Soil hydraulic properties have important effects on describing water flow, solute and gass transports and also are important in hydrological studies. Although spectral information over visible near-infrared and shortwave infrared range, as a rapid, cost-effective and non-destructive method, has been recently applied to predict a number of soil properties, only few attempts have been conducted to predict soil hydraulic properties. The objective of this study was to assess whether inclusion of soil spectral data as a uniqe set of the predictors and alternative to basic soil properties would improve water retention predictions. Consequently, a number of 174 soil samples were taken and the spectral reflectances of the soils over 350 to 2500 nm range were measured, using a handheld spectroradiometer apparatus. The water retention at six matric potentials of -330, -1000, -3000, -5000, -10000 and -15000 cm were also measured by using preassure plate apparatus. Four scenarios including spectrotransfer functions (STFs), pedotransfer functions (PTFs), spectropedotransfer functions (SPTFs) and Rosetta PTFs were investigated. The transfer functions were first derived and compared with each other as well as with Rosetta PTFs afterwards. Based on the obtained results, basic soil properties and water retention parameters indicated high and significant (1% significancancy level) correlations with spectral reflectance values particularly in near and shortwave infrared ranges. The STFs indicated higher accuracy (R2>0.60; RMSR<0.011 cm3 cm-3) than the others especially at mid and dry end of retention curve. Although SPTFs and PTFs provided similar predictions, but PTFs were estimated narrowly better predictions at wet-end part of retention curve (-330 and -1000 cm). Weak predictions were obtained by Rosetta PTFs for all water contents particularly at the wet part of retention curve. These results suggest the efficacy of the spectral data, which can be used as an indirect method to predict soil water retention status.

Quantitative description of infiltration process is crucial for many applications in hydrologic cycle. The direct measurement of infiltration is time consuming, expensive and often impractical because of the large spatial and temporal variability. Any indirect parametric estimation of this process would be quite useful. Although, the so-called pedotransfer functions (PTFs) are widely used as an indirect method to predict the soil hydraulic properties, no attention has made to indirect estimation of infiltration. The objective of this study was to develop and verify some parametric PTFs to predict the infiltration process under three different land uses; namely pasture, wheat and fallow. For this purpose, 123 double ring infiltration data were collected. The parameters of four infiltration models were then obtained, using sum of least squares error method. Basic soil properties of the two upper pedogenic layers such as initial water content, bulk density, particle-size distributions, organic carbon and gravel contents, CaC03 percent, field moisture capacity and penpanent wilting point water contents were measured for each sampling location. The parametric PTFs were then developed to predict the parameters of the infiltration models, using the step wise regression method. The accuracy of the derived PTFs was evaluated using MAMD, MRMSD, SDMRMSD and MPearson statistics. The results indicated that the PTFs derived for the land under fallow have the best performance on cumulative infiltration prediction. Under pasture, wheat and fallow land uses the derived PTFs for Philip, Horton and Kostiakov-Lewis models were the best predictor of infiltration, respectively.

Field capacity (FC) and permanent wilting point (PWP) are efficacious in determining net irrigation water depth. However, direct measurement of these properties is tedious, time consuming and costly especially on large scale. Soil pedotransfer functions (PTFs) as the indirect methods can replace by the direct methods. In this study, performance of the six available pedotransfer functions on FC and PWP moisture content predicting was evaluated on 112 soil samples that were collected from the north and northeast regions of Iran. The Root Mean Square Error (RMSE) values of menioned available PTFs were changed between 0.05 to 0.17 and 0.03 to 0.13 in moisture prediction on FC and PWP points, respectively. Therefore new PTFs were developed by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) techniques based on soil properties (90 samples) and the results were validated on different soils (22 samples). The results showed that both MLR technique with assigning the RMSE values approximately 0.035, 0.01, 0.027 and 0.024 to predict soil moisture content on FC and PWP, total available water and specific yield and ANN technique with assigning the values approximately 0.013, 0.007, 0.015 and 0.013 to the same properties, evaluated in appropriate performance. The results also showed that using variables such as geometric mean and geometric standard deviation particle diameter, fractal dimension and air-entry suction, for the first one on input variables of PTFs, improved the accuracy of the results significantly, although accepting of this theory requires more studies.

Parametric description of the soil water retention curve is crucial for many applications and mode ling water movement and solute transport in the unsaturated zone. Due to high spatial and temporal variability of this hydraulic characteristic, its direct measurement requires a large numbers of samples. The direct measurements, in the other hand, is rather time consuming and costly. Thus, the indirect methods such as pedotransfer functions (PTFs), as an alternative, are increasingly employed for practical and modeling purposes. The objective of this study was to derive and verify some pedotransfer functions based on the geometric soil particle size variables to estimate the van Genuchten parameters. Consequently, the geometric mean and geometric standard deviation of particle diameters as input parameters were used to describe the pore-size distribution in PTFs. Therefore, 34 soil samples were randomly collected from Karaj area. The particle-size distributions and in situ bulk densities were determined with the hydrometery and core methods, respectively. The soil water retention curves for the entire range of interest were obtained using the pressure plate apparatus. The so-called easily obtainable variables were separated into two groups: (i) particle size distribution and bulk density, (ii) bulk density, geometric mean diameter and the geometric standard deviation of soil particle diameters. The parametric PTFs of these two group variables were developed, using the stepwise regression method. The derived pedotransfer functions were also verified and compared with some other collected data. The results indicated that the second group of variables can better predict the van Genuchten parameters. The coefficient of determination of 94 and 69 percents were obtained for the saturation and a, respectively.

In our country, regardless of a limited area, soil organic carbon is very low in most areas of production such as agriculture and rangeland. This fact suggests that slight change in the amount of organic carbon can have high impact on soil properties and thus its quality in arid and semi-arid conditions. Determination of soil organic carbon (SOC) is of great importance because of its role in physical, chemical and biological properties. This research was aimed to estimate soil organic carbon using pedotransfer functions and the independent variables of soil physical and chemical properties in Damavand Rangelands. For this purpose, 60 soil samples were taken systematically at a soil depth of 0-30 cm in Damavand rangelands and soil organic carbon, pH, lime, nitrogen, sand, silt and clay were determined. Results showed that the average percentage of organic carbon was 0.49 percent and the minimum and maximum were 0.1% and 0.92%, respectively with a normal distribution. According to the obtained results, soil organic carbon was significantly positively correlated with clay and then with the nitrogen. One of the methods used to estimate soil organic carbon is a multivariate linear regression model where the dependent variables (clay, nitrogen, sand, silt, soil pH, bulk density and the percentage of gravel) are used, having a high correlation coefficient with organic carbon.

Soil water retention curve is needed to describe the availability of soil water to plant, and to study modeling of water and solute transport in unsaturated soils. The direct measurement of this curve is time-consuming, difficult and costly. Recently, many attempts have been made to predict water retention curve indirectly from soil physical and chemical properties. pedotransfer functions (PTFs) comprise the indirect methods. Predicting water retention curve and Van Genuchten equation parameters by using pedotransfer functions were the main research objectives. In current study fourty loamy soil samples (35 for prediction and 5 for validation) were randomly collected form Karaj area. Particle size distribution, bulk density, calcium carbonate equivalent and organic carbon as independent variables, were determined by the hydrometer, clod, acid neutralization and Walkly Black methods, respectively. Water retention curve for the soils were obtained, experimentally using pressure plates. The variables were separated in two groups: (1) particle size distribution, bulk density, organic carbon and calcium carbonate percentage. (2) geometric mean and geometric standard deviation of particle size, bulk density, organic carbon and calcium carbonate percentage. The most optimum combination of independent variables for estimating soil water retention curve and Van Gemichten equation parameters were selected by the regression method. The regression equations for two independent variable groups were obtained using multiple linear regression and the PTFs were compared. The results indicate that there is a significant relationship between measured and predicted values. The relationship was significant at 0.1% probability level for pointed PTFs, at 1% for n, and 5% for er of Van Genuchten predicting function. Using first group of variables for estimating soil water retention curve and Van genuchten equations parameters was better than the second group. Statistical analysis for the evaluation of PTFs indicated that GMER (Geometric mean error ratio) values were close to 1 and GSDER (Geometric standard deviation of the error ratio) values were small. The results indicate that presented functions were valid.  

In order to prevent land degradation and soil and water pollution, realizing the respective processes and quantifying their relationships is unavoidable. Infiltration process is one of the most important components of the hydrological cycle. On the other hand, the direct measurement of infiltration process is laborious, time consuming and expensive. In this study, the possibility of predicting cumulative infiltration in specific time intervals, using readily available soil data and pedotransfer functions (PTFs) was investigated. For this purpose, 210 double ring infiltration data were collected from different regions of Iran. Soil texture ranged from loam to clay. Basic soil properties of the two upper pedogenic horizons including initial water content, bulk density, particle-size distributions, organic carbon, gravel content, CaCO3 percent and soil water contents at field capacity and permanent wilting point were determined on each soil sample. The parametric PTFs were then developed to predict the cumulative infiltration at times 5, 10, 15, 20, 30, 45, 60, 90, 120, 150, 180, 210, 240, 270 minutes after the start of the infiltration test and the time of basic infiltration rate, using the stepwise regression method. The results of reliability test indicated that all derived PTFs underestimated the cumulative infiltration. Also, the obtained RMSEs at small times were lower than those obtained at the ending times of the infiltration process. EF statistic had positive values and increased with time increasing. The EF values indicated that the efficiency of the derived PTFs improved during the time increasing. Also, developed PTFs had a mean RMSD of 6.90 cm in estimating the cumulative infiltration curve. Results indicated that at the 1% probability level, the estimated cumulative infiltration curve can be accepted as one of the replicates of a reliable infiltration test.

Background and Objectives: Soil bulk density (BD) is important because of its direct effect on soil properties such as porosity, soil moisture availability, and hydraulic conductivity and its indirect effects on root growth and crop yield. Environmental processes and agronomic practices induce soil bulk density to vary greatly in both space and time. On the other hand, measuring it on a large scale requires a lot of time and is not economical. As a result, indirect methods are used to measure the bulk density when performing large-scale field activities. pedotransfer functions (PTFs) have been broadly implemented as indirect cost-effective and time-saving methods in predicting soil bulk density. The purpose of this study is to evaluate the existing pedotransfer functions in order to determine the bulk density for different soils of Sistan region as well as calibration and provide new pedotransfer functions for the study area. Materials and Methods: After reviewing different reference, 64 different pedotransfer functions (PTFs) published in different sources were selected to estimate the bulk density. These pedotransfer functions were selected in such a way that 1)in a wide range of time scale (from 1957 up to date), 2) from wide regional, 3) from various soil land uses 4) from all types of regression techniques and 5) only using common and easily measured predictors such as sand, silt, clay and organic carbon. The soil samples collected in this study was 220 data, which was obtained from 110 points at two depths of 0-15 and 15-30. Three indicators of absolute mean error (ME), root mean square error (RMSE) and standard deviation of the predicted error (SDPE) were used to evaluate. Results: Among the existing pedotransfer functions, Benites et al. (2007) with ME value equal to-0. 0008, RMSE value equal to 0. 1038 and SDPE equal to 0. 1033 had the best results. Based on the RMSE value of Yang et al. (2007) with a value of 0. 1038 with a rank of 1 and based on SDPE function with a value between 0. 0976 Leonaviciute (2000) had the best results. For the study area, 5 presented relationships including linear relationship between BD and OC, linear relationship between OC and BD squares, exponential relationship between BD and OC, linear relationship between BD and OC logarithm and polynomial relationship between OC and BD were presented. Conclusion: Based on the results it can be concluded that soil organic carbon (OC) is the most important factor in predicting soil bulk density and using soil organic carbon alone, soil bulk density can be predicted with relative accuracy. It can also be concluded that the 5 relationships developed in this study can be used to obtain the apparent density in the study area.

The parametric description of the soil water retention curve and hydraulic conductivity are crucial to study water movement and solute transport in the unsaturated soil. Despite the progresses made in the direct measurement of hydraulic properties, the majority of these techniques remained time consuming and costly. Further, due to inherent temporal and spatial variability of these hydraulic characteristics in the field conditions, large number of samples is required to properly characterize area of land. An alternative is then the use of so-called pedotransfer functions (PTFs) that estimate missing soil characteristics form easily obtainable soil properties. In arid and semi-arid areas, gypsum is a major solid component of the soil. The objective of this study was to derive some pedotransfer functions to predict the van Genuchten and Mualem-van Genuchten parameters of some gypsiferous soils. Consequently, 35 soil samples with gypsum content ranging from 3.8 to 32.7 percent were collected. The gypsum content was measured by Acetone method, the bulk density by volumetric method, the particle size distribution by covering the particles with Barium Sulfate and the soil water retention curve was obtained using pressure plates apparatus. The easily obtainable variables were in to two groups (i) particle size distribution, bulk density and gypsum content, (ii) bulk density, gypsum content, geometric mean and geometric standard deviation of particle diameter. The stepwise linear regression method was used to derive the PTFs. Two types parametric functions were derived, using these variables. Comparison of the results indicated that the first group of variables had better prediction of the van Genuchten and the Mualem-van Genuchten parameters than the second group. The t test method was used to compare the hydraulic parameters of the soil samples with and without gypsum. This indicated that the gypsum removal increased the water retention at matric potentials of 0, -10, -33, -100, -300, -500 and -1500 KPas (P<0.01). Analyzing the data in more details, a significant difference between the hydraulic parameters was obtained for the soils with and without gypsum compounds.

Estimating the soil moisture curve has an important role in modeling water movement and solute transport in the soils. Saturated water content is one of the important parameters in soil studies which is used to estimate the soil water retention curve and unsaturated hydraulic conductivity. pedotransfer functions are as undirected methods which estimate soil time consuming parameters from readily measured parameters. The multi-linear regression and artificial neural network methods were used to develop the pedotransfer functions. In this research, soil texture, bulk density, soil particle density, organic material percent and lime content percent as readily measured parameters and saturated water content as time consuming parameter were considered. In this study, using soil readily measured parameters in 136 soil samples, 14 models of multi-linear regression and 6 models of artificial neural network were evaluated in order to estimate saturated water content. Finally, measured and estimated values of soil saturated water content were compared and each model ability was evaluated by statistical indices. The indices of Geometric Mean Error Ratio (GMER), Akaike's Information Criterion (AIC) and Root Mean Square Error (RMSE) showed that Minasny et al and shinoset et al models had better estimation of saturated water content. The results showed that low content of organic materials had the significant effect on the accuracy of neural network models estimation but lime percent had not the significant effect on the so called models.