Agricultural Water Management 201 (2018) 70–82

Contents lists available at ScienceDirect

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

Soil water and salinity dynamics under sprinkler irrigated almond exposed
to a varied salinity stress at diﬀerent growth stages

T⁎

V. Phogata,b, , T. Pitta, J.W. Coxa,c, J. Šimůnekd, M.A. SkewesaaSouth Australian Research and Development Institute, GPO Box 397, Adelaide SA 5001, Australia
CCS Haryana Agricultural University, Hisar 125004, Indiac
The University of Adelaide, PMB1 Glen Osmond SA 5064, AustraliadDepartment of Environmental Sciences, University of California, Riverside, CA 92521, USAb

A R T I C L E I N F O

A B S T R A C T

Keywords:
AlmondHYDRUS-2D
Salinity
Leaching
Water balance
Water use eﬃciency

Water use and salinity dynamics in the soils are the crucial management factors inﬂuencing the productivity andlong-term sustainability of almond and associated environment. In this study, HYDRUS-2D was calibrated andvalidated on measured spatial and temporal water contents and soil salinities (ECe) distributions under almond
irrigated with diﬀerent water qualities (ECiw) at diﬀerent physiological stages. During two irrigation seasons(2014–15 and 2015–16), less saline irrigation water (average ECiw 0.78 dS/m) was substituted for recycled
irrigation water (average ECiw 1.9 dS/m) in three phenologically diﬀerent growth stages; pre-pit hardening,
kernel growth, and post-harvest, along with no and full substitution during the entire season. Graphical andstatistical comparisons (RMSE, MAE, ME, the Nash and Sutcliﬀe model eﬃciency, and the coeﬃcient of determination) between measured and simulated values of water contents and ECe in the soil showed a close
agreement in all treatments. The water balance data revealed that the seasonal crop evapotranspiration of almond (ETc) varied from 850 to 955 mm in diﬀerent treatments over the two seasons which represented 68–79%
of the water application. Trees irrigated with only less saline water through the two seasons (average ECiw
0.78 dS/m) showed 10% higher plant water uptake as compared to those irrigated with recycled water only
(average ECiw 1.9 dS/m). Substituting less saline irrigation during the kernel growth phase, between pit-hardening and harvest, showed greater water uptake by almond and lower salinity buildup in the soil as compared totreatments that substituted less saline irrigation early or late in the season. For all treatments, the average dailyroot zone ECe (2.4–3.7 dS/m) remained above the level of the almond salinity tolerance threshold (ECe = 1.5 dS/m) throughout the period of investigation. Water use eﬃciency of almonds varied in a narrow range(0.21–0.25 kg m−3) for diﬀerent treatments. Deep drainage below the root zone (2 m) varied from 22.4–31.1%of the total water application (Rainfall + Irrigation), which was episodic and insuﬃcient to contain the salinity
below the almond threshold. This study provided a greater understanding of soil water and salinity dynamics
under almond irrigated with waters of varying qualities.

1. Introduction

1982; Maas, 1990; Pitman and Laüchli, 2002; Assouline et al., 2015).
Impacts of soil salinity are varied and highly inﬂuenced by the soil type,inherent layering and hydraulic heterogeneity, quality and quantity ofirrigation, the method of irrigation, rainfall amounts and distribution
patterns, and salt tolerance of crops. At the same time, the installationof highly eﬃcient, partial cover, irrigation systems in high-value crops
including almond has a narrow wetted area that tends to concentratesalts in the rootzone. During periods of water scarcity, when irrigators
are forced to limit their leaching fractions or forced to apply groundwater/recycled water, salts can rapidly accumulate to dangerous levels.
Almond production in the arid and semi-arid regions of the world

Water is becoming increasingly scarce worldwide, and sustainable
use of the available water resources is the major water policy challenge
for the future. In water-stressed regions, strong constraints due to natural climatic variability and increased use by other users make the allocation of water the core challenge for water resource management(Alcon et al., 2013). In addition to issues related to water quantity, thequality of water plays an important role in the sustainability of irrigatedlands, especially in the context of salinity build up that could adversely
impact the agricultural/horticultural crop productivity (Bresler et al.,⁎Corresponding author.
E-mail address: vinphogat@gmail.com (V. Phogat).

https://doi.org/10.1016/j.agwat.2018.01.018
Received 24 October 2017; Received in revised form 9 January 2018; Accepted 19 January 20180378-3774/ © 2018 Elsevier B.V. All rights reserved.

Agricultural Water Management 201 (2018) 70–82

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recycled water, (2) to simulate water movement and salinity dynamics
for other experimental irrigation treatments including substituting lesssaline irrigation water for the resident recycled water irrigation at threephenologically diﬀerent growth stages, and (3) to evaluate the water
use eﬃciency of almonds under the diﬀerent irrigation treatments.

remains vulnerable to water availability and soil salinity. Althoughalmond is a drought tolerant tree (Torrecillas et al., 1996), its production and proﬁtability are highly dependent on the supply of irrigation water. The adoption of eﬃcient irrigation techniques can increase
the production by as much as ten-fold compared to non-irrigated lands(Egea et al., 2010). On the other hand, almond is sensitive to salinity,
having a production threshold with respect to the electrical conductivity of the soil saturation extract (ECe) of 1.5 dS/m and a reductionin the growth rate of 19% for a unit increase in salinity beyond thethreshold (Maas, 1990). Thus, the salinity levels higher than thethreshold anytime during the cropping season can have a serious impact on growth and nut production. Therefore, proper irrigation management in the orchard is very important to obtain sustainable yield,
leach harmful chemicals from the root zone, save precious water, andincrease the shelf life of the fruits. Precise information on the salinity
impact at diﬀerent growth stages can help develop more robust orchardsalinity management guidelines.
Diﬀerent deﬁcit irrigation options (regulated deﬁcit irrigation, RDI;
sustained deﬁcit irrigation, SDI; and partial root zone drying, PRD),
including their impact at diﬀerent growth stages of almond (Girona
et al., 2005; Goldhamer and Viveros, 2000; Romero et al., 2004;
Goldhamer et al., 2006; Egea et al., 2010; Puerto et al., 2013) have been
extensively studied over the almond growing season (Nortes et al.,2009; Egea et al., 2013; Monks et al., 2017). These investigations
highlighted the impact of reduced water applications on various physiological parameters, kernel size, and kernel yield. Other studies focussed on the almond water requirement and crop coeﬃcients (Stevens
et al., 2012; Espadafor et al., 2015; García-Tejero et al., 2015), an irrigation system design (Phogat et al., 2012), and water productivity ofalmonds under reduced water applications (García-Tejero et al., 2011;
Phogat et al., 2013). On the other hand, the studies on the implicationsof the application of saline/recycled water irrigation on the water
availability to almonds and salinity dynamics in the soil are sparse.
Notably, the salinity related studies are speciﬁcally focused on theidentiﬁcation of tolerant root stocks (Gradziel and Kester, 1998;
Camposeo et al., 2011) and genotypes (Rouhi et al., 2007; Sorkheh
et al., 2012; Rajabpoor et al., 2014; Bahrami et al., 2015). Franco et al.
(2000) reported a 46% reduction in the almond kernels when seasonally irrigated with high salinity water (4.6 dS/m) compared to whenless saline water (0.8 dS/m) was used. Nightingale et al. (1991) observed the greatest accumulation of salinity (5.7 dS/m) beneath thetrickle line source for 50% ETC irrigation and laterally away from thetrickle line for 100 and 150% ETC irrigation. Therefore, the informationon the impact of saline/recycled water irrigation at diﬀerent growthstages of almond on the water balance and salinity dynamics in the soil
could improve the understanding of the use of such water for the almond production and ensure the long-term sustainability of high-valuehorticulture crops.
Predictive science and numerical models such as HYDRUS-2D(Šimůnek et al., 2016) present an excellent opportunity to gauge theimpacts of irrigation practices (Kandelous and Šimůnek, 2010; Ramos
et al., 2012; Phogat et al., 2012; González et al., 2015), water quality(Hassan et al., 2005; Ramos et al., 2011; Hassanli et al., 2016) andclimate change (Austin et al., 2010) on potential water and salinity
hazards and to control oﬀsite movement of costly inputs into the surface and subsurface water bodies (Phogat et al., 2014). Moreover, thepartial wetting pattern and irregular root water uptake in a drip-irrigated orchard make it diﬃcult to apply and interpret standard waterbalance techniques and require a large number of measurements (BenAsher, 1979). Thus, a numerical model (HYDRUS-2D) was employed toevaluate the impact of diﬀerent qualities of water applications to almonds at diﬀerent growth stages on the water balance and salinity
dynamics in the soil.
The objectives of this study are (1) to calibrate and validate
HYDRUS-2D to describe water content distributions and spatiotemporalsalinity dynamics in the soils under sprinkler-irrigated almond with

2. Materials and methods
2.1. Experimental details
The experimental site was established at a mature almond plantation located in the Northern Adelaide Plains irrigation district, approximately 35 km north of Adelaide, South Australia (34.628°S and
138.683°E). The orchard was planted in 1998 and designed to have two
adjacent rows of a commercial variety, Nonpareil, bordered on eitherside by pollinators, Price and Keane. All trees were grafted to Brightpeach hybrid rootstock with rows planted in a north-south direction.
Trees were spaced at a distance of 5.5 m within the rows and 7.5 m
between rows.
The trial was designed as a randomized unblocked design, with four
treatments replicated four times, plus an additional demonstration plot.
Each treatment plot consisted of micro-sprinklers (ﬁve trees) along atree line and was three rows wide. It covered a double row of Nonpareiltrees plus a single row of a pollinator variety. All soil and plant measurements were collected from the three central trees in the middle row.
The treatment infrastructure was installed before the 2013–14 irrigation season and was designed to substitute recycled (more saline) water
irrigation (2 seasons average ECiw = 1.9 dS/m) with less saline water (2seasons average ECiw = 0.78 dS/m) at one of the three phenological
growth stages. The treatment when recycled water irrigation was used
for the entire irrigation season represents the control treatment (A). In
treatment B, less saline water was applied between the buds burst andpit hardening (BB-PH) stages. In treatment C, less saline irrigation wasapplied between the pit hardening and harvest (PH-H) stages, whereas
in treatment D, less saline irrigation was applied between the harvest
and leaf fall (H-LF) stages. The number of days during the three physiological stages when less saline water was applied in treatments B, C,
and D was 84, 108, and 73 during 2014–15 and 88, 101, and 88 during
and 2015–16, respectively. In addition, a non-replicated plot (treatment
E) of trees was irrigated with less saline water during the entire season
for demonstration purposes. Table 1 gives details about the four replicated irrigation treatments and the non-replicated demonstration
treatment. Further details about the trial design are described in Pittet al. (2015).
Irrigations were scheduled to replace estimated tree evapotranspiration, which was evaluated based upon a modiﬁed version of
the protocol developed by the Almond Board of Australia (2011). The
Almond Board of Australia (ABA) provides a spreadsheet based on a
Table 1
The timing of exposure to non-saline irrigation water in treatments A–D (replicated) andtreatment E (a non-replicated demonstration plot).
Treatments

Irrigation

Growth Stages
1

2
*

#

BB to PH#P
A - Control
B
C
D
E

Saline all year
Non-saline at BB-PH
Non-saline at PH-H
Non-saline at H-LD
Non-saline all year

* Bud burst.
Pit hardening.
λHarvest.
η
Leaf drop.
#

71

SalineNon-saline
SalineSalineNon-saline

H to H3
λSalineNon-saline

H to LDη

SalineNon-saline

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V. Phogat et al.

water budgeting tool to facilitate this process. The spreadsheet requires
the user to enter daily estimates of pan evaporation from a class A pan
installed on their property and uses “in-house” crop factors (Cf) within
the spreadsheet to calculate the crop water requirement for the day(Almond Board of Australia, 2011). The potential crop water use (ETC)
is the result of a product of measured pan evaporation (ETpan) and agiven crop factor on any particular day during the season. Therefore,
data on irrigation depths for the experimental site were sourced fromthe collaborating grower and cross-checked against ﬂow meters locatedin each treatment plot. Applied depths of irrigation were recorded
weekly through the irrigation season to ensure the same depth of irrigation was applied to all treatments at each growth stage. The amountof seasonal irrigation to almonds was slightly higher (8%) during
2015–16 as compared to 2014–15. The orchard was irrigated with EinDor (70 L/h) micro-sprinklers spaced every 5.5 m, halfway between
trees, along the length of tree rows spaced at 7.5 m. This resulted in an
application rate of 1.7 mm/h. The spatial extent of sprinklers was over a
280-cm radial area and there was a small overlap from neighboring
sprinklers. Irrigation water was distributed more or less uniformly on
the soil surface along a tree row to a distance of 280 cm on both sides of
the tree line. While sprinkler irrigation with saline water can cause
direct salt injury to the crop foliage, it remained an understory intervention for almond trees at the experimental site.
The recycled water was drawn from the Bolivar Wastewater
Treatment Plant. The wastewater used for irrigation in this study wasclass A recycled water and as per the guidelines Class A water has received a level of treatment greater than classes B to D and is suitable forunrestricted irrigation to all crops and fodder types (Department of
Human Services and South Australian Environmental Protection
Agency, 1999). The less saline irrigation water was supplied with apotable water connection (SA Water), installed in 2013 adjacent to thetrial site. Water samples were continuously collected throughout thegrowing season via sprinklers, and the electrical conductivity of theirrigation water (ECiw) was assessed. In treatment A (recycled water),the ECiw varied from 1.01 to 2.54 and 1.2–2.18 dS/m during 2014–15
and 2015–16 seasons, respectively, with corresponding average ECiw of
1.94 dS/m and 1.81 dS/m. Similarly, in treatment E (less saline water),the ECiw varied from 0.72 to 0.78 dS/m (average 0.76 dS/m) and
0.83–0.86 dS/m (average 0.85 dS/m), respectively during 2014–15 and2015–16.
The experimental site was managed as per standard commercial
orchard practices including a full nutrient, fungicide, and herbicide
spray program plus mechanical harvest operations. A mid-row covercrop of various volunteer weeds and grasses (both annual and perennial) were controlled with occasional slashing and under tree herbicide operations. The experimental site was fertilized via foliar,
broadcast and fertigation methods as reported in Pitt et al. (2015). The
variation in ECiw due to fertigation was not considered in the modelingsimulations as the amount and timing of fertilization application is similar across all the treatments.
Soils at the site consist of a sandy loam to loam in the surface 35-cm
horizon, followed by hard and calcareous clay (35–110 cm), and variable clay soil zoning below 110 cm, as characterized by Dowley andFitzpatrick (2001). They noted that the tree root development is good in
the upper 0.6 m of the proﬁle and reported good water movement in thesurface layer, with 24-h irrigation (at a rate of 2.5 mm/h) wetting thesoil to a depth of 45–50 cm. The moisture characteristics, bulk density,
soil textures, particle size distribution, and chemical properties of thesoils of this region have also been documented in the APSIM Soil database (2016) and the ASRIS (2011) database. The particle size distribution and the bulk density of various depths were used to estimate
the soil hydraulic parameters using ROSETTA (Schaap et al., 2001), a
pedo-transfer function software package that uses neural networks topredict soil hydraulic parameters from soil texture and related data.
However, values of residual (θr) and saturated (θs) water contents weretaken from the databases mentioned above. The values of soil hydraulic

Table 2
Optimized soil hydraulic parameters at the experimental site used in the HYDRUS-2D
simulations.
Soil depth (cm)

1
2
3

0–15
15–25
25–35

4
5
6
7
8

35–55
55–110
110–130
130–150
150–200

TextureSandy loam
Loam
Sandy clay
loam
Clay loam
Clay loam
Silty clay loam
Silty clay loam
Silty clay loam

θr

θs

α

n

Ks

l

cm3 cm−3

cm−1–

cm/day

–

0.06
0.12
0.18

0.42
0.37
0.40

0.075
0.059
0.041

1.89
1.48
1.40

120
80
65

0.5
0.5
0.5

0.23
0.22
0.19
0.20
0.19

0.45
0.40
0.37
0.38
0.37

0.027
0.045
0.02
0.032
0.02

1.23
1.40
1.20
1.32
1.20

40
70
30
40
30

0.5
0.5
0.5
0.5
0.5

parameters (α, n, and Ks) were further optimized during calibration.
The optimized soil hydraulic parameters for diﬀerent soil depths usedin the present modeling study are given in Table 2.
2.2. Meteorological data
HYDRUS-2D requires daily rainfall, potential evaporation (Es), andtranspiration (Tp) as inputs, which deﬁne the dynamic climatic variability experienced by a plant. Data on rainfall and daily reference
evapotranspiration (ET0) during the modeling period (2014–15 and2015–16) for the experimental site were generated by running a data
drill (Jeﬀrey et al., 2001) which generate the data for a site dependingon the available climate data records in the adjoining Bureau of Meteorology (BOM) observatories. The observatories closest to the experimental site are Edinburg Raaf (6.9 km), Roseworthy (16 km), andParaﬁeld (16 km). Crop coeﬃcients (Kc), which were obtained from the
IRES irrigation scheduling software developed by Rural Solutions SA(Rural Solutions SA, 2011), were used to estimate daily ETC for almonds. Daily ETC was divided into potential Tp and Es following
Belmans et al. (1983) using measured LAI at the experimental site. LAI
was determined using the method described in Fuentes et al. (2014)
using an SLR Camera (Leica Digilux 2, New Jersey, USA). LAI was
measured three times during both seasons: just after pit-hardening(November), before harvest (February), and in the weeks approachingsenescence (April). Four under canopy images were collected from eachof three central trees using a sensor located at the ground level and
1.5 m away from the trunk. Four images captured the north, south, east,
and west portions of the canopy. Images were processed using an algorithm developed by Fuentes et al. (2014). Daily potential Tp and Es
values were then used as time-variable atmospheric boundary conditions in the model, along with precipitation data for the site during thesimulation period. Seasonal rainfall at the experimental site amounted
to 327 and 373 mm during 2014–15 and 2015–16 seasons, respectively.
Daily ET0, rainfall, and the amount of irrigation applied to almonds
during the 2014–15 and 2015–16 seasons are shown in Fig. 1.
2.3. Water content and soil salinity measurements
Soil water contents were measured on a monthly basis during the2015 and 2016 irrigation seasons using a 503 DR Hydroprobe neutron
moisture meter (CPN International, California, USA). Twelve aluminum
access tubes were installed in May 2014 for the monitoring of spatiotemporal moisture contents distribution in the soil in diﬀerent treatments. The tubes were installed in three replicates in treatments A, C,
and D, two replicates in treatment B, and a single replicate in the nonreplicated treatment E. Access tubes were installed 90 cm from thesprinkler emitters across the tree line. The probe was setup to measure
at 10 cm depth increments between 20 and 60 cm and 20 cm depth
increments between 60 and 200 cm. At the same time, undisturbed soil
samples were collected from three separate cores for the determination
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V. Phogat et al.

Feddes macroscopic approach (Feddes et al., 1978) implemented in the
HYDRUS software. The following critical values of pressure heads foralmond in the Feddes et al. (1978) model were used: h1 = −10,h2 = −25, h3 = −500 to −800, and h4 = −15000 cm. These values
were taken from previous investigations in South Australia (Phogat
et al., 2012, 2013). The reduction of root water uptake due to salinity
stress, α2(hϕ), was described by adopting the salinity threshold andslope function (Maas, 1990). The salinity threshold (ECT) for almonds
has a value of ECe of 1.5 dS m−1 and a slope (s) of 19%. As required by
HYDRUS-2D, these values were converted into the electrical conductivity at the actual soil water content (ECsw), utilizing the linear
relationship (Pitt et al., 2017) and initial water content values.
Fig. 1. Daily reference crop evapotranspiration (ET0, line graph), rainfall (blue bars), andirrigation (green bars) applied to almonds during 2014–15 and 2015–16. (For interpretation of the references to colour in this ﬁgure legend, the reader is referred to the web
version of this article.)

2.5. Soil salinity simulation
The distribution of soil solution salinity (ECsw) was simulated as a
non-reactive solute (Ramos et al., 2011; Phogat et al., 2012; Phogat
et al., 2014; Wang et al., 2014). These studies demonstrated a goodprediction of salinity under intensive irrigation and fertigation, similar
to practices in the region under study. The longitudinal dispersivity was
assumed as one/tenth of the modeling domain (with the transverse
dispersivity being one-tenth of the longitudinal dispersivity) (Bevenet al., 1993; Cote et al., 2003) and molecular diﬀusion in water equal to1.66 cm2/day (Phogat et al., 2014). Measured values of irrigation watersalinity (ECiw) were used as a time variable input for the root zone soil
salinity prediction. The rainfall chemistry analyzed by Cresswell et al.
(2010) for diﬀerent locations in Australia provided reliable information
about rainfall salinity (ECrw) for the Adelaide region, which is close to
the experimental site. The average salinity of rainfall was 0.12 dS/m,and this value was used for all salinity simulations.

of bulk density, for the conversion of gravimetric to volumetric watercontents, and for the calibration of the Neutron Probe.
Soil samples were collected from every plot of all treatments at the
beginning of 2014–15 and at the end of each growth stage (bud burst,
pit hardening, harvest) during the 2014–15 and 2015–16 seasons.
Samples were collected 100 cm into the mid-row from the sprinkler
emitter and in 10 cm increments to a depth of 160 cm using a hydraulicsoil sampling rig with a 50 mm diameter collection tube (Christies
Engineering, Horsley Park, Australia). Soil salinity was measured as theelectrical conductivity of 1:5 soil:water extracts (EC1:5) on duplicate
samples using a temperature compensated conductivity meter (model
CON510, Eutech, Singapore). However, soil salinity was reported as theelectrical conductivity of the extract from a saturated paste (ECe) using
a conversion factor (ECe = 5.89 × EC1:5, R2 = 0.72) that was generated
by analyzing paired data from 35 soil samples covering all depths,
which had been split so that both EC1:5 and ECe could be measured. All
salinities were determined following the method of Rayment and
Higginson (1992). More details about the sampling and the analysis aredescribed in Pitt et al. (2017).

2.6. Modeling domain and initial and boundary conditions
A two-dimensional modeling domain was constructed with a width
equal to the half distance between tree lines (375 cm) and a verticaldepth of 200 cm (Fig. 2). The domain represents a right half crosssection of the almond tree plant spacing. The domain was discretized
into 23,505 elements, with a ﬁner mesh size on the upper boundary
where the time-variable ﬂux and atmospheric boundary conditions
were imposed. Measured values of water contents and soil salinities,
recorded on 10/6/2014, were used as the initial conditions. The watercontents were measured using a Neutron Probe, and soil salinity wasmeasured on disturbed soil samples collected during the probe calibration. The top boundary consisted of a time-variable ﬂux boundary
condition along the 280-cm width and an atmospheric boundary condition on the rest of the upper boundary (100 cm). A 15% rainfall interception by the tree was assumed, which is comparable to the reported value for similar deciduous trees (Xiao et al., 2000). Uniform
distributions of net rainfall and irrigation were imposed as a timevariable ﬂux boundary condition. However, the understory rainfall
distribution and interception by trees could vary depending on the

2.4. Root distribution
Most almond roots at the experimental site are distributed inshallow depths (0–25 cm). Very few roots were encountered in the
25–50 cm depth due to the presence of hard clay at the shallow depth,
which doesn’t allow the roots to penetrate deeper as the penetration
resistance exceeds 2 MPa (Dowley and Fitzpatrick, 2001). The root
distribution considered in the model was based on a rough estimate of
the volume of roots recovered from various depth ranges (in incrementsof 10 cm from the surface to a depth of 160 cm) from soil samples
collected using a hydraulic rig. As the roots were only observed above
the 50 cm soil depth, their distribution was assumed in the model
within the 0–50 cm region with a maximum root density at the 20-cm
soil depth. Root water uptake from this region was computed using the

Fig. 2. A modeling domain (375 × 200 cm in the X–Z plane) representing
the right half spacing of the mid-row along the almond tree line, showing
imposed boundary conditions and the distribution of soil textural layers inthe soil proﬁle at the experimental site.

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canopy characteristics. The side boundaries were no ﬂow boundaries,
and free drainage was imposed at the lower boundary. The model wascalibrated using water content and salinity distributions during theentire season (July 10, 2014, to June 30, 2015). The model simulated
soil solution salinity values (ECsw) were converted to the soil salinity atsaturation (ECe) utilizing the corresponding water content data and a
relation developed for the experimental soil samples. Validation was
performed using data from July 1, 2015 to June 30, 2016 in the control(recycled water irrigation) (treatment A) by comparing measured andsimulated values of the water contents and salinities at various depths(20, 30, 40, 50, 60, 80, 100, 120, 140, and 160 cm). In treatment A,
irrigations were composed of recycled water having an average ECiw of
1.94 dS/m (varied from 1.01 to 2.54 dS/m) and 1.81 dS/m (ranged
from 1.2 to 2.18 dS/m) during the 2014–15 and 2015–16 seasons, respectively.
The calibrated and validated model was then used to simulate waterand salinity dynamics under other treatments (B, C, and D) where recycled water was substituted with less saline water (ECiw of < 0.8 dS/m) between diﬀerent phenological stages of the almond growth.
Simulations were also performed for treatment E where less saline
water was used throughout the almond season. Simulated spatiotemporal dynamics of water contents and soil salinities were comparedwith the corresponding measured values in all treatments.

N∑ (Mi − Si)2
E = 1 − iN= 1∑ (Mi − M )2

The range of E lies between−∞ and 1.0 (perfect ﬁt). An eﬃciency
value between 0 and 1 is generally viewed as an acceptable level of
performance. Additionally, the coeﬃcient of determination (R2) between the simulated and measured water contents and soil salinities
was also estimated as:⎛⎜
R2 = ⎜⎜⎜⎝(1)

2.8. Statistical parameters
The model’s performance was evaluated by comparing measured(M) and HYDRUS-2D simulated (S) values of water contents and electrical conductivities (ECe) in the soil at diﬀerent times, and calculating
the mean error (ME), the mean absolute error (MAE), and the root meansquare error (RMSE) as follows:N1∑ (Mi − Si)
N i=1

(2)

N

MAE =

1
∑ Mi − Si
N i=1

(3)

N

RMSE =1∑ (Mi − Si)2
N i=1

(6)

Model calibration and validation were performed using measured
values of water contents in the soil at diﬀerent depths and at diﬀerent
growth stages of almond in the saline water treatment (A; control).
Measured and simulated water contents at diﬀerent times during model
calibration (2014–15) are compared in Fig. 3a–d. Diﬀerent ﬁgures represent diﬀerent growth stages of almond (bud burst, pit hardening,
harvest, and leaf drop). The simulated distribution (S) of the watercontent in the soil proﬁle matched well with the measured (M) values at
all times during the growing season. However, there was a small overestimation of water contents at the harvest stage (Fig. 3c) between the
60–120 cm soil depth. Such occasional deviations in simulated values
are unavoidable and are on the same order of magnitude as measurement errors of the sensors (e.g., Ganjegunte et al., 2012; Evett et al.,2012).
Similarly, statistical errors (see Section 2.8) evaluating diﬀerences
between measured and simulated soil water contents indicated a closeﬁt (Table 3, the ﬁrst row). The RMSE and MAE values obtained bycomparing measured and simulated soil water contents at diﬀerent
depths and at diﬀerent times varied from 0.020 to 0.036 and0.014–0.030 cm3 cm−3, respectively. Similarly, the mean error estimated at diﬀerent times also varied in a narrow range(−0.030–0.015 cm3 cm−3) (Table 3). The negative ME values indicateda slight under-estimation of water contents by the model. However,small ME values indicate a good match between measured and simulated values. Also, the seasonal modeling eﬃciency of 0.79 indicatesthat the model provided a good representation of water movement
under experimental conditions. Similarly, the coeﬃcient of determination (R2; 0.85) for measured and simulated water contents also
showed a close correspondence. Hence, estimated values of soil hydraulic parameters used in the model represent well the experimental
soil.
During validation, the calibrated HYDRUS-2D model was run withthe weather and irrigation input (treatment A) for the second season(2015–16). Overall, there was a good match between modeled andmeasured water contents at diﬀerent depths and times as shown in
Fig. 3e–h. Small deviations at some depths are common under ﬁeld
conditions. Similarly, the statistical comparison between measured andsimulated soil water contents during validation (2015–16) showed aslightly wider error range (Table 3) as compared to calibration. However, the RMSE (0.16–0.38 cm3cm−3), MAE (0.11–0.34 cm3 cm−3), andME (−0.034–0.006 cm3 cm−3) values ranged within accepted limits
reported in similar studies (e.g., Deb et al., 2011; Phogat et al., 2012,2013, 2014, 2017; Ramos et al., 2011, 2012). The seasonal modelingwhere Y is the average kernel yield (kg/tree) and ETC is the average
seasonal actual evapotranspiration (m3 of water/tree) estimated by
HYDRUS-2D for diﬀerent treatments.

ME =

2

⎞
∑ (Mi − M )(Si − S )
⎟
i=1
⎟
N
N
2 ⎟
2
∑ (Mi − M ) ∑ (Si − S ) ⎟
i=1
i=1
⎠

3.1. Model calibration and validation for water content distributions in thesoils

The yield was measured on the three central trees of each treatment
plot. Prior to commercial harvest operations, the orchard ﬂoor was
raked clear under the target trees. Trees were mechanically shaken on
16 February 2015 and 10 February 2016. Nuts were left to dry on the
ground for four days before the experimental pickup operation.
Water productivity of almonds for actual ET losses (WPETC) was
estimated using the average seasonal ETC values obtained fromHYDRUS-2D simulations for two seasons and measured average yielddata for diﬀerent treatments given as:

Y
ETC

N

3. Results and discussion

2.7. Yield components and water productivity

WPETC =

(5)

i=1

(4)

Model eﬃciency (E) was estimated as proposed by Nash andSutcliﬀe (1970), which is given as:74

Agricultural Water Management 201 (2018) 70–82

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Fig. 3. Comparison of measured (M) and HYDRUS-2D simulated (S) water content distributions in the soil at indicated times during 2014–15 (a, b, c and d) and 2015–16 (e, f, g and h)under almond irrigated with saline water (treatment A).

water contents at the harvest stage in treatment D (see the fourthcolumn in Fig. 4) at depths between 40 and 140 cm depth. Nevertheless,the statistical measures, such as error parameters (RMSE, MAE, andME), model eﬃciency and R2 between measured and simulated watercontents, indicated a good match in all treatments (Table 3).
Despite some deviations, simulated results have shown a fairly good
agreement with measured water contents in all treatments, as substantiated by diﬀerent statistical error parameters, the model eﬃciency,
and R2 values. Similar deviations between measured and simulated
water contents have been reported in other similar investigations (e.g.,
Ramos et al., 2012; Phogat et al., 2012, 2013, 2017; González et al.,2015). The extent of deviations can be attributed to various causes,
possibly including diﬀerent root distribution characteristics due to soil
heterogeneity, which was assumed constant in all treatments. Modifying input parameters and calibrating the model for each treatment
would probably improve HYDRUS-2D predictions for each treatment.
Other causes for deviations between measured and simulated watercontents, which could also be involved in the current investigation,
were explained by Ramos et al. (2012) and are related to ﬁeld measurements, model inputs, and model structural errors. Additionally, themeasurement of soil water contents by sensors is also not free of errors,eﬃciency (0.78) was also slightly lower than the corresponding value
obtained during calibration. However, the R2 value (0.86) between
measured and simulated seasonal water content distributions indicateda close match. All statistical measures comparing measured and simulated water contents during calibration and validation thus indicatethat HYDRUS-2D is able to reliably simulate water distribution patterns
in the soil under evaluated experimental conditions during the entire
almond growing season.
3.2. Water content distributions in other treatments
The calibrated and validated model was then used to simulate watercontent distributions in other treatments (B, C, D, and E) where irrigations with recycled water were substituted with less saline water
during diﬀerent growth stages. Water content distributions in thesetreatments (B, C, D, and E) during both seasons (2014–15 and 2015–16)
at selected times are compared in Fig. 4. Water content depth distributions showed a good match between measured and simulated values at the bud burst growth stage in treatment B (see the second
column in Fig. 4) and at the pit hardening stage in treatment C (see thethird column in Fig. 4). However, there was a small over-estimation in

Table 3
Statistical errors (RMSE, MAE, ME), model eﬃciency, and the coeﬃcient of determination (R2) for the comparison between measured and simulated soil water contents for diﬀerent
treatments during 2014–15 and 2015–16.
Treatment

Year

RMSE

MAE

ME

Model eﬃciency

R2

0.014–0.030
0.011–0.034
0.013–0.40
0.012–0.035
0.011–0.058
0.014–0.056
0.016–0.045
0.012–0.039
0.015–0.036
0.022–0.037−0.030 to 0.015−0.034 to 0.006−0.040 to 0.026−0.032 to 0.010−0.058 to 0.016−0.56 to −0.013−0.038 to 0.020−0.034 to −0.002−0.030 to 0.018−0.032 to 0.019

0.79
0.78
0.72
0.74
0.48
0.56
0.46
0.60
0.74
0.66

0.85
0.86
0.73
0.80
0.64
0.80
0.57
0.74
0.76
0.73

(cm3 cm−3)
2014–15a2015–16b
2014–15b
2015–16b
2014–15b
2015–16b
2014–15b
2015–16b
2014–15b
2015–16b

A
B
C
D
E

a
b

0.020–0.036
0.016–0.038
0.018–0.046
0.015–0.040
0.015–0.62
0.017–0.60
0.018–0.051
0.014–0.048
0.017–0.040
0.025–0.048

Calibration period.
Validation period.

75Agricultural Water Management 201 (2018) 70–82

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Fig. 4. Comparison of measured (M) and HYDRUS-2D simulated (S) water content distributions in the soil in diﬀerent treatments (B, C, D, E) at indicated times during the 2014–15 (toprow) and 2015–16 (bottom row) almond season.

over-estimation of measured ECe values at the bud burst stage between
40 and 80 cm soil depths. Thereafter, the model-produced ECe dynamics
remained within the extent of deviations observed in measured ECe
replicates at all stages of the almond growth. The statistical error
parameters, i.e., RMSE, MAE, and ME estimated at diﬀerent times between average measured and simulated ECe values varied from 0.60 to
0.86, 0.47–0.56, and −0.35–0.49 dS/m (Table 4) and matched wellwith other studies (e.g., Ramos et al., 2012). The model eﬃciency(0.98) and R2 (0.73) also indicated a close match between measured
and simulated ECe values during the entire season (2014–15).
Similarly, during the validation period (2015–16) simulated ECevalues at diﬀerent depths and times are in close agreement with measured replicated values except for a small under-estimation in thedue to numerous assumptions and inherent complexities in the soil
(e.g., Rosenbaum et al., 2010; Ganjegunte et al., 2012; Evett et al.,2012), which may contribute to a similar extent of deviations in watercontents as obtained by modelling predictions. Finally, the soil spatial
variability in diﬀerent treatments could lead to spatially dynamic root
growth, which is not considered in the present modeling investigation.
3.3. Model calibration and validation for salinity dynamics in the soils
Simulated ECe (S) dynamics in the soil proﬁle under almond iscompared with measured values at diﬀerent times at 4 locations (A1,A2, A3 and A4) in treatment A during 2014–15 (Fig. 5a–d) and2015–16 (Fig. 5e–h). During calibration (2014–15), there was a slight

Fig. 5. Comparison of measured (at diﬀerent locations A1, A2, A3 and A4) and HYDRUS-2D simulated (S) electrical conductivities in the soil at saturation (ECe) at indicated times during
2014–15 (a, b, c, and d) and 2015–16 (e, f, g, and h) under almond irrigated with saline water (treatment A).

76

Agricultural Water Management 201 (2018) 70–82

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Table 4
Statistical errors (RMSE, MAE, ME), model eﬃciency, and the coeﬃcient of determination (R2) for measured and simulated ECe in the soil in diﬀerent treatments during 2014–15 and2015–16.
Treatment

Year

RMSE

MAE

ME

Model eﬃciency

R2

0.47–0.56
0.57–0.90
0.53–0.86
0.66–1.02
0.44–0.69
0.46–0.82
0.50–0.87
0.78–1.22
0.72–1.06
0.49–1.32−0.35 to 0.49
−0.03 to 0.76
−0.66 to −0.29−0.55 to 0.62
−0.25 to 0.18
−0.72 to 0.45
−0.67 to 0.12
−1.09 to 0.69
−0.33 to 0.38
−0.22 to 1.09

0.98
0.49
0.44
0.32
0.52
0.23
0.43
0.20
0.38
0.08

0.73
0.64
0.69
0.51
0.57
0.37
0.54
0.37
0.40
0.24

(dS m−1)
2014–15a2015–16b
2014–15b
2015–16b
2014–15b
2015–16b
2014–15b
2015–16b
2014–15b
2015–16b

A
B
C
D
E

a
b

0.60–0.86
0.71–1.09
0.63–1.05
0.82–1.27
0.59–0.85
0.64–1.20
0.64–1.17
1.13–1.49
0.83–1.26
0.50–1.60

Calibration period.
Validation period.

wider range (0.82–1.27 dS/m) in the following season (2015–16). Similarly, MAE varied from 0.66-1.02 dS/m during 2015–16. Apart fromthese deviations, the model eﬃciency values (0.44 and 0.32) illustrate areasonable prediction while R2 values (0.69 and 0.51) indicate reduced
congruency in the comparison. Overall, similar deviations in ECe wererecorded in treatment C (Table 4). However, deviations between measured and simulated ECe in treatments D and E were comparatively
larger as compared to other treatments (A, B, and C) but the variability
remained within a reported range (Ramos et al., 2011, 2012).
Deviations between ﬁeld-measured and simulated variables can be
attributed to diﬀerent causes, including errors related to ﬁeld measurements, model inputs, and structural errors (Ramos et al., 2012).
Ramos et al. (2012) have discussed extensively various causes that may
result in deviations between ﬁeld measurements and model simulations. In addition to various causes of errors mentioned in Ramos et al.
(2012), an additional factor causing deviations in our study especiallyfor ECe values was the destructive sampling. While the model predicted
the salinity dynamics at the same spot, ﬁeld measurements were madeat diﬀerent locations each time. Moreover, the soils at the experimentalsite have immense spatial heterogeneity (nine diﬀerent textures at
diﬀerent depths; Table 2), which had a strong impact on the measured

60–140 cm soil depth at the pit hardening stage (Fig. 5e to h). The
RMSE, MAE, and ME values between average measured and simulated
ECe values at diﬀerent soil depths and growth stages varied from 0.71 to
1.09, 0.57–0.90, and −0.3–0.76 dS/m (Table 4). However, the modeleﬃciency (0.49) and R2 (0.64) values were lower compared to thecorresponding valued observed during calibration (2014–15), which is
a common occurrence.

3.4. Soil salinity dynamics in other treatments
The calibrated and validated model was then used to simulate
salinity dynamics in the soil in other treatments (Fig. 6). Simulated
salinity distributions in the soil in diﬀerent treatments (B to E) werecompared with corresponding measured ECe replicates (B1, B2, and B3for treatment B; C1, C2, C3, and C4 for treatment C; and D1, D2, D3,
and D4 for treatment D) during both seasons. Simulated ECe values
remained within the measured range of diﬀerent replicates during bothseasons. However, small under- or over-estimations also occurred
during some time in diﬀerent treatments. Among the error estimates intreatment B, RMSE varied from 0.63-1.05 dS/m and MAE ranged from
0.53-0.86 dS/m during 2014–15 (Table 4). However, RMSE showed aFig. 6. Comparison of measured and HYDRUS-2D simulated (S) electrical conductivities in the soil at saturation (ECe) in diﬀerent treatments (B, C, D, E) at indicated times during the
2014–15 (top row) and 2015–16 (bottom row) almond seasons.

77Agricultural Water Management 201 (2018) 70–82

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ECe values as shown in diﬀerent replicates in treatments A, B, C, and D.
In treatment E, ECe measurements were made only at one location andhence, there was a wider variation among measured and simulated ECevalues. The assumption of a similar conversion factor for ECe and EC1:5
for both years have also contributed to the extent of variability in estimated ECe values.
The model input parameters, especially soil hydraulic parametersand solute dispersivities, may also introduce huge errors if not properly
measured at the ﬁeld site. In our study, these parameters were takenfrom other local studies or from the literature, which may have introduced errors in simulated values even though the model was properly calibrated and validated for the ﬁeld site. Calibration and validation of the model only for one treatment could also result in errors insimulated values. In spite of these deviations between measured andsimulated ECe values, the error estimates are lower than reported inother studies (Ramos et al., 2011, 2012).

Table 5
Seasonal water balance components for almond in diﬀerent treatments (A, B, C, D, and E)
during 2014–15 and 2015–16. Seasonal evapotranspiration, drainage and depletion/
storage data was evolved from modelling simulations for diﬀerent treatments. Water
balance error represents the extent of error between source (irrigation, rainfall), sink
(Evapotranspiration, drainage), and soil depletion/storage data. Depletion is considered
as source term when ﬁnal wetness in the soil was lower than the initial wetness and sink(storage) term if vice versa.
Treatments

AB

3.5. Seasonal water balance
The simulated water balance revealed that evapotranspiration losses(ETC) by almonds under recycled water irrigations (treatment A) during
2014–15 amounted to 849.9 mm, which was 68.2% of the total applied
water (rainfall + irrigation). Similarly, during 2015–16, although theseasonal ETC (868.2 mm) was slightly higher, the drainage was reduced
by 1% compared to the previous season. The error in the water balance
simulations in A was 0.7 and 0.5% during 2014–15 and 2015–16 seasons, respectively, which is not uncommon in such situations. Deepdrainage in treatment A represented 31.1% of the total applied water,which is an important amount of water that helps in transporting salts
out of the root zone.
Monthly drainage ﬂuxes below the 2-m soil depth under the almondtree irrigated with saline water (treatment A) during calibration(2014–15) and validation (2015–16) is shown in Fig. 7. Large deep
drainage (80 mm) during July 2014 is the result of a low water requirement by the tree due to its dormancy and the early bud sprout
stage coupled with high late winter rains. Deep drainage in othermonths during the ﬁrst season (2014–15) varied between
13.7–39.2 mm. Deep drainage during September and October 2014 wasvery low because of a high water requirement by the almond tree
during this period of profuse vegetative growth. Similarly, during
2015–16 more deep drainage (57.7 mm) occurred during the earlyseason (August 2015) as compared to mid- and late-period. However,enormous deep drainage occurred during May and June months of thesecond season as compared to 2014–15. This was ascribed to higher
rain during May-June 2016, which was almost double the amount

C

D

E

Components

Irrigation
RainfallSoil depletion (+)/storage (−)
Evapotranspiration (ETC)
Drainage
Water balance error*
Irrigation
RainfallSoil depletion (+)/storage (−)
Evapotranspiration (ETC)
Drainage
Water balance error*
Irrigation
RainfallSoil depletion (+)/storage (−)
Evapotranspiration (ETC)
Drainage
Water balance error*
Irrigation
RainfallSoil depletion (+)/storage (−)
Evapotranspiration (ETC)
Drainage
Water balance error*
Irrigation
RainfallSoil depletion (+)/storage (−)
Evapotranspiration (ETC)
Drainage
Water balance error*

2014–15

2015–16

(mm)

(%)#

(mm)

(%)#

889.4
326.9
30.4
849.9
387.8
9
889.4
326.9
15.3
878.6
359.3
−6.3
889.4
326.9
−18.29
880.9
303.8
13.31
889.4
326.9
−2.1
859.8
361.8
−7.4
889.4
326.9
−28.1
941.1
265.6
−18.5

71.3
26.2
2.4
68.2
31.1
0.7
72.2
26.5
1.2
71.3
29.2
−0.5
74.2
27.3
−1.5
73.5
25.4
1.1
73.2
26.9
−0.2
70.8
29.8
−0.6
74.9
27.5
−2.4
79.2
22.4
−1.6

960.4
372.7
−81.8
868.2
376.9
6.2
960.4
372.7
−78.2
892.8
352.1
10
960.4
372.7
−62.1
923.5
321.1
26.4
960.4
372.7
−80.9
877.2
367.7
7.3
960.4
372.7
−72.6
954.7
289.2
16.6

76.8
29.8
−6.5
69.4
30.1
0.5
76.5
29.7
−6.2
71.1
28.1
0.8
75.6
29.3
−4.9
72.7
25.3
2.1
76.7
29.8
−6.5
70.1
29.4
0.6
76.2
29.6
−5.8
75.7
22.9
1.3

* is(∑ source − ∑ sin k ) .
#represents% of source (irrigation + rainfall ± depletion/storage).

received during the same period of the 2014–15 season. This kind of
rainfall during the dormant period could be helpful in driving salts outof the root zone. This suggests that the presence of higher amounts ofsalts in irrigation water not only reduced water uptake by almond trees
but it may have also encouraged greater hydraulic ﬂow through the soil
proﬁle. A similar monthly drainage pattern was observed in othertreatment as well.
Simulated seasonal ETC of almond in treatment B was increased by
3.4 and 2.8%, during 2014–15 and 2015–16, respectively, as comparedto the corresponding values in treatment A (Table 5). This suggests thatswitching to irrigation with non-saline water during the bud burst andpit hardening stages exposed the almond tree to less osmotic pressure in
the root zone and increased root water uptake, thereby increasing thewater consumption. As a consequence, this favorable impact also reduced the drainage component by 6–7% compared to treatment A(Table 5). In treatment C, where less saline irrigation was used between
the pit hardening and harvest stages, seasonal ETC of almond increased
as compared to the control treatment (treatment A). The extent of anincrease in ETC was also higher than in treatment B, especially during
2015–16. Seasonal ETC increased by 3.6 and 6.4% during the 2014–15
and 2015–16 seasons, respectively, as compared to treatment A. On theother hand, seasonal drainage in treatment C was reduced by 21.7 and14.8% during 2014–15 and 2015–16, respectively, as compared totreatment A, irrigated with saline water throughout the entire season.
This suggests that the impact of switching to less saline irrigation onwater uptake by almond was slightly more eﬀective during the midstage of almond growth (treatment C) compared to the initial period

Fig. 7. Simulated monthly drainage (below the 2-m depth) under almond irrigated withsaline water (treatment A) during (a) 2014–15 and (b) 2015–16.

78

Agricultural Water Management 201 (2018) 70–82

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all treatments. A notable diﬀerence between the two seasons was thelower initial average root zone salinity during the 2015–16 season in all
the treatments. The root zone ECe again increased during late September till mid-October in both seasons, which corresponds with atremendous vegetative growth and an increased water requirement by
the atmosphere, which may have reduced the leaching fraction andresulted in salts built up in the root zone. Another reason for an increase
in ECe in the soil during October was the high salinity of irrigationwater as compared to the rest of the season. However, the average rootzone ECe in the soil during the mid-season ﬂuctuated between 4 and5 dS/m. On the other hand, the crop harvest and consequent leaf drop
coupled with a reduced atmospheric demand and early winter rains had
a tremendous impact on the sharp decline in the root zone ECe during
both seasons. Post-harvest irrigation also contributed to a gradual reduction in the ECe due to an increased tendency of generating high deep
drainage. Hence, the root zone salinity was reduced to a seasonal
minimum level of 3.9 dS/m at the end of 2014–15. However, theminimum root zone ECe of 3.7 dS/m in treatment A during the 2015–16
season was observed during September 2015 (Fig. 8), which was lower
than the ﬁnal ECe (4.07 dS/m) in the soil.
Simulated daily average root zone salinity of the saturation extract(ECe) in treatment B showed a similar seasonal pattern as observed intreatment A (Fig. 8). Nevertheless, the average ECe was lower than intreatment A, especially between the bud burst to pit hardening stages
when less saline water was applied in treatment B. Later on, although
the salinity gradually increased, it remained below the corresponding
ECe in treatment A. In the following season (2015–16), a similar seasonal pattern was observed in daily ECe in the root zone as observed
during the previous year (2014–15).
In treatment C, the average root zone ECe was slightly higher thanthe corresponding value in treatment B during the initial stage of2014–15. However, the ECe later on reduced in response to the application of less saline water between the pit hardening to harvest stages.
Thereafter, the ECe in treatment C remained lower than in treatment B
until the end of the season. The average ECe during 2015–16 also remained lower in treatment C as compared to treatment B since the same
irrigation water was applied from the beginning to the end of the season
except for a few days during the bud burst to pit hardening stages whenless saline irrigation water was applied in treatment B. Hence, lower
root zone salinity in treatment C than in treatment B supported higher
seasonal water uptake reported in the previous section.
On the other hand, in treatment D during 2015–16, the daily salinity
remained higher than in treatments B and C throughout the season,
except in May-June when non-saline water was applied during the postharvest stage. During the initial stage, the salinity was slightly lower
than in treatment A. Thereafter, the average root zone ECe in treatment
D continued to remain close to that in treatment A during both seasons
as irrigation was performed with recycled water with higher salts
content. Hence, post-harvest irrigation with less saline water may
prepare the soil for better root development in the ensuing season due
to less osmotic stress. However, the impact is relatively short-lived as
irrigation with saline water recommences at the beginning of the newseason which increases the salt pressure again to the same level.
Therefore, only a limited impact on root water uptake was observed
when less saline water was applied during the harvest to leaf drop
stages (D).
The average daily root zone ECe in treatment E was much lower thanin the other treatments during both seasons (2014–15 and 2015–16) asa result of the application of less saline water during the entire almondseason (Fig. 8). However, the average ECe remained higher than thealmond threshold salinity (1.5 dS/m). This suggests that less saline
water (ECiw < 0.8 dS/m) adds enough salts into the soil that continue
to build up during the season if they are not leached out of the root zone
by applying more irrigation water. The comparison of season patternsof ECe during the 2014–15 and 2015–16 seasons showed that the gap
between treatment E and other treatments is widening throughout the(treatment B). Similarly, seasonal ETC of almond in treatment D was
almost at par with that in treatment A, because it only increased by 1.2
and 1.0% during the 2014–15 and 2015–16 seasons, respectively,
compared to treatment A (Table 5). Consequently, drainage losses also
decreased by 6.7 and 2.4% during 2014–15 and 2015–16, respectively,
as compared to the corresponding values in treatment A (recycled water
irrigation). This shows that switching to less saline irrigation between
the harvest and leaf fall stages had the least impact on water uptake byalmond. During this period, the water demand is progressively reduced
as the tree is dropping its leaves to enter into the dormant phase. Additionally, mechanical harvesting also inﬂuences water uptake as tree
shaking causes considerable canopy defoliation and disturbs the naturalsoil-plant coherence.
Irrigation with less saline water during the entire season (treatmentE) produced the highest amount of seasonal ETC by almond (Table 5).
Seasonal ETC increased by 10.7 and 10% during the 2014–15 and2015–16 seasons, respectively, as compared to irrigation with saline
water (treatment A). This indicates early commencement of enhanced
plant water use in non-saline treatment (Pitt et al., 2015). Moreover,
the reduction in the osmotic stress due to fewer salts being added to thesoil by irrigation (treatment E) played a key role in enhanced water
uptake by the almond tree. Consequently, the lower osmotic pressuresat the soil-root interface presumably resulted in a lower ionic stresswithin the leaf tissues, slowing the senescence of older leaves (Munns
and Tester, 2008) and enhancing root water uptake. It is also wellknown that the almond tree is very sensitive to the soil salinity and aunit increase in ECe in the root zone above threshold (1.5 dS/m) may
reduce water uptake and hence almond yield by 19% (Maas, 1990). On
the other hand, a decrease in drainage component amounted to 31.5
and 23.3% during 2014–15 and 2015–16, respectively, as compared totreatment A. This is quite signiﬁcant and may have a great impact onsalts leaching from the soil. Overall, the modeling error in the waterbalance ranged from −1.6 to 2.1% across all treatments, which indicates good simulations of the water balance under complex conditions.
3.6. Seasonal salinity dynamics
There is an increased chance of salts accumulation in the root zone
in treatment A compared to the other treatments as the almonds were
irrigated with recycled water with an average ECiw of 1.94 dS/m (varied
from 1.01- 2.54 dS/m) and 1.81 dS/m (ranges from 1.2- 2.18 dS/m)during the 2014–15 and 2015–16 seasons, respectively. During the ﬁrst
season, initial average root zone salinity (ECe) drops steadily (Fig. 8)
because of signiﬁcant leaching as a result of high deep drainage during
July 2014 (Fig. 8) generated by winter rain. A similar drop in the
average root zone salinity also occurred during the 2015–16 season in

Fig. 8. Simulated average daily root zone ECe in diﬀerent treatments under almondduring the (a) 2014–15 and (b) 2015–16 seasons.

79Agricultural Water Management 201 (2018) 70–82

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Table 6
Yield (kernel in shell), simulated evapotranspiration (ETC), and water use eﬃciency(WUE) by an almond tree in diﬀerent treatments during the 2014–15 and 2015–16 seasons.
Treatments

Yield (kg/tree)

ETC (m3/tree)

WUEETc (kg/m3)

2014–15ABCDE
LSD* (A–D)
2015–16

8.57
8.59
8.92
8.55
9.25
NS

35.06
36.24
36.34
35.47
38.82

0.24
0.24
0.25
0.24
0.24

8.37
8.44
8.43
8.57
8.56
NS

35.81
36.83
38.09
36.18
41.44

0.23
0.23
0.22
0.24
0.21

LSD* (A–D)

Fig. 9. Simulated daily root zone salinity (ECe) in diﬀerent treatments (A, B, C, D, and E)
averaged for diﬀerent growth stages (BB - Bud Burst, pH - Pit Hardening, H - Harvest, andLD - Leaf Drop) of almond during 2014–15 and 2015–16.

* Least Square Diﬀerence (LSD) for treatments A to D only. Treatment E had a single
replicate, hence not included in the statistical analysis.

season over the years. Hence, these simulations need to be conducted
for a longer time to fully understand the salinity dynamics over multiple
years. This could have drastic implications on the judicious use of saline
water for almond cultivation.
The model-simulated, daily root zone salinity averaged during different stages of almond growth in all treatments is shown in Fig. 9. It
can be seen that the average salinity in treatment A remained higher
than in other treatments as a result of the application of saline water
irrigation during the entire almond growing season. The average ECe
was 3.8, 4.6, and 4.9 dS/m between the bud burst (BB)-pit hardening(pH), pH -harvest (H), and H-leaf drop (LD) growth stages, respectively,
during 2014–15, while the corresponding ECe during 2015–16 slightly
increased to 4.0, 5.0 and 5.5 dS/m, respectively, during the same stagesof almond growth. In contrast, the average ECe was always lower intreatment E during all growth stages as compared to the other treatments since irrigation with non-saline water was applied during theentire season. Hence, the average ECe of the root zone during the BBpH, pH -H, and H-LD stages was 2.8, 2.9, and 3.1 dS/m, respectively,
during 2014–15. While the salinity in treatment E during the following
season (2015–16) decreased slightly during the BB-pH stage, it increased to 3.5 dS/m during the H-LD stage (Fig. 9). Overall, the averagesalinity in treatment E between the BB-pH, pH -H, and H-LD stages
during 2014–15 decreased by 27, 37, and 37%, respectively, as compared to treatment A. However, the corresponding reduction during
2015–16 was 37, 41, and 36%, respectively. The seasonal average rootzone ECe over the two seasons in treatment E was reduced by 35% ascompared to the corresponding ECe in treatment A.
In the other treatments (B, C, and D), the average ECe between
diﬀerent growth stages remained between values observed in treatments A and E (Fig. 9). However, the impact of switching to irrigation
with less saline water during diﬀerent growth stages in diﬀerent
treatments is worth noticing. For example, the average ECe in treatment
B during 2014–15 was lower than in treatments C and D between the
BB-pH stages, which corresponds to the time when irrigation with lesssaline water was applied. However, this trend was not maintained in
the following year (2015–16) where ECe in treatment C was slightly
lower than in treatment B. It must be noted that the average ECe intreatment C was lower than in treatments B and D at all other growthstages. However, at the end of the seasons the average ECe in treatment
C was almost in the middle of values obtained in treatments A and E.
Therefore, the average ECe between the BB-pH, pH -H, and H-LD stages
in treatment C was reduced by 15, 21, and 20%, respectively, as compared to those in treatment A. The seasonal average of the root zone ECeover the two seasons was reduced by 13, 18, and 6.4% in treatments B,
C, and D, respectively, as compared to treatment A. These results

suggest that switching to irrigation with less saline water between the
pH -H stages could produce a better outcome in terms of lower salinity
in the root zone accompanied by increased root water uptake in thistreatment. However, there could be diﬀerent seasonal ECe developments if similar amounts of less saline water were applied in all treatments. It must be noted that the volume of good quality water appliedin treatment C was higher than volumes applied in treatments B and D.
3.7. Yield and water use eﬃciency of almonds
Yield attributes did not elicit a signiﬁcant yield response to thetiming of the reduced salt pressure (Table 6). Previous almond studies
have suggested that yield components can be relatively insensitive to a
stress in the ﬁrst years of exposure and that residual eﬀects tend topresent in the subsequent years (Goldhamer and Smith, 1995). Similar
investigations assessing the timing of salt stress on Colombard grapes
also found a negligible yield response early in the life of the trial butthat diﬀerences increased with time (Stevens et al., 1999). If yields
within the current trial are to respond to salinity treatments, it is likelythat changes will occur only once treatment carryover eﬀects frompreceding years are included. However, Sanden et al. (2014) concluded
that almonds are capable of much higher yields under even more salineconditions than old published standards. The almond water use eﬃciency
inrelationtocrop
evapotranspiration(WUEETc;
0.24–0.25 kg m−3) also did not show any treatment eﬀect because there
were non-signiﬁcant diﬀerences in the yield and seasonal ETC losses
were also varied in a narrow range across all treatments. However, similar WUE values (0.20–0.33 kg m−3) have been reported in several
studies (Romero et al., 2004; Goldhamer et al., 2006; Girona et al.,2005; Egea et al., 2010; Goldhamer and Fereres, 2017). On the other
hand, the WUE of almonds in the Riverland region of Australia (Phogat
et al., 2013) was almost double (0.43 kg m−3) the value estimated inthis study (0.24–0.25 kg m−3). Apart from high soil salinity, sub-soilconstraints, saline water irrigation at the experimental site, growing
conditions (solar radiation, warm temperatures) coupled with pests anddiseases infestation, the density of plantings, the frequency of irrigationand fertigation played a key role in the wide disparity in the WUEETc at
two locations. Although almonds are among the species capable ofmaintaining high gains in water productivity under increasing soil
water deﬁcit (Prichard et al., 1992; Goldhamer and Viveros, 2000;
Girona et al., 2005; Phogat et al., 2013), a similar resilience under theosmotic stress has not yet been established. This behavior is due in partto the adaptation of trees to conditions with strong water stress bydeveloping more compact crowns with a higher fruit density than thoseof fully irrigated trees (Egea et al., 2010). On the other hand, the80Agricultural Water Management 201 (2018) 70–82

V. Phogat et al.

research. The leaves granted to Dr. Vinod Phogat by CCS Haryana
Agricultural University for availing Post-Doctoral studies are highly
acknowledged.

evaluation of the impact of timing of salinity stress (treatment eﬀect) is
more complex to ascertain as salts are highly dynamic in the soil
system.
The average dry weight of a single kernel (1.08 g) and WUEETc of0.25 kg m−3 suggest that 4 Liters of water are required to produce asingle kernel, which is comparable to “one US gallon per nut” reported
by Goldhamer and Fereres (2017). In fact, four liters of water accountonly for the pure form of water consumed by the tree to produce asingle nut, neglecting the issues associated with all soluble salts remaining in the soil system. These salts may stockpile in the soil overyears and can hamper the sustainable almond production if they are notremoved from the root zone. On the other hand, high amounts of salts
leached below the root zone can adversely aﬀect groundwater resources
and can inﬂict signiﬁcant environmental damage. Hence, there exists a
trade-oﬀ between irrigation with saline water and pollution hazards ofgroundwater resources, which requires special attention to sustainable
water management practices. Such a production system has to deal witha “salinity paradox,” which resembles the so-called “eﬃciency
paradox” (Scott et al., 2014), a phenomenon that essentially overturns
whatever is gained through improved water use eﬃciency (Ward and
Pulido-Velazquez, 2008).

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4. Conclusions
A numerical model (HYDRUS-2D) was successfully calibrated(2014–15) and validated (2015–16) using seasonal water content distributions and salinity dynamics in the soil under almond irrigated with
diﬀerent quality waters. Simulated results revealed that irrigation withsaline water had a notable impact on the root zone salinity dynamics
and almond water uptake. Seasonal evapotranspiration (ETC) of almonddecreased by 10% and the overall seasonal average root zone ECe increased by 54% for trees receiving saline irrigation (two seasons
average ECiw of 1.85 dS/m) across the entire irrigation season as compared to trees receiving non-saline irrigation (ECiw < 0.8 dS/m). Other
treatments tested the response of trees to substituting saline water with
non-saline water during three phenologically diﬀerent growth stages:
before pit-hardening, between pit-hardening and harvest, and following
harvest. Despite the inter-treatment diﬀerences in timing and depth ofsaline and non-saline irrigation, the seasonal patterns of soil salinity(ECe) were similar across all treatments. However, irrigation with nonsaline water between diﬀerent growth stages reduced the buildup ofsalts in the soil proﬁle. Despite this reduction, the average root zone ECeremained above the level of the almond salinity tolerance(ECe = 1.5 dS/m) during the entire season even for soils that received
less saline water during the entire irrigation season. The model suggested that winter rainfall was not very eﬀective in reducing potentiallyhigh soil salinity levels below those that would not adversely inﬂuence
the normal growth of the almond. Similarly, replacing irrigation withsaline recycled water with non-saline water was ineﬀective in containing the root zone ECe below the crop threshold. However, thesubstitution of less saline water during diﬀerent growth stages resulted
in insigniﬁcant diﬀerences in the yield. This suggests that the current
salinity threshold (1.5 dS/m) for almond may need a comprehensive
examination in light of this and other recent studies (e.g., Sanden et al.,2014). The results of this study also demonstrated that deep drainage
below the root zone (2 m) is irregular, and its magnitude was highly
varied during the almond season. This warrants further investigation, as
does the development of appropriate reclamation strategies. Restricteddrainage conditions, coupled with salts resurgence due to capillaryaction, may complicate adequate salt removal from the soil system.
Acknowledgements
The authors wish to thank the Australian Water Recycling Centre of
Excellence, the South Australian River Murray Sustainability Program(SARMS 3IP) and the Goyder Institute for Water for funding this81Agricultural Water Management 201 (2018) 70–82

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