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

In recent years, inverse numerical solution methods have been considered by many researchers to address the problems of water movement in soil. In this study, HYDRUS-2D/3D software was used to simulate water infiltration through double-rings infiltrometers in soils with different textures using the inverse solution approach. For this purpose, the infiltration data obtained by double-rings method from 63 points of different regions in Isfahan were used as model input. The studied soils were classified into seven textural classes including Sandy Loam (SL), Sandy Clay Loam (SCL), Loam (L), Silty Loam (SiL), Clay Loam (CL), Silty Clay Loam (SiCL) and SiC (Silty Clay). Good agreement was observed between the measured and simulated cumulative infiltration data, in all soil textures. Coefficients of determination (R2) were 0.998, 0.999, 0.992, 0.996, 0.983, 0.976 and 995 for SL, SCL, L, SiL, CL, SiCL and SiC textures, respectively. Increasing the percentage of clay in the soil textures increased the simulation error. The highest simulation error was observed in SiC (NRMSE = 0.045) and the lowest simulation error was observed in SL (NRMSE = 0.015). In general, the simulated double-rings infiltration data using HYDRUS -2D / 3D software and the inverse numerical solution approach had acceptable accuracy and high reliability in all studied textures.

Soil water Infiltration plays an important role in the water cycle of nature. However, since the direct measurement of soil water infiltration is laborious, time-consuming and expensive. Therefore, in this study was conducted to investigate the possibility of estimating the coefficients of water infiltration equations such as Kastiakov, Kastiakov-Lewis, Horton and USA Soil Conservation Service (SCS) using readily available soil properties and to evaluate the validity of these functions outside of their derivation regions. Therefore, soil physical properties and cumulative infiltration were measured in two different regions (T1 and T2). Parametric functions were derived using T1 location measurements and validation was performed using independent T2 measurements. Cumulative infiltration was measured using double rings with three replications at 78 points. Parametric functions were created using multiple linear regression. The highest accuracy of parametric functions in the derivation stage was related to SCS equations with the coefficient of explanation (R2) equal to 0.66 and the lowest accuracy with the value of R2 equal to 0.04 was related to the power of Horton equation. In the validation stage, the accuracy of the functions showed a large decrease. R2 changes range from 0.00 to 0.26, normalized root mean square error (nRMSE) range from 1.76 to 80.93 and geometric mean error ratio (GMER) range from 0.94 to 1.73 Was obtained. Therefore, in this study, the use of parametric functions to estimate the coefficients of infiltration equations outside of their derivation regions was not efficient.

Infiltration is a key process which can be considered for scheduling, design, management and optimization of irrigation systems. Therefore, it is necessary to choose an appropriate infiltration model to optimize water consumption in agriculture and to study the water cycle on a small and large scale. The purpose of this study was to investigate the sensitivity of the coefficients of Kostiakov, Kostiakov-Lewis and Horton infiltration equations to the simultaneous changes of initial moisture and soil bulk density and its effect on the performance of infiltration equations in different soils. In order to represent the effect of the sensitivity of the coefficients of infiltration equations on the efficiency of those equations at different moisture and bulk densities, first the coefficients of the equations are calculated for all infiltration data (fitting step) and then at a specific moisture and bulk density for each soil are calculated and for the moisture and other densities were used (evaluation step). For this purpose, cumulative infiltration data in eight soil textures, three bulk densities including 1.3, 1.4 and 1.5 g / cm3 and five volumetric moisture levels of 0.1, 0.15, 0.2, 0.25 and 0.3 was prepared using HYDRUS-1D. Based on the results, sensitivity of the coefficients of infiltration equations to changes in moisture and bulk density from high to low, respectively, the coefficient of the Kostiakov equation (ak), the coefficient of the Kostiakov-Lewis equation (akl), the power of the Kostiakov equation (bk), the coefficient of the Horton equation (bh) And the power of the Kostiakov-Lewis equation (bkl) was obtained. The mean increase of RMSE of the Kostiakov, Kostiakov-Lewis and Horton equations in the evaluation stage was respectively 3.25, 34 and 2.8 times the fitting stage. Therefore, the lowest and the highest sensitivity occurred in the Horton and the Kostiakov-Lewis equations respectively.

The process of infiltration is one of the most important components of the hydrological cycle and field water management. The direct measurement of infiltration process is difficult, time spending and expensive. Therefore, infiltration models are used. Kostiakov, Kostiakov-Lewis, Philip, Soil Conservation Service of America, Horton and Overton models infiltration based on double ring results carried out in two wheat and bean fields to the determination of best infiltration model in the Jahad Nasr Khuzestan province. The results showed that the mean infiltration rate in wheat field was 35.47 cm hr-1 in 15.83 percent moisture before first irrigation up to 6.35 cm hr-1 in soil moisture content of 29.20 percent after second irrigation. The average infiltration rate in the bean field with initial moisture was 16.43 percent before the first irrigation from 61.63 to 13.70 cm hr-1 in the soil moisture content of 23.10 percent after the second irrigation. The lowest and highest RMSE for coefficients of cumulative infiltration models in wheat field were obtained from Philip and Horton equations with 1.21 and 3.14 cm with R2 of 0.93 and 0.95 and the bean field was 1.57 and 3.42 cm with R2 of 0.90 and 0.92, respectively. The lowest and highest RMSE of the infiltration rate in the wheat field were obtained Philip and Horton equations with 0.46 and 0.63 cm min-1 and in the bean field were 0.90 and 1.40 cm min-1, respectively. The accuracy rating of the studied models in two fields showed that Kostiakov-Lewis and Horton models have the highest accuracy in the infiltration rate and Kostiakov-Lewis and Phillip models in cumulative infiltration.

Infiltration is one of the most important parts of hydrologic cycle by which surface run off, groundwater recharge and design of irrigation systems can be linked. Numerous equations, some entirely empirical and others physically based, have been proposed over the years to express infiltration as a function of time. Among these equations, due to the simplicity and capability of fitting on most measured infiltration test data, the Kostiakov infiltration equation is widely used for design of irrigation systems. Influence of initial soil water content is not considered in this important equation. This study aims to investigate the effect of initial soil water content on empirical parameters of Kostiakov infiltration equation and to modify the equation based on influence of initial soil water content. In this research, two uniform sandy soils were prepared by passing a sandy soil through sieves. Following that, the two soil samples were dried at oven at 105 oC for 24 hr. Each dry soil sample was placed in a transparent acrylic cylinder with inner diameter of 7 cm and smoothly compacted by kicking a rubber hammer on cylinder body. After that, an initial water depth was placed on soil surface and falling head cumulative infiltration depth through the soil column was measured with the time. After diminishing of water level on soil surface, the humid soil column was drained by using a vacuum pomp. Then, the soil column water content was measured and the falling head infiltration test was repeated on the soil column. For two studied soil columns, falling head infiltration tests were conducted in three different values of initial soil water contents. To extend the experimental results to the field scale, a falling head infiltration test was also conducted on a clay textured field by using the double ring infiltrometers, with inner ring diameter of 25 cm, and outer ring diameter of 40 cm. Both the inner and outer rings were filled with the same initial water depth. Falling water level in inner ring was measured before water level would reach the soil surface. Simultaneously with last reading of water level, the two rings were pooled out and vertical distance from the soil surface to the wetting front was measured using an auger. Additionally, a steel cylinder was used and a soil sample with volume of 293 cm3 was taken from the vicinity of field test location and field initial soil water content was measured. For laboratory and field tests, wetness increment behind the wetting front was estimated by dividing the total infiltrated water through the soil by the depth of wetting front. The water content behind the wetting front of laboratory and field tests was also estimated by adding the wetness increment with initial water content. For laboratory and field tests, the Kostiakov infiltration equation is fitted on measured cumulative infiltration test data and empirical parameters of the equation are estimated for each test. Comparison between measured cumulative infiltration tests data of each laboratory soil column in different values of initial water contents indicated that water infiltration through the soil is strongly influenced by initial soil water content. With increasing initial soil water content, cumulative infiltration depth through the soil is significantly reduced. Additionally, laboratory results indicated that water content behind the wetting front of infiltration is a constant value and is not influenced by initial soil water content. Laboratory results indicated that the power of Kostiakov equation (b) is a constant value and is not influenced by initial soil water content, while the coefficient of equation (a) is strongly influenced by initial water content. With increasing initial soil water content, the a coefficient of Kostiakov equation is reduced. After analysis of experimental results, a coefficient of Kostiakov infiltration equation is related to the initial soil water content and the water content behind the wetting front of infiltration. Laboratory results were extended to the field scale. Based on laboratory results, the b coefficient of Kostiakov equation for the studied field was taken as a constant value. The obtained empirical equation for the a coefficient of Kostiakov equation was used and the a coefficient for the studied field was related to the initial soil water content and the water content behind the wetting front.

Moisture flow through unsaturated zone is vital in agricultural engineering, soil science, groundwater hydrology, and environmental engineering. Moisture flow through unsaturated zone is complex due to the dependencies of flow and storage properties on the pressure head and is commonly analyzed by solving Richards’ equation. Solution of Richards’ equation requires the knowledge of soil hydraulic conductivity and water content versus pressure head functions referred to herein as the soil hydraulic properties. Since these functions are highly nonlinear, direct laboratory and field measurements are tedious, time consuming, and involve considerable uncertainty (Hari Prasad et al., 2010). An alternative to the direct determination is to employ the parameter estimation methods using inverse procedure for the determination of hydraulic properties. Indirect methods are divided into several categories, including methods based on pedotransfer functions, semi-physical, and inverse methods (Abbasi, 2007). Determination of hydraulic properties by inversion of in situ measured moisture contents, pressure heads and cumulative infiltration have become an alternative to direct measurements due to decrease in the computational costs and development of efficient optimization algorithms. Inverse solutions based on the Richards’ equation are now increasingly used for estimating the unsaturated soil hydraulic properties. The HYDRUS-1D model is one of the advanced models have been widely used to simulate one-dimensional water movement in soil. Examples of numerical studies in which infiltration data were used to inversely estimate the near-saturated soil hydraulic properties using HYDRUS model are by Simunek and van Genuchten (1996) and Rashid et al (2015). Water quality can impact on soil hydraulic conductivity changes. The effect of salinity on the hydraulic conductivity of soil has been studied by many researchers, including Moutier et al. (1998) et al. and Levy (2005). The researchers reported that increasing the salinity has increased the hydraulic conductivity of the soil. The purpose of this research was to evaluate saline water effect on unsaturated hydraulic properties and estimate these properties inversely using infiltration data.

One of the methods to increase water application efficiency in agricultural lands is surge irrigation. Simulation and design of this method rely on accurate estimation of infiltration equation coefficients. Commonly used methods to estimate infiltration equations coefficients need to numerous field measurements and to time consume. Therefore, to evaluate surge factor in furrow infiltration under non- continuous flow, a field study was conducted for surge flow with on-time of 30 min, cycle ratio of 1/2, with four surges, at two irrigation events. Surge infiltration was measured by blocked furrow infiltrometers which adapted for non- continuous condition. The results indicated that non- continuous flow has a potential to decrease cumulative infiltration with the application of different surges. Decreasing infiltration due to non-continuous flow caused decreasing deep percolation and improving flow advance along furrows, consequently, it will be caused increase in irrigation efficiency and improve water distribution in the farm. With increase in irrigation events, cumulative infiltration decreased. Cumulative infiltration at the first 30 min in the first irrigation (with 9363 ml m-1) is more than the second event (with 5858 ml m-1). The differences between infiltration rates from two to four surges were not significant. Application flow with more than three surges did not cause changes in infiltration rate and water efficiency. Infiltration equation parameters (k and a) were obtained by regression analysis for different surges of tow irrigation events. The exponent of the equation was 0.43. The parameter of K in The first irrigation was more than that was from the second irrigation. In the present study, surge factor was evaluated for two irrigation events with several flow surges. The surges factors can be used to estimate cumulative infiltration in the different surges of the first and the second irrigation events based on the first surge.

Infiltration is one the most sensitive hydraulic parameters that has more effect on the efficiency of water, especially in surface irrigation design and management. The fact that infiltration is affected by several factors of soil and irrigation method makes it a complex parameter. Evaluating the impacts of irrigation water head on cumulative infiltration and using this parameter in infiltration models will lead to better performance of the models which are widely used in different conditions. So far, many studies have been conducted, in the field of infiltration. Forman et al (2006) investigated the effect of water height on parameters of Kostiakov soil infiltration equation in the soils with Sandy loam, loam and Silt showed that the parameters k and a had the most variability in sandy loam soil texture. However, the parameter k, in the Silty soil texture, showed little variation due to water height changes. Valyntzas et al (2009) studied the effect of water height as an upstream boundary condition on the parameters of Kostiakov equation in Sandy Clay Loam soil texture and showed that by modifying the infiltration coefficient based on water height, the simulation results of infiltration by HYDRUS improved. The purpose of this study was to investigate the effect of different water heights on the infiltration process, in some soil texture in Foumanat plain in Guilan province. In this way, 17 soil samples that had more than 15% Clay and less than 30% Sand were gathered. Infiltration tests were carried out with three replications in a completely randomized design, in a physical model. The factors included soil texture with 17 levels: the height of water with three levels (3, 5 and 7 centimeters) and three levels of the time. The results also showed that height of water on the soil surface had more impact on Loam soil texture than Clay soil texture. Infiltration testing time was 600 minutes and cumulative infiltration values were calculated in 30, 360 and 600 minutes. The physical model includes a water tank, manometer for reading the water level, reservoir water for fixing water level on the surface of soil and polyethylene cylinder with an internal diameter of 15.23 cm and a height of 35 cm, as a soil box. To maintain the soil water level, a float was used. Analysis of variance hypotheses for normal distribution was done. After passing normal distribution test, Analysis of variance and the comparison of averages were done by Tukey’s method at 5% level using SAS software. Results showed that treatment of water height had a significant effect on cumulative infiltration of the four soil samples in Silty Clay and Loam Silty Clay soil textures. Although, the results of statistical tests showed no significant effect between water height treatments (3, 5 and 7 cm) in 13 soil samples, the difference between the amount of infiltrated water in the samples should be considered due to the critical situation of water resources and necessity of increasing in irrigation efficiency. Comparison of the difference percentage of cumulative infiltration in different soil textures showed that, apart from the 3 cm water height treatment, in the two other water height treatments, Clay Loam, Silty Clay Loam, Silty loam and Silty Clay soil textures had the highest percentage of difference between the two time periods, respectively. On the others in estimating of cumulative infiltration, Clay Loam texture has more sensitivity to the cumulative time of infiltration. The difference of cumulative infiltration between treatments 3 and 5 cm was more than the other two treatments (5 and 7 cm), despite the equal difference of 2 cm in the water height. The reason for this result could be explained by reducing the influence of gravity due to increasing the height of water on the soil surface. Investigating the three infiltration time treatments (30, 360 and 600 minutes) showed that in 14 soil samples, there were significant differences between cumulative infiltration in 30 min with infiltration time in 360 and 600 minutes (p

Infiltration is one of the most important parameters affecting irrigation. For this reason, measuring and estimating this parameter is very important, particularly when designing and managing irrigation systems. Infiltration affects water flow and solute transport in the soil surface and subsurface. Due to temporal and spatial variability, Many measurements are needed to explain the average soil infiltration characteristics under field conditions. Stochastic characteristics of the different natural phenomena led to the application of random variables and time series in predicting the performance of these phenomena. Time-series analysis is a simple and efficient method for prediction, which is widely used in various sciences. However, a few researches have investigated the time-series modeling to predict soil infiltration characteristics. In this study, capability of time series in estimating infiltration rate for different soil textures was evaluated.
For this purpose, the 60 and 120 minutes data of double ring infiltrometer test in Lali plain, Khuzestan, Iran, with its proposed time intervals (0, 1, 3, 5, 10, 15, 20, 30, 45, 60, 80, 100, 120, 150, 180, 210, 240 minutes) were used to predict cumulative infiltration until the end of the experiment time for heavy (clay), medium (loam) and light (sand) soil textures. Moreover, used parameters of Kostiakov-Lewis equation recommended by NRCS, 24 hours cumulative infiltration curves were applied in time-series modeling for six different soil textures (clay, clay loam, silty, silty loam, sandy loam and sand). Different time-series models including Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), autoregressive integrated moving average (ARIMA), ARMA model with eXogenous variables (ARMAX) and AR model with eXogenous variables (ARX) were evaluated in predicting cumulative infiltration. Autocorrelation and partial autocorrelation charts for each variable time-series models were investigated. The evaluation indices were the coefficient of determination (R2), root of mean square error (RMSE) and standard error (SE).
The results showed that the AR(p), ARX(p,x) and ARMAX(p,q,x) time series models with various degrees 1, 2, 3 successfully predicted infiltration rates for duration of the test in different soils. Significant correlation between actual and estimated values of cumulative infiltration was almost obtained. The values of SE varied between 2 and 5 percent for three soil textures in Lali plain. Reducing input data from two hours to one hour did not have major impact on infiltration prediction. The results of 24 hours cumulative infiltration also indicated standard error of estimated infiltration varied between 2 and 21% for six different soil textures. Similarly, there was a very good correlation between the actual and predicted values of 24 hours cumulative infiltration. The prediction error increased with increasing prediction time (4 hours vs. 24 hours). The time-series models had accurate performances to predict cumulative infiltration until 12 hours, therefore, they would be as a useful tool to predict soil infiltration characteristics for irrigation purposes. The RMSE values for predicting 24 hours cumulative infiltration were 0.5, 2.6, 4.1, 4.9, 7.5 and 11.8 cm for clay, clay loam, silt, silty loam, sandy loam and sand, respectively. The SE values also were 2.6, 11.7, 13.9, 14.9, 17.2 and 21.6 % for clay, clay loam, silt, silty loam, sandy loam and sand, respectively. Time-series modeling showed better performance in heavy and moderate soils than in light soils. However, the performance of the time-series modeling for predicting infiltration for the double ring test with four hours experiment time was better for light soil textures as compared to heavy and moderate soil textures. Therefore, more studies are needed to investigate the capability of time series modeling to predict infiltration with more experiment data, particularly for heavy and moderate soil textures.
The results indicated that the experiment time of the double ring test could be reduced from four to one hour by using time series models in various soil textures and consequently the cost of soil infiltration measurements would be decreased. Using initial 120 min infiltration data, the time-series models could successfully predict the 12 hours cumulative infiltration. Comparison between the results of times-series models and actual data indicated the application of time-series models in predicting soil infiltration characteristics was efficient.

Soil water infiltration and its characteristics are important in water management both in agriculture and hydrology. Water repellency of soil، a phenomenon that often occurs in forest soils، reduces infiltration greatly and enhances soil degradation by various ways. In this study two clay loam and sandy loam soils were sampled from Kaleybar forest area، East Azarbaijan province. They were artificially hydrophobized into five different degrees of water repellency by using stearic acid. Water drop penetration time test (WDPT) was applied to assess the severity of the repellency. Effects of the repellency were investigated on cumulative infiltration and infiltration coefficients of the Kostiakov and Philip models. Results indicated that in the both soils، cumulative and average infiltration rate (average of triplicate infiltration measurement data) at the beginning of the experiment and the average steady state infiltration rate decreased with increasing of repellency from degree 1 to 5. The average value of the initial infiltration rate decreased from 1. 37 to 0. 31 in sandy loam soil، and from 1. 50 to 0. 23 in clay loam soil with increasing the severity of the repellency from degree one to five. Steady state infiltration rate was greatly reduced from 0. 037 to 0. 0001 in sandy loam soil، and from 0. 02 to 0. 008 in clay loam soil. Water repellency significantly affected Kostiakov c and Philip sorptivity S. The Kostiakov model in comparison to the Philip model was more appropriate for estimating cumulative infiltration in different degrees of water repellency.

Efficient water use in agriculture is a priority in arid and semi-arid regions where water for irrigation is scarce. Super absorption polymers (SAP) could be considered as an approach to increase water productivity in rainfed and irrigated farming. The present study was carried out to evaluate the effect of stockosorb super absorption polymer on the Kostiakov-Lewis Equation parameters and the flow advance curve in the furrow irrigation method. The experiment was conducted based on a randomized block design with factorial arrangement having 3 replications. The main plots included four levels of SAP included 0 (control), 5, 7, and 9 g m-2 polymer into 25 cm of soil depth, and two levels of inflow rate included 0.5 and 0.7 l/s as sub main plots. The data were collected during four irrigation events. The results showed that applying SAP had a significant effect on the flow advance time and cumulative infiltration depth. Flow advance time and cumulative infiltration depth values were increased because of using SAP. Using SAP may have suitable effects on infiltration rate especially after the first irrigation event that reduces the cumulative infiltration substantially. Hence, the performance of surface irrigation methods and its water productivity can be improved. The results of statistical analysis also indicated that the effect of SAP and inflow rate levels on the improvement of infiltration was significant at 1% level. Using 9 g.m-2 SAP into 25 cm of soil with inflow rate of 0.5 l/s in plots having 90 m length increased the cumulative infiltration by 67% as compared to the control treatment. In addition, decreasing of inflow rate increased cumulative infiltration.