土壤持水特性参数与土壤质地尺度关系的联合多重分形分析

Soil water-retention characteristics at measurement scales are generally different from those at application scales, and there is scale disparity between them and soil physical properties. The relationships between two water-retention parameters, the scaling parameter related to the inverse of the air-entry pressure (αvG, cm-1) and the curve shape factor related to soil pore-size distribution (n) of the van Genuchten water-retention equation, and soil texture (sand, silt, and clay contents) were examined at multiple scales. One hundred twenty-eight undisturbed soil samples were collected from a 640-m transect located in Fuxin, China. Soil water-retention curves were measured and the van Genuchten parameters were obtained by curve fitting. The relationships between the two parameters and soil texture at the observed scale and at multiple scales were evaluated using Pearson correlation and joint multifractal analyses, respectively. The results of Pearson correlation analysis showed that the parameter αvG was significantly correlated with sand, silt, and clay contents at the observed scale. Joint multifractal analyses, however, indicated that the parameter αvG was not correlated with silt and sand contents at multiple scales. The parameter n was positively correlated with clay content at multiple scales. Sand content was significantly correlated with the parameter n at the observed scale but not at multiple scales. Clay contents were strongly correlated to both water-retention parameters because clay content was relatively low in the soil studied, indicating that water retention was dominated by clay content in the field of this study at all scales. These suggested that multiple-scale analyses were necessary to fully grasp the spatial variability of soil water-retention characteristics.     

土壤水分特征曲线的分形模拟

Many empirical models have been developed to describe the soil water retention curve （SWRC）. In this study, a fractal model for SWRC was derived with a specially constructed Menger sponge to describe the fractal scaling behavior of soil; relationships were established among the fractal dimension of SWRC, the fractal dimension of soil mass, and soil texture; and the model was used to estimate SWRC with the estimated results being compared to experimental data for verification. The derived fractal model was in a power-law form, similar to the Brooks-Corey and Campbell empirical functions. Experimental data of particle size distribution （PSD）, texture, and soil water retention for 10 soils collected at different places in China were used to estimate the fractal dimension of SWRC and the mass fractal dimension. The fractal dimension of SWRC and the mass fractal dimension were linearly related. Also, both of the fractal dimensions were dependent on soil texture, i.e., clay and sand contents. Expressions were proposed to quantify the relationships. Based on the relationships, four methods were used to determine the fractal dimension of SWRC and the model was applied to estimate soil water content at a wide range of tension values. The estimated results compared well with the measured data having relative errors less than 10% for over 60% of the measurements. Thus, this model, estimating the fractal dimension using soil textural data, offered an alternative for predicting SWRC.     

基于人工神经网络的数据处理和多目标群方法用土壤传递函数(PTFs)估算保水性

Pedotransfer functions(PTFs) have been developed to estimate soil water retention curves(SWRC) by various techniques.In this study PTFs were developed to estimate the parameters(θ s,θ r,α and λ) of the Brooks and Corey model from a data set of 148 samples.Particle and aggregate size distribution fractal parameters(PSDFPs and ASDFPs,respectively) were computed from three fractal models for either particle or aggregate size distribution.The most effective model in each group was determined by sensitivity analysis.Along with the other variables,the selected fractal parameters were employed to estimate SWRC using multi-objective group method of data handling(mGMDH) and different topologies of artificial neural networks(ANNs).The architecture of ANNs for parametric PTFs was different regarding the type of ANN,output layer transfer functions and the number of hidden neurons.Each parameter was estimated using four PTFs by the hierarchical entering of input variables in the PTFs.The inclusion of PSDFPs in the list of inputs improved the accuracy and reliability of parametric PTFs with the exception of θ s.The textural fraction variables in PTF1 for the estimation of α were replaced with PSDFPs in PTF3.The use of ASDFPs as inputs significantly improved α estimates in the model.This result highlights the importance of ASDFPs in developing parametric PTFs.The mGMDH technique performed significantly better than ANNs in most PTFs.     

用于估算伊朗盐土和盐碱土土壤水分特征曲线的土壤传递函数(PTFs)研究

Soil water retention data are essential for irrigation scheduling and determination of irrigation frequency.However,direct measurement of this characteristic is time consuming and expensive and furthermore its spatial and temporal variabilities in field scales increase the number of measurements.Different pedotransfer functions,such as Saxton et al.,Campbell,Vereecken et al.,Rawls and Brakensiek,Wo¨sten et al.,Rajkai et al.,Ghorbani Dashtaki and Homaee,Zacharias and Wessolek,and Rosetta,were evaluated to estimate soil water retention of saline and saline-alkali soils collected from south of Tehran,Iran.The saturation-extract conductivity of all the 68 samples and exchangeable sodium percentage of more than half of them were measured to be greater than 4 dS m-1 and 15%,respectively.The calculated Akaike’s information criterion values showed that Saxton et al.and Campbell models were the best in estimation of soil water retention curve and total available water,respectively.     

分形模型在利用颗粒分布数据评价土壤持水性质中的应用

Soil water retention characteristics are the key information required in hydrological modeling.Frac-tal models provide a practical alternative for indirectly estimating soil water retention characteristics from particle-size distribution data.Predictive capabilities of three fractal models,i.c.,Tylcr-Wheatcraft model,Rieu-Sposito model,and Brooks-Corey model,were fully evaluated in this work using experimental data from an international database and literature.Particle-size distribution data were firstly interpolated into 20 classes using a van Genuchten-type equation.Fractal dimensions of the tortuous pore wall and the pore surface were then calculated from the detailed particle-size distribution and incorporated as a parameter in fractal water retention models.Comparisons between measured and model-estimated water retention cha-racteristics indicated that these three models were applicable to relatively different soil textures and pressure head ranges.Tyler-Whcatcraft and Brooks-Corey models led to reasonable agreements for both coarse-and medium-textured soils,while the latter showed applicability to a broader texture range.In contrast,Rieu-Sposito model was more suitable for fine-textured soils.Fractal models produced a better estimation of water contents at low pressure heads than at high pressure heads.     

Field-scale spatial variability of soil calcium in a semi-arid region: Implications for soil erosion and site-specific management

Excess calcium(Ca) in soils of semi-arid and arid regions has negative effects on soil structure and chemical properties, which limits the crop root growth as well as the availability of soil water and nutrients. Quantifying the spatial variability of soil Ca contents may reveal factors influencing soil erosion and provide a basis for site-specific soil and crop management in semi-arid regions. This study sought to assess the spatial variability of soil Ca in relation to topography, hydraulic attributes, and soil types for precision soil and crop management in a 194-ha production field in the Southern High Plains of Texas,USA. Soils at four depth increments(0–2, 0–15, 15–30, and 30–60 cm) were sampled at 232 points in the spring of 2017. The Ca content of each sample was determined with a DP-6000 Delta Premium portable X-ray fluorescence(PXRF) spectrometer. Elevation data was obtained using a real-time kinematic GPS receiver with centimeter-level accuracy. A digital elevation model(DEM) was derived from the elevation data, and topographic and hydraulic attributes were generated from this DEM. A generalized least-squares model was then developed to assess the relationship between soil Ca contents of the four layers and the topographic and hydraulic attributes. Results showed that topographic attributes, especially slope and elevation, had a significant effect on soil Ca content at different depths(P 0.01). In addition, hydraulic attributes, especially flow length and sediment transport index(STI), had a significant effect on the spatial distribution of soil Ca. Spatial variability of soil Ca and its relationships with topographic and hydraulic attributes and soil types indicated that surface soil loss may occur due to water or wind erosion, especially on susceptible soils with high slopes. Therefore, this study suggests that the application of PXRF in assessing soil Ca content can potentially facilitate a new method for soil erosion evaluation in semi-arid lands. The results of this study provide valuable information for site-specific soil conservation and crop management.     

内蒙古荒漠化草地土壤碳、氮养分及土壤呼吸的空间异质性研究

There is a limited knowledge of spatial heterogeneity in soil nutrients and soil respiration in the semi-arid and arid grasslands of China. This study investigated the spatial differences in soil nutrients and soil respiration among three desertified grasslands and within two shrub-dominated communities on the Ordos Plateau of Inner Mongolia, China in 2006. Both soil organic carbon (SOC) and total nitrogen (TN) were significantly different (P < 0.01) among the three desertified grasslands along a degradation gradient. Within the two shrub-dominated communities, the SOC and TN contents decreased with increasing distance from the main stems of the shrub, and this “fertile island” effect was most pronounced in the surface soil. The total soil respirations during the growing season were 131.26, 95.95, and 118.66 g C m-2, respectively, for the steppe, shrub, and shrub-perennial grass communities. The coefficient of variability of soil respiration was the highest in the shrub community and lowest in the steppe community. CO2 effluxes from the soil under the canopy of shrub were significantly higher than those from the soil covered with biological crusts and the bare soil in the interplant spaces in the shrub community. However, soil respiration beneath the shrubs was not different from that of the soil in the inter-shrub of the shrub-perennial grass community. This is probably due to the smaller shrub size. In the two shrub-dominated communities, spatial variability in soil respiration was found to depend on soil water content and C:N ratio.     

重金属迁移与土壤性质的关系

Cu, Zn, Pb and Hg runoff from yellow limestone soil and purple soils and the relationships between the mobility of the heavy metals and the soil characteristics were studied in laboratory using a rainfall simulator. The results showed that the concentrations of soluble Zn in surface runoff were significantly negatively correlated with the contents of < 0.002 mm particles and CEC of the soils, indicating that Zn was mostly adsorbed by clays in the soils. The contents of Cu and Hg in surface runoff were positively related to their contents in the soils. The amounts of Cu, Zn, Pb and Hg removed by surface runoff were influenced by the amounts of soil and water losses and their contents in the soils, and were closely related to the contents of soil particles 1~0.02 mm in size.     

Plant-available water capacity of soils at a regional scale: Analysis of fixed and dynamic field capacities

Estimation of the plant-available water capacity (PAWC) of soils at a regional scale helps in adopting better land use planning, developing suitable irrigation schedules for crops, and optimizing the use of scarce water resources. In the current study, 72 soil profiles were sampled from the Barossa region of South Australia to estimate pedo-transfer functions deduced from easily estimated soil properties. These functions were then used to estimate the fixed (10 and 33 kPa) and dynamic pressure head (h_fc) water contents at field capacity (FC) for minimum drainage flux (0.01 and 0.001 cm d^-1), which serves as the upper boundary for plant-available water in soils. The estimated residual water content was corrected for subsoil constraints, especially the exchangeable sodium percentage (ESP). The results showed that the mean values of h_fc in sand-dominated light and medium textured soils (i.e., sand, loamy sand, sandy loam, and loam) varied in a narrow range (15.8–18.2 kPa), whereas those in the clay-dominated heavy textured soils (i.e., clay loam) showed a wide range (11.3–49.3 kPa). There were large differences in PAWC for dynamic FC (PAWC_fc) and fixed FC at 10 kPa (PAWC₁₀), 33 kPa (PAWC₃₃), and a mix of 10 and 33 kPa (PAWC_10,33) pressure heads depending on soil texture. Normally, the difference between PAWC at 10 kPa and h_fc (∆PAWC₁₀) was positive, whereas that between 33 kPa and h_fc (∆PAWC₃₃) was negative across all sites. Nevertheless, the estimation of PAWC assuming a fixed FC at 10 and 33 kPa pressures (i.e., PAWC_10,33) for sandy, clay, and silty soils reduced the difference between fixed and dynamic pressure PAWCs to < 10% across the region. The estimation of PAWC was improved by incorporating the impact of subsoil constraints, such as high ESP, which was more pronounced for clay and silty soils. These findings demonstrate the inherent inconsistencies between static pressure and flux-based dynamic FC estimations in soils. Soil heterogeneity, intra-texture variability, subsoil constraints, and swell-shrink clays can have great impacts on the water retention capacity in response to dynamic and fixed pressure FC values.     

可耕种坡地的土壤水力参数非均质性变化

The spatial variations of the soil hydraulic properties were mainly considered in vertical direction. The objectives of this study were to measure water-retention curves, θ（ψ）, and unsaturated hydraulic conductivity functions, K（ψ）, of the soils sampled at different slope positions in three directions, namely, in vertical direction, along the slope and along the contour, and to determine the effects of sampling direction and slope position of two soil catenas. At the upper slope positions, the surface soils （0-10 cm） sampled in the vertical direction had a lower soil water content, 0, at a certain soil water potential （-1 500 kPa 〈 ψ 〈 -10 kPa） and had the greatest unsaturated hydraulic conductivity, K, at ψ 〉 -10 kPa. At the lower slope positions, K at ψ〉 -10 kPa was smaller in the vertical direction than in the direction along the slope. The deep soils （100 110 cm） had similar soil hydraulic properties in all the three directions. The anisotropic variations of the hydraulic properties of the surface soils were ascribed to the effects of natural wetting and drying cycles on the structural heterogeneity. These results suggested that the anisotropy of soil hydraulic properties might be significant in influencing soil water movement along the slope and need to be considered in modeling.     

Performance of pedotransfer functions in predicting soil water characteristics of soils in Norway

Abstract

Pedotransfer functions (PTFs), predicting the soil water retention curve (SWRC) from basic soil physical properties, need to be validated on arable soils in Norway. In this study we compared the performance of PTFs developed by Riley (1996), Rawls and Brakensiek (1989), Vereecken et al. (1989), Wösten et al. (1999) and Schaap et al. (2001). We compared SWRCs calculated using textural composition, organic matter content (SOM) and bulk density as input to these PTFs to pairs of measured water content and matric potential. The measured SWRCs and PTF input data were from 540 soil horizons on agricultural land in Norway. We used various statistical indicators to evaluate the PTFs, including an integrated index by Donatelli et al. (2004). The Riley PTFs showed good overall performance. The soil specific version of Riley is preferred over the layer specific, as the latter may introduce a negative change in water content with increasing matric potential (h). Among the parameter PTFs, Wösten's continuous PTF showed the overall best performance, closely followed by Rawls&B and Vereecken. The ANN-based continuous PTF of Schaap showed poorer performance than its regression based counterparts. Systematic errors related to both particle size and SOM caused the class PTFs to perform poorly; these PTFs do not use SOM as input, and are therefore inappropriate for soils in Norway, being highly variable in SOM. The PTF performance showed little difference between soil groups. Water contents in the dry range of the SWRC were generally better predicted than water contents in the wet range. Pedotransfer functions that included both SOM and measured bulk density as input, i.e. Wösten, Vereecken and Rawls&B, performed best in the wet range.     

沿海滩涂农田耕层土壤饱和导水率间接方法的评价

为筛选和构建适合苏北沿海滩涂围垦农田耕层土壤饱和水力传导率间接估算的土壤转换函数,在典型地块实测土壤饱和导水率和相关土壤基本性质的基础上,分析了11种根据基本土壤性质预测饱和导水率的转换函数方法的适用性,同时探讨了基于人工神经网络方法的土壤转换函数的预测效果。结果表明:滩涂围垦农田耕层土壤平均饱和导水率为10.04 cm/d,属低透水强度;在现有的土壤饱和导水率转换函数中,Vereecken函数是最适合滩涂围垦农区土壤、具有最佳预测精度的转换函数,其预测均方根误差为8.154,其次是Li、Campbell和Rawls函数;以砂粒、粘粒、容重和有机质作为输入因子,基于人工神经网络的土壤转换函数较Vereecken函数其预测均方根误差降至7.920,在输入因子中增加土壤盐分指标可进一步提高饱和导水率的预测精度,其预测均方根误差降为7.634。本文的研究结果显示利用人工神经网络方法建立的转换函数可有效提高滩涂盐渍农田土壤饱和导水率的预测精度。     

Use of class pedotransfer functions based on texture and bulk density of clods to generate water retention curves

Abstract. Water retention properties of 219 horizons were measured in Cambisols, Luvisols and Fluvisols, mainly from the Paris basin. We derived class pedotransfer functions (class PTFs) based on texture alone and in a second stage class PTFs based on classes combining texture and clod bulk density. The performance of these two types of PTFs were discussed at −330 and −15000 hPa water potential on an independent set of 221 horizons. Results showed that PTFs based on sets grouped by texture and clod bulk density provide estimates with an accuracy that is (i) greater than with class PTFs based on texture alone, and (ii) similar to the estimation accuracy recorded with continuous PTFs. As a consequence, the lack of interest in class PTFs should be reconsidered to bridge the gap between the available basic soil data and hydraulic properties which are generally missing, particularly when pertinent soil characteristics can be derived from the data available in soil databases. The two types of class PTFs providing gravimetric water contents at seven water potentials ranging from −10 to −15 000 hPa were converted to volumetric water content using the soil bulk density. Finally, the parameters of van Genuchten's water retention curve model were computed for every class PTF.     

Estimating the water retention curve from soil properties: comparison of linear, nonlinear and concomitant variable methods

The unsaturated soil hydraulic functions involving the soil–water retention curve (SWRC) and the hydraulic conductivity provide useful integrated indices of soil quality. Existing and newly devised methods were used to formulate pedotransfer functions (PTFs) that predict the SWRC from readily available soil data. The PTFs were calibrated using a large soils database from Hungary. The database contains measured soil–water retention data, the dry bulk density, sand, silt and clay percentages, and the organic matter content of 305 soil layers from some 80 soil profiles. A three-parameter van Genuchten type function was fitted to the measured retention data to obtain SWRC parameters for each soil sample in the database. Using a quasi-random procedure, the database was divided into “evaluation” (EVAL) and “test” (TEST) parts containing 225 and 80 soil samples, respectively. Linear PTFs for the SWRC parameters were calculated for the EVAL database. The PTFs used for this purpose particle-size percentages, dry bulk density, organic matter content, and the sand/silt ratio, as well as simple transforms (such as logarithms and products) of these independent variables. Of the various independent variables, the eight most significant were used to calculate the different PTFs. A nonlinear (NL) predictive method was obtained by substituting the linear PTFs directly into the SWRC equation, and subsequently adjusting the PTF parameters to all retention data of the EVAL database. The estimation error (SSQ) and efficiency (EE) were used to compare the effectiveness of the linear and nonlinearly adjusted PTFs. We found that EE of the EVAL and the TEST databases increased by 4 and 7%, respectively, using the second nonlinear optimization approach. To further increase EE, one measured retention data point was used as an additional (concomitant) variable in the PTFs. Using the 20 kPa water retention data point in the linear PTFs improved the EE by about 25% for the TEST data set. Nonlinear adjustment of the concomitant variable PTF using the 20 kPa retention data point as concomitant variable produced the best PTF. This PTF produced EE values of 93 and 88% for the EVAL and TEST soil data sets, respectively.     

Applicability of site-specific pedotransfer functions and rosetta model for the estimation of dynamic soil hydraulic properties under different vegetation covers

Background, Aims, and Scope  During the last decades, different methods have been developed to determine soil hydraulic properties in the field and laboratory. These methodologies are frequently time-consuming and/or expensive. An indirect method, named Pedotransfer Functions (PTFs), was developed to predict soil hydraulic properties using other easily measurable soil (physical and chemical) parameters. This work evaluates the use of the PTFs included in the Rosetta model (Schaap et al. 2001) and compares them with PTFs obtained specifically for soils under two different vegetation covers. Methods  Rosetta software includes two basic types of pedotransfer functions (Class PTF and Continuous PTF), allowing the estimation of van Genuchten water retention parameters using limited (textural classes only) or more extensive (texture, bulk density and one or two water retention measurements) input data. We obtained water retention curves from undisturbed samples using the ‘sand box’ method for potentials between saturation and 20 kPa, and the pressure membrane method for potentials between 100 and 1500 kPa. Physical properties of sampled soils were used as input variables for the Rosetta model and to determine site-specific PTFs. Results  The Rosetta model accurately predicts water content at field capacity, but clearly underestimates it at saturation. Poor agreement between observed and estimated values in terms of root mean square error were obtained for the Rosetta model in comparison with specific PTFs. Discrepancies between both methods are comparable to results obtained by other authors. Conclusions  Site-specific PTFs predicted the van Genuchten parameters better than Rosetta model. Pedotransfers functions have been a useful tool to solve the water retention capacity for soils located in the southern Pyrenees, where the fine particle size and organic matter content are higher. The Rosetta model showed good predictions for the curve parameters, even though the uncertainty of the data predicted was higher than for the site-specific PTFs. Recommendations and Perspectives  The Rosetta model accurately predicts the retention curve parameters when the use is related with wide soil types; nevertheless, if we want to obtain good predictors using a homogenous soil database, specific PTFs are required. ESS-Submission Editor: Prof. Zhihong Xu, PhD (zhihong.xu@griffith.edu.au)     

不同盆栽基质水分特征曲线的对比分析与模拟

现有关于盆栽控水模拟土壤干旱条件的试验中多采用含水率作为水分胁迫阈值,然而由于基质配比不同导致含水率相同的基质的水分状况也不尽相同,这导致各研究间结果难以对比和参考。为快速获取盆栽基质水分特征曲线,建立基质水分特征曲线预测模型。该研究以盆栽控水试验常用的泥炭土、蛭石和珍珠岩为基质材料,测定了不同配比基质的水分特征曲线,通过不同方法（多元回归模型、人工神经网络）建立了其预测模型。结果表明,人工神经网络模型对基质水分特征曲线的预测精度高于多元回归;相较于人工神经网络,多元回归模型的稳定性更高。综合考虑模型的精度和稳定性,多元回归模型是预测作物盆栽基质水分特征曲线的最佳模型。该模型为基质水分特征曲线快速获取以及相关作物干旱胁迫研究间的对比提供了方法和依据。     

Prediction of soil saturated water content using evolutionary polynomial regression (EPR)

The measurement of saturated water content (SWC) is necessary in the estimation of soil water retention and unsaturated hydraulic conductivity curves. In several studies, pedotransfer functions (PTFs) were developed to predict SWC. Among them, evolutionary polynomial regression (EPR) is one that can operate on large quantities of data in order to capture nonlinear and complex interactions between the variables of the system. In this study, the evolutionary data-mining technique was used to derive new PTFs and different methods were evaluated, such as the soil porosity method, Rosetta method, and others, for the estimation of SWC. For this purpose, 270 soil samples (3:1 ratio for development and validation) from three data sets were used. Among 190 PTFs provided by EPR, one equation with the highest accuracy and the least number of inputs was selected. The EPR predictions were compared with the experimental results as well as the PTFs proposed in previous studies. Comparison of the statistical indicators showed that the ‘proposed PTF’ and ‘porosity method’ are the best and worst methods for the prediction of SWC, respectively. Also, good predictions were achieved from the proposed approaches by the groups of Scheinost, Vereecken, and Williams.