Agricultural Water Management 121 (2013) 73–80

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

Solute transport in a loamy soil under subsurface porous clay pipe irrigation
A.A. Siyal a,∗ , M. Th. van Genuchten b , T.H. Skaggs ca

Department of Land and Water Management, Sindh Agriculture University Tandojam, Pakistan
Department of Mechanical Engineering, Federal University of Rio de Janeiro, UFRJ, Rio de Janeiro, RJ, 21945-970, BrazilcU.S Salinity Laboratory, USDA-ARS, 450 W. Big Springs Rd., Riverside, CA 92507, USAba r t i c l e

i n f oArticle history:
Received 28 July 2012
Accepted 10 January 2013
Keywords:
Soil salinization
Subsurface irrigation
Okra
Model prediction
HYDRUS-2D

a b s t r a c t
Subsurface porous clay pipe irrigation is widely considered to be a very promising method for smallscale irrigation in arid regions. Unfortunately, salt accumulation at and near the soil surface using thismethod may affect germination of direct-seeded crops. Predicting salt movement and accumulation withclay pipe irrigation will allow producers to anticipate the need for leaching to control salinity in the soil
root zone. The HYDRUS-2D model was used to simulate the accumulation of salt from a subsurface clay
pipe irrigation system, installed at 30 cm depth, during the growing season of okra (Abelmoschus esculentus) irrigated with water having a salinity of 1.1 dS m−1 . The loamy soil proﬁle had an initial salinityof 2.3 dS m−1 . Predicted electrical conductivity (ECe ) values at the end of the growing season correlated
signiﬁcantly (R2 = 0.952) with measured saturated paste ECe data obtained at the end of the ﬁeld experiments. Salinity was found to be relatively low around the pipes, but increased with distance away fromthe pipes. Measured and predicted soil salinity levels were especially higher above the clay pipes. Our
results indicate that proper management of salt accumulation is vital for sustainable crop production
whenever subsurface irrigation systems are being implemented.
© 2013 Elsevier B.V. All rights reserved.

1. Introduction
Chronic water shortages in arid regions of the world are compelling farmers to adopt more efﬁcient irrigation methods forsustainable crop production. While in many developed countries
high-tech micro-irrigation methods such as sprinkler and drip
irrigation are used increasingly, many farmers in developing
countries are reluctant to adopt these methods due to their highinitial cost of installation and costly maintenance. Traditional
irrigation methods such as subsurface pitcher and porous clay
pipe irrigation (Ashraﬁ et al., 2002; Qiaosheng et al., 2007; Siyal
et al., 2009) are often preferred by poor farmers in small scale
irrigation projects because of their low cost and high irrigation
efﬁciency. Subsurface clay pipe irrigation may be used to improveirrigation uniformity and water use efﬁciency in a number of different cropping systems. The method allows one to supply therequired amount of water by seepage from below the soil surface
to the crop at the right place and the right time. This should reducewater losses by evaporation and deep percolation while increasing
crop yield and quality.

∗ Corresponding author. Tel.: +92 3353340405; fax: +92 222765300.
E-mail addresses: siyal@yahoo.com (A.A. Siyal), rvangenuchten@hotmail.com(M.Th. van Genuchten), Todd.Skaggs@ars.usda.gov (T.H. Skaggs).
0378-3774/$ – see front matter © 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.agwat.2013.01.005

In subsurface porous clay pipe irrigation, water and solutes not
only spread downward and sideways but also move upward due
to capillarity and surface evaporation, thus causing salts to accumulate at or near the soil surface. The accumulated salts may be
harmful to crops that are subsequently grown at the site, especially directly seeded crops because of their sensitivity to high levelsof salinity during germination and establishment (Hussain et al.,
1997; Mer et al., 2000; Roberts et al., 2009). Salt accumulation during subsurface clay pipe irrigation is a particular concern in arid
regions where annual potential evapotranspiration (ET) is muchhigher than precipitation. Thus, special management techniques
are needed to prevent salt accumulation and the resulting harmful
effects on germination or seed emergence (Hanson and Bendixen,
1995; Hanson, 2003).
The design and management of subsurface porous clay pipe
irrigation systems requires an understanding of water and solute
distribution patterns in the soil around the pipes (Siyal and Skaggs,2009). Optimal management of salt accumulation during and following porous clay pipe irrigation is especially important for
high-value crops that are often salt-sensitive. Understanding patterns of salt accumulation at the end of the growing season enable
one to foresee a possible need to leach accumulated salts fromthe near surface by surface ﬂooding or some other method. This
understanding may result from a combination of well-designed
ﬁeld experiments and numerical modeling.

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A.A. Siyal et al. / Agricultural Water Management 121 (2013) 73–80

Numerical models are now widely used to simulate the ﬂow ofwater and the transport of solutes in soils (Šimůnek and Bradford,2008). The HYDRUS-2D software (Šimůnek et al., 1999; Šimůnek
and van Genuchten, 2008) is a numerical model capable of simulating many vadose zone ﬂow and transport processes subject to awide range of boundary conditions and irrigation regimes. In thepresent study, the ability of HYDRUS-2D to predict the salt distribution after a full cropping season was determined using correlations
between model-predicted and ﬁeld-measured values of the electrical conductivity of the soil saturation extract (ECe ). The data
pertain to a subsurface clay pipe ﬁeld experiment carried out inSouth-East Pakistan on a medium-textured soil cropped with okra.
In a previous study, Siyal and Skaggs (2009) showed that HYDRUS2D provided excellent predictions of observed wetting patterns inthe same soil after 5 days of irrigation using the same porous clay
pipe irrigation system. The speciﬁc objectives of this study were to
predict ECe values at the end of the okra cropping season, and to correlate the model-predicted ECe values with the actual ECe values inorder to determine the accuracy of HYDRUS-2D model predictions.

2. Methods and materials
A subsurface irrigation system with baked porous clay pipes was
installed in an area of 500 m2 (20 m × 25 m) at the experimentalﬁeld of the Faculty of Agricultural Engineering, Sindh Agriculture University Tandojam, Pakistan (Fig. 1). The site is located at
25◦ 25 28” N, 68◦ 32 25” E, about 26 m above mean sea level. The
clay pipe irrigation system was very similar to the system previously described in detail by Siyal and Skaggs (2009). Pipes 20 m long
were buried at a depth of 30 cm, with a spacing of 1 m. The pipes
were formed by cementing together segments having a length of
40 cm and an outside diameter of 13.1 ± 0.2 cm (Fig. 1). More details
are given by Siyal and Skaggs (2009).
One of the installed irrigation pipes was chosen at random forﬁeld characterization measurements (the eighth out of 25 installed
pipes). At three random locations along the selected pipeline, soil
samples were taken on both sides of the pipe, at lateral distances of
20 and 40 cm from the centre of the pipeline, at depths of 0–20,
20–40, 40–60, 60–80 and 80–100 cm. In total, 60 samples weretaken (3 locations × 4 distances × 5 depths). These samples showedthat the mean initial soil proﬁle ECe down to a depth of 100 cm was
2.3 ± 0.08 dS m−1 . The hydrometer method was used to determine
the soil particle size distribution (Craze et al., 2003). The textureof the soil according to the USDA system was found to be mostly
loam, with the sand fraction ranging from 38 to 56%, the silt fraction from 27 to 39%, and the clay fraction from 15 to 26% (Table 1).
The soil bulk density was determined at several locations down
to 100 cm depth using a core sampler with a diameter of 1.5 cm.
The bulk density measurements ranged from 1.31 to 1.37 g cm−3 .
No obvious trend in the bulk density versus depth or across theﬁeld was observed; hence we used the average value of 1.34 g cm−3
to convert gravimetric water content data into volumetric watercontents. The soil was different from that used in the previous study(Siyal and Skaggs, 2009). The van Genuchten-Mualem (VGM) soil
hydraulic parameters were estimated from soil textural data andthe bulk density using the Rosetta pedotransfer functions (Schaap
et al., 2001) as implemented in HYDRUS-2D. Soil texture and related
hydraulic properties are given in Table 1.
Laboratory-measured saturated hydraulic conductivities (AbuZreig and Atoum, 2004) of the walls of four randomly selected
pipes were found to be 0.051, 0.046, 0.055, and 0.048 cm d−1 . The
average saturated hydraulic conductivity (Ks ) value of 0.05 cm d−1
was used in the simulations. Other hydraulic parameters of theclay pipe were taken to be:  s = 0.35,  r = 0.042, ˛ = 0.000001 cm−1and n = 1.3 (where  s and  r are the saturated and residual watercontents, respectively, and ˛ and n are van Genuchten–Mualem
shape parameters). The small value of ˛ ensured that the pipe walls
remained saturated during the calculations. We refer to Siyal andSkaggs (2009) for more details about the hydraulic conductivity
measurements of the clay pipes.
Tube-well water having an ECw of 1.1 dS m−1 was supplied continuously to the clay pipe laterals throughout the okra growing
season at a constant positive pressure head of 100 cm. Okra seeds
were drilled in rows separately at a depth 2 cm above the clay pipe
laterals using a hand drill on 11 February 2009. At the end of thegrowing season (12 April 2009), soil samples were taken again fromthe same locations and depths. Hence, in total 60 soil samples weretaken again, which were assumed to represent the entire experimental area. Once brought into the laboratory, the samples were
air dried and ground to pass through a 2 mm sieve before preparing
saturation pastes and determining ECe values. Due to the presumed
lateral symmetry of the wetted zone, the two salt concentrations forthe same depths and distances on both sides of the pipe were averaged for subsequent data analysis. No signiﬁcant rainfall occurred
during the entire growing period to contribute either to irrigationor to leaching of salts from the root zone. The total water applied
to okra was 10 cm compared to 50 cm usually required for okra
with conventional surface irrigation methods, thus demonstrating the potential saving in water provided by clay pipe subsurface
irrigation.
2.1. Numerical simulations
Simulations of water ﬂow and solute transport using HYDRUS2D were based on the Richards equation for water ﬂow and theequilibrium advection-dispersion equation for solute transport in a
two-dimensional variably-saturated medium. The general Richards
equation is given by:∂= ∇ · (K · ∇ h + K)
∂t

(1a)

which for a two-dimensional isotropic medium such as in our study
reduces to



∂
∂
∂h
=
K(h)
∂t
∂x
∂x



+

∂
∂z



K(h)

∂h
∂z



+

∂K(h)
∂z

(1b)

where x is the horizontal coordinate (L), z is the vertical coordinate(L), t is time (T),  is the volumetric water content (L3 L−3 ), h is thepressure head (L), and K(h) is the unsaturated hydraulic conductivity function (LT−1 ). The soil water retention, (h), and hydraulicconductivity, K(h), functions needed in Eq. (1b) were describedusing the functions of van Genuchten (1980):
(h) = r +s − r[1 + |˛h|n ]

(2)

m



1/m m

K(h) = Ks Se0.5 1 − (1 − Se

2

)

(3)

where  r ,  s , Ks , ˛ and n are soil hydraulic parameters as deﬁned
previously, m = 1–1/n, and Se is effective saturation given bySe =(h) − rs − r

Solute transport was describedadvection–dispersion equation given by:∂c= ∇ · (D · ∇ c − qc)
∂t

(4)
using

the

general(5)where c is the solution concentration (ML−3 ), q is theDarcy–Buckingham water ﬂux vector (LT−1 ), and D is the dispersion
tensor (L2 T−1 ), which was described using standard expressions(e.g., Bear, 1972) in terms of the longitudinal (εL ) and transverse

A.A. Siyal et al. / Agricultural Water Management 121 (2013) 73–80

75

Fig. 1. Baked clay pipes and their installation at the experimental ﬁeld.

Table 1
Soil texture and related hydraulic properties.
Soil depth (cm)

0–20
20–40
40–60
60–80
80–100Percentage of soil separates

Sand

Silt

Clay

38.2
40.8
39.5
45.6
55.0

41.6
40.5
43.2
36.3
26.8

20.2
18.7
17.3
18.1
18.2

Soil texture

Loam
Loam
Loam
Loam
Sandy loam

Dry bulk
density (g/cm3 )

1.31
1.34
1.33
1.34
1.37

Soil hydraulic properties r (cm3 cm−3 ) s (cm3 cm−3 )Ks (cm d−1 )˛ (cm−1 )

n

0.065
0.061
0.058
0.059
0.058

0.422
0.411
0.407
0.413
0.419

20.80
19.27
22.05
22.77
31.22

0.008
0.008
0.007
0.010
0.016

1.56
1.55
1.57
1.51
1.45

(εT ) dispersivities (L). Eq. (5) holds for non-reactive transport, andhence ignores any sorption or precipitation/dissolution of salts inthe proﬁle.
Eqs. (1) and (5) were used to simulate water and salt movement in a vertical cross-section containing a functioning clay pipe.
Because of symmetry, only the right side of the vertical cross section
was simulated. Hence the computational domain was rectangular
except for a section on the left side where the clay pipe was represented on the domain boundary by a half-circle (Figs. 2 and 3). The
ﬂow domain and computational grid were the same as those usedpreviously by Siyal and Skaggs (2009).
2.2. Initial and boundary conditions
The initial pressure head distribution in the soil proﬁle wasdetermined from the measured water contents of soil samples
taken when the irrigation pipes were installed. These water content measurements were converted to pressure heads using the soil
water retention characteristic given by Eq. (2) using the loam soil
parameter values listed in Table 1. Consistent with these observed
values, the initial pressure head was assumed to increase linearlywith depth in the proﬁle, from −1000 cm at the soil surface (z = 0)
to −600 cm at the bottom (z = 100 cm) as shown in Fig. 2. The clay
pipe internal boundary nodes were assigned a constant pressure
head of 100 cm, being equal to the pressure head imposed during

Fig. 2. Linear distribution of initial soil water pressure head (cm) in the soil proﬁle.

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A.A. Siyal et al. / Agricultural Water Management 121 (2013) 73–80

Fig. 4. Trajectories of the water particles originating from clay pipe.

Fig. 3. HYDRUS-2D model domain and boundary conditions.

the study. The remaining portion of the left boundary was set asa zero ﬂux condition (due to symmetry in the simulated proﬁle).
The surface boundary condition was speciﬁed as an “atmosphericboundary condition” (Šimůnek et al., 1999). The potential evaporation and transpiration rates needed for the boundary condition
were determined by ﬁrst calculating a reference evapotranspiration ET0 (t) using the Penman–Monteith method. The potential crop
evapotranspiration ETp (t) was then computed by multiplying thereference evapotranspiration with a crop coefﬁcient (Kc ):ETp (t) = Kc (t) · ET0 (t)(6)

ET0 (t) was discretized in daily time steps whereas Kc (t) variedwith annual crop growth stage (initial, crop development, midseason, and late season stages) as described by Allen et al. (1998).
Data from Allen et al. (1998) for beans (which are similar to okra)
were used to specify the value of Kc during each growth stage. The
potential evaporation rate Ep (t) may be estimated from ETp (t) using
an equation of the form:
Ep (t) = ETp (t) · f (t)(7)

where f(t) is typically formulated based on Beer’s law and the timevariation of the canopy cover (e.g. Pachepsky et al., 2004). Because
we did not have data for the time-variation of the canopy cover,
we followed (Jiménez-Martinez et al., 2009) and speciﬁed f(t) as
being a function that decreased sinusoidally from a value of 1 attime t = 0 d (planting) to a value of 0 at time t = 30 d (full canopy
cover). The reasoning for this choice is that initially after planting
transpiration would be about zero and thus Ep would be equal toETp (f = 1), whereas with full canopy (achieved around t = 30 d inour case) evaporation would be close to zero (f = 0). With ETp andEp given by Eqs. (6) and (7), respectively, the potential transpiration
Tp (t) was speciﬁed by:
Tp (t) = ETp (t) − Ep (t)(8)

The bottom boundary was speciﬁed as a free drainage (unit gradient) condition while the right boundary was a zero ﬂux condition(Fig. 3).

Soil salinity is typically quantiﬁed in terms of the electrical conductivity of the soil water or a soil water extract. Assuming that thesoil solution electrical conductivity is simply proportional to thesolution salt concentration, transport simulations can be performedin terms of electrical conductivities rather than concentrations Eq.
(5). The initial salt concentration of the soil saturation extract was
1440 mg/L, which was converted to electrical conductivity (dS m−1 )by dividing by a factor of 640 (U.S. Salinity Laboratory Staff, 1954).
This ECe value (2.3 dS m−1 ) was then converted to the electrical conductivity that would exist in the soil water (ECsw ) at time t = 0 (soilsampling time) using the relationship:
ECsw =Sp b
ECe
100 w(9)

where Sp is soil saturation percentage, b is soil dry bulk density(g cm−3 ), w is density of water (g cm−3 ), and  is volumetric soil
water content (cm3 cm−3 ).
The initial salt concentration of soil in the ﬂow domain was thus
set equal to 9.97 dS m−1 at the top and 9.26 dS m−1 at the bottom
during simulations. The conductivity of the irrigation water (ECiw )was set equal to the measured value (1.1 dS m−1 ). At the end of thesimulation, Eq. (9) was used to convert the simulated ECsw values
to ECe , which allowed for comparison with measured values.
Solute entering the soil system was described using a thirdtype (or ﬂux type) boundary condition at the porous clay pipe wall.
No solute was assumed to be taken up by the plants. A Neumannboundary condition for solute transport was used at the bottomof the soil proﬁle. For the simulations we used a longitudinal dispersivity equal to one-tenth of the depth of the ﬂow domain (i.e.
εL = 10 cm), which is consistent with various studies indicating that
εL is about one-tenth the scale of a transport experiment (Bevenet al., 1993; Cote et al., 2003; Phogat et al., 2011). The transversal
dispersivity (εT ) was assumed to be one-tenth of the longitudinal
dispersivity (e.g., Hanson et al., 2006).
The model of Feddes et al. (1978) modiﬁes root water uptake
depending on the water status of the soil and is parameterized interms of four pressure head values, h1 > h2 > h3 > h4 . The parameterization is such that uptake occurs at the potential rate when
the pressure head is in the optimal range h3 ≤ h ≤ h2 , drops off
linearly when h > h2 or h < h3 , and becomes zero when h ≤ h4 or
h ≥ h1 . The parameter h3 is expected to be a function of theA.A. Siyal et al. / Agricultural Water Management 121 (2013) 73–80

77

Fig. 5. Water content in the soil proﬁle at the end of the 60 days crop season. (a) 20 cm from the centre of the pipe and (b) 60 cm from the centre of the pipe.

evaporative demand. HYDRUS permits the speciﬁcation of high(h3high ) and low (h3low ) values of this parameter for high (rH) andlow (rL) potential transpiration rates, respectively. An interpolation
scheme is further used to determine h3 for intermediate conditions(Šimůnek et al., 1999). For our simulations, we used literature values for beans (which are similar to okra): h1 = −10 cm, h2 = −25 cm,
h3high , = −750 cm, h3low = −2000 cm, h4 = −8000 cm, rH = 0.5 cm d−1 ,and rL = 0.1 cm d−1 . The okra root distribution in the soil proﬁlewas described using the model of Vrugt et al. (2001) with roots
extending 20 cm laterally and 60 cm vertically.

2.3. Evaluation of model predictions

MBE =

i=1

(Pi − Oi )nn(P − Oi )2
i=1 i

RMSE =

n
R2 = 1 − i=1n

(Pi − Oi )22n(Oi − Ō)
i=1

(11)

(12)

where n is the number of data points; Pi is the ith predicted data
point; Oi is the ith observed data; and Ō is the mean of observed
data. MBE can identify potential bias (i.e., underestimation andoverestimation) in the predicted values, whereas RMSE and R2 giveoverall measures of the goodness-of-ﬁt.
3. Results and discussion

The agreement of HYDRUS-2D model predictions with the measured data was quantiﬁed in terms of three statistical measures:the mean bias error (MBE), the root mean square error (RMSE), andthe coefﬁcient of determination (R2 ). These parameters are deﬁned
as (Willmott, 1982):

n



(10)

Fig. 4 shows water particle trajectories originating from the pipe
wall after 3 days of irrigation. The trajectories provide a clear illustration of the direction of water movement from the clay pipe into
the medium-textured soil. Again, in this system, water moves not
only downward due to gravity but also upward as a result of capillarity, surface evaporation, and, to a lesser extent, the appliedpressure head. Water moving upward is either used by plants tomeet transpiration, or lost to the atmosphere while leaving the salts

Fig. 6. Simulated distribution of salt (ECe ) in loamy soil after 60 days crop season.

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A.A. Siyal et al. / Agricultural Water Management 121 (2013) 73–80

Fig. 7. Measured and predicted electrical conductivity of soil saturated extract (ECe ) after 60 days of irrigation.

in the soil. Because of evaporation, the soil water content decreases
signiﬁcantly near the soil surface, as shown in Fig. 5.
Fig. 6 shows the computed distribution of salt (ECe ) within theroot zone after the 60 days okra growing season. Results show relatively high salt concentrations above the clay pipe near the soil
surface and within the root zone, but lower concentrations near theclay pipe at 30 cm depth. Fig. 7 compares predicted and measured
ECe values at the end of the growing season. Results clearly indicate
salt accumulation near the wetting front laterally, as well as accumulation of salt at or near the soil surface. Measured and simulated
salinity patterns are nearly identical, with salinity increasing with
distance from the pipe, and being highest at the soil surface. Similarsalinization patterns were also reported by Morvant et al. (1997),
Cox (2001), Badr and Taalab (2007), Lamm et al. (2007) and Trenton
et al. (2008) for subsurface drip irrigation.
Fig. 8 shows computed soil water contents and ECe values versus
time at different soil depths 20 cm laterally away from the centreof the clay pipe. The plots show that the soil water content at a
depth of 5 cm initially decreases to about 0.09 cm3 cm−3 becauseof surface evaporation, but then the wetting front arrives and thewater content increases to approximately 0.15 and 0.17 cm3 cm−3
after 30 and 60 days, respectively. At the same 5 cm depth, ECeinitially increases from 2.3 to 2.6 dS m−1 after 9 days of irrigation,
but then gradually decreases to 2.4 dS m−1 , before again increasing
to 2.9 dS m−1 . In contrast, the soil water content at the 35 cm soil
depth rapidly increases to 0.23 cm3 cm−3 after 2 days of irrigationand thereafter slowly climbs to around 0.29 cm3 cm−3 , while thecorresponding ECe gradually decreases to 1.8 dS m−1 after 60 daysof irrigation. At 90 cm, the soil water content remains unchanged
until the arrival of the wetting front after about 30 days, and thengradually increases to 0.27 cm3 cm−3 after 60 days. Similarly, ECeremains unchanged for 30 days and then gradually decreases to alittle less than 2.0 dS m−1 after 60 days.
In all, the results in Figs. 7 and 8 indicate that salts accumulate predominantly near the soil surface. This accumulation of salt
near the soil surface after one growing season can adversely affect
the germination and emergence of a new crop. Knowledge of salt

Fig. 8. Computed change in (a) soil water content (cm3 cm−3 ) and (b) electricalconductivity of soil saturated extract (ECe ) against time at different soil depths in a
proﬁle located at 20 cm from the centre of the pipe.

A.A. Siyal et al. / Agricultural Water Management 121 (2013) 73–80

Fig. 9. Correlation of measured and predicted end-of-season saturation paste electrical conductivity (ECe ) at different depths in the soil proﬁle at lateral distances of
20 and 40 cm from the centre of the pipe.

distributions at the end of a growing season would allow producers more ﬂexibility in managing salt accumulation, and could
eliminate operating cost associated with unnecessary soil leaching.
Fig. 9 shows a comparison of predicted end-of-season ECe values with values measured for different soil depths (0–20, 20–40,
40–60, 60–80 and 80–100 cm) at lateral distances of 20 cm and40 cm from the centre of pipe, at 3 locations along the pipe. The
results show a strong relationship between observed and simulated values (R2 = 0.952, a slope close to one, and an intercept nearzero). Based on the goodness-of-ﬁt parameters (MBE, RMSE, R2 )in Fig. 9 and a visual inspection of the agreement between the79

simulated and measured values, we conclude that the salt distribution in the soil proﬁle following subsurface porous clay pipe
irrigation could be predicted well using HYDRUS-2D. The results
provide support for using HYDRUS-2D as a useful tool for investigating, designing and managing subsurface clay pipe irrigation
systems.
In order to determine the long term effect of subsurface clay
pipe irrigation on salt distributions, HYDRUS-2D was used to simulate salt distributions assuming beans were grown on the same
soil for two years. Simulation results (Fig. 10) show that two
years of continuous irrigation with the subsurface clay pipe system leads to further salinization of soil proﬁle above the clay pipe:
the ECe increased to approximately 5 dS m−1 near the soil surface. However, the salt concentration below the pipe decreased to
1.8 dS m−1 . The relatively high near-surface-salinity predicted after
two years indicates a need for leaching salts from the soil proﬁlebefore establishing a new crop. This then requires surface water
applications unless occasional rain alleviates the salt accumulation
problem.
4. Conclusion
Salt accumulation in soil due to subsurface porous clay pipe
irrigation was studied experimentally and by means of HYDRUS-2D
numerical simulations. An okra crop was grown on a loam soil withan initial soil salinity of ECe = 2.3 dS m−1 . The subsurface clay pipe
irrigation system was installed at 30 cm depth and the irrigationwater had a salinity of ECiw = 1.1 dS m−1 . Both measured and predicted soil salinity patterns showed higher soil salinity in the soil
proﬁle above the clay pipe, and lower salinity around the clay pipe
lateral. A strong relationship existed between observed and simulated salinity values (R2 = 0.952, slope close to one, intercept nearzero), although overall the model tended to somewhat underestimate the measured values. In arid regions it may be necessary
to use occasional surface water application to leach salts from thesurface soil, especially during the initial stages of the growing season when crops are typically most sensitive to salinity. The results
suggest that HYDRUS-2D can be used as a useful tool for investigating, designing and management of subsurface clay pipe irrigation
systems.
Acknowledgment
The Higher Education Commission (HEC) of Pakistan is gratefully
acknowledged for funding the project “Use of subsurface irrigation
system to meet the water crisis in the country” under the National
Research Program for Universities (NRPU).
References

Fig. 10. Predicted electrical conductivity (ECe ) of soil saturated extract of the soil
proﬁle after 2 years of irrigation.

Abu-Zreig, M., Atoum, F.M., 2004. Hydraulic characteristics of clay pitchers produced
in Jordan. Can. Biosyst. Eng. 46 (1), 15–20.
Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. Irrigation and Drainage. Paper No.
56. FAO, Rome, Italy.
Ashraﬁ, S., Gupta, A., Singh, M.B., Izumi, N., Loof, R., 2002. Simulation of inﬁltrationfrom porous clay pipe in subsurface irrigation. Hydrological Sciences Journal 47(2), 253–268.
Badr, M.A., Taalab, A.S., 2007. Effect of drip irrigation and discharge rate on waterand solute dynamics in sandy soil and tomato yield. Australian Journal of Basicand Applied Science 1 (4), 545–552.
Bear, J., 1972. Dynamics of ﬂuids in porous media. Elsevier, New York.
Beven, K.J., Henderson, D.E., Reeves, A.D., 1993. Dispersion parameters for undisturbed partially saturated soil. Journal of Hydrology 143, 19–43.
Cote, C.M., Bristow, K.L., Charlesworth, P.B., Cook, F.J., Thorburn, P.J., 2003. Analysis
of soil wetting and solute transport in subsurface trickle irrigation. Irrigation
Science 22, 143–156.
Cox, D.A., 2001. Growth, nutrient content and growth medium electrical conductivity of poinsettia irrigated by sub irrigation or from overhead. Journal Plant
Nutrition 24, 523–533.

80

A.A. Siyal et al. / Agricultural Water Management 121 (2013) 73–80

Craze, B., Holman, G., Chapman, G.A., Stone, M.J., 2003. Soil survey standard test
methods. Dep. of Land and Water Conservation, Sydney, NSW, Australia.
Feddes, R.A., Kowalik, P.J., Zaradny, H., 1978. Simulation of ﬁeld water use and crop
yield. In: Simulation Monographs. Pudoc, Wageningen, The Netherlands.
Hanson, B.R., Simunek, J., Hopmans, J.W., 2006. Evaluation of urea-ammoniumnitrate fertigation with drip irrigation using numerical modeling. Agricultural
Water Management 86, 102–113.
Hanson, B.R., Bendixen, W.E., 1995. Drip irrigation controls soil salinity under row
crops. California Agriculture 49 (4), 19–23.
Hanson, B.R., 2003. Drip irrigation increases tomato yields in salt-affected soil of San
Joaquin Valley. California Agriculture 57, 132–137.
Hussain, G., Al-Jaloud, A.A., Al-Shammary, S.A., Karimulla, S., Al-Aswad, S.O., 1997.
Effect of saline irrigation on germination and growth parameters of barley(Hordeum vulgare L.) in a pot experiment. Agricultural Water Management 34,
125–135.
Jiménez-Martinez, J., Skaggs, T.H., van Genuchten M.Th. Candela, L., 2009. A rootzone modelling approach to estimating groundwater recharge from irrigatedareas. Journal of Hydrology 367, 138–149.
Lamm, F.R., Ayars, J.E., Nakayama, F.S., 2007. Micro-irrigation for crop production:
design, operation, and management. Development in Agricultural Enginerring,
vol. 13. Elsevier, UK, p. 147.
Mer, R.K., Prajith, P.K., Pandya, D.H., Pandey, A.N., 2000. Effect of salts on germination of seeds and growth of young plants of Hordeum vulgare, Triticum aestivum,
Cicer arietinum and Brassica juncea. Journal of Agronomy and Crop Science 185,
209–217.
Morvant, J.K., Dole, J.M., Allen, E., 1997. Irrigation systems alter distribution of roots,
soluble salts, nitrogen and pH in the root medium. Horticulture Technology 7,
156–160.
Pachepsky, Y.A., Smettem, K.R.J., Vanderborght, J., Herbst, M., Vereecken, H., Wosten,
J.H.M., 2004. Reality and ﬁction of models and data in soil hydrology. In: Feddes, R.A., et al. (Eds.), Unsaturated-Zone Modeling. Kluwer Academic Publishers,
Dordrecht, the Netherlands.
Phogat, V., Mahadevan, M., Skewes, M., Cox, J.W., 2011. Modelling soil waterand salt dynamics under pulsed and continuous surface drip irrigationof almond and implications of system design. Irrigation Science 30 (4),315–333.

Qiaosheng, S., Zuoxin, L., Zhenying, W., Haijun, L., 2007. Simulation of the soil wetting
shape under porous pipe sub-irrigation using dimensional analysis. Irrigation
and Drainage 56, 389–398.
Roberts, T.L., Lazarovitch, N., Warrick, A.W., Thompson, T.L., 2009. Modeling salt
accumulation with subsurface drip irrigation using HYDRUS-2D. Soil Science
Society of America Journal 73, 233–240.
Schaap, M.G., Leij, F.J., van Genuchten, M.Th., 2001. ROSETTA: a computer programfor estimating soil hydraulic properties with hierarchical pedotransfer functions. J. Hydrol. 251, 163–176.
Šimůnek, J., Sejna, M., van Genuchten, M.Th., 1999. The HYDRUS-2D Software
Package for Simulating The Two-Dimensional Movement of Water, Heat, andMultiple Solutes in Variably-Saturated Media. IGWMC-TPS 53, Version 2.0, International Ground Water Modeling Centre. Colorado School of Mines, Golden,
CO.
Šimůnek, J., van Genuchten, M.Th., 2008. Modeling non equilibrium ﬂow and transport with HYDRUS. Vadose Zone Journal 7 (2), 782–797.
Šimůnek, J., Bradford, S.A., 2008. Vadose Zone Modeling: Introduction and importance. Vadose Zone Journal 7 (2), 581–586.
Siyal, A.A., van Genuchten, M.Th, Skaggs, T.H., 2009. Performance of pitcher irrigation
systems. Soil Science 174 (6), 312–320.
Siyal, A.A., Skaggs, T.H., 2009. Measured and simulated wetting patterns underporous clay pipe subsurface irrigation. Agricultural Water Management 96 (6),893–904.
Trenton, L.R., Scott, A.W., Arthur, W.W., Thomas, L.T., 2008. Tape depth and germination method inﬂuence patterns of salt accumulation with subsurface drip
irrigation. Agricultural Water Management 95 (6), 669–677.
U.S. Salinity Laboratory Staff, 1954. Diagnosis and improvement of saline and alkalisoils. Handbook, vol.60. U.S. Dept. Agriculture, U.S. Government Printing Ofﬁce,
Washington, DC, p. 160.
van Genuchten, M.Th., 1980. A closed-form equation for predicting the hydraulicconductivity of unsaturated soils. Soil Science Society of America Journal 44,
892–898.
Vrugt, J.A., Hopmans, J.W., Simunek, J., 2001. Calibration of a two-dimensional root
water uptake. Soil Science Society of America Journal 65, 1027–1037.
Willmott, C.J., 1982. Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society. 63 (11), 1309–1313.