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.     

使用积分法估算原状土van Genuchten模型参数

The van Genuchten model is the most widely used soil water retention curve (SWRC) model. Two undisturbed soils (clay and loam) were used to evaluate the accuracy of the integral method to estimate van Genuchten model parameters and to determine SWRCs of undisturbed soils. SWRCs calculated by the integral method were compared with those measured by a high speed centrifuge technique. The accuracy of the calculated results was evaluated graphically, as well as by root mean square error (RMSE), normalized root mean square error (NRMSE) and Willmott’s index of agreement (l). The results obtained from the integral method were quite similar to those by the centrifuge technique. The RMSEs (4.61 × 10-5 for Eum-Orthic Anthrosol and 2.74 × 10-4 for Los-Orthic Entisol) and NRMSEs (1.56 × 10-4 for Eum-Orthic Anthrosol and 1.45 × 10-3 for Los-Orthic Entisol) were relatively small. The l values were 0.973 and 0.943 for Eum-Orthic Anthrosol and Los-Orthic Entisol, respectively, indicating a good agreement between the integral method values and the centrifuge values. Therefore, the integral method could be used to estimate SWRCs of undisturbed clay and loam soils.     

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.     

植被覆盖和雨强对附着在沉积物上的养分流失, 泥沙颗粒组成及体积分形维数的影响

Vegetation and rainfall are two important factors affecting soil erosion and thus resulting in nutrient loss in the Chinese Loess Plateau.A field experiment was conducted to investigate the effects of rainfall intensities(60,100 and 140 mm h-1) and vegetation(Caragana korshinskii) coverages(0%,30% and 80%) on soil loss,nutrient loss,and the composition and volume fractal dimension of eroded sediment particles under simulated rainfall conditions.The results showed that vegetation cover,rainfall intensity and their interaction all had significant effects on sediment transport and the sedimentbound nutrient loss.Higher rainfall intensity and lower coverage led to higher sediment and nutrient losses.Positive linear relationships were observed between soil loss and nutrient loss.The treatments showed more significant effects on the enrichment ratio(ER) of nitrogen(ERN) than organic matter(EROM) and phosphorus(ERP).Compared with the original surface soil,the eroded sediment contained more fine particles.Under the same coverage,the clay content significantly decreased with increasing rainfall intensity.The ER of sediment-bound nutrients was positively correlated with that of clay,suggesting that the clay fraction was preferentially eroded and soil nutrients were mainly adsorbed onto or contained within this fraction.There were increments in the fractal dimension of the sediment particles compared to that of the original surface soil.Moreover,the fractal dimension was positively correlated with clay,silt,and sediment-bound OM,N,and P contents,whereas it was negatively correlated with sand content.This study demonstrated that fractal dimension analysis can be used to characterize differences in particle-size distribution and nutrient loss associated with soil erosion.     

从连续的粒径大小分布数据估算土壤水分特征曲线

Soil moisture characteristic curve (SMC) is a fundamental soil property and its direct measurement is tedious and time consuming. Therefore, various indirect methods have been developed to predict SMC from particle-size distribution (PSD). However, the majority of these methods often yield intermittent SMC data because they involve estimating individual SMC points. The objectives of this study were 1) to develop a procedure to predict continuous SMC from a limited number of experimental PSD data points and 2) to evaluate model predictions through comparisons with measured values. In this study, an approach that allowed predicting SMC from the knowledge of PSD, parameterized by means of the closed-form van Genuchten model (VG), was used. Through using Mohammadi and Vanclooster (MV) model, the parameters obtained from fitting of VG to PSD data were applied to predict SMC curves. Since the residual water content (θ r ) could not be obtained through fitting of VG-MV integrated model to PSD data, we also examined and compared four different methods estimating θ r . Results showed that the proposed equation (MV-VG integrated model) provided an excellent fit to all the PSD data and the model could adequately predict SMC as measured in forty-two soils sampled from different regions of Iran. For all soils, the method in which θ r was obtained through parameter optimization procedure provided the best overall predictions of SMC. The two methods estimating θ r with Campbell and Shiozawa (CS) model resulted in less accuracy than the optimization procedure. Furthermore, the proposed model underestimated the moisture content in the dry range of SMC when the value of θ r was assumed to equal zero. θ r could be attributed to the incomplete desorption of water coated on soil particles and the accurate estimation of θ r was critical in prediction of SMC, especially for fine-textured soils at high suction heads. It could be concluded that the advantages of our approach were the continuity, robustness, and independency of model performance on soil type, allowing to improve predictions of SMC from PSD at the field and watershed scales.     

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.     

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.     

引入分维理论评价土壤对优势流的敏感性

Food dye Brilliant Blue was introduced as the tracer in a dye-tracing experiment to obtain dye profile patterns of sandy loam soil,aeolian sandy soil,percolating paddy soil and permeable paddy soil.The dyed soil profiles were then photographed and the photos were scanned into a computer.Edited with certain software,only the dyed areas were left on the profile photos,which indicted the preferential flow paths for water and solute transport.Fractal dimensions of the dye patterns were calculated according to Arnold‘s function.Soil particle size distribution was analyzed by pipette method.The regression analysis showed that there was significant relationship between soil clay content and fractal dimension D of the dye pattern of soil profile.Based on the experiment results,the possibility of introducing fractal dimension to estimation of soil sensitivity to preferential flow is discussed.     

控制中国非点源污染的生物经济学战略

While non-point pollution from agriculture has become an increasingly serious problem in China, some progress has been made in studying the causal biophysical processes. However, few studies have assessed the economic consequences of non-point pollution in China or the policy options that could be employed to combat it. In this work a sustainable strategy to control non-point pollution from crop production, which involved taxing excessive inputs of irrigation water and fertilizer, was proposed. The approach taken to assess these measures combined biophysical and economic models,having a trade-off between economic returns and an improved environment. A proven and practical spatially referenced water and nutrient management model was used to determine the quantities of excessive irrigation water and fertilizer for specific soil and land use. Also, a set of indicators were proposed for evaluating the effects of agricultural economic output and agricultural practices on the environment.     

积分方法用于校定逆转型土壤盐渍剖面的磁感式电导

Various calibration methods have been propounded to determine profiles of apparent bulk soil electrical conductivity （ECa） and soil electrical conductivity of a saturated soil paste extract （ECe） or a 1：5 soil water extract （EC1：5） using an electromagnetic induction instrument （EM38）. The modeled coefficients, one of the successful and classical methods hitherto, were chosen to calibrate the EM38 measurements of the inverted salinity profiles of characteristic coastal saline soils at selected sites of Xincao Farm, Jiangsu Province, China. However, this method required three parameters for each depth layer. An integration approach, based on an exponential decay profile model, was proposed and the model was fitted to all the calibration sites. The obtained model can then be used to predict EC1：5 at a certain depth from electromagnetic measurements made using the EM38 device positioned in horizontal and vertical positions at the soil surface. This exponential decay model predicted the EC1：5 well according to the results of a one-way analysis of variance, and the further comparison indicated that the modeled coefficients appeared to be slightly superior to, but not statistically different from, this exponential decay model. Nevertheless, this exponential decay model was more significant and practical because it depended on less empirical parameters and could be used to perform point predictions of EC1：5 continuously with depth.     

Hysteresis of the Water Retention Curve: Wetting Branch Simulation Based on the Drying Curve

Hysteresis of the soil water retention curve (SWRC), manifested as a difference between equilibrium curves of soil wetting and drying (hysteresis loop), is a phenomenon specific to soil hydrology, which is of practical importance for calculating irrigation norms. We have attempted to derive a model of the wetting curve from the drying curve. Modeling was based on parameters proposed by van Genuchten [6] and certain soil physical characteristics. SWRC hysteresis characteristics were obtained experimentally using capillarimetric measurements for wetting and drying curves at soil water pressures ranging from 0 to –800 cmH₂O. Two models, М-1 and М-2, based on the hypothesis of dissimilarity of parameters α for wetting (α_w) and drying (α_d) and the constancy of other parameters of the van Genuchten SWRC approximation for both curves of the hysteresis loop, have been developed for wetting curve assessment based on the drying curve. The M-1 error (RMSE = 0.05 cm^–1) was less than that of M-2 (RMSE = 0.06 cm^–1), which used clay content and soil density as predictors, as well as that of the well-known model proposed by Kool and Parker [11]. This approach to derive the wetting curve from the drying curve for a presumed correlation between the values of one parameter and equal values of the other parameters, can be used to predict an estimate of the SWRC hysteresis for a specific soil.     

灰色关联及非线性规划法构建传递函数估算黑土水力参数

土壤水分特征曲线和饱和导水率是重要的水力参数,为了简便准确获取这些参数,以松嫩平原黑土区南部为研究区域,采集136个采样点土样用于测定不同土层土壤水分特征曲线、饱和导水率以及土壤理化性质,并运用灰色关联分析确定影响土壤水力参数的主要土壤理化性质,采用非线性规划构建土壤分形维数、有机质、干容重、土壤颗粒组成与土壤水分特征曲线、饱和导水率之间的土壤传递函数,并通过与现有土壤传递函数对比分析进行精度验证。结果表明:1)土壤分形维数是估算土壤水分特征曲线模型参数和饱和导水率的主要参数之一,同时,干容重和有机质含量也在不同土层土壤传递函数中起到重要的作用;2)通过验证分析,不同土层各参数平均绝对误差接近于0,均方根误差值也都较小,其中在不同土层土壤传递函数估算的土壤含水率均方根误差分别为0.022、0.017cm~3/cm~3;3)对比分析其他已存的土壤水分特征曲线和饱和导水率的土壤传递函数,该文构建的土壤传递函数均方根误差值均较小,决定系数值都在0.66以上,表明估算精度较高,均好于其他方法估算精度,具有良好的区域适应性。综上,所构建的土壤水分特征曲线和饱和导水率土壤传递函数可以用于松嫩平原黑土区土壤水力参数估算。     

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.     

Deforestation effects on soil quality and water retention curve parameters in eastern Ardabil,Iran

The land use change from natural to managed ecosystems causes serious soil degradation. The main objective of this research was to assess deforestation effects on soil physical quality attributes and soil water retention curve (SWRC) parameters in the Fandoghlou region of Ardabil province, Iran. Totally 36 surface and subsurface soil samples were taken and soil water contents measured at 13 suctions. Alfa (α) and n parameters in van Genuchten (1980) model were estimated by fitting SWRC data by using RETC software. The slope of SWRC at inflection point (S_P) was calculated by Dexter (2004) equation. The results indicated that with changing land use from forest (F) to range land (R) and cultivated land (C), and also with increasing soil depth from 0–25 to 75–100 cm in each land use, organic carbon, micropores, saturated and available water contents decreased and macropores and bulk density increased significantly (P < 0.05). The position of SWRC shape in F was higher than R and C lands at all soil depths. Changing F to R and C lands and also increasing soil depth in each land use significantly (P < 0.05) increased α and decreased n and S_P. The average values of S_P were obtained 0.093, 0.051 and 0.031 for F, R and C, respectively. As a result, deforestation reduced soil physical quality by affecting SWRC parameters.     

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)