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

Water infiltration to soil play an important role in irrigation management, storage moisture in soil especially in dry and semi-dry area and increasing agronomy yield. Understanding water infiltration to soil is of important in designing and applying water conservation methods, flood and runoff control and soil erosion management. Additionally, accurately measuring water infiltration to soil in different times is more important for predicting water storage in root zone, irrigation designing and planning and agronomy management. In other hand, water infiltration to soil is a base for precision agriculture, therefore, producing accurate maps for water infiltration to soil play an important role in land management and applying precision agriculture. Modeling soil water infiltration at the field scale with ruler of calcareous, saline and sodic conditions is important for a better understanding of infiltration processes in these soils and future of infiltration modeling. The present study aimed for estimating water infiltration to soil at different times using soil spatial prediction functions and spatial estimators.

in present study, 72 soil samples were collected using a random sampling method in Marvdasht plain, Fars Province. In selected points, soil bulk density, sand silt, clay, pH, electrical conductivity, calcium carbonate, solution sodium, solution calcium and magnesium and organic carbon contents. For measuring water infiltration to soil, the double ring method were used. Multiple linear regression (MLR), artificial neural network (ANN) and spatial estimators were used for deriving soil spatial prediction functions models between water infiltration to soil in different times including 5, 10, 20, 45, 90, 150, 210 and 270 min. In this study, the readily available soil properties and auxiliary variables such as remote sensing and topography data were used in soil spatial prediction functions.

The results of evaluating regression and artificial neural networks models based on the mean error (ME), coefficient of determination (R2) and root mean square error (RMSE) criteria in testing phase were showed that the developed artificial neural network models in present study performed better than multiple linear regression models in water infiltration to soil prediction at different times. Moreover, the results showed that the combined estimators (artificial neural network - Kriging) performed better than ordinary kriging model for estimating water infiltration to soil.

In totally, the results of this study showed that the applying soil spatial prediction functions (using auxiliary variables such as remote sensing data and topography data with the readily available soil properties) had a great potential to predict spatial estimation of water infiltration to soil at most considered times.

Soil saturated hydraulic conductivity is one of the most important physical characteristics of soils that affects water movement in soil. . The aim of this study was to determine the most important parameters in prediction and modeling of saturated hydraulic conductivity from conveniently available parameters, using the decision tree and error estimator cross validation and re- substitution. In this study, 72 soil samples with six different textures were collected from the village of Morgmalek and Shahrekord District. Conveniently available soil properties were introduced into software in 4 scenarios (the first scenario: pH, EC, % sand, % clay, OM%, CaCO3, mean weight diameter of dry aggregate (MWD dry), mean weight diameter of wet aggregate (MWD wet), BD; the second scenario: CaCO3 , OM%, % sand, % clay, BD, % gravel, electrical resistivity, dielectric constant, root penetration resistivity; the third scenario: pH, EC, Geometric mean diameter )dg(, Geometric standard  deviation )σg(, OM%, CaCO3, mean weight diameter of dry aggregate (MWD dry), mean weight diameter of wet aggregate (MWD wet), BD; and the fourth scenario: CaCO3, OM%, dg, σg BD, % gravel, electrical resistivity, dielectric constant, root penetration resistivity). Saturated hydraulic conductivity was measured with single ring. The results showed that moisture followed by structural features such as (σg) in the first scenario, and OM and BD in the second scenario, BD and MWD in the third scenario, and OM and BD in the fourth scenario were the most important parameter affecting saturated hydraulic conductivity. Correlation between predicted data by decision tree and measured data in the second and fourth scenarios were 0.83 and 0.82, respectively, and 0.79 in the first and third scenarios. All four scenarios were successful in modeling with respect to error rate and %RMSE. However, the %RMSE in the second and fourth scenarios was lower and the correlation coefficient was higher than the other two scenarios. The RMSE values in the four scenarios were 0.79, 0.83, 0.79, and 0.82, respectively.

Water infiltration is one of the most important properties of soil. The importance of infiltration process has led to development of several theoretical and empirical infiltration models. However, the applicability of these models is strongly subjected to the method of infiltration measurement. In this study, double ring infiltration data were collected from different regions of Iran with different soil textures. On the other hand, HYDRUS-1D model was used to simulate vertical infiltration through forward solution of the Richards’ equation. Van Genuchten-Mualem model was used to quantify the soil hydraulic properties. For this purpose, the hydraulic parameters of van Genuchten-Mualem model were optimized using inverse modeling in the HYDRUS, for each region's soil and were used for simulation. In order to evaluate the accuracy of the Horton, Kostiakov, Kostiakov-Louis and Philip models, based on measured and simulated infiltration data, mean error (ME), root mean square error (RMSE), mean absolute mean error (MAME), Pearson correlation coefficient (r) and modeling efficiency (EF) statistics were used. The results indicated that the Kostiakov-Lewis model has had the best performance in different soil textures based on measured double ring infiltration data. Horton model has had the best performance based on HYDRUS simulated infiltration data in different soil textures. The Philip model had the least efficiency in estimating cumulative infiltration based on both measured and simulated infiltration data.

Quantitative description of the spatial variability of soil hydraulic characteristics is crucial for planning, management and the optimum application. Field measurement of infiltration is very expensive, time-consuming and laborious. Soil structure also important effects on water infiltration in the soil. The objectives of this study were to determine the spatial variability of water infiltration, to select the most appropriate infiltration model, to calculate the parameters of relevant models, and to quantify the soil structure by using the fractal geometry. Infiltration parameters were estimated by using some physical soil properties, as well as fractal parameters, in this research. To achieve these purposes, 161 sites were selected and their infiltration was measured by using the constant head double-ring infiltrometers method in a systematic array of 500*500 m. The observed infiltration data from all examined sites were fitted to three selected infiltration models. Soil bulk density (BD), soil water content, soil particle size distribution, soil aggregate size distribution (ASD), organic carbon content (OC), saturation percentage (SP), soil pH and electrical conductivity (EC) were also measured in all 161 sites. For the quantitative assessment of soil structure, the aggregate size distribution, fractal parameters of the Rieu and Sposito model as well as the mean weight diameters (MWD) and geometric mean diameter (GMD) were also obtained. The obtained results indicated that the infiltration rates of the studied areas had generally low basic infiltration rates (1.1-31.1 cm hr-1) for most sites with the average of 6.69 cm hr-1. According to all obtained results and based on the least-square method, the Philip model was selected as the best performing model to account for infiltration. The aggregate size distribution demonstrated a fractal behavior, and the infiltration parameters could be significantly correlated with the fractal parameters and other soil physical properties.

Infiltration is one of the important parameters of the soil, which affects many hydrological processes in the watersheds. The importance of the Infiltration has led to various laboratory methods used to measure this process. Various methods have been developed for measuring the infiltration, which are based on the measurement of the vertical flow of water to the soil. One of the standard methods for measuring infiltration, is double-ring infiltrometers method. In this method, it is assumed that the external cylinder prevents lateral flow and creates a completely vertical flow in the soil. Therefore, the present study was conducted to evaluate the double-ring infiltrometers method for measuring the vertical infiltration compared to actual vertical infiltration data simulated using HYDRUS-1D software. In this study, in order to evaluate the double- ring infiltration data, infiltration was measured in several regions with different soil textures by double- ring infiltrometers. Then, HYDRUS-1D numerical model was used to simulate infiltration and the vertical infiltration data were obtained through forward solution of the Richards equation. van Genuchten-Mualem model was used to quantitatively soil hydraulic properties of the Richards equation. In order to the hydraulic parameters of van Genuchten-Mualem model were optimized using inverse modeling in the HYDRUS, for each region's soil Assessment of measured data was performed using mean error (ME), root mean square error (RMSE), coefficient of determination (R2) and normalized mean square root (NRMSE). Findings: Comparison of simulated and measured infiltration data showed that double ring infiltration data is much higher than the simulated vertical infiltration data. The measured infiltration data in all soil texture was much different from that of simulated data. The lowest error in the measurement of vertical infiltration was observed in sandy loam soil that is a light texture. The values of coefficient of determination, root mean square error, mean error and normalized mean square root (R2, RMSE, ME and NRMSE) were 0.87, 8.51, -4.45, and 0.18 respectively in this texture. In measuring the infiltration of a double-rings, the size of the cylinders used, as well as the buffer index, is of great importance. Therefore, the use of cylinders of different sizes influences on the final infiltration values. The double-ring infiltrometers method has a lower error in the Sandy texture than heavy texture and the contribution of lateral infiltration is less in this texture. Therefore, the double-ring infiltrometers has a higher accuracy in light texture.

The infiltration process is one of the most important components of the hydrologic cycle. Quantifying the infiltration water into soil is of great importance in watershed management. Prediction of flooding, erosion and pollutant transport all depends on the rate of runoff which is directly affected by the rate of infiltration. Quantification of infiltration water into soil is also necessary to determine the availability of water for crop growth and to estimate the amount of additional water needed for irrigation. Thus, an accurate model is required to estimate infiltration of water into soil. The ability of physical and empirical models in simulation of soil processes is commonly measured through comparisons of simulated and observedvalues. For these reasons, a large variety of indices have been proposed and used over the years in comparison of infiltration water into soil models. Among the proposed indices, some are absolute criteria such as the widely used root mean square error (RMSE), while others are relative criteria (i.e. normalized) such as the Nash and Sutcliffe 1970) efficiency criterion (NSE). Selecting and using appropriate statistical criteria to evaluate and interpretation of the results for infiltration water into soil models is essential because each of the used criteria focus on specific types of errors. Also, descriptions of various goodness of fit indices or indicators including their advantages and shortcomings, and rigorous discussions on the suitability of each index are very important. The objective of this study is to compare the goodness of different statistical criteria to evaluate infiltration of water into soil models. Comparison techniques were considered to define the best models: coefficient of determination (R2), root mean square error (RMSE), efficiency criteria (NSEI) and modified forms (such as NSEjI, NSESQRTI, NSElnI and NSEiI). Comparatively little work has been carried out on the meaning and interpretation of efficiency criteria (NSEI) and its modified forms used to evaluate the models. The collection data of 145 point-data of measured infiltration of water into soil were used. The infiltration data were obtained by the Double Rings method in different soils of Iran having a wide range of soil characteristics. The study areas were located in Zanjan, Fars, Ardebil, Bushehr and Isfahan provinces. The soils of these regions are classified as Mollisols, Aridisols, Inceptisols and Entisols soil taxonomy orders. The land use of the study area consisted of wheat, barley, pasture and fallow land.The parameters of the models (i.e. Philip (18), Green and Ampt (3), SCS (23), Kostiakov (6), Horton (5), and Kostiakov and Lewis (11) models) were determined, using the least square optimization method. All models were fitted to experimental infiltration data using an iterative nonlinear regression procedure, which finds the values of the fitting parameters that give the best fit between the model and the data. The fitting process was performed using the MatLab 7.7.0 (R2008b) Software Package. Then, the ability of infiltration of water into soil models with the mean of coefficient of determination (R2), root mean square error (RMSE), efficiency criteria(NSEI) and modified forms (such as NSEjI, NSESQRTI,NSElnI and NSEiI) were determined and goodness of criteria was compared for the selection of the best model. The results showed the mean of RMSE for all soils cannot always be a suitable index for the evaluation of infiltration of water into soil models. A more valid comparison withNSEI, NSEjI, NSESQRTI, NSElnI indices indicated that these indices also cannot apparently distinguish among the infiltration models for the estimation of cumulative infiltration. These indices are sensitive to the large amount of data. The NSEiI index with giving more weight to infiltration data in shorter times was selected as the most appropriate index for comparing models. According to the NSEiI index, Kostiakov and Lewis, Kostiakov, SCS, Philip, Horton, and Green and Ampt models were the best models in approximately 72.42, 4. , 26.9, 53.11, 11.73 and 1.0 percent of soils, respectively. The results of this study indicated that the ability of modified forms of NSE indices in evaluation of infiltration of water into soil models depend on the influence of models from infiltration data values in different time series. This encourages us to be cautious on the application and interpretation of statistical criteria when evaluating the models.

Although solutions of transient water flow can be obtained by numerical modeling, their application may be limited in part as root water uptake is generally considered to be one-dimensional only. The objective of this study was to evaluation accuracy of two-dimensional root water uptake model by Hydrus-2D in the Lysimeter. The root water uptake model was incorporated into a two-dimensional flow model, and parameters of governing equation were optimized based on minimizing the residuals between measured and simulated water content data. Water content was measured at different points and layers. To calibrate the flow, a genetic algorithm was used. With the optimized parameters of model, simulated and measured water contents during the 30 days period were in excellent agreement. Based on results, R2 values generally ranging between 0.92 and 0.94 and a root mean squared error (RMSE) of 0.0236 m3m-3. In this Paper effect of soil hydraulic properties on Hydrus simulation was evaluated by several predict of neural network. Finally, Result illustrated that Hydrus-2D is extremely flexible in simulation of soil water content pattern.

This study focused on the effect of organic manures as well as potassium fertilizer on some soil properties and onion yield at the field condition. The experiments were carried out as factorial in a randomized complete block design with three replications. The treatments were poultry manure (10 t ha-1)، alfalfa residue (10 t ha-1) and control each together with two levels of potassium fertilizer as K2O (0 and 250 kg ha-1). The results showed that application of poultry manure and alfalfa residue resulted in yield increasing about 57. 7 and 40. 9 % in comparison to the control، respectively. On average، the onion yield for the potassium treatment was 7. 8 % higher than for the untreated one. The application of poultry manure and alfalfa residue increased final infiltration rate 73. 2 and 56. 1 %، respectively. Inversely، potassium fertilizer caused a significant reduction in the final infiltration rate. Moreover، application of organic manures particularly poultry manure، significant increases in saturated moisture، porosity، EC، organic carbon and available phosphorous and decrease in bulk density and pH was observed، while potassium only increased soil EC، significantly. Application of poultry manure and alfalfa residue increased soil organic carbon 129. 8 and 80. 2 % and available phosphorous 104. 8 and 51. 9 %، respectively. Among different soil properties، organic carbon showed the highest influence on yield increase.

Quantitative description of infiltration process is needed for both irrigated and rainfed agriculture for optimal management and designs purposes. The most difficult task in the field, however, is to determine the spatial variability of infiltration process, which is of great importance for precision agriculture. The objective of this study was to quantify spatial variability of infiltration models by using scaling in Kavar plain, east of Shiraz, Iran. A number of 161 infiltration experiments were conducted on a systematic squared grid pattern with 500*500m over the 4000 ha area, using double-ring method. The observed infiltration data from all the experimental sites were fitted to three selected infiltration models. The best-fitted model for individual sites was identified. Using the least-squares technique, the Philip model was selected as the best model to describe infiltration process. Infiltration rates of the area generally indicated low basic infiltration rates (1.11–31.11 cm hr−1) for most sites, and average amount of them was 6.69 cm hr−1. The parameters of Philip model (S and A) showed a wide variation among the test sites. The sorptivity-based scaling factor αS and the transmissivity-based scaling factor αA were computed and the observed infiltration data were scaled. Scaling achieved through αA was found better than through αS. Optimum scaling factors αopt were then obtained by the least-squares. Scaling factors based on the arithmetic, geometric and harmonic means of αS and αA were

Puddling is the most common method of land preparation for lowland rice cultivation. The purpose of this study was to assess the influence of various intensities of puddling on percolation rate، water retention by soil and the amount of water used for different puddling intensities in three dominant soil textures of paddy fields in Guilan province. Undisturbed soil samples were taken from 3 different soils including silty clay، clay loam and loam، with 3 replications. The soil samples were puddled by a laboratory apparatus with different intensities. The results showed that the low puddling intensity treatment caused a 29. 3، 32. 4 and 36% reduction of percolation rate in silty clay، silty loam and loam textures، respectively. Increasing puddling intensity from low to medium reduced percolation rate significantly، but high intensity was not effective. Soil moisture characteristic curves of all three soils showed that water retention was increased by puddling treatments. Water retention in silty clay was higher than the other two soils. The high intensity treatment needed more water than low intensity for puddling. Increasing puddling intensity from medium intensity to high intensity caused 15. 4، 14. 1 and 16. 3% increase in the amount of water required for puddling in silty clay، silty loam and loam textures، respectively. Generally، in all the three studied soil textures، the amount of water used for high-intensity puddling was more than medium-intensity puddling، while it had no significant effect on water percolation rate and soil water retention.

Spatial variability of infiltration parameters were investigated in about 4000 ha of Kavar plain, east of Shiraz, Iran. For this purpose, infiltration was measured on a systematic squared grid pattern with 500 by 500 m in 161 sites using double ring infiltrometer. Soil bulk density (BD), initial soil water content, soil texture, organic carbon content (OC), saturation percentage (SP), pH, and EC were also measured. The interpolation of infiltration parameters in non-sampled areas were predicted using kriging, inverse distance weighted, and co-kriging methods. The experimental semi-variograms were fitted to linear, exponential, gaussian, and spherical models. Cross validation method using statistical parameters of MBE, MAE, MSE, and RMSE was used to choose the most accurate interpolation method. The spatial distribution map of parameters was obtained using the best interpolation method. Estimating the coefficient of a in Kostiakov model using co-kriging method (spherical model) and co-factor of BD with R2=0.99, nugget effect of -0.002, and range of 3380 m was superior to kriging method. The power b of Kosiakov was estimated by exponential mode and kriging method with R2= 0.97, nugget effect of 0.005 and range of 14410 m. To estimate sorptivity parameter (S) of Philip model, the co-kriging method with spherical model and co-factor of BD with R2=0.99 and range of 2973 m was preferred to kriging method. Likewise, estimation of transmissivity parameter of Philip model using co-kriging method (Gaussian model) with R2=0.996, nugget effect of 0.0003, and range of 3862 m showed high accuracy. Using soil moisture content, BD, texture, SP, and organic carbon as co-factors of co-kriging method, resulted in relatively better estimation of infiltration model parameters.

Spatial variability of infiltration has an important role on the hydrological processes. Spatial variability of infiltration is affected by extrinsic and intrinsic properties of soils. The objective of this study was to investigate the spatial variations of infiltration parameters through the variation of soil intrinsic properties and land use variation (as an extrinsic factors) using geostatistics methods. Therefore، 95 single ring infiltration data were measured from a field with rangeland and degraded rangeland land uses، simultaneously. The parameters of the Philip model were then obtained using measured infiltration data for all sampling points. The spatial variations of the parameters were assessed using the whole measured data and measured data at rangeland land use، separately. The mean infiltration data in the rangeland land use were higher than the degraded rangeland. It seems that cultivation activities that led to degrading in the rangeland are one of the reasons for decreasing the infiltration at the degraded rangeland comparing to protected rangeland. The geostatistical analysis indicated that strong spatial variability in Philip parameters exist in the investigated area، such that about 10 percent of saturated hydraulic conductivity variation and 35 percent of the variation in sorptivity parameters was random in the rangelands. The variography of the parameters showed that the land use change affected saturated hydraulic conductivity parameter more than sorptivity parameter، so that results on decreasing the spatial structure of saturated hydraulic conductivity and sorptivity parameters from 89 and 64 percents to 56 and 53 percents، respectively. The comparison between the result of this study and the past study showed that the spatial variability of hydraulic conductivity depend on the study area. The results indicated that the geostatistics method was a proper method for predicting the infiltration parameters in the study area with 0. 08 and 0. 19 RMSE for saturated hydraulic conductivity and sorptivity، respectively.

Infiltration is a significant process which controls the fate of water in the hydrologic cycle. The direct measurement of infiltration is time consuming, expensive and often impractical because of the large spatial and temporal variability. Artificial Neural Networks (ANNs) are used as an indirect method to predict the hydrological processes. The objective of this study was to develop and verify some ANNs to predict the infiltration process. For this purpose, 123 double ring infiltration data were collected from different sites of Iran. The parameters of some infiltration models were then obtained; using sum squares error optimization method. Basic soil properties of the two upper pedogenic layers such as 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 obtained for each sampling point. The feedforward multilayer perceptron was used for predicting the infiltration parameters. Two ANNs types were developed to estimate infiltration parameters. The developed ANNs were categorized into two groups; type 1 and type 2 ANNs. For developing type 1 ANNs, the basic soil properties of the first upper soil horizon were used as inputs, hierarchically. While for developing type 2 ANNs the basic soil properties of the two upper soil horizons were used as inputs, using principal component analysis technique. Evaluation results of these two types ANNs showed the better performance of type 1 ANNs in predicting the infiltration parameters. Therefore, this type of ANNs was used for predicting the cumulative infiltration. The reliability test indicated that the developed ANNs for Philip model have the best performance to predict cumulative infiltration with a mean RMSE of 6.644 cm. The developed ANNs for Horton, Kostiakov-Lewis and Kostiakov have the next best ranks, respectively.