In order to predict the behavior of soil-related phenomena, it is necessary to have knowledge about unsaturated flow and using models that provide optimal estimates of the retention curve and hydraulic conductivity of soils. Despite the widespread use of the classic van Genuchten-Mualem model (VGM), this model usually performs poorly in predicting hydraulic conductivity and modification of some of its parameters seems necessary. In this research, 283 soils from different textures of the UNSODA bank were selected and divided into two sections of calibration and validation and their soil parameters were exported and categorized. Then, by defining the modified unsaturated hydraulic conductivity (Ksc) instead of the saturated hydraulic conductivity (Ks) and determining the limits for l and n parameters, the hydraulic conductivity-moisture function of VGM were solved using 24600 pairs of points li and nj for each soil of the three main soil texture classes. In the following, the optimal l value (l̂) of each texture class was selected based on the minimum value of the hydraulic conductivity estimation error using the root mean square error (RMSE) index and the n values that had created the minimum errors, were selected as the optimal pore size distribution coefficients of the hydraulic conductivity-moisture function (n̂opt). In order to create pedotransfer functions for estimating n̂opt, we ran stepwise regression in MATLAB software considering the condition of statistical significance (P-value=0.05) for independent variables and functions for each soil texture class. After creating pedotransfer functions, the results of the proposed method of this research (MVGM) were compared with the VGM results using RMSE and Nash-Sutcliffe (NSE) indices. The results showed that in both sections of creation and validation functions, the MVGM performed better in estimating hydraulic conductivity and had a higher efficiency index in all textural classes of soil.

Quantification of soil water retention curve (WRC) is essential to study soil water movement in unsaturated soils. Direct measurement of soil moisture and matrix potential is too costly and time consuming. Moreover, due to the phenomenon of hysteresis, moisture in the drying branch cannot be used on wetting branch. Therefore, using the indirect method to find the relationship between the two branches of the WRC is needed. Development and use of the pedotransfer function (PTF) is one of the indirect methods. The purpose of this study was to provide a function for the parameters of van Genuchten model estimation in wetting branch using drying branch data. Required information in this research was collected from soil data base (Unsuda). These data include moisture retention curve data of drying and wetting and soil bulk density of 21 soil samples classified in sandy (10 samples), clay loam and loam (7samples), and silty clay loam (4samples) texture. To provide the functions, all samples were used. Results showed that nw in van Genuchten model was estimated with good accuracy, while the introduced function for the parameter aw had lower performance. The model obtained for estimation of the parameters of van Genuchten model in the wetting branch of WRC had a higher accuracy in low potentials. Results show that PTF are more powerful than Parlange hysteresis model for estimation of WRC in the wetting branch.

The modeling of water movement through soils typically requires the functional representation of soil hydraulic properties, such as the water retention curve. Among different water retention models, van Genuchten (VG) model is an appropriate one. The objectives of this study were to quantify relationships between the VG model parameters and soil physical properties and to select equations that are useful for prediction of VG model for soil water retention curve. Fifty four soil samples were used for soil water characteristics determination by ceramic plate extractor, soil particle size determination by hydrometer method, soil bulk density and soil organic matter. Multiple regressions were used to determine the relationships between m and hp (soil water suction at reflection point of the soil water retention curve) and the aforementioned soil physical properties. The results indicated that multiple regression models may be used to estimate, with acceptable accuracy, the water retention curve of VG type from soil sand content, bulk density and Organic matter percent. The proposed regression models were used for prediction of soil water retention curve for three different soils with an acceptable accuracy.

Proper soil hydraulic functions that accurately predict soil water characteristic curve and hydraulic conductivity are essential for studies of unsaturated flow in soils. In this study, 8 soils from different texture classes of UNSODA soil bank were selected and the optimal validity of shape factor (n) of the RETC software (using retention curve function of van Genuchten-Mualem (VGM) model) was investigated for predicting the value of unsaturated hydraulic conductivity at different moisture contents. Since the predicted results of this model were poor, we attempted to investigate a separate shape factor such as 􀝊 􀷜 for the hydraulic conductivity-moisture (K-θ ) function of VGM Model. To this end, 24 soil samples from different texture classes of UNSODA were selected and their measured parameters were analyzed by regression analysis to find a suitable pedotransfer function for estimating 􀝊 􀷜 . The developed function confirmed the relationship of 􀝊 􀷜 with both saturated moisture (θ s) and organic matter contents with a correlation coefficient of r = 0. 745 at significant level of P = 0. 0005. Also, to validate the developed pedotransfer function, the hydraulic conductivity values corresponding to the measured moistures for the 8 selected soils in the validation section were calculated based on the two shape factors obtained from the pedotransfer function (􀝊 􀷜 ) and the RETC (n) and the results were compared with the measured values. The statistical indices of root mean square error of model (RMSEM) and Nash-Sutcliff model efficiency coefficient (NSE) showed that the shape factor of the developed pedotransfer function compared to the RETC, had a better performance in predicting unsaturated hydraulic conductivity values.

Availability of accurate information regarding permeability of soil is needed in planning for the development projects, especially hydraulic structures. Laboratory determination of unsaturated permeability of soil involves high costs and is very time taking. Therefore, it is preferred to determine it using such indirect methods as making use of soil-water characteristic curve. These indirect methods are of constant parameters the determination of which needs such special information of soil water characteristic curve as residual and saturated water contents. Through initially examining a statistical model (not a function of residual and saturated water contents) the permeability coefficients of unsaturated soil that were in acceptable correlation with famous models were determined. The constant parameters of some of the equations for estimation of the permeability of unsaturated soil were then estimated. The results finally show a high accuracy of the proposed model for a determination of the constant parameters.

Heating affects soil physical and chemical properties and sometimes causes soil water repellency. In order to investigate heating effect on water retention curve, an organic soil was heated in a muffle furnace preheated at 100 (T100), 200 (T200), 300 (T300), 400 (T400) and 500oC (T500). Hence, the soil was exposed to five heating treatments in three replications, in a completely randomized design. Water repellency was determined by using water drop penetration time method and aqueous ethanol method. Water retention curve of the treatments was determined by means of sand box and pressure plate. The van Genuchten equation's parameters were obtained by minimizing mean square procedure and using RETC software. Then, heating effects were determined using SAS8 and LSD model. Some properties of water retention curve in heated and unheated soils were measured. The results showed that the original soil had low water repellency, while, at 300oC, strong water repellency appeared, but disappeared by increasing the temperature. Van Genuchten equation's parameters changed by increasing temperature and water repellency. The hydrophobic soils had low saturation water content compared to the other treatments with similar texture. T500 had the least saturation water content, probably because higher temperature decreased organic matter and increased sand and ash content. The value of a in hydrophobic soil was lower than other treatments with the same texture, but the “n” parameter in hydrophobic soils was higher than that in T100 and T200 treatments. Since this parameter is lower in heavy soils than light soils, it was low in T400 and T500 with sandy loam texture compared with the other treatments with loam texture. Water content at field capacity and wilting point diminished by increasing temperature.

Soft computing techniques have been extensively studied and applied in the last three decades for scientific research and engineering computing. The purpose of this study was to investigate the abilities of multilayer perceptron neural network (MLP) and neuro-fuzzy (NF) techniques to estimate the soil-water retention curve (SWRC) from Khozestan sugarcane Agro-Industries data. Sensitivity analysis was used for determining the model inputs and appropriate data subset. Also, in this paper, the van Genuchten and Fredlund and xing models were used to predict SWRC. Measured soil variables included particle size distribution, organic matter, bulk density, calcium carbonate, sodium adsorption ratio, electrical conductivity, acidity, mean weight diameter, plastic and liquid limit, resistance of soil penetration, water saturation percentage and water content for matric potentials -33, -100, -500 and -1500 kPa. The results of this study in terms of various statistical indices indicated that both MLP and NF provide good predictions but the neural network provides better predictions than neuro-fuzzy model. For example, using MLP and NF models values of NMSE at prediction θs, θr, α, n and m in Fredlund and Xing equation corresponded to (0.059, 0.065), (0.154, 0.162), (0.109, 0.117), (0.129, 0.135) and (0.129, 0.145), respectively. Furthermore, α and n parameters at the first depth, and ϴr and α parameters at the second depth in Fredlund and Xing equation were estimated with higher accuracy compared with equivalent parameters in van Genuchten equation.

Accurate estimation of root water uptake is essential for irrigation management and yield prediction. In this study, impact of deficit irrigation on water uptake amount was studied in two macro models (Feddes; Van Genuchten) and one micro model (Van Lier) using greenhouse data of chickpea. Irrigation treatments included five irrigation levels of 120, 100, 80, 60, and 40 percent of water requirement. To estimate potential water uptake, 120% treatment was used. Also 100% and 80% treatments were used for calibration of the models and 60% and 40% treatments for validation of the models. Hydraulic parameters of soil and root water uptake for each of the models were estimated utilizing GLUE method. The results of calibration stage indicated Van Genuchten model is able to simulate root water uptake well, as the parameters of R2, NS, NRMSE, MAE, ME, and dare 0. 935, 0. 91, 5. 51, 0. 42, 0. 92, 0. 95, respectively. The values in validation stage are 0. 872, 0. 82, 11. 73, 0. 6, 0. 82, 0. 9, respectively. The findings also showed the micro model presented by Van Lier has the weakest function due to very simplifying assumptions (considering fixed root diameter and disregarding root tolerance).

Unsaturated flow in porous media is described by Richards' equation. In order to use this equation, hydraulic conductivity function has to be specified. Brooks - Corey, van Genuchten and van Genuchten- Mualem' s methods have been used for the determination of this function for a Sandy Loam soil in Khuzestan. Undisturbed samples from a top soil were collected in metal cylinders for determination of soil moisture characteristics on sandbox tensiometer and in pressure plate apparatus. RETC program has been used for the derivation of the Van Genuchten's parameters. Results were compared with those obtained from methods suggested by Saxton et al. (1986), Gregson et al. (1987), and Hutson and Cass (1987). Hutson and Cass's model gave comparatively more accurate results. Saxton's model using only soil texture data also produced acceptable regression at high pressure head values.