农田土壤水盐特性的空间变异性研究
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摘要
土壤水盐特性是用于描述土壤水分、盐分运动过程与分布情况的参数。研究土壤水盐特性的空间变异性对于科学研究土壤环境、土壤农作、土壤水分运动等学科都具有重要的指导意义。通过对杨凌塿土直线取样(方案一)、新疆盐碱土空间网格取样(方案二)和直线取样(方案三)的样品分析测定,采用经典统计学原理、地统计学原理、空间自相关原理、小波分析、多重分形和联合多重分形等方法对其进行空间分析,从不同角度对比揭示土壤水盐特性的空间变异性。得到以下主要结论:
(1)经典统计学原理分析可知,方案一中的Brooks-Corey模型拟合参数(α)和饱和导水率(Ks)的变异系数大于1,表现为强变异;Brooks-Corey模型拟合参数(n)的变异系数介于0.1到1之间,表现为中等变异水平;Brooks-Corey模型饱和含水量(θs)、质量含水率(θ)及容重(ρ)的分布较为均匀,且变异系数小于0.1,表现为弱变异。方案二中绝大多数土壤水盐特性表现为中等变异水平,只有Na~+在大、小尺度上表现为强变异。方案三中各土壤水盐特性绝大多数表现为中等变异强度,只有pH表现为弱变异。
(2)地统计学分析表明,三方案中各土壤水盐特性的半方差函数基本上可以用球状模型来模拟,属于中等变异程度。
(3)空间自相关理论分析表明,方案一中四个方向上的各土壤水盐特性的空间自相关程度不一,但同一方向上呈现类似的变化趋势,Moran's I系数的变化范围在[-0.3,0.3],表明随机分布格局相对占较大比重。方案二中三尺度下土壤各水盐特性的Moran's I系数变化具有相似性,在-0.8～0.6范围内波动。方案三中各土壤水盐特性的Moran's I系数变化各异,但均在-0.5~0.4之间变化。
(4)由小波方差图谱分析可知,方案一和三中的各土壤水盐特性在整个空间上至少表现出两个尺度结构,而绝大多数的土壤水盐特性最大尺度结构分别出现在200m和64m处,这就说明在此处引起土壤水盐特性的空间变化的信息较为丰富,空间分布结构比较复杂。
(5)从多重分形理论来分析,在无标度尺度-4~4内,各土壤水盐特性存在多重分形特征,方案一中θs、θ及ρ的多重结构较弱,而Ks、α及n的多重分形结构特征明显。通过对广义分形维数的计算,表明θs、θ及ρ的空间变异性较弱,Ks的空间变异性很强,α的空间变异性较强,n的空间变异性强,且Ks比α和n具有更为复杂的空间分布结构。方案三中土壤含盐量、滴水穿透时间、pH、Na~+、Mg~(2+)和Ca~(2+)均具有多重分形结构,但pH的多重分形特征不明显,表明pH的空间变异性较弱。
(6)利用联合多重分形对土壤水盐特性的空间关联性进行分析表明,方案一中Ks和α、n,ρ和n之间的空间关联性较弱,Ks和θs、ρ、θ,ρ和α之间的空间关联性较强,方案三中土壤含盐量与pH、Na~+、Mg~(2+)、Ca~(2+),Na~+与Mg~(2+)、Ca~(2+),Mg~(2+)和Ca~(2+)、滴水穿透时间之间存在一定的空间关联性。
Soil hydraulic and saline properties are parameters used to describe soil water and salt movement and distribution. Study on spatial variability of soil hydraulic and saline properties had great significance on guiding the development and application of soil environment, soil tillage, and soil water movement. Based on the measurement of Lou soil samples taken in yangling region along straight lines (scheme one), saline-alkalin sail samples taken in Xinjiang region at different scales of grids (scheme two) and along straight lines (scheme three). Spatial variability of soil hydraulic and saline properties were analyzed by means of principles of classical statistics, geostatistics, spatial autocorrelation theory, wavelet analysis, the BP artificial neural network, multifractal theory, and joint multifractal theory. In order to reveal spatial variability of soil hydraulic and saline properties from different angles. The main conclusions were as following:
(1) From the analysis of classical statistics for scheme one, variation coefficients of Brooks-Corey model fitting parameter (α) and saturated hydraulic conductivity (Ks) were larger than 1, belonged to strong variations; variation coefficients of Brooks-Corey model fitting parameter (n) varied from 0.1 to 1, belonged to moderate variation; Brooks-Corey model saturated water content (θs), mass water content(θ) and bulk density(ρ) distributed uniformly, and variation coefficients were smaller than 0.1, belonged to weak variation. For scheme two, most soil hydraulic and saline properties varied to a moderate degree. For scheme three, most of the soil hydraulic and saline properties varied to a moderate degree while pH belonged to weak variation.
(2) From the analysis of Geostatistics, most semi-variance theoretical models of soil hydraulic and saline properties of three schemes could be fit with spherical models, and showed moderate variation level.
(3) From the analysis of spatial autocorrelation theory for scheme one, spatial autocorrelation degree of soil hydraulic and saline properties were different in four directions, Moran’s I coefficients ranged from -0.3 to 0.3, showed that random distribution took up a large proportion. For scheme two, Moran’s I coefficients of all the soil hydraulic and saline properties were quite similar in variation, which ranged from -0.8 to 0.6 at all the three scales. For scheme three, Moran’s I coefficients of all the soil hydraulic and saline properties were different, but which ranged from -0.5 to 0.4.
(4) From the analysis of the wavelet variance map, for scheme one and three, every soil hydraulic and saline properties in space showed at least two scale structures, most of their biggest scale structure appeared near 200 and 64 meter respectively. It suggested that in these positions, the spatial variability information of soil hydraulic and saline properties were more abundant, and spatial distribution structure were more complex.
(5) From the analysis of multifractal theory, in scale-free scale from -4 to 4, soil hydraulic and saline properties had multifractal feature. For scheme one, the multifractal features ofθs,θandρwere weak, and the multifractal features of Ks,αand n were obvious. According to the calculations of generalized dimensions of soil hydraulic and saline properties, spatial variability ofθs,θandρwere weak, the spatial variability of Ks was stronger thanαand n, suggested that spatial structure of Ks was more complex. For scheme three, soil salt content, WDPT, pH, Na~+, Mg~(2+), and Ca~(2+) had multifractal structure, but multifractal features of pH was not obvious, suggested that spatial variability of pH was weak.
(6) From joint multifractal analyse for spatial correlation of soil hydraulic and saline properties, for scheme one, the result showed that in the first program spatial correlation between Ks andα, n, and betweenρand n were weak, spatial correlation between Ks andθs,ρ,θ, and betweenρandαwere strong. For scheme three, spatial correlation between salt content and pH, Na~+, Mg~(2+), Ca~(2+), between Na~+ and Mg~(2+), Ca~(2+), between Mg~(2+) and Ca~(2+) , WDPT were strong.
(1)经典统计学原理分析可知,方案一中的Brooks-Corey模型拟合参数(α)和饱和导水率(Ks)的变异系数大于1,表现为强变异;Brooks-Corey模型拟合参数(n)的变异系数介于0.1到1之间,表现为中等变异水平;Brooks-Corey模型饱和含水量(θs)、质量含水率(θ)及容重(ρ)的分布较为均匀,且变异系数小于0.1,表现为弱变异。方案二中绝大多数土壤水盐特性表现为中等变异水平,只有Na~+在大、小尺度上表现为强变异。方案三中各土壤水盐特性绝大多数表现为中等变异强度,只有pH表现为弱变异。
(2)地统计学分析表明,三方案中各土壤水盐特性的半方差函数基本上可以用球状模型来模拟,属于中等变异程度。
(3)空间自相关理论分析表明,方案一中四个方向上的各土壤水盐特性的空间自相关程度不一,但同一方向上呈现类似的变化趋势,Moran's I系数的变化范围在[-0.3,0.3],表明随机分布格局相对占较大比重。方案二中三尺度下土壤各水盐特性的Moran's I系数变化具有相似性,在-0.8～0.6范围内波动。方案三中各土壤水盐特性的Moran's I系数变化各异,但均在-0.5~0.4之间变化。
(4)由小波方差图谱分析可知,方案一和三中的各土壤水盐特性在整个空间上至少表现出两个尺度结构,而绝大多数的土壤水盐特性最大尺度结构分别出现在200m和64m处,这就说明在此处引起土壤水盐特性的空间变化的信息较为丰富,空间分布结构比较复杂。
(5)从多重分形理论来分析,在无标度尺度-4~4内,各土壤水盐特性存在多重分形特征,方案一中θs、θ及ρ的多重结构较弱,而Ks、α及n的多重分形结构特征明显。通过对广义分形维数的计算,表明θs、θ及ρ的空间变异性较弱,Ks的空间变异性很强,α的空间变异性较强,n的空间变异性强,且Ks比α和n具有更为复杂的空间分布结构。方案三中土壤含盐量、滴水穿透时间、pH、Na~+、Mg~(2+)和Ca~(2+)均具有多重分形结构,但pH的多重分形特征不明显,表明pH的空间变异性较弱。
(6)利用联合多重分形对土壤水盐特性的空间关联性进行分析表明,方案一中Ks和α、n,ρ和n之间的空间关联性较弱,Ks和θs、ρ、θ,ρ和α之间的空间关联性较强,方案三中土壤含盐量与pH、Na~+、Mg~(2+)、Ca~(2+),Na~+与Mg~(2+)、Ca~(2+),Mg~(2+)和Ca~(2+)、滴水穿透时间之间存在一定的空间关联性。
Soil hydraulic and saline properties are parameters used to describe soil water and salt movement and distribution. Study on spatial variability of soil hydraulic and saline properties had great significance on guiding the development and application of soil environment, soil tillage, and soil water movement. Based on the measurement of Lou soil samples taken in yangling region along straight lines (scheme one), saline-alkalin sail samples taken in Xinjiang region at different scales of grids (scheme two) and along straight lines (scheme three). Spatial variability of soil hydraulic and saline properties were analyzed by means of principles of classical statistics, geostatistics, spatial autocorrelation theory, wavelet analysis, the BP artificial neural network, multifractal theory, and joint multifractal theory. In order to reveal spatial variability of soil hydraulic and saline properties from different angles. The main conclusions were as following:
(1) From the analysis of classical statistics for scheme one, variation coefficients of Brooks-Corey model fitting parameter (α) and saturated hydraulic conductivity (Ks) were larger than 1, belonged to strong variations; variation coefficients of Brooks-Corey model fitting parameter (n) varied from 0.1 to 1, belonged to moderate variation; Brooks-Corey model saturated water content (θs), mass water content(θ) and bulk density(ρ) distributed uniformly, and variation coefficients were smaller than 0.1, belonged to weak variation. For scheme two, most soil hydraulic and saline properties varied to a moderate degree. For scheme three, most of the soil hydraulic and saline properties varied to a moderate degree while pH belonged to weak variation.
(2) From the analysis of Geostatistics, most semi-variance theoretical models of soil hydraulic and saline properties of three schemes could be fit with spherical models, and showed moderate variation level.
(3) From the analysis of spatial autocorrelation theory for scheme one, spatial autocorrelation degree of soil hydraulic and saline properties were different in four directions, Moran’s I coefficients ranged from -0.3 to 0.3, showed that random distribution took up a large proportion. For scheme two, Moran’s I coefficients of all the soil hydraulic and saline properties were quite similar in variation, which ranged from -0.8 to 0.6 at all the three scales. For scheme three, Moran’s I coefficients of all the soil hydraulic and saline properties were different, but which ranged from -0.5 to 0.4.
(4) From the analysis of the wavelet variance map, for scheme one and three, every soil hydraulic and saline properties in space showed at least two scale structures, most of their biggest scale structure appeared near 200 and 64 meter respectively. It suggested that in these positions, the spatial variability information of soil hydraulic and saline properties were more abundant, and spatial distribution structure were more complex.
(5) From the analysis of multifractal theory, in scale-free scale from -4 to 4, soil hydraulic and saline properties had multifractal feature. For scheme one, the multifractal features ofθs,θandρwere weak, and the multifractal features of Ks,αand n were obvious. According to the calculations of generalized dimensions of soil hydraulic and saline properties, spatial variability ofθs,θandρwere weak, the spatial variability of Ks was stronger thanαand n, suggested that spatial structure of Ks was more complex. For scheme three, soil salt content, WDPT, pH, Na~+, Mg~(2+), and Ca~(2+) had multifractal structure, but multifractal features of pH was not obvious, suggested that spatial variability of pH was weak.
(6) From joint multifractal analyse for spatial correlation of soil hydraulic and saline properties, for scheme one, the result showed that in the first program spatial correlation between Ks andα, n, and betweenρand n were weak, spatial correlation between Ks andθs,ρ,θ, and betweenρandαwere strong. For scheme three, spatial correlation between salt content and pH, Na~+, Mg~(2+), Ca~(2+), between Na~+ and Mg~(2+), Ca~(2+), between Mg~(2+) and Ca~(2+) , WDPT were strong.
引文
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朱磊,周清,王康,杨金忠. 2009.基于多重分形理论的土壤水非均匀流动分析.水科学进展,20(3):392~397.
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Caniego F J, Espejo R, Martin M A.2005. Multifractal scaling of soil spatial variability. Ecological Modelling, 182: 291~303.
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Tsegaye T, Hill R L. 1998. Intensive tillage effects on spatial variability of soil physical properties. Soil Science, 163 (2): 143~154.
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周慧珍,龚子同. 1996.土壤空间变异性研究.土壤学报,33(3):232~241.
朱磊,周清,王康,杨金忠. 2009.基于多重分形理论的土壤水非均匀流动分析.水科学进展,20(3):392~397.
朱益玲,刘洪斌,江希流. 2004.江津市紫色土中N、P养分元素区域空间变异性研究.环境科学, 25(1):138~143.
Abuelgasim A A, Ross W D, Gopal S.1999. Change detection using adaptive fuzzy neural networks: Environmental damage assessment after the Gulf War. Remote Sensing of Environment, 70(2): 208~223.
Beaulieu P, Lowell K. 1994. Spatial autocorrelation among forest stands identified from the interpretation of aerial photographs. Landscape Urban Plan, 29(2~3): 161~169.
Bird N, Cruz D M, Saa A.2006. Fractal and multifractal analysis of pore~scale images of soil. Journal of Hydrology, 322: 211~219.
Caniego F J, Espejo R, Martin M A.2005. Multifractal scaling of soil spatial variability. Ecological Modelling, 182: 291~303.
Cerri C E P, Bernoux M, Chaplot V.2004. Assessment of soil property spatial variation in an Amazon pasture: basis for selecting an agronomic experimental area. Geoderma, 123(1~2): 51~68.
Comegna V, Damiani P, Sommella A. 1998. Use of a fractal model for determining soil water retention curves. Geoderma, 85(4): 307~323.
Ersahin S, Brohi A R. 2006. Spatial variation of soil water content in topsoil and subsoil of a Typic Ustifluvent. Agricultural Water Management, 83(1~2): 79～88.
Filgueira R R, Fournier L L, Sarli G O.1999. Sensitivity of fractal parameters of soil aggregates to different management practices in a Phaeozem in central Argentina. Soil & Tillage Research, 52: 217~222.
Gaston L A, Locke M A, Zablotowicz R M.2001. Spatial variability of soil properties and weed populations in the Mississippi Delta. Soil Sci Soc Am J, 65: 449~459.
Gerke H H, Hangen E, Schaaf W.2001. Spatial variability of potential water repellency in a lignitic mine soil afforested with Pinus nigra. Geoderma, 102: 255-274.
Glick B. 1979. The spatial autocorrelation of cancer mortality. Soc Sci Med Pt D, 13(2): 123~130. Hariharan G, Kannan K, Sharma Renganathan Kal. 2009. Harr wavelet in estimating depth profile of soil temperature. Applied Mathematics and Computation, 210: 119~125.
Huang G H, Zhang R D, Huang Q Z. 2006. Modeling soil water retention curve with a fractal method. Pedosphere, 16(2): 137~146.
Iqbal J, Thomasson J A, Jenkins J N.2005. Spatial Variability Analysis of Soil Physical Properties of Alluvial Soils. Soil Sci Soc Am J, 69: 1338~1350.
Jumars P A. 1978. Spatial autocorrelation with RUM (Remote Underwater Manipulator): vertical and horizontal structure of a bathyal benthic community. Deep Sea Res, 25(7): 589~604.
Kravchenko A, Zhang R. 1998. Estimating the soil water retention from particle size distributions: A fractal approach. Soil Science, 163: 171~ 179. 57
Kravchenko A N, Boast C W, Bullock D G. 1999. Multifractal analysis of soil spatial variability. Agronomy Journal, 91: 1033–1041.
Li Yi, Li Min, Robert Horton. 2011. Single and joint multifractal analysis of soil particle size distributions. Pedosphere, 21(1): 75-83.
Mallat S. 1989. A theory for multi-resolution signal decomposition: the wavelet representation. IEEE Trans on PAMI, 11(7): 674~693.
Mallat S. 1989. Multi-frequency channel decompositions of images and wavelet models. IEEE Trans on Acoustics, Speech and Signal Processing, 37(12): 2091~2110. Meyer Y. 1990. Ondeletters et operateurs. Paris: Hermann Press, 1~200.
Meneveau C, Sreenivasan K R, Kailasnath P.1990. Joint multifractal measures: Theory and application to turbulence. Phys. Rev. A, 41(2): 894～913.
Miranda J G V, Montero E, Alves M C.2006. Multifractal characterization of saprolite particle~size distributions after topsoil removal. Geoderma, 134: 373~385.
M?rtberga Ulla, Karlstr?mb Anders. 2005. Predicting forest grouse distribution taking account of spatial autocorrelation. Journal for Nature Conservation, 13: 147~159.
Oleschko K, Figueroa B, S.2000. Mass fractal dimensions and some selected physical properties of contrasting soils and sediments of Mexico. Soil & Tillage Research, 55: 43~61.
Overmars K P, Koning G H J, Veldkamp A. 2003. Spatial autocorrelationin multi-scale land use models. Ecol Modeling, 164(2~3): 257~270.
Perrier E, Rieu M, Sposoto G. 1996. Models of the water retention curve for soils with a fractal pore size distribution. Water Resources Research, 32: 3025~3031.
Pont O, Turiel A, Pérez~Vicente C J. 2009. Empirical evidences of a common multifractal signature in economic, biological and physical systems. Physica A, 388: 2025~2035.
Premo L S. 2004. Local spatial autocorrelation statistics quantify multi~scale patterns in distributional data: an example from the Maya Lowlands. Journal of Archaeological Science, 31: 855~866.
Reys J, Montouri B D. 1999. US regional income convergence: a spatial econometric perspective. Region Stud, 33(2): 143~156.
Saldana A, Stein A, Zinck J.A. 1998. Spatial variability of soil properties at different scales within three terraces of the Henares River (Spain). Catena, 33: 139–153.
Schaap Marcel G, Leij Feike J. 1998. Using neural networks to predict soil water retention and soil hydraulic and saline conductivity. Soil & Tillage Research, 47: 37~42.
Si B C. 2008. Spatial scaling anglyses of soil physical properties: a review of spectral and wavelet methods. Vadose Zone Journal, 7(2): 547~562.
Shu Q, Liu Z, Si B. 2008. Characterizing scale-and location-dependent correlation of water retention parameters with soil physical properties using wavelet techniques. Journal of Environmental Quality, 37: 2284~2292
Sokal R R, OdenN L, Thomson B A. 1998. Local spatial autocorrelation in biological variables. Biol J Linn Soc, 65(1): 41~62.
Tsegaye T, Hill R L. 1998. Intensive tillage effects on spatial variability of soil physical properties. Soil Science, 163 (2): 143~154.
Tyler S W, Wheateraft S W. 1990. Fractal process in soil water retention. Water Resources Research, 26:1017~1054.
Wang D, Fu B J, Zhao W W.2008. Multifractal characteristics of soil particle size distribution under different land~use types on the Loess Plateau, China. Catena, 72: 29~36.
Warner T A, Shank M C. 1997. Spatial autocorrelation analysis of hyper spectral imagery for feature selection. Remote Sens Environ, 60(1): 58~70.
Western A W, Bl?schl G, Grayson R B. 1998. Geostatistical characterisation of soil moisture patterns in the Tarrawarra catchment. Journal of Hydrology, 205(1~2): 20~37.
Xu Y F, Dong P. 2004. Fractal approach to hydraulic properties in unsaturated porous media. Chaos, Solitons and Fractals, 19(2): 327~337.
Yang Qiu, Bojie Fu, Jun Wang. 2001. Spatial variability of soil moisture content and its relation to environmental indices in a semi~arid gully catchment of the Loess Plateau, China. Journal of Arid Environments, 49: 723~750.
Yu L, Qi D W. 2008. Analysis and processing of decayed log CT image based on multifractal theory. Computers and electronics in agriculture, 63: 147~154.
Zeleke T B, Si B C. 2005. Scaling relationships between saturated hydraulic conductivity and soil physical properties. Soil Science Society of America Journal, 69: 1691~1702.
Zeleke T B, Si B C. 2006. Characterizing scale~dependent spatial relationships between soil properties using multifractal techniques. Geoderma, 134: 440~452.