M.T. Pozan; M.M. Chari; P. Afrasiab
Abstract
Introduction: Infiltration is found to be the most important process that influences uniformity and efficiency of surface irrigation. Prediction of infiltration rate is a prerequisite for estimating the amount of water entering into the soil and its distribution. Since the infiltration properties are ...
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Introduction: Infiltration is found to be the most important process that influences uniformity and efficiency of surface irrigation. Prediction of infiltration rate is a prerequisite for estimating the amount of water entering into the soil and its distribution. Since the infiltration properties are a function of time and space, a relatively large number of field measurements is needed to represent an average of farm conditions (Bautista and Wallender, 1985). In recent years, researchers have proposed methods to reduce the requirement of the regional and field data in order to describe water dynamic in the soil. One of these methods is scaling which at the first was presented by Miller and Miller (1956) and developed on the similar media theory in the soil and water sciences (Miller and Miller, 1956; Sadeghi et al., 2016). According to similar media theory, soils can be similar, provided that different soils can be placed on a reference curve with ratios of a physical characteristic length, called "scaling factor". The objective of the present study was scaling the Philip infiltration equation and analyzing the spatial variability of infiltration characteristics by using minimum field measurements. In this research, a new method was presented for scaling infiltration equation and compared with previous methods scaling including: based on sorptivity (), transmissivity (), the optimum scaling factors () arithmetic, geometric and harmonic.
Materials and Methods: The basic assumption of scaling through this method was “the shape of the infiltration characteristics curve is almost constant despite the variations in the rate and depth of infiltration”. The data required for infiltration scaling were a reference infiltration curve (whose parameters are known) and the depth of water infiltrated within a specified time period in other infiltration curves. In this method, first, equation infiltration parameters are specified for one infiltration curve, called the reference infiltration curve (). If, for other infiltration equations, the depth of water infiltrated is obtained after the specified time(ts) (for example, depth of infiltration water after 4 hours), the scale factor (Fs, dimensionless) is equal to the depth of water infiltrated after ts in the reference infiltration equation to depth of infiltrated water after ts even infiltration equation is as follows:
(1)
where Ii (i=1,2, …,n) is depth of infiltrated water after a given time (ts) for each infiltration families and is depth of infiltrated water after a given time in reference, and and are parameters of reference curve.In order to assess the proposed scaling method, root mean square error (RMSE), mean bias error (MBE) and coefficient of determination (R2) were used for a totally 24 infiltration tests.
Results and Discussion: The parameters of this model (i.e. sorptivity S and transmissivity factor A) showed a wide variation among the study sites. The variation of these parameters showed no significant difference between sorptivity and transmissivity factors. In addition, Talsama et al. (1969) illustrated that there is a weak relationship between sorptivity and saturated hydraulic conductivity. Results showed that scaling achieved using αA was better than that obtained using αS. Mean curve was chosen as reference curve and scale curve was obtained by different methods. The results of statistical analysis showed that the proposed method had the best performance (RMSE=0.006, MBE=0.0019 and R2=0.9996). In order to evaluate the effect of the reference curve selection on the results, the scaled cumulative infiltration curve based on different reference curves (different infiltration equation) was evaluated. The results showed that the selection of the reference infiltration curve is optional and each cumulative infiltration families can be selected as the reference curve. For defining the relationship between and , , αS، αA ، ، ، data, a statistical analysis was performed. According to our results, had the highest correlation with .
Conclusion: In this study, a new method for penetration scaling was presented. In this method, the infiltration curve can be obtained using the minimum information including a reference curve and the depth of infiltrated water after a given time. The selection of the reference infiltration curve is optional and each cumulative infiltration equation can be selected as the reference curve. In the light of the results of this research, it can be concluded that the proposed method in this study is promising to be used for surface irrigation management.
M.M. Chari; B. Ghahraman; K. Davary; A. A. Khoshnood Yazdi
Abstract
Introduction: Water and soil retention curve is one of the most important properties of porous media to obtain in a laboratory retention curve and time associated with errors. For this reason, researchers have proposed techniques that help them to more easily acquired characteristic curve. One of these ...
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Introduction: Water and soil retention curve is one of the most important properties of porous media to obtain in a laboratory retention curve and time associated with errors. For this reason, researchers have proposed techniques that help them to more easily acquired characteristic curve. One of these methods is the use of fractal geometry. Determining the relationship between particle size distribution fractal dimension (DPSD) and fractal dimension retention curve (DSWRC) can be useful. However, the full information of many soil data is not available from the grading curve and only three components (clay, silt and sand) are measured.In recent decades, the use of fractal geometry as a useful tool and a bridge between the physical concept models and experimental parameters have been used.Due to the fact that both the solid phase of soil and soil pore space themselves are relatively similar, each of them can express different fractal characteristics of the soil .
Materials and Methods: This study aims to determine DPSD using data soon found in the soil and creates a relationship between DPSD and DSWRC .To do this selection, 54 samples from Northern Iran and the six classes loam, clay loam, clay loam, sandy clay, silty loam and sandy loam were classified. To get the fractal dimension (DSWRC) Tyler and Wheatcraft (27) retention curve equation was used.Alsothe fractal dimension particle size distribution (DPSD) using equation Tyler and Wheatcraft (28) is obtained.To determine the grading curve in the range of 1 to 1000 micron particle radius of the percentage amounts of clay, silt and sand soil, the method by Skaggs et al (24) using the following equation was used. DPSD developed using gradation curves (Dm1) and three points (sand, silt and clay) (Dm2), respectively. After determining the fractal dimension and fractal dimension retention curve gradation curve, regression relationship between fractal dimension is created.
Results and Discussion: The results showed that the fractal dimension of particle size distributions obtained with both methods were not significantly different from each other. DSWRCwas also using the suction-moisture . The results indicate that all three fractal dimensions related to soil texture and clay content of the soil increases. Linear regression relationships between Dm1 and Dm2 with DSWRC was created using 48 soil samples in order to determine the coefficient of 0.902 and 0.871 . Then, based on relationships obtained from the four methods (1- Dm1 = DSWRC, 2-regression equationswere obtained Dm1, 3- Dm2 = DSWRC and 4. The regression equation obtained Dm2. DSWRC expression was used to express DSWRC. Various models for the determination of soil moisture suction according to statistical indicators normalized root mean square error, mean error, relative error.And mean geometric modeling efficiency was evaluated. The results of all four fractalsare close to each other and in most soils it is consistent with the measured data. Models predict the ability to work well in sandy loam soil fractal models and the predicted measured moisture value is less than the estimated fractal dimension- less than its actual value is the moisture curve.
Conclusions: In this study, the work of Skaggs et al. (24) was used and it was amended by Fooladmand and Sepaskhah (8) grading curve using the percentage of developed sand, silt and clay . The fractal dimension of the particle size distribution was obtained.The fractal dimension particle size of the radius of the particle size of sand, silt and clay were used, respectively.In general, the study of fractals to simulate the effectiveness of retention curve proved successful. And soon it was found that the use of data, such as sand, silt and clay retention curve can be estimated with reasonable accuracy.
