Wetting patterns in a subsurface irrigation system by using reservoirs of different permeabilities: experimental and HYDRUS-2D/3D modeling

Wetting patterns in a subsurface irrigation system by using reservoirs of different permeabilities: experimental and HYDRUS-2D/3D modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Wetting patterns in a subsurface irrigation system by using reservoirs of different permeabilities: experimental and HYDRUS-2D/3D modeling Zahra Jafari, SayedHamid Matinkhah, Mohammad Reza Mosaddeghi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1201561/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract In this study, wetting patterns around the reservoirs with different permeability (low, medium, and high) were assessed in the field and simulated using the HYDRUS-2D/3D software. The results showed that the highest soil water content was observed near the reservoir and it decreased with increasing distance from the reservoir. In the high permeability treatment, a large volume of water was released for almost three days, so that, more water was deep-percolated and lowered the soil water available to plant roots. In the low and medium permeability treatments, the water content slowly moved and observed that the maximum soil water content occurred in the 20-40 cm layer overtime. The HYDRUS-2D/3D software was able to simulate water flow and soil water content during the growth period of plants with a high correspondence between measured and simulated soil water contents ( R 2 = 0.90-0.95). HYDRUS-2D/3D subsurface irrigation reservoirs rangeland arid lands Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Water scarcity in the arid and semi-arid regions is a major concern. High-performance irrigation systems, such as surface or subsurface drip irrigation systems, are often recommended to overcome this problem and to dramatically increase the water use efficiency over that of traditional irrigation systems (Kandelous and Šimůnek, 2010). Subsurface irrigation with a ring-shaped emitter is one of the irrigation techniques in arid lands. Emitters are usually made from rubber although ring-shaped emitters have been successfully used for irrigation; its current design and operation are purely empirical. Besides, the current design of the ring-shaped emitter does not allow one to easily detect malfunctions because the emitter is fully covered with a permeable textile. As a result, it is not easy to repair it quickly (Saefuddin et al., 2019). Transient simulation models allow one to consider the physical processes governing the flow of water and chemicals in the unsaturated root-soil zone and consequently to evaluate the shape and the dimension of the wetting patterns as a function of the amount of applied water. The knowledge of the wetted soil volume dimensions as a function of time contributes, for each soil type, to identify proper design parameters (emitter spacing and distance between laterals) and the duration of irrigation providing to wet a fixed soil depth (Provenzano, 2007). Deterministic models define mathematically individual processes as well as interactions between them, and each set of input data leading to a unique and reproducible prediction (Šimůnek et al., 1999; Wagenet and Hutson, 1992). Using physically based simulation models also contributes to develop management scenarios and strategies for irrigation, aiming to find indications for water saving and consequently for increasing water use efficiency. To assess the accuracy of the models, it is necessary to proceed for the calibration and validation by using experimental measurements (Provenzano, 2007). Several empirical, analytical, and numerical models have been developed to simulate water flow, soil water content pattern and wetting dimensions in surface/subsurface irrigation systems (e.g., Philip, 1968; Warrick, 1974; Schwartzman and Zur, 1986; Angelakis et al., 1993; Chu, 1994; Ben-Asher and Phene, 1996; Cook et al., 2003). Due to advances in computer speed, and the public availability of numerical models simulating water flow and solute transport in the soils, many researchers have become interested in using such models for evaluating water flow in soils with subsurface irrigation systems (e.g., Ben-Asher and Phene, 1996; Cote et al., 2003; Skaggs et al., 2004; Provenzano, 2007). Among different models, HYDRUS-2D/3D (Šimůnek et al., 2008, 2016) is one of the most widely used dynamic, physically based models to simulate soil water dynamics. One of the advantages of this software is that its input parameters are closely related to soil physical properties, which could be measured either in-situ or in the lab (Karandish and Šimůnek, 2016b). The applicability of HYDRUS-2D/3D to numerous plant/soil scenarios have been demonstrated experimentally, including surface drip (Skaggs et al., 2004; Mubarak et al., 2009; Mailhol et al., 2011; Phogat et al., 2012; Li et al., 2015), and subsurface drip irrigation systems (Kandelous and Šimůnek, 2010; Mailhol et al., 2011). Kandelous et al. (2011) calibrated HYDRUS-2D/3D to simulate water movement from a subsurface drip irrigation system by comparing simulated results with measured soil water contents in several field experiments. Kandelous and Simunek (2010b) used HYDRUS-2D/3D to evaluate laboratory and field data involving water movement in a clay loam soil from point-water sources buried at different depths and with different discharge rates . Kandelous and Simunek (2010) investigated the capability of HYDRUS-2D/3D to estimate the dimensions of the wetted zone under laboratory and field conditions. A comparison of measurements and simulations exhibited root mean square error (RMSE) from 0.011 to 0.045 cm 3 cm 3 . Jahantigh (2021) studied the effects of irrigation methods of subsurface, clay pot and drop on Mulberry growth in dry land region and stated by establishing vegetation while saving limited water consumption in the region, along with its economic aspects for the stakeholders, should be effective as windbreaks and improvement of the environment for the development of the region. . Dastorani et al. (2010) evaluated the efficiency of surface and subsurface irrigation in dryland environments with planting pistachio trees and measuring their yield and concluded that considerable difference in efficiency of the two irrigation systems and relatively higher preference for a subsurface system over the traditionally used surface method. Precipitation in arid lands is low and often highly variable; therefore, efficient irrigation is the best approach for managing limited water supplies and irregular precipitation events. Furthermore, so many ,far research hasn’t been done for species that are cultivated in the natural arena, especially in the arid areas of Iran. The main objectives of this study were: 1) to compare the water distribution patterns around reservoirs made with different permeabilities in the irrigation, and 2) to assess the efficiency of the HYDRUS-2D/3D software in simulating water content distribution in the subsurface irrigation. 2 Methods And Materials 2.1. Experimental site The experiment was conducted at the rangelands of the Isfahan University of Technology, central Iran. The study site is characterized by dry and hot summers and mild and humid winters. The cultivated field with an area of 1000 m 2 is located in latitude 32° 43' N and longitude 51° 33' E, 1600 m above sea level. The mean annual temperature is 17.0°C, the mean annual rainfall is 116.9 mm and the mean annual relative humidity of air is 38%. The hottest and coldest months of the year are reported in July and January, respectively. The maximum and minimum absolute temperatures in the area are +48°C and –30°C, respectively (Soltani, 2004). The climate of this region according to Emberger’s climate classification is arid. 2.2. Soil analysis Soil samples were collected from the 0–50 cm layer below the surface to measure chemical and physical properties. The soil EC and pH were determined in the saturated extract (Slavich and Petterson, 1993). Soil texture was measured by the hydrometer method and soil texture class was determined using the USDA (United States Department of Agriculture) soil texture triangle (Bouyoucos, 1962). The bulk density was determined using the core method (Grossman and Reinsch, 2002), and the porosity of soil samples ( f ) was calculated using bulk density and particle density (i.e., 2.65 Mg/m 3 ). Field capacity (FC) and permanent wilt point (PWP) were determined using a pressure plate at matric potentials of –33 and –1500 kPa, respectively (Klute, 1986). Physical and chemical properties of the studied soil are presented in Table 1 . Table 1 Physical and chemical properties of the soil Depth pH EC Sand‌ Clay Silt FC PWP Bulk density (Mg/m 3 ) Porosity (m 3 /100 m 3 ) (cm) (-) (dS/m) (kg/100kg) 0–50 8.81 2.00 48.70 21.80 29.50 13.50 8.40 1.36 51.00 Note: EC, electrical conductivity; FC, water content at field capacity; PWP, water content at permanent wilting point. 2.3. Experimental design and treatments The reservoirs were made with a height of 50 cm and an outer diameter of 11 cm (i.e., a volume of 4749.25 cm 3 ) to irrigate the saplings. We used plaster molds to prepare the wet reservoirs, and then the reservoirs were cooked in a pottery kiln at 900°C. The planting pits, 50 cm×50 cm (width and depth) with a distance of 2 m, were drilled and the saplings together with reservoirs were placed in the pits. A lid was placed on the mouth of each reservoir to avoid evaporation loss. The dimensions of the reservoirs are presented in Table 2 . All the treatments were under the same environmental conditions. In this research, three permeability treatments for the reservoirs were considered. Various permeabilities in the reservoirs were attained by adding different amounts of wheat straw and manure to the reservoirs. Table 2 Geometric properties of the reservoirs used in this study. Parameters Reservoir dimensions Thickness (cm) 1 Height (cm) 50 Inner diameter (cm) 10 Outer diameter (cm) 11 Volume (cm 3 ) 4749 Area (cm 2 ) 1917 Tree of Heaven ( Ailanthus altissima ), Chinaberry ( Melia azedarach ), White mulberry ( Morus alba ) and Black locust ( Robinia pseudoacacia ) were planted in March 2016. 2.4. Determination of water depletion from the reservoirs The percentage of water loss through the reservoir walls was determined by filling the reservoir installed in the soil with water up to its neck level. After a specific time, the reservoir was filled with the same water up to the same level to replenish the water loss. The percentage of water released per unit of time was calculated by Eq. 1 (Naik et al., 2008): \(P=\frac{100v}{Vt}\) (1) where P is the percentage of water loss per unit of time through the reservoir, V is the neck level capacity of the reservoir (cm 3 ), v is the volume of water required to fill up the reservoir again to its neck level between two consecutive fillings (cm 3 ), and t (h) is the time elapsed between two consecutive fillings which was 24 h for all the reservoir. 2.5. Measuring saturated hydraulic conductivity (K s ) of the reservoirs A modified falling-head method was adapted to measure the saturated hydraulic conductivity ( K s ) of the whole reservoirs using tap water. The reservoirs were first submerged, with the reservoir full of water, in a tap water bath for three days for saturation. After that, the reservoir, full of water, was submerged to its neck in a water bucket for K s measurement. The water level in the bucket was kept constant using overflow. A manometer (length = 100 cm and diameter = 1cm) tube was inserted into a rubber stopper and fitted tightly to the mouth of the reservoir. A schematic diagram of the experimental setup is shown in Fig. 1 . Changes in the water head, which is the height of the water level in the manometer tube above the free water surface of the bucket, were monitored with time. The falling-head equation for the calculation of K s is given by Eq. 2 (Stein, 1990): \(\text{ln}\left(\frac{h}{{h}_{0}}\right)=\frac{A{K}_{\text{s}}}{aL}t\) (2) where h 0 is initial height of water level in the manometer tube above the free water surface (cm), h is height of water level in the manometer tube at time t (cm), A is the surface area of reservoir (cm 2 ), L is average wall thickness of the reservoir (m), K s is saturated hydraulic conductivity (m/s), and t is cumulative time (s). A plot of ln( h / h 0 ) versus time, t , gives a straight line. a is cross-sectional area of manometer tube (m 2 ). The K s of reservoirs can be calculated from the slope of the line if the other terms (i.e., L , A , and a ) are known. The average thickness of the reservoir was 1 cm. 2.6. Determination and modeling of soil hydraulic properties Soil hydraulic properties including soil water characteristic curve and saturated hydraulic conductivity were measured on the intact samples. Intact soil samples were collected from three locations in the field by pressing core samplers of diameter of 5.3 cm and height of 4.5 cm into the soil with minimum soil disturbance. Then, the soil samples were immersed and saturated in water for 48 h. The saturated soil samples were consecutively equilibrated at the matric suctions ( h ) of 0, 2, 5, 10, 20, 50, 100, 330, 500, 1000, 2000, 5000, 10000, and 15000 cm using a pressure plate (Gee and Ward, 2000 ). After applying the last pressure, the samples were dried in the oven and weighted. The gravimetric water content values were converted to volumetric water content ( θ ) by multiplying by the bulk density ( ρ b ). The saturated hydraulic conductivity ( K s ) of the soil was measured on the same samples using the constant-head method. The K s was calculated using the Darcy (1856) equation: \({K}_{\text{s}}=\frac{V\times L}{A\times t\times \varDelta H}\) (3) where, K s is the saturated hydraulic conductivity [LT −1 ], V is the volume of water outflow [L 3 ], L is the soil column height [L], A is the cross-sectional area of soil [L 2 ], Δ H is the hydraulic potential difference between the two ends of the sample [L] and t is the time difference from t 1 to t 2 [T]. Soil hydraulic properties were fitted to the van Genuchten-Mualem model (Eqs. 4-6): (Schaap et al, 2006) (4) \(K\left({S}_{e}\right)={K}_{s}{S}_{e}^{l}{\left[1+{(1-{S}_{1}^{1/m})}^{m}\right]}^{2}\) (5) where \({S}_{e}=\frac{\theta -{\theta }_{r}}{{\theta }_{s}-{\theta }_{r}}. m=1-\frac{1}{n}\) (6) where θ s is saturated water content [L 3 L −3 ], θ r is residual water content [L 3 L −3 ], S e is effective saturation; K ( S e ) is unsaturated hydraulic conductivity [LT −1 ], and n and α are shape parameters, and l is the soil pore tortuosity parameter, which is estimated to be about 0.5 on average for many soils. The Solver tool in MS Excel was used to fit the van Genuchten-Mualem model to the measured data of soil water characteristics curve and optimize its parameters. The optimized hydraulic parameters for the studied soil are presented in Table 3 . Table 3 Optimized van Genuchten-Mualem hydraulic parameters of the studied soil, and reservoir Permeeability Soil hydraulic parameters Soil - \({\theta }_{\text{r}}\) (cm 3 / cm 3 ) \({\theta }_{\text{s}}\) (cm 3 / cm 3 ) n \(\alpha\) (1/cm) \(\) \({K}_{\text{s}}\) (cm/day) l 0-10 cm 0.068 0.381 1.09 0.008 62 0.5 Reservoir Low 0.068 0.381 1.09 0.008 0.06 0.5 Medium 0.068 0.381 1.09 0.008 0.08 0.5 High 0.068 0.381 1.09 0.008 0.2 0.5 θ s : saturated water content, θ r : residual water content, S e is effective saturation, n and α are shape parameters, K s : saturated hydraulic conductivity, and l : soil pore tortuosity parameter. 2.7. Field measurements and numerical modeling of water flow The soil water content was measured one, three, and six days after irrigation at horizontal distances of 5, 25, and 50 cm from the reservoir and at depths of 0-10, 10-25, 25-40, and 40-75 cm from the soil surface 2 years after establishment sapling with TDR apparatus (TRIME-FM model with 0.1% accuracy) (Fig. 2 .). The water content distribution in the soil was modeled using HYDRUS-2D/3D (Simunek et al., 1999). Assuming a homogeneous and isotropic soil, the governing equation for water flow is the 2D Richards Equation as follows: \(\frac{\partial \theta }{\partial t}=\frac{\partial }{\partial x}\left[K \left(h\right)\frac{\partial h}{\partial x}\right]+\frac{\partial }{\partial z}\left[K \left(h\right)\frac{\partial h}{\partial z}+K \left(h\right)\right]-S\) (7) where, θ is volumetric water content [L 3 L −3 ], h is pressure head [L], t is time [T], x and z are horizontal and vertical space coordinates, respectively, and K s is saturated hydraulic conductivity [LT −1 ]. The S [T −1 ] as a sink term indicates the rate of water uptake by the roots from the soil.HYDRUS-2D/3D uses the Galerkin finite-element method to solve the transport equations as explained in detail by Simunek et al. (1999). The domain was defined by an axisymmetrical 2D geometry. The soil and reservoirs were defined as two soil materials. The reservoir were defined as variable head. In addition, boundary conditions on the soil surface were defined as atmospheric boundary condition to consider evaporation from the soil surface. The right and left sides of the modeled environment are no flux, because the range of effect of the reservoirs is up to this boundary. We simulated only the right side of the presumed symmetric profile. Thus, the boundaries of the finite-element mesh are rectangular except on the right edge near the upper right-hand corner where the reservoir is located (Fig. 3 ). The simulated environment in the model is a range of 100 cm in width and 150 cm in height. The dimensions of the overall mesh were 3 cm and around the reservoir was 1.6 cm. 2.8. Root distribution and water uptake parameters Plant-root distribution influences soil water and salinity distributions in the root domain under micro-irrigation. Root distribution is quantified by \(\beta\) ( r , z ) (dimensionless) in 2D geometry as follows: \(\beta \left(r.z\right)=\left[1-\frac{z}{{z}_{m}}\right]\left[(1-\frac{r}{{r}_{m}})\right]{e}^{-(\frac{{p}_{z}}{{z}_{m}}\left|{z}^{*}-z\right|+\frac{{p}_{r}}{{r}_{m}}\left|{r}^{*}-r\right|)}\) (8) where z and r are the distances [L] from the plant origin in the directions of z (depth), and r (radius), respectively, and z m and r m are the maximum rooting lengths [L] from the plant origin in the z and r directions, respectively. In Eq. 8, \({r}^{\text{*}}\) and \({ z}^{\text{*}}\) , and \({p}_{\text{z}}\) (-) and \({p}_{\text{r}}\) (-) are all empirical parameters controlling spatial root distribution. These empirical parameters were considered to provide zero root water uptake (RWU) beyond z m to account for asymmetrical RWU in the vertical and radial directions and to allow maximum RWU within 0 to z m (Vrugt et al., 2001b). Root distribution parameters by investigating the distribution of plant roots in the field, reviewing resources and with the help of HYDRUS 2D/3D software are shown in Table 4 . Table 4. Root distribution parameters and parameters of the Feddes model (i.e., threshold and critical soil water pressures, h ) for the studied plants. In the absence of osmotic stress, the actual rate of root water uptake, S ( h ), in any point of the simulation domain is computed according to the multilinear model proposed by Feddes et al. ( 1978 ). In the model, it is assumes that S ( h ) is proportional to the maximum (potential) root uptake rate occurring when water is not limiting plant transpiration: S ( h ) = α ( h ). S p (9) where α ( h ) is a dimensionless prescribed function of soil water pressure head and S p is the potential (maximal) water uptake rate by roots (cm 3 cm −3 d −1 ). Parameters of the Feddes model for water uptake by root are as follows: h 1 is the pressure head below which plant roots begin to extract water from the soil, h 2 is pressure head below which plant roots extract water at the maximum rate, h 3.low is limiting pressure head below which plant roots are not able to extract water at the maximum rate at potential transpiration rate of 0.1 cm day −1 , h 3.high is limiting pressure head at potential transpiration rate of 0.5 cm day −1 , h 4 is pressure head below which plant roots stop to take up water, which is usually taken at wilting point, r 2H is potential transpiration rate at which the limiting pressure head allows extracting water at the maximum rate and r 2L is potential transpiration rate at which the limiting pressure head allows extracting water at the minimum rate. Parameters of root water uptake by reviewing the sources, considering the water requirement of the plants and with the help of HYDRUS 2D/3D software are presented in Table 4 . 2.9. Evaluation of simulation model predictions The agreement of HYDRUS-2D/3D software predictions with the measured data was quantified in terms of three statistical measures: the mean bias error (ME), the root square error (RMSE), and the coefficient of determination ( R 2 ) defined as follows (Willmott, 1982 ): \(\text{M}\text{E}=\sum _{i=1}^{N}({P}_{i}-{O}_{i})/N\) (10) \(\text{R}\text{M}\text{S}\text{E}=\sqrt{\sum _{i=1}^{N}{({P}_{i}-{O}_{i})}^{2}/N}\) (11) \({R}^{2}=\frac{\sum _{i=1}^{N}{({P}_{i}-{O}_{i})}^{2}}{\sum _{i=1}^{N}{({O}_{i}-\overline{O})}^{2}}\) (12) where N is the number of data points, \({P}_{\text{i}}\) is the i th predicted data point, \({O}_{\text{i}}\) is the i th observed data, and \(\overline{O}\) is the mean of observed data. ME can identify potential bias (i.e., underestimation and overestimation) in the predicted values, whereas RMSE and \({R}^{2}\) give overall measures of the goodness-of-fit. 3 Results And Discussion 3.1. Water depletion from the reservoirs The percentage of water depletion of the reservoirs with different permeabilities is presented in Table 5 . We defined three treatments, namely, reservoirs with low, medium, and high permeability in this study according to the percentage of water depletion. At high cooking temperature, organic materials were completely burned and different pore spaces were produced in their places. Therefore, this resulted in different permeabilities of the reservoirs concerning the quantity of the organic matter. Therefore, it was expected that the organic materials had minimal nutritional and direct influences on the survival and growth of seedlings. The saturated hydraulic conductivity of reservoirs is also presented in Table 5 . The \({K}_{\text{s}}\) varied from 0.06 to 0.2 cm/day. Table 5 Percentage of water depletion and saturated hydraulic conductivity ( \({K}_{\text{s}}\) ) from reservoirs with different permeabilities Reservoir type Materials used for the preparation Percentage of water depletion after 24 h (%) Permeability \({K}_{\text{s}}\) (cm/day) R 1 95% clay + 5% manure 22 low 0.06 R 2 95% clay + 5% wheat straw 24 R 3 Clay soil 30 Medium 0.08 R 4 60% clay + 40% manure 48 High 0.2 R 5 60% clay + 40% wheat straw 50 3.2. Soil water characteristic curve Figure 3 shows the laboratory-measured and predicted data of soil water characteristic curve. The van Genuchten-Mualem model has an excellent fit to the measured data. 3.3. Optimal values of soil hydraulic parameters from inverse modeling Soil hydraulic parameters were optimized in the inverse modeling by HYDRUS-2D/3D. The HYDRUS-2D/3D software has been widely used for inverse modeling of water flow (Simunek et al., 2012). The estimated soil hydraulic parameters by inverse solution are presented in Table 6 . The optimized values of soil hydraulic parameters can be compared with those estimated by fitting the van Genuchten-Mualem model to the laboratory-measured data. The optimized θ s values (0.50 cm 3 cm -3 ) were higher than the measured value (0.38 cm 3 cm -3 ). The parameter n determines the shape of soil water characteristic curve and its optimized values (average 1.56) were higher than the fitted value (1.09). α for the model fitting to the measured data was equal to 0.008 cm -1 which is greater than its optimized value (0.0018 cm -1 ). Thus, the unsaturation zone occurs at lower matric suctions for the fitted set of parameters than the optimized set of parameters. The measured \({K}_{\text{s}}\) (62 cm/day) was greater than the optimized value (average 35 cm/day). The differences between the optimized and fitted sets of parameters can be explained by the fact that the optimized parameters are obtained by using the dynamic soil water content distribution in different parts of the soil profile and taking into account both wetting and drying processes, but in the laboratory, the hydraulic properties of the soil samples were measured at equilibrium condition (e.g., in pressure plate) or at steady-state condition (e.g., constant-head method). Table 6 The van Genuchten-Mualem parameters optimized in the inverse modeling by HYDRUS-2D/3D software. Permeability treatment \({\theta }_{\text{s}}\) (cm 3 / cm 3 ) \({K}_{\text{s}}\) (cm/ day) n \(\alpha\) (1/cm) Low permeability 0.50 31 1.46 0.0018 Medium permeability 0.50 37 1.50 0.0017 High permeability 0.50 37 1.72 0.0019 Average 0.500 35 1.56 0.0018 3.4. Water distribution and wetting pattern in the soil Wetting patterns in the soil and the spatial distribution of soil water and matric potential depends on soil hydraulic properties, emitter discharge rates, spacing and placement of emitters, irrigation amount and frequency, water uptake rates, and root distribution patterns (Gardenas et al. 2005 ). As shown in Figures 4-6, the water slowly moved from the reservoir with low permeability, but it moved faster and in all directions from the reservoir with high permeability. In other words, with increasing the hydraulic conductivity of the reservoir, the dimensions of the wetted area around the reservoir increased and the water content moved faster. As it can be seen, in the low permeability treatment, the water content values are at the lowest values after 24 h, the water content values slowly increased after three days, and finally, the soil around of the plant is soaked and water is slowly provided to the plant. The trend was the same in the moderate permeability treatment, but in the high permeability treatment, a large volume of water is discharged for almost three days, so the soil water content after one day is more than the other two treatments, and more water was deep-percolated and lowered the soil water available to plant roots. One of the important goals of pressurized irrigation systems, especially drip irrigation systems, is to prevent deep percolation and water outflow from the plant root area (Khorami et al., 2013 ). Irrigation system should be designed so that water is evenly distributed in the root zone. In the low permeability and medium permeability treatments, it is observed that the maximum soil water content occurred in the 20-40 cm layer overtime. The soil water content after three days of irrigation in the low permeability treatment ranged from 0.25 to 0.14 cm 3 cm −3 . In the moderate permeability treatment, it changed from 0.25 to 0.13 cm 3 cm −3 , and finally, in the high permeability treatment, the water content varied from 0.26 to 0.12 cm 3 cm −3 . Such differences might be related to different discharge rates and hydraulic conductivities of the used reservoirs. Li et al. ( 2004 ) stated that the dimensions and shape of the wetting pattern in the subsurface irrigation depended on the soil type and layering, the reservoir outlet flow rate, the time of cessation of irrigation, etc. In their research, they examined the distribution of the wetting pattern in the subsurface irrigation for two loamy and sandy soils, which can show the shape of the moisture distribution in the horizontal and vertical sections with exponential functions. They stated that with increasing discharge rate, the horizontal distribution of water content would increase, and with decreasing discharge rate, the water content distribution would increase in the vertical direction. The distribution of soil water content around the reservoir must be known for the proper management of subsurface systems to wet the plant root zone uniformly. It is also aimed to increase the efficiency of the water/fertilizer use, and to maintain a dry soil surface for low water losses due to evaporation. The horizontal and vertical expansion of the water content profiles is a function of the flow rate and the operating time of the system (Mohamadzade et al., 2015). The water content distribution at different distances and depths and for different treatments at one, three, and six days after irrigation in different permeability treatments are presented in Figs. 4 to 6 and using Surfer 10.0 software and the Kriging method. The subsurface irrigation system is based on the use of continuous water content, which determines the proper and uniform irrigation. In general, the highest soil water content was observed near the reservoir and the water content decreases with increasing distance from the reservoir. In the subsurface drip irrigation, the water content profile around the reservoir takes on the shape of concentric ellipses due to the gravitational effect (Oron et al., 1999 ). Several fields and laboratory studies have been carried out to study the effect of emitter discharge rate on the size of the wetting pattern. Some studies suggested that an increase in the discharge rate, for equal volumes of applied water, results in an increase in horizontal spreading but a decrease in wetted depth (Bresler et al., 1971 ; Ah Koon et al., 1990 ; Khan et al., 1996 ; Li et al., 2003 ). 3.5. Comparison of measured and simulated soil water content data The calibration results of measured and simulated values of soil water content with HYDRUS-2D/3D software in reservoirs with low, medium and high permeabilities are shown in Fig. 7. HYDRUS-2D/3D software was able to simulate the water flow and soil water content with high values of coefficient of determination ( R 2 = 0.90-0.95). This shows the high accuracy of simulation of soil water content pattern in the two-dimension, which was higher in the lower discharges (Table 7 ). Kandelous et al. (2010) simulated the water movement in a subsurface drip irrigation system and concluded that the correspondence between simulations and observations was very good. Nayebloei et al. (2015) simulated the 2D water content distribution in the subsurface drip irrigation and stated that soil water flow can be reasonably simulated while root water uptake and evaporation processes are both actively going on. Table 7 Statistics of calibration of measured and simulated values of soil water content with HYDRUS-2D/3D software. Treatment R 2 ME RMSE Low permeability 0.95 0.001 0.038 Medium permeability 0.93 0.005 0.048 High permeability 0.90 0.002 0.710 R 2= Coefficient of determination , ME= Mean Error, and RMSE= Root mean square error 4 Conclusions In this study, reservoirs with different permeabilities were made by using manure and wheat straw, so that, the saturated hydraulic conductivity ( K s ) of reservoirs varied from 0.06 to 0.2 cm/day. Then, the reservoirs were used in the field for reservoir irrigation of saplings and HYDRUS-2D/3D software was used to simulate wetting pattern in the soil profile around the reservoir. With increasing the hydraulic conductivity of the reservoirs, the dimensions of the wetted area increased and the water content moved faster in the soil. The water slowly released from the reservoirs with low and medium permeabilities, but in the high permeability treatment, a large volume of water was released for almost three days, so that, more water was deep-percolated which lowered the soil water available to plant roots leading to adverse effects on the plant growth. In the low and medium permeability treatments, it was observed that the maximum soil water content occurred in the 20-40 cm layer overtime. HYDRUS-2D/3D software was able to simulate soil water content during the growth period of plants with a high correspondence between the measured and predicted data. This finding showed the high accuracy of simulating the water content pattern in the two-dimension by HYDRUS-2D/3D. It is suggested that the results of HYDRUS-2D/3D simulations and different irrigation scenarios be used to determine the water content distribution and suitable places for planting seedlings. Abbreviations 2D/3D= Two-dimensional/Three- dimensional °C= Temperature pH= Power of the concentration of the hydrogen ion: Millimeter cm= Centimeter mm= Millimeter q s = Saturated water content, q r = Residual water content, S e = Effective saturation, K s = Saturated hydraulic conductivity l= Soil pore tortuosity parameter. R 2 = Coefficient of determination ME= Mean Error RMSE= Root mean square error TDR= Time domain reflectometry h= Pressure head q- Volumetric water content r b = Bulk density EC= Electrical conductivity FC= Water content at field capacity PWP= Water content at permanent wilting point. Declarations 5 Acknowledgements The Isfahan University of Technology, Iran (1974) supported this study. We thank especially the anonymous reviewers and the editor for their careful reading and many insightful comments and suggestions. Kindly, I am writing to submit our manuscript entitled “ Wetting patterns in a subsurface irrigation system by using reservoirs of different permeabilities: experimental and HYDRUS-2D/3D modeling ”, for publication in your journal. We appreciate your consideration of our manuscript, and we look forward to receiving comments from your respected reviewers. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The manuscript has not been previously published, is not currently submitted for review to any other journal, and will not be submitted elsewhere before a decision is made by this journal. References Ah Koon PD, Gregory PJ, Bell JP (1990) Influence of drip irrigation emission rate on distribution and drainage of water beneath a sugarcane and a fallow plot. Agric Water Manag 17:267–282 Angelakis AN, Rolston DE, Kadir TN, Scott VN (1993) Soil-water distribution under trickle source. J Irrig Drain Eng ASCE 119:484–500 Ben-Asher J, Phene CJ (1996) Surface and subsurface drip irrigation: an analysis by a numerical model. Rep. Jacob Blaustein Institute for Desert Research, Ben Gurion University of Negev, Sede Boker Campus, Negev, Israel Bresler E, Heller J, Diner N, Ben-Asher J, Brandt A, Goldberg D (1971) Infiltration from a trickle source. II: experimental data and theoretical predictions. Soil Sci Soc Am Proc. 35, 683–689 Chu ST (1994) Green-Ampt analysis of wetting patterns for surface emitters. J Irrig Drain Eng ASCE 120(2):414–421 Cook FJ, Thorburn PJ, Fitch P, Bristow KL (2003) WetUp: a software tool to display approximate wetting pattern from drippers. Irrig Sci 22:129–134 Cote CM, Bristow KL, Charlesworth PB, Cook FJ, Thorburn PJ (2003) Analysis of soil wetting and solute transport in subsurface trickle irrigation. Irrig Sci 22:143–156 Darcy H 1856. Les Fontaines Publiques de la Ville de Dijon [The Public Fountains of the City of Dijon].Dalmont, Paris Dastorani MT, Heshmati M, Sadeghzadeh MA (2010) Evaluation of the efficiency of surface and subsurface irrigation in dryland environments. Irrigation and Drainage: The journal of the International Commission on Irrigation and Drainage 59(2):129–137 Feddes RA, Kowalik P, Zarandy H (1978) Simulation of Field Water Use and Crop Yield. Pudoc. Wageningen. The Netherlands Gardenas AI, Hopman JW, Hanson BR, Šimůnek. J (2005) Two-dimensional modeling of nitrate leaching for various fertigation scenarios under micro-irrigation. Agri Water Manage 74(3):219–242 Gee GW, Ward AL (2000) Innovations in two-phase measurements of soil hydraulic properties.p. 241-269. In M.Th. van Genuchten (ed.). Proceedings of an International Conference on Methods for Estimating the Hydraulic Propertise of Understand Soils. U.C. Reverside Press. Riverside, CA Jahantigh M (2021) Effects of irrigation methods of subsurface, clay pot and drop on Mulberry growth in dry land region (Case study: Sistan area). J Water Soil Manage Model 1(2):28–39 Kandelous MM, Šimůnek. J (2010) Numerical simulations of water movement in a subsurface drip irrigation system under field and laboratory conditions using HYDRUS-2D. Agric Water Manage 97:1070–1076 Khan AA, Yitayew M, Warrick AW (1996) Field evaluation of water and solute distribution from a point source. J Irrig Drain Eng 22(4):221–227 Khorami M, Alizadeh A, Ansari H (2013) Simulation of water movement and redistribution of soil moisture in subsurface drip irrigation by HYDRUS 2D/3D model. J Water and Soil (Agri Sci Tech 27(4):702–692 Kandelous MM, Šimůnek J, van Genuchten MT, Malek K (2011) Soil water content distributions between two emitters of a subsurface drip irrigation system. Soil Sci Soc Am J 75:488 Kandelous MM, Šimunek J (2010b) Numerical simulations of water movement in a subsurface drip irrigation system under field and laboratory conditions using HYDRUS-2D. Agric. Water Manage 97:1070–1076 Karandish F, Šimunek J (2016b) Numerical and machine-learning modeling of soil water content for sustainable water management in agriculture under water stress. J Hydro 543:892–909 Koumanov KS, Hopmans JW, Schwankl LJ, Andreu L, Tuli A (1997) Application efficiency of micro-sprinkler irrigation of almond trees. Agri Water Manage 34(3):247–263 Li X, Shi H, Simunek JS, Gong X, Peng Z (2015) Modeling soil water dynamics in a drip-irrigated intercropping field under plastic mulch. Irrig Sci 33:289–302 Li J, Zhang J, Rao M (2004) Wetting patterns and nitrogen distributions as affected by fertigation strategies from a surface point source. J Agric Water Manage 67(2):89–104 Li J, Zhang J, Ren L (2003) Water and nitrogen distribution as affected by fertigation of ammonium nitrate from a point source. Irrig Sci 22(1):12–30 Mailhol JC, Ruelle P, Walser S, Schu¨ tze N, Dejean C (2011) Analysis of AET and yield predictions under surface and buried drip irrigation systems using the Crop Model PILOTE and HYDRUS-2D. Agric. Water Manage 98:1033–1044 Mohammadzade F, Gheysari M, Landi E (2015) Development and Evaluation of Estimation Models of Wetting Pattern of Drippers in a Sandy Soil with High Gravel. J Water and Soil Sci 19(71):287–297 Mubarak I, Mailhol JC, Angulo-Jaramillo R, Ruelle P, Boivin P, Khaledian M (2009) Temporal variability in soil hydraulic properties under drip irrigation. Geoderma 150:158e165 Nayebloie F, Kouchakzadeh M, Ebrahimi K, Homaee M, Abbasi F (2015) Simulation of 2D Soil Moisture Distribution under Subsurface Drip Irrigation. Iranian, J Soil and Water Res 46(2):222–229 Oron G, DeMalach Y, Gillerman L, David I, Rao V (1999) Improved saline-water use under subsurface drip irrigation. Agric Water Manage 39(1):19–33 Philip JR (1968) Steady infiltration from buried point sources and spherical cavities. Water Resour Res 4:1039–1047 Provenzano G (2007) Using HYDRUS-2D simulation model to evaluate wetted soil volume in subsurface drip irrigation systems. J Irrig Drain Eng 133(4):342–349 Saefuddin R, Saito H, Šimůnek J (2019) Experimental and numerical evaluation of a ring-shaped emitter for subsurface irrigation. Agric water manage 211:111–122 Schaap MG, Van Genuchten MT (2006) A modified Mualem–van Genuchten formulation for improved description of the hydraulic conductivity near saturation. Vadose Zone J 5(1):27–34 Schwartzman M, Zur B (1986) Emitter spacing and geometry of wetted soil volume. J Irrig Drain Eng 112(3):242–253 Simuunek J, Sejna M, van Genuchten MTh (1999) ‘‘The HYDRUS-2D software package for simulating the two-dimensional movement of water, heat, and multiple solutes in variably saturated media.’’ IGWMC-TPS 53, Version 2.0. International Ground Water Modeling Center, Colorado School of Mines, Golden, Colo Skaggs TH, Trout TJ, Sˇimu˚ nek J, Shouse PJ (2004) Comparison of HYDRUS-2D simulations of drip irrigation with experimental observations. J Irrig Drain Eng ASCE 130(4):304–310 Simůnek J, van Genuchten MTh, Šejna M (2016) Recent developments and applications of the HYDRUS computer software packages. Vadose Zone J 15(7):25. doi: 10.2136/vzj2016.04.0033 Skaggs TH, Trout TJ, Simunek J, Shouse PJ (2004) Comparison of HYDRUS-2D simulations of drip irrigation with experimental observations. J Irrig Drain Eng 130:304–310 Sun Y, Zhou H, Qin Y, Lammers PS, Berg A, Deng H, Cai X, Wang D, Jones SB (2014) Horizontal monitoring of soil water content using a novel automated and mobile electromagnetic access-tube sensor. J Hydro 516:50–55 Wagenet RJ, Hutson JL (1992) “LEACHM: A process based model of water and solute movement, transformation, plant uptake, and chemical reactions in the unsaturated zone. ” Centre of Environmental Research, Cornell Univ., Ithaca, N.Y Warrick AW (1974) Time-dependent linearized infiltration. I. Point sources. Soil Sci. Soc. Am. Proc. 38, 383–386 Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 63(11):1309–1313 Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Jan, 2022 Reviewers invited by journal 24 Jan, 2022 Editor assigned by journal 09 Jan, 2022 First submitted to journal 23 Dec, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1201561","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":78796041,"identity":"bbd80678-de10-4cf9-b689-b13f2a683ddd","order_by":0,"name":"Zahra Jafari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie3RsUrEMBjA8S8Evi6hXQOKfYWWDjfd3au0FM6lCG43dMhUl+CtCuIzOLkaCHTy3sDhbrmpQ13kBhHT9hCHhnMULv8hBMKPLyEALte/jHULgicAVLo0e3o4wGOEKUM2rz0h4g8EekK2Vb8fiK3wZl23e/BD5q23Knt8C8Fjkw2UM/DP1Cgh8iq/l4CxZJeRyp53saAsFlDngH46SigUibkbkhdYgCGaDAQVIBu/GAZNQj4B5zLYGfKg5wP5shPGi4SaKZnk3RShs56Qyk44bxJ6HmEuuZmS1jqvKF7fZbc5s5FwVSSkWdZTGSzo+77U01Wgn9r2Y3YRynEyFNW/Xtct6c9/WSuPnLtcLtdJ9w1kLE+ja1Wk2AAAAABJRU5ErkJggg==","orcid":"","institution":"Isfahan University of Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Jafari","suffix":""},{"id":78796042,"identity":"62e62d25-f01f-4c88-b391-7a4445d556b8","order_by":1,"name":"SayedHamid Matinkhah","email":"","orcid":"","institution":"Isfahan University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"SayedHamid","middleName":"","lastName":"Matinkhah","suffix":""},{"id":78796043,"identity":"9ee4adee-220f-4c44-830f-81e21f3df2fe","order_by":2,"name":"Mohammad Reza Mosaddeghi","email":"","orcid":"","institution":"Isfahan University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Reza","lastName":"Mosaddeghi","suffix":""}],"badges":[],"createdAt":"2021-12-24 07:36:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1201561/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1201561/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":17741847,"identity":"12669297-887c-4a60-ab3e-83c50313a4f3","added_by":"auto","created_at":"2022-01-28 16:12:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17667,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the falling-head permeameter used to measure the saturated hydraulic conductivity of the reservoirs.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-1201561/v1/22b630de487b67b8a1543a7f.png"},{"id":17741853,"identity":"88a6b87c-0aea-46a9-ba5e-202422a53e29","added_by":"auto","created_at":"2022-01-28 16:12:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":145112,"visible":true,"origin":"","legend":"\u003cp\u003eDispersion of sampling points in soil profiles used in the reverse solution process\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1201561/v1/d499d62f9f54f7140429dd7a.png"},{"id":17741852,"identity":"5ac963c4-9e6e-4d36-9078-2927621ae7bf","added_by":"auto","created_at":"2022-01-28 16:12:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":151323,"visible":true,"origin":"","legend":"\u003cp\u003eBoundary conditions and geometric dimensions of the soil environment simulated in HYDRUS-2D/3D software.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-1201561/v1/be42aee4f95b21914159ebd7.png"},{"id":17741851,"identity":"6a441066-898c-4250-ab6c-f2918b40be65","added_by":"auto","created_at":"2022-01-28 16:12:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":11544,"visible":true,"origin":"","legend":"\u003cp\u003eThe laboratory-measured data of soil water characteristic curve and prediction of van Genuchten-Mualem model.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-1201561/v1/8806035d390c46962d8ea675.png"},{"id":17741848,"identity":"dd9b8559-e00d-4a0e-9128-86df4a241cf7","added_by":"auto","created_at":"2022-01-28 16:12:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":409367,"visible":true,"origin":"","legend":"\u003cp\u003eWetting pattern in the soil around reservoirs with low permeability: a) one day, b) three days, and c) six days after irrigation.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-1201561/v1/a562909f6d7ff33ea7c6a14a.png"},{"id":17742054,"identity":"59f401e8-e92e-481d-951e-3b48bba3ddf3","added_by":"auto","created_at":"2022-01-28 16:15:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":426999,"visible":true,"origin":"","legend":"\u003cp\u003eWetting pattern in the soil around reservoirs with medium permeability: a) one day, b) three days, and c) six days after irrigation.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-1201561/v1/df45512d054e0c5e2ad613d4.png"},{"id":17742055,"identity":"402b06b6-be01-4534-9810-869d801cf627","added_by":"auto","created_at":"2022-01-28 16:15:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":490919,"visible":true,"origin":"","legend":"\u003cp\u003eWetting pattern in the soil around reservoirs with high permeability: a) one day, b) three days, and c) six days after irrigation.\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-1201561/v1/a8e7416e71d9f71689adea01.png"},{"id":17742053,"identity":"1805c3fa-68d3-4888-a443-d24e8df01364","added_by":"auto","created_at":"2022-01-28 16:15:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":58909,"visible":true,"origin":"","legend":"\u003cp\u003eResults of calibration of measured and simulated values of soil water content with HYDRUS-2D/3D software in reservoirs with a) low permeability, b) medium permeability, and c) high permeability.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-1201561/v1/42386bdef3af64c1b0f62a20.png"},{"id":17742056,"identity":"97d80bc3-120d-4d87-8f9f-01a14c31d747","added_by":"auto","created_at":"2022-01-28 16:15:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2193451,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1201561/v1/18fe4cec-fafc-41f3-8d0d-b13825f99fc4.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eWetting patterns in a subsurface irrigation system by using reservoirs of different permeabilities: experimental and HYDRUS-2D/3D modeling\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWater scarcity in the arid and semi-arid regions is a major concern. High-performance irrigation systems, such as surface or subsurface drip irrigation systems, are often recommended to overcome this problem and to dramatically increase the water use efficiency over that of traditional irrigation systems (Kandelous and \u0026Scaron;imůnek, 2010). Subsurface irrigation with a ring-shaped emitter is one of the irrigation techniques in arid lands. Emitters are usually made from rubber although ring-shaped emitters have been successfully used for irrigation; its current design and operation are purely empirical. Besides, the current design of the ring-shaped emitter does not allow one to easily detect malfunctions because the emitter is fully covered with a permeable textile. As a result, it is not easy to repair it quickly (Saefuddin et al., 2019).\u003c/p\u003e\n\u003cp\u003eTransient simulation models allow one to consider the physical processes governing the flow of water and chemicals in the unsaturated root-soil zone and consequently to evaluate the shape and the dimension of the wetting patterns as a function of the amount of applied water. The knowledge of the wetted soil volume dimensions as a function of time contributes, for each soil type, to identify proper design parameters (emitter spacing and distance between laterals) and the duration of irrigation providing to wet a fixed soil depth (Provenzano, 2007). Deterministic models define mathematically individual processes as well as interactions between them, and each set of input data leading to a unique and reproducible prediction (\u0026Scaron;imůnek et al., 1999; Wagenet and Hutson, 1992). Using physically based simulation models also contributes to develop management scenarios and strategies for irrigation, aiming to find indications for water saving and consequently for increasing water use efficiency. To assess the accuracy of the models, it is necessary to proceed for the calibration and validation by using experimental measurements (Provenzano, 2007).\u003c/p\u003e\n\u003cp\u003eSeveral empirical, analytical, and numerical models have been developed to simulate water flow, soil water content pattern and wetting dimensions in surface/subsurface irrigation systems (e.g., Philip, 1968; Warrick, 1974; Schwartzman and Zur, 1986; Angelakis et al., 1993; Chu, 1994; Ben-Asher and Phene, 1996; Cook et al., 2003). Due to advances in computer speed, and the public availability of numerical models simulating water flow and solute transport in the soils, many researchers have become interested in using such models for evaluating water flow in soils with subsurface irrigation systems (e.g., Ben-Asher and Phene, 1996; Cote et al., 2003; Skaggs et al., 2004; Provenzano, 2007). Among different models, HYDRUS-2D/3D (\u0026Scaron;imůnek et al., 2008, 2016) is one of the most widely used dynamic, physically based models to simulate soil water dynamics. One of the advantages of this software is that its input parameters are closely related to soil physical properties, which could be measured either in-situ or in the lab (Karandish and \u0026Scaron;imůnek, 2016b). The applicability of HYDRUS-2D/3D to numerous plant/soil scenarios have been demonstrated experimentally, including surface drip (Skaggs et al., 2004; Mubarak et al., 2009; Mailhol et al., 2011; Phogat et al., 2012; Li et al., 2015), and subsurface drip irrigation systems (Kandelous and \u0026Scaron;imůnek, 2010; Mailhol et al., 2011). Kandelous et al. (2011) calibrated HYDRUS-2D/3D to simulate water movement from a subsurface drip irrigation system by comparing simulated results with measured soil water contents in several field experiments. Kandelous and Simunek (2010b) used HYDRUS-2D/3D to evaluate laboratory and field data involving water movement in a clay loam soil from point-water sources buried at different depths and with different discharge rates\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e Kandelous and Simunek (2010) investigated the capability of HYDRUS-2D/3D to estimate the dimensions of the wetted zone under laboratory and field conditions. A comparison of measurements and simulations exhibited root mean square error (RMSE) from 0.011 to 0.045 cm\u003csup\u003e3\u003c/sup\u003e cm\u003csup\u003e3\u003c/sup\u003e. Jahantigh (2021) studied the effects of irrigation methods of subsurface, clay pot and drop on Mulberry growth in dry land region and stated by establishing vegetation while saving limited water consumption in the region, along with its economic aspects for the stakeholders, should be effective as windbreaks and improvement of the environment for the development of the region. . Dastorani et al. (2010) evaluated the efficiency of surface and subsurface irrigation in dryland environments with planting pistachio trees and measuring their yield and concluded that considerable difference in efficiency of the two irrigation systems and relatively higher preference for a subsurface system over the traditionally used surface method.\u003c/p\u003e\n\u003cp\u003ePrecipitation in arid lands is low and often highly variable; therefore, efficient irrigation is the best approach for managing limited water supplies and irregular precipitation events. Furthermore, so many ,far research hasn\u0026rsquo;t been done for species that are cultivated in the natural arena, especially in the arid areas of Iran. \u003c/p\u003e\n\u003cp\u003eThe main objectives of this study were: 1) to compare the water distribution patterns around reservoirs made with different permeabilities in the irrigation, and 2) to assess the efficiency of the HYDRUS-2D/3D software in simulating water content distribution in the subsurface irrigation.\u003c/p\u003e"},{"header":"2 Methods And Materials","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e2.1. Experimental site\u003c/h2\u003e\n \u003cp\u003eThe experiment was conducted at the rangelands of the Isfahan University of Technology, central Iran. The study site is characterized by dry and hot summers and mild and humid winters. The cultivated field with an area of 1000 m\u003csup\u003e2\u003c/sup\u003e is located in latitude 32\u0026deg; 43\u0026apos; N and longitude 51\u0026deg; 33\u0026apos; E, 1600 m above sea level. The mean annual temperature is 17.0\u0026deg;C, the mean annual rainfall is 116.9 mm and the mean annual relative humidity of air is 38%. The hottest and coldest months of the year are reported in July and January, respectively. The maximum and minimum absolute temperatures in the area are +48\u0026deg;C and \u0026ndash;30\u0026deg;C, respectively (Soltani, 2004). The climate of this region according to Emberger\u0026rsquo;s climate classification is arid.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.2. Soil analysis\u003c/h2\u003e\n \u003cp\u003eSoil samples were collected from the 0\u0026ndash;50 cm layer below the surface to measure chemical and physical properties. The soil EC and pH were determined in the saturated extract (Slavich and Petterson, 1993). Soil texture was measured by the hydrometer method and soil texture class was determined using the USDA (United States Department of Agriculture) soil texture triangle (Bouyoucos, 1962). The bulk density was determined using the core method (Grossman and Reinsch, 2002), and the porosity of soil samples (\u003cem\u003ef\u003c/em\u003e) was calculated using bulk density and particle density (i.e., 2.65 Mg/m\u003csup\u003e3\u003c/sup\u003e). Field capacity (FC) and permanent wilt point (PWP) were determined using a pressure plate at matric potentials of \u0026ndash;33 and \u0026ndash;1500 kPa, respectively (Klute, 1986). Physical and chemical properties of the studied soil are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePhysical and chemical properties of the soil\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDepth\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSand\u0026zwnj;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClay\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSilt\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePWP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBulk density\u003c/p\u003e\n \u003cp\u003e(Mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePorosity\u003c/p\u003e\n \u003cp\u003e(m\u003csup\u003e3\u003c/sup\u003e/100 m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(cm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(-)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(dS/m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e(kg/100kg)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\"\u003eNote: EC, electrical conductivity; FC, water content at field capacity; PWP, water content at permanent wilting point.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e2.3. Experimental design and treatments\u003c/h2\u003e\n \u003cp\u003eThe reservoirs were made with a height of 50 cm and an outer diameter of 11 cm (i.e., a volume of 4749.25 cm\u003csup\u003e3\u003c/sup\u003e) to irrigate the saplings. We used plaster molds to prepare the wet reservoirs, and then the reservoirs were cooked in a pottery kiln at 900\u0026deg;C. The planting pits, 50 cm\u0026times;50 cm (width and depth) with a distance of 2 m, were drilled and the saplings together with reservoirs were placed in the pits. A lid was placed on the mouth of each reservoir to avoid evaporation loss. The dimensions of the reservoirs are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eAll the treatments were under the same environmental conditions. In this research, three permeability treatments for the reservoirs were considered. Various permeabilities in the reservoirs were attained by adding different amounts of wheat straw and manure to the reservoirs.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeometric properties of the reservoirs used in this study.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReservoir dimensions\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThickness (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInner diameter (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOuter diameter (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolume (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTree of Heaven (\u003cem\u003eAilanthus altissima\u003c/em\u003e), Chinaberry (\u003cem\u003eMelia azedarach\u003c/em\u003e), White mulberry (\u003cem\u003eMorus alba\u003c/em\u003e) and Black locust (\u003cem\u003eRobinia pseudoacacia\u003c/em\u003e) were planted in March 2016.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003e2.4. Determination of water depletion from the reservoirs\u003c/h2\u003e\n \u003cp\u003eThe percentage of water loss through the reservoir walls was determined by filling the reservoir installed in the soil with water up to its neck level. After a specific time, the reservoir was filled with the same water up to the same level to replenish the water loss. The percentage of water released per unit of time was calculated by Eq.\u0026nbsp;1 (Naik et al., 2008):\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P=\\frac{100v}{Vt}\\) \u003c/span\u003e\u003c/span\u003e(1)\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cem\u003eP\u003c/em\u003e is the percentage of water loss per unit of time through the reservoir, \u003cem\u003eV\u003c/em\u003e is the neck level capacity of the reservoir (cm\u003csup\u003e3\u003c/sup\u003e), \u003cem\u003ev\u003c/em\u003e is the volume of water required to fill up the reservoir again to its neck level between two consecutive fillings (cm\u003csup\u003e3\u003c/sup\u003e), and \u003cem\u003et\u003c/em\u003e (h) is the time elapsed between two consecutive fillings which was 24 h for all the reservoir.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003e2.5. Measuring saturated hydraulic conductivity (K\u003csub\u003es\u003c/sub\u003e) of the reservoirs\u003c/h2\u003e\n \u003cp\u003eA modified falling-head method was adapted to measure the saturated hydraulic conductivity (\u003cem\u003eK\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e) of the whole reservoirs using tap water. The reservoirs were first submerged, with the reservoir full of water, in a tap water bath for three days for saturation. After that, the reservoir, full of water, was submerged to its neck in a water bucket for \u003cem\u003eK\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e measurement. The water level in the bucket was kept constant using overflow. A manometer (length = 100 cm and diameter = 1cm) tube was inserted into a rubber stopper and fitted tightly to the mouth of the reservoir. A schematic diagram of the experimental setup is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Changes in the water head, which is the height of the water level in the manometer tube above the free water surface of the bucket, were monitored with time. The falling-head equation for the calculation of \u003cem\u003eK\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e is given by Eq. 2 (Stein, 1990):\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{ln}\\left(\\frac{h}{{h}_{0}}\\right)=\\frac{A{K}_{\\text{s}}}{aL}t\\) \u003c/span\u003e\u003c/span\u003e(2)\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cem\u003eh\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e is initial height of water level in the manometer tube above the free water surface (cm), \u003cem\u003eh\u003c/em\u003e is height of water level in the manometer tube at time \u003cem\u003et\u003c/em\u003e (cm), \u003cem\u003eA\u003c/em\u003e is the surface area of reservoir (cm\u003csup\u003e2\u003c/sup\u003e), \u003cem\u003eL\u003c/em\u003e is average wall thickness of the reservoir (m), \u003cem\u003eK\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e is saturated hydraulic conductivity (m/s), and \u003cem\u003et\u003c/em\u003e is cumulative time (s). A plot of ln(\u003cem\u003eh\u003c/em\u003e/\u003cem\u003eh\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e) versus time, \u003cem\u003et\u003c/em\u003e, gives a straight line. a is cross-sectional area of manometer tube (m\u003csup\u003e2\u003c/sup\u003e). The \u003cem\u003eK\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e of reservoirs can be calculated from the slope of the line if the other terms (i.e., \u003cem\u003eL\u003c/em\u003e, \u003cem\u003eA\u003c/em\u003e, and \u003cem\u003ea\u003c/em\u003e) are known. The average thickness of the reservoir was 1 cm.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003e2.6. Determination and modeling of soil hydraulic properties\u003c/h2\u003e\n \u003cp\u003eSoil hydraulic properties including soil water characteristic curve and saturated hydraulic conductivity were measured on the intact samples. Intact soil samples were collected from three locations in the field by pressing core samplers of diameter of 5.3 cm and height of 4.5 cm into the soil with minimum soil disturbance. Then, the soil samples were immersed and saturated in water for 48 h. The saturated soil samples were consecutively equilibrated at the matric suctions (\u003cem\u003eh\u003c/em\u003e) of 0, 2, 5, 10, 20, 50, 100, 330, 500, 1000, 2000, 5000, 10000, and 15000 cm using a pressure plate (Gee and Ward, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). After applying the last pressure, the samples were dried in the oven and weighted. The gravimetric water content values were converted to volumetric water content (\u003cem\u003e\u0026theta;\u003c/em\u003e) by multiplying by the bulk density (\u003cem\u003e\u0026rho;\u003c/em\u003e\u003csub\u003eb\u003c/sub\u003e). The saturated hydraulic conductivity (\u003cem\u003eK\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e) of the soil was measured on the same samples using the constant-head method. The \u003cem\u003eK\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e was calculated using the Darcy (1856) equation:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({K}_{\\text{s}}=\\frac{V\\times L}{A\\times t\\times \\varDelta H}\\) \u003c/span\u003e\u003c/span\u003e(3)\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere, \u003cem\u003eK\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e is the saturated hydraulic conductivity [LT\u003csup\u003e\u0026minus;1\u003c/sup\u003e], \u003cem\u003eV\u003c/em\u003e is the volume of water outflow [L\u003csup\u003e3\u003c/sup\u003e], \u003cem\u003eL\u003c/em\u003e is the soil column height [L], \u003cem\u003eA\u003c/em\u003e is the cross-sectional area of soil [L\u003csup\u003e2\u003c/sup\u003e], \u0026Delta;\u003cem\u003eH\u003c/em\u003e is the hydraulic potential difference between the two ends of the sample [L] and \u003cem\u003et\u003c/em\u003e is the time difference from \u003cem\u003et\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e to \u003cem\u003et\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e [T].\u003c/p\u003e\n\u003cp\u003eSoil hydraulic properties were fitted to the van Genuchten-Mualem model (Eqs. 4-6): \u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003e(Schaap et al, 2006)\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e (4)\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(K\\left({S}_{e}\\right)={K}_{s}{S}_{e}^{l}{\\left[1+{(1-{S}_{1}^{1/m})}^{m}\\right]}^{2}\\) \u003c/span\u003e\u003c/span\u003e(5)\u003c/p\u003e\n\u003cp\u003ewhere\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{e}=\\frac{\\theta -{\\theta }_{r}}{{\\theta }_{s}-{\\theta }_{r}}. m=1-\\frac{1}{n}\\) \u003c/span\u003e\u003c/span\u003e(6)\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003e\u0026theta;\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e is saturated water content [L\u003csup\u003e3\u003c/sup\u003eL\u003csup\u003e\u0026minus;3\u003c/sup\u003e], \u003cem\u003e\u0026theta;\u003c/em\u003e\u003csub\u003er\u003c/sub\u003e is residual water content [L\u003csup\u003e3\u003c/sup\u003eL\u003csup\u003e\u0026minus;3\u003c/sup\u003e], \u003cem\u003eS\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e is effective saturation; \u003cem\u003eK\u003c/em\u003e(\u003cem\u003eS\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e) is unsaturated hydraulic conductivity [LT\u003csup\u003e\u0026minus;1\u003c/sup\u003e], and \u003cem\u003en\u003c/em\u003e and \u003cem\u003e\u0026alpha;\u003c/em\u003e are shape parameters, and \u003cem\u003el\u003c/em\u003e is the soil pore tortuosity parameter, which is estimated to be about 0.5 on average for many soils.\u003c/p\u003e\n\u003cp\u003eThe Solver tool in MS Excel was used to fit the van Genuchten-Mualem model to the measured data of soil water characteristics curve and optimize its parameters. The optimized hydraulic parameters for the studied soil are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOptimized van Genuchten-Mualem hydraulic parameters of the studied soil, and reservoir\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePermeeability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eSoil hydraulic parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\theta }_{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e (cm\u003csup\u003e3\u003c/sup\u003e/ cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\theta }_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e (cm\u003csup\u003e3\u003c/sup\u003e/ cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003e (1/cm)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({K}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e (cm/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003el\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-10 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eReservoir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u0026theta;\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e: saturated water content, \u003cem\u003e\u0026theta;\u003c/em\u003e\u003csub\u003er\u003c/sub\u003e: residual water content, \u003cem\u003eS\u003c/em\u003e\u003csub\u003ee\u003c/sub\u003e is effective saturation, \u003cem\u003en\u003c/em\u003e and \u003cem\u003e\u0026alpha;\u003c/em\u003e are shape parameters, \u003cem\u003eK\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e: saturated hydraulic conductivity, and \u003cem\u003el\u003c/em\u003e: soil pore tortuosity parameter.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e2.7. Field measurements and numerical modeling of water flow\u003c/h2\u003e\n \u003cp\u003eThe soil water content was measured one, three, and six days after irrigation at horizontal distances of 5, 25, and 50 cm from the reservoir and at depths of 0-10, 10-25, 25-40, and 40-75 cm from the soil surface 2 years after establishment sapling with TDR apparatus (TRIME-FM model with 0.1% accuracy) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.).\u003c/p\u003e\n \u003cp\u003eThe water content distribution in the soil was modeled using HYDRUS-2D/3D (Simunek et al., 1999). Assuming a homogeneous and isotropic soil, the governing equation for water flow is the 2D Richards Equation as follows:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\partial \\theta }{\\partial t}=\\frac{\\partial }{\\partial x}\\left[K \\left(h\\right)\\frac{\\partial h}{\\partial x}\\right]+\\frac{\\partial }{\\partial z}\\left[K \\left(h\\right)\\frac{\\partial h}{\\partial z}+K \\left(h\\right)\\right]-S\\) \u003c/span\u003e\u003c/span\u003e(7)\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere, \u003cem\u003e\u0026theta;\u003c/em\u003e is volumetric water content [L\u003csup\u003e3\u003c/sup\u003eL\u003csup\u003e\u0026minus;3\u003c/sup\u003e], \u003cem\u003eh\u003c/em\u003e is pressure head [L], \u003cem\u003et\u003c/em\u003e is time [T], \u003cem\u003ex\u003c/em\u003e and z are horizontal and vertical space coordinates, respectively, and \u003cem\u003eK\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e is saturated hydraulic conductivity [LT\u003csup\u003e\u0026minus;1\u003c/sup\u003e]. The \u003cem\u003eS\u003c/em\u003e [T\u003csup\u003e\u0026minus;1\u003c/sup\u003e] as a sink term indicates the rate of water uptake by the roots from the soil.HYDRUS-2D/3D uses the Galerkin finite-element method to solve the transport equations as explained in detail by Simunek et al. (1999).\u003c/p\u003e\n\u003cp\u003eThe domain was defined by an axisymmetrical 2D geometry. The soil and reservoirs were defined as two soil materials. The reservoir were defined as variable head. In addition, boundary conditions on the soil surface were defined as atmospheric boundary condition to consider evaporation from the soil surface. The right and left sides of the modeled environment are no flux, because the range of effect of the reservoirs is up to this boundary. We simulated only the right side of the presumed symmetric profile. Thus, the boundaries of the finite-element mesh are rectangular except on the right edge near the upper right-hand corner where the reservoir is located (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The simulated environment in the model is a range of 100 cm in width and 150 cm in height. The dimensions of the overall mesh were 3 cm and around the reservoir was 1.6 cm.\u003c/p\u003e\n\u003cdiv class=\"Section3\" id=\"Sec10\"\u003e\n \u003ch2\u003e2.8. Root distribution and water uptake parameters\u003c/h2\u003e\n \u003cp\u003ePlant-root distribution influences soil water and salinity distributions in the root domain under micro-irrigation. Root distribution is quantified by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e(\u003cem\u003er\u003c/em\u003e,\u003cem\u003ez\u003c/em\u003e) (dimensionless) in 2D geometry as follows:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta \\left(r.z\\right)=\\left[1-\\frac{z}{{z}_{m}}\\right]\\left[(1-\\frac{r}{{r}_{m}})\\right]{e}^{-(\\frac{{p}_{z}}{{z}_{m}}\\left|{z}^{*}-z\\right|+\\frac{{p}_{r}}{{r}_{m}}\\left|{r}^{*}-r\\right|)}\\) \u003c/span\u003e\u003c/span\u003e(8)\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cem\u003ez\u003c/em\u003e and \u003cem\u003er\u003c/em\u003e are the distances [L] from the plant origin in the directions of \u003cem\u003ez\u003c/em\u003e (depth), and \u003cem\u003er\u003c/em\u003e (radius), respectively, and \u003cem\u003ez\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e and \u003cem\u003er\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e are the maximum rooting lengths [L] from the plant origin in the \u003cem\u003ez\u003c/em\u003e and \u003cem\u003er\u003c/em\u003e directions, respectively. In Eq. 8, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}^{\\text{*}}\\)\u003c/span\u003e\u003c/span\u003e and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ z}^{\\text{*}}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({p}_{\\text{z}}\\)\u003c/span\u003e\u003c/span\u003e (-) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({p}_{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e (-) are all empirical parameters controlling spatial root distribution. These empirical parameters were considered to provide zero root water uptake (RWU) beyond \u003cem\u003ez\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e to account for asymmetrical RWU in the vertical and radial directions and to allow maximum RWU within 0 to \u003cem\u003ez\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e (Vrugt et al., 2001b). Root distribution parameters by investigating the distribution of plant roots in the field, reviewing resources and with the help of HYDRUS 2D/3D software are shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Root distribution parameters and parameters of the Feddes model (i.e., threshold and critical soil water pressures,\u0026nbsp;\u003cem\u003eh\u003c/em\u003e) for the studied plants.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eIn the absence of osmotic stress, the actual rate of root water uptake, \u003cem\u003eS\u003c/em\u003e(\u003cem\u003eh\u003c/em\u003e), in any point of the simulation domain is computed according to the multilinear model proposed by Feddes et al. (\u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e). In the model, it is assumes that \u003cem\u003eS\u003c/em\u003e(\u003cem\u003eh\u003c/em\u003e) is proportional to the maximum (potential) root uptake rate occurring when water is not limiting plant transpiration:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eS\u003c/em\u003e(\u003cem\u003eh\u003c/em\u003e) = \u003cem\u003e\u0026alpha;\u003c/em\u003e(\u003cem\u003eh\u003c/em\u003e).\u003cem\u003eS\u003c/em\u003e\u003csub\u003ep\u003c/sub\u003e (9)\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003e\u0026alpha;\u003c/em\u003e(\u003cem\u003eh\u003c/em\u003e) is a dimensionless prescribed function of soil water pressure head and \u003cem\u003eS\u003c/em\u003e\u003csub\u003ep\u003c/sub\u003e is the potential (maximal) water uptake rate by roots (cm\u003csup\u003e3\u003c/sup\u003e cm\u003csup\u003e\u0026minus;3\u003c/sup\u003e d\u003csup\u003e\u0026minus;1\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eParameters of the Feddes model for water uptake by root are as follows: \u003cem\u003eh\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e is the pressure head below which plant roots begin to extract water from the soil, \u003cem\u003eh\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e is pressure head below which plant roots extract water at the maximum rate, \u003cem\u003eh\u003c/em\u003e\u003csub\u003e3.low\u003c/sub\u003e is limiting pressure head below which plant roots are not able to extract water at the maximum rate at potential transpiration rate of 0.1 cm day\u003csup\u003e\u0026minus;1\u003c/sup\u003e, \u003cem\u003eh\u003c/em\u003e\u003csub\u003e3.high\u003c/sub\u003e is limiting pressure head at potential transpiration rate of 0.5 cm day\u003csup\u003e\u0026minus;1\u003c/sup\u003e, \u003cem\u003eh\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e is pressure head below which plant roots stop to take up water, which is usually taken at wilting point, \u003cem\u003er\u003c/em\u003e\u003csub\u003e2H\u003c/sub\u003e is potential transpiration rate at which the limiting pressure head allows extracting water at the maximum rate and \u003cem\u003er\u003c/em\u003e\u003csub\u003e2L\u003c/sub\u003e is potential transpiration rate at which the limiting pressure head allows extracting water at the minimum rate. Parameters of root water uptake by reviewing the sources, considering the water requirement of the plants and with the help of HYDRUS 2D/3D software are presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003ch2\u003e2.9. Evaluation of simulation model predictions \u003c/h2\u003e\n\u003cp\u003eThe agreement of HYDRUS-2D/3D software predictions with the measured data was quantified in terms of three statistical measures: the mean bias error (ME), the root square error (RMSE), and the coefficient of determination (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) defined as follows (Willmott, \u003cspan class=\"CitationRef\"\u003e1982\u003c/span\u003e):\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{M}\\text{E}=\\sum _{i=1}^{N}({P}_{i}-{O}_{i})/N\\) \u003c/span\u003e\u003c/span\u003e(10)\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{R}\\text{M}\\text{S}\\text{E}=\\sqrt{\\sum _{i=1}^{N}{({P}_{i}-{O}_{i})}^{2}/N}\\) \u003c/span\u003e\u003c/span\u003e(11)\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}=\\frac{\\sum _{i=1}^{N}{({P}_{i}-{O}_{i})}^{2}}{\\sum _{i=1}^{N}{({O}_{i}-\\overline{O})}^{2}}\\) \u003c/span\u003e\u003c/span\u003e(12)\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003eN\u003c/em\u003e is the number of data points, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e is the i\u003csup\u003eth\u003c/sup\u003e predicted data point, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({O}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e is the i\u003csup\u003eth\u003c/sup\u003e observed data, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\overline{O}\\)\u003c/span\u003e\u003c/span\u003e is the mean of observed data. ME can identify potential bias (i.e., underestimation and overestimation) in the predicted values, whereas RMSE and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e give overall measures of the goodness-of-fit.\u003c/p\u003e"},{"header":"3 Results And Discussion","content":"\u003ch2\u003e3.1. Water depletion from the reservoirs\u003c/h2\u003e\n\u003cp\u003eThe percentage of water depletion of the reservoirs with different permeabilities is presented in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. We defined three treatments, namely, reservoirs with low, medium, and high permeability in this study according to the percentage of water depletion. At high cooking temperature, organic materials were completely burned and different pore spaces were produced in their places. Therefore, this resulted in different permeabilities of the reservoirs concerning the quantity of the organic matter. Therefore, it was expected that the organic materials had minimal nutritional and direct influences on the survival and growth of seedlings. The saturated hydraulic conductivity of reservoirs is also presented in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({K}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e varied from 0.06 to 0.2 cm/day.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePercentage of water depletion and saturated hydraulic conductivity (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({K}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e) from reservoirs with different permeabilities\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReservoir type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaterials used for the preparation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage of water depletion after 24 h (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePermeability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({K}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e (cm/day)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% clay + 5% manure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% clay + 5% wheat straw\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClay soil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60% clay + 40% manure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60% clay + 40% wheat straw\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"Section2\" id=\"Sec12\"\u003e\n \u003ch2\u003e3.2. Soil water characteristic curve\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the laboratory-measured and predicted data of soil water characteristic curve. The van Genuchten-Mualem model has an excellent fit to the measured data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec13\"\u003e\n \u003ch2\u003e3.3. Optimal values of soil hydraulic parameters from inverse modeling\u003c/h2\u003e\n \u003cp\u003eSoil hydraulic parameters were optimized in the inverse modeling by HYDRUS-2D/3D. The HYDRUS-2D/3D software has been widely used for inverse modeling of water flow (Simunek et al., 2012). The estimated soil hydraulic parameters by inverse solution are presented in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. The optimized values of soil hydraulic parameters can be compared with those estimated by fitting the van Genuchten-Mualem model to the laboratory-measured data. The optimized \u003cem\u003e\u0026theta;\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e values (0.50 cm\u003csup\u003e3\u003c/sup\u003e cm\u003csup\u003e-3\u003c/sup\u003e) were higher than the measured value (0.38 cm\u003csup\u003e3\u003c/sup\u003e cm\u003csup\u003e-3\u003c/sup\u003e). The parameter \u003cem\u003en\u003c/em\u003e determines the shape of soil water characteristic curve and its optimized values (average 1.56) were higher than the fitted value (1.09). \u003cem\u003e\u0026alpha;\u003c/em\u003e for the model fitting to the measured data was equal to 0.008 cm\u003csup\u003e-1\u003c/sup\u003e which is greater than its optimized value (0.0018 cm\u003csup\u003e-1\u003c/sup\u003e). Thus, the unsaturation zone occurs at lower matric suctions for the fitted set of parameters than the optimized set of parameters. The measured \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({K}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e (62 cm/day) was greater than the optimized value (average 35 cm/day). The differences between the optimized and fitted sets of parameters can be explained by the fact that the optimized parameters are obtained by using the dynamic soil water content distribution in different parts of the soil profile and taking into account both wetting and drying processes, but in the laboratory, the hydraulic properties of the soil samples were measured at equilibrium condition (e.g., in pressure plate) or at steady-state condition (e.g., constant-head method).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab6\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe van Genuchten-Mualem parameters optimized in the inverse modeling by HYDRUS-2D/3D software.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePermeability treatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\theta }_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e (cm\u003csup\u003e3\u003c/sup\u003e/ cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({K}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e (cm/ day)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003e(1/cm)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow permeability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium permeability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh permeability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec14\"\u003e\n \u003ch2\u003e3.4. Water distribution and wetting pattern in the soil\u003c/h2\u003e\n \u003cp\u003eWetting patterns in the soil and the spatial distribution of soil water and matric potential depends on soil hydraulic properties, emitter discharge rates, spacing and placement of emitters, irrigation amount and frequency, water uptake rates, and root distribution patterns (Gardenas et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAs shown in Figures 4-6, the water slowly moved from the reservoir with low permeability, but it moved faster and in all directions from the reservoir with high permeability. In other words, with increasing the hydraulic conductivity of the reservoir, the dimensions of the wetted area around the reservoir increased and the water content moved faster. As it can be seen, in the low permeability treatment, the water content values are at the lowest values after 24 h, the water content values slowly increased after three days, and finally, the soil around of the plant is soaked and water is slowly provided to the plant. The trend was the same in the moderate permeability treatment, but in the high permeability treatment, a large volume of water is discharged for almost three days, so the soil water content after one day is more than the other two treatments, and more water was deep-percolated and lowered the soil water available to plant roots. One of the important goals of pressurized irrigation systems, especially drip irrigation systems, is to prevent deep percolation and water outflow from the plant root area (Khorami et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Irrigation system should be designed so that water is evenly distributed in the root zone. In the low permeability and medium permeability treatments, it is observed that the maximum soil water content occurred in the 20-40 cm layer overtime. The soil water content after three days of irrigation in the low permeability treatment ranged from 0.25 to 0.14 cm\u003csup\u003e3\u003c/sup\u003e cm\u003csup\u003e\u0026minus;3\u003c/sup\u003e. In the moderate permeability treatment, it changed from 0.25 to 0.13 cm\u003csup\u003e3\u003c/sup\u003e cm\u003csup\u003e\u0026minus;3\u003c/sup\u003e, and finally, in the high permeability treatment, the water content varied from 0.26 to 0.12 cm\u003csup\u003e3\u003c/sup\u003e cm\u003csup\u003e\u0026minus;3\u003c/sup\u003e. Such differences might be related to different discharge rates and hydraulic conductivities of the used reservoirs. Li et al. (\u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e) stated that the dimensions and shape of the wetting pattern in the subsurface irrigation depended on the soil type and layering, the reservoir outlet flow rate, the time of cessation of irrigation, etc. In their research, they examined the distribution of the wetting pattern in the subsurface irrigation for two loamy and sandy soils, which can show the shape of the moisture distribution in the horizontal and vertical sections with exponential functions. They stated that with increasing discharge rate, the horizontal distribution of water content would increase, and with decreasing discharge rate, the water content distribution would increase in the vertical direction.\u003c/p\u003e\n \u003cp\u003eThe distribution of soil water content around the reservoir must be known for the proper management of subsurface systems to wet the plant root zone uniformly. It is also aimed to increase the efficiency of the water/fertilizer use, and to maintain a dry soil surface for low water losses due to evaporation. The horizontal and vertical expansion of the water content profiles is a function of the flow rate and the operating time of the system (Mohamadzade et al., 2015). The water content distribution at different distances and depths and for different treatments at one, three, and six days after irrigation in different permeability treatments are presented in Figs. 4 to 6 and using Surfer 10.0 software and the Kriging method. The subsurface irrigation system is based on the use of continuous water content, which determines the proper and uniform irrigation. In general, the highest soil water content was observed near the reservoir and the water content decreases with increasing distance from the reservoir. In the subsurface drip irrigation, the water content profile around the reservoir takes on the shape of concentric ellipses due to the gravitational effect (Oron et al., \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSeveral fields and laboratory studies have been carried out to study the effect of emitter discharge rate on the size of the wetting pattern. Some studies suggested that an increase in the discharge rate, for equal volumes of applied water, results in an increase in horizontal spreading but a decrease in wetted depth (Bresler et al., \u003cspan class=\"CitationRef\"\u003e1971\u003c/span\u003e; Ah Koon et al., \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e; Khan et al., \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e; Li et al., \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec15\"\u003e\n \u003ch2\u003e3.5. Comparison of measured and simulated soil water content data\u003c/h2\u003e\n \u003cp\u003eThe calibration results of measured and simulated values of soil water content with HYDRUS-2D/3D software in reservoirs with low, medium and high permeabilities are shown in Fig.\u0026nbsp;7. HYDRUS-2D/3D software was able to simulate the water flow and soil water content with high values of coefficient of determination (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.90-0.95). This shows the high accuracy of simulation of soil water content pattern in the two-dimension, which was higher in the lower discharges (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Kandelous et al. (2010) simulated the water movement in a subsurface drip irrigation system and concluded that the correspondence between simulations and observations was very good. Nayebloei et al. (2015) simulated the 2D water content distribution in the subsurface drip irrigation and stated that soil water flow can be reasonably simulated while root water uptake and evaporation processes are both actively going on.\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab7\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistics of calibration of measured and simulated values of soil water content with HYDRUS-2D/3D software.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eME\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow permeability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium permeability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh permeability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eR\u003csup\u003e2=\u003c/sup\u003e Coefficient of determination\u003csup\u003e,\u003c/sup\u003e ME= Mean Error, and RMSE= Root mean square error\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eIn this study, reservoirs with different permeabilities were made by using manure and wheat straw, so that, the saturated hydraulic conductivity (\u003cem\u003eK\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e) of reservoirs varied from 0.06 to 0.2 cm/day. Then, the reservoirs were used in the field for reservoir irrigation of saplings and HYDRUS-2D/3D software was used to simulate wetting pattern in the soil profile around the reservoir.\u003c/p\u003e \u003cp\u003eWith increasing the hydraulic conductivity of the reservoirs, the dimensions of the wetted area increased and the water content moved faster in the soil. The water slowly released from the reservoirs with low and medium permeabilities, but in the high permeability treatment, a large volume of water was released for almost three days, so that, more water was deep-percolated which lowered the soil water available to plant roots leading to adverse effects on the plant growth. In the low and medium permeability treatments, it was observed that the maximum soil water content occurred in the 20-40 cm layer overtime. HYDRUS-2D/3D software was able to simulate soil water content during the growth period of plants with a high correspondence between the measured and predicted data. This finding showed the high accuracy of simulating the water content pattern in the two-dimension by HYDRUS-2D/3D.\u003c/p\u003e \u003cp\u003eIt is suggested that the results of HYDRUS-2D/3D simulations and different irrigation scenarios be used to determine the water content distribution and suitable places for planting seedlings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e2D/3D= Two-dimensional/Three- dimensional\u003c/p\u003e\n\u003cp\u003e\u0026deg;C= Temperature\u003c/p\u003e\n\u003cp\u003epH= Power of the concentration of the hydrogen ion: Millimeter\u003c/p\u003e\n\u003cp\u003ecm= Centimeter\u003c/p\u003e\n\u003cp\u003emm= Millimeter \u003c/p\u003e\n\u003cp\u003eq\u003csub\u003es\u003c/sub\u003e= Saturated water content,\u003c/p\u003e\n\u003cp\u003eq\u003csub\u003er\u003c/sub\u003e= Residual water content, \u003c/p\u003e\n\u003cp\u003eS\u003csub\u003ee\u003c/sub\u003e = Effective saturation, \u003c/p\u003e\n\u003cp\u003eK\u003csub\u003es\u003c/sub\u003e= Saturated hydraulic conductivity\u003c/p\u003e\n\u003cp\u003el= Soil pore tortuosity parameter.\u003c/p\u003e\n\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e=\u003csup\u003e \u003c/sup\u003eCoefficient of determination\u003c/p\u003e\n\u003cp\u003eME= Mean Error\u003c/p\u003e\n\u003cp\u003eRMSE= Root mean square error\u003c/p\u003e\n\u003cp\u003eTDR= Time domain reflectometry\u003c/p\u003e\n\u003cp\u003eh= Pressure head\u003c/p\u003e\n\u003cp\u003eq- Volumetric water content\u003c/p\u003e\n\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003csub\u003eb\u003c/sub\u003e= Bulk density \u003c/p\u003e\n\u003cp\u003eEC= Electrical conductivity\u003c/p\u003e\n\u003cp\u003eFC= Water content at field capacity\u003c/p\u003e\n\u003cp\u003ePWP= Water content at permanent wilting point.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e5 Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Isfahan University of Technology, Iran (1974) supported this study. We thank especially the anonymous reviewers and the editor for their careful reading and many insightful comments and suggestions.\u003c/p\u003e\n\u003cp\u003eKindly, I am writing to submit our manuscript entitled \u0026ldquo;\u003cstrong\u003eWetting patterns in a subsurface irrigation system by using reservoirs of different permeabilities: experimental and HYDRUS-2D/3D modeling\u003c/strong\u003e\u0026rdquo;, for publication in your journal. We appreciate your consideration of our manuscript, and we look forward to receiving comments from your respected reviewers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eThe manuscript has not been previously published, is not currently submitted for review to any other journal, and will not be submitted elsewhere before a decision is made by this journal.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAh Koon PD, Gregory PJ, Bell JP (1990) Influence of drip irrigation emission rate on distribution and drainage of water beneath a sugarcane and a fallow plot. Agric Water Manag 17:267\u0026ndash;282\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngelakis AN, Rolston DE, Kadir TN, Scott VN (1993) Soil-water distribution under trickle source. J Irrig Drain Eng ASCE 119:484\u0026ndash;500\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBen-Asher J, Phene CJ (1996) Surface and subsurface drip irrigation: an analysis by a numerical model. Rep. Jacob Blaustein Institute for Desert Research, Ben Gurion University of Negev, Sede Boker Campus, Negev, Israel\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBresler E, Heller J, Diner N, Ben-Asher J, Brandt A, Goldberg D (1971) Infiltration from a trickle source. II: experimental data and theoretical predictions. Soil Sci Soc Am Proc. 35, 683\u0026ndash;689\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChu ST (1994) Green-Ampt analysis of wetting patterns for surface emitters. J Irrig Drain Eng ASCE 120(2):414\u0026ndash;421\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCook FJ, Thorburn PJ, Fitch P, Bristow KL (2003) WetUp: a software tool to display approximate wetting pattern from drippers. Irrig Sci 22:129\u0026ndash;134\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCote CM, Bristow KL, Charlesworth PB, Cook FJ, Thorburn PJ (2003) Analysis of soil wetting and solute transport in subsurface trickle irrigation. Irrig Sci 22:143\u0026ndash;156\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarcy H 1856. Les Fontaines Publiques de la Ville de Dijon [The Public Fountains of the City of Dijon].Dalmont, Paris\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDastorani MT, Heshmati M, Sadeghzadeh MA (2010) Evaluation of the efficiency of surface and subsurface irrigation in dryland environments. Irrigation and Drainage: The journal of the International Commission on Irrigation and Drainage 59(2):129\u0026ndash;137\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeddes RA, Kowalik P, Zarandy H (1978) Simulation of Field Water Use and Crop Yield. Pudoc. Wageningen. The Netherlands\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGardenas AI, Hopman JW, Hanson BR, Šimůnek. J (2005) Two-dimensional modeling of nitrate leaching for various fertigation scenarios under micro-irrigation. Agri Water Manage 74(3):219\u0026ndash;242\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGee GW, Ward AL (2000) Innovations in two-phase measurements of soil hydraulic properties.p.\u0026nbsp;241-269. In M.Th. van Genuchten (ed.). Proceedings of an International Conference on Methods for Estimating the Hydraulic Propertise of Understand Soils. U.C. Reverside Press. Riverside, CA\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJahantigh M (2021) Effects of irrigation methods of subsurface, clay pot and drop on Mulberry growth in dry land region (Case study: Sistan area). J Water Soil Manage Model 1(2):28\u0026ndash;39\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKandelous MM, Šimůnek. J (2010) Numerical simulations of water movement in a subsurface drip irrigation system under field and laboratory conditions using HYDRUS-2D. Agric Water Manage 97:1070\u0026ndash;1076\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan AA, Yitayew M, Warrick AW (1996) Field evaluation of water and solute distribution from a point source. J Irrig Drain Eng 22(4):221\u0026ndash;227\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhorami M, Alizadeh A, Ansari H (2013) Simulation of water movement and redistribution of soil moisture in subsurface drip irrigation by HYDRUS 2D/3D model. J Water and Soil (Agri Sci Tech 27(4):702\u0026ndash;692\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKandelous MM, Šimůnek J, van Genuchten MT, Malek K (2011) Soil water content distributions between two emitters of a subsurface drip irrigation system. Soil Sci Soc Am J 75:488\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKandelous MM, Šimunek J (2010b) Numerical simulations of water movement in a subsurface drip irrigation system under field and laboratory conditions using HYDRUS-2D. Agric. Water Manage 97:1070\u0026ndash;1076\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarandish F, Šimunek J (2016b) Numerical and machine-learning modeling of soil water content for sustainable water management in agriculture under water stress. J Hydro 543:892\u0026ndash;909\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoumanov KS, Hopmans JW, Schwankl LJ, Andreu L, Tuli A (1997) Application efficiency of micro-sprinkler irrigation of almond trees. Agri Water Manage 34(3):247\u0026ndash;263\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Shi H, Simunek JS, Gong X, Peng Z (2015) Modeling soil water dynamics in a drip-irrigated intercropping field under plastic mulch. Irrig Sci 33:289\u0026ndash;302\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Zhang J, Rao M (2004) Wetting patterns and nitrogen distributions as affected by fertigation strategies from a surface point source. J Agric Water Manage 67(2):89\u0026ndash;104\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Zhang J, Ren L (2003) Water and nitrogen distribution as affected by fertigation of ammonium nitrate from a point source. Irrig Sci 22(1):12\u0026ndash;30\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMailhol JC, Ruelle P, Walser S, Schu\u0026uml; tze N, Dejean C (2011) Analysis of AET and yield predictions under surface and buried drip irrigation systems using the Crop Model PILOTE and HYDRUS-2D. Agric. Water Manage 98:1033\u0026ndash;1044\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammadzade F, Gheysari M, Landi E (2015) Development and Evaluation of Estimation Models of Wetting Pattern of Drippers in a Sandy Soil with High Gravel. J Water and Soil Sci 19(71):287\u0026ndash;297\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMubarak I, Mailhol JC, Angulo-Jaramillo R, Ruelle P, Boivin P, Khaledian M (2009) Temporal variability in soil hydraulic properties under drip irrigation. Geoderma 150:158e165\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNayebloie F, Kouchakzadeh M, Ebrahimi K, Homaee M, Abbasi F (2015) Simulation of 2D Soil Moisture Distribution under Subsurface Drip Irrigation. Iranian, J Soil and Water Res 46(2):222\u0026ndash;229\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOron G, DeMalach Y, Gillerman L, David I, Rao V (1999) Improved saline-water use under subsurface drip irrigation. Agric Water Manage 39(1):19\u0026ndash;33\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhilip JR (1968) Steady infiltration from buried point sources and spherical cavities. Water Resour Res 4:1039\u0026ndash;1047\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProvenzano G (2007) Using HYDRUS-2D simulation model to evaluate wetted soil volume in subsurface drip irrigation systems. J Irrig Drain Eng 133(4):342\u0026ndash;349\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaefuddin R, Saito H, Šimůnek J (2019) Experimental and numerical evaluation of a ring-shaped emitter for subsurface irrigation. Agric water manage 211:111\u0026ndash;122\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchaap MG, Van Genuchten MT (2006) A modified Mualem\u0026ndash;van Genuchten formulation for improved description of the hydraulic conductivity near saturation. Vadose Zone J 5(1):27\u0026ndash;34\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwartzman M, Zur B (1986) Emitter spacing and geometry of wetted soil volume. J Irrig Drain Eng 112(3):242\u0026ndash;253\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimuunek J, Sejna M, van Genuchten MTh (1999) \u0026lsquo;\u0026lsquo;The HYDRUS-2D software package for simulating the two-dimensional movement of water, heat, and multiple solutes in variably saturated media.\u0026rsquo;\u0026rsquo; IGWMC-TPS 53, Version 2.0. International Ground Water Modeling Center, Colorado School of Mines, Golden, Colo\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkaggs TH, Trout TJ, Sˇimu˚ nek J, Shouse PJ (2004) Comparison of HYDRUS-2D simulations of drip irrigation with experimental observations. J Irrig Drain Eng ASCE 130(4):304\u0026ndash;310\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimůnek J, van Genuchten MTh, Šejna M (2016) Recent developments and applications of the HYDRUS computer software packages. Vadose Zone J 15(7):25. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2136/vzj2016.04.0033\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkaggs TH, Trout TJ, Simunek J, Shouse PJ (2004) Comparison of HYDRUS-2D simulations of drip irrigation with experimental observations. J Irrig Drain Eng 130:304\u0026ndash;310\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Y, Zhou H, Qin Y, Lammers PS, Berg A, Deng H, Cai X, Wang D, Jones SB (2014) Horizontal monitoring of soil water content using a novel automated and mobile electromagnetic access-tube sensor. J Hydro 516:50\u0026ndash;55\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWagenet RJ, Hutson JL (1992) \u0026ldquo;LEACHM: A process based model of water and solute movement, transformation, plant uptake, and chemical reactions in the unsaturated zone. \u0026rdquo; Centre of Environmental Research, Cornell Univ., Ithaca, N.Y\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarrick AW (1974) Time-dependent linearized infiltration. I. Point sources. Soil Sci. Soc. Am. Proc. 38, 383\u0026ndash;386\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWillmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 63(11):1309\u0026ndash;1313\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"HYDRUS-2D/3D, subsurface irrigation, reservoirs, rangeland, arid lands","lastPublishedDoi":"10.21203/rs.3.rs-1201561/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1201561/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, wetting patterns around the reservoirs with different permeability (low, medium, and high) were assessed in the field and simulated using the HYDRUS-2D/3D software. The results showed that the highest soil water content was observed near the reservoir and it decreased with increasing distance from the reservoir. In the high permeability treatment, a large volume of water was released for almost three days, so that, more water was deep-percolated and lowered the soil water available to plant roots. In the low and medium permeability treatments, the water content slowly moved and observed that the maximum soil water content occurred in the 20-40 cm layer overtime. The HYDRUS-2D/3D software was able to simulate water flow and soil water content during the growth period of plants with a high correspondence between measured and simulated soil water contents (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.90-0.95).\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Wetting patterns in a subsurface irrigation system by using reservoirs of different permeabilities: experimental and HYDRUS-2D/3D modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-01-28 16:12:34","doi":"10.21203/rs.3.rs-1201561/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2022-01-24T13:56:20+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-01-24T13:30:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-01-09T22:17:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Water Resources Management","date":"2021-12-24T02:36:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fc7ac2b4-c784-46e9-8784-b6fd82667843","owner":[],"postedDate":"January 28th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-06-15T14:02:05+00:00","versionOfRecord":[],"versionCreatedAt":"2022-01-28 16:12:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1201561","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1201561","identity":"rs-1201561","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}