Pedotransfer functions for estimating soil water retention curve of Sicilian soils

Pedotransfer functions (PTFs) make use of routinely surveyed soil data to estimate soil properties but their application to soils different from those used for their development can yield inaccurate estimates. This investigation aimed at evaluating the water retention prediction accuracy of eight existing PTFs using a database of 217 Sicilian soils exploring 11 USDA textural classes. PTFs performance was assessed by root mean square differences (RMSD) and average differences (AD) between estimated and measured data. Extended Nonlinear Regression (ENR) technique was adopted to recalibrate or develop four new PTFs and Wind’s evaporation method was applied to validate the effectiveness of the relationships proposed. PTFs evaluation resulted in RMSD and AD values in the range 0.0630–0.0972 and 0.0021–0.0618 cm³ cm^–3, respectively. Best and worst performances were obtained respectively by PTF-MI and PTF-ZW. ENR allowed to recalibrate PTF-MI and PTF-ZW with improvements of RMSD (0.0594 and 0.0508 cm³ cm^–3) and to develop two relationships that improved RMSD by 75–78% as compared to PTF-MI. The results confirmed the potential of ENR technique in calibrating existing PTFs or developing new ones. Validation conducted with an independent dataset suggested that recalibrated/developed PTFs represent a viable alternative for water retention estimation of Sicilian soils.     

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.     

估计太湖地区水稻土水分特征曲线的物理-经验方法研究

为寻求适于太湖地区水稻土水分特征曲线估算的简便方法,本文以实测土壤基本性质和土壤水分特征曲线为基础,对13种物理-经验方法估计土壤水分特征曲线的效果进行综合评价。结果表明,基于形状相似性的物理-经验方法预测效果要优于多数分形方法,但均差于基于Kravchenko-Zhang分维方法和B rooks-Corey水分特征曲线方程的孔隙表面分形模型(简称KZBC模型)。物理-经验方法在不同压力水头段对含水量的预测精度有差异,在实际工作中不仅要考虑模型的适用性,还要考虑其适用的压力水头范围。KZBC模型估计土壤水分特征曲线精度最高,实用性也较强,是适用于本研究区水分特征曲线估计的最佳方法。     

Point pedotransfer functions for prediction of water retention of selected soil series in a semi-arid region of western Iran

This study was conducted to derive point pedotransfer functions (PPTFs) for soil water retention (SWR) in western Iran. Topsoil and subsoil of 63 soil series, which were representative of different regions of Hamadan province, were sampled. Soil water retention was determined by the sand box and pressure plate at matric suctions (h _m) of 0, 1, 2, 5, 10, 25, 50, 100, 200, 500, 1000 and 1500 kPa. PPTFs were derived through multiple linear regressions for the topsoils and subsoils. These used particle size distribution, bulk density, organic matter, calcium carbonate and gravel contents as easily-available inputs. To increase the accuracy of the PPTFs, saturated water content was also included as an input variable in a group of PPTFs but they are not better as assessed using the Akaike Information Criterion. All of the PPTFs were statistically significant (p < 0.001) and could be used to predict the SWR. The absolute effect of bulk density on the SWR diminished as h _m increased. Bulk density decreased the SWR for low h _m and increased it for high h _m. In the wet range, organic matter increased the SWR. Clay and silt increased SWR whereas gravel decreased it. The effect of calcium carbonate on SWR was negligible.     

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.     

Estimating the wetting branch of the soil water retention curve from grain-size fractions

The development of a theoretical method for estimating the wetting branch of the soil water retention curve (SWRC) is required for knowledge of the soil infiltration process. The aim of this study was to derive the theoretical functions to represent the wetting branch of the SWRC based on the Jensen method, and then compare the Jensen method and Kool & Parker (KP87) model for estimating the wetting branch of the SWRC. Fifteen soil samples with varying basic properties (e.g., grain-size distribution and bulk density (BD)) were selected from the Unsaturated Soil Hydraulic Database (UNSODA) to test these two methods. Results showed that the Jensen method (root mean squared error (RMSE) = 0.057 cm³ cm⁻³) produced a substantially better performance in predicting the wetting branch of the SWRC than the KP87 (RMSE = 0.089 cm³ cm⁻³) for the 15 samples. The range of the scaled mean bias error (SMBE) between the Jensen method-predicted and measured soil water contents at all pressure heads was −0.529 to 0.402. A positive linear relationship between the SMBE and silt content was observed for the Jensen method. The findings of this study have practical significance for simulating the soil infiltration in the unsaturated zone.     

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.     

Evaluation of pedotransfer functions for estimating the soil water retention points

Direct measurement of soil moisture has been often expensive and time-consuming. The aim of this study was determining the best method to estimate the soil moisture using the pedotransfer functions in the soil par2 model. Soil samples selected from the database UNSODA in three textures include sandy loam, silty loam and clay. In clay soil, the Campbell model indicated better results at field capacity (FC) and wilting point (WP) with RMSE = (0.06, 0.09) and d = (0.65, 0.55) respectively. In silty loam soil, the Epic model had accurate estimation with MBE = 0.00 at FC and Campbell model had the acceptable result of WP with RMSE = 0.03 and d = 0.77. In sandy loam, Hutson and Campbell models had a better result to estimation the FC and WP than others. Also Hutson model had an acceptable result to estimation the TAW (Total Available Water) with RMSE = (0.03, 0.04, 0.04) and MBE = (0.02, 0.01, 0.01) for clay, sandy loam and silty loam, respectively. These models demonstrate the moisture points had the internal linkage with the soil textures. Results indicated that the PTFs models simulate the agreement results with the experimental observations.     

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泥炭土-蛭石复配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,预测精度R2≥0.950,平均误差接近0。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。     

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)     

基于土壤传递函数的土壤持水特性间接估计及其空间变异性研究

A total of 107 soil samples were taken from the city of Qingdao,Shandong Province,China.Soil water retention data at 2.5,6,10,33,100,300,and 1 500 kPa matric potentials were measured using a pressure membrane apparatus.Multiple linear regression (MLR) was used to develop pedotransfer functions (PTFs) for single point estimation and van Genuchten parameter estimation based on readily measurable soil properties,i.e.,MLR-based point (MLRP) PTF and MLR-based parametric (MLRV) PTF.The double cross-validation method was used to evaluate the accuracy of PTF estimates and the stability of the PTFs developed in this study.The performance of MLRP and MLRV PTFs in estimating water contents at matric potentials of 10,33,and 1 500 kPa was compared with that of two existing PTFs,the Rawls PTF and the Vereecken PTF.In addition,geostatistical analyses were conducted to assess the capabilities of these PTFs in describing the spatial variability of soil water retention characteristics.Results showed that among all PTFs only the Vereecken PTF failed to accurately estimate water retention characteristics.Although the MLRP PTF can be used to predict retention characteristics through traditional statistical analyses,it failed to describe the spatial variability of soil water retention characteristics.Although the MLRV and Rawls PTFs failed to describe the spatial variability of water contents at a matric potential of 10 kPa,they can be used to quantify the spatial variability of water contents at matric potentials of 33 and 1 500 kPa.     

Evaluation of pedotransfer functions predicting hydraulic properties of soils and deeper sediments

Eight pedotransfer functions (PTF) originally calibrated to soil data are used for evaluation of hydraulic properties of soils and deeper sediments. Only PTFs are considered which had shown good results in previous investigations. Two data sets were used for this purpose: a data set of measured pressure heads vs. water contents of 347 soil horizons (802 measured pairs) from Bavaria (Southern Germany) and a data set of 39 undisturbed samples of tertiary sediments from deeper ground (down to 100 m depth) in the molasse basin north of the Alps, containing 840 measured water contents vs. pressure head and unsaturated hydraulic conductivity. A statistical analysis of the PTFs shows that their performance is quite similar with respect to predicting soil water contents. Less satisfactory results were obtained when the PTFs were applied to prediction of water content of sediments from deeper ground. The predicted unsaturated hydraulic conductivities show about the same uncertainty as for soils in a previous study. Systematic deviations of predicted values indicate that an adaptation of the PTFs to the specific conditions of deeper ground should be possible in order to improve predictions.     

Assessment of the usefulness of particle size distribution measured by laser diffraction for soil water retention modelling

Particle size distribution (PSD) is a major soil characteristic, which is essential and commonly used for the development of pedotransfer functions (PTFs) to estimate the water retention of soils. The laser diffraction method (LDM) became a popular alternative to the standard sieve‐hydrometer method (SHM) of PSD measurement. Unfortunately, PSDs determined with LDM and SHM methods differ sometimes substantially. Moreover, it is claimed that the laser diffraction method underestimates finer fractions in favor of coarser fractions. Several authors have tried to elaborate on methods to recalculate LDM PSD into its SHM counterparts, but no universal methodology has been developed to this date. In this paper, we use PSD determined by LDM directly for PTF development and compare it with the classical PTF approach based on PSD measured by SHM. Four different PTF models based on LDM particle size distribution data were developed, with different PSD characteristics taken as the models' input variables. The possibility of using alternative PSD characteristics, such as deciles, area moment mean and volume moment mean, for PTF development was examined. The accuracy of PTF models constructed on the basis of LDM‐measured PSD was comparable with that of the developed models using texture data obtained from SHM, giving approximately the same RMSE and R² values. Our study shows that LDM‐measured particle size distribution may be directly used for PTF developments without any recalculations to their sieve‐hydrometer counterparts.     

Comparison of the prediction efficiency of two pedotransfer functions for soil cation‐exchange capacity

The aim of this study was to provide knowledge of the prediction efficiency of two pedotransfer functions for soil cation‐exchange capacity (CEC), i.e., the approaches of Krogh et al. (2000) and Scheinost (1995). Potential CEC, i.e., CEC at pH 8.1, was predicted for 30 samples of German soils showing a strong variation of soil organic C and clay content which are important soil characteristics for CEC. The results were compared to analyzed potential CEC of the samples by coefficient‐of‐efficiency (EC) criterion of Nash and Sutcliffe (1970). Significant deviations between observed and measured CEC were found for both functions. The approach of Scheinost (1995) showed a higher prediction efficiency (EC = 0.77 in comparison to EC = 0.26 for Krogh et al., 2000) and better results under extrapolative conditions.