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.     

Estimating forest soil bulk density using boosted regression modelling

Soil bulk density (ρ) is an important physical property, but its measurement is frequently lacking in soil surveys due to the time‐consuming nature of making the measurement. As a result pedotransfer functions (PTFs) have been developed to predict ρ from other more easily available soil properties. These functions are generally derived from regression methods that aim to fit a single model. In this study, we use a technique called Generalized Boosted Regression Modelling (GBM; Ridgeway, 2006 ) which combines two algorithms: regression trees and boosting. We built two models and compared their predictive performance with published PTFs. All the functions were fitted based on the French forest soil dataset for the European demonstration Biosoil project. The two GBM models were Model G3 which involved the three most frequent quantitative predictors used to estimate soil bulk density (organic carbon, clay and silt), and Model G10, which included ten qualitative and quantitative input variables such as parent material or tree species. Based on the full dataset, Models G3 and G10 gave R² values of 0.45 and 0.86, respectively. Model G3 did not significantly outperform the best published model. Even when fitted from an external dataset, it explained only 29% of the variation of ρ with a root mean square error of 0.244 g/cm³. In contrast, the more complex Model G10 outperformed the other models during external validation, with a R² of 0.67 and a predictive deviation of ±0.168 g/cm³. The variation in forest soil bulk densities was mainly explained by five input variables: organic carbon content, tree species, the coarse fragment content, parent material and sampling depth.     

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。     

Improvement in estimation of soil water retention using fractal parameters and multiobjective group method of data handling

Soil water retention characteristic is required for modeling of water and substance movement in unsaturated soils and need to be estimated using indirect methods. Point pedotransfer functions (PTFs) for prediction of soil water content at matric suctions of 1, 5, 25, 50, and 1500 kPa were developed and validated using a data-set of 148 soil samples from Hamedan and Guilan provinces, Iran, by multiobjective group method of data handling (mGMDH). In addition to textural and structural properties, fractal parameters of the power-law fractal models for both particles and aggregates distributions were also included as predictors. Their inclusion significantly improved the PTFs’ accuracy and reliability. The aggregate size distribution fractal parameters ranked next to the particle size distribution (PSD) in terms of prediction accuracy. The mGMDH-derived PTFs were significantly more reliable than those by artificial neural networks but their accuracies were practically the same. Similarity between the fractal behavior of particle and void size distributions may contribute to the improvement of the derived PTFs using PSD fractal parameters. It means that both distributions of the pore and particle size represent the fractal behavior and can be described by fractal models.     

Estimating soil ammonium adsorption using pedotransfer functions in an irrigation district of the North China Plain

Extensive use of chemical fertilizers in agriculture can induce high concentration of ammonium nitrogen(NH4⁺_-N) in soil. Desorption and leaching of NH4⁺_-N has led to pollution of natural waters. The adsorption of NH4⁺_-N in soil plays an important role in the fate of the NH4⁺_-N. Understanding the adsorption characteristics of NH4⁺_-N is necessary to ascertain and predict its fate in the soil-water environment, and pedotransfer functions(PTFs) could be a convenient method for quantification of the adsorption parameters. Ammonium nitrogen adsorption capacity, isotherms, and their influencing factors were investigated for various soils in an irrigation district of the North China Plain. Fourteen agricultural soils with three types of texture(silt, silty loam, and sandy loam) were collected from topsoil to perform batch experiments. Silt and silty loam soils had higher NH4⁺_-N adsorption capacity than sandy loam soils.Clay and silt contents significantly affected the adsorption capacity of NH4⁺_-N in the different soils. The adsorption isotherms of NH4⁺_-N in the 14 soils fit well using the Freundlich, Langmuir, and Temkin models. The models’ adsorption parameters were significantly related to soil properties including clay,silt, and organic carbon contents and Fe²⁺ and Fe³⁺ ion concentrations in the groundwater. The PTFs that relate soil and groundwater properties to soil NH4⁺_-N adsorption isotherms were derived using multiple regressions where the coefficients were predicted using the Bayesian method. The PTFs of the three adsorption isotherm models were successfully verified and could be useful tools to help predict NH4⁺_-N adsorption at a regional scale in irrigation districts.     

青海三江源地区土壤容重转换函数的建立与比较

Bulk density (BD) is an important soil physical property and has significant effect on soil water conservation function. Indirect methods, which are called pedotransfer functions (PTFs), have replaced direct measurement and can acquire the missing data of BD during routine soil surveys. In this study, multiple linear regression (MLR) and artificial neuron network (ANN) methods were used to develop PTFs for predicting BD from soil organic carbon (OC), texture and depth in the Three-River Headwater region of Qinghai Province, China. The performances of the developed PTFs were compared with 14 published PTFs using four indexes, the mean error (ME), standard deviation error (SDE), root mean squared error (RMSE) and coefficient of determination (R²). Results showed that the performances of published PTFs developed using exponential regression were better than those developed using linear regression from OC. Alexander (1980)-B, Alexander (1980)-A and Manrique and Jones (1991)-B PTFs, which had good predictions, could be applied for the soils in the study area. The PTFs developed using MLR (MLR-PTFs) and ANN (ANN-PTFs) had better soil BD predictions than most of published PTFs. The ANN-PTFs had better performances than the MLR-PTFs and their performances could be improved when soil texture and depth were added as predictor variables. The idea of developing PTFs or predicting soil BD in the study area could provide reference for other areas and the results could lay foundation for the estimation of soil water retention and carbon pool.     

Field-scale spatial variability of saturated hydraulic conductivity on a recently constructed artificial ecosystem

Saturated hydraulic conductivity (K_s) influences water storage and movement, and is a key parameter of water and solute transport models. Systematic field evaluation of K_s and its spatial variability for recently constructed artificial ecosystems is still lacking. The objectives of the present study were; (1) to determine saturated hydraulic conductivity of an artificial ecosystem using field methods (Philip-Dunne, and Guelph permeameters), and compare their results to the constant-head laboratory method; (2) to evaluate the spatial variability of K_s using univariate and geostatistical analyses, and (3) to evaluate the ability of five pedotransfer functions to predict K_s. The results showed that K_s varied significantly (p < 0.05) among methods, probably reflecting differences in scales of measurement, flow geometry, assumptions in computation routines and inherent disturbances during sampling. Mean K_s values were very high for all methods (38.6-77.9 m day^− 1), exceeding values for natural sandy soils by several orders of magnitude. The high K_s values and low coefficients of variation (26-44%) were comparable to that of well-sorted unconsolidated marine sands. Geostatistical analysis revealed a spatial structure in surface K_s data described by a spherical model with a correlation range of 8 m. The resulting kriged map of surface K_s showed alternating bands of high and low values, consistent with surface structures created by wheel tracks of construction equipment. Vertical K_s was also spatially structured, with a short correlation range of 40 cm, presumably indicative of layering caused by post-construction mobilization and deposition of fine particles. K_s was linearly and negatively correlated with dry soil bulk density (ρ_b) (r² = 0.73), and to a lesser extent silt plus clay percentage (Si + C) (r² = 0.21). Combining both ρ_b and Si + C significantly (p < 0.05) improved the relationship and gave the best predictor of K_s (r² = 0.76). However, evaluation of five PTFs developed for natural soils showed that they all underestimated K_s by an order of magnitude, suggesting that application of water balance simulation models based on such PTFs to the present study site may constitute a bias in model outputs. Overall, the study demonstrated the influence of material handling, construction procedures and post-construction processes on the magnitude and spatial variability of K_s on a recently constructed artificial ecosystem. These unique hydraulic properties may have profound impacts on soil moisture storage, plant water relations and water balance fluxes on artificial ecosystems, particularly where such landforms are intended to restore pre-disturbance ecological and hydrological functions.     

Pedotransfer functions for estimating plant available water and bulk density in Swedish agricultural soils

Abstract

Pedotransfer functions (PTFs) to estimate plant available water were developed from a database of arable soils in Sweden. The PTFs were developed to fulfil the minimum requirements of any agro-hydrological application, i.e., soil water content at wilting point (θ _wp ) and field capacity (θ _fc ), from information that frequently is available from soil surveys such as texture and soil organic carbon content (SOC). From the same variables we also estimated bulk density (ρ) and porosity (ε), which seldom are included in surveys, but are needed for calculating element mass balances. The seven particle-size classes given in the data set were aggregated in different ways to match information commonly gained from surveys. Analysis of covariance and stepwise multiple linear regression were used for quantifying the influence of depth, particle size class, textural class and soil organic carbon on the characteristic variables. PTFs developed from other data sets were also tested and their goodness-of-fit and bias was evaluated. These functions and those developed for the Swedish database were also tested on an independent data set and finally ranked according to their goodness of fit. Among single independent variables, clay was the best predictor for θ _wp , sand (or the sum of clay and silt) for θ _fc and SOC for ρ and ε. A large fraction of the variation in θ _wp and θ _fc is explained by soil texture and SOC (up to 90%) and root mean square errors (RMSEs) were as small as 0.03 m³ water m^?3 soil in the best models. For the prediction of ρ and ε in the test data set, the best PTF could only explain 40–43% of the total variance with corresponding RMSEs of 0.14 g cm^?3 and 5.3% by volume, respectively. Recently presented PTFs derived from a North American database performed very well for estimating θ _wp (low error and bias) and could be recommended for Swedish soils if measurements of clay, sand and SOC were available. Although somewhat less accurately, also θ _fc could be estimated satisfactorily. This indicates that the determination of plant available water by texture and SOC is rather independent of soil genesis and that certain PTFs are transferable between continents.     

Neural network models to predict cation exchange capacity in arid regions of Iran

Design and analysis of land‐use management scenarios requires detailed soil data. When such data are needed on a large scale, pedotransfer functions (PTFs) could be used to estimate different soil properties. Because existing regression‐based PTFs for estimating cation exchange capacity (CEC) do not, in general, apply well to arid areas, this study was conducted (i) to evaluate the existing models and (ii) to develop neural network‐based PTFs for predicting CEC in Aridisols of Isfahan in central Iran. As most researches have found a significant correlation between CEC and soil organic matter content (OM) and clay content, we also used these two variables for modelling of CEC. We tested several published PTFs and developed two neural network algorithms using multilayer perceptron and general regression neural networks based on a set of 170 soil samples. The data set was divided into two subsets for calibration and testing of the models. In general, the neural network‐based models provided more reliable predictions than the regression‐based PTFs.     

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)     

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.     

A Comparison of Five Different Techniques to Determine Hydraulic Conductivity of a Riparian Soil in North Bavaria, Germany

Soil saturated hydraulic conductivity (K_s) is a predominant input factor when forecasting the vertical transport of contaminants through the soil or when estimating the flood retention capacity of the soil. Displacement of contaminants in the soil over extended periods of time can be attributed mainly to matrix flow, whereas flow through macropores becomes significant under untypically wet conditions, e.g., during spills or rain storms. To obtain matrix conductivities for a soil, the effects of macropores should be excluded. However, the K_s values of a soil profile are unlikely to be reflected solely by pedotransfer tables based on soil texture and bulk density. In this study, we examined five different methods (pedotransfer table, soil core, borehole permeameter, particle-size distribution curve, and instantaneous profile) to determine K_s values for a mercury-contaminated riparian soil for subsequent simulation of long-term mercury displacement toward groundwater. We found that the determined K_s values increased in the following order: borehole permeameter < particle-size distribution curve < pedotransfer table < instantaneous profile < soil core. The instantaneous profile method yielded K_s values of matrix flow, which additionally reflected the structure-related features of K_s values as provided by the soil core method. Despite being labor intensive and requiring expensive field sensors, the instantaneous profile method may provide the best representative in-situ K_s values for the studied site.     

Pedotransfer functions to assess adsorbed phosphate using iron oxide content and magnetic susceptibility in an Oxisol

Adsorbed phosphate in soils can be chemically extracted; however, this process is both time‐consuming and not cost‐effective if large numbers of samples have to be analysed. Indirect assessment of adsorbed phosphate by pedotransfer functions (PTFs) can help optimize fertilizer strategies. This study aimed to evaluate the spatial variability of adsorbed phosphate (P_ads), iron oxides and magnetic susceptibility (MS) in oxisols and to calibrate PTFs to predict P_ads. A total of 308 soil samples were collected from Hapludox and Eutrudox soils formed from sandstone in Brazil. The contents of clay (196–607 g/kg), iron oxides (40–165 g/kg), MS (1.2–29 × 10^?6 m³/kg) and P_ads (327–842 mg/kg) were in the range of typical values for these highly weathered soils. This study showed that the attributes studied were spatially dependent. Geomorphic surfaces enabled understanding of spatial variability and helped to develop a more efficient sampling scheme to calibrate PTFs. Moreover, the adsorbed phosphate in these oxisols could be predicted by a PTF using iron oxides and MS as predictors. The MS attribute enabled the most accurate prediction (concordance coefficient = 0.95, root‐mean‐square error = 46 mg/kg and relative improvement in root‐mean‐square error = ?4.12) of spatial variability through PTF compared to other predictors.     

Selection of a Suitable Soft Computing Model for Estimation of Soil Cation Exchange Capacity

ABSTRACT

Measuring of soil cation exchange capacity (CEC) is difficult and time-consuming. Therefore, it is essential to develop an indirect approach such as pedotransfer functions (PTFs) to predict this property from more readily available soil data. The aim of this study was to compare multiple linear and nonlinear regression, adaptive neurofuzzy inference system, and an artificial neural network (ANN) model to develop PTFs for predicting soil CEC. One hundred and seventy-one soil samples were used into two subsets for training and testing of the models. The model's prediction capability was evaluated by statistical indicators that include RMSE, R², MBE, and RI. Results showed that the ANN model had the most reliable prediction when compared with other models. This study provides a strong basis for predicting soil CEC and identifying the most determinant properties influencing soil CEC in the north regions of Iran. Analytical framework results could be applied to other parts of the world with similar challenges.

Abbreviations: ANFIS: Adaptive Neuro-Fuzzy Inference System; ANN: Artificial Neural Network; CEC: Cation Exchange Capacity; CV: Coefficient of Variation; FFBP: Feed-Forward Back-Propagation; FIS: Fuzzy Inference System; MBE: Mean Bias Error; MF: Membership Function; MLR: Multiple Linear Regressions; MNLR: Multiple Non-Linear Regressions; MLP: Multi-layer Perceptron; OC: Organic Carbon; PTFs: Pedotransfer Functions; R²: Determination Coefficient; RI: Relative Improvement; RMSE: Root Mean Square Error; SD: Standard Deviation     

Soybean seedling root growth as influenced by soil texture,matric suction and bulk density

Abstract

Laboratory experiments were conducted under controlled conditions to determine the effect of five matric suctions (0.05, 0.10, 0.30, 1.00 and 3.00 bars) and three bulk densities (1.10, 1.30 and 1.50 g.cm^?3) on the moisture content, penetrometer resistance and soybean (Glycine max L.) root growth in six different soil textural groups (sand, silt, clay and their combinations).

The different textural groups were compacted in PVC pipes 4.4 cm ID and 10 cm long and placed in pressure cells to obtain the desired matric suction. After equilibrium five pregerminated soybean seedlings were fixed on the soil surface. At the end of 48 hours root elongation was measured.

There was an increase in root growth in all the textural groups at all the bulk densities when the matric suction was increased from 0.05 to 0.30 bar. There was however a gradual decrease in root growth as the matric suction increased from 0.30 to 3.0 bars. The reduction in root growth at low and high matric suctions was related to moisture content, change in soil resistance and redox status of the soil system.

The measured penetrometer resistance values were directly related to the level of compaction, soil matric suction and also were dependent upon the texture. Close relationships were recorded between redox potentials and soil matric suction.     

Integral energy of conventional available water, least limiting water range and integral water capacity for better characterization of water availability and soil physical quality

Different approaches have been proposed for quantification of soil water availability for plants but mostly they do not fully describe how water is released from the soil to be absorbed by the plant roots. A new concept of integral energy (E_I) was suggested by Minasny and McBratney (Minasny, B., McBratney, A.B. 2003. Integral energy as a measure of soil-water availability. Plant and Soil 249, 253-262) to quantify the energy required for plants to take up a unit mass of soil water over a defined water content range. This study was conducted to explore the E_I concept in association with other new approaches for soil water availability including the least limiting water range (LLWR) and the integral water capacity (IWC) besides conventional plant available water (PAW). We also examined the relationship between E_I and Dexter's index of soil physical quality (S-value). Twelve agricultural soils were selected from different regions in Hamadan province, western Iran. Soil water retention and penetration resistance, Q, were measured on undisturbed samples taken from the 5-10 cm layer. The PAW, LLWR and IWC were calculated with two matric suctions (h) of 100 and 330 hPa for field capacity (FC), and then the E_I values were calculated for PAW, LLWR and IWC. There were significant differences (P < 0.01) between the E_I values calculated for PAW₁₀₀, PAW₃₃₀, LLWR₁₀₀, LLWR₃₃₀ and IWC. The highest (319.0 J kg⁻¹) and the lowest (160.7 J kg⁻¹) means of E_I were found for the E_I(IWC) and E_I(PAW330), respectively. The E_I values calculated for PAW₁₀₀, LLWR₁₀₀ and LLWR₃₃₀ were 225.6, 177.9 and 254.1 J kg⁻¹, respectively. The mean value of E_I(PAW330) was almost twice as large as the mean of E_I(IWC) showing that IWC is mostly located at lower h values when compared with PAW₃₃₀. Significant relationships were obtained between E_I(IWC) and h at Q = 1.5 MPa, and E_I(LLWR100) or E_I(LLWR330) and h at Q = 2 MPa indicating strong dependency of E_I on soil strength in the dry range. We did not find significant relationships between E_I(PAW100) or E_I(PAW330) and bulk density (ρ_b) or relative ρ_b (ρ_b-rel). However, E_I(LLWR100) or E_I(LLWR330) was negatively and significantly affected by ρ_b and ρ_b-rel. Both E_I(PAW100) and E_I(PAW330) increased with increasing clay content showing that a plant must use more energy to absorb a unit mass of PAW from a clay soil than from a sandy soil. High negative correlations were found between E_I(PAW100) or E_I(PAW330) and the shape parameter (n) of the van Genuchten function showing that soils with steep water retention curves (coarse-textured or well-structured) will have lower E_I(PAW). Negative and significant relations between E_I(PAW100) or E_I(PAW330) and S were obtained showing the possibility of using S to predict the energy that must be used by plants to take up a unit mass of water in the PAW range. Our findings show that E_I can be used as an index of soil physical quality in addition to the PAW, LLWR, IWC and S approaches.     

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.