Agricultural Water Management 97 (2010) 1070–1076

Contents lists available at ScienceDirect

Agricultural Water Management
journal homepage: www.elsevier.com/locate/agwat

Numerical simulations of water movement in a subsurface drip irrigation system
under ﬁeld and laboratory conditions using HYDRUS-2D
Maziar M. Kandelous, Jiří Šimůnek ∗Department of Environmental Sciences, University of California Riverside, Riverside, CA 92521, USAa r t i c l e

i n f oArticle history:
Received 9 September 2009
Accepted 13 February 2010
Available online 15 March 2010
Keywords:
Subsurface drip irrigation
HYDRUS-2D
Wetting dimensionsa b s t r a c t
Due to the decreasing availability of water resources and the increasing competition for water between
residential, industrial, and agricultural users, increasing irrigation efﬁciency, by methods like subsurface drip irrigation (SDI) systems, is a pressing concern for agricultural authorities. To properly manage
SDI systems, and increase the efﬁciency of the water/fertilizer use while reducing water losses due
to evaporation, the precise distribution of water around the emitters must be known. In this paper,
the Windows-based computer software package HYDRUS-2D, which numerically simulates water, heat,
and/or solute movement in two-dimensional, variably-saturated porous media, was used to evaluate the
distribution of water around the emitter in a clay loam soil. The simulation results were compared with
two sets of laboratory and ﬁeld experiments involving SDI with emitters installed at different depths,
and were evaluated using the root-mean-square-error (RMSE). The RMSE at different locations varied
between 0.011 and 0.045 for volumetric water contents, and between 0.98 and 4.36 cm for wetting
dimensions. Based on these values, it can be concluded that the correspondence between simulations
and observations was very good.
© 2010 Elsevier B.V. All rights reserved.

1. Introduction
Water scarcity in arid and semi-arid regions is a major concern for water and agricultural authorities around the world. Highperformance irrigation systems, such as surface or subsurface drip
irrigation (SDI) systems, are often recommended to overcome thisproblem and to dramatically increase the efﬁciency of water use
over that of traditional irrigation systems.
In designing subsurface drip irrigation systems for row crops,
the dimensions of the wetted volume and the distribution of soil
moisture within this volume are two of the main factors in determining installation depth and spacing of drippers to obtain an
optimum distribution of water in the crop root zone. Since thesource of water is at a certain depth when SDI is used, the soil
surface usually remains drier than for the surface drip irrigation.
This leads to the reduction of evaporation from the soil surface,and consequently to an increase in transpiration and overall water
use efﬁciency (Romero et al., 2004). However, a deep installationof emitters can increase water losses due to deep percolation anddecrease availability of water for crop roots (Dukes and Scholberg,2005). The precise distribution of moisture around the emittersmust be known in order to properly manage SDI systems to wet∗ Corresponding author. Tel.: +1 951 827 7854; fax: +1 951 827 7854.
E-mail address: jiri.simunek@ucr.edu (J. Šimůnek).
0378-3774/$ – see front matter © 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.agwat.2010.02.012

the crop root zone uniformly, which will increase the efﬁciency of
the water/fertilizer use, and to maintain a dry soil surface to reducewater losses due to evaporation.
Several empirical, analytical, and numerical models have beendeveloped to simulate soil moisture pattern and wetting front
dimensions for surface/subsurface drip irrigation systems (e.g.,
Philip, 1968; Warrick, 1974; Schwartzman and Zur, 1986; Angelakis
et al., 1993; Chu, 1994; Ben-Asher and Phene, 1996; Moncef et al.,
2002; Cook et al., 2003). Due to advances in computer speed, andthe public availability of numerical models simulating water ﬂow
and solute transport in soils, many researchers have become interested in using such models for evaluating water ﬂow in soils with
subsurface irrigation systems (e.g., Meshkat et al., 1999; Schmitz etal., 2002; Ben-Asher and Phene, 1996; Cote et al., 2003; Mmolawa
and Or, 2003; Skaggs et al., 2004; Lazarovitch et al., 2005, 2007;
Provenzano, 2007).
HYDRUS-2D (Šimůnek et al., 1999) is a well-known Windowsbased computer software package used for simulating water, heat,
and/or solute movement in two-dimensional, variably-saturated
porous media. This model’s ability to simulate water movement
for subsurface drip irrigation conditions has been assessed by many
researchers (for references, see Šimůnek et al., 2008). For example,
Skaggs et al. (2004) compared HYDRUS-2D simulations of ﬂow froma subsurface drip irrigation line source with observed ﬁeld data
involving a sandy loam soil and a SDI system with a 6-cm installation depth and 3 discharge rates. They found very good agreement

M.M. Kandelous, J. Šimůnek / Agricultural Water Management 97 (2010) 1070–1076

between simulated and observed soil moisture data. Ben-Gal et al.
(2004) explained that one of the main problems with SDI systems
is soil saturation near the emitter and its effects on emitter discharge resulting from the net pressure on the emitter outlet. To
solve this problem, they installed the drip tube in a trench, andﬁlled it with gravel to eliminate saturation and net pressure aroundthe emitter. Ben-Gal et al. (2004) then simulated their conditions
using HYDRUS-2D, and found good agreement between observed
and simulated data. Lazarovitch et al. (2005) modiﬁed HYDRUS-2D
further so that it could account for the effects of back-pressure on
the discharge reduction using the dripper characteristic function.
Provenzano (2007) assessed the accuracy of HYDRUS-2D by comparing simulation results and experimental observations of matricpotential for SDI systems in a sandy loam soil with a 10-cm installation depth, and also found satisfactory agreement.
The main objective of this study is to further investigate the
HYDRUS-2D model’s capacity for simulating water movement in
the soil from a buried point source and estimating dimensions of
the wetted zone. The simulated results are compared with laboratory and ﬁeld data involving SDI systems with different emitter
installation depths and discharges. While both Skaggs et al. (2004)
and Provenzano (2007) were evaluating SDI experiments, in which
drippers were installed at relatively shallow depths (6 and 10 cm,
respectively), in this study emitters were installed at multiple
depths, down to 30 cm. Additionally, both Skaggs et al. (2004) and
Provenzano (2007) carried out their studies on sandy loams, while
a clay loam, a much heavier-textured soil, is used in this study.
Finally, Skaggs et al. (2004) and Provenzano (2007) experimented
on either thoroughly mixed or repacked soils, while a ﬁeld part ofour study was performed on undisturbed soil proﬁles.
2. Materials and methods
Two sets of experiments were carried out using a subsurface
drip irrigation system. While the ﬁrst set of experiments was performed in the laboratory on a lysimeter system involved both
inﬁltration and redistribution and movement of the moisture front
could be visually observed through transparent walls, the second
set of experiments was carried out in the ﬁeld and concerned only
inﬁltration.
2.1. Laboratory lysimeter experiments
The laboratory experiments were carried out on a lysimeter(2 m × 1 m × 1.2 m) ﬁlled with a clay loam soil (33.5% clay, 39.7% silt,
and 26.8% sand) at the central laboratory of the College of Agricultural and Natural Resources of the University of Tehran, Iran. Before
the soil was ﬁlled into the lysimeter, the lysimeter walls, made from
Plexiglas, were treated with glue and sprayed with sand to create acoarse surface, in order to prevent preferential ﬂow along the walls(Kandelous and Šimůnek, 2010). The air-dried soil was ﬁlled into
the lysimeter with an average soil bulk density of 1.35 g cm−3 .
Soil moisture sensors (model EA514-054, provided by ELE International, UK) were installed at several locations around the emitter
during the ﬁlling of the lysimeter with the air-dried soil. The locations of the sensors are displayed in Fig. 1. Prior to installationin the lysimeter, the soil moisture sensors were individually calibrated in the laboratory to determine the soil moisture–resistancecurve. The electrical resistance sensors were installed in a cylindrical container with a radius of 10 cm, a height of 20 cm, and initially
ﬁlled with the saturated soil used in the lysimeter. The soil container was left open at the top for one day to allow evaporation,and then sealed for another day to allow redistribution. Once thesoil reached an equilibrium state, the resistance measured by thesensors was read and the soil container was weighted to determine

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Fig. 1. A schematic of a laboratory lysimeter with a connected water reservoir andsoil moisture sensors.

the water loss. This procedure was repeated several times until thesoil became dry. Gravimetric water contents measured by weighing the soil container were converted to volumetric water contents
using the soil bulk density.
The emitter installed at a depth of 30 cm was connected to awater reservoir (a 160-mm diameter PVC pipe) located on a scale
3 m above the emitter using a 20-mm nominal diameter polyethylene pipe. The emitter was installed close to one of the transparent
lysimeter walls and in the center of the visible area. The lysimeter
represented a half space of a subsurface drip irrigation problem,and could thus be treated mathematically as an axisymetrical problem. Emitter installation is described in detail by Kandelous andŠimůnek (2010).
The ﬁrst experiment was conducted to measure soil-wetting
patterns during the irrigation. The initial average water content, measured on soil samples taken during packing, was
0.07 cm3 cm−3 . The shape of the wetting front was drawn visually on the transparent walls of the lysimeter during the irrigation
experiment, and wetting dimensions (vertical upward, vertical
downward and horizontal) were then measured for different volumes of applied irrigation water. The average emitter discharge forthe ﬁrst laboratory experiment was 3.6 × 10−7 m3 s−1 .
After the ﬁrst experiment, the soil in the lysimeter was irrigatedusing sprinklers, and the water was allowed to redistribute forabout one month to achieve a relatively uniform soil water content
distribution. The second experiment, in which soil water content
changes were recorded using the moisture sensors, was carried out
with an average emitter discharge of 3.7 × 10−7 m3 s−1 . The average
initial water content for the second experiment, obtained from theinstalled moisture sensors, was 0.16 cm3 cm−3 . Soil moisture wasrecorded 24, 48, 72, and 96 h after the irrigation experiment started.
The soil surface was covered with a plastic sheet to minimize evaporation during the ﬁrst four days of the experiment, during which
the redistribution of soil moisture was recorded.
Soil hydraulic parameters for the soil in the lysimeter were estimated using the ROSETTA (Schaap et al., 2001) software. ROSETTA
is a software package that evaluates pedotransfer functions thatuse neural network models to predict soil hydraulic parametersfrom soil texture and related data for the van Genuchten–Mualem
model (van Genuchten, 1980). The parameters required for themost complex ROSETTA model are the bulk density (1.35 g cm−3 ),percentages of sand (26.8%), silt (39.7%), and clay (33.5%), andwater contents for pressures of −33 and −1500 kPa (0.25 and 0.133,
respectively). For these input variables, ROSETTA predicted the following soil hydraulic parameters of the van Genuchten–Mualem
model:  r = 0.06 cm3 cm−3 ,  s = 0.41 cm3 cm−3 , ˛ = 2.3 m−1 , n = 1.34,
and Ks = 2.5 × 10−6 m s−1 , assuming l = 0.5 (Table 1).

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Table 1
Soil hydraulic parameters for the van Genuchten–Mualem model (van Genuchten, 1980).
 r (cm3 cm−3 )Lysimeter
Field

0.06
0.07

 s (cm3 cm−3 )
0.41
0.38

2.2. Field experiments
Field experiments were carried out at the Research Field of theCollege of Agricultural and Natural Resources of the University of
Tehran, Iran, on a clay loam soil (32.5% clay, 36.5% silt, and 31.0%sand). The emitters were installed at 3 depths of 5, 15, and 25 cm
below the soil surface in a set up similar to that in the laboratory.
Details can also be found in Kandelous and Šimůnek (2010). Waterfrom a reservoir was delivered with a millimeter precision to theemitter using a small pump. The volume of water in the reservoir
was recorded every 15 min to account for variations in water discharge. Four experiments were conducted to characterize watercontent distribution around the emitter. Two experiments had theemitter at a depth of 5 cm (experiments A and B), one at 15 cm(experiment C), and one at 25 cm (experiment D) below the soil surface. The average emitter discharges for experiments A, B, C, and D
were 5.86 × 10−7 , 8.06 × 10−7 , 5.72 × 10−7 , and 7.33 × 10−7 m3 s−1 ,respectively. At the end of each irrigation experiment (2 h after theend of the experiment D), the soil surrounding the emitter was
excavated to expose a vertical soil proﬁle with the emitter in thecenter. Soil samples were then taken from locations 0, 12.5, and25 cm away from the emitter and at 0, 10, 20, 30, and 40 cm depths,
to characterize the soil water content in the wetting pattern using
a 3-cm long steel soil sampler with a 3-cm inside diameter. Samples were collected by pressing the soil sampler horizontally into
the proﬁle at selected locations. Volumetric water contents were
determined by multiplying gravimetric water contents using an
average bulk density of 1.55 g cm−3 . The average initial water content for the ﬁeld experiments was 0.13 cm3 cm−3 . This value, which
was used in the numerical simulations, was obtained from samples
taken randomly around the experimental ﬁeld from 6 depths of 10,
20, 30, 40, 50, and 60 cm.
Ten additional subsurface drip irrigation experiments, including
5 experiments with the emitter depth of 15 cm and 5 experimentswith the emitter depth of 30 cm, were carried out to determine thedimensions of the wetted zone. Each experiment was conductedin a different location of the same ﬁeld, and involved inﬁltrationfrom a single emitter. The emitter discharge in these experiments
varied from 5.28 × 10−7 to 9.69 × 10−7 m3 s−1 . Different irrigation
volumes were used for different irrigation experiments. At the endof each irrigation experiment, the soil around the emitter was
dug out, and the distance of the wetting front from the emitter
was measured in the horizontal, vertical downward, and vertical upward directions. Maximum distances between the wetting
front and the emitter in particular directions (upward, downward,
and horizontal) were used in comparisons with results simulatedby HYDRUS-2D. The average initial water content for these ﬁeld
experiments, which was used in the numerical simulations, was
0.1 cm3 cm−3 .
Due to the natural spatial heterogeneity of soils in the ﬁeld,
expected lack of uniformity of soil hydraulic properties, and thelikely effects of soil structure on water ﬂow in the ﬁeld, experimental data collected in the ﬁeld from one additional subsurface
drip irrigation were used with the inverse option available in
HYDRUS-2D to estimate the effective soil hydraulic parameterscharacterizing ﬁeld conditions. ROSETTA was used ﬁrst to provide initial estimates of soil hydraulic parameters. The saturated
hydraulic conductivity estimated by ROSETTA (Ks = 5 × 10−7 m s−1 )was very low, and did not allow HYDRUS to provide a good descrip-

˛ (m−1 )Ks (m s−1 )

n

2.3
1

−6

2.5 × 10
5 × 10−6

1.34
1.89

l
0.5
0.5

tion of soil water contents during the inﬁltration experiments.
ROSETTA, taking into account only soil textural properties, did notprovide a good estimate of the saturated hydraulic conductivity
for the undisturbed ﬁeld soil, which was also likely affected by thesoil structure, contrary to the repacked soil used in the laboratory.
Therefore, the inverse solution option of HYDRUS-2D was used toestimate the saturated hydraulic conductivity and parameter n forthe ﬁeld soil.
One additional subsurface drip irrigation experiment was conducted speciﬁcally for this purpose. The emitter was installed
30 cm below the soil surface, and the average emitter discharge
was 7.5 × 10−7 m3 s−1 for 5 h. The average dry bulk density andthe average initial water content for this ﬁeld experiment were1.55 g cm−3 and 0.13 cm3 cm−3 , respectively. At the end of theirrigation experiment, the soil surrounding the emitter was excavated, and soil samples were taken in three locations (0, 12.5,
and 25 cm away from the emitter) and at four depths (10, 20,
30, and 40 cm). Water contents measured at these samples werethen used in the inverse analysis. Parameters n and Ks were
optimized using HYDRUS-2D to get a better description of themeasured wetting pattern for this control experiment. The ﬁnal
estimates of soil hydraulic parameters for a clay loam soil were: r = 0.07 cm3 cm−3 ,  s = 0.38 cm3 cm−3 (measured in the laboratory on soil samples), ˛ = 1 m−1 , n = 1.89 (1.39 was estimated by
ROSETTA), and Ks = 5 × 10−6 m s−1 (5 × 10−7 m s−1 was estimatedby Rosetta), assuming l = 0.5 (Table 1).
2.3. Numerical modeling
Since only one emitter was used as a point source of waterin both sets of experiments, water movement during both theinﬁltration and the redistribution phases could be considered anaxisymmetrical process. The following Richards equation is thegoverning equation for water ﬂow in a homogenous and isotropic
soil:
∂
1 ∂
=
r ∂r
∂t



rK(h)

∂h
∂r



∂
∂z

+





K(h)

∂h
+ K(h)∂z(1)

where  = volumetric water content (L3 L−3 ); h = soil water pressure head (L); t = time (T); r = radial space coordinate (L);z = vertical space coordinate (L); and K = hydraulic conductivity(L T−1 ). The soil hydraulic properties were modeled using the van
Genuchten–Mualem constitutive relationships (van Genuchten,1980) as follows:(h) =⎧ −⎪⎨ r +  s r⎪⎩n

1 + ˛h

m,

h<0
(2)

s ,



h≥0



K(h) = Ks Sel 1 − 1 − Se
where Se =

1/m

m

2

 − r,s − r

,
m=1−

1
n

(3)

where  s = saturated water content (L3 L−3 );  r = residual water content (L3 L−3 ); Ks = saturated hydraulic conductivity (L T−1 ); and ˛(L−1 ), n and l = shape parameters.

M.M. Kandelous, J. Šimůnek / Agricultural Water Management 97 (2010) 1070–1076

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Fig. 2. A comparison of measured and simulated soil water contents for the lysimeter experiment (— simulated with  s = 0.41 cm3 cm−3 , - - - simulated with  s = 0.38 cm3 cm−3 ,䊉 observations).

HYDRUS-2D uses the Galerkin ﬁnite-element method to solve
the governing water ﬂow equation. The transport domain, forwhich the numerical solution was obtained, was rectangular(100 cm wide and 150 cm deep, discretized into 2000 nodes),except for the semicircle on the left side of the domain representing
the dripper. The location of the semicircle depended on the locationof the dripper in the different experiments. Initial conditions for all
simulations were given in terms of constant water contents. During
water application, a variable ﬂux boundary condition was used at
the emitter. The water ﬂux, considered to be constant for each inﬁltration experiment, was calculated by dividing the water discharge
by the surface area of the emitter. At the end of the irrigation event,the emitter boundary became a zero-ﬂux boundary. The remaining
part of the left boundary was a zero-ﬂux boundary both during andafter the irrigation event. Zero-ﬂux boundary conditions were also
used at the right and bottom boundaries, since the computational
ﬂow domain was large enough for these boundaries not to affectwater ﬂow in the domain. A zero ﬂux boundary condition was also
used at the soil surface as evaporation could be neglected due tothe plastic mulch used during and after irrigation.
2.4. Statistical analysis
The root-mean-square-error (RMSE) for both simulated andmeasured volumetric water contents, as well as wetting dimensions, was calculated to provide a quantitative comparison of thegoodness-of-ﬁt between measured and simulated data.
3. Results and discussion
3.1. Soil water distribution
Fig. 2 shows the measured and simulated volumetric watercontent distributions for the second lysimeter experiment 1, 2, 3,

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M.M. Kandelous, J. Šimůnek / Agricultural Water Management 97 (2010) 1070–1076

Fig. 3. A comparison of measured and simulated soil water contents for the ﬁeld experiments (— simulation, 䊉 observations).

Fig. 4. A comparison of measured and simulated wetting dimensions for (A) the lysimeter laboratory experiment, and (B) and (C) ﬁeld experiments with emitter depths of
15 and 30 cm, respectively (— simulation, 䊉 observations).

M.M. Kandelous, J. Šimůnek / Agricultural Water Management 97 (2010) 1070–1076

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Table 2
Statistical comparison of measured and simulated data for both lysimeter and ﬁeld experiments.
Measurement

Exp. conditions

Laboratoryb

Soil water contents

Laboratoryc

Field

Laboratoryb
Wetting Dimensions
Field

Emitter depth (cm)Days after irrigation
One
Two
Three
Four
One
Two
Three
Four
End of Irrigation

Distance direction
UpwardHorizontal
Downward
Horizontal
Downward
UpwardHorizontal
Downward

30

30
5
5
15
25

30
15
30

R2

RMSEa

0.65
0.62
0.68
0.66
0.66
0.57
0.61
0.60
0.77
0.88
0.98
0.60

0.045
0.020
0.012
0.014
0.031
0.016
0.011
0.011
0.035
0.029
0.012
0.028

0.99
0.99
0.99
0.94
0.99
0.98
0.71
0.99

0.98
2.11
2.81
2.55
4.36
2.1
3.87
3.48

Root-mean-square-error was evaluated for the soil water content in volumetric units (cm3 cm−3 ) and for wetting dimensions in cm.
Soil hydraulic properties predicted by ROSETTA.
c
Soil hydraulic properties predicted by ROSETTA, adjusted porosity.

aband 4 days after irrigation. Two simulations were carried out forthis laboratory experiment. While all the soil hydraulic parameters
were estimated by ROSETTA in the ﬁrst simulation, in the second
simulation the saturated water content  s was lowered from the
Rosetta-predicted value of 0.41 to a value of 0.38, measured in
the laboratory for the ﬁeld soil, to obtain a better correspondence
between measurements and simulations. Differences between themeasured and the simulated data were greater during the ﬁrst day
than during the remaining three days. The sensor placed at a depthof 5 cm showed the same results for all days, and thus it is possible that there was a poor contact between this sensor and thesoil, resulting in incorrect measurements. The RMSE for simulations
conducted using a saturated water content equal to 0.41 cm3 cm−3 ,predicted by ROSETTA, and 0.38 cm3 cm−3 , measured in the laboratory for the ﬁeld experiment, varied from 0.012 to 0.045 and from0.011 to 0.031, respectively. Since these values are comparable to
the results obtained by Skaggs et al. (2004), it can be concluded
that the accuracy of the simulations for all 4 sampling times was
satisfactory.
Fig. 3 shows the measured and simulated volumetric water contents for four ﬁeld experiments (two with emitters at a depth of
5 cm with different emitter discharges, one with the emitter at a15-cm depth, and one with the emitter at a 25-cm depth) immediately after irrigation ended (except for experiment D, where watercontents were measured 2 h after irrigation ended). As shown in
Table 2, the RMSE for the ﬁeld experiments varied, as with thelysimeter experiment, from 0.012 to 0.035. Considering the spatial heterogeneity of soil properties in the ﬁeld, it can be concluded
that the correspondence between simulations and observations isvery good.
Taking into account both laboratory and ﬁeld results, and better
correspondence between simulated and measured soil water contents in the laboratory experiment when a lower value of porosity
was used than that predicted by ROSETTA, it can be concluded that
poorer correspondence between simulations and measurementsin the laboratory experiments during the ﬁrst day was most likely
due to the inadequacy of the soil hydraulic parameters estimatedby ROSETTA. While soil hydraulic parameters for the ﬁeld soil were
calibrated using an independent dynamic ﬂow experiment, for thelaboratory experiments they were estimated using ROSETTA fromtextural (hence, static) soil properties. Better correspondence forthe ﬁeld experiments was achieved using an inverse analysis of
the collected dynamic ﬂow data to obtain effective soil hydraulicproperties.
3.2. Wetting dimensions (upward, downward and horizontal)
The wetting dimensions observed during lysimeter and ﬁeld
experiments are compared with HYDRUS-2D simulations in Fig. 4.
A statistical comparison between observed and simulated data ispresented in Table 2. No comparison was made for the upward
direction for experiments with the emitter installed at a depth of
15 cm since, as HYDRUS-2D predicted, the wetting front reached
the soil surface during these experiments. The value of the RMSE
for different distances varied from 0.98 to 4.36 cm for the lysimeter
experiments and from 2.1 to 3.87 cm for the ﬁeld experiments.
4. Summary and conclusions
For this research, multiple subsurface drip irrigation experiments were carried out under both laboratory and ﬁeld conditions.
The purpose of these experiments was an evaluation of the accuracyof HYDRUS-2D for simulating water movement in clay loam soils,
for predicting the spatial distribution of soil water contents, and for
determining the upward, downward, and horizontal dimensions of
the wetting zone, for buried point sources with variable discharge
conditions.
Experiments in a laboratory lysimeter were carried out for a clay
loam soil with the emitter placed 30 cm below the soil surface. Soil
moisture sensors were installed at different locations around theemitter to measure the spatial distribution of the soil water content.
The soil hydraulic properties used in the laboratory experimentwere estimated using the ROSETTA model available in HYDRUS2D. A comparison of measured and simulated data showed that the
HYDRUS-2D predictions were very accurate, especially when theporosity was adjusted to observed values.
Four experiments with emitters located at different depths (2with the emitter at a depth of 5 cm using different emitter discharges, and one each with emitters at depths of 15 and 25 cm)
were carried out under ﬁeld conditions. Soil water contents were1076

M.M. Kandelous, J. Šimůnek / Agricultural Water Management 97 (2010) 1070–1076

obtained from samples collected at various locations from thevertical soil proﬁles that were exposed after the soil was excavated at the end of the irrigation experiments. The effective soil
hydraulic properties for the ﬁeld experiments were obtained using
the HYDRUS-2D model and the inverse analysis of collected data.
As with the lysimeter experiments, good agreement between measured and simulated data was obtained for ﬁeld experiments.
Like Skaggs et al. (2004) and Provenzano (2007), both of whomused HYDRUS-2D to analyze subsurface drip irrigation experiments
carried out on sandy loam soil (with drippers located at depthsof 6 and 10 cm, respectively), we can conclude that HYDRUS-2D
performed well for drippers installed in deeper depths of the clay
loam soil. We should note that while experiments by Skaggs etal. (2004) and Provenzano (2007) were carried out on either thoroughly mixed or repacked soils, respectively, our ﬁeld study was
performed on undisturbed soil. This suggests that HYDRUS-2D
can be a good tool for designing and managing subsurface drip
irrigation systems for soils with different textures. HYDRUS-2D
results can be substantially improved when its input parameters,i.e., the effective soil hydraulic parameters, are calibrated against a
dynamic ﬂow experiment.
Twelve observations were recorded for the upward, downward,
and horizontal dimensions of the wetting pattern in the laboratory
experiment. As with soil water content, good agreement between
measured and simulated dimensions was obtained in all directions.
Ten experiments, i.e., 5 with emitters at a 15-cm depth and 5 withemitters at a 30-cm depth, were carried out to measure wetting
dimensions in the ﬁeld. As with the measurements of soil watercontents, upward, downward, and horizontal dimensions of thewetting pattern were measured after the vertical soil proﬁles wereexcavated. A comparison of measured and simulated data for bothsoil water contents and wetting dimensions showed that the accuracy of HYDRUS-2D predictions was good.
In the past, several researchers have attempted to estimate wetting dimensions using various empirical, analytical, and numericalmodels (e.g., Schwartzman and Zur, 1986; Angelakis et al., 1993;
Chu, 1994; Moncef et al., 2002; Cook et al., 2003; Singh et al., 2006;
Lazarovitch et al., 2007; Kandelous and Šimůnek, 2010). HYDRUS2D proved to be an equally good, if not better, tool for estimating
wetting dimensions, as well as soil water content distributions, fordrip irrigation systems with emitters installed at different depths.
Considering all previous and current studies, it can be concluded
that HYDRUS can successfully simulate both temporal and spatial
soil water content distributions, as well as the dimensions of thesoil wetting pattern, the two main factors usually considered fordrip irrigation system design, for different emitter depths (surface/subsurface drip irrigation), different soil types, and different
initial and boundary conditions. HYDRUS’s capacity to simultaneously evaluate soil wetting pattern dimensions, soil water content,
and matric potential distributions during and after irrigation makeit a good and useful tool for designing, monitoring, and managing
drip irrigation systems.
Additional experiments will be carried out using various soil
types to illustrate HYDRUS-2D’s capacity for application to a broad
range of soils. Experiments will also be performed with multiple emitters to evaluate whether HYDRUS can simulate water
movement in soils where two or more wetting patterns overlap. However, this will require fully three-dimensional simulations

using HYDRUS-2D/3D (Šimůnek et al., 2008). Ultimately, we plan
to develop an optimization tool that will be able to optimize all themain factors involved in the design of the subsurface drip irrigation
system, including emitter installation depth, emitter spacing, timeof water application, and emitter discharge for given conditions.
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