颖,尚松浩,毛晓敏,等. 区域地下水位插值的整体-局部组合方法[J]. 农业工程学报,2011,27(8):63-68.Wang Chunying, Shang Songhao, Mao Xiaomin, et al. Combined global-local interpolation method for regional groundwater level[J]. Transactions of the Chinese Society and Agricultural Engineering(Transactions of the CSAE), 2011, 27(8): 63-68. (in Chinese with English abstract)
    [12] Yamamoto J K. Correcting the smooth effect of ordinary Kriging estimates[J]. Mathematical Geology, 2005, 37(11): 69-94.
    [13] Yamamoto J K. Estimation or simulation? That is the question[J]. Computational Geoscience, 2008, 12(4): 573-591.
    [14] 杨雨亭,尚松浩,李超. 土壤水分空间插值的克里金平滑效应修正方法[J]. 水科学进展,2010,21(2):208-213.Yang Yuting, Shang Songhao, Li Chao. Correcting the smoothing effect of ordinary Kriging estimates in soil moisture interpolation[J]. Advances in Water Science, 2010, 21(2): 208-213. (in Chinese with English abstract )
    [15] 朱蕾,黄敬峰.山区县域尺度降水量空间插值方法比较[J]. 农业工程学报,2007,23(7):80-85.Zhu Lei, Huang Jingfeng . Comparison of spatial interpolation method for precipitation of mountain areas in county scale[J]. Transactions of the Chinese Society and Agricultural Engineering(Transactions of the CSAE), 2007, 23(7): 80-85. ( in Chinese with English abstract )
    [16] 许迪,李益农,李福祥,等. 农田土地精细平整施工测量网格间距的适宜性分析[J]. 农业工程学报,2005,21(2):51-55. Xu Di, Li Yinong, Li Fuxiang, et al. Analysis of feasible grid space in agricultural land levelling survey[J]. Transactions of the Chinese Society and Agricultural Engineering (Transactions of the CSAE), 2005, 21(2): 51-55. (in Chinese with English abstract)
    [17] Zhang Shaohui, Xu Di, Li Yinong. A two-dimensional surface water flow model of basin irrigation based on a hybrid numerical method[J]. Journal of Irrigation and Drainage Engineering, 2012, 138(9): 799-808.
    [18] Monestiez P, Dubroca L, Bonnin E, et al. Geostatistical modelling of spatial distribution of Balaenoptera physalus in the Northwestern Mediterranean Sea from sparse count data and heterogeneous observation efforts[J]. Ecological Modelling, 2006, 193(3): 615-628.
    [19] Vicente Serrano S M, Sánchez S, Cuadrat J M. Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature[J]. Climate Research, 2003, 24(2): 161-180.
    [20] Hutchinson M F. Interpolation of rainfall data with thin plate smoothing splines. Part II: Analysis of topographic dependence[J]. Journal of Geographic Information and Decision Analysis, 1998, 2(2): 152-167.
    [21] 林忠辉,莫兴国,李宏轩,等. 中国陆地区域气象要素的空间插值[J]. 地理学报,2002,57(1):47-56.Lin Zhonghui, Mo Xingguo,Li Hongxuan, et al. comparison of three spatial interpolation methods climate variables in china[J]. Acta Geographica Sinica, 2002, 57(1): 47-56.
    [22] Kostiakov A N. On the dynamics of the coefficient of water-percolation in soils and on the necessity for studying it from a dynamic point of view for purposes of amelioration [C].Transactions of the 6th Communication of the Int. Paris: International Soil Science Society, 1932.
    [23] US Department of Agriculture. WinSRFR 3.1 Help and Manual Surface Irrigation Analysis, Design and Simulation[M]. Maricopa, US: US Department of Agriculture, 2009.
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出版历程
  • 收稿日期:  2015-05-20
  • 修回日期:  2015-08-09
  • 发布日期:  2015-08-31

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