Parametric Pedotransfer Functions Research Articles

Pedotransfer functions for soil hydraulic properties developed from a hilly watershed of Eastern India

Saturated hydraulic conductivity ( K s) and water retention characteristics (θ − h) of soil are needed to characterize the hydrological response of watersheds. Direct measurement of these two hydraulic properties at multiple locations in a watershed is time-consuming and costly. Alternatively, pedotransfer functions (PTFs) may be used to indirectly estimate these properties from easy-to-measure soil properties. However, in many developing countries such PTFs are not available because of the lack of large databases typically required to develop PTFs. In this study, PTFs were developed from a limited number of soils collected from a hilly watershed located in the Western Catchment of the Chilika Lake, Orissa, India. Point PTF for K s and parametric PTFs for the parameters of van Genuchten water retention model were developed through multiple linear regression technique. Soil texture, organic carbon contents, pH, and elevation were used as the input variable for these new PTFs. These PTFs were validated against the two national datasets on the Benchmark Soils of India and those representing the Indo-Gangetic Plains. All the three PTFs showed the best performance when applied to soils of the watershed from where the PTFs were developed. When applied to soils outside the study area, these PTFs performed similar to established PTFs, which are generally derived from very large databases. A significant result of this study is that robust PTFs may be developed from a limited number of soil samples provided there is sufficient variability in soil properties. This study shows that soils collected from hilly watersheds may possess such variability. Small area of a watershed is also convenient to collect large number of soil samples needed to develop robust PTFs.

Deriving point and parametric pedotransfer functions of some gypsiferous soils

Parametric description of the soil water retention curve as well as the hydraulic conductivity curve is needed for modelling water movement and solute transport in the vadose zone. The objective of this study was to derive pedotransfer functions (PTFs) to predict the water retention curve and the van Genuchten and the van Genuchten–Mualem parameters of some gypsiferous soils. Consequently, 185 gypsiferous soil samples were collected and their physical properties were measured. The particle size distribution was determined in 2 steps: (i) with gypsum, by covering the particles with barium sulphate; (ii) without gypsum, using the hydrometry method. The easily obtainable variables were grouped as (1) particle size distribution, bulk density, and gypsum content; and (2) bulk density, gypsum content, geometric mean, and geometric standard deviation of the particle diameter. Stepwise multiple linear regression method was used to derive the PTFs. Two types of parametric and point functions were derived using these variables. The first group of variables predicted water retention and the van Genuchten and van Genuchten–Mualem parameters better than the second group. The gypsum content appeared to be the second dominant parameter for predicting water retention at 0, −330, −1000, −3000, −5000, and −15 000 cm. The derived PTFs were compared with the Rosetta database as independent dataset. The validity test indicated that in order to predict the hydraulic properties of gypsiferous soils the derived PTFs are more accurate than what can be obtained from the Rosetta database. Removal of gypsum increased the water retention at pressure heads of 0, –100, –330, –1000, –3000, –5000, and –15 000 cm (P < 0.01). The results also indicated that hydraulic parameters were different for the same soil with and without gypsum.

Pedotransfer functions estimating soil hydraulic properties using different soil parameters

AbstractEstimates of soil hydraulic properties using pedotransfer functions (PTF) are useful in many studies such as hydrochemical modelling and soil mapping. The objective of this study was to calibrate and test parametric PTFs that predict soil water retention and unsaturated hydraulic conductivity parameters. The PTFs are based on neural networks and the Bootstrap method using different sets of predictors and predict the van Genuchten/Mualem parameters. A Danish soil data set (152 horizons) dominated by sandy and sandy loamy soils was used in the development of PTFs to predict the Mualem hydraulic conductivity parameters. A larger data set (1618 horizons) with a broader textural range was used in the development of PTFs to predict the van Genuchten parameters. The PTFs using either three or seven textural classes combined with soil organic mater and bulk density gave the most reliable predictions of the hydraulic properties of the studied soils. We found that introducing measured water content as a predictor generally gave lower errors for water retention predictions and higher errors for conductivity predictions. The best of the developed PTFs for predicting hydraulic conductivity was tested against PTFs from the literature using a subdata set of the data used in the calibration. The test showed that the developed PTFs gave better predictions (lower errors) than the PTFs from the literature. This is not surprising since the developed PTFs are based mainly on hydraulic conductivity data near saturation and sandier soils than the PTFs from the literature. Copyright © 2007 John Wiley & Sons, Ltd.

Pedotransfer functions for point and parametric estimations of soil water retention curve

A water retention curve is required for the simulation studies of water and solute transport in unsaturated or vadose zone. Unlike the direct measurement of water retention data, pedotransfer functions (PTFs) have attracted the attention of researchers for determining water retention curves from basic soil properties. The objective of this study was to develop and validate point and parametric PTFs for the estimation of water retention curve from basic soil properties such as particle-size distribution, bulk density, and porosity using multiple-linear regression technique and comparing the performances of point and two parametric methods using some evaluation criteria. 140 soil samples were collected from three different databases and divided as 100 and 40 for the derivation and validation of the PTFs. All three methods predicted water contents at selected water potentials and combined water retention curves pretty well, but van Genuchten’s model performed the best in prediction. However, the differences among the methods in point and water retention curve predictions were not statistically significant (p > 0.05). Prediction accuracies were evaluated by the coefficient of determination (R<sup>2</sup>) and the root mean square error (RMSE) between the measured and predicted values. The R<sup>2</sup> and RMSE were 0.962 and 0.036, 0.994 and 0.067, and 0.946 and 0.082 for point and parametric (van Genuchten, and Brooks and Corey) methods, respectively, in predicting combined water retention curve. The three methods can be alternatively used in the estimation of water retention curves, but parametric methods are preferred for yielding continuous water retention functions used in flow and transport modeling.

Point and parameter pedotransfer functions for water retention predictions for Danish soils

Estimates of soil water retention characteristics using pedotransfer functions (PTFs) are useful in many studies, such as hydrological modelling and soil mapping. The objective of this study was to calibrate and validate point and parametric PTF models based on neural networks and the Bootstrap method using different sets of predictors. The point PTF models estimated retention points at −1, −10, −100, and −1500 kPa pressure and the parametric PTF models estimated the van Genuchten retention parameters. A Danish soil data set (3226 horizons) dominated by sandy and sandy loamy soils was used in the analysis. The data were split up into a calibration data set ( N=1618 horizons) and a testing data set ( N=1608). The data were evaluated with the root mean square residuals (RMSR) and the Akaike Information Criterion (AIC), both obtained from measured and predicted water contents at the four retention points. The results for the point PTF models show similar predictions using detailed soil textural classification (seven textural classes) compared to a simplified textural classification (sand, silt, and clay). In general, we found that adding bulk density (BD) and soil organic matter (SOM) as predictors increased the prediction capability. The RMSR values varied between 0.037 and 0.051 cm 3 cm −3 and the lowest RMSR values were found for the models that used the most detailed data. The AIC followed the change in RMSR closely. The RMSR values for the parametric PTF models were generally 0.011 cm 3 cm −3 higher than the point PTF models using the same predictors. This was mainly due to an imperfect fit of the van Genuchten retention model to the retention data at 1500 kPa. Adding measured soil water content at −10 kPa in the parametric PTF models reduced the RMSR by 0.006 cm 3 cm −3. Adding additional soil water contents measured at −1 kPa, −100 kPa, and −1500 kPa improved the predictions only to a minor degree. The uncertainty in the prediction of water content using both the point and parametric PTF increased with increasing clay content. Another finding was that the absolute uncertainty in point PTF predictions of water content at −10 kPa was generally higher than the uncertainty found at −1, −100, and −1500 kPa.

Development of pedotransfer functions using a group method of data handling for the soil of the Pianura Padano–Veneta region of North Italy: water retention properties

Pedotransfer functions (PTFs) with quantified uncertainty for estimating water retention from soil structure and texture, organic carbon content, bulk density and derived variables are presented for the soils of the Pianura Padano–Veneta region of North Italy. A data set of 153 soil horizons from 59 soil profiles was used for calibration of parametric PTFs using a group method of data handling (GMDH); the proposed PTFs provide estimations for the Brooks and Corey water retention parameters and for water content at −5, −10, −33 and −1500 kPa matric potentials. Soil structure descriptors were retained as essential input variables to be included in the PTFs for the estimation of the Brooks and Corey water retention parameters. Calibrated PTFs were validated against two independent data sets: a local one of 122 soil horizons and a data set of 78 soil horizons selected from the UNSODA data set. Point and parametric neural network PTFs were also trained and validated on the same data sets in order to evaluate which of the two methods is more accurate. In the case of point PTFs, GMDH PTFs performed better with the exception of water content at −1500 kPa; in the case of parametric PTFs, given an a posteriori correction of the parameters estimates outside calibration ranges, neural network provided slightly better validation results, but were less accurate in the case of the training data set. Results show that the proposed GMDH PTFs provide good results for the local validation data set and that for input data within the PTFs calibration range, the estimation holds the same degree of accuracy also for the UNSODA data set. Results suggest that whenever PTFs calibrated on a local data set are available, their use should be recommended, provided an a priori control of input variable ranges. Bootstrap results and Monte Carlo analysis highlighted the need for extending the calibration data set to include poorly represented coarse textured soils in order to reduce estimation variance and improve PTFs accuracy.

The Neuro‐m Method for Fitting Neural Network Parametric Pedotransfer Functions

Parametric pedotransfer functions (PTFs), which predict parameters of a model from basic soil properties are useful in deriving continuous functions of soil properties, such as water retention curves. The common method for deriving parametric water retention PTFs involves estimating the parameters of a soil hydraulic model by fitting the model to the data, and then forming empirical relationships between basic soil properties and parameters. The latter step usually utilizes multiple linear regression or artificial neural networks. Neural network analysis is a powerful tool and has been shown to perform better than multiple linear regression. However neural‐network PTFs are usually trained with an objective function that fits the estimated parameters of a soil hydraulic model. We called this the neuro‐p method. The estimated parameters may carry errors and since the aim is to be able to estimate water retention, it is sensible to train the network to fit the measured water content. We propose a new objective function for neural network training, which predicts the parameters of the soil hydraulic model and optimizes the PTF to match the measured and observed water content, we called this neuro‐m method. This method was used to predict the parameters of the van Genuchten model. Using Australian soil hydraulic data as a training set, neuro‐m predicted the water retention from bulk density and particle‐size distribution with a mean accuracy of 0.04 m3 m−3 The relative improvement of neuro‐m over neural networks that was optimized to fit the parameters (neuro‐p) is 13%. Compared with a published neural network PTF, the new method is 30% more accurate and less biased.

Comparison of different approaches to the development of pedotransfer functions for water-retention curves

Pedotransfer functions (PTFs) for estimating water-retention from particle-size and bulk density are presented for Australian soil. The water-retention data sets contain 733 samples for prediction and 109 samples for validation. We present both parametric and point estimation PTFs using different approaches: multiple linear regression (MLR), extended nonlinear regression (ENR) and artificial neural network (ANN). ENR was found to be the most adequate for parametric PTFs. Multiple linear regression cannot be used to predict van Genuchten parameters as no linear relationship was found between soil properties and the curve shape parameters. Using the prediction set, ANN performance was similar to the ENR performance for the prediction dataset, but ENR performed better on the validation set. Since ANN is still considered as a black-box approach, the ENR approach which has a more physical basis is preferred. Point estimation PTFs were estimated for water contents at −10, −33 and −1500 kPa. Multiple linear regression performed better for point estimation. An exponential increase trend was found between particles <2 μm and water contents held at −10, −33 and −1500 kPa. The point estimation ANN did not improve prediction compared to MLR. Increasing the number of functions and parameters in developing PTFs does not necessary improve the prediction. The effect of parameter uncertainty, differences in texture determination and spatial variability on the error in prediction is also discussed.