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 ...
Read More
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.
Ali Akbar Moosavi; Mohammad Omidifard
Abstract
Introduction: Saturated hydraulic conductivity and the other hydraulic properties of soils are essential vital soil attributes that play role in the modeling of hydrological phenomena, designing irrigation-drainage systems, transportation of salts and chemical and biological pollutants within the soil. ...
Read More
Introduction: Saturated hydraulic conductivity and the other hydraulic properties of soils are essential vital soil attributes that play role in the modeling of hydrological phenomena, designing irrigation-drainage systems, transportation of salts and chemical and biological pollutants within the soil. Measurement of these hydraulic properties needs some special instruments, expert technician, and are time consuming and expensive and due to their high temporal and spatial variability, a large number of measurements are needed. Nowadays, prediction of these attributes using the readily available soil data using pedotransfer functions or using the limited measurement with applying the geostatistical approaches has been receiving high attention. The study aimed to determine the spatial variability and prediction of saturated (Ks) and near saturated (Kfs) hydraulic conductivity, the power of Gardner equation (α), sorptivity (S), hydraulic diffusivity (D) and matric flux potential (Фm) of a calcareous soil.
Material and Methods: The study was carried out on the soil series of Daneshkadeh located in the Bajgah Agricultural Experimental Station of Agricultural College, Shiraz University, Shiraz, Iran (1852 m above the mean sea level). This soil series with about 745 ha is a deep yellowish brow calcareous soil with textural classes of loam to clay. In the studied soil series 50 sampling locations with the sampling distances of 16, 8 , and 4 m were selected on the relatively regular sampling design. The saturated hydraulic conductivity (Ks), near saturated hydraulic conductivity (Kfs), the power of Gardner equation (α), sorptivity (S), hydraulic diffusivity (D) and matric flux potential (Фm) of the aforementioned sampling locations was determined using the Single Ring and Droplet methods. After, initial statistical processing, including a normality test of data, trend and stationary analysis of data, the semivariograms of each studied hydraulic attributes were calculated in various directions and their surface semivariograms were also prepared to determine the isotropic or anisotropic behavior of each studied soil attributes. Since all of studied soil hydraulic attributes were isotropic variables, therefore, the omnidirectional semivariograms were calculated and different theoretical models were fitted to them. The best fitted semivariogram models were determined using the determination coefficient, R2, and the residual sum of the square, RSS. The parameters of the best fitted models to the experimental semivariograms were also determined. The prediction of study hydraulic attributes was carried out using the parameters of semivariogram models by applying the ordinary Kriging approach. Predictions were also carried out using the Inverse Distance Weighing approach. The results of predictions were compared to each other using the Jackknifing evaluation approach and the suitable prediction method was determined and zoning was performed using the results of introducing prediction method. All of the semivariogram calculations and modeling, prediction of zoning of study hydraulic attributes were performed using the GS+ 5.1 software packages.
Results and Discussion: Results indicated that all of the studied soil hydraulic attributes belonged to the weak to moderated spatial correlation classes and the spherical model was the best fitted model for their semivariograms (except for Kfs and D that their best semivariogram models were exponential). The sill of all semivariograms ranged between 0.0003 to 0.419 for the S and Kfs, respectively. The nugget effects and the Range parameter of all semivariograms were located between 0.00015 to 0.108 for the S and Фm, and 211 to 6.4 m for Ks and D, respectively. Results also indicated that 3.5 and 50% of total variation of D and Ks was spatially structured and the other was random, respectively. The spatial correlation classes of near saturated soil hydraulic conductivity and soil hydraulic diffusivity were week, whereas, the spatial correlation classes of the other studied soil hydraulic attributes were moderate. Results revealed that the Inverse Distance Weighting method was the most suitable approach for the prediction of all studied soil hydraulic attributes in the present study. Comparison of the calculated statistical evaluation measures (i.e. Determination coefficient, R2, Mean residual error, MRE, mean square error, MSE, Normalized mean square error, NRMSE and geometric mean error ratio, GMER) and the final determined order of precision showed that the most and the least accurate predictions were obtained for Ks and Фm, respectively.
Conclusion: It is suggested in the cases that we need to map the hydraulic attributes or need their quantities in a large number; geostatistical prediction be performed using the limited measurements to reduce the needed time and costs.
