Soil Water Retention Curve Model Research Articles

A novel segmental model to describe the complete soil water retention curve from saturation to oven dryness

Traditional water retention models that use the residual water content parameter fail to accurately describe the dry end of the soil water retention curve (SWRC). We developed two- and three-segment smooth, continuous SWRC models, acknowledging the existence of three saturation states, for water contents ranging from saturation to oven-dryness. The equations near the oven dryness end in both models were obtained based on the assumption of proportionality between the soil water content and the logarithm of suction. The equations in the wet range for both models were modified from the van Genuchten model and/or Brook-Corey model(s) by removing the residual water content parameter. The continuity and smoothness conditions at the junction points reduced the fitting parameters of the three- and two- segment models from 9 to 4 and from 6 to 3, respectively. Both models were tested with water retention data from the Washington soil database and the Unsaturated Soil Hydraulic Database (UNSODA) over nearly the entire range. Both models were also compared with seven existing SWRC models and were evaluated on the basis of the adjusted coefficient of determination (adj R2), the root mean square error (RMSE) and the Kashyap information criteria (KIC). The testing results showed that the three-segment model performed better than the two-segment model at fitting the SWRC at the dry end. For the three-segment model, the adj R2 value was greater than 0.90, the RMSE was less than 0.02, and the mean KIC value was −67.0, and these values were superior to those of the other models. In addition, the three-segment model was robust and stable for extrapolation of values in the dry range when data near the dry end were unavailable.

An empirical soil water retention model based on probability laws for pore‐size distribution

ABSTRACTKnowledge of the soil water retention curve (SWRC) is critical to mathematical modeling of soil water dynamics in the vadose zone. Traditional SWRC models were developed based on bundles of cylindrical capillaries (BCCs) using a residual water content, which fail to accurately describe the dry end of the curve. This study improved and expanded on the traditional BCC models. Specifically, the total water retention was treated as a weighed superposition of capillary and adsorptive components. We proposed a mathematical continuous expression for water retention from saturation to oven dryness, which also allowed for a partition of capillary and adsorptive retention. We further evaluated six capillary retention functions using different probability laws for pore‐size distribution—namely, the log‐logistic, Weibull, lognormal, two‐parameter van Genuchten (VG), three‐parameter VG (or Dagum), and Fredlund–Xing (FX) distributions. Model testing against 144 experimental data showed better agreement of the proposed model with experimental observations than the traditional approaches that use the residual water content. The Dagum and FX distributions, which have one more degree of freedom, provided better agreement with experimental data than the other four distributions. The log‐logistic and lognormal distributions fitted the experimental data better than the Weibull and VG distribution for loam soils. In addition, the fitted weighting factor w using the log‐logistic and lognormal distributions better correlated to soil clay content than the other four distributions. Our study suggests that the log‐logistic and lognormal distributions are more suitable to model soils’ pore‐size distribution than other tested distributions.

Reducing uncertainties and improving sand soil-water retention curve (SWRC) predictions for hazard screening analyses

An energy-based laboratory-testing program was undertaken to investigate the effects of different testing methods, numerical model fits, and soil fabrics and densities on the soil-water retention curve (SWRC) using a poorly graded sand. Four different reconstitution energies and three saturation levels were used to generate different soil fabrics and structure within a narrow band of possible densities, as limited by the mechanical properties of the soil particles. Tests were performed using a “transient retention imbibition method” and a Fredlund device to develop a statistically representative laboratory SWRC. Testing results for the poorly graded sand indicate little aleatory variability in SWRC from the soil structure. The dominant source of data variability is a function of the epistemic uncertainty associated with the testing methods and fitting models but can be accounted for by a bounded mean SWRC. This bounding allows for the development of a laboratory “proxy” soil, representative of generalized sand SWRC behavior, for use as a hazard screening tool for modeling unsaturated sand behavior. The proxy soil SWRC is compared with other generalized SWRC models and independent SWRC field and laboratory tests, wherein the proxy soil SWRC yields significant increases in accuracy between the estimated and field SWRC behavior.

Comparison of three models fitting the soil water retention curves in a degraded alpine meadow region

Soil water retention curve (SWRC) plays an important role in simulating soil water movement and assessing soil water holding capacity and availability. Comparison of fitness between different models to determine the best SWRC model of specific regions is required. In this study, three popular models, van Genuchten, Brooks Corey and Gardner model, were selected for comparing in a degraded alpine meadow region on the eastern Tibetan Plateau. Fitness, error distribution along with key parameters were compared. For each soil horizon, the soil moisture content at all soil water potentials decreased consistently with degradation, thereby integrally moving the SWRCs of all soil depths downward with degradation. The differences in SWRCs across various degradation degrees diminished along with soil depth and soil water potential. The Adj.r2 values of van Genuchten, Brooks Corey and Gardner models ranged in 0.971–0.995, 0.958–0.997, and 0.688–0.909, respectively. The van Genuchten and Brooks Corey models significantly (p < 0.05) outperformed the Gardner model, and have no significant differences in fitness. The fitness of all three models showed no significant changes with degradation. Regardless of degradation degree and soil depth, the fitting error of van Genuchten and Brooks Corey models was mainly distributed in the higher (from –100 hPa to –500 hPa) and lower (below –10000 hPa) potential sections. With regard to the parameters of van Genuchten and Brooks Corey models, the field capacity (θs), and permanent wilting moisture were highly coherent with Adj.r2 values of higher than 0.98, while the curve shape parameter (θr), and air entry pressure of the Brooks Corey model were much lower than those of the van Genuchten model with Adj.r2 values of lower than 0.91. The SWRCs with varying degrees of degradation are best fitted by both van Genuchten and Brooks Corey models but cannot be fitted by Gardner model. Soil water holding capacity decreased with degradation especially in the top soil (0 cm to 30 cm), but the curve shape of all SWRCs did not change significantly with degradation.

Modelling effects of root growth and decay on soil water retention and permeability

Plant roots can change the soil water retention curve (SWRC) and saturated permeability (ksat) of vegetated soils. However, there is no model that could capture both the effects of root growth and root decay on these soil hydraulic properties simultaneously. This note proposes a new void ratio function that can model the decrease and increase in soil void ratio due to root occupancy (upon growth) and root shrinkage (upon decay), respectively, in an unsaturated vegetated coarse-grained soil. The function requires two root parameters; namely, root volume ratio and root decay ratio, both of which can be readily measured through root excavation and image-based analysis. The new function is incorporated into a void ratio–dependent SWRC model for predicting the SWRC of vegetated soils. Similarly, the same function can be combined with the Kozeny–Carman equation for predicting ksat. The model prediction is then compared with a set of new field test data and an existing laboratory dataset for a silty sand vegetated with plant species under the family Schefflera. Good agreements are obtained between the measurements and predictions.

Prediction of soil water retention curve using Bayesian updating from limited measurement data

• A new updating algorithm based on the Bayesian framework predicts soil water retention curve with limited measured data. • Prediction algorithm is effective to reduce the uncertainty of parameters and soil water retention curve prediction. • Reliable soil water retention curve can be obtained using prediction algorithm even with incomplete prior knowledge. A soil water retention curve is one of the fundamental elements used to describe unsaturated soil. The accurate determination of soil water retention curve requires sufficient available information. However, the amount of measurement data is generally limited due to the restriction of time or test apparatus. As a result, it is a challenge to determine the soil water retention curve from limited measurement data. To address this problem, a Bayesian framework is proposed. In the Bayesian framework, Bayesian updating can be employed using the posterior distribution that is obtained by the Markov chain Monte Carlo sampling method with the Delayed Rejection Adaptive Metropolis algorithm. The parameters of soil water retention curve model are represented by the sample statistics of updating posterior distribution. A new updating algorithm based on Bayesian framework is proposed to predict the soil water retention curve using the ideal data and the limited measurement data of the granite residual soil and sand. The results show that the proposed prediction algorithm exhibits an excellent capability for more accurately determining the soil water retention curve with limited measured data. The uncertainty of updating parameters and the influence of the prior knowledge can be reduced. The converged results can be derived using the proposed prediction algorithm even if the prior knowledge is incomplete.

Performance evaluation of different soil water retention functions for modeling of water flow under transient condition

ABSTRACT ABSTRACT: Description and prediction of water flow through unsaturated soils is necessary to understand their hydraulic properties, including soil water retention curve (SWRC). Many models have been developed for estimation of SWRC and many researchers compared water retention curve derived from these models with the measured values. In this paper, in addition to comparing measured and derived SWRC, a functional evaluation of SWRC for modeling of soil water movement was carried out using van Genuchten, Brooks-Corey, Campbell and Hutson-Cass models in three sites including Loamy sand, Loam and Clay loam soils. Therefore, the functional behavior of SWRC was quantitatively compared by applying mentioned SWRC to numerical code (HydroGeoSphere) to simulate soil profile drainage under steady-state and transient conditions. The agreement between simulated and measured free drainages values was evaluated using statistical criteria including mean absolute error (MAE), modified index of agreement (d’), modified coefficient efficiency (E’), and t-test. The results demonstrated that the van Genuchten model was slightly better than the other models for estimation of SWRC (MAE 0.014 – 0.016,E’ 0.80 – 0.87 and d’ 0.90 – 0.93) while according to t-test, it was found that the measured and estimated SWRC using various models did not differ significantly. Therefore, it is expected that the simulated free drainage using mentioned SWRC models did not differ significantly with observed values. But the results demonstrated that the simulated free drainage using Brooks-Corey model for Loamy sand soil and van Genuchten and Brooks-Corey models for Loam soil differed significantly (p ≤ 0.05) with measured values.

Evaluation of soil water retention curve model from saturation to oven-dryness and development of pedotransfer functions for predicting model parameters

Evaluation of soil water retention curve model from saturation to oven-dryness and development of pedotransfer functions for predicting model parameters

Simple Model on Water Retention and Permeability in Soil Mixed with Lignocellulose Fibres

Natural or lignocellulose fibres have been widely used for reinforcing soils in geotechnical infrastructures by using their mechanical reinforcement. However, less attention has been taken to the hydraulic properties of soil-lignocellulose fibre composites, namely Soil Water Retention Curve (SWRC) and soil water permeability. These hydraulic properties are the key parameters when conducting transient seepage analysis in reinforced slope stability calculation. Till now, there is no model yet that can capture SWRC and water permeability of soil-lignocellulose fibre composite. This technical note aims to develop a new and simple model for predicting the SWRC and water permeability of soils mixed with lignocellulose fibres. The model considers the void ratio change by incorporating the air void from fibres. The void ratio function is then fed into a void-ratio-dependent SWRC model. SWRCs and water permeability of soils mixed with two lignocellulose fibres (jute and coir) were measured systematically to provide high quality data to validate the proposed model. There were three replicates for each case. It shows that the presence of pore structures in natural fibres reduced air entry value of soils from 8 kPa to 2–3 kPa, while it had no effects on desorption rates. Moreover, those pores in lignocellulose fibres increased the water flow path, resulting in increased water permeability. As demonstrated by dye tracer experiments, the increased water flow was along the cellulose, hemicellulose fibrils inside the fibre and soil-fibre interface. The comparisons between experimental measurements and model predictions indicate that the proposed simple model can capture the effects of natural fibres on soil hydraulic properties quite well, with the maximum discrepancy less than 15% and 28% for SWRC and water permeability, respectively.

Parameter Estimation for Soil Water Retention Curve Using the Salp Swarm Algorithm

This paper employs an optimization algorithm called the salp swarm algorithm (SSA) for the parameter estimation of the soil water retention curve model. The SSA simulates the behavior of searching for food of the salp swarm and manages to find the optimal solutions for optimization problems. In this paper, parameter estimation of the van Genuchten model based on nine soil samples, covering eight soil textures, is conducted. The optimization problem that minimizes the difference between the measured and the estimated water content is formulated, and the SSA is applied to solve this problem. To validate the competitive advantage of the SSA, the experimental results are compared with Particle Swarm Optimization algorithm, the Differential Evolution algorithm and the RETC program, which indicates that SSA performs better than the three methods.

Parameter estimation of the soil water retention curve model with Jaya algorithm

Parameter estimation of the soil water retention curve model using Jaya algorithm is studied in this paper. Different from conventional heuristic search algorithms, Jaya algorithm is a swarm intelligence algorithm without algorithm-specific parameters. The effectiveness of Jaya algorithm is validated via estimating parameters of the Van Genuchten model based on collected experimental data of six soil samples. An optimization problem is formulated to minimize the difference between the measured and modeled water content while Jaya algorithm is employed to solve this optimization problem. To assess the estimation performance, Jaya algorithm is benchmarked with the Differential Evolution, RETC and Particle Swarm Optimization algorithms and computational results prove the superiority of the proposed method over state-of-the-art methods.

Estimation of soil moisture using confined compression curve parameters

The confined compression curve (CCC) represents the relationship between the logarithm of the applied stress and the void ratio. There are several similarities between the soil water retention curve (SWRC) and the CCC. The aim of this study was to modify the Dexter SWRC model to fit the experimental CCC data by replacing the matric suction and water content in the Dexter model with the normal stress and void ratio, respectively, as well as using the CCC parameters and characteristics (pre-compression stress, compression, and swelling indices) as predictors to estimate the SWRC. We collected 150 soil samples from five provinces of Iran. The SWRC, CCC, and basic properties of the soil samples (clay, silt/sand, and bulk density) were measured. The Dexter model was applied to the experimental data for both the CCC and SWRC, and their parameters were calculated. The CCC parameters and basic soil properties were used to estimate the soil moisture at five input levels with the Dexter model. The best results were obtained using the basic properties of soil as predictors, as well as with the parameters of the Dexter model obtained by its fitting to the CCC data. The integral root mean squared error was reduced from 0.059 and 0.061 (in the first step) to 0.053 and 0.056gg−1 in the training and testing steps, respectively. The relative improvements in the SWRC estimates showed that improvements of 4.9% to 11.9% were obtained by using the CCC parameters as predictors. These improved estimates can be attributed to the apparent similarities between the two curves as well as the impacts of similar factors on these curves and the correlation between them.

An Approach to Estimate Wetting Path of Soil–Water Retention Curve from Drying Path

The Soil–Water Retention Curve (SWRC) is an important part of any constitutive relationship for unsaturated soils. The SWRC is a hysteretic curve, and geotechnical problems such as unsaturated slope stability analysis can lead to different results based on each branches. Although the majority of SWRC models can just predict the initial drying curve, the wetting path in many practical projects is critical and in fact occurs. In this research, an approach is presented to estimate the wetting path of genetic-based model as a sample of accurate models using initial drying. For this purpose, based on Feng and Fredlund (in: Proceedings of the 52nd Canadian geotechnical conference, Regina, Sask, 1999) hysteresis model of SWRC and using more than 180 soil samples, which are results from pressure plate tests carried out on clay, silty clay, sandy loam and loam, statistical values are suggested for the gravimetric water content difference of main drying and wetting curves at various suctions. The results show acceptable accuracy of the method.

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.

The use of soil water retention curve models in analyzing slope stability in differently structured soils

This study analyzes whether and at what rate the parameterization of the Soil Water Retention Curve (SWRC) affects the analysis of shallow slope stability for differently structured unsaturated soils. Advanced empirical or physically-based equations of SWRCs have been proposed in literature to describe soil systems characterized by the so-called bimodal porous domain. In unsaturated soils, SWRC affects the stability assessment in two ways. It influences the resistance properties in terms of shear strengths, which depend on the soil water suction; and it affects the hydrological process modeling (e.g. infiltration) directly influencing soil moisture patterns and indirectly influencing slope stability. Most of the formulations proposed to predict the shear strength of unsaturated soils require the definition of an χ parameter that tunes the contribution of the suction effect to a rate proportional to the saturation conditions.In this study, a set of experiments was carried out in order to analyze both the mechanical and hydrological effects of SWRC on slope stability. First, three SWRC models were calibrated on different soil textures. Then, slope stability analyses were carried out on a synthetic hillslope supposed to be characterized alternatively and homogenously by the different soils. The factor of safety (FS) of the slope was computed first, at given states of hydrological conditions (i.e., fixed soil moisture), and then at dynamic hydrological conditions simulated by solving the 1D Richards's equation. Two different formulations of the χ parameter were also used. Finally, a sensitivity analysis of the SWRC models and the χ formulations for slope stability were evaluated for different slope angles and mechanical properties.The results indicated that for clayey (and bimodal) soils, changes in FS obtained with different SWRC models can be significant, especially at soil moisture values close to the residual zone. In sandy (and unimodal) soils, the choice of χ formulations can be more important. The variation of FS decreases as the slope angle increases or the friction angle decreases.

Intrinsic Relationship between Matric Potential and Cation Hydration

Core Ideas Exchangeable cations are important in water sorption, hydraulic conductivity, swelling, flocculation–deflocculation, and fertility. Intrinsic relationship exists between matric potential and cation hydration at very low water contents. Proposed model predicts soil water content at low matric potentials for different exchangeable cations in soil. Model is applicable for soil water retention models and quantification of different types of exchangeable cations. The origin of matric potential in the tightly adsorbed soil water retention (SWR) regime, where matric potentials are generally less than −150 MPa, remains unsettled. Surface tension in soil pores and van der Waals attraction (VDW) near particle surfaces are known, respectively, as the sources of matric potential for capillary and adsorbed film water. However, theories based on VDW fail to predict the SWR for matric potentials less than −150 MPa. Recent studies show that cation hydration occurs at matric potentials much lower than those predicted by considering a particle surface hydration originating from the VDW forces. With this basis, a theoretical soil water retention curve (SWRC) model for drying path is proposed to quantify matric potential and its retained water by accounting for all water sorption mechanisms on or within soil particles, particularly the cation–water molecule or charge–dipole interaction. It is found that clays at very low soil water content, typically less than a few percentage gravimetric, charge–dipole interaction energy, dominate matric potential. Experimental SWR data for various cation‐exchanged soils confirms the validity of the theoretical SWRC model and its accuracy, indicating that cation hydration is mainly responsible for SWR for matric potentials less than −150 MPa. Furthermore, the predicted lowest matric potentials at zero water content for various soils agree well with experimental data. The lowest matric potential of a soil depends mainly on the type of exchangeable cations and thus is not a universal constant for all soils. These results have potential applications in using SWRC for the determination of cation exchange capacity and types of exchangeable cations in soils.

Generalized Soil Water Retention Equation for Adsorption and Capillarity

AbstractA new soil water retention curve (SWRC) equation, generalized from previous models and the latest understanding of soil-water interaction, explicitly accounts for capillary and adsorptive processes. Under the assumption of local thermodynamic energy equilibrium, soil water is one of two types (capillary and adsorptive), and occurs in three retention regimes in the order from high to low matric potential: Capillary, adsorbed film, and tightly adsorbed soil water. A new equation for adsorptive water as a function of matric potential is introduced. The transition between adsorption and capillary regimes is smoothly described with a cavitation probability function imposed on a commonly used SWRC model. Three parameters (adsorption capacity, adsorption strength, and mean cavitation suction) are defined, replacing the commonly used parameters of residual moisture content, pore structure, and residual suction. The soil water retention data for 21 soils, representing a variety of soil compositions, are us...

PEDOTRANSFER FUNCTIONS CAPABILITY TO SIMULATE BEHAVIOUR OF SMECTITIC SOILS IN ESTIMATION OF VARIOUS SOIL WATER RETENTION CURVE MODELS

For modelling the flow transport in unsaturated conditions, we can use hydraulic properties which are expensive and time-consuming to be obtained directly because of high variability and complexity of soil systems. Few studies have been done about pedotransfer functions (PTFs) in smectitic soils. Moreover, the utility of fractal parameters in the prediction of soil water retention curve (SWRC) have not been investigated in these soils. In this study, PTFs have been made for estimating the parameters of van Genuchten (VG) and Dexter models by regression and artificial neural networks methods. Therefore, 69 soil samples were collected from Guilan Province, Iran. Fractal and non-fractal models were fitted to the particle size distribution (PSD) and micro-aggregate size distribution (ASD) and their parameters were calculated. To create PTFs, the parameters of PSD and ASD models were used as estimators. The comparison of the results of the two models of Dexter and VG shows the priority of Dexter model for the purpose of testing of smectitic soils. The results showed the superiority of Fredlund et al. PSD model parameters and fractal parameters of ASD, in the estimation of Dexter and VG SWRC models, respectively. This outcome may be related to the higher accuracy of Fredlund et al. PSD model in the description of the PSD data in the clayey soils. However, the higher number of parameters in comparison to the number of fractal model parameters may be another reason.

A new and simple water retention model for root-permeated soils

Vegetation can affect soil hydrology not only through evapotranspiration but also through changes in soil water retention curve (SWRC). Models that are able to predict root-induced change in SWRC are limited. Such models often contain many empirical parameters that are not easy to be obtained and calibrated. This letter proposes a new and simple model with only one root parameter, namely root volume ratio Rv, needed for predicting the SWRC of a root-permeated soil. The new model considers void ratio change through volume reduction of air void of soil due to the presence of roots. The modified void ratio of a root-permeated soil is then fed into a void-ratio-dependent SWRC model to predict any resultant change in SWRC. The performance of this new model is validated against three case studies. Good agreement between measurements and predictions is obtained, with discrepancies of degree of saturation &lt;13% for a given suction.

Effects of various input levels and different soil water retention curve models on water content estimation using different statistical methods

This study investigated the impact of different input variables on the predictability of the water content using soil water retention curve (SWRC) models. The particle and aggregate size distribution model parameters were calculated by fitting the Perrier model to the related distributions for 75 soil samples. Nine SWRC models were fitted to the experimental data and their coefficients were obtained. The regression method was used to estimate the coefficients for nine SWRC models at three input levels. Cluster analysis classified the SWRC models into more homogeneous groups according to the accuracy of their predictions. The SWRC estimated using the Gardner model had the highest accuracy, but it was not an appropriate model for the soils because of its low fitting accuracy. Boltzman, Campbell, and Fermi models obtained the highest accuracy after the Gardner model. The Durner model yielded the lowest prediction accuracy due to the lack of correlation between the input variables and coefficients in this model. Thus, the water content predictions obtained using different SWRC models varied because different input variables were employed.