Estimating Soil Water Retention Research Articles

Using near-infrared spectroscopy to estimate soil water retention curves with the van Genuchten model

Using near-infrared spectroscopy to estimate soil water retention curves with the van Genuchten model

Estimating soil water retention curves over the entire saturation range: A thermal conductivity-based approach

The relationship between soil water content (θ) and suction (h, referring to the absolute value of pressure head), is described by the soil water retention curve (SWRC). Our earlier research (Fu et al. (2021, 2023a) [J. Hydrol.127171]; [J. Hydrol.129898]) recognized underlying correlations between SWRCs and soil thermal conductivity (λ) versus θ curves, and developed methodologies to ascertain the parameters of the van Genuchten (vG) equation using λ(θ) measurements, described by the Ghanbarian & Daigle (GD) equation, and basic soil characteristics. Limitations intrinsic to the van Genuchten equation restrict the GD-vG approach to generate precise estimates only in the wet and medium suction range, specifically h ranging from 0 to 150 mH2O. The validity of these approaches in the dry region remains uncertain. In this study, we associated the Peters-Durner-Iden (PDI) model parameters to those of the GD model. An initial examination was performed on the linearization processes needed to derive the hydraulic continuity water content (θhc) from the capillary water component as characterized by the PDI model and to choose the suction at oven dryness (h0) based on PDI model performance. Subsequently, two piecewise functions and two pedo-transfer functions were formulated to compute the PDI model parameters utilizing soil porosity, particle size distribution, and GD parameters, based on a calibration dataset comprising 25 different soils. The new GD-PDI approach was subsequently assessed with six independent soils and juxtaposed with the previous GD-vG approach. The GD-PDI approach outperformed the GD-vG approach, particularly within the dry range.

Evaluation of Pedotransfer Functions to Estimate Soil Water Retention Curve: A Conceptual Review

The soil water retention curve (SWRC) is a vital soil property used to evaluate the soil’s water holding capacity, a critical factor in various applications such as determining soil water availability for plants, soil conservation and management, climate change adaptation, and mitigation of flood risks. Estimating SWRC directly in the field and laboratory is a time-consuming and laborious process and requires numerous instruments and measurements at a specific location. In this context, various estimation approaches have been developed, including pedotransfer functions (PTFs), over the past three decades to estimate soil water retention and its associated properties. Despite the efficiencies, PTFs and semi-physical approach-based models often have several limitations, particularly in the dry range of the SWRC. PTFs-based modeling has become a key research topic due to readily available soil data and cost-effective methods for deriving essential soil parameters, which enable more efficient decision-making in sustainable land-use management. Therefore, advancement and adjustment are necessary for reliable estimations of the SWRC from readily available data. This article reviews the evaluation of the current and past PTFs for estimating the SWRC. This study aims to evaluate PTF techniques and semi-physical approaches based on soil texture, bulk density, porosity, and other related factors. Additionally, it also assesses the performance and limitations of various common semi-physical models proposed and developed by Arya and Paris, Haverkamp and Parlange, the Modified Kovács model by Aubertin et al., Chang and Cheng, Meskini-Vishkaee et al., Vidler et al., and Zhai et al. This assessment will be effective for researchers in this field and provide valuable insight into the importance of new PTFs for modeling SWRC.

Developing novel ensemble models for predicting soil hydraulic properties in China’s arid region

Accurately quantifying the mass (water, nutrients, and carbon) and energy exchange processes between the Earth’s atmosphere, biosphere, and lithosphere requires the accurate parameterization of soil hydraulic properties (SHPs) and their spatial heterogeneity. Because direct measurements of SHPs are difficult, time-consuming, and impossible at larger spatial scales, various pedotransfer functions (PTFs) have been developed in the last few decades, providing divergent estimates of SHPs from readily measurable variables. However, existing PTFs are mostly developed for specific regions and may not be suitable for other pedoclimatic conditions. Here, PTFs were developed using multiple machine-learning algorithms to estimate SHPs and examined the weaknesses and strengths of each method in estimating the average and variability of SHPs across China’s arid region. The optimal PTFs were applied to a 1 × 1 km2 regional map of texture and bulk density, thus producing maps of the saturated hydraulic conductivity (Ks), the parameters of the van Genuchten (VG) formulation, field capacity (θfc), wilting point (θwp), plant available water (θpa), and soil macroporosity (ϕm) in the 0–2 m soil profile throughout the region. The results indicate that the ensemble model with the averaging method (EMA) is the most robust for estimating all SHPs. The EMA-PTFs for Ks yielded the best performance for sand textures, followed by sandy loam, loam, silty loam, silty clay loam, loamy sand, and clay loam textures. There were no significant differences in estimating soil water retention curve parameters among the different soil texture classes. Using the same observed data set, we demonstrated that the new EMA-PTFs outperformed those of existing PTFs, such as Rosetta and HYPRES, with RMSE values decreasing by 25–83 % depending on the SHPs. Furthermore, our SHP datasets exhibited significantly deeper soil profiles and higher accuracy than other available regional and global SHP products. The VG retention parameter α shows the greatest variation vertically throughout the 0–2 m soil profile, followed by ln(Ks), θr, θpa, θfc, θwp, ϕm, θs and n. All SHPs exhibit much lower variability in the desert than in other areas, likely due to the homogeneous particle size distribution of desert areas. This study provides more accurate SHP estimates in China’s arid region than the existing SHP products by developing advanced PTFs and highlights the inconsistency in SHPs among different global or regional products; the uncertainties induced by PTFs should thus be considered in future terrestrial biosphere modeling.

Point Pedotransfer Functions (PPTF) for Estimating Soil Water Retention Curve by Multiple Linear Regression Methods

The aim of this study was to derive and find the best Point Pedotransfer Functions (PPTF) for estimating SWRC for Iraqi soils using the multiple regression methods. Soil samples were collected from 30 different sites of Iraqi soil at two depths: 0-0.3 m and 0.3-0.6 m, in order to obtain the required database for the derivation of PTF, which includes the soil particle size distribution, carbonate minerals, bulk density, particles density, organic matter, saturated hydraulic conductivity, capillary height and the boundaries of available water. Measured characteristics were considered as explanatory variables (EV). Volumetric water content was measured at suctions 0, -5, -10, -33, -500, -100, -500, -1000, and -1500 kPa and considered as response variables (RV). PPTF was derived using SAS statistical software based on multiple linear regression. Three methods for fitting the PPTF to EV were Maximum R2 Improvement (MR), Backward Elimination (BW) and Forward selection (FW). The prediction accuracy was evaluated through Mean Bias Error (MBE), Mean Absolute Error (MAE), root mean squared error (RMSE) and Coefficient Of Determination (R2). The results showed that all EVs were included in the PPFT derivation by MR method, while in the BW and FW methods the significant level of entery (SLENTRY) of EV was 0.1. It was also found that variables w1, capillary height, available water, bulk density, and porosity were included in the derivation of most of the models using the BW and FW method. Besides no statistically significant differences were found among the values of MBE, MAE, RMSE, and R2 of the PPTF although the number of selected EV are different.

Estimating soil water retention curves from thermal conductivity measurements: A percolation-based effective-medium approximation

A soil water retention curve (SWRC) describes the relationship between soil water content (θ) and suction (h, also the absolute value of pressure head). Earlier work indicated that correlations existed between the percolation-based effective medium approximation (P-EMA) thermal conductivity (λ) model parameters and soil hydraulic properties. In this study, the critical water content (θc) of the P-EMA model was related to the pore size distribution parameter (m) of the van Genuchten model, water content at the inflection point of a SWRC (θi) and hydraulic continuity water content (θhc). And a pedo-transfer function was established to estimate the van Genuchten model parameter α from soil properties and P-EMA parameters. Based on these relationships, three approaches were developed to estimate the van Genuchten models parameters from λ(θ) measurements, porosity, sand and clay contents. The three approaches were then validated on six independent soils, and results showed that all of the approaches estimated θ well at selected h values, with the average root mean square errors from 0.025 to 0.029 cm3 cm−3, the average mean relative absolute errors ranging from 0.111 to 0.157, and the average Akaike Information Criterion from −18.3 to −16.2. Two new approaches outperformed the original Fu et al approach but with fewer input parameters (no need for organic carbon content), thus also facilitating their broader application.

Measuring soil freezing characteristic curve with thermo‐time domain reflectometry

AbstractThe soil freezing characteristic curve (SFCC) has applications in determining the soil water retention curve (SWRC) and modelling thermal and hydrological processes in cold regions. Accurate measurement of the SFCC in the laboratory has been constrained to the thawing process because of the interference of supercooling during the freezing process. In this study, we introduce a dynamic method based on the technique of thermo‐time domain reflectometry (thermo‐TDR) for measuring the SFCC quickly and accurately. A simple iced‐toothpick approach was used for eliminating supercooling during a freezing experiment. The performance of the thermo‐TDR‐based method was evaluated by comparing the measurements with the equilibration method on two soils of different textures. The results showed that the supercooling phenomena during the freezing process could be eliminated effectively by inserting iced‐toothpicks into the samples at soil freezing point, and both the SFCC (at a freezing rate of 2.5°C h−1) and the soil thawing characteristic curves (STCC) could be determined accurately with the thermo‐TDR‐based method. The thermo‐TDR measured SFCCs agreed well with estimates from the SWRCs, with an average root‐mean square error (RMSE) of 0.02 m3 m−3. The dynamic thermo‐TDR‐based method has the advantages of being easy to operate, time‐saving, and applicable for in situ monitoring of SFCC, and could be used to determine SWRCs indirectly.Highlights The thermo‐TDR method can measure the soil freezing characteristic curve (SFCC) accurately. Supercooling during the freezing process was eliminated by using an iced‐toothpick approach. The slow‐rate dynamic method provided reliable SFCCs as compared with the equilibration method. The thermo‐TDR measured SFCCs could be used to estimate soil water retention curves.

Data correlation structure controls pedotransfer function performance

Soil hydraulic properties are essential for modeling and evaluating several soil–water processes. The use of pedotransfer functions (PTFs) is a viable alternative in the analysis of these processes due to the high costs or difficulty in measuring these properties. We aimed to (1) examine the performance of a physically-based PTF and a pool of empirical PTFs from both temperate and tropical climates to estimate soil water retention under subtropical climate and (2) performed correlation analysis between PTF inputs, outputs and estimation residuals to help gain understanding why some PTFs are more successful than others in a given study area. The study was carried out in the Pelotas River Watershed, Southern Brazil, where samples were taken at 100 locations in the 0–0.20-m soil layer along a 25-km-spatial transect. For each point, clay, silt, fine sand, total sand, and organic carbon contents were measured along with bulk and particle density, macro, micro, and total porosity, and the soil water retention curve. The PTFs were used to estimate field capacity, permanent wilting point, and the available water (AW) content. The performance of PTFs from different climate zones was mixed, and similarity in the data correlation structure between PTF development and application data sets appeared to be a good predictor of their predictive power. There was no clear grouping in such correlation structures within climate-zones, and we conclude that the often claimed geoclimatic difference or similarity between an empirical PTF’s origin and its application site is not, or at least not the sole driver of a PTF’s expected performance.

Evaluation of Pedotransfer Functions for Estimating Soil Water Retention Curve of Ap Horizon Soils for Various Soil Series of Reclaimed Tidal Flat Soil

This investigation was to evaluate the applicability and prediction accuracy of pedotransfer function (PTF) to estimate the water retention curves of Ap horizon soils for five soil series which cannot be directly used to cultivate upland crops other than rice because of their high salinity. Soil water retention curves (SWRCs) were obtained from 150 undisturbed soil samples collected from the Ap horizons of which soil textures were grouped into sandy loam with high contents of sand (>60%) and low clay contents (<10%) and silt loam with relatively high silt content (>60%) and low sand content (<10%). Soil-water retention characteristics between 0 to −50 kPa and −50 to −1500 kPa were measured using the sandbox, kaolin-plate, and pressure chamber methods, respectively. The SWRCs were also estimated by Rawls and Brakensiek PTF (RB-PTF). The measured SWRCs of sandy loam and silt loam were compared against those obtained from RB-PTF. The SWRCs estimated with the modeled data closely fit the measured data of sandy loam and silt loam although saturated volume water content (θs) and residual volume water content (θr) of RB-PTF were higher than those of the measured mean for both soil textures. The air entry point (α) and the steepness of the water-retention curve (n) were higher in sandy loam than those of silt loam. SWRCS estimated by the RB-PTF yielded the best fit of all soil samples from the five series, whereas the MBE values less than zero indicate that θv measured at the water potentials is under-predicted. The r2 values greater than 0.9 for sandy loam and silt loam represent RB-PTF and are suitable for predicting SWRCs in RTFS because the measured data points do not vary around the estimated regression line.

Estimating Soil Water Retention Curve by Extreme Learning Machine, Radial Basis Function, M5 Tree and Modified Group Method of Data Handling Approaches

AbstractThis study was conducted to assess the applicability of novel types of neural network methods (extreme learning machine (ELM), radial basis function (RBF), modified group method of data handling (M‐GMDH)) and M5 tree methods for the prediction of the soil water retention curve (SWRC) and compare their performance with that of derived methods and pedotransfer functions (PTFs) of other studies for soils in Iran. Then, 15 PTFs were developed. Predictions were evaluated by the integral root mean square error (IRMSE), integral mean error (IME), Akaike's information criterion (AIC), and coefficient of determination (R2). The RBF‐based PTFs were better than the M5 tree, M‐GMDH, and ELM‐based PTFs in terms of the IRMSE criterion in the testing step. Also, PTFs and methods developed in the present study were more reliable than other derived PTFs and methods by different researchers. The values of the IRMSE and R2 in the best PTFs (with inputs of sand, clay, total porosity and the moisture content at field capacity and permanent wilting point) of the testing data set of the RBF method were 0.037 cm3cm−3 and 0.988, respectively. For the testing data set, the average values of the IRMSE criterion for all the PTFs of the RBF, ELM, M‐GMDH, and M5 tree methods were 0.051, 0.062, 0.055, and 0.054 cm3cm−3, respectively. Therefore, the differences were considerable only between the ELM and other methods. The IRMSE criterion results of the testing data set showed the suitability of the RBF method in the development of PTFs for the prediction of the SWRC.

Estimating soil water retention curves from soil thermal conductivity measurements

The soil water retention curve represents the relationship between soil water content (θ) and matric potential (ψ). The van Genuchten (vG) model is commonly used to characterize the shape of a θ(ψ) curve. Based on the similarities between θ(ψ) curves and soil thermal conductivity (λ) versus θ curves, Lu and Dong proposed a unified conceptual λ(θ) model (LD model) for estimating λ(θ) curves from θ(ψ) curves. Their work makes it possible to relate the shapes of λ(θ) curves to θ(ψ) curves. In this study, we present an empirical approach to estimate the vG model parameter m from the LD model shape parameter p based on a model calibration with θ(ψ) and λ(θ) datasets obtained from 10 soils. The saturated water content θs and the vG model parameter α are estimated from selected soil properties (i.e., bulk density, particle density, particle size distribution and organic carbon content), and the residual water content θr is estimated from the LD model parameter θf. For model evaluation, the θ(ψ) curves of six soils were estimated from measured λ(θ) values and selected soil properties, and were compared to direct θ(ψ) measurements. The proposed method performed well with root mean square errors of estimated θ values ranging from 0.015 to 0.052 cm3 cm−3 and bias ranging from −0.009 to 0.040 cm3 cm−3. We conclude that the proposed method accurately estimates θ(ψ) curves from λ(θ) curves and selected soil properties.

Pedotransfer functions for estimating soil hydraulic properties from saturation to dryness

Current pedotransfer functions (PTFs) for estimating soil hydraulic curves are mostly developed to predict parameters of the Mualem-van Genuchten hydraulic functions. The Mualem-van Genuchten functions are recognised to be inadequate in representing soil water retention hydraulic conductivity curves at low pressure head ranges. This study presents neuroFX, a suite of PTFs, for estimating soil water retention and unsaturated hydraulic conductivity curves from saturation to complete dryness based on the Fredlund-Xing-Wang (FXW) model. The PTFs were calibrated using the neuro-m neural networks approach with three different sets of inputs: (1) SSCBD uses the sand, silt, and clay fractions, and bulk density; (2) SSC uses the sand, silt and clay fractions, and (3) soil textural class input. NeuroFX PTFs were trained using fitted parameters of the FXW model from selected data in the UNSODA database and validated using 5-fold cross-validation that was repeated 10 times. NeuroFX provides an uncertainty estimate of hydraulic parameters. The prediction quality of neuroFX was compared with two existing PTFs: ROSETTA and Brunswick-Weber (BW) PTFs. Based on multiple criteria, we found that neuroFX performed better than ROSETTA and BW PTFs in the same test sample. NeuroFX PTF with the SSCBD input yielded the best prediction of soil water retention and unsaturated hydraulic conductivity curves with RMSE in water content of 0.052 cm3 cm−3 and RMSE in log10(K) = 0.732 (in the magnitude of cm day−1), indicating the importance of including bulk density in the input of PTFs. NeuroFX was then used to map parameters of the FXW model for the whole of the continental USA over 6 depth intervals. The code of neuroFX in R software is available in Supplementary Material 1.

Pedotransfer functions for estimating soil water retention properties of northern China agricultural soils: Development and needs*

AbstractMany agro‐environmental studies focusing on the efficient management of soils and water resources make use of soil water simulation models. Reliable soil hydraulic properties are critical for ensuring the accuracy of model simulations. However, soil hydraulic parameters in northern China are generally derived using external pedotransfer functions (PTFs) that do not take into account the specificities of local edaphoclimatic conditions due to the lack of a better alternative. Therefore, the main objective of this paper was to develop PTFs for estimating the soil water retention curve (SWRC) in northern China agricultural soils (named PTF‐ANC). A total of 440 soil horizons were collected from the existing literature. A flexible soil‐textural conversion program was first developed to harmonize soil particle‐size data into the United States Department of Agriculture (USDA) classification system. The SWRC parameters of the van Genuchten model were also generated by curve fitting. Then, the PTF‐ANC were developed using artificial neural networks, with soil texture and bulk density being used as input data and with a basic three‐layer back‐propagation neural network. The PTF‐ANC showed an acceptable accuracy when predicting the SWRC, indicating a strong application potential for northern China soils. Comparison of estimates with two widely used external PTFs also showed that these were not suitable for characterizing the SWRC of northern China agricultural soils. This is due to the fact that the main soil textures (silt and silty loam textures) found in northern China soils were misrepresented in those external soil databases. Overall, this paper presented the absolute necessity of developing specific PTFs for northern China agricultural soils.

Using Different Approaches of Particle Size Analysis for Estimation of Water Retention Capacity of Soils: Example of Keszthely Mountains (Hungary)

PSD (particle size distribution) is a key factor affecting soil hydro-physical properties (e.g. hydraulic conductivity and water retention), which makes its determination essential. Climate change increases the importance of water retention and permeability as extreme weather events can severely impair the water supply of drought-sensitive vegetation. The amount of water in soils is expected to decrease. The modified Thornthwaite model considers soil properties such as root depth, topsoil layer thickness and particle size distribution (silt and clay fraction) of soil particles combined with the most significant soil properties. At the beginning of the research, we developed a laser diffraction method to replace the standard based “pipette” sedimentation method. The theoretical background of laser diffraction measurements is already known, but their practical application for estimating soil water retention capacity is still poorly understood. The pre-sieving of soil aggregates, the pre-treatment (disaggregation and dispersion) of the samples greatly influence the obtained results. In addition to the sedimentation method, laser diffraction measurements (Malvern Mastersizer 3000) were applied with three variants of pre-treatment. For comparison, the results of a Leptosol, a Cambisol, and a Luvisol were prepared for the first modified Thornthwaite water balance model. Significant differences appeared, especially during drought periods, which could be a basis for studying soil drought sensitivity. The development of our method can estimate the water retention capacity of soil, which could support adaptive forest management plans against climatic and pedological transformations.

Estimating soil water retention for wide ranges of pressure head and bulk density based on a fractional bulk density concept

Soil water retention determines plant water availability and contaminant transport processes in the subsurface environment. However, it is usually difficult to measure soil water retention characteristics. In this study, an analytical model based on a fractional bulk density (FBD) concept was presented for estimating soil water retention curves. The concept allows partitioning of soil pore space according to the relative contribution of certain size fractions of particles to the change in total pore space. The input parameters of the model are particle size distribution (PSD), bulk density, and residual water content at water pressure head of 15,000 cm. The model was tested on 30 sets of water retention data obtained from various types of soils that cover wide ranges of soil texture from clay to sand and soil bulk density from 0.33 g/cm3 to 1.65 g/cm3. Results showed that the FBD model was effective for all soil textures and bulk densities. The estimation was more sensitive to the changes in soil bulk density and residual water content than PSD parameters. The proposed model provides an easy way to evaluate the impacts of soil bulk density on water conservation in soils that are manipulated by mechanical operation.

Pedotransfer functions for estimating soil water retention curve of Sicilian soils

ABSTRACT 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.

Estimating Soil Water Retention Curve by Inverse Modelling from Combination of In Situ Dynamic Soil Water Content and Soil Potential Data

Soil water retention curves (SWRCs) are crucial for characterizing soil moisture dynamics, and are particularly relevant in the context of irrigation management. Inverse modelling is one of the methods used to parameterize models representing these curves, which are closest to the field reality. The objective of this study is to estimate the soil hydraulic properties through inverse modelling using the HYDRUS-1D code based on soil moisture and potential data acquired in the field. The in situ SWRCs acquired every 30 min are based on simultaneous soil water content and soil water potential measurements with 10HS and MPS-2 sensors, respectively, in five experimental fields. The fields were planted with drip-irrigated lettuces from February to March 2016 in the Chrey Bak catchment located in the Tonlé Sap Lake region, Cambodia. After calibration of the van Genuchten soil water retention model parameters, we used them to evaluate the performance of HYDRUS-1D to predict soil moisture dynamics in the studied fields. Water flow was reasonably well reproduced in all sites covering a range of soil types (loamy sand and loamy soil) with root mean square errors ranging from 0.02 to 0.03 cm3 cm−3.

Approaches for Estimating Soil Water Retention Curves at Various Bulk Densities With the Extended Van Genuchten Model

AbstractSoil bulk density (ρb) variations influence soil hydraulic properties, such as the water retention curve (WRC), but they are usually ignored in soil water simulation models. We extend the van Genuchten WRC model parameters to account for ρb variations using a series of empirical expressions. WRC measurements made on eight soils with various ρb, and textures are used to calibrate these ρb‐related empirical equations. Accordingly, two approaches are developed to estimate WRCs of soils at various ρb. Another eight soils with a wide range of ρb and textures are used to evaluate the accuracy of the new approaches. Approach 1 estimates WRCs for each soil at various ρb using a WRC measurement made at a reference ρb and the soil texture fractions. This approach gives reasonable WRC estimates for the eight validation soils, with an average root‐mean‐square error (RMSE) of 0.025 m3/m3 and an average determination coefficient (R2) of 0.94. For Approach 2, a WRC measurement made at a reference ρb and one additional water content‐matric potential value measured at a different ρb value are used, which produces WRC estimates with an average RMSE of 0.017 m3/m3 and an average R2 of 0.97. The methodology used in Approach 2 is also applied to the Brooks and Corey WRC model to obtain accurate and precise WRC estimates. The proposed approaches have the potential to be incorporated into simulation models for estimating soil hydraulic properties that are affected by transient and variable ρb.

Pedotransfer functions to estimate soil water content at field capacity and permanent wilting point in hot Arid Western India

Characterization of soil water retention, e.g., water content at field capacity (FC) and permanent wilting point (PWP) over a landscape plays a key role in efficient utilization of available scarce water resources in dry land agriculture; however, direct measurement thereof for multiple locations in the field is not always feasible. Therefore, pedotransfer functions (PTFs) were developed to estimate soil water retention at FC and PWP for dryland soils of India. A soil database available for Arid Western India (N=370) was used to develop PTFs. The developed PTFs were tested in two independent datasets from arid regions of India (N=36) and an arid region of USA (N=1789). While testing these PTFs using independent data from India, root mean square error (RMSE) was found to be 2.65 and 1.08 for FC and PWP, respectively, whereas for most of the tested ‘established’ PTFs, the RMSE was >3.41 and >1.15, respectively. Performance of the developed PTFs from the independent dataset from USA was comparable with estimates derived from ‘established’ PTFs. For wide applicability of the developed PTFs, a user-friendly soil moisture calculator was developed. The PTFs developed in this study may be quite useful to farmers for scheduling irrigation water as per soil type.

Analysis of the suitability of Polish soil texture classification for estimating soil water retention and hydraulic properties

Abstract The objective of this study was to examine whether the Polish soil textural classification is useful for evaluation of soil water retention and hydraulic properties and, furthermore, for determining which textural classes are characterized by the highest diversity of soil water retention and hydraulic properties. The texture triangle was divided into a 1% grid of particle-size classes resulting in 5151 different data points. For each data point, soil water retention parameters and saturated hydraulic conductivity were obtained using the ROSETTA program. The silt classes showed the highest uncertainty in the estimation of the saturated water content based on the soil texture. These classes are characterized by high variations of saturated water content within the class. Estimations of field capacity and permanent wilting point on the basis of textural classes are encumbered with highest errors for gp, pg, pl and pyg soils, which are characterized by the highest values of coefficient of variation. Saturated soil hydraulic conductivity is better classified into homogeneous classes by the Polish texture classes than by the clusters obtained by the k-means cluster analysis based on the soil hydraulic and retention properties. Soil water retention parameters are better classified into homogeneous groups by the k-means cluster analysis than by the traditional textural classes. Cluster analysis using the k-means can be helpful for grouping similar soils from the point of view of their retention properties.