جستجوی مقالات مرتبط با کلیدواژه « scaling » در نشریات گروه « آب و خاک »

Infiltration in the soil is one of the determining parameters in irrigation, which affects all factors related to irrigation evaluation. On the other hand, spatial and temporal variability of infiltration parameters are a major physical limitation in managing and achieving high application efficiency of surface irrigation. The objective of the present study is to evaluate scaling method in estimating the infiltration equation parameters in furrow irrigation based on Elliott-Walker, Shepard et al., Ebrahimian et al., infilt, Valiants and Milapali methods, and also to evaluation the accuracy of the middle point and end point for the scale process. For this purpose, a reference infiltration curve is considered and then using the time of to reach the middle and end of the furrow, the scale factor of that furrow is obtained. In this research, the measured data for six furrows with soil texture and different input data were considered. According to the results, if the furrow end point is used to calculate the scaling factor in the Scaling method, the accuracy of this method will be significantly increased .Based on the evaluation, Ebrahimian's method had the best results. In the infilt method, there is no noticeable difference between using the middle point and the end point. In many cases, using the middle point in order to obtain the parameters of the penetration equation overestimates the advance value. The simplicity of the one-point scaling method is one of the advantages of this method in comparison with other methods of obtaining penetration parameters

Measuring the soil water retention curve in the laboratory is time consuming and costly. For this purpose, researchers have developed some methods to reduce measurements. One of these methods is scaling. The objective of this study is to scaling the soil moisture retention curve using Brooks-Corey model for all textural classes. This method requires a reference curve and the moisture content in a specified suction. In this method the scaling factor is the logarithm of water content in a specified suction (e.g.) in the reference soil to the logarithm of water content in the same suction of the proposed soil. The scaling factor obtained by the proposed method was evaluated with the scaling factor obtained by the statistical optimization method. In this study, 11 different soil texture classes data provided by Rawls et al. were used. The results showed that the scaling factor obtained based on the water content of ,  and  is close to the optimum scaling factor. The mean value of the sum of squares error for the proposed and optimization methods were 0.047 and 0.045, respectively. The mean value of the geometric mean error for the proposed and optimization methods were 1.024 and 1.047, respectively. The results showed that the reference curve is optional and each soil can be used as the reference curve. In the case of De-scaling, the soil water retention curve obtained by the proposed method was fitted very well to the one obtained from Brooks-Corey model.

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.

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.

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 .

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.

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In most cases, advance data is used for evaluating border irrigation. Due to soil variability, as well as initial and boundary conditions in border irrigation, water advance rate varies considerably in different borders. Scaling techniques helped to reduce the required measurements in soil and water issues. The aim of this study was to scale the volume balance equation and provide a simple equation to determine water advance in border irrigation. For this purpose, 21 borders, including cultivated and uncultivated borders with slope of 0.001 to 0.005, roughness of 0.017 to 0.211, length of 91.4 to 100 m, and discharge rate of  0.08 to 0.16 m3/m/min were used. Scale factors were defined such that the volume balance equation remained independent from soil and initial conditions. The scaled advance curves showed certain patterns. As a result, empirical equations were fitted to the scaled solutions. The empirical equation was evaluated for prediction of water advance in the border. The root mean square error obtained from the observed and calculated values by the experimental equation for the different borders, in most cases, was less than 5 minutes, and the mean absolute error value was less than 10%. The determination coefficient of the final advance from observed and calculated values by the experimental equation was 0.93. In general, simple form and independent to the soil type equations presented are advantages of this method.

Infiltration is one of the most important physical parameters of the soil, which plays an important role in the hydrological cycle. The locaton variability in analyzing issues of water flow in the soil, at large levels such as the catchment area, is very difficult and costly. The use of scaling methods is a practical solution to the problems of soil variability. After presenting the theory of similar environments, scaling methods were proposed to overcome the problem of soil variability. In this research, the Richards equation was solved in a wide range of moisture (saturated moisture up to the remaining moisture content) for sandy soil, and the scale of accumulated penetration values was presented using the scaling of the results of solving this equation. These values were approximated with the triple-form of the Philip equation and, using regression models, for each sentence of this equation, an empirical relation was established for water penetration in soil. Then, using two sandy soils and clay in a specific moisture, the experimental effect was measured for both scaled time scales of 0.1 and 0.01 which included short and long periods. Then, using two sandy soils and clay in a specific moisture, the experimental Efficiency was measured for both scaled time scales of 0.1 and 0.01 which included short and long periods evaluated. Of the four available scenarios, the highest mean square error value was obtained at 0.0053 for clay and for long periods of time. cuase to the fact that this criterion was high in relation to other scenarios, the effect of gravity was calculated in long periods of time. The condition of applying the scaling method used in the research is not the effect of gravity on the capillary force. Also, the influence of the proposed relationship with the comparison of field data measured by Barry et al. (1995) on a sandy soil with a fixed height of water on the soil surface of Boehne et al. (1993) using the Rain simulator , Conducted penetration tests on two clay soils under cultured and uncoated conditions was evaluated. The results showed that the aforementioned relationship can provide an acceptable estimate (with the highest root mean square error of 3%) compared to the measured values of water penetration in the soil.

The rate and duration of downward flow during redistribution process determines the effective soil water storage at any time. This property is vitally important, particularly in arid and semi-arid regions where plants must rely for long periods of time on the remained soil water of the root zone. In this study a new approach for scaling of soil moisture redistribution process based on the Green-Ampt redistribution theory was developed. Using the scaled results of numerical solution of the general flow (Richards’ equation), an empirical equation for predicting the soil moisture profile during redistribution process was derived. An important advantage of the empirical equation is adopting the effect of hysteresis in soil retention curve on redistribution process. To validate the proposed empirical equation, its outputs were compared with those of Richards’ solution for 11 soil textural classes (from sand to clay). The comparison showed negligible amount of error for all of the 11 soil textural classes and for a wide range of initial conditions. However, some deviations from results of Richards’ solution were observed under high initial infiltrated water depth and/or high initial soil water content. Therefore, a model which can estimate the soil moisture content at any depth and time during redistribution phase with accuracy of numerical models and simplicity in application of analytical models was obtained.