Soil Water Retention Curve Data Research Articles

SWRC interpretation of iron ore tailings blends focusing on saturation assessment of dry stacking

ABSTRACT The study addresses challenges in dry stacking of fine-grained materials by blending itabirite iron ore slime tailings with coarser flotation tailings. It focuses on flow dynamics, capillary saturation, and the necessity to prevent large, saturated volumes. Geotechnical tests were conducted on various blends, deriving unsaturated hydraulic conductivity functions using Fredlund and Xing’s methodology. The Soil-Water Retention Curve data, combined with transient 1D analysis, assesses capillary rise. Results show capillary rise up to 50 meters with over 80% saturation, and also influenced by weather conditions penetrating up to 20 meters. Integrating up to 30% slime content in flotation tailings mitigates saturation issues. Empirical and numerical findings enable inferring saturation tendencies in distinct iron ore tailings mixtures, establishing a procedural framework for practical field applications.

New insights into the correlation between soil thermal conductivity and water retention in unsaturated soils

AbstractThe heat transfer and water retention in soils, governed by soil thermal conductivity (λ) and soil water retention curve (SWRC), are coupled. Soil water content (θ) significantly affects λ. Several models have been developed to describe λ(θ) relationships for unsaturated soils. Ghanbarian and Daigle presented a percolation‐based effective‐medium approximation (P‐EMA) for λ(θ) with two parameters: scaling exponent (ts) and critical water content (θc). In this study, we explored the new insights into the correlation between soil thermal conductivity and water retention using the P‐EMA and van Genuchten models. The θc was strongly correlated to selected soil hydraulic and physical properties, such as water contents at wilting point (θpwp), inflection point (θi), and hydraulic continuity (θhc) determined from measured SWRCs for a 23‐soil calibration dataset. The established relationships were then evaluated on a seven‐soil validation dataset to estimate θc. Results confirmed their robustness with root mean square error ranging from 0.011 to 0.015 cm3 cm−3, MAE ranging from 0.008 to 0.013 cm3 cm−3, and R2 of 0.98. Further discussion investigated the underlying mechanism for the correlation between θc with θhc which dominate both heat transfer and water flow. More importantly, this study revealed the possibility to further investigate the general relationship between λ(θ) and SWRC data in the future.

Spatial Properties of Soil Physical Quality Index S in Black Soil Croplands under Permanent Gully Erosion

Soil physical quality (SPQ) is a limiting factor affecting crop production. However, the impact of gully erosion on the SPQ index S, defined by Dexter as the inflection point of the soil water retention curve (SWRC), remains unclear, especially when considering different latitudinal regions. This study aimed to apply Dexter’s S-theory to evaluate the distribution of index S in black soils adjacent to various gully positions and investigate its relationship with bulk density (Bd), soil organic matter (SOM), and particle percentage. Soil properties (SWRC, Bd, SOM, and particle percentage) from nine gullies in croplands in three latitudinal regions (Harbin, Hailun, and Nenjiang in Heilongjiang province) were determined at the gully edge (GE0) and 50 m beyond the edge into the croplands (GE50) at the following gully units: head, mid-upper, middle, mid-lower, tail, and conjunctions between main gully and gully branch. The S-index was calculated using parameters such as n, θs, and θr, with SWRC data fitted into the van Genuchten model. The results showed spatial variations in the S-index across latitudinal regions, with slightly higher S-values in Harbin than in Hailun and Nenjiang. The S-index also showed noticeable differences at GE0 and GE50 and at the junctions between the main gully and its branches. Approximately 51% of the samples at GE0 and 28.2% of the samples at GE50 had S-values below 0.035, which Dexter proposed as the boundary between good and poor SPQ, indicating a degradation of SPQ at the gully-surrounding areas. A decreased S-index in the gully vicinity was significantly (p < 0.05) associated with increased bulk density (1.33 vs. 1.21 g cm−3 for GE0 and GE50) and decreased SOM (36.80 vs. 39.36 g kg−1 for GE0 and GE50). In summary, this study indicates that gully erosion affects the farmland S-index at the gully-surrounding areas through SOM and Bd. Accordingly, measures suited to the increase in the S-index of the gully-surrounding areas may be implemented to maximize the crop yield of farmlands.

An Unsaturated Hydraulic Conductivity Model Based on the Capillary Bundle Model, the Brooks‐Corey Model and Waxman‐Smits Model

AbstractSoil unsaturated hydraulic conductivity (K), which depends on water content (θ) and matric potential (ψ), exhibits a high degree of variability at the field scale. Here we first develop a theoretical hydraulic‐electrical conductivity (σ) relationship under low and high salinity cases based on the capillary bundle model and Waxman and Smits model which can account for the non‐linear behavior of σ at low salinities. Then the K‐σ relationship is converted into a K(θ, ψ) model using the Brooks‐Corey model. The model includes two parameters c and γ. Parameter c accounts for the variation of the term (λ + 2)/(λ + 4) where λ is the pore size distribution parameter in the Brooks‐Corey model, and the term m‐n where m and n are Archie's saturation and cementation exponents, respectively. Parameter γ is the sum of the tortuosity factor accounting for the differences between hydraulic and electrical tortuosity and Archie's saturation exponent. Based on a calibration data set of 150 soils selected from the UNSODA database, the best fitting log(c) and γ values were determined as −2.53 and 1.92, −4.39 and −0.14, −5.01 and −1.34, and −5.79 and −2.27 for four textural groups. The estimated log10(K) values with the new K(θ, ψ) model compared well to the measured values from an independent data set of 49 soils selected from the UNSODA database, with mean error (ME), relative error (RE), root mean square error (RMSE) and coefficient of determination (R2) values of 0.02, 8.8%, 0.80 and 0.73, respectively. A second test of the new K(θ, ψ) model using a data set representing 23 soils reported in the literature also showed good agreement between estimated and measured log10(K) values with ME of −0.01, RE of 9.5%, RMSE of 0.77 and R2 of 0.85. The new K(θ, ψ) model outperformed the Mualem‐van Genuchten model and two recently published pedo‐transfer functions. The new K(θ, ψ) model can be applied for estimating K under field conditions and for hydrologic modeling without need for soil water retention curve data fitting to derive a K function.

Enhancing rhizosphere soil water retention in wheat through colonization with endophytic fungus Serendipita indica

Effect of S. indica colonization of wheat (Triticum aestivum) on water retention and physical quality of the rhizosphere soil was investigated in a greenhouse study. Colonized and uncolonized seedlings of four wheat cultivars (i.e., Roshan, Ghods, Pishtaz, and Kavir) were grown in rhizoboxes. Plants were harvested and intact samples were taken from the rhizosphere to measure soil organic carbon (SOC), hot-water soluble carbohydrates (HWSC), and soil water retention curve (SWRC). Water contents at field capacity (θFC) and permanent wilting point (θPWP), and plant available water (PAW) were obtained from the measured SWRC data. The integral energy (EI(PAW)) and integral water storage (WI(PAW)), two alternative indices of soil water availability and physical quality, were computed by integration of SWRCs over the PAW range with respect to water content and matric potential, respectively. Low EI(PAW) and high WI(PAW) indicate improved soil physical quality and better soil water availability to plant. The results showed that the SOC storage and HWSC of the wheat rhizosphere were approximately 146 and 83% greater than those of unplanted soils, respectively. Moreover, compared with the uncolonized counterparts, S. indica colonization significantly increased the SOC and HWSC by 76 and 134%, respectively. The presence of plant roots and S. indica colonization improved soil water retention by increasing SOC and HWSC and subsequently enhancing portion of structural porosity (i.e., pore space between the soil aggregates in the size range of 0.5–500 μm, mainly includingmacropores and mesopores). Despite having the same PAW, colonized soil samples had lower EI(PAW) (≈ 133 J kg−1) compared to uncolonized counterparts (≈ 153 J kg−1). This finding implies that plant roots take up water from the colonized wheat-planted soils easier than from the uncolonized counterparts. Compared to uncolonized samples, an increase of 50% in S1 (Dexter's index of soil physical quality) of the colonized Kavir cultivar [with the lowest root extension, SOC (i.e., 0.97 g kg−1), and HWSC (i.e., 0.059 g kg−1)] showed that S. indica can improve soil physical quality due to greater structural pores. Among the four wheat cultivars, the rhizosphere of Roshan (≈ 1.60 g kg−1) and Ghods (1.61 g kg−1) had the highest SOC values. Higher HWSC (i.e., 0.138 g kg−1) and more extensive root distribution for Roshan, especially upon co-cultivation with S. indica, led to greater contribution of structural pores in water retention in the rhizosphere soil when compared with three other cultivars. To conclude, S. indica colonization not only in the presence of plant roots and by increasing SOC and HWSC improves the physical quality of the rhizosphere soil but also the fungus by itself enhances soil physical quality due to enhancing portion of structural porosity involving in water transition and storage.

Fractal-based hydraulic model of unsaturated flow in deformable soils considering the evolution of pore size distribution

This paper presents a fractal-based hydraulic model for unsaturated flow, which includes the soil water retention curve (SWRC) and relative hydraulic conductivity curve (RHCC), and considers the evolution of pore size distribution (PSD) during soil deformation. In this model, soil PSD is assumed to follow the fractal scaling law, and soil deformation is characterized by a change in the fractal dimension and air entry value. By combining the intrinsic constraint between void ratio and fractal dimension, the fractal description of PSD evolution during volumetric deformation of soils is clearly defined, and the void ratio-dependent SWRC and RHCC are derived. All model parameters can be calibrated from the SWRC data of soils under two initial void ratios, and can then be used to predict the SWRC and RHCC of the soil under arbitrary deformation states. Eight experimental data sets, including SWRC and RHCC data under different initial void ratios, are used to evaluate the proposed model. Results show that: (1) the fractal-based hydraulic model can well describe the influence of volumetric deformation on the hydraulic properties of unsaturated soils, and (2) ignoring the evolution of PSD during soil compression will significantly overestimate the water retention ability and hydraulic conductivity of soils.

Contribution of capillary pressure to effective stress for unsaturated soils: Role of wet area fraction and water retention curve

Estimating the contribution of capillary pressure to the effective stress is critical for studying the mechanical and hydraulic behavior of unsaturated soils. Based on experimental evidence, we clarify that the degree of saturation (Sr) rather than matric suction (s) is more suitable as a variable for modeling Bishop's effective stress parameter (χ). The wet area fraction, defined as the ratio of the wet external area of soil particles to the total surface area, is then introduced as an alternative to the parameter χ. Because the contact area between soil particles and pore water is affected by both hydraulic (matric suction) and mechanical (volumetric deformation) states, the soil water retention curve (SWRC) is embedded in the proposed model to simulate the influences of pore size distribution and hydraulic hysteresis on shear strength. The proposed model requires only the material parameters of soils in the reference state and then can be used to predict the strength characteristics in other mechanical or hydraulic states. The shear strength data under different test conditions and the corresponding SWRC data are adopted to validate the new model. The results reveal that the wet area fraction could well predict the contribution of capillary pressure to the shear strength of unsaturated soils.

Testing numeric and logarithmic scales for hydraulic-energy indices and functions to assess physical earthworm effects on soil structure

Sufficient soil physical quality (SPQ) is needed to support the provision of multiple ecosystem services including food and fibre production, cycling of nutrients, cycling and storage of carbon, filtration of water and a habitat for biodiversity. Several SPQ indices are used to ascertain the SPQ of a soil and are often derived from the soil water retention curve (SWRC), built with multiple volumetric water content (θ) points across the pressure head (h) range, from saturation to oven-dried soil. The integral area under the SWRC has been used as an indicator for evaluating SPQ. The present study compared five hydraulic-energy SPQ indices and two cumulative hydraulic-energy functions based on the SWRC integral calculated on θ[log10(h)] basis and θ(h) in terms of their ability to track and refine soil structural information over time. For this, both approaches were applied to the SWRC data developed to examine the relationship between SPQ and earthworm abundance under dung pat and non-dung pat treatments from deposition to decay and beyond at a grassland field site in Ireland. Results showed that plant available water content (PAWC) and drainable porosity (DP) can be examined with either numeric (θ(h)) and logarithmic (θ[log10(h)]) scales and outcomes were not significantly different. Although the absolute hydraulic-energy SPQ indices of aeration and water retention presented distinct results, they both resulted in the same information with respect to soil structure status and this was consistent across numeric or logarithmic scales. However, the relative hydraulic-energy indices of aeration, water retention and air-water revealed more information with respect to water retention and drainage processes than when calculated on a logarithmic basis. Over the study duration, it was shown that more than 85% of the integral energy was used for storing water in the PAWC range and was dominant over the aeration process regardless of the treatment imposed. The results also indicate high and medium effect sizes for some parameters suggesting that a larger population or sample size could enhance the correlations and significance above those found in the present study.

Importance of volumetric shrinkage curve (VSC) for determination of soil–water retention curve (SWRC) for low plastic natural soils

The study of unsaturated behavior of soils pivots around the relationship between soil suction and the amount of water content present in the soil mass, which is termed as soil–water retention curve (SWRC). The drying SWRC is generally determined by neglecting the volume change behavior of the soil for low plastic soils. Such a procedure is bound by an inherent assumption that the soil exhibit zero or negligible volume change and can lead to approximations in important SWRC parameters, including saturated volumetric water content, residual water content, and fitting parameters, which are essential for the estimation of unsaturated hydraulic characteristics of the soil. This study deals with the determination of SWRC parameters for a range of soils starting from non-plastic to high plastic soils, with and without considering volume change characteristics. For this purpose, the SWRC of six different natural soils were experimentally measured using a miniature tensiometer (T5) and a dew point potentiometer (WP4C), while the VSC of the used soils were measured using the balloon method. Efforts were made to critically understand the influence of no volume change assumption on the SWRC parameters, unsaturated hydraulic conductivity function, and flow of water through 1D soil column. The experimental results indicated that the no volume change assumption underpredicted the water retention curves of low plastic soils, and this error in SWRC increases with the soil plasticity. The measured VSC of the used soils further revealed that there could be a range of saturated volumetric water content (VWC) depending on the initial state of the soil sample, which also influences the sensitivity of the SWRC parameters. Therefore, the present study proposed a new methodology to determine unambiguous SWRC (in terms of VWC-suction) for a particular soil type combining both the measured VSC data and SWRC data. Based on the obtained results, it was recommended that the details of VSC must be incorporated along with the measured SWRC data for soils having plasticity and clay content higher than 15 and 11%, respectively.

Fitting process-dependence performance of the van Genuchten soil water retention model to simulate the soil water flow

Determining the parameters of the soil water retention curve (SWRC) is essential to model the water flow in soil and investigate the water and nutrients transport around the roots of plants and ensure optimal management of irrigation. The unweighted least squares regression (ULS) is the most common approach applied to fit the SWRC functions to the observed data-points to optimize their parameters. The main purpose of this study was to evaluate the ULS and the weighted least squares regression (WLS) fitting process of SWRC and their impact on simulations of soil water flow. In this regard, the measured SWRC data in six parallel samples from given depth were fitted to the SWRC equations to optimize their parameters, through either the WLS or the ULS. In the WLS approach, the weights were calculated as the inverse of the variance in each pressure head of different iterations (six repetitions in this study). The results showed that although WLS generally resulted in an increased error in estimating the SWRC in the van Genuchten-Mualem model (RMSE = 0.027 and 0.043 cm3 cm−3 in the ULS and WLS methods, respectively), it improved the accuracy of the estimations at lower water contents (dry-end), as compared to the ULS. However, the efficiency of the ULS and WLS in estimating the SWRC was different from that in simulating the soil water flow. The water flow in the soil was more accurately simulated using the hydraulic parameters obtained by the WLS (with RMSE = 0.029 and 0.026 cm3 cm-3 at x = 0 and x = 20 cm, respectively, in the WLS, and RMSE = 0.062 and 0.065 cm3 cm-3 at x = 0 and x = 20 cm, respectively, in the ULS), especially at the low water range. While there was an important difference between the ULS and WLS in terms of estimating the SWRC and water flow simulations, other dispersion scenarios should be evaluated in future studies to compute the error of the fitting processes at low-pressure heads.

Comparison of Soil Physical Quality Indicators Using Direct and Indirect Data Inputs Derived from a Combination of In‐Situ and Ex‐Situ Methods

Core Ideas Soil quality indicators such as S and PAWC are considered as static indicators with low sensitivity. The soil quality indicator AWr is considered a dynamic indicator with high sensitivity. Indirect pedotransfer function based SWRCs are appropriate for static baseline indicators. Soil monitoring programs should use AWr as a sensitive and reliable soil quality indicator. The quality of a soil is its ability to deliver functions providing ecosystem services, human health and well‐being. Soil physical quality (SPQ) values use different parts of the soil water retention curve (SWRC) to calculate SPQ. For example, the plant available water capacity (PAWC) method is the difference in water content between permanent wilting point and field capacity. The S‐index uses the slope of the SWRC at its inflection point and the relative air‐water energy (AWr) is the integral of “dry” divided by the “wet” area of the SWRC. Increasing demand for soil monitoring policies calls for reliable and sensitive soil quality indicators (SQIs). The objectives of the study were to assess the sensitivity and applicability of SPQ indicators using direct and indirect data inputs. The indirect approach provided sufficient data complexity for the PAWC and S‐index values, but the more complex AWr required the direct approach. PAWC and S‐index values were identified as static SPQ indicators. The values obtained from these approaches should be used to form baseline static datasets and therefore have an indicative role only. The AWr value was identified as a dynamic SPQ indicator and provided required sensitivity to pick up temporal changes in SPQ. This indicator could be used at multiple scales and could even guide grassland management in terms of SPQ. Higher SWRC data resolution will require more complex hydraulic models to fit and will ultimately improve the accuracy of soil hydraulic data and improve the sensitivity of AWr as a SPQ indicator.

Estimation of Water Retention Curve for Soils of Bahia, Brazil, Based on Soil Particle-Size Analysis

Soil water retention curve (SWRC) is an important soil attribute because it is a soil quality indicator and is fundamental to study water dynamics in the soil-plant-atmosphere system. Since the conventional SWRC determination is laborious and time-consuming, making it difficult to process a large volume of samples, pedotransfer functions have been used to estimate it by using other soil physical attributes easily determined. Thus, this study aimed to apply Arya-Paris model to SWRC estimation for soils of Bahia state, Brazil, based on soil particle-size analysis, and to compare estimated and determined data of SWRC. Samples were collected from horizons A and AB and/or B and/or C, for a total of 15 soils and 62 horizons. Particle-size was determined by automatic soil particle-size analyzer (PSA) based on g-ray attenuation and traditional Bouyoucos’ hydrometer (BH) method. Arya-Paris model showed better SWRC predictions for sandy soils, followed by clayey, loamy, and very clayey soils. Good model performance was observed for all soils together. The α 1 scaling factor provided better predictions, followed by α 3, and α 2 showed unsatisfactory behavior. BH method, using only 7 soil particle-size fractions, gave slightly higher predictions than PSA using 30 soil particle-size fractions.

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.

Modeling the soil water retention properties of same-textured soils with different initial void ratios

This study presents a method of predicting the soil water retention curve (SWRC) of a soil using a set of measured SWRC data from a soil with the same texture but different initial void ratio. The relationships of the volumetric water contents and the matric suctions between two samples with different initial void ratios are established. An adjustment parameter (β) is introduced to express the relationships between the matric suctions of two soil samples. The parameter β is a function of the initial void ratio, matric suction or volumetric water content. The function can take different forms, resulting in different predictive models. The optimal predictive models of β are determined for coarse-grained and fine-grained soils using the Bayesian method. The optimal models of β are validated by comparing the estimated matric suction and measured data. The comparisons show that the proposed method produces more accurate SWRCs than do other models for both coarse-grained and fine-grained soils. Furthermore, the influence of the model parameters of β on the predicted matric suction and SWRC is evaluated using Latin Hypercube sampling. An uncertainty analysis shows that the reliability of the predicted SWRC decreases with decreasing water content in fine-grained soils, and the initial void ratio has no apparent influence on the reliability of the predicted SWRCs in coarse-grained and fine-grained soils.

Enhanced pedotransfer functions with support vector machines to predict water retention of calcareous soil

Knowledge of soil hydraulic properties is of major importance for land management in dry‐land areas. The most important properties are the soil–water retention curve (SWRC) and hydraulic conductivity characteristics. Direct measurement of the SWRC is time and cost prohibitive. Pedotransfer functions (PTFs) use data mining tools to predict SWRC. Modern data mining techniques enable accurate predictions and good generalization of SWRC data. In this research we explore whether the use of support vector machines (SVMs) could improve the accuracy of prediction of SWRC. The novelty of our work is in the application of SVM data mining techniques, which are seldom used in soil research, to a limited dataset from Syria. The soil studied is calcareous and the climate is arid, for which no PTFs have been developed. Seventy‐two undisturbed soil samples were taken from four different agro‐climatic zones of Syria. The soil water contents at eight matric potentials were determined and selected as output variables. The data were split into two subsets: a training set with 54 samples for model calibration or PTF development and a test set with 18 samples for PTF validation. An overview of the theoretical foundation of this new approach and the use of specific kernel functions is given. Then, the model parameters were optimized with ninefold cross‐validation and a grid search method. The predictions of the SVM‐based PTFs were analysed with the coefficient of determination (R2) and root mean square error (RMSE). Our results showed that the accuracy of SVM was better in terms of RMSE and R2 than multiple linear regression (MLR) and the artificial neural network (ANN). The results support previous findings that the SVM approach performs better than MLR and the ANN. Furthermore, improvements in predictions of SWRC with the three data mining techniques were obtained by replacing the more conventional organic matter in the PTF with the plastic limit (PL). Therefore, SVM and PL markedly improved the accuracy of prediction of SWRC for calcareous soil.Highlights Pedotransfer functions (PTFs) to predict the soil–water retention curve of calcareous soil. Improved prediction of water retention with support vector machines (SVMs) The plastic limit (PL) appeared to be a significant predictor variable. The results suggest the use of SVMs and PL to improve and develop PTFs further.

Deforestation effects on soil quality and water retention curve parameters in eastern Ardabil, Iran

The land use change from natural to managed ecosystems causes serious soil degradation. The main objective of this research was to assess deforestation effects on soil physical quality attributes and soil water retention curve (SWRC) parameters in the Fandoghlou region of Ardabil province, Iran. Totally 36 surface and subsurface soil samples were taken and soil water contents measured at 13 suctions. Alfa (α) and n parameters in van Genuchten (1980) model were estimated by fitting SWRC data by using RETC software. The slope of SWRC at inflection point (SP) was calculated by Dexter (2004) equation. The results indicated that with changing land use from forest (F) to range land (R) and cultivated land (C), and also with increasing soil depth from 0–25 to 75–100 cm in each land use, organic carbon, micropores, saturated and available water contents decreased and macropores and bulk density increased significantly (P < 0.05). The position of SWRC shape in F was higher than R and C lands at all soil depths. Changing F to R and C lands and also increasing soil depth in each land use significantly (P < 0.05) increased α and decreased n and SP. The average values of SP were obtained 0.093, 0.051 and 0.031 for F, R and C, respectively. As a result, deforestation reduced soil physical quality by affecting SWRC parameters.

Soil Water Retention Curve from Saturated Hydraulic Conductivity for Sandy Loam and Loamy Sand Textured Soils

Knowledge of the soil hydraulic properties is indispensable to solve many soil and water management problems related to agriculture, ecology, and environmental issues. Soil hydraulic properties are key factors that regulate the movement of groundwater and transport of solutes. The most important hydraulic properties of soils are the soil water retention curve (SWRC) and saturated hydraulic conductivity (ks). Determination of these hydraulic properties is needed for many studies and applications related to irrigation, drainage, water movement and solute transport in the soil. Although many advances are made for direct measurements of SWRC and ks, they are usually time consuming and costly. Some attempts have been made to indirectly predict these hydraulic properties. The objective of this study was to derive an empirical relationship to estimate soil water retention curve from saturated hydraulic conductivity. One hundred and one soil samples were collected from agricultural and forest sites at different depths, at different locations in the Pavanje river basin that lies in the southern coastal region of Karnataka, India. The saturated hydraulic conductivity was measured in the laboratory by variable falling head method using Permeameter and soil water retention curve data was obtained through pressure plate apparatus for all the agricultural and forest soils. Prediction accuracies were evaluated using coefficient of determination, root mean square error, mean error, Akaike Information Criteria, between measured and predicted values. The results obtained from the prediction of soil moisture retention curve from saturated hydraulic conductivity, shows that the developed relationship are reasonably useful to get the soil moisture retention curve for the soils of agricultural and forested hillslopes of the region under consideration.

Extrapolative capability of two models that estimating soil water retention curve between saturation and oven dryness.

Accurate estimation of soil water retention curve (SWRC) at the dry region is required to describe the relation between soil water content and matric suction from saturation to oven dryness. In this study, the extrapolative capability of two models for predicting the complete SWRC from limited ranges of soil water retention data was evaluated. When the model parameters were obtained from SWRC data in the 0–1500 kPa range, the FX model (Fredlund and Xing, 1994) estimations agreed well with measurements from saturation to oven dryness with RMSEs less than 0.01. The GG model (Groenevelt and Grant, 2004) produced larger errors at the dry region, with significantly larger RMSEs and MEs than the FX model. Further evaluations indicated that when SWRC measurements in the 0–100 kPa suction range was applied for model establishment, the FX model was capable of producing acceptable SWRCs across the entire water content range. For a higher accuracy, the FX model requires soil water retention data at least in the 0- to 300-kPa range to extend the SWRC to oven dryness. Comparing with the Khlosi et al. (2006) model, which requires measurements in the 0–500 kPa range to reproduce the complete SWRCs, the FX model has the advantage of requiring less SWRC measurements. Thus the FX modeling approach has the potential to eliminate the processes for measuring soil water retention in the dry range.

Estimating soil water retention function from its particle-size distribution

Knowledge of the soil water retention curve (SWRC) is indispensable for characterizing and modeling water flow and solute transport in soils. However, since direct measurement of the SWRC in a laboratory is expensive, time-consuming, and laborious, the SWRC has been frequently estimated from more easily measurable soil properties. Previously, we formulated an asymmetry-based pore-solid fractal (PSF) model that addresses asymmetries in fractal dimensions between the distributions of particlesize (PSD) and pore-size (POD) to better estimate the SWRC from the PSD data of a soil. Despite this effort, however, the asymptotic problems of a single power-law function when soil water contents are close to saturation still remains unresolved. To overcome such drawbacks, we addressed functional similarities between the cumulative PSD (cPSD) and the SWRC to avoid the asymptotic problems and the concept of the slope of the SWRC at its inflection point to treat a bimodal POD, and evaluated the performance of both models using experimental PSD and SWRC data from the UNSODA database (103 soils for model calibration and 46 soils for model validation). Some limitations of the performance of the models were discussed by applying the models to various soils. The fit of the cPSD and the SWRC to the calibration dataset showed that both models performed well irrespective of soil textures and the square of the Pearson product-moment correlation coefficient was 0.987 and 0.965, respectively. The values of the inflection points of the cPSD (pc) increased with increasing sand fraction, while those of the SWRC (hc) decreased, indicating that coarse-textured soils had smaller particle-size and larger suction head at the inflection points than finer-textured soils. Quadratic regression relationship between the shape-related parameters (mp for the cPSD and mh for the SWRC) showed that the magnitude of mh changed little in the lower range of mp but increased abruptly in the higher range of mp, indicating that the slope of the SWRC becomes steeper as the soil texture becomes coarser. Overall, the cPSD model fitted the measured data reasonably well, indicating that the residual fraction (Fr) that accounts for the contribution of fine-size colloidal fraction to soil water retention was adequate. Validation results with various soils showed that the performance of the SWRC model was dependent on the accuracy of the estimated inflection point. Although the cPSD model performed better than the SWRC model, the fitting results indicated that the adoption of functional similarities between the cPSD and the SWRC and the concept of point of inflection was adequate.

Evaluation of soil water retention curve with the pore–solid fractal model

Empirical models have been developed to describe the soil water retention curve (SWRC). Applications of the fractal theory may provide a useful tool to fill the gap between the use of empirical models and physical interpretation of their parameters. Especially a more generalized model for the SWRC has been developed based on the pore–solid fractal (PSF) distribution. The PSF model covers several existing models as its special cases and theoretically provides a direct way to estimate the SWRC fractal dimension from particle size distributions (PSDs). The first objective of this study was to evaluate the SWRC with the PSF distribution using more than 400 data sets of PSD and SWRC. The second objective was to establish a relationship between the fractal dimension and soil texture. Analyses of the data sets showed that the fractal dimension values estimated using the PSD data were consistently smaller than those estimated using the SWRC data. Therefore, using the fractal dimension from PSD to predict the SWRC will result in underestimation. To resolve the problem, the soil data sets of PSD and SWRC were used to establish relationships between the fractal dimensions in the PSF model of SWRC and soil clay content as well as soil texture class. Independent SWRC data sets were used to test the methods. Predicted results using the PSF model of SWRC with the fractal dimension estimated using clay content or soil texture class were compared with the measured soil water retention data. Linear regressions of the predicted and measured SWRC showed good agreement for relatively fine texture soils with the coefficients of determination ( r 2) of 0.940.