Parametric Pedotransfer Functions Research Articles

Studying Unimodal, Bimodal, PDI and Bimodal-PDI Variants of Multiple Soil Water Retention Models: II. Evaluation of Parametric Pedotransfer Functions Against Direct Fits

A high-resolution soil water retention data set (81 repacked soil samples with 7729 observations) measured by the HYPROP system was used to develop and evaluate the performance of regression parametric pedotransfer functions (PTFs). A total of sixteen soil hydraulic models were evaluated including five unimodal water retention expressions of Brooks and Corey (BC model), Fredlund and Xing (FX model), Kosugi (K model), van Genuchten with four free parameters (VG model) and van Genuchten with five free parameters (VGm model). In addition, eleven bimodal, Peters–Durner–Iden (PDI) and bimodal-PDI variants of the original expressions were studied. Six modeling scenarios (S1 to S6) were examined with different combinations of the following input predictors: soil texture (percentages of sand, silt and clay), soil bulk density, organic matter content, percent of stable aggregates and saturated water content (θs). Although a majority of the model parameters showed low correlations with basic soil properties, most of the parametric PTFs provided reasonable water content estimations. The VGm parametric PTF with an RMSE of 0.034 cm3 cm−3 was the best PTF when all input predictors were considered. When averaged across modeling scenarios, the PDI variant of the K model with an RMSE of 0.045 cm3 cm−3 showed the highest performance. The best performance of all models occurred at S6 when θs was considered as an additional input predictor. The second-best performance for 11 out of the 16 models belonged to S1 with soil textural components as the only inputs. Our results do not recommend the development of parametric PTFs using bimodal variants because of their poor performance, which is attributed to their high number of free parameters.

Evaluation of point and parametric pedotransfer functions for predicting soil water retention and availability in soils of southwestern Saudi Arabia

Simulation of water flow and solute transport in the soil environment requires accurate estimates of the soil water retention characteristics (SWRC). In this study, multiple linear regression (MLR) was used to develop two site-specific pedotransfer functions (PTFs), a point (MLRP) and a parametric (MLRF), using soil properties of 43 soil samples collected from the Jazan region, southwest of Saudi Arabia. The accuracy of the developed PTFs and four existing PTFs to determine the SWRC, predict soil water content (SWC) at − 10, − 33, and − 1500 kPa, and estimate available water content (AWC) was assessed. The MLRP and the Schaap PTFs produced the best estimate of SWC, with smaller root mean square error (RMSE) (0.023–0.053 cm3 cm−3), and larger D-index (0.8–0.9) and Akaike information criterion (AIC) (− 303.7 to − 240.7), respectively. The largest prediction errors in the estimation of SWC were observed at matric potential close to field capacity (FC). For the AWC, the Schaap PTF provided the best prediction (RMSE = 0.014 cm3 cm−3, D-index = 0.93, AIC = − 359.9), followed by the MLRP PTF (RMSE = 0.027 cm3 cm−3, D-index = 0.83, AIC = − 302.1), whereas the Vereecken, Gupta and Larson, and the MLRF PTFs produced less accurate predictions of the AWC. The MLRP PTF proved to be more accurate compared to other tested PTFs in the prediction of SWC at both FC and permanent wilting point (PWP). In contrast, the MLRF PTF produced a relatively large error in the estimation of SWC at FC.

Parametric Estimation of Water Retention Using Mgmdh Method and Principal Component Analysis

Performing a primary analysis, such as principal component analysis (PCA) may increase accuracy and reliability of developed pedotransfer functions (PTFs). This study focuses on the usefulness of the soil penetration resistance (PR) and principal components (PCs) as new inputs along with the others to develop the PTFs for estimating the soil water retention curve (SWRC) using a multi-objective group method of data handling (mGMDH). The Brooks and Corey (1964) SWRC model was used to give a description of the water retention curves and its parameters were determined from experimental SWRC data. To select eight PCs, PCA was applied to all measured or calculated variables. Penetration resistance, organic matter (OM), aggregates mean weight diameter (MWD), saturated hydraulic conductivity (Ks), macro porosity (Mp), micro porosity (Mip) and eight selected PCs were used as predictors to estimate the Brooks and Corey model parameters by mGMDH. Using PR or OM, Ks and MWD, improved the estimation of SWRC in some cases. Using the predicted PR can be useful in the estimation of SWRC. Using either the MP and Mip or the eight PCs significantly improved the PTFs accuracy and reliability. It would be very useful to apply PCA on the original variables as a primary analysis to develop parametric PTFs.

Sensitivity analysis of point and parametric pedotransfer functions for estimating water retention of soils in Algeria

Abstract. Improving the accuracy of pedotransfer functions (PTFs) requires studying how prediction uncertainty can be apportioned to different sources of uncertainty in inputs. In this study, the question addressed was as follows: which variable input is the main or best complementary predictor of water retention, and at which water potential? Two approaches were adopted to generate PTFs: multiple linear regressions (MLRs) for point PTFs and multiple nonlinear regressions (MNLRs) for parametric PTFs. Reliability tests showed that point PTFs provided better estimates than parametric PTFs (root mean square error, RMSE: 0.0414 and 0.0444 cm3 cm−3, and 0.0613 and 0.0605 cm3 cm−3 at −33 and −1500 kPa, respectively). The local parametric PTFs provided better estimates than Rosetta PTFs at −33 kPa. No significant difference in accuracy, however, was found between the parametric PTFs and Rosetta H2 at −1500 kPa with RMSE values of 0.0605 cm3 cm−3 and 0.0636 cm3 cm−3, respectively. The results of global sensitivity analyses (GSAs) showed that the mathematical formalism of PTFs and their input variables reacted differently in terms of point pressure and texture. The point and parametric PTFs were sensitive mainly to the sand fraction in the fine- and medium-textural classes. The use of clay percentage (C %) and bulk density (BD) as inputs in the medium-textural class improved the estimation of PTFs at −33 kPa.

Pedotransfer Functions for Estimating Soil Hydraulic Properties: A Review

Characterization of soil hydraulic properties is important to environment management; however, it is well recognized that it is laborious, time-consuming and expensive to directly measure soil hydraulic properties. This paper reviews the development of pedotransfer functions (PTFs) used as an alternative tool to estimate soil hydraulic properties during the last two decades. Modern soil survey techniques like satellite imagery/remote sensing has been used in developing PTFs. Compared to mechanistic approaches, empirical relationships between physical properties and hydraulic properties have received wide preference for predicting soil hydraulic properties. Many PTFs based on different parametric functions can be found in the literature. A number of researchers have pursued a universal function that can describe water retention characteristics of all types of soils, but no single function can be termed generic though van Genuchten (VG) function has been the most widely adopted. Most of the reported parametric PTFs focus on estimation of VG parameters to obtain water retention curve (WRC). A number of physical, morphological and chemical properties have been used as predictor variables in PTFs. Conventionally, regression algorithms/techniques (statistical/neural regression) have been used for calibrating PTFs. However, there are reports of utilizing data mining techniques, e.g., pattern recognition and genetic algorithm. It is inferred that it is critical to refine the data used for calibration to improve the accuracy and reliability of the PTFs. Many statistical indices, including root mean square error (RMSE), index of agreement (d), maximum absolute error (ME), mean absolute error (MAE), coefficient of determination (r2) and correlation coefficient (r), have been used by different researchers to evaluate and validate PTFs. It is argued that being location specific, research interest in PTFs will continue till generic PTFs are developed and validated. In future studies, improved methods will be required to extract information from the existing database.

Towards using pedotransfer functions for estimating infiltration parameters

ABSTRACTWater infiltration into soils is an important component of hydrological processes. Direct measurement of infiltration is time consuming, expensive and often involves large spatial and temporal variability. The objective of this study was to develop and verify parametric pedotransfer functions (PTFs) to predict infiltration parameters. Consequently, 119 double-ring infiltration data were collected. The parameters of Philip, Kostiakov, Kostiakov-Lewis and Horton models were obtained, using the sum of squares error optimization method. Some parametric PTFs were then derived to predict the parameters of the infiltration models, using stepwise regression analysis. The results indicated a reasonable estimation of infiltration parameters by the derived PTFs. These results were more accurate when the land use of the studied area was considered. Overall results of this study suggest infiltration-based PTFs could be established as a reasonable indirect method for estimating infiltration parameters.Editor M.C. Acreman; Associate editor N. Verhoest

Preliminary investigation to estimate soil NAPL retention using parametric pedotransfer functions

Abstract Organic liquid retention of soils is a primary input variable for modelling the nonaqueous phase liquid transport and behaviour in the subsurface. In environmental and soil physical practice, it is mainly determined by scaling based on the water retention of soils or with charts of average empirical values of organic liquid retention or the fitting parameters of hydraulic functions. Predicting the fitting parameters of organic liquid retention curves with pedotransfer functions might be a promising alternative method, but this topic has only been researched to a limited extent. Thus we investigated the applicability of different hydraulic functions (3- and 4- parameter form of the van Genuchten equation and Brutsaert equation) for fitting organic liquid retention characteristics. Multivariate linear regression was used to build and develop pedotransfer functions, modelling relations between original and transformed values of basic soil properties and organic liquid retention. We attempted to generate parametric pedotransfer functions. According to our results, the applicability of hydraulic functions for fitting nonaqueous phase liquid retention curves to the experimental data was proven. The investigations gave promising results for the possibility to estimate soil nonaqueous phase liquid retention with parametric pedotransfer functions.

Assessment of pedotransfer functions for estimating soil water retention curves for the amazon region

Knowledge of the soil water retention curve (SWRC) is essential for understanding and modeling hydraulic processes in the soil. However, direct determination of the SWRC is time consuming and costly. In addition, it requires a large number of samples, due to the high spatial and temporal variability of soil hydraulic properties. An alternative is the use of models, called pedotransfer functions (PTFs), which estimate the SWRC from easy-to-measure properties. The aim of this paper was to test the accuracy of 16 point or parametric PTFs reported in the literature on different soils from the south and southeast of the State of Pará, Brazil. The PTFs tested were proposed by Pidgeon (1972), Lal (1979), Aina & Periaswamy (1985), Arruda et al. (1987), Dijkerman (1988), Vereecken et al. (1989), Batjes (1996), van den Berg et al. (1997), Tomasella et al. (2000), Hodnett & Tomasella (2002), Oliveira et al. (2002), and Barros (2010). We used a database that includes soil texture (sand, silt, and clay), bulk density, soil organic carbon, soil pH, cation exchange capacity, and the SWRC. Most of the PTFs tested did not show good performance in estimating the SWRC. The parametric PTFs, however, performed better than the point PTFs in assessing the SWRC in the tested region. Among the parametric PTFs, those proposed by Tomasella et al. (2000) achieved the best accuracy in estimating the empirical parameters of the van Genuchten (1980) model, especially when tested in the top soil layer.

Surrogate estimation of soil water retention characteristics of seasonally impounded soils

Measurement of soil hydraulic properties is an arduous task, especially to meet the large scale data requirements. This has prompted numerous studies on surrogate estimation of hydraulic properties. Objective of this study was to build regional hydraulic database and develop pedotransfer functions (PTF) for estimation of water retention characteristics (WRC) of seasonally impounded shrink-swell soils. Soil samples from 41 soil profiles were analyzed in the laboratory for soil-water retention at nine pre-determined suction pressure points and basic soil properties. These data were used for calibrating neural PTFs to predict Van Genuchten (VG) function parameters for prediction of WRC from basic soil properties data. Routine method of deriving and relating retention function parameters to basic soil properties and Neuro-m method resulted in unacceptable PTFs (RMSE > 0.05 m3 m-3) . In place of parameters derived from SWRC, we used ‘Rosetta’ estimates of VG parameters to relate basic soil properties. Resultant hierarchical parametric PTFs with prediction accuracy of RMSE

Soil water-retention prediction from pedotransfer functions for some Indian soils

This paper discusses the development of pedotransfer functions (PTFs) and uses a multiple nonlinear regression technique to validate point and parametric PTFs for the estimation of a water-retention curve from basic soil properties such as particle-size distribution, bulk density and organic matter content. One hundred soil samples were collected at different depths from different locations in the Pavanje river basin that lies within the southern coastal region of Karnataka, India. Prediction accuracies were evaluated using the coefficient of determination (R 2), root mean square error (RMSE) and mean error (ME) between measured and predicted values. Overall, both point and parametric methods predicted water contents at selected water potentials with considerable accuracy. However, prediction of the soil water-retention curve using PTFs by point estimation method was relatively more successful (best case R 2 = 0.983) for the sampled soils. F-tests were also conducted for all cases. For one regression equation, the p-value was zero and for other equation, values were close to zero. Critical comparative analysis was carried out on the performances of the point and parametric methods. Use of the developed PTFs is suggested for the prediction of a water-retention curve for loamy sand and sandy loam soils in this area of the coastal region of southern India.

Evaluation of pedotransfer functions for predicting water retention of soils in Lower Congo (D.R. Congo)

The soil water retention curve (SWRC) is important to solve many soil and water management problems related to agriculture, ecology, and environmental issues. However, it is well recognized that its direct measurement is laborious, time-consuming and expensive. An alternative is the estimation of the SWRC by pedotransfer functions (PTFs), which are well documented for temperate soils. Few works, however, have been devoted to PTFs for tropical soils. The main objective of this study was to evaluate the ability of a number of published “point” and “parametric” PTFs to predict water retention of soils in the Lower Congo (the South-Western region of the Democratic Republic of Congo). The “point” PTFs of Oliveira et al. (2002) and Dijkerman (1988) performed best at −33kPa, while those of Arruda et al. (1987) and Pidgeon (1972) were best at −1500kPa. Regarding the parametric PTFs which predicted the Van Genuchten, 1980 parameters, the “tropical” PTFs of Hodnett and Tomasella (2002) and the “temperate” PTFs of Schaap et al. (2001) gave the best results. Preliminary results of this evaluation study suggest that estimates of water content by several existing “temperate” as well as “tropical” PTFs may induce errors in the outputs of watershed models used in various agricultural studies under the humid tropics. Large discrepancies in the derived soil hydraulic data can substantially reduce the quality of the modelling results particularly in regions where soils may have been formed and evolved in similar climatological and pedological conditions as soils from the Lower Congo. We further found that a predictor such as dithionite-citrate-bicarbonateextractable iron (DCB-Fe) has great potential to reduce the uncertainty of PTFs for predicting water retention parameters of tropical soils.

A pseudo-continuous neural network approach for developing water retention pedotransfer functions with limited data

Summary In this study, a new approach, which we called pseudo-continuous, to develop pedotransfer functions (PTFs) for predicting soil–water retention with an artificial neural network (ANN) was introduced and tested. It was compared with ANN PTFs developed using traditional point and parametric approaches. The pseudo-continuous approach has a continuous performance, i.e. it enables to predict water content at any desirable matric potential, but without the need to use a specific equation, such as the one by van Genuchten. Matric potential is considered as an input parameter, which enables to increase the number of samples in the training dataset with a factor equal to the number of matric potentials used to determine the water retention curve of the soil samples in the dataset. Generally, the pseudo-continuous functions performed slightly better than the point and parametric functions. The root mean square error (RMSE) of the pseudo-continuous functions when considering local data for training and testing, and with both bulk density and organic matter as extra input variables on top of sand, silt and clay content, was 0.027 m 3 m −3 compared to 0.029 m 3 m −3 for both the point and parametric PTF. The increased number of samples in the training phase and the selection of matric potential as an input variable enabling to predict water content at any desired matric potential are the most important reasons why pseudo-continuous functions would need more intention in the future. Uniformity in the training and test dataset was shown to be important in deriving PTFs. We finally recommend the use of pseudo-continuous PTFs for further improvement and development of PTFs, in particular when datasets are limited.

Parametric pedotransfer functions of a simple linear scale model for soil moisture retention curve

Soil moisture retention (SMR) curve is a basic soil property. Since, direct measurement of the SMR curve is costly and time-consuming; it can be estimated using pedotransfer functions (PTFs) based on more easily available soil properties. Parametric PTFs estimate the parameters of a closed-form analytical equation for representation of SMR curve. In this study, we derived parametric PTFs for the model of Groenevelt and Grant (2004) which include three adjustable fitting parameters (K0, K1 and n). Our results tended to consider the constant value of 5 for the parameter of K0, and the parameters K1 and n were estimated based on more easily available soil properties by using twenty-two soils. The value of soil water content at matric potential of 1500 kPa (qwp) must also be available for this model. In this study, we considered two conditions for entering the qwp in the model: using measured qwp and estimated qwp with more easily available soil properties considering sand fraction of the soil, organic matter content, geometric mean particle-size diameter and geometric standard deviation of the particle-size diameter. Finally, the derived PTFs were evaluated for eight independent soils. The results indicate that there was no considerable difference between the two mentioned conditions for using qwp to estimate the SMR curve. Also, the results demonstrated that the proposed model in this study can be used for estimating the SMR curve with acceptable accuracy. Key words: Model of Groenevelt and Grant, soil moisture retention curve, pedotransfer functions.

Deriving and validating pedotransfer functions for some calcareous soils

Summary The unsaturated soil hydraulic properties are needed for many different applications in soil hydrology. Pedotransfer functions (PTFs) have proven to be useful to indirectly estimate these parameters from more easily obtainable soil data. Until now no studies have been conducted to derive or verify PTFs for calcareous soils, which hydraulically may not behave the same as non-calcareous soils. The objectives of this study were to assess the influence of soil CaCO3 on the soil water retention characteristics of some calcareous soils, and to derive PTFs for these soils. Two data sets were used to derive and evaluate the established PTFs. Data set 1 containing 220 samples was employed as a calibration set for multiple linear regression. An independent data set containing 55 soil samples from a different location served to verify the derived PTFs. No significant difference in accuracy was found between the PTFs with and without CaCO3 in terms of estimating specific soil water retention values or the van Genuchten soil hydraulic parameters. Compared with the Rosetta PTFs of Schaap et al. (2001) , the derived point and parametric PTFs provided better accuracy with average RMSE values of 0.028 and 0.107 cm3 cm−3, respectively.

Estimating Water Retention with Pedotransfer Functions Using Multi-Objective Group Method of Data Handling and ANNs

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 γ) 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.

Derivation and validation of pedotransfer functions for estimating soil water retention curve using a variety of soil data

Soil textural components are the most common inputs in pedotransfer functions (PTFs), although other soil properties, such as bulk density, organic matter content and structural indexes, are also used. The possibility of using the geometric mean d g and the geometric standard deviation σ g of soil particle diameters instead of soil particle size distribution to derive PTFs was investigated. Soil samples (234) having different particle size distributions were collected randomly. The predictor variables were separated into two groups: (i) particle-size distribution and bulk density, (ii) bulk density, geometric mean and geometric standard deviation of particle diameters. Two point PTF types and two parametric PTF forms were developed to predict six points on the retention curve and the parameters of the van Genuchten model, using the stepwise regression method. The derived PTFs were compared with the Rosetta package. The results indicated that the second set of variables accounted for 65 and 90% of the variation in α and 0 s parameters of the van Genuchten, model respectively. The results also indicated that these variables better explained the variation of the n parameter (the shape parameter of the van Genuchten model) and the water contents at -30, -100, -300, -500 and -1500 kPa matric potentials. It was concluded that, at the drier end of the retention curve, d g and σ g predict the water contents better than the soil bulk density. Although the coefficients of determination of the derived PTFs with the second set of variables were higher than that of the first group, the first set of variables gave more reliable estimates. Results showed that the descriptors of the particle size distribution (d g and σ g ) did not improve the quality of soil water retention prediction. The reliability test indicated that the derived PTFs predict soil hydraulic properties better than the Rosetta package.

Alternative methods in the development of pedotransfer functions for soil hydraulic characteristics

Soil hydraulic properties are needed in the modeling of water flow and solute movement in the vadose zone. Pedotransfer functions (PTFs) have received the attention of many researchers for indirect determination of hydraulic properties from basic soil properties as an alternative to direct measurement. The objective of this study was to compare the performance of cascade forward network (CFN), multiple-linear regression (MLR), and seemingly unrelated regression (SUR) methods using prediction capabilities of point and parametric PTFs developed by these methods. The point PTFs estimated field capacity (FC), permanent wilting point (PWP), available water capacity (AWC), and saturated hydraulic conductivity (Ks) and the parametric PTFs estimated the van Genuchten retention parameters. A total of 180 soil samples was extracted from the UNSODA database and divided into two groups as 135 for the development and 45 for the validation of the PTFs. The model performances were evaluated with three statistical tools: the maximum error (ME), the model efficiency (EF), and the D index (D) using the observed and predicted values of a given parameter. Despite the fact that the differences among the three methods in prediction accuracies of the point and parametric PTFs were not statistically significant (p > 0.05) except θr and α (p < 0.05) based on the ANOVA test, overall MLR and SUR were somewhat better than CFN in prediction of the point PTFs, whereas CFN performed better than the other two methods in prediction of the parametric PTFs. The F.F values of FC and θr for CFN, MLR, and SUR methods were 0.705. 0.805, 0.795 and 0.356, −0.290, −0.290, respectively, which refer to the best and worst predictions. Properties (Ks, θr, α) having some difficulty in prediction were better predicted by CFN and SUR methods, where these methods predict all hydraulic properties from basic soil properties simultaneously rather than individually as in MLR. This suggests that multivariate analysis using such functional relationships between hydraulic properties and basic soil properties can be utilized in developing more accurate point and parametric PTFs with less time and effort.

Estimation of soil hydraulic properties using proximal spectral reflectance in visible, near-infrared, and shortwave-infrared (VIS–NIR–SWIR) region

Characterization of soil hydraulic properties is an important step for assessing soil water regime in agricultural fields. Because direct measurement of soil hydraulic properties at multiple locations is costly and time-consuming, pedotransfer functions (PTF) are conveniently used to estimate these properties from easily measurable basic soil properties. Over the last two decades, several studies have demonstrated that basic soil properties of surface soils may be rapidly estimated by measuring soil spectral reflectance. In this study, we evaluated a PTF approach to use proximal spectral reflectance over the visible, near-infrared, and shortwave-infrared (VIS–NIR–SWIR) region (350–2500 nm) as predictor variable in place of basic soil properties. To develop these transfer functions, spectral reflectance of air-dried and sieved soil samples was measured using a handheld spectroradiometer equipped with a contact probe. Transfer functions in the form of multiple linear regression relationships between soil hydraulic properties and different attributes of measured spectral reflectance were developed. These new transfer functions are called spectrotransfer functions (STF). Both the parametric PTFs and STFs for the parameters of van Genuchten water retention model ( α and n) and point PTF for saturated hydraulic conductivity ( K s) were evaluated using the root-mean-squared error ( RMSE). Results show that STFs have the similar accuracy as PTFs for estimating hydraulic properties. Specifically, STFs developed with the absorption features of proximal spectral reflectance performed better than the PTFs for estimating α. Among three hydraulic parameters for which the STFs were developed, van Genuchten parameter n is well predicted with comparatively lower values of RMSE. Thus, this study shows that proximal spectral reflectance of soil may be used for rapid estimation of soil hydraulic properties in a large area with accuracy comparable to PTFs. A rigorous testing in different geographical regions is warranted to establish the utility of STFs as a method for estimating soil hydraulic properties.

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 &lt; 0.01). The results also indicated that hydraulic parameters were different for the same soil with and without gypsum.