Journal of Hydrology 513 (2014) 504–516

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Journal of Hydrology
journal homepage: www.elsevier.com/locate/jhydrol

Seasonal simulation of water, salinity and nitrate dynamics under drip
irrigated mandarin (Citrus reticulata) and assessing management options
for drainage and nitrate leaching
V. Phogat a,⇑, M.A. Skewes a, J.W. Cox a,b, G. Sanderson c, J. Alam d, J. Šimůnek eaSouth Australian Research and Development Institute, GPO Box 397, Adelaide, SA 5001, Australia
The University of Adelaide, PMB1, Glen Osmond, SA 5064, Australia
Dareton Agricultural Research and Advisory Station, PO. Box 62, Dareton, NSW 2717, AustraliadRural Solutions SA, GPO Box 1671, Adelaide, SA 5001, AustraliaeDepartment of Environmental Sciences, University of California, Riverside, CA 92521, USAbca r t i c l e

i n f oArticle history:
Received 9 January 2014
Received in revised form 31 March 2014
Accepted 4 April 2014
Available online 16 April 2014
This manuscript was handled by Peter K.
Kitanidis, Editor-in-Chief, with theassistance of Markus Tuller, Associate Editor
Keywords:
Modelling
HYDRUS
Mandarin
Fertigation
Nitrate leaching
Soil salinitys u m m a r y
Estimation of all water ﬂuxes temporally and spatially within and out of the crop root zone, andevaluation of issues like salinity and nutrient leaching, are necessary to fully appraise the efﬁciency ofirrigation systems. Simulation models can be used to investigate these issues over several seasons
when the cost of long term monitoring is prohibitive. Model results can be used to advise growers
if improvements are required to various aspects of irrigation system operations. In this study,
HYDRUS-2D was used to evaluate data measured during one season in a young mandarin (Citrus
reticulata) orchard, irrigated with an intensive surface drip fertigation system. Water contents, salinities,
and nitrate concentrations measured weekly in the ﬁeld were compared with model predictions.
The temporal mean absolute error (MAE) values between weekly measured and simulated watercontents ranged from 0.01 to 0.04 cm3 cm3. However, modelling error (MAE) was slightly larger at
10 cm depth (0.04 cm3 cm3), as compared to greater depths (0.02–0.03 cm3 cm3). Similarly, the errors
1), aswere larger in the surface soil layer (25 cm depth) for nitrate–nitrogen, NO
3 –N (1.52 mmol(c) Lcompared to greater depths. The spatial and temporal soil solution salinity (ECsw) and NO3 –N data
showed accumulation of salts and nitrate within the soil up until day 150 of the simulation (December,2006), followed by leaching due to high precipitation and over irrigation at later times. Only 49% of
applied water was used by the mandarin trees, while 33.5% was leached. On the other hand, thesimulation revealed that a signiﬁcant amount of applied nitrogen (85%) was taken up by the mandarin
trees, and the remaining 15% was leached. The results indicate that the irrigation and fertigation scheduleneeds modifying as there was overwatering from December onwards.
Different permutations and combinations of irrigation and fertigation scheduling were evaluated to
optimise the water and nitrogen uptake and to reduce their leaching out of the crop root zone. Slightly
higher nitrogen uptake (1.73 kg ha1) was recorded when fertigation was applied second to last hourin an irrigation event, as compared to applying it earlier during an irrigation event. Similarly, a 20% reduction in irrigation and N application produced a pronounced reduction in drainage (28%) and N leaching(46.4%), but it also decreased plant N uptake by 15.8% and water uptake by 4.8%, and increased salinity
by 25.8%, as compared to the normal practice. This management would adversely impact the sustainability of this expensive irrigation system. However, reducing only irrigation by 30% during the 2nd half ofthe crop season (January to August) reduced drainage and N leaching by 37.2% and 50.5%, respectively,
and increased N uptake by 6.9%. Such management of irrigation would be quite promising for the sustainability of the entire system. It is concluded that judicious manipulations of irrigation and fertilizer applications can be helpful in designing drip irrigation schedules for perennial horticultural crops to achieve
improved efﬁciency of irrigation and fertigation applications and reduced contamination of receiving
water bodies.
Ó 2014 Elsevier B.V. All rights reserved.

⇑ Corresponding author. Tel.: +61 8 83039567; fax: +61 8 83039473.
E-mail address: vinod.phogat@sa.gov.au (V. Phogat).
http://dx.doi.org/10.1016/j.jhydrol.2014.04.008
0022-1694/Ó 2014 Elsevier B.V. All rights reserved.

V. Phogat et al. / Journal of Hydrology 513 (2014) 504–516

1. Introduction
Micro-irrigation has become the optimal standard for irrigationand fertigation of horticultural crops in Australia, due to increased
water scarcity and higher costs of fertilizers over the last decade.
Intensive fertigation schedules have been developed to increase
yield and quality of many permanent horticultural crops, including
mandarin. This combines drip irrigation and fertigation to deliver
water and nutrients directly to the roots of the crop, with the aimof synchronizing the applications with crop demand (Assouline,
2002; Gärdenäs et al., 2005) and maintaining the desired concentration and distribution of ions and water in the soil (Bar-Yosef,
1999). The overall aim of these interventions is to develop an irrigation and nutrient management program that increases yield andfruit quality, while reducing leaching. The fundamental principleof drip fertigation is to apply water and nutrients regularly to asmall volume of soil at a low application rate and at a highfrequency to closely meet crop demand (Falivene et al., 2005). However, the potential for movement of water and mineral nutrients,
especially nitrogen (as nitrate), below the root zone and into theground- and then surface-waters using these approaches is stillhigh. This is due to a number of factors: amount and intensity of
precipitation, the large amounts of water and nutrients being
applied, the limited capacity of roots to take up these nutrients,
and to the ability of irrigators to manage drainage and henceleaching.
Citrus is one of the important horticultural crops being grownunder intensive fertigation systems in Australia. The vast majorityof citrus plantings are oranges (73%), with the rest split between
mandarins (20%), lemons and limes (5%), and grapefruit (2%)(Horticulture Australia Limited, 2008). About 75% of the Australian
citrus industry is located in the Murray-Darling Basin, utilising thelighter-textured free-draining soils adjacent to the Murray, Darlingand Murrumbidgee rivers, and thus potential off-site effects ofpoorly managed fertigation may have wider implications.
Irrigated horticulture has, in general, been identiﬁed as the
major source of nitrogen in drainage waters in the Murray Darling
Basin (Harrison, 1994). A signiﬁcantly high nitrate level has beenreported in drainage water (60 mg L1) and soil solution(100 mg L1) under grapevines (Correll et al., 2010) in the Murray
Darling Basin. These values are signiﬁcantly higher than theAustralian environmental trigger value (0.5 mg L1 for lowland rivers) for nitrate (ANZECC and ARMCANZ, 2000). Leaching of nitrates
from soils under perennial horticulture may pose a potential threat
to groundwater.
The main sources of nitrate in mandarin production are mineral
fertilizers. Nitrate is removed from the soil by plant uptake or
through decomposition by micro-organisms in the process ofdenitriﬁcation. In well-aerated soils typical of this region, denitriﬁcation is often negligible because of a lack of favourable conditions(Alva et al., 2006). Nitrate, being an anion, moves freely inthese mineral soils, and hence has the potential to leach into
groundwater and waterways if fertigation is not well scheduled(Paramasivam et al., 2002; Gärdenäs et al., 2005; White, 2006).
Several researchers have reported substantial leaching (6–45%) ofapplied N in citrus cultivation under ﬁeld conditions (Wang andAlva, 1996; Paramasivam and Alva, 1997; Paramasivam et al.,
2002; Sluggett, 2010). Syvertsen and Jifon (2001) found that N
leaching was higher under weekly fertigated orange trees thanunder daily or monthly fertigated trees. Syvertsen and Sax (1999)
reported that increasing the number of fertigation events could
signiﬁcantly reduce N leaching. However, they observed 38–52%
leaching of N from fertilizer, and the nitrogen use efﬁciency
ranging between 25% and 44% in Hamlin orange trees. Other
researchers (Clothier et al., 1988; Li and Liu, 2011) have reported

505

that nitrate accumulates toward the boundary of the wetted
volume for most combinations of drip emitter discharge, input
concentrations, and volumes applied. These studies suggest that
there is a need for efﬁcient tools, capable of describing andquantifying nitrate leaching, as well as nitrate uptake by crops,
which in turn would help in designing and managing drip irrigation systems and achieving a high N fertilizer use efﬁciency,
thereby limiting the export of this nutrient as a pollutant todownstream water systems.
In addition to nitrate leaching, salinity is also an importantfactor inﬂuencing the sustainability of the citrus production worldwide, as citrus species are relatively salt sensitive. The reported
value of the average threshold electrical conductivity of saturation
extract (ECe) and slope for oranges (Citrus sinensis) are 1.7 dS m1
and 16%, respectively (Maas and Hoffmann, 1977). Salt damage isusually manifested as leaf burn and defoliation, and is associatedwith accumulation of toxic levels of Na+ and/or Cl in leaf cells.
Under drip irrigation there are many factors inﬂuencing the distribution of soil water and salts, and hence the water use efﬁciency(WUE), such as water quality, dripper discharge rate (Liu et al.,2012), irrigation water depth (Hanson et al., 2006), and irrigation
frequency (El-Hendawy et al., 2008).
Simulation models have been valuable research tools in studies
involving complex and interactive processes of water ﬂow andsolute transport through the soil proﬁle, as well as the effects ofmanagement practices on crop yields and the environment (Pangand Letey, 1998; Li et al., 2003). HYDRUS-2D (Šimůnek et al.,2011) has been used extensively in evaluating the effects of soil
hydraulic properties, soil layering, dripper discharge rates, irrigation frequencies, water quality, and timing of nutrient applications
on wetting patterns and solute distribution (e.g., Cote et al., 2003;
Lazarovitch et al., 2005; Gärdenäs et al., 2005; Hanson et al., 2006;
Ajdary et al., 2007; Phogat et al., 2009; Šimůnek and Hopmans,
2009; Li and Liu, 2011; Phogat et al., 2012a,b, 2013a,b; Ramos
et al., 2011, 2012). Although these studies demonstrate well theimportance of numerical modelling in the design and managementof irrigation and fertigation systems for various crops, most studies
involving salinity and nitrate leaching are based on either an analysis of hypothetical scenarios, or are carried out for annual crops.
Hence, there is a need to carry out modelling studies for perennial
horticultural crops such as mandarin, using experimental resultsfrom ﬁeld studies involving modern irrigation systems such as drip.
The objectives of the present investigation were to evaluate
water, salt (ECsw), and nitrate (NO3 –N) movement in soil below
young mandarin tree using HYDRUS-2D, and to evaluate various
irrigation and fertigation strategies for controlling deep drainage
and nitrate leaching, whilst maintaining soil salinity below thethreshold for mandarin. This approach will help us understandthe best irrigation and fertigation management practices to be
adopted in future practical applications, with the goal to increaseroot water and nutrient uptake.

2. Materials and methods
2.1. Field experiment
The ﬁeld experiment was conducted at the Dareton Agriculturaland Advisory Station (34.10°S and 142.04°E), located in the
Coomealla Irrigation Area, 3 km from Dareton and 10 km from
Wentworth in New South Wales (NSW). The research station forms
part of the Sunraysia fruit growing district of NSW and Victoria
located in the Murray Darling Basin.
An experimental site with an intensive fertigation system, consisting of various mandarin (Citrus reticulata) varieties budded onto

V. Phogat et al. / Journal of Hydrology 513 (2014) 504–516

a number of rootstock varieties (Volkameriana, C35, Cleopatra
Mandarin, Trifoliata, Swingle Citrumelo and Citrange), was established in October 2005. The trees were planted at a spacing of
5 m  2 m. The actual monitoring and measurements were initiated in August 2006. The trees were managed and fertilized following current commercial practices, although the amounts of applied
fertilizer varied. The soils of the site are alkaline (Class IIIA), with
red sandy loam from the surface to 90-cm depth, and loam below(90–150 cm). The total organic carbon content is very low (0.4%) in
the ﬁrst 30 cm, and below 0.25% in the remainder of the root zone.
The climate is characterized as dry, with warm to hot summers andmild winters. The total rainfall during the experimental period
from 21 August 2006 (DOY 233) to 20 August 2007 (DOY 232)
was 187 mm (Fig. 1), which was slightly below average for thearea. Potential evapotranspiration is normally high and equal to1400 mm per year. Mild frost conditions occur during the wintermonths. Weather data were collected from an automated weather
station located within the research station.
2.2. Irrigation, fertigation and measurements
Irrigation water was supplied through a surface drip irrigation
system, with drip lines placed on both sides of the tree line at a distance of 60 cm. Laterals had 1.6 L h1 pressure compensating
online drippers spaced at 40 cm, resulting in 10 drippers per tree.
Irrigation was performed weekly/bi-weekly, depending on theplant requirement, and the total seasonal irrigation was 432.8 mm.
The crop was irrigated to replace estimated crop evapotranspiration (ETC.) for previous days. Reference crop evapotranspiration(ET0) was calculated using the FAO 56 method (Allen et al.,1998). ETC. was calculated using the equation:

ET C ¼ ET 0  K c  Ac

ð1Þ

where Kc is the crop coefﬁcient and Ac is the crop age coefﬁcient.
The Kc values were compiled by the Irrigated Crop Management
Service (ICMS) at Rural Solutions, South Australia. Kc values weretaken from the FAO 56 report and adjusted for the Southern Hemisphere. Ac was used to correct ET0 for the age of the crop and its
impact on canopy area (RMCWMB, 2009). Mandarin is an evergreen
tree that requires nitrogen throughout the year. Nitrogen fertilizer
was applied as ammonium nitrate and mono ammonium phosphate. The amount and timing of fertilizers injected into the irrigation water during the crop growth season is shown in Fig. 2. Total
seasonal amounts of applied ammonium nitrate and mono ammonium phosphate fertilizers were equal to 508.1 and 139.4 kg ha1,respectively. While irrigation was applied continuously during multiple hours, fertigation was applied during a one hour interval.

Irrigation/Rainfall (mm)30
Rainfall25

Irrigation
20
15

Fertilizers added (kg ha-1)

506

20
AN

16

MAP

12
8
4
0

Fig. 2. Fertigation schedule followed during the experimental period (21 August
2006 to 20 August 2007) (AN represents Ammonium nitrate, black bars; MAP
represents Mono-ammonium phosphate, blue bars). (For interpretation of thereferences to colour in this ﬁgure legend, the reader is referred to the web version ofthis article.)

Water for irrigation was pumped directly from the Murray
River. The salinity of the irrigation water (ECw) was monitored
daily, and ranged between 0.09 and 0.19 dS m1, well below the
ECw threshold for irrigation of orange, a close relative of mandarin(1.1 dS m1; Ayers and Westcot, 1989).
Daily soil water content measurements were collected using
SentekÒ EnviroSCANÒ logging capacitance soil water sensors,
installed adjacent to the drip line (approximately 10 cm away fromthe dripper) at depths of 10, 25, 50, 80, and 110 cm. The EnviroSCAN probes were calibrated for the experimental site by thegravimetric method.
Soil water was sampled on a weekly basis using SoluSAMPLERs™ (Biswas, 2006; Biswas and Schrale, 2007). The SoluSAMPLER is a porous ceramic cup connected to a PVC sample
reservoir and the tubing from the reservoir to the soil surface,which is used to apply suction and then extract soil solution within
24 h. The experimental site had SoluSAMPLERs located at depths of
25, 50, 100, and 150 cm at a horizontal distance of 10 cm from thedrip emitter. The SoluSAMPLERs used in this study were developed
at the South Australian Research and Development Institute(SARDI) and are distributed by Sentek Pty, Ltd.
The extracted soil solution was analysed to determine ECsw and
the NO
3 –N content. Nitrate was determined by the Auto-analyser(cadmium reduction) procedure of Maynard and Kalra (1993).
2.3. Modelling software
The HYDRUS-2D software package (Šimůnek et al., 2011) wasused to simulate the transient two-dimensional movement ofwater and solutes in the soil. This program numerically solves
the Richards’ equation for variably-saturated water ﬂow, andadvection–dispersion equations for both heat and solute transport.
The model additionally allows speciﬁcation of root water uptake,which affects the spatial distribution of water, salts and nitrate
between irrigation cycles. The solute transport equation considers
the advective–dispersive transport in the liquid phase, as well as
diffusion in the gaseous phase. The theoretical part of the model
is described in detail in the technical manual (Šimůnek et al.,2011) and in Šimůnek et al. (2008).

10

2.4. Input parameters

50
233 261 289 317 345

8

36

64

92 120 148 176 204 232

DOY
Fig. 1. Rainfall received (red bars) and irrigation applied (blue bars) during theexperimental period (21 August 2006 to 20 August 2007). DOY represents the Julian
day of the year. (For interpretation of the references to colour in this ﬁgure legend,
the reader is referred to the web version of this article.)

2.4.1. Soil hydraulic properties
Soil hydraulic properties were described using the van Genuchten–Mualem constitutive relationships (van Genuchten, 1980). The
parameters for these constitutive relationships (except for the
120–150 cm soil depth) were optimised using data from a lysimeter experiment (Phogat et al., 2013b) (Table 1) involving similarsoils as in the current study.

507

V. Phogat et al. / Journal of Hydrology 513 (2014) 504–516

Table 1
Soil hydraulic parameters used in the modelling study (the residual water content hr, the saturated water content hs, van Genuchten shape parameters (a, n and l), and thesaturated hydraulic conductivity Ks).
Soil depth (cm)Texturehr (cm3 cm3)hs (cm3 cm3)a (cm1)n

Ks (cm day1)

l

0–30
30–60
60–90
90–120
120–150

Loamy sandLoamy sandLoamy sand
Loam
Loam

0.060
0.060
0.050
0.050
0.046

0.37
0.36
0.34
0.33
0.36

0.02940.02680.03080.03000.0346

1.92
1.91
1.99
1.85
1.41

116.88107.04113.28
79.20
27.89

0.5
0.5
0.5
0.5
0.5

2.4.2. Root water uptake
The spatial root distribution is deﬁned in HYDRUS-2D according
to Vrugt et al. (2001a):Xðx; zÞ ¼ 1 

z
zm


1


x ðzpz jz zjþxpx jx xjÞ
m
e m
xm

ð2Þ

where xm and zm are the maximum width and depth of the rootzone (cm), respectively, z* and x* describe the location of the maximum root water uptake, from the soil surface in the vertical direction (z*) and from the tree position in the horizontal direction (x*),
and px and pz are empirical coefﬁcients.
We considered a simple root distribution model, in which theroots of young mandarin trees expanded horizontally into all available space between tree lines (xm = 200 cm), were concentrated
mainly below the drip emitter (x* = 60 cm, z* = 20 cm) where waterand nutrients were applied, and extended to a depth of 60 cm(zm = 60 cm). The parameters deﬁning the maximum root water
uptake in vertical and horizontal directions (z* and x*) were also
based on our earlier experience in similar studies (Phogat et al.,2012a,b, 2013a,b). No signiﬁcant volume of roots was found outside of the speciﬁed area in ﬁeld observations.
The reduction of root water uptake due to the water stress,
a1(h), was described using the well-known piecewise linear relation, developed by Feddes et al. (1978):a1 ðhÞ ¼

8
0;
h > h1 or h 6 h4>>>
> hh1 ; h < h 6 h<
2
1
h2 h1>1;h3 < h 6 h2>>>
: hh4 ; h < h 6 h
43
h3 h4

ð3Þ

where h1, h2, h3, and h4 are the threshold parameters. Water uptake
is at the potential rate when the pressure head is between h2 and h3,
decreases linearly when h > h2 or h < h3, and becomes zero whenh < h4 or h > h1. The following parameters of the Feddes et al.
(1978) model were used: h1 = 10, h2 = 25, h3 = 200 to 1000,
h4 = 8000 cm, which were taken from Taylor and Ashcroft (1972)
for orange.
The reduction of root water uptake due to the salinity stress,a2(h/), was described by adopting the Maas and Hoffmann(1977) salinity threshold and slope function. The salinity threshold(ECT) for orange (closely related to mandarin) corresponds to avalue for the electrical conductivity of the saturation extract(ECe) of 1.7 dS m1, and a slope (s) of 16%. As required by
HYDRUS-2D, these values were converted into ECsw, assuming that
the ECsw/ECe ratio was 2, which is a common approximation used
for soil water contents near ﬁeld capacity in light-textured soils(U.S. Salinity Laboratory Staff, 1954; Skaggs et al., 2006).
Plant uptake of non-adsorbing nutrients such as nitrate is controlled mainly by mass ﬂow of water uptake (Barber, 1995). Therefore, it was assumed that nitrate was either passively taken up by
the tree with root water uptake (Šimůnek and Hopmans, 2009) or
moved downward with soil water.

2.4.3. Solute parameters
Soil solution salinity (ECsw) distribution in soil was modelled asa nonreactive solute (e.g., Skaggs et al., 2006; Ramos et al., 2011;
Wang et al., 2014). These studies demonstrated that this approach
can be successfully used in environments under intensive irrigation and fertigation management. Additionally, Ramos et al.
(2011) reported that similar salinity distributions were obtained
when this simple approach of EC modelling using HYDRUS wascompared with much more complex predictions involving consideration of precipitation/dissolution and ion exchange as done withUNSATCHEM, particularly when the soil solution is under-saturated with calcite and gypsum.
Nitrogen transport was simulated by means of a sequential
ﬁrst-order decay chain, implemented in HYDRUS-2D. Hence, N
reaction or transformation processes, other than nitriﬁcation, were
not considered. Similar assumptions have also been made in previous studies involving modelling of the nitrate transport is soil
(Ramos et al., 2011, 2012). We also assumed that inherent soilorganic N was mineralised directly into NO
3 –N, consistent withother studies (Wang et al., 2010; Tafteh and Sepaskhah, 2012).
Nitrate (NO3 –N) was assumed to be present only in the dissolved phase (with the distribution coefﬁcient, Kd = 0 cm3 g1).
Ammonium (NH+4–N) was assumed to adsorb to the solid phasewith a Kd value of 3.5 cm3 g1 (e.g., Hanson et al., 2006; Ramos
et al., 2012). The nitriﬁcation of NH+4–N to NO
3 –N thus acts as asink for NH+4–N and as a source for NO
3 –N. First-order rate constants for solutes in the liquid and solid phases were set to be
0.2 d1. These were taken from a review of published data presented by Hanson et al. (2006), and represent the centre of therange of reported values.
The longitudinal dispersivity (eL) was considered to be 20 cm
and the transverse dispersivity (eT) was taken as one-tenth of eL.
These values have been optimised in similar studies involving solute transport in ﬁeld soils (e.g., Cote et al., 2003; Mallants et al.,2011).

Fig. 3. A schematic view of the model domain (2D) showing considered boundary
conditions based on the experimental layout, plant and drip spacing, and locationsof monitoring equipments.

V. Phogat et al. / Journal of Hydrology 513 (2014) 504–516

2.4.4. Initial and boundary conditions
A time-variable ﬂux boundary condition was applied to a 20 cm
long boundary directly below the dripper, centred on 60 cm fromthe top left corner of the soil domain (Fig. 3). The ﬂux boundary
condition with a ﬂux q was deﬁned as:

q¼

v olume of water applied=day

ð4Þ

surface wetted area

where the volume of water applied (L3) varied for different irrigation events and was calculated by multiplying the dripper discharge
rate by irrigation time, and the surface wetted area (L2) was approximately 800 cm2 (i.e., 20 cm  40 cm). The length of the boundary
was selected to ensure that all water could inﬁltrate into the soil
without producing positive surface pressure heads, because positive
pressure heads at the ﬂux boundary could make the numerical code
unstable. During irrigation, the drip line boundary was held at aconstant water ﬂux, q. The atmospheric boundary condition was
assumed for the remainder of the soil surface during periods of irrigation, and for the entire soil surface during periods between irrigation. A no-ﬂow boundary condition was established at the left andright edges of the soil proﬁle, to account for ﬂow and transport symmetry. A free drainage boundary condition was assumed at the bottom of the soil proﬁle. All these boundary conditions are illustrated
in Fig. 3. The mathematical details of applying the boundary conditions to a domain similar to the current one can be obtained from
Phogat et al. (2012a).
The initial soil water content distribution was based on EnviroSCAN measured values and varied from 0.1 to 0.25 cm3 cm3 in thesoil domain (0–150 cm). Measured values of ECsw and NO
3 –N inthe soil were used as initial conditions in the model. The ECsw varied from 0.8–1.5 dS m1 and NO
3 –N concentrations ranged
between 0.16 and 1.07 mmol(c) L1 in the soil proﬁle (0–150 cm).
The third-type Cauchy boundary conditions were imposed at
the soil surface and at the free drainage boundary for solute transport (ECsw, NH+4–N and NO3 –N) and no ﬂux boundary was imposedon the sides of the domain.
2.4.5. Flow domain and simulation
In this approach, the drip tubing can be considered as a line
source (Fig. 3), because in a twin line drip irrigation system withclosely spaced drippers the wetted pattern from adjacent drippers
merges to form a continuous wetted strip along the drip lines(Falivene et al., 2005). Water movement was therefore treated asa two-dimensional (in the vertical plane) process (Skaggs et al.,2004). Our ﬁeld observations of the wetting pattern on the soil surface during experiments also supported this approach. The transport domain was set as a rectangle with a width of 250 cm (halfof the lateral spacing between tree rows) and a depth of 150 cm.
The transport domain was discretised into 2172 ﬁnite element
nodes, which corresponded to 4191 triangular elements (Fig. 3).
Observation nodes corresponded to the locations where EnviroSCAN probes (depths of 10, 25, 50, 80, 100, and 110 cm) and SoluSAMPLERs (depths of 25, 50, 100, and 150 cm) were installed, at a
distance of 10 cm from the emitter source (Fig. 3).
2.4.6. Estimation of potential evaporation and transpiration
HYDRUS-2D requires daily estimates of potential evaporation(Es) and transpiration (Tp). In this study, these parameters wereobtained by combining the daily values of reference evapotranspiration (ET0), determined by the FAO Penman–Monteith method,
and the dual crop coefﬁcient approach (Allen et al., 1998; Allenand Pereira, 2009), as follows:

ET C ¼ ðK cb þ K e ÞET 0

ð5Þ
1

where ETC. is the evapotranspiration (LT ), Kcb is the basal crop
coefﬁcient, which represents the plant transpiration component,

and Ke is the soil evaporation coefﬁcient. Standard mandarin Kcb values (Allen et al., 1998) were adjusted for the local climate, taking
into consideration crop height, wind speed, and minimum relative
humidity averages for the period under consideration. The valuesof daily potential transpiration (Tp) and soil evaporation (Es) thus
obtained (Fig. 4) were used as time-variable boundary conditions(see Fig. 3) in the model, along with the precipitation received at
the site during the experimental period. The seasonal Tp amounted
to 696 mm and Es to 174 mm. The maximum Tp of 4.4 mm occurred
on 10th January 2007 (DOY 10), when the most adverse weather
conditions occurred.
2.5. Scenario analysis for controlling deep drainage and N losses
The nitrogen balance for the mandarin crop was evaluated for
two fertigation strategies. First, the fertigation pulse was appliedat the beginning of each irrigation event (Fert A). Second, the fertigation pulse was applied near the end of each irrigation event (Fert
B). It is a common practice that irrigation water is initially and at
the end free of fertilizer, to ensure a uniform fertiliser applicationand ﬂushing of the drip lines (Gärdenäs et al., 2005). Therefore, fertigation applications were simulated to either start one hour after
irrigation started or to end one hour before irrigation stopped.
Nitrate management strategies also include a judicious manipulation of irrigation and N fertilizer applications, and increasing or
decreasing the frequency of applications. These interventions
should improve N uptake by plants and reduce N leaching out ofthe plant root zone (Harrison, 1994). The evaluated scenarios aredescribed in Table 2. Scenario, S1, illustrates the impact of applying
the same volume of water in small irrigation events (<5 mm). Scenarios S2 and S3 then represents the reduction of the irrigation volume application by 10% and 20%, respectively. Scenarios S4 and S5
are based on decreasing the nitrogen application by 10–20%,
respectively, while scenarios S6 and S7 represent a combined
reduction in irrigation and fertigation by 10–20%, respectively. Fivescenarios (S8 to S12) were executed, in which irrigation was
reduced during the second half of the crop season, i.e., between
January and August, by 10%, 20%, 30%, 40%, and 50%, respectively.
2.6. Statistical analysis
A mean absolute error (MAE) has been reported (Willmott andMatsuura, 2005) to be a good parameter for comparing modelling
results with observed values. It was calculated by comparing
weekly measured (M) and corresponding HYDRUS-2D simulated(S) values of water contents, electrical conductivities of soil solution (ECsw), and nitrate concentrations (NO3 –N) in soil as follows:

MAE ¼

N
1X
jMi  Si j
N i¼1

ð6Þ

5

Potential fluxes (mm)508

Evaporation

4

Transpiration

3
2
1
0

233 261 289 317 345

8

36

64

92 120 148 176 204 232

DOY
Fig. 4. Daily potential transpiration (Tp) and potential soil evaporation (Es)estimated using the dual crop coefﬁcient approach during the study period. DOY
represents the Julian day of the year 2006–2007.

509

V. Phogat et al. / Journal of Hydrology 513 (2014) 504–516
Table 2
Various scenarios evaluated for optimising irrigation and fertigation of a mandarin
orchard.
Scenario

Reduction in irrigation (I) and/or fertigation (F)

S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12

All irrigation events 65 mm
10% less I during the entire season
20% less I during the entire season
10% less F during the entire season
20% less F during the entire season
10% less I and F during the entire season
20% less I and F during the entire season
10% less I during January–August, 2007
20% less I during January–August, 2007
30% less I during January–August, 2007
40% less I during January–August, 2007
50% less I during January–August, 2007

Here, N is the number of comparisons.

3. Results and discussion
3.1. Moisture distribution

3.2. Soil solution salinity distribution
Comparison of simulated electrical conductivities of soil solution (ECsw) with weekly measured values at different depths (25,50, 100 and 150 cm) are shown in Fig. 6. Despite of low irrigationwater salinity (0.09–0.2 dS m1) and low initial soil salinity (0.8–
1.5 dS m1), the measured ECsw increased in the soil with the onsetof irrigation at all depths, except at 150 cm where the increase insalinity occurred only after December 2006. Subsequently, adecreasing trend was observed in ECsw later in the season. The
higher amount of irrigation compared to ETC. and an signiﬁcantamount of precipitation (Fig. 1) during this period resulted in a
reduction in soil solution salinity.
On the other hand, the model over-predicted ECsw at a depth of
25 cm from October to December 2006 and under-predicted it at a
depth of 100 cm during the same period. However, at a depth of
150 cm, simulated values remained constant till January 2007,
indicating a delayed response. The increase in simulated ECsw
values was delayed at 100 and 150 cm depths as compared tomeasured values. Both set of values matched well at a depth of
50 cm and the proﬁle average of ECsw also showed a close match.
It is signiﬁcant to note that irrigation with good quality water(ECw < 0.2 dS m1) in our study led to the development ofsigniﬁcant levels of measured ECsw (0.34–2.32 dS m1; mean
1.17 dS m1). However, the ECsw values remained below the threshold of salinity tolerance (ECe = 1.7 dS m1 or ECsw = 3.4 dS m1) oforange throughout the season (Ayers and Westcot, 1989; ANZECC
and ARMCANZ, 2000).
The (temporal) MAEs between weekly measured and simulated
ECsw in the soil ranged from 0.08 to 0.76 dS m1 (Table 3), which
are acceptable for a complex and highly dynamic soil system, withthe exception of a few divergent values obtained between mid
October and December (DOY 290-365). The disagreement in ECsw
values during this period was correlated with corresponding
ﬂuctuations and low values of water contents, especially at soil
depths of 10 and 25 cm and this variability was transferred to

0.30

0.30

0.20

0.20

0.20

0.10

0.10

0.00

0.00

S

240
268
296
324
352
15
43
71
100
134
162
205

0.00

M

240
268
296
324
352
15
43
71
100
134
162
205

0.10

240
268
296
324
352
15
43
71
100
134
162
205

0.30

0.30

0.20

0.20

0.20

0.10

0.10

0.10

0.00

0.00

0.00

240
268
296
324
352
15
43
71
100
134
162
205

0.30

240
268
296
324
352
15
43
71
100
134
162
205

0.30

240
268
296
324
352
15
43
71
100
134
162
205

Water contents (cm3 cm-3 )The water contents measured weekly by EnviroSCAN at different depths (10, 25, 50, 80 and 100 cm) at a horizontal distance of
10 cm from the dripper, and corresponding values simulated by
HYDRUS-2D during the entire growing season are illustrated inFig. 5. The measured water contents remained similar at 10(0.2 cm3 cm3) and 80 cm (0.1 cm3 cm3) cm, ﬂuctuated between
0.1 and 0.2 cm3 cm3 at 25 and 50 cm, and stayed higher than
0.2 cm3 cm3 at 110 cm soil depths throughout the growing season, indicating a favourable moisture regime in the crop root zone.
However, the simulated water contents were lower than the measured values during the initial period at a depth of 10 cm and during the mid period at a depth of 110 cm. The simulated values
matched the measured values more closely at soil depths of 25
and 50 cm, which is the most active root zone for water and nutrient uptake for citrus (Mikhail and El-Zeftawi, 1979). However, theproﬁle average water distribution matched well.
The MAE between weekly measured and simulated moisture content values across all locations varied from 0.01 to 0.04 cm3 cm3,indicating a good agreement between the two sets of values(Table 3). Slightly higher temporal MAE values during the midseason agreed well with the variation shown in Fig. 5. Similarly,
the MAE values at 10, 25, 50, 80, and 110 cm soil depths (Table 3)
at a 10 cm lateral distance from the dripper also revealed thatthe variation between measured and simulated water contents
remained between 0.02 and 0.04 cm3 cm3. However, thedifferences were slightly higher at 10 cm depth (0.04 cm3 cm3) ascompared to greater depths (0.02–0.03 cm3 cm3). Higher
variations at the surface depth (10 cm) are to be expected because
this part of the soil proﬁle is inﬂuenced by soil evaporation, whichpeaks in day time and is low at night time, while the assumptionof a constant atmospheric boundary ﬂux for daily time steps in themodel (Ramos et al., 2012) deviated from the actual transientconditions existing at the surface boundary. Other studies (Vrugt
et al., 2001b; Skaggs et al., 2010; Phogat et al., 2012a,b, 2013a,b;
Ramos et al., 2012) also showed a similar magnitude of variations
between measured and predicted water contents.

DOY

DOY

DOY

Fig. 5. Comparison of weekly measured (M) and simulated (S) water contents at indicated depths in the soil proﬁle under a mandarin tree.

510

V. Phogat et al. / Journal of Hydrology 513 (2014) 504–516

Table 3
Temporal and spatial mean absolute error (MAE) values between measured and simulated water contents, soil solution salinities (ECsw), and nitrate–nitrogen (NO
3 –N)concentrations.
Nb

Nb

Water content
Mean

Range

Mean

(cm3 cm3)

c

Range

NO
3 –N
Mean

(dS m1)

Range(mmol(c) L1)

Temporal MAE valuesa
48
0.03

0.01–0.04

47

0.34

0.08–0.76

48

0.89

0.10–1.97

Spatial MAE valuesncDepth (cm)Error (cm3 cm3)nc

Depth (cm)Error (dS m1)nc

Depth (cm)Error (mmol(c) L1)

353
353
353
353
353

0.04
0.03
0.02
0.02
0.03

47
47
47
47
–

25
50
100
150
–

0.36
0.47
0.36
0.19
–

48
48
48
48
–

25
50
100
150
–

1.52
0.64
0.73
0.63
–

10
25
50
80
110

MAE for temporal data were calculated across 5 depths (i.e. n = 5) at weekly interval of the trial.
Represents the number of weekly comparisons.
Represents number of values in each error calculation.

4

4

3

3
M

S

2

1

1

0

0
240
268
296
324
352
15
43
71
100
134
162
205

4

3

3

3

2

2

2

1

1

1

0

0

0
240
268
296
324
352
15
43
71
100
134
162
205

4

240
268
296
324
352
15
43
71
100
134
162
205

4

240
268
296
324
352
15
43
71
100
134
162
205

ECsw (dS m-1 )

2

240
268
296
324
352
15
43
71
100
134
162
205

a
b

Nb

ECsw

DOY

DOY

DOY

Fig. 6. Comparison of measured (M) and simulated (S) values of soil solution salinity (ECsw) at indicated depths in the soil proﬁle under a mandarin tree.

the ECsw values. Differences between measured and simulated ECsw
values at 50 cm depth were relatively higher (MAE = 0.47 dS m1)
than at other depths (Table 3). The mean MAE at 25, 100, and150 cm depths ranged from 0.19 to 0.36 dS m1, showing a good
agreement with the measured values at these depths.
The spatial distribution of ECsw in the soil proﬁle at various
dates is depicted in Fig. 7. It can be seen that salts remained
restricted to roughly the upper 50 cm of the soil proﬁle until

December (between 28/11/2006 and 17/01/2007 in Fig. 7). The
salts mass was later pushed deeper due to high rainfall (55 mm
in January, 29% of seasonal rain). The downward movement of salts
continued in February and March (8/03/2007 in Fig. 7), because in
March the amount of irrigation was higher than ETC. (Fig. 2). It ispertinent to note here that the ECsw distribution under the dripper
remained lower as compared to the adjoining soil at all times,
because a continuous water application in this region pushes theFig. 7. Spatial distribution of simulated soil solution EC (ECsw, dS m1) in the soil proﬁle at indicated times.

V. Phogat et al. / Journal of Hydrology 513 (2014) 504–516

salts towards the outer boundary of the wetting front. The drainage
ﬂux during and after March transported salts vertically downwards, thereby making the soil directly beneath the dripper relatively salt free by the end of the season. Applying additional
water at the end of the season could be a strategy to create a salt
free rootzone which may encourage vigorous root development,
and assist the plant growth in the ensuing season.
3.3. Nitrate nitrogen distribution
Comparison of weekly measured and daily simulated nitrate–
nitrogen (NO3 –N) concentrations at different depths (25, 50, 100
and 150 cm) in the soil proﬁle is illustrated in Fig. 8. Over-prediction
was observed at a depth of 25 cm from October to November 2006,
which coincided with similar over-prediction for salinity. Similarly,both measured and simulated values matched well at a depth of
50 cm, while a delayed response in predicted nitrate contents wasobserved at lower depths. However, a fairly good correspondence
was observed between proﬁle averaged NO
3 –N contents. The tem1
poral MAE values for NO
3 –N ranged from 0.1 to 1.97 mmol(c) L(Table 3). Similar differences between measured and HYDRUS-2D
simulated values were also reported in another study (Ramos et al.
(2012) involving simulations of nitrogen under ﬁeld cropped
conditions. Additionally, MAE at a 25 cm depth (Table 3) had ahigher value (1.52 mmol(c) L1) than at greater depths (0.63–0.73mmol(c) L1). A similar match of nitrate distributions has beenreported in other studies as well (Ajdary et al., 2007; Ramos et al.,2012; Tournebize et al., 2012).
The reason for differences in ECsw and NO
3 –N values may bepartially due to the fact that model reports point values, whereas
the SoluSAMPLER draws in solution from a sampling area of a certain volume, the size of which depends on the soil hydraulic properties, the soil water content, and the applied suction within theceramic cup (Weihermuller et al., 2005; Ramos et al., 2012;
Phogat et al., 2012a). Hence the measured parameters consideredin modelling may not represent the inherent spatial variability ofthe soil. In addition, while a homogeneous soil environment isassumed by the model, the ﬁeld site could be far more heterogeneous and anisotropic. Also, the model simulations considered only
a 2D movement of nitrogen and the nitriﬁcation process, whilemore complex nitrate processes (e.g., mineralisation, ammoniﬁcation, denitriﬁcation, immobilization through carbon–nitrogencomplex formation and microbial interaction) were not taken into
account. Ramos et al. (2012) documented numerous factors inﬂuencing the correspondence between measurements and simulations of water contents and solute concentrations in the soil

511

under drip irrigation conditions and these factors are relevant also
for the present investigation. These factors, including those mentioned above, may modify the error in the simulated NO
3 –Nvalues.
The simulated movement of nitrate–nitrogen (NO3 –N) in thesoil under a mandarin tree at various dates is shown in Fig. 9.
Nitrate fertigation increased the nitrogen content in the soil withtime, as is evident from an increasing size of the concentration
plume below the dripper as the season progressed. This indicatesthat the plant was not able to take up all nitrogen added throughfertigation, and thus nitrogen built up in the soil over time, leading
to a maximum concentration values in January (17/01/07 in
Fig. 11). Ultimately, nitrogen started moving downwards after late
January, when there was high rainfall and total water additions
exceeded ETC. Alva et al. (2006) also detected greater variationsin NO
3 –N concentrations in the 0–15 cm depth horizon, as compared to greater depths in a ﬁeld experiment involving citrus.
The seasonal NO
3 –N concentrations in the domain varied from0.01–7.03 mmol(c) L1. Hutton et al. (2008) reported higher mobilization of nitrate at a shallower depth under drip irrigation ofgrapevine, and seasonal root zone nitrate concentrations ranging
between 0 and 11.07 mmol(c) L1 in the Murrumbidgee Irrigation
Areas in Australia.
As the season continued and plant uptake was reduced, excesswater further mobilised nitrate–nitrogen out of the root zone, as
is evident from 27/04/07 and beyond (Fig. 9). At the end of the crop
season, little nitrogen remained in the soil system, and what did
remain was well beyond the reach of the plants. This nitrogen is
expected to continue leaching downwards over time and becomea potential source of nitrate–nitrogen loading to the ground water.
Additionally, peak NO
3 –N concentrations in the soil proﬁle(7.03 mmol(c) L1) and in drained water (NO
3 –N concentration at
the 150 cm depth, 2.14 mmol(c) L1) were signiﬁcantly higher thanthe Australian environmental standard (ANZECC and ARMCANZ,
2000) for protection of 80% (17 mg NO3 L1 = 0.27 mmol(c) NO
3 –N
L1) and 95% of species (0.7 mg NO3 L1 = 0.01 mmol(c) NO
3 –N
L1). The NO
3 –N concentrations in the soil solution also occasionally exceeded the level of Australian drinking water quality standard (NRMMC, 2011) for nitrate (100 mg NO3 L1 = 1.61 mmol(c)1NO). High levels of nitrate–nitrogen below the crop root
3 –N Lzone are undesirable, as some recharge to groundwater aquifers
can occur, in addition to ﬂow into downstream rivers, which areused for drinking water and irrigation. These ﬁndings are consistent with other studies (Barlow et al., 2009; Correll et al., 2010),
in which high nitrate concentrations in drainage water under drip
and furrow fertigated irrigation systems have been reported.

Fig. 8. Comparison of measured (M) and simulated (S) values of soil solution nitrate–nitrogen (NO3 –N) at indicated depths in the soil proﬁle under a mandarin tree.

512

V. Phogat et al. / Journal of Hydrology 513 (2014) 504–516

1Fig. 9. Spatial distribution of simulated soil solution NO
) in the soil proﬁle at indicated times.
3 –N (mmol(c) L

3.4. Water and nitrogen balance
The seasonal water balance was computed from cumulative
ﬂuxes calculated by HYDRUS-2D. Estimated water balance components above and below the soil surface under a mandarin tree arepresented in Table 4. It can be seen that in a highly precise drip irrigation system, a large amount of applied water (210.9 mm)
drained out of the rootzone, even though the amount of irrigation
applied was based on estimated ETC. This drainage corresponded to33.5% of applied water, and occurred because highly permeable
light textured soils, such as those found in this study, are prone
to deep drainage whenever the water application exceeds ETC.
The drainage amount in our study falls within the range ofrecharge ﬂuxes to groundwater reported by Kurtzman et al.
(2013) under citrus orchards in a semiarid Mediterranean climate.
Mandarin root water uptake amounted to 307.3 mm, which constitutes about 49% of applied water. Root water uptake slightly
increased (3.5%) when the model was run without considering solute (salt) stress (not shown here), which is not a signiﬁcant difference. It further substantiates the results obtained for seasonal ECsw
in Fig. 6, where salinity remained below threshold (3.4 dS m1)
over the season. Evaporation accounted for 17.7% of the total water
applied through irrigation and rainfall. The modelling study overestimated the sink components of the water balance by 4.79 mm(0.77%, Table 4).
There were major differences between water input and output
from January 2007 onwards (Fig. 10). During this period, irrigation(I) and precipitation (P) signiﬁcantly exceeded tree water uptake(S_W), which eventually resulted in deep drainage (Dr_W) fromMarch 2007 onwards. Therefore, current irrigation schedulingrequires adjustment during this period. This illustrates how simulations were helpful in evaluating the overall water dynamics insoil under the mandarin tree.

Water compoments (mm)

120

a

(mm)

(%)

Sources
Irrigation
RainfallSoil depletion

432.68171.13
21.80

69.16
27.35
3.48

Sinks
Root water uptake
Drainage
Evaporation
Water balance error

307.3
210.94111.56
4.79

48.80
33.50
17.70
0.77aWater balance error ð%Þ ¼

P

P

W source P

W source

W sink 100 .

80
60

Dr_W
S_W
P
I

40
20
0
-20
-40
-60
-80

Fig. 10. Monthly irrigation (I, mm), precipitation (P), water uptake (S_W) by amandarin tree, and deep drainage (Dr_W) from the soil during the study period(from August 2006 to August 2007).

The nitrogen balance is presented in Table 5. The nitrogen fer+
tilizer was applied either in the form of NH+4 or NO
3 , but NH4 trans
forms quickly to NO3 through the process of nitriﬁcation. Model
simulations showed that nitriﬁcation of NH+4 was very rapid andmost of the NH+4–N converted to NO
3 before it moved to a depthof 20 cm, and no traces of NH+4 were observed below this depth.
It is apparent that the nitriﬁcation of NH+4 took place in the upper
soil layer, which contains organic matter and moisture that supports microorganisms (Nitrosomonas and Nitrobactor), facilitating
the nitriﬁcation of NH+4. Though NH+4 was initially nitriﬁed to NO
2and consequently to NO
3 , NO2 was short-lived in the soil andTable 5Components of the nitrogen balance under a mandarin crop for fertigation at the
beginning (Fert A) and at the end (Fert B) of an irrigation event.

Table 4
Simulated components of the seasonal water balance under a young mandarin tree.
Components

100

N source

N balance (kg ha1)

Fert A

Fert B

NH+4–N

Soilinitial
Added
Adsorbed on soil
UptakeLeachedNitriﬁcation
Soilend

0
105.6
0
0.71
0
104.9
0

0
105.6
0
0.71
0
104.9
0

NO
3 –N

Soilinitial
Added
UptakeLeachedSoilend22.8
88.8
167.11
31.3
20.21
0.98

22.8
88.8
168.84
31.1
20.09
1.63

Mass balance erroraaMass balance error ð%Þ ¼

PP

W input PW output

W input 100.

V. Phogat et al. / Journal of Hydrology 513 (2014) 504–516+
decayed to NO
3 quickly. Therefore, the simulated plant NH4–N
1

uptake was only 0.71 kg ha . Hence, the NO3 –N form was responsible for most of the plant uptake, corresponding to about 85% of
the applied nitrogen. The monthly N applications were slightly
higher than plant uptake during the ﬂowering (August–October)and fruit growth (January–March) periods (Fig. 11). However, themonthly uptake was slightly higher than the N application
between these periods.
High frequency of N applications in small doses resulted in similar nitrogen uptake efﬁciency (61–75%) in citrus as in other studies (Syvertsen and Smith, 1995; Quiñones et al., 2007). Similarly,
Scholberg et al. (2002) reported doubling of nitrogen use efﬁciency
as a result of frequent application of N in a dilute solution. Slightly
higher uptake (1.73 kg ha1) was recorded when fertigation wasapplied in second last hour of an irrigation event (Fert B), as compared to when it was applied early in the irrigation event (Fert A,
Table 5). Hence, it can be concluded that timing of fertigation does
not have a major impact in a normal fertigation schedule withsmall and frequent N doses within an irrigation event in light textured soils. Similar results were also obtained in our earlier study
in a lysimeter planted with an orange tree (Phogat et al., 2013b),
which revealed that timing of fertilizer N applications in small
doses in an irrigation event with a low emitter rate had little
impact on the nitrogen uptake efﬁciency.
Nitrate–nitrogen leaching accounted for only 15% of the applied
nitrogen (Table 5). Monthly N balance (Fig. 11) revealed that mostof the N leaching happened between March 2007 and August 2007,
which was correlated with the extent of deep drainage occurring
during this period. NO
3 –N losses ranging from 2% to 15% were
illustrated by Paramasivam et al. (2002) and Alva et al. (2006),
attributable in part to an improved management of N, which could
be a contributor in the current estimation.

3.5. Strategies for controlling water and nitrogen losses
In our study, it is evident that there were signiﬁcant deep drainage (33%) and nitrate–nitrogen leaching losses (15%), which could
be reduced by appropriate management. Hence, different simulations involving the reduction of irrigation and fertigation applications during the whole or part of the crop season wereconducted, to optimize water and nitrogen uptake and to reduce
their losses from the soil (Table 6).
Increasing the irrigation frequency with short irrigation events(S1) while maintaining the same irrigation volume, had no impact
on deep drainage (Dr_W) and N leaching (Dr_N). However, the seasonal salinity increased by 11% compared to the standard practice.
This conﬁrms that the current irrigation schedule followed withrespect to the irrigation frequency seems to be optimal under the

N components (kg ha-1)

35
30
25
20

N added
N uptake
N leached

15
10
5
0

Fig. 11. Simulated monthly values of nitrogen added (N added), nitrogen uptake (Nuptake) by a young mandarin tree, and nitrogen leached (N leached) from the soil
during the study period (from August 2006 to August 2007).

513

experimental conditions. In S2, Dr_W and Dr_N were reduced by
14.4% and 19%, respectively, but salinity increased by 11%. However, a sustained reduction in irrigation by 20% (S3) eventually
reduced the Dr_W and Dr_N by 28.1 and 38.3%, respectively, atthe expense of a 4.9% decline in plant water uptake, but with a
4% increase in N uptake. However, salinity increased by 25.8% compared to the normal practice, which would likely have a signiﬁcant
impact on plant growth.
Scenarios S4 and S5 were based on decreasing the nitrogen
application by 10% and 20%, resulting in a decrease in N leachingby 7.4% and 14.8%, respectively, along with a much higher reduction in plant N uptake (10.4% in S4 and 19.7% in S5), suggestingthat the reduction in the fertilizer application alone is not a viableoption to control N leaching under standard conditions. A combined reduction in irrigation and fertigation by 10% (S6) further
reduced N leaching by 5.5%, compared to reducing irrigation alone(S2), but at the same time plant N uptake was reduced by 5% morethan in S2. Similarly, reducing irrigation and N application by 20%(S7) produced a pronounced reduction in N leaching (46.4%) andwater drainage (28%), but it also resulted in a decrease in plant N
uptake by 15.8% and water uptake by 4.8%, compared to normal
practice. At the same time, salinity increased by 25.8%, which issimilar to S3. The reduction in plant water and N uptake would
have a major impact on plant growth and yield, and would
adversely impact the sustainability of this expensive irrigation system. Hence, reducing fertilizer applications does not seem to be agood proposition under the current experimental conditions, as it
results in an appreciable decline in plant N uptake. However,Kurtzman et al. (2013) reported that a 25% reduction in the application of N fertilizer is a suitable agro-hydrological strategy tolower the nitrate ﬂux to groundwater by 50% under different environmental conditions. Rather, reducing irrigation alone seems to
be a better option to control the deep drainage and N leachinglosses under the conditions encountered at the experimental site.
Additionally, it is worth noting that in S3 and S7 the salinity(ECsw) during a period between October and December at a depthof 25 cm, and during December at a depth of 50 cm, increased considerably, and was higher than the threshold level (Fig. 12), conﬁrming that a sustained reduction in irrigation (S3) andfertigation (S7) is not a viable agro-hydrological option for controlling water and N leaching under the mandarin orchard.
However, it seems unnecessary to reduce irrigation applications
uniformly across the season as suggested by Lido9 n et al. (2013).
Rather, irrigation could more proﬁtably be reduced only during aparticular time period when excess water was applied. The waterand N balance data in our study revealed that an imbalance
between water applications and uptake happened during the second half of the crop season, i.e., from January till August 2007,
resulting in maximum drainage (Fig. 10) and N leaching (Fig. 11),
coinciding with the fruit maturation and harvesting stage. Hence,
there is a need to reschedule irrigation within this period, rather
than reducing water applications throughout the entire season.
Keeping this in mind, the following 5 scenarios (S8 to S12, Table 6)
were executed, in which irrigation was reduced during the second
half of the crop season, i.e., between January and August, by 10%,
20%, 30%, 40%, and 50%, respectively.
Scenarios S10, S11, and S12 showed an enormous potential for
reducing water and N losses. In S10, Dr_W and Dr_N were reduced
by 8% and 4% more than in S7, N uptake was increased by 6.9%
(compared with a reduction in S7), and salinity was also 4% lessthan in S7, which seems quite promising. On the other hand, in
S11 and S12, the Dr_W and Dr_N were reduced to a greater extent(50–58% water and 70–80% N leaching) than in S10, and soil salinity increased substantially (40.3% and 58.7% higher than normal
practice), due to a considerable reduction in the leaching fraction.
This is also shown in Fig. 12, which shows that monthly soil

514

V. Phogat et al. / Journal of Hydrology 513 (2014) 504–516

Table 6Percent increase (+)/decrease () in water uptake (S_W), drainage (Dr_W), N uptake (S_N), N leaching (Dr_N), and electrical conductivity of the soil solution (EC_sw) in different
scenarios of water and fertilizer applications, compared to the normal practice.
Scenario

Reduction in irrigation (I)/fertigation (F)

S_W

Dr_W

S_N

Dr_N

EC_sw

S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12

Irrigation events 65 mm
10% I, full season
20% I, full season
10% F, full season
20% F, full season
10% I and F, full season
20% I and F, full season
10% I, January–August, 2007
20% I, January–August, 2007
30% I, January–August, 2007
40% I, January–August, 2007
50% I, January–August, 2007

0.25
2.17
4.88
0.18
0.21
2.06
4.83
0.30
0.87
1.66
2.68
4.11

1.16
14.4028.150.06
0.03
14.25
28.18
12.74
25.41
37.16
49.89
57.91

1.01
2.64
4.07
10.38
19.72
7.05
15.66
1.90
4.36
6.89
9.79
12.76

0.06
19.07
38.29
7.40
14.76
24.6046.43
15.53
32.97
50.52
69.53
80.51

11.29
11.29
25.81
0.00
0.00
11.29
25.81
5.65
13.71
21.77
40.32
58.87

6

(a) 25 cm
S3ECsw (dS m-1)

5

S7

S10

S11

S12

4
3
2
1
0

3.5

ECsw (dS m-1)

3(b) 50cm

2.5
2
1.5
1
0.5
0

Fig. 12. Monthly average soil solution salinity (ECsw, dS m1) at (a) 25 cm depth and(b) 50 cm depth in the soil proﬁle under different scenarios (see Table 6). Horizontal
lines show the threshold salinity level for citrus (3.4 dS m1).

solution salinity (ECsw) in S11 and S12 at the 25 and 50 cm soil
depths increased dramatically between January and August.
Although ECsw remained below the threshold level, except at a
50 cm depth in S12 during March 2007, there is a signiﬁcant
likelihood of it increasing further in subsequent seasons, which
would ultimately impact the growth and yield of mandarin trees.
Hence, under current conditions, Scenario S10 represents the best
option to control excessive water and N losses, and high salinity,
and to increase the water and N efﬁciency for mandarin trees.
Other permutations and combinations, involving fertilizer
reductions along with S10, did not provide further improvements
in controlling water and N leaching. It is concluded thatsimulations of irrigation and fertilizer applications, using HYDRUS,
can be helpful in identifying strategies to improve the water and N
efﬁciency for drip irrigation systems of perennial horticultural
crops.
4. Conclusions
This study demonstrates the importance of combining strategic
monitoring with numerical modelling to assess water movement,

salinity distribution, and nitrogen management under drip irrigation systems in young mandarin orchards in Australia. HYDRUS2D was used to predict seasonal water, salt, and nitrate dynamics
in soils. Modelling results were compared with measured valuesof moisture content, soil solution salinity (ECsw), and nitrate–nitrogen (NO3 –N) in the soil proﬁle during the complete season.
Graphical and statistical comparisons of measured and simulated values of water contents, ECsw, and NO
3 –N concentrationsin the soil under a mandarin tree showed a consistent performanceof HYDRUS-2D for modelling water, salinity, and nitrogen transport. The temporal mean absolute errors (MAE) for water contents,
ECsw, and NO
3 –N concentrations were within acceptable limits.
However, MAE showed divergent values at shallow depths (10–25 cm) due to the assumption of a constant surface boundary ﬂux
during a particular daily time step, which deviated from normal
diurnal ﬂuctuations in the real-time evaporation ﬂux. Other reasons for deviations between predicted and observed NO
3 –N contents were attributed to the model considering only a simple
linear movement of nitrogen, rather than considering all complex
processes (e.g., mineralisation, ammoniﬁcation, denitriﬁcation,
immobilization through carbon–nitrogen complex formation, andmicrobial interactions).
The simulated water and nutrient balances showed that the irrigation scheduling at the experimental site from December
onwards needed to be modiﬁed in order to control deep drainage(33.5% of applied water) and nitrate leaching (15% of appliedNO
3 –N). Sustained reduction of irrigation and/or fertilization by
10–20% reduced water (14–28%) and NO
3 –N (19–46%) losses
appreciably, but these strategies reduced the leaching fraction
and/or plant N uptake to a level where root zone ECsw increased
to substantially higher values than the recommended threshold(3.4 dS m1) and plant N uptake was reduced (7–20%), both ofwhich may affect plant growth and yield, and in turn would
adversely impact the sustainability of expensive irrigation systems.
Other evaluated scenarios focused on reducing irrigation (by10–50%) between January and August, when a mismatch between
irrigation applications and plant uptake was observed. A 30%
reduction in irrigation during this period provided the best scenario, in which both water and NO
3 –N leaching were reduced by
37% and 52%, respectively, and plant N uptake was increased by
7%, compared to the normal practice. However, a further reduction
in irrigation by 40 and 50% reduced the water (50–58%) and NO
3–
N (70–80%) losses to a great extent, but increased salinity in theroot zone to a level much higher than the tolerance threshold of
mandarin.
This study forms the basis for future evaluation of irrigation,salinity, and nitrate–nitrogen dynamics under drip fertigation systems in ﬁelds with horticultural trees, and for future exploration ofways to ﬁne-tune irrigation schedules in order to better control

V. Phogat et al. / Journal of Hydrology 513 (2014) 504–516

excessive drainage and N losses. However, there is a need to further improve the modelling estimates by considering all processesof the nitrogen cycle in the soil system. It is concluded that suchstudies would help in improving irrigation and fertigation programs for horticultural crops irrigated with drip irrigation systems,
and would lead to more efﬁcient and less environmentally detrimental crop management practices.
Acknowledgements
The authors wish to thank Dr. Tapas Biswas for initiating theexperimental set up and Dareton Agricultural Research and Advisory Station farm staff for managing the site and plantings. The
ﬁnancial support of the National Program for Sustainable Irrigation
is gratefully acknowledged.
References
Ajdary, K., Singh, D.K., Singh, A.K., Khanna, M., 2007. Modelling of nitrogen leachingfrom experimental onion ﬁeld under drip fertigation. Agric. Water Manag. 89,
15–28.
Allen, R.G., Pereira, L.S., 2009. Estimating crop coefﬁcients from fraction of groundcover and height. Irrig. Sci. 28, 17–34.
Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration:
Guidelines for Computing Crop Water Requirements. FAO Irrigation andDrainage Paper No. 56, FAO, Rome, Italy.
Alva, A.K., Paramasivam, S., Obreza, T.A., Schumann, A.W., 2006. Nitrogen best
management practice for citrus trees: I. Fruit yield, quality, and leaf nutritional
status. Sci. Hortic. 107, 233–244.
ANZECC, ARMCANZ, 2000. National water quality management strategy: Australianand New Zealand guidelines for fresh and marine water quality: volume 1: theguidelines (Chapters 1–7). Australia and New Zealand Environment andConservation Council and Agriculture and Resource Management Council ofAustralia and New Zealand.
Assouline, S., 2002. The effects of microdrip and conventional drip irrigation onwater distribution and uptake. Soil Sci. Soc. Am. J. 66, 1630–1636.
Ayers, R.S., Westcot, D.W., 1989. Water Quality for Agriculture. FAO Irrigation andDrainage Paper No. 29, FAO, Rome, Italy.
Barber, S.A., 1995. Soil Nutrient Bioavailability: A Mechanistic Approach. John Wiley& Sons, New York.
Barlow, K., Bond, W., Holzapfel, B., Smith, J., Hutton, R., 2009. Nitrogenconcentrations in soil solution and surface run-off on irrigated vineyards inAustralia. Aust. J. Grape Wine Res. 15, 131–143.
Bar-Yosef, B., 1999. Advances in fertigation. Adv. Agron. 65, 1–77.
Biswas, T.K., 2006. Simple and inexpensive tools for root zone watch. Aust.
Nutgrower J. 20, 14–16.
Biswas, T., Schrale, G., 2007. Sentek SoluSAMPLER: a tool for managing salt andnutrient movement in the root zone. In: Instruction Manual Version 2.0. Sentek
Pty Ltd., Stepney, SA, Australia.
Clothier, B.E., Green, S.R., Sauer, T.J., 1988. The movement of ammonium nitrate into
unsaturated soil during unsteady absorption. Soil Sci. Soc. Am. J. 52, 340–345.
Correll, R., Smith, L., Waller, T., Zandona, R., Christen, E., Kookana, R., 2010.
Managing root zone water quality. Project Report, CSIRO Land and Water,
CSIRO, Australia.
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. Irrig. Sci. 22,
143–156.
El-Hendawy, S.E., Abd El-Lattief, E.A., Ahmed, M.S., Schmidhalter, U., 2008. Irrigation
rate and plant density effects on yield and water use efﬁciency of drip-irrigatedcorn. Agric. Water Manag. 95, 836–844.
Falivene, S., Goodwin, I., Williams, D., Boland, A., 2005. Introduction to OpenHydroponics. In: NPSI Fact sheet. National Program for Sustainable Irrigation.
Feddes, R.A., Kowalik, P.J., Zaradny, H., 1978. Simulation of Field Water Use and Crop
Yield. Simulation Monographs, Pudoc, Wageningen, The Netherlands.
Gärdenäs, A.I., Hopmans, J.W., Hanson, B.R., Šimůnek, J., 2005. Two-dimensionalmodeling of nitrate leaching for various fertigation scenarios under microirrigation. Agric. Water Manag. 74, 219–242.
Hanson, B.R., Šimůnek, J., Hopmans, J.W., 2006. Evaluation of urea–ammonium–
nitrate fertigation with drip irrigation using numerical modeling. Agric. Water
Manag. 86, 102–113.
Harrison, J., 1994. Review of nutrients in irrigation drainage in the Murray-Darling
Basin. CSIRO Report, Water Resources Series, No. 11, CSIRO, Australia.
Horticulture Australia Limited, 2008. <http://www.australiafresh.com.au/fruit
and_nuts/fruit/citrus.asp?src=side>.
Hutton, R., Holzapfel, B., Smith, J., Hutchinson, P., Barlow, K., Bond, W., 2008.
Inﬂuence of irrigation and fertilizer management on the movement of waterand nutrients within and below rootzone of vines for sustainable grape
production. CRC for Viticulture Report S2.3.6.
Kurtzman, D., Shapira, R.H., Bar-Tal, A., Fine, P., Russo, D., 2013. Nitrate ﬂuxes togroundwater under citrus orchards in a Mediterranean climate: observation,

515

calibrated models, simulations and agro-hydrological conclusions. J. Contam.
Hydrol. 151, 93–104.
Lazarovitch, N., Šimůnek, J., Shani, U., 2005. System-dependent boundary condition
for water ﬂow from subsurface source. Soil Sci. Soc. Am. J. 69 (1), 46–50.
Li, J., Liu, Y., 2011. Water and nitrate distributions as affected by layered-texturalsoil and buried dripline depth under subsurface drip fertigation. Irrig. Sci.,
http://dx.doi.org/10.1007/s00271-010-0255-z (published online).
Li, R., Ma, J., Zhang, R., 2003. Estimating nitrate leaching with a transfer function
model incorporating net mineralization and uptake of nitrogen. J. Environ. Qual.
32, 1455–1463.
9
Lidon, A., Ramos, C., Ginestar, D., Contreras, W., 2013. Assessment of LEACHN and asimple compartmental model to simulate nitrogen dynamics in citrus orchards.
Agric. Water Manag. 121, 42–53.
Liu, M., Yang, J., Li, X., Liu, G., Yu, M., Wang, J., 2012. Distribution and dynamics ofsoil water and salt under different drip irrigation regimes in northwest China.
Irrig. Sci., http://dx.doi.org/10.1007/s00271-012-0343-3 (published online).
Maas, E.V., Hoffmann, G.J., 1977. Crop salt tolerance – current assessment. ASCE J.
Irrig. Drain. Div. 103, 115–134.
Mallants, D., van Genuchten, M.Th., Šimůnek, J., Jacques, D., Seetharam, S., 2011.
Leaching of contaminants to groundwater. In: Swartjes, F.A. (Ed.), Dealing with
Contaminated Sites: From Theory towards Practical Applications. Springer, The
Netherlands, pp. 787–850.
Maynard, D.G., Kalra, Y.P., 1993. Nitrate and exchangeable ammonium nitrogen. In:
Carter, M.R. (Ed.), Soil Sampling and Methods of Analysis. Lewis Publishers, Boca
Raton, FL, pp. 25–38.
Mikhail, E.H., El-Zeftawi, B.M., 1979. Effect of soil types and rootstocks on root
distribution, chemical composition of leaves and yield of valencia oranges. Aust.
J. Soil Res. 17, 335–342.
NRMMC, 2011. Australian drinking water guidelines, Paper 6, National water
quality management strategy. National Health and Medical Research Council,
National Resource Management Ministerial Council, Commonwealth ofAustralia, Canberra.
Pang, X.P., Letey, J., 1998. Development and evaluation of ENVIRO-GRO, an
integrated water, salinity, and nitrogen model. Soil Sci. Soc. Am. J. 62, 1418–
1427.
Paramasivam, S., Alva, A.K., 1997. Nitrogen recovery from controlled-releasefertilizers under intermittent leaching and dry cycles. Soil Sci. 162, 447–453.
Paramasivam, S., Alva, A.K., Fares, A., Sajwan, K.S., 2002. Fate of nitrate and bromide
in an unsaturated zone of a sandy soil under citrus production. J. Environ. Qual.
31, 671–681.
Phogat, V., Malik, R.S., Kumar, S., 2009. Modeling the effect of canal bed elevation on
seepage and water table rise in a sand box ﬁlled with loamy soil. Irrig. Sci. 27,
191–200.
Phogat, V., Mahadevan, M., Skewes, M., Cox, J.W., 2012a. Modeling soil water andsalt dynamics under pulsed and continuous surface drip irrigation of almondand implications of system design. Irrig. Sci. 30 (4), 315–333.
Phogat, V., Skewes, M.A., Cox, J.W., Mahadevan, M., 2012b. Modelling the impact ofpulsing of drip irrigation on the water and salinity dynamics in soil in relation
to water uptake by an almond tree. WIT Transactions on Ecology andEnvironment, vol. 168. WIT Press, pp. 101–113, ISSN 1743–3541, http://dx.doi.org/10.2495/SI120091.
Phogat, V., Skewes, M.A., Mahadevan, M., Cox, J.W., 2013a. Evaluation of soil plantsystem response to pulsed drip irrigation of an almond tree under sustained
stress conditions. Agric. Water Manag. 118, 1–11.
Phogat, V., Skewes, M., Cox, J.W., Alam, J., Grigson, G., Šimůnek, J., 2013b. Evaluationof water movement and nitrate dynamics in a lysimeter planted with an orange
tree. Agric. Water Manag. 127, 74–84.
Quiñones, A., Marti9 nez-Alcantara, B., Legaz, F., 2007. Inﬂuence of irrigation systemand fertilization management on seasonal distribution of N in the soil proﬁleand on N-uptake by citrus trees. Agric. Ecosyst. Environ. 122, 399–409.
Ramos, T.B., Šimůnek, J., Goncalves, M.C., Martins, J.C., Prazeres, A., Castanheira, N.L.,
Pereira, L.S., 2011. Field evaluation of a multicomponent solute transport model
in soils irrigated with saline waters. J. Hydrol. 407, 129–144.
Ramos, T.B., Šimůnek, J., Goncalves, M.C., Martins, J.C., Prazeres, A., Pereira, L.S.,
2012. Two-dimensional modeling of water and nitrogen fate from sweetsorghum irrigated with fresh and blended saline waters. Agric. Water Manag.
111, 87–104.
River Murray Catchment Water Management Board (RMCWMB), 2009. Water
allocation plan for the river Murray prescribed watercourse. <http://
www.samdbnrm.sa.gov.au/Portals/9/PDF’s/Water/
River%20Murray%20Water%20Alloc%20plan.pdf> (accessed on 17.05.13).
Scholberg, J.M.S., Parsons, L.R., Wheaton, T.A., McNeal, B.L., Morgan, K.T., 2002. Soil
temperature, nitrogen concentration, and residence time affect nitrogen uptake
efﬁciency in citrus. J. Environ. Qual. 31, 759–768.
Šimůnek, J., Hopmans, J.W., 2009. Modeling compensated root water and nutrient
uptake. Ecol. Model. 220, 505–521.
Šimůnek, J., van Genuchten, M.Th., Šejna, M., 2008. Development and applications of
the HYDRUS and STANMOD software packages, and related codes. Vadose Zone
J. 7 (2), 587–600.
Šimůnek, J., van Genuchten, M.Th, Sejna, M., 2011. The HYDRUS software package
for simulating two- and three-dimensional movement of water, heat, andmultiple solutes in variably-saturated media. In: Technical Manual, Version 2.
PC Progress, Prague, Czech Republic, pp 258.
Skaggs, T.H., Trout, T.J., Šimůnek, J., Shouse, P.J., 2004. Comparison of HYDRUS-2D
simulations of drip irrigation with experimental observations. J. Irrig. Drain.
Eng. 130 (4), 304–310.

516

V. Phogat et al. / Journal of Hydrology 513 (2014) 504–516

Skaggs, T.H., van Genuchten, M.Th., Shouse, P.J., Poss, J.A., 2006. Macroscopic
approaches to root water uptake as a function of water and salinity stress. Agric.
Water Manag. 86, 140–149.
Skaggs, T.H., Trout, T.J., Rothfuss, Y., 2010. Drip irrigation water distribution pattern:effects of emitter rate, pulsing and antecedent water. Soil Sci. Soc. Am. J. 74,
1886–1896.
Sluggett, A.F., 2010. Solute dynamics in advanced fertigated horticulture. Thesis,
School of Chemistry, Physics and Earth Science, Flinders University, SA,
Australia.
Syvertsen, J.P., Jifon, J.L., 2001. Frequent fertigation does not affect citrus tree
growth, fruit yield, nitrogen uptake, and leaching losses. Proc. Fl. State Horticult.
Soc. 114, 88–93.
Syvertsen, J.P., Sax, S.M., 1999. Fertigation frequency, wetting pattern, and nitrateleaching from lysimeter-grown citrus trees. Proc. Fl. State Horticult. Soc. 112, 9–14.
Syvertsen, J.P., Smith, M.L., 1995. Nitrogen leaching, N uptake efﬁciency and water
use from citrus trees fertilized at three N rates. Proc. Fl. State Horticult. Soc. 108,
151–155.
Tafteh, A., Sepaskhah, A.R., 2012. Application of HYDRUS-1D model for simulatingwater and nitrate leaching from continuous and alternate furrow irrigatedrapeseed and maize ﬁelds. Agric. Water Manag. 113, 19–29.
Taylor, S.A., Ashcroft, G.M., 1972. Physical Edaphology. Freeman and Co., San
Francisco, California, USA, pp. 434–435.
Tournebize, J., Gregoire, C., Coupe, R.H., Ackerer, P., 2012. Modelling nitratetransport under row intercropping system: vines and grass cover. J. Hydrol.
440–441, 14–25.

U.S. Salinity Laboratory Staff, 1954. Diagnosis and improvement of saline and alkalisoils. In: USDA Handbook 60. United States Department of Agriculture,
Washington, USA.
van Genuchten, M.Th., 1980. A closed-form equation for predicting the hydraulicconductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898.
Vrugt, J.A., Hopmans, J.W., Šimůnek, J., 2001a. Calibration of a two-dimensional root
water uptake model. Soil Sci. Soc. Am. J. 65, 1027–1037.
Vrugt, J.A., van Wijk, M.T., Hopmans, J.W., Šimůnek, J., 2001b. One-, two-, and threedimensional root water uptake functions for transient modeling. Water Resour.
Res. 37 (10), 2457–2470.
Wang, F.L., Alva, A.K., 1996. Leaching of nitrogen from slow-release urea sources insandy soils. Soil Sci. Soc. Am. J. 60, 1454–1458.
Wang, H., Ju, X., Wei, Y., Li, B., Zhao, L., Hu, K., 2010. Simulation of bromide andnitrate leaching under heavy rainfall and high-intensity irrigation rates in North
China Plain. Agric. Water Manag. 97, 1646–1654.
Wang, Z., Jin, M., Šimůnek, J., van Genuchten, M.Th., 2014. Evaluation of mulcheddrip irrigation for cotton in arid Northwest China. Irrig. Sci. 32, 15–27.
Weihermuller, L., Kasteel, R., Vanderborght, J., Putz, T., Vereecken, H., 2005. Soilwater extraction with a suction cup: results of numerical simulations. Vadose
Zone J. 4, 899–907.
White, R.E., 2006. Principles and Practices of Soil Science: The Soil as a Natural
Resource. Blackwell Publishing, UK.
Willmott, C.J., Matsuura, K., 2005. Advantages of the mean absolute error (MAE)over the root mean square error (RMSE) in assessing average model
performance. Clim. Res. 30, 79–82.