JWSS - Journal of Water and Soil Science

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To obtain soil-moisture characteristic curve experimentally is time-consuming and usually subject to considerable errors. So, many investigators have tried to predict soil-moisture characteristic curve by different models. One of these models predicts soil moisture characteristic curve based on soil particle size distribution and bulk density. In this model, soil particle size distribution curve is divided into a number of segments, each with a specific particle radius and cumulative particle mass greater than that of the radius. Using these data, soil-moisture characteristic curve was estimated. In this model, a scale factor, α, is used which may be considered as a constant, or obtained by logistic or linear procedures. The average values of α for clay, silty clay, sandy loam, two loam soils, and two silty clay loam soils were 1.159, 1.229, 1.494, 1.391, 1.393, 1.253 and 1.254, respectively. For most conditions, soil particle size distribution curve is not available, but only the percentages of clay, silt, and sand could be obtained using soil textural data, which is not enough to draw a precise soil particle size distribution curve. In this situation, a precise soil particle size distribution curve must be initially developed on the basis of which the soil moisture characteristic curve can be predicted. In this study, using soil textural data of seven different soils, soil moisture characteristic curve of each was estimated. In these estimations, logistic and linear methods were used to obtain the α value. Then, the results were compared with those of measured soil moisture characteristic curve. For estimation of soil particle size distribution curve, two extreme values for soil particle radius, 125 and 999 m, were used. The results indicated that using particle radius of 999 µm is more appropriate. On the other hand, it was found that for clay, silty clay, and sitly clay loam texture, it is more appropriate to employ a linear equation to determine for estimating soil-moisture characteristic curve while the logistic equation can be more appropriately used for loam and sand loam textures.

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Direct measurement of soil unsaturated hydraulic conductivity (K(h) or K(θ)) is difficult and time-consuming, and often in many applied models, predicting hydraulic conductivity is carried out according to measurements of soil retention curve and saturated hydraulic conductivity (Ks). However, using KS as a matching point in many procedures may result in over-estimation of unsaturated hydraulic conductivity in dry regions. Therefore, the unsaturated hydraulic conductivity at inflection point of retention curve (Ki) and Ks was used as a matching point to predict K(h). For measurement of K(h), 30 soil samples were collected based on variety of soil texture (8 texture classes from sandy to clay) and other chemical and physical properties. In addition to Ks, K(θ) values of undisturbed samples were measured using multi-step outflow method at matric suctions of 0.1, 0.2, 0.3, 0.5 0.7, 1 bar and inflection point of retention curve by using hanging water column and pressure plate. Then, the measured K(h), and water diffusivity (D(θ)) values were compared to the predicted values of van Genuchten and Brooks and Corey models (with Mualem and Burdine constraint). The results showed that for 80% of the samples, the van Genuchten–Mualem model with Ki was the best model for predicting K(h) (i.e. using Ki as a matching point in the van Genuchten–Mualem model resulted in best fitting to measured data). Also, in 6.7 % of samples (two sandy clay samples), Brooks and Corey-Mualem model with Ki and in 13.3 % soil samples (2 silty clay and 2 silty clay loam samples), van Genouchten–Mualem model had a best fitting to K(h) measured data. Furthermore, in 20 % samples (4 clay loam, and 2 silt loam textures), the accuracy and efficiency of van Genuchten–Mualem with Ki and van Genuchten–Mualem models in predicting K(h) were almost similar. According to t-Student test, the mean of RMSE and GSDER of van Genuchten–Mualem model with Ki was significantly less than van Genuchten–Mualem model at P < 0.01. In 90 percent of samples, van Genuchten-Mualem and Brooks and Corey-Burdine theory had the best fitting to the measured data of water diffusivity, but in some cases van Genuchten-Burdine model with Ki was the best model for predicting D(θ).

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Hydraulic conductivity is an important parameter in the design of geotechnical structures such as earth dam, floor construction, retaining walls and environmental structures. In unsaturated soils, hydraulic conductivity is a function of moisture content and soil water suction i.e. soil moisture characteristic curve. In this study, the values of unsaturated hydraulic conductivity in two soil types (Ramjerdi and Molasadra core dam series) at 5 different compactions using Gardner method were measured. Then, the unsaturated hydraulic conductivity was estimated by different models using the soil moisture characteristic curve and was compared with measured values. The results showed that Fredlund and Xing models predict the soil moisture characteristic curves more accurately compared with van Genuchten model. For Ramjerdi soil series and up to nearly 0.25 volumetric water content, (VGM) and (FM) models indicated a good estimation for unsaturated soil conductivity. Also, for Molasadra core dam none of the models resulted in acceptable estimations for unsaturated hydraulic conductivity.

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