Mohammad Hossein Mohammadi

Predicting Soil Moisture Characteristic Curves from Continuous Particle-Size Distribution Data

Abstract Soil moisture characteristic curve (SMC) is a fundamental soil property and its direct measurement is tedious and time consuming. Therefore, various indirect methods have been developed to predict SMC from particle-size distribution (PSD). However, the majority of these methods often yield intermittent SMC data because they involve estimating individual SMC points. The objectives of this study were 1) to develop a procedure to predict continuous SMC from a limited number of experimental PSD data points and 2) to evaluate model predictions through comparisons with measured values. In this study, an approach that allowed predicting SMC from the knowledge of PSD, parameterized by means of the closed-form van Genuchten model (VG), was used. Through using Mohammadi and Vanclooster (MV) model, the parameters obtained from fitting of VG to PSD data were applied to predict SMC curves. Since the residual water content (θ r ) could not be obtained through fitting of VG-MV integrated model to PSD data, we also examined and compared four different methods estimating θ r . Results showed that the proposed equation (MV-VG integrated model) provided an excellent fit to all the PSD data and the model could adequately predict SMC as measured in forty-two soils sampled from different regions of Iran. For all soils, the method in which θ r was obtained through parameter optimization procedure provided the best overall predictions of SMC. The two methods estimating θ r with Campbell and Shiozawa (CS) model resulted in less accuracy than the optimization procedure. Furthermore, the proposed model underestimated the moisture content in the dry range of SMC when the value of θ r was assumed to equal zero. θ r could be attributed to the incomplete desorption of water coated on soil particles and the accurate estimation of θ r was critical in prediction of SMC, especially for fine-textured soils at high suction heads. It could be concluded that the advantages of our approach were the continuity, robustness, and independency of model performance on soil type, allowing to improve predictions of SMC from PSD at the field and watershed scales.

Predicting the solute breakthrough curve from soil hydraulic properties.

Soil hydrodynamic dispersivity and therefore inert solute transport are largely determined by soil hydraulic properties. In short travel times or long distances, diffusion is not a significant process. Thus, in these cases, only the convection of fluid in different pores determines the breakthrough curve (BTC) shape. Because convection of fluid in different pores is described by pore fluid velocity distribution and consequently pore size distribution, it is possible to connect BTC to pore size distribution or pore fluid velocity distribution. The objectives of this study were to develop a model relating soil moisture characteristic (SMC) to BTC and to evaluate model predictions through comparisons with measured values. In this article, an approach that allowed predicting inert solute transport from knowledge of the SMC, parameterized by means of the closed-form van Genuchten-Mualem model, is presented. It was assumed that only part of the total soil porosity contributes to the transport process. The immobile water content and consequently the actual relative pore volume were predicted based on the SMC. A heaviside-type miscible displacement was performed, imposing a saturated unit gradient flow. The derived model was then tested on eight different disturbed soils. Results showed that the modeled and measured BTCs were in good agreement, especially during the initial stages of the breakthrough. It was further shown that only a small part of the soil pores (∼4%-13%) contributes significantly to the convective solute transport. Results indicated that when the relative pore volume is increased, the accuracy of the model decreases. Prediction errors were attributed to estimating immobile water content, the assumption of the tortuosity factor, and neglecting of diffusion, which may become significant as the travel time increases.Abbreviations: BTC: breakthrough curve, SMC: soil moisture characteristics curve, HCC: hydraulic conductivity curve, SHC: soil hydraulic characteristics, VGM: van Genuchten-Mualem model, CDE: convection dispersion equation

Indirect estimation of the Convective Lognormal Transfer function model parameters for describing solute transport in unsaturated and undisturbed soil

Solute transport in partially saturated soils is largely affected by fluid velocity distribution and pore size distribution within the solute transport domain. Hence, it is possible to describe the solute transport process in terms of the pore size distribution of the soil, and indirectly in terms of the soil hydraulic properties. In this paper, we present a conceptual approach that allows predicting the parameters of the Convective Lognormal Transfer model from knowledge of soil moisture and the Soil Moisture Characteristic (SMC), parameterized by means of the closed-form model of Kosugi (1996). It is assumed that in partially saturated conditions, the air filled pore volume act as an inert solid phase, allowing the use of the Arya et al. (1999) pragmatic approach to estimate solute travel time statistics from the saturation degree and SMC parameters. The approach is evaluated using a set of partially saturated transport experiments as presented by Mohammadi and Vanclooster (2011). Experimental results showed that the mean solute travel time, μ(t), increases proportionally with the depth (travel distance) and decreases with flow rate. The variance of solute travel time σ²(t) first decreases with flow rate up to 0.4-0.6 Ks and subsequently increases. For all tested BTCs predicted solute transport with μ(t) estimated from the conceptual model performed much better as compared to predictions with μ(t) and σ²(t) estimated from calibration of solute transport at shallow soil depths. The use of μ(t) estimated from the conceptual model therefore increases the robustness of the CLT model in predicting solute transport in heterogeneous soils at larger depths. In view of the fact that reasonable indirect estimates of the SMC can be made from basic soil properties using pedotransfer functions, the presented approach may be useful for predicting solute transport at field or watershed scales.

Some physiological responses of wheat and bean to soil salinity at low matric suctions

Abstract The effect of soil matric suction (2-33 kPa) and salinity (soil solution electrical conductivity 0.7-8 dS m−1 for bean and 2-20 dS m−1 for wheat) on some physiological characteristics of bean and wheat in a clay loam soil under greenhouse condition was investigated. The results showed that the leaf chlorophyll content index and potassium concentration decrease under salinity stress and increase with matric suction from 2 to 33 kPa suction for both plants. The wheat chlorophyll content index declines during the stress spell but bean chlorophyll content index remains nearly constant. The lowest values of the content of soluble sugars and the highest values of leaf proline content are observed at2 kPa matric suction (highest aeration stress) for bean and wheat. As matric suction increases from 2 to 6 kPa, the soluble sugars increases and proline content decreases significantly and then soluble sugars decreases and proline content increases until 10 kPa suction, and the soluble sugars remains nearly constant at the higher matric suctions for both plants. While the electrical conductivity effect on the soluble sugars is not significant, the values of proline content for both crop increase significantly with electrical conductivity. It was shown that the aeration stress can result in more considerable and rapid physiological responses, in comparison with salinity stress. There is a strong correlation between wheat and bean chlorophyll content index and potassium concentration under salinity and aeration stresses.

Characterizing Spatial Variability of Soil Textural Fractions and Fractal Parameters Derived from Particle Size Distributions

Abstract Soil particle size distribution (PSD) is a fundamental physical property affecting other soil properties. Characterizing spatial variability of soil texture is very important in environmental research. The objectives of this work were: 1) to partition PSD of soil samples into two scaling domains using a piecewise fractal model to examine and evaluate the relationship between fractal dimensions of scaling domains and soil clay, silt and sand fractions, and 2) to assess the capability of the fractal parameters as an index used in a geostatistical approach reflecting the spatial variability of soil texture. In this research, fractal features of particle size distribution of soil samples were studied using fractal geometry and then the geostatistics approach was applied to characterize the spatial variability of fractal and soil textural parameters. 75 soil samples were considered in this study. The samples were collected from a flat field located on lands of University of Guilan, in Iran. Fractal dimensions of PSD samples were calculated and it was found that there were two scaling domains, D M1 and D M2 for the PSD of soil samples, then D M1 and D M2 were used to characterize different ranges of soil particle sizes and their proportion to the soil textural parameters. Statistical correlations between D M1 and D M2 and soil textural parameters were analyzed using regression techniques. There was a positive correlation between D M1 and clay content with R 2 =0.924, a negative correlation between D M1 and silt content with R 2 =0.801 and a negative correlation between D M2 and sand content with R 2 =0.913. The relationships between the geometric mean diameter (d g ) and D M1 and D M2 were also taken into account. The d g had a negative correlation with D M1 and D M2 with R 2 = 0.569 and 0.682 respectively. Semivariograms of fractal dimensions and soil textural parameters were calculated and the maps of spatial variation of D M1 and D M2 and soil PSD parameters were provided using ordinary kriging. The results showed that in addition to the statistical correlations between D M1 and D M2 and particle size fractions, there were spatial correlations between D M1 and D M2 and particle size fractions. According to the semivariogram models and validation parameters applied to the models, it was found that the fractal parameters had powerful spatial structure and could better describe the spatial variability of soil texture.

Evaluation of models for description of wet aggregate size distribution from soils of different land uses

Abstract A proper description of aggregate size distribution (ASD) with an optimum mathematical model would be useful in modeling and monitoring land use effect. The objective of this study was to evaluate the suitability of six cumulative distribution models, namely, Jaky, normal, log-normal, Rosin-Rammler, Fredlund and a mass-based fractal model with wet aggregate size distribution (WASD) data sets from a given range of soil structural properties. The models were tested on wet sieving data of samples that had been collected from a number of different land use types (dry farmland, rangeland and forestland). Three statistical criteria, namely, coefficient of determination (R2), Mallows statistics (Cp), and Akaike’s information criterion (AIC), were used for evaluating model performance, based on the least sum of square error and number of fitting parameters. Analysis of R2 showed that the Fredlund three-parameter model showed the best performance in all of the soils apart from the number of parameters. The log-normal model gave a good fit on WASD from rangeland and forestland; it was the best especially in dry farmland. The normal model provided a good description of WASD from the rangeland and forest. However, it failed in dry farmland. According to Cp and AIC as the evaluation criteria, the fractal model was the optimum to describe WASD for all of the land uses. The Fredlund, log-normal, Jaky and Rosin-Rammler models ranked next in the given order.