Irrig Sci (2013) 31:1009–1024
DOI 10.1007/s00271-012-0383-8

ORIGINAL PAPER

Modeling the effects of saline water use in wheat-cultivated lands
using the UNSATCHEM model
Fatemeh Rasouli • Ali Kiani Pouya •Jiřı́ Šimůnek

Received: 28 April 2012 / Accepted: 25 July 2012 / Published online: 23 August 2012
Ó Springer-Verlag 2012

Abstract Waters of poor quality are often used to irrigate
crops in arid and semiarid regions, including the Fars
Province of southwest Iran. The UNSATCHEM model was
first calibrated and validated using field data that werecollected to evaluate the use of saline water for the wheat
crop. The calibrated and validated model was thenemployed to study different aspects of the salinization
process and the impact of rainfall. The effects of irrigationwater quality on the salinization process were evaluatedusing model simulations, in which irrigation waters ofdifferent salinity were used. The salinization process under
different practices of conjunctive water use was alsostudied using simulations. Different practices were evaluated and ranked on the basis of temporal changes in rootzone salinity, which were compared with respect to thesensitivity of wheat to salinity. This ranking was thenverified using published field studies evaluating wheatyield data for different practices of conjunctive water use.
Next, the effects of the water application rate on the soil
salt balance were studied using the UNSATCHEM simulations. The salt balance was affected by the quantity ofCommunicated by J. Ayars.
F. Rasouli (&)  A. Kiani Pouya
Department of Salinity Research, Fars Research Center
for Agriculture and Natural Resources, Bakhshande Blvd.,
Zarghan, Fars, Irane-mail: rasouli@farsagres.ir
A. Kiani Pouyae-mail: ali.kiani@farsagres.ir
J. Šimůnek
Department of Environmental Sciences,
University of California Riverside, Riverside, CA 92521, USAe-mail: Jiri.Simunek@ucr.edu

applied irrigation water and precipitation/dissolution reactions. The results suggested that the less irrigation water isused, the more salts (calcite and gypsum) precipitate fromthe soil solution. Finally, the model was used to evaluate
how the electrical conductivity of irrigation water affects
the wheat production while taking into account annual
rainfall and its distribution throughout the year. The maximum salinity of the irrigation water supply, which can besafely used in the long term (33 years) without impairing
the wheat production, was determined to be 6 dS m-1.
Rainfall distribution also plays a major role in determining
seasonal soil salinity of the root zone. Winter-concentrated
rainfall is more effective in reducing salinity than a similar
amount of rainfall distributed throughout autumn, winter,and spring seasons.

Introduction
Waters of poor quality are often used to irrigate crops inarid and semiarid regions, including the Fars Province insouthwest Iran. It is well known that due to high concentrations of soluble salts, the use of such waters may result
not only in the decrease of crop yield, but also in the
reduction in soil water infiltration capacity (e.g., McNeal
1974; Shainberg and Levy 1992; Qadir et al. 2000). A
variety of strategies have been adopted to overcome
problems associated with soil salinity, including improving
the productivity of saline soils mainly through leaching ofexcess soluble salts, blending and reusing of saline drainage waters, selecting of tolerant varieties of suitable crops,
and using appropriate agronomic practices (Rhoades et al.
1992). Adoption of suitable salinity control measuresrequires determination of salt and water movement throughthe soil profile and prediction of crop response to soil water

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and soil salinity, subject to various climatic, edaphic, andagronomic factors (Ferrer and Stockle 1999).
Wheat is the main winter cereal crop planted over an
area of almost 600 thousand hectares in the Fars Province.
About 44 % of the total cultivated area in the Fars Province
is cultivated with this crop. Wheat is ranked as a moderately salt-tolerant crop (Maas and Grattan 1999) that can besafely irrigated with moderately saline water, although anincrease in water salinity may cause a reduction in thewheat grain yield. However, the wheat yield is also
affected by several other factors, including soil, crop, andenvironmental conditions, which interact with soil salinity
to cause different yield responses.
Several methods are available for a thorough assessment of what impact water quality and existing farm
irrigation management may have on soil salinity (e.g.,
Kelleners and Chaudhry 1998; Sharma and Rao 1998). By
far, the most widely used method for salinity assessment
is detailed soil sampling and subsequent laboratory measurement of salinity (Cheraghi et al. 2007). However, thetraditional soil salinity assessment method requires considerable time, expense, and effort and cannot fully cover
the spatial and temporal patterns of variability at the field
scale. There has been considerable success in using
ground-based geophysical measurements of the apparent
soil electrical conductivity (ECa) to assess salinity acrossindividual fields, including the electrical resistivity (ER)or electromagnetic induction (EM) surveys (e.g., Corwin
and Lesch 2003). However, even these methods are currently too time consuming to be applied cost-effectively
at regional scales (Lobell et al. 2010). Therefore, there isa need for the development of more practical (i.e., quicker
and cheaper) methods and tools to determine and evaluatesoil salinity in order to improve decision-making
processes.
Mathematical models that consider and integrate various
climate, crop, and soil factors have been suggested as
useful tools for assessing the best management practices
for saline conditions (e.g., Ramos et al. 2011, 2012).
Steady-state and transient water flow and solute transportmodels are the two main classes of currently available
models for the assessment of salinity management. Steadystate models, which assume steady-state water flow
through the soil profile and constant soil solution concentrations at any point of the root zone at all times, are notsuitable for irrigated lands under saline conditions (Leteyand Feng 2007; Corwin et al. 2007; Letey et al. 2011). A
large number of transient flow and transport models,
including UNSATCHEM (Šimůnek et al. 1996), SWAP
(van Dam et al. 1997), HYDRUS-1D (Šimůnek et al.
2008), and HYSWASOR (Dirksen et al. 1993), among
many others, have been developed to simulate integrated
effects of climate, soil, and plants.

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Irrig Sci (2013) 31:1009–1024

UNSATCHEM is a transient flow and transport modelthat considers the effects of many variables and factors, suchas the initial soil salinity, time and amount of rainfall, water
quality, and blending of saline and fresh waters (Bradford
and Letey 1992; Kaledhonkar and Keshari 2006; Gonçalves
et al. 2006; Ramos et al. 2011, 2012; Kaledhonkar et al.
2012). The UNSATCHEM model can additionally accountfor chemical reactions, such as aqueous complexation, cationexchange, and salts precipitation and/or dissolution, which
are important for assessing the effects of the quality of irrigation water in arid and semiarid regions.
The UNSATCHEM model is a suitable tool for studies
evaluating salt movement in soils irrigated with brackish
waters because it can accurately predict not only soil watercontents and concentrations of soluble ions, but also integral variables, such as electrical conductivity, the sodium
adsorption ratio, and sodium exchange percentage (Gonçalves et al. 2006; Ramos et al. 2011). For example, Suarez
et al. (2006) modeled the effects of rainfall on the permeability of a soil with a high sodium content. Theirsimulation results demonstrated that for regions where
rainfall is significant, the Na hazard is considerably greaterthan what would be suggested by a simple application ofcommonly used EC–SAR hazard relationships.
Studies assessing soil salinity using numerical models
are very limited in Iran. For example, Vaziri (1995) evaluated four desalinization models in a field study conductedin two regions of Rudasht and Kangavar. Four different
types of models were compared: (1) a serial reservoir
model, (2) a theoretical plat-thickness model, (3) anumerical convection–dispersion model, and (4) a numerical model that considered both continuous and intermittent
leaching processes. Results showed that the highest accuracy was achieved in both regions and for both conditions
using the convection–dispersion model. Droogers et al.
(2000) used the SWAP model to evaluate field experimental data from the Isfahan Province. They concluded
that the SWAP model can successfully predict water andsalt balances in the root zone for different irrigation management scenarios with steady-state conditions.
Considering the importance of using numerical models
for a better understanding of salinity processes and forevaluating various salinity management options, the UNSATCHEM model was, in the present study, first calibrated
and validated against the experimental data involving theuse of saline water for irrigating wheat (Wahedi 1995).
Next, further simulations were carried out using the UNSATCHEM model to obtain better understanding about
strategies involving the conjunctive use of waters of different qualities, to evaluate the effects of different irrigation water qualities, rainfall amounts, and saline waters andto develop the guidelines for the management of salinity inwheat-cultivated lands.

Irrig Sci (2013) 31:1009–1024

1011

Materials and methods
Model description
UNSATCHEM is a numerical model for simulating
movement of water, heat, carbon dioxide, and solute in
one-dimensional, variably saturated media (Šimůnek et al.
1996; Šimůnek and Suarez 1994a, b). The model numerically solves the Richards equation for water flow andconvection–dispersion type equations for heat, carbon
dioxide, and solute transport. The flow equation incorporates a sink term to account for water uptake by plant roots:


oh
o
oh
¼
Kh þ Kh  S
ð1Þ
ot oz
oz
where h (L3 L-3) is the volumetric water content, t (T) is time,
Kh (L T-1) is the unsaturated hydraulic conductivity, h (L) is
the soil water pressure head, z (L) is the vertical spatial
coordinate, and S (T-1) defines the root water uptake term.
Root water uptake is simulated as a function of depth andtime, and is a function of the pressure and osmotic heads toaccount for water and salinity stresses, respectively.
Transport of seven major ions, namely Ca2?, Mg2?,
Na?, K?, HCO3-, SO42-, and Cl-, is considered by UNSATCHEM. Solute transport of each aqueous species issimulated using the advection–dispersion equation:


ohck
ock
o
ock
Dh
þ qb
¼
 qck
k ¼ 1; . . .; 7
ð2Þ
oz
ot
ot
oz
where ck (M L-3) is the total dissolved concentration of
aqueous species k, ck (M M-1) is the concentration associated with the solid phase (i.e., sorbed and precipitated), qb(M L-3) is the soil bulk density, D (L2 T-1) is the dispersion
coefficient, and q (L T-1) is the Darcy water flux. The
transport equation is solved in UNSATCHEM using aGalerkin finite-element method (van Genuchten 1987).
The UNSATCHEM model accounts for equilibrium
chemical reactions between major ions, such as aqueous
complexation, cation exchange, and precipitation–dissolution (Šimůnek and Suarez 1994a, b). Activity coefficients are
determined using either modified Debye–Huckel or Pitzer
equations to calculate single-ion activities. All cations in thesolution are assumed to be in equilibrium with the sorbed
cations, which balance the negatively charged sites of the soil.
Cation exchange is a dominant chemical process for the major
cations in the solution in the unsaturated zone (Suarez 2001).
The UNSATCHEM model uses a Gapon-type expression todescribe exchange equilibrium between the sorbed and dissolved cations (White and Zelazny 1986):
Kij ¼

aþ 1=acbþ
i ðcj Þbþ 1=bcaþj ðcj Þð3Þ

where b and a are the valences of species i and j,
respectively, and Kij is the Gapon selectivity coefficient(dimensionless). Parentheses indicate ion activity(dimensionless), and c in (3) is the concentration ofadsorbed cations (mmolc kg-1).
The model considers a set of solid phases, including
calcite (CaCO3), gypsum (CaSO42H2O), and others. The
model allows for precipitation of a mineral whenever the
product of molar concentrations of their constituent ionsraised to the power of their respective stoichiometriccoefficients exceeds the Ksp value of the mineral (Šimůnek
and Suarez 1994a, b).
Root water uptake is simulated as a function of depth andtime, and is a function of the pressure and osmotic heads toaccount for water and salinity stresses, respectively:
Sðh; pÞ ¼ a1 ðhÞa2 ðpÞSp

ð4Þ
3

-3

-1

where Sp is the potential water uptake rate (L L T ) in
the root zone, a1(h) and a2(p) are the pressure and osmotic
stress response functions (dimensionless), respectively, andp is the osmotic head (L).
The S-shaped stress response functions proposed by van
Genuchten (1987) were used to evaluate the reduction due topressure and salinity stresses for the rate of water extraction.
Reduction functions a1(h) and a2(p) for pressure and salinity
stresses are described by the following pair of functions:a1 ðhÞ ¼1þ
a2 ðpÞ ¼
1þ

1
 p

ð5Þ

1
 p

ð6Þ

h
h50

p
p50

where h50 and p50 are the pressure and osmotic heads at which
the water extraction rates are reduced by 50 % during conditions of negligible osmotic and pressure stress, respectively, and p (dimensionless) is an empirical constant.
Field experiments
The data for calibration and validation of the model wereobtained from a research project reported by Wahedi(1995). The field experiment was conducted in Sarvestan,
Fars Province (29.12°N, 53.12°E), to study the effects ofthree irrigation water salinities (2.5, 6.5, and 11.5 dS m-1)
and three leaching requirements corresponding to 10, 25,
and 50 % reductions in the grain yield of winter wheat(namely LR10 %, LR25 %, and LR50 %). The experiment
was conducted in a randomized block design with four
replications during three consecutive years (1991–1994).
Seeds were planted on November 28 and grain was harvested on June 25, 27, and 21 in the first, second, and thirdyear of the study, respectively.

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Irrigation was provided using the basin irrigation system. Two irrigations (with a total of 210 mm) were applied
before the vegetative cover reached 50 % during the winterseason in the first 2 years of the study. In the third year of
the experiment, an additional irrigation was provided inthis growth stage (with a total of 310 mm). Six additional
irrigations were carried out afterward, with a total of
480 mm plus the amount of leaching required to obtain 10,25, and 50 % yield reductions as calculated using theleaching requirement equation:
LR ¼ 

ECiw

5ECe  ECiw

ð7Þ

where LR is the leaching requirement, ECiw (dS m-1) is theelectrical conductivity of irrigation water, and ECe* (dSm-1) is the salinity level, at which a particular yield loss isobtained. ECe* values that resulted in 10, 25, and 50 %yield reductions in wheat were 7.4, 9.5, and 13 dS m-1,
respectively, according to the Maas and Hoffman equation(1977). Leaching requirement, total applied water at different ECiw and desired yield reductions during 3 years of
the field experiment are presented in Table 1.
Water samples were analyzed for sodium (Na?), magnesium (Mg2?), calcium (Ca2?), potassium (K?), bicarbonate (HCO3-), carbonate (CO32-), chloride (Cl-),
sulfate (SO42-), and water electrical conductivity (ECiw).
The electrical conductivity of water samples was measured
in the field. Na? and K? concentrations were measured
using the emission flame photometer. Ca2? and Mg2? were
measured by EDTA titrimetry. HCO3- was determinedusing titration with HCl and Cl- was titrated by silver
nitrate, while SO42- was obtained using the gravimetricmethod (Richards 1954). The chemical composition ofirrigation waters is presented in Table 2. The calcium

carbonate content of soil was determined by treating 0.5 gof dried soil with HCl, followed by back titration of
unreacted acid with NaOH (Loeppert and Suarez 1996).
Cation-exchange capacity (CEC) and exchangeable cations
were measured according to Amrhein and Suarez (1990).
The hydrometric method was used to determine the soil
texture (Bouyoucos 1962). The soil texture was clay loam
between 0 and 50 cm depth with a high percentage of
calcium carbonate and a low value of the organic carbon.
The soil is classified as Carbonatic, Thermic, Typic Calcixerepts according to soil taxonomy (Soil Survey Staff
1999). Soil texture, calcium carbonate content, cationexchange capacity, and Gapon’s selectivity coefficientswere measured before experiment. To assess soil salinity,
soil samples were taken from 0–10, 10–30, and 30–50 cm
depths three times during the wheat growing season every
year over 3 years of the experiment. Averaged salinity
during the season was reported for each depth.
Daily meteorological data were obtained from the Sarvestan Weather Station. Total annual rainfall was 309, 276,
and 67 mm during the first, second, and third year of theexperiment, respectively. Reference evapotranspiration,
ETo, was estimated using the FAO Penman–Monteith
method (Allen et al. 1998). Crop evapotranspiration was thencalculated as the product of ETo and crop coefficient (Kc)using the AQUACROP model. Crop parameters required for
calculating actual evapotranspiration were adopted from
Najafi Mirak (2008). The Sarvestan region has two distinct
weather periods: a cool, rainy season (winter) from
November to April (with about 97 % of the total rainfall) anda hot, dry season (summer) from May to October (with only
about 3 % of the total rainfall). Required climatic parameters(ET and rainfall), soil properties, and initial concentrations
for simulations are presented in Table 3.

Table 1 Leaching requirement, total applied water at different ECiw, and desired yield reductions during 3 years of field experimentECiw(dS m-1)

Desired yield
reduction (%)

Soil salinity levelat which a desired
yield reductionis achieved (dS m-1)

Applied water
before vegetative
cover reached
50 % (mm)Crop waterrequirements(mm)

Leaching
requirements(mm)

Total applied
water (mm)

2.5

10

7.4

210a (310)b

480

34.8

725a (825)b

2.5

25

9.5

210 (310)

480

26.7

717 (817)

2.5

50

13

210 (310)

480

19.2

709 (809)

6.5

10

7.4

210 (310)

480

102.3

792 (892)

6.5

25

9.5

210 (310)

480

76.1

766 (866)

6.5
11.5

50
10

13
7.4

210 (310)
210 (310)

480
480

53.3
216.5

743 (843)
906 (1,006)

11.5

25

9.5

210 (310)

480

153.3

843 (943)

11.5

50

13

210 (310)

480

103.2

793 (893)aApplied water in the first 2 years, b Applied water in the third year

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Irrig Sci (2013) 31:1009–1024

1013

Table 2 Chemical composition of irrigation waterEC(dS m-1)

Ca2?
(meq L-1)

Mg2?
(meq L-1)

Na?
(meq L-1)

K?
(meq L-1)

HCO3(meq L-1)

SO42(meq L-1)

Cl(meq L-1)

2.5

5.5

5.0

11.9

0.4

3.7

5.8

12.5

4.0

11.2

12.4

17.1

0.5

1.1

11.3

28.2

6.0

18.5

18.0

22.1

0.5

4.1

17.0

37.2

6.5

18.5

18.0

27.1

0.5

4.1

17.0

46.2

8.0

19.4

21.3

49.3

0.5

5.5

28.5

54.9

10.0

21.1

31.7

54.3

0.4

4.4

34.3

61.4

11.5

21.0

29.1

60.9

0.4

3.9

22.5

92.0

The UNSATCHEM model setup
The UNSATCHEM simulations were conducted using theatmospheric boundary condition (BC) with surface runoff.
However, the surface runoff option was insignificant since
specified daily precipitation and irrigation fluxes werelower than the infiltration capacity of the upper soil layer.
The upper BC consisted of daily values of rainfall andpotential evapotranspiration, which was partitioned into
potential transpiration and potential evaporation using themodified approach of Ritchie (1972). The AQUACROP

model was applied to calculate soil evaporation and croptranspiration separately based on the fraction of green
canopy ground cover (Raes et al. 2009).
Free drainage BC was used at the bottom of the soil
profile. The root growth was modeled using a logistic
growth function, assuming the initial root growth time set
at the planting date and 50 % root growth halfway throughthe growing season. Parameters for the van Genuchten
model (van Genuchten 1980) describing soil hydraulicproperties were derived from soil textural information andthe soil bulk density using the ROSETTA program (Schaap

Table 3 Parameter values used in the UNSATCHEM model
Soil parameter

Value

Unit

Soil classification

Carbonatic, Thermic, Typic Calcixerepts–

Soil texture

Clay loam–

Soil bulk density

1.35

g cm-3Ks

6.24

cm day-1

a

0.019

cm-1

n

1.31

–

hr

0.095

–

hs

0.41

–

Cation-exchange capacity

165

mmolc kg-1 soil

Soil hydraulic parameters

Gapon’s selectivity coefficients
Ca–Na exchange

10.3

Ca–Mg exchange

0.92

Ca–K exchange

0.41

Initial dissolved concentrations
In plots irrigated with ECiw = 2.5 and LR10 %, LR25 %, and LR50 %3.7, 4.2, 3.7–
dS m-1

In plots irrigated with ECiw = 6.5 and LR10 %, LR25 %, and LR50 %9.4, 8.9, 8.2

dS m-1

In plots irrigated with ECiw = 11.5 and LR10 %, LR25 % and LR50 %
Initial sorbed concentrations (Ca; Mg; Na; K)

13.9, 13.8, 15.7

dS m-1

105, 35, 10, 15

mmolc kg-1 soil

CalcitePotential ET (1st, 2nd, and 3rd year)400
646, 695, 686

gr kg-1mm

Long-term mean potential ET

635

mm

Crop coefficient (initial, developing, and ripening stages)

0.51, 1.05, 0.4

Rainfall (1st, 2nd, and 3rd year)309, 276, 67mm

Long-term mean rainfall

250

mm

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Irrig Sci (2013) 31:1009–1024

1.2

p ¼ 3:580EC þ 1:366

Relative Yield

1
0.8
0.6
y = -0.0561x + 1.3485
2

0.4

R = 0.9229

0.2
0

2

4

6

8

10

12

14

-1

Average Root-zone Salinity (dS m )Fig. 1 Relationship between the relative wheat grain yield and theaverage root-zone salinity

et al. 2001). Since field data related to CO2 concentrations(cm3 cm-3), fluxes, and production were not available, theCO2 concentrations were assumed to increase linearly from
0.00033 (the CO2 concentration in the atmosphere) at thesoil surface to 0.022 at the 50 cm soil depth.
The relationship between the relative yield of wheat (theGhods cultivar) and the mean value of soil salinity during
the growing season for 3 years of experiment is illustrated
in Fig. 1. The first segment of the relationship shows nochange in the relative yield up to a soil salinity threshold of
about 6.9 dS m-1. The second segment is given by thefollowing equation:
Yr ¼ 0:0561ECe þ 1:388

R2 ¼ 0:92

ð8Þ

where Yr is the relative yield (the actual wheat yield divided by the maximum wheat yield) and ECe is the averageroot-zone salinity in dS m-1. This equation provides thestandard values of threshold and slope for yield reduction
due to salt stress (Maas and Hoffman 1977) of 6.9 dS m-1
and 5.61 % decrease per 1 dS m-1 increase above thethreshold, respectively.
Note that the threshold value obtained in our study
indicated a slightly higher salt tolerance than reported by
Maas and Grattan (1999) (i.e., 6 dS m-1) and lower thanrecorded by Francois et al. (1986) (8.6 dS m-1). In
experiments with different wheat cultivars in an extremely
dry region of Iran (the Yazd province), Ranjbar (2005)
found that wheat cultivars were less salt tolerant than intemperate regions. A threshold of 5.92 dS m-1 and slope of
4.5 % decrease per 1 dS m-1 for a Ghods cultivar were
proposed. According to the results obtained from 150 farms
in 15 provinces in the north, south, and central parts ofIran, the soil salinity threshold, above which wheat yield
started to decline, was found to be 6.8 dS m-1 (Siadat andSaadat 1998). Differences between different studies can be
attributed to different experimental conditions, including
genotypes, soils, climate, and/or agronomic practices.
The relationship between the electrical conductivity andthe osmotic pressure head is given by:

123

R2 ¼ 0:996

ð9Þ

where p has units of meters and EC has units of dS m-1.
Equation (9) was determined using UNSATCHEM (Šimůnek
et al. 1996) to compute the osmotic head p for the solution
composition of irrigation water reported by Wahedi (1995).
In the UNSATCHEM model, the water and salinity
stresses on root water uptake are defined using the empirical parameters h50 and p50. These parameters represent thepressure and osmotic heads, at which the water extraction
rate is reduced by 50 % due to water and salinity stresses,
respectively. Dehghanian (2004) reported h50 for wheat of-29.7 m. According to Wahedi (1995), the soil salinitythat causes 50 % loss in the wheat yield is 15.82 dS m-1.
According to Eq. (9), this corresponds to the osmoticpressure head p50 of -55.3 m.
Data from experimental plots with irrigation water withthe salinity of 2.5 dS m-1 were used for model calibrationand that of 6.5 and 11.5 dS m-1 for model validation. For
both calibration and validation, simulations were carriedout for three leaching requirements in 3 years. Simulated
salinities at depths of 0–10, 10–30, and 30–50 cm, averaged over 3 years, were compared with measured salinities
at corresponding depths. Three-year average soil salinities
were used for calibration and validation based on priorrecommendations (e.g., Gan et al. 1997) that 3–5 years ofdata that include average, wet, and dry years should be
used for model calibration and evaluation.
Statistical evaluation
Modeling results were evaluated using both graphical andstatistical methods. In the graphical approach, measured
values of soil salinity were plotted against simulated values. Agreement between simulated and observed values
was evaluated using the root mean square error (RMSE)
and the relative error (RE), which are defined as follows(Kobayashi and Salam 2000):
 X
0:5
1
RMSE ¼ðSimi  Obsi Þ2
ð10Þ
n

RMSE
RE ¼
ð11Þ
Obsavgwhere Simi and Obsi are simulated and observed values,
respectively; n is the number of data points included in thecomparison; and Obsavg is the mean observed value. RE
was used to evaluate the quality of the RMSE value. The
simulation is considered to be excellent when a relative
error is less than 10 %, good if it is greater than 10 % andless than 20 %, fair if it is greater than 20 % and less than
30 %, and poor if it is greater than 30 % (Loague andGreen 1991).

Irrig Sci (2013) 31:1009–1024

Alternative irrigation scenarios
Three alternative scenarios were considered when evaluating the effects of different irrigation waters on thedevelopment of salinity using UNSATCHEM. While norainfall was considered in the first scenario, the other two
scenarios considered daily rainfall data. In all alternative
simulations, the longitudinal dispersivity of 9 cm obtainedby calibration against the field data was used.
Scenario 1 Temporal changes in the quality of irrigationwater can significantly influence the salinization process.
These changes can occur when farmers get an occasional
supply of good quality canal water. In such a situation,saline and canal waters can be used either alternately or
mixed (blended), depending on the availability of goodquality water. The effects of various options involving
either an alternative use of different waters or theirblending on soil salinity were investigated using the calibrated UNSATCHEM model. In this scenario, the wheat
crop was considered and rainfall events were neglected.
Simulations were carried out for six sub-scenarios: for twocases when only either low–salinity (LS) or high-salinity(HS) waters were used, and for four modes of conjunctiveuse (Table 4), namely LS:HS (alternating irrigations withlow- and high-salinity waters, starting with low-salinity
water), HS:LS (alternating irrigations with low- and highsalinity waters, starting with highly saline water), 3LS:3HS(initial three irrigations with low-salinity water followed bythree irrigations with highly saline water), and Mix (1:1),
where LS and HS stands for low and high salinity,
respectively. Eight irrigations, each of 9 cm, were appliedin each simulation. UNSATCHEM was run with daily
values of long-term mean potential evapotranspiration. The
EC of irrigation water of 2 (LS), 6 (Mix), and 10 (HS) dS
m-1 was used.
Scenario 2 In lands cultivated with wheat in the Fars
Province, about 35–45 % of applied water percolates
below the root zone into deeper soil layers, leaching salts
and increasing the wheat yield even when high-salinity
irrigation waters are used (Cheraghi and Rasouli 2008).
However, applying large amounts of water is not sustainable in arid and semiarid areas (such as in Iran and specifically in the Fars Province). Therefore, optimizing theleaching fraction while preserving the crop yield andminimizing the amount of irrigation water is of primaryinterest. This scenario involved four practical irrigation
treatments (1.1ET, 1.2ET, 1.3ET, and 1.4ET, where ET is
the potential evapotranspiration) while considering or
ignoring annual rainfall. Water was applied at one-day
intervals, such as with a drip irrigation system. The use ofpressurized irrigation (e.g., sprinkler or drip irrigation) is
one of the goals of sustainable agriculture in Iran. Long-

1015

term mean atmospheric data (potential ET and rainfall) andan ECiw of 6 dS m-1 were considered in this scenario.
Other input data required for simulations are given in
Table 3. For each simulation, various salt balance components, including leaching, precipitation (of calcite andgypsum), and final soil salinity were calculated for the soil
depth of 0–50 cm.
Scenario 3 This scenario was carried out to evaluate themaximum electrical conductivity of irrigation water (ECiw)that can maintain an average root-zone salinity of less than
6.9 dS m-1, while considering annual rainfall and evapotranspiration. Three 3-year time series of rainfall, representing dry, average, and above-average rainfall years,
were analyzed. Irrigation waters with ECiw of 4, 6, 8, and10 dS m-1 were tested.
The historical rainfall record over the past 33 years for
the Sarvestan area was obtained from the Meteorologicalstatistics of the Fars province (2011). Long-term average
precipitation calculated over a time period from 1978 to2011 was considered a normal year. The year with annual
rainfall either 50 % below or above the long-term mean
was assumed to be a dry or wet year, respectively. Annual
rainfall values were then sorted from the driest year to thewettest year. If the rainfall of the ith year in the orderedseries was Pi, then the probability that rainfall was lower
than Pi was given by the order number (i) of a year divided
by the total number of years (n) plus 1 [Prob(P B Pi) = i/(n ? 1)]. The rainfall distribution of the ith year was used
to determine results with Pi probability.
To evaluate the influence of rainfall distribution, two
rainfall records with similar annual rainfall averages but
different distributions throughout the year were selected.
Two years (2005–2006 and 2010–2011) were compared.
Total annual rainfall was almost the same (207 mm) inboth years. In 2005–2006, rainfall was concentrated
in winter (87 % of rainfall occurred from November toMarch), whereas in 2010–2011, rainfall was more uniformly distributed throughout the winter and spring (only
38 % occurred from November to March). Irrigationwater was assumed to have a salinity of 6 dS m-1 in themodel.
One additional scenario was carried out to evaluate how
particular irrigation water affects the wheat yield over thelong term. In this scenario, a 33-year record of historicaldaily rainfall was considered to determine the effect ofirrigation waters with different ECiw on the average rootzone salinity. In this scenario, the quantity of irrigationwater was assumed to be 120 % of the potential evapotranspiration. The simulation was run for 33 years and thesimulated series of soil salinity was used to establish theprobability of obtaining values below or above thethreshold value of soil salinity.

123

1016

Irrig Sci (2013) 31:1009–1024

Results and discussion

During model calibration, the longitudinal dispersivity was
fitted using the trial-and-error method until the simulated
and measured soil salinities agreed within acceptable limits. Good agreement between the simulated and measured
data was obtained by adjusting the dispersivity value to
9 cm, which is in the range of values suggested by many
authors (e.g., Leijnse et al. 1996; Bejat et al. 2000; Vanderborght and Vereecken 2007).
Vanderborght and Vereecken (2007) wrote a review ondispersivities for solute transport modeling in soils (thedata base contains 635 entries derived from 57 publications). Data are provided for a wide range of transportdistances, flow rates, soil textures, pore water velocities,
and scales of experiments (field, column, or core). Dispersivity values ranged between 0.1 and 481.1 cm. Dispersivities obtained for conditions most similar to our study(a relatively heavy soil texture, field condition, and travel
distance of 50 cm) were in the range from 2.7 to 13.5 cm.
We chose the minimum, maximum, and mean dispersivity
values for inverse simulations. In each model run, simulation results were compared against the measured soil
salinity data. Differences between measured and simulated
values were evaluated by means of the RMSE. Differences
between measured and simulated values were minimized
with a dispersivity of 9 cm. Therefore, this value was used
for remaining simulations in the present study. A comparison between measured and simulated soil salinities forexperimental treatments with irrigation water with asalinity of 2.5 dS m-1 and three different leachingrequirements is depicted in Figs. 2 and 3.
The RMSE of 0.34 dS m-1 (11.2 % relative error)
shows that the UNSATCHEM model is able to predict soil
salinities with an acceptable accuracy. Graphical display ofresults for the calibration (Fig. 3) and validation (Fig. 4)

0
0
10

5

10

15

ECiw= 2.5 dS m
LR10%

-1

LR50%
2

-1

1

RMSE= 0.34 dS m
RE= 11.2%

0
0

1

2

Fig. 2 Comparison between measured and simulated (with the
UNSATCHEM model) soil salinities for experimental treatmentswith irrigation water with salinity of 2.5 dS m-1 and three different
leaching requirements (LR10 %, LR25 %, and LR50 % are leachingrequirements for 10, 25, and 50 % yield reductions, respectively)

periods indicates that the UNSATCHEM model adequately
described experimental data, although the calibration
results showed a better match than the validation results.
RMSE values for the calibration period ranged from 0.31 to
0.38, while RE ranged from 10.5 to 12.3 % (Fig. 5). RMSE
values ranged from 0.52 to 1.01 dS m-1 and from 1.60 to2.13 dS m-1 for the validation period with water salinitiesof 6.5 and 11.5 dS m-1, respectively. UNSATCHEM
predicted soil salinity with relative errors of 10.6 and14.9 % when using irrigation water with medium- andhigh-level salinity, respectively.
Higher differences between simulated and measured
salinities for experiments with the highest salinity of irrigation water (11.5 dS m-1) indicate that the model performs better for lower salinity levels. However, RE
remained in the range of 10 to 20 %, which, according toevaluation guidelines, indicates a good accuracy of themodel simulation. This result suggests that UNSATCHEM
provides reasonably accurate predictions of soil salinity for
different ranges of water quality and quantity. The

Soil Salinity (dS m -1)
0
0
10

5

10

15

ECiw= 2.5 dS m-1
LR 25%

Soil Salinity (dS m-1)
20

0

10

30

30

30

40

40

50

50

60

60

50

Simulated

5

10

15

0

20

Measured4

Measured Soil Salinity(dS m )

20

40

3
-1

20
-1

LR25%

20

60

123

3

Soil Salinity (dS m-1 )Soil Depth (cm)Fig. 3 Graphical comparison
between measured andsimulated soil salinities forexperimental treatments withirrigation water with salinity of
2.5 dS m-1 and three different
leaching requirements (LR10 %,LR25 %, and LR50 % areleaching requirements for 10,
25, and 50 % yield reductions,
respectively)

LR10%(dS m )

Calibration and validation of UNSATCHEM
for modeling the use of saline water for irrigation

Simulated Soil Salinity4

ECiw= 2.5 dS m-1
LR 50%

20

Irrig Sci (2013) 31:1009–1024

0

5

10

15

0

0

5

10

15

0

20

20

30

30

30

40

40

40

50

50

60

60

0
0
10

5

10

15

20

0

5

10

15

20

10

0

ECiw= 11.5 dS m -1
LR 25%

10

20

20

20

30

30

30

40

40

40

50

50

50

60

60

60

UNSATCHEM model can thus be used to evaluate theeffects of various factors on the development of salinity for
different situations.
Scenario 1: Salinization for different modesof conjunctive water use
Different scenarios (Table 4) with cyclic and blending useof irrigation water were evaluated using the UNSATCHEM
model. The average root-zone salinity throughout the season for the LS:HS, HS:LS, 3LS:3HS, and mixed (1:1)
modes were 6.31, 5.93, 6.04, and 5.56 dS m-1,

20

5

10

15

20

0

0

ECiw= 11.5 dS m -1
LR 10%

15

ECiw= 6.5 dS m
LR50%

10

20

Measured
Simulated

10

-1

-1

ECiw= 6.5 dS m
LR25%

10

20

60

5

0

0

ECiw= 6.5 dS m
LR10%

50

Soil Depth(cm)

20
-1

10

Soil Salinity (dS m-1)

Soil Salinity (dS m-1)

Soil Salinity (dS m-1)

Soil Depth (cm)Fig. 4 Validation of the
UNSATCHEM model forexperimental treatments withsalinities of irrigation water of
6.5 and 11.5 dS m-1 and threedifferent leaching requirements(LR10 %, LR25 %, and LR50 %are leaching requirements for
10, 25, and 50 % yield
reductions, respectively)

1017

ECiw= 11.5 dS m -1
LR 50%

respectively. The lowest soil salinity was obtained for themixed (1:1) mode and the highest for the LS:HS mode.
Since the salt tolerance of wheat varies at different
stages of its growth, seasonal changes in root-zone salinity
can influence the crop yield substantially. Time changes of
the average root-zone salinity for different modes of conjunctive water use are shown in Fig. 6. For the 3LS:3HS
mode, soil salinity remained lower than 6.9 dS m-1 (thethreshold limit for yield reduction) up to 120 days after
planting. Although for this mode, soil salinity was relatively high during the last 40 days of the growth period (upto 11.2 dS m-1), salinity during this growth phase does not

Table 4 Results simulated using UNSATCHEM for different modes of the conjunctive water use (Scenario 1)
Conjunctive
use practiceDescription

Soil salinity at
different depths(dS m-1)
0–30

30–60

60–90

Average rootzone
salinity(dS m-1)

LS

Irrigations with low-salinity water only

3.52

3.88

4.53

3.97

3LS ? 3HS

Three irrigations with low-salinity water, followed by three irrigations
with high-salinity water

4.88

5.08

8.17

6.04
6.31

LS ? HS

Alternate irrigations with low-salinity and high-salinity waters

4.28

5.85

8.82

HS ? LS

Alternate irrigations with high-salinity and low-salinity water

4.27

5.19

8.33

5.93

MIX (1:1)

Irrigations with water mixed from low- and high-salinity waters (1:1)

4.26

5.27

8.17

5.56

HS

Irrigations with high-salinity water only

7.73

8.50

13.31

9.85

123

1018

Irrig Sci (2013) 31:1009–1024
3
RMSE
RE
16
2
12

1.5

RE(%)

-1

RMSE (dS m )

2.5

20

8

1

4

0.5

0

0
LR 10% LR 25% LR 50% LR 10% LR 25% LR 50% LR 10% LR 25% LR 50%
ECiw=2.5

ECiw=6.5

ECiw=11.5

Treatments

Fig. 5 The performance statistics comparing simulated versus measured data (RMSE root mean square error, RE relative error)

12-1

Soil Salinity (dS m )14
Mix(1:1)

3LS+3HS

HS+LS

HS+LS

10
8
6
4
2
0
0

20

40

60

80

100

120

140

160

180

200

Day After Planting

Fig. 6 Seasonal changes in average soil salinity for different modesof conjunctive water use

have a considerable effect on wheat’s function and performance. For the LS:HS mode, soil salinity was low during
the first 35 days. After that, soil salinity started fluctuating
between 4.9 and 6.9 dS m-1, depending on whether goodor poor-quality water was used for irrigation. For the HS:LS
mode, high-salinity water was applied during the early stageof the wheat growth. Although soil salinity was thenreduced when high-quality water was used for irrigation
later on, this practice should not be used because earlysalinity may adversely affect germination, a growth stage
negatively affected by high salinity. For the mix (1:1) mode,soil salinity fluctuated around 5.38 dS m-1 from the
beginning to the end of the growing season.
The early stages of wheat growth are considered mostsensitive to the salt stress, and its salt tolerance increases
with growth (Maas and Poss 1989; Ranjbar 2010). In orderto minimize the salinity stress, good quality water must be
allocated for the most sensitive stages of crop growth.
Therefore, the most suitable modes of conjunctive water
use with low- and high-salinity waters during the growing
season, while controlling initial soil salinity, are 3L:3H,
followed by the LS:HS, and mix (1:1) modes. The optimal

123

strategy of conjunctive water use for the wheat crop is todelay as much as possible the use of saline waters, which
may lead to the salinity stress, while always keeping salt
concentrations within permissible limits, especially during
early crop stages.
Wheat yields reported by Minhas and Gupta (1993a) andRanjbar (2010) validate this recommendation. Experiments
carried out on Iranian wheat cultivars grown in sand containing 200 mM of salt have indicated that the wheat yield
was reduced by a half in treatments that exposed wheat tosalts during early vegetative growth stages, compared totreatments in which wheat encountered increased salinity
during later reproductive stages (Ranjbar 2010). It was
concluded that the more delayed irrigation with saline
water is, the more water productivity can be achieved.
Experiments of Minhas and Gupa (1993a) showed thatwhen irrigations with saline water start at jointing, nonsaline/saline waters are used cyclically, and salinity is
increased gradually, wheat yield is higher than in othertreatments. They estimated that salinity (expressed asEC50) during the salt-tolerant stage (dough to maturity) canbe as much as 1.4 times higher than during the salt-sensitive growth stage (seedling to root crown) (i.e., 13.2 vs.
9.3 dS m-1). Simulation results indicate that up to 90 % of
the potential wheat yield can be achieved under conjunctive water use when non-saline water is used until the third
post-sowing irrigation, while water with an ECiw of 51.4 dS
m-1 is used thereafter (Minhas and Gupta 1993b).
The above discussion suggests that initial soil salinity,
quality of irrigation water applied during an early growthstage, and practices of conjunctive water use have a significant impact on temporal changes in root-zone salinity
and that these changes affect the wheat yield.
Scenario 2: Salinization for different amounts
of applied water
The salt balance components for different amounts of irrigation water (1.1, 1.2, 1.3, or 1.4 ET) and annual rainfall (norainfall or average rainfall) are given in Table 5. The soil
profile salt balance (initial and final mass of salts) is given for
the upper layer of soil (50 cm). The salt balance was significantly affected by the amount of applied irrigation waterand the presence of rainfall. Results suggest that salt accumulation was inversely proportional to the quantity of
applied water. The less the irrigation water was applied, thehigher was the salinization risks of soils under irrigation. Soilsalinity increased by 8.6, 23.5, and 58 % when the quantityof applied water decreased from 1.4 ET to 1.3, 1.2, and 1.1
ET, respectively. The corresponding values when annual
rainfall of 300 mm was considered in simulations were 2.7,
5.5, and 13.7 %, respectively. Since 140 mm of rainfall(during the growth season) met evapotranspiration needs ofIrrig Sci (2013) 31:1009–1024

1019

the plants, this quantity of water was subtracted from the total
irrigation water used. Thus, the salt input to the soil throughirrigation, as given in Table 5, was reduced by 1.4 kg m-2.
Furthermore, 53 % (160 cm) of rainfall occurred during thewinter months, percolating through soil and leaching salts
below the upper soil layer. Hence, the amount of salt stored in
the soil was reduced by rainfall.
Chemical precipitation/dissolution reactions also affected
the salt balance in the soil profile. Results showed that
because irrigation water is high in gypsum and carbonate
minerals, salts tend to precipitate in the soil profile in all
irrigation treatments. The relative amount of salts that precipitated varied inversely with the amount of applied water
and/or rainfall. More salts precipitated when less irrigationwater was applied, especially if accompanied by a lack ofrain conditions. For conditions without rainfall, 0.89, 0.83,
0.76, and 0.68 kg m-2 salts were estimated to precipitate for
1.1, 1.2, 1.3, and 1.4 ET irrigation treatments, respectively.
Less salts precipitated when soil received 300 mm of rainfall. The amounts of precipitated salts for sub-scenarios withand without rainfall and for different irrigation quantities
during the growing season are shown in Fig. 7. This figure
shows that the less the water for irrigation was applied, theearlier the salts started precipitating and the more the salts
precipitated during the entire season. Additionally, during
the rainy season (from 10 to 150 days after planting), no salt
precipitation occurred in the soil profile.
Table 5 shows that salts leaching from the top 50 cm ofsoil and the amount of applied water were closely related.
More leaching of salts occurred when more irrigation water
was applied. Under no rainfall conditions, 1.07 and2.55 kg m-2 of salts were leached below the depth of
50 cm when irrigation was equal to 1.1 and 1.4 ET,
respectively. A fraction of leached salts below the depth of
50 cm to salt input through irrigation was 0.68, 0.61, 0.52,
and 0.36 for irrigation equal to 1.4, 1.3, 1.2, and 1.1 ET.
Fraction of leached salts thus decreased with reduced irrigation. A similar trend was also observed for conditions
with rainfall. Salt losses under no rainfall conditions werehigher than under conditions with rainfall. This can be
attributed to the higher quantity of salt input through irrigation in the absence of rainfall.

A combination of chemical precipitation and leaching
resulted in favorable root-zone salinity. For sustainableagriculture, irrigation should be managed such that the salt
precipitation is maintained at high levels without a significant reduction in the crop production (Bresler et al. 1982).
Simulated results showed that 26 and 30 % of salts appliedwith irrigation water in the 1.2 and 1.1 ET irrigation treatments, respectively, contributed to the precipitation process
when no rain was considered. Salt concentrations (Fig. 8)
exceeded 7 dS m-1 after the 115th and 135th day after
planting, respectively, and were continuously increasing
until the end of the season. When rainfall was considered,soil salinity for all irrigation treatments remained below 7 dS
m-1 during the growing season. These results indicate that an
application of irrigation water at 1.3 ET (a leaching fractionof 23 %) would guarantee the optimal wheat yield under dry
conditions (no rainfall) in the study area. On the other hand,the 1.1 ET irrigation treatment (a leaching fraction of 10 %)can be successfully used in normal years with average
rainfall of 300 mm.
Scenario 3: Evaluation of the maximum ECiw
to maintain the average root-zone salinity
below the soil salinity threshold
The maximum EC of the irrigation water (ECiw) thatmaintains an average root-zone salinity below 6.9 dS m-1
during the growing season was evaluated for different rates
of rainfall (for dry, average, and above-average rainfall
years). Simulation results indicate that the seasonal-average root-zone salinity over the 3-year period for subaverage rainfall conditions (annual rainfall of 110 mm)
was 4.6, 6.7, 9.1, and 10.1 dS m-1 when the EC of irrigation water was 4, 6, 8, and 10 dS m-1 (Fig. 9), respectively. Corresponding root-zone salinities for average
rainfall conditions were 4.6, 6.8, 9.1, and 10.1 dS
m-1—almost the same. Finally, wetter conditions (withaverage rainfall of 450 mm) resulted in average root-zone
salinity of 3.2, 4.8, 6.4, and 7.4 dS m-1, respectively. For
all irrigation waters in the above-average rainfall years andfor an ECiw of 4 dS m-1 in average rainfall years, theseasonal root-zone salinity was lower than the ECiw of

Table 5 Components of the salt balance (kg m-2) for simulations involving wheat crop and various water applications (Scenario 2)
Rain = 0
1.4 ET

Rainfall = 300 mm
1.3 ET

1.2 ET

1.1 ET

1.4 ET

1.3 ET

1.2 ET

1.1 ET

Initial

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

Irrigation

3.74

3.47

3.20

2.94

2.35

2.08

1.81

1.54

Leaching

2.55

2.13

1.67

1.07

1.66

1.24

0.91

0.56

Final

0.81

0.88

1.00

1.28

0.73

0.75

0.77

0.83

Precipitation (calcite and gypsum)

0.68

0.76

0.83

0.89

0.26

0.39

0.43

0.45

123

1020

Irrig Sci (2013) 31:1009–1024

Salt Precipitation (kg m-2)

0.9
1.1 ET
1.3 ETR-1.1 ET
R-1.3 ET

0.8
0.6

1.2 ET
1.4 ET
R-1.2 ET
R-1.4 ET

0.5
0.3
0.2
0.0
0

20

40

60

80
100 120 140
Day After Planting

160

180

200

Fig. 7 Changes in salt precipitation during wheat growing season(ET evapotranspiration, R simulation considering annual rainfall)14

1.1 ET
1.3 ETR-1.1 ET
R-1.3 ET

Soil Salinity (dS m-1)

1210

1.2 ET
1.4 ET
R-1.2 ET
R-1.4 ET8
6
4

R2 ¼ 0:978

ECiw ¼ 1:069ECt  1:083

R2 ¼ 0:975

ECiw ¼ 1:306ECt  0:704
Above-average rainfall

0
0

20

40

60

80

100

120

140

160

Fig. 8 Changes in soil salinity during wheat growing season (ETevapotranspiration, R simulations considering annual rainfall)

14

ECiw= 4 dS/m
ECiw= 6 dS/m
ECiw= 8 dS/m
ECiw= 10 dS/m

12
10
8
6
4
2
0

Subnormal

NormalAbove-normal

RainfallFig. 9 Simulated root-zone salinity for various rainfall rates anddifferent qualities of irrigation water

irrigation water, due to rain-induced salt leaching during
the cool winter rainy season.
During sub-average and average rainfall years, irrigationwaters with salinities of 4 and 6 dS m-1 can be safely usedwithout any loss in the wheat yield. However, irrigation

123

R2 ¼ 0:993

ð12Þ
ð13Þ
ð14Þ

180

Day After Planting-1ECiw ¼ 1:042ECt  0:978Sub-average rainfall

Average rainfall

2

Average Root zone Salinity (dS m )water salinities of 8 and 10 dS m-1 produced reductions inwheat yield of 12.1 and 12.4 %, and 18.0 and 18.6 % in
sub-average and average rainfall years, respectively. For
above-average rainfall years, soil salinity remained below
the salinity threshold except when irrigation water withsalinity of 10 dS m-1 was used. However, even this case
did not produce a dramatic reduction in wheat yield.
Reduced grain yield can be attributed to osmotic stress.
In saline soils, osmotic potential can be so low that plants
cannot overcome it and cannot take up enough water,
suffering from the effects of osmotic or salinity stress(Heikal and Shaddad 1981). Osmotic stress reduces germination, seedling growth, leaf expansion, and the root
growth rate, leading to a reduction in the yield of the wheat
crop (Läuchli and Grattan 2007).
The following relationships between permissible irrigation water salinity (EC*iw) and the wheat salinity
threshold (ECt) for different rainfall series were developed
based on simulated results discussed above:

These relationships (Eqs. 12–14) can be used to estimate
the salinity of irrigation water that will not produce averagesoil salinity higher that the threshold for wheat production.
Considering a soil salinity threshold of 6.9 dS m-1 andsubstituting it into Eqs. 12–14, the maximum salinity ofirrigation water for different rainfall conditions can be estimated. The maximum salinity of irrigation water for the
optimum production of wheat is 6.21 and 6.29 dS m-1 for
sub-average and average rainfall years, respectively. Interestingly, the dryer 3-year period resulted in average root-zone
salinity values equal to those for the average 3-year period,
suggesting that rainfall distribution also plays an importantrole in determining the soil salinity. Irrigation water with asalinity of up to 8.31 dS m-1 can be applied without any
reduction in wheat yield in above-average rainfall years.
These results suggest that consideration of rainfall
would produce lower leaching requirements or higher
permissible ECiw, thus allowing farmers to use either less
irrigation water or water with lower quality (Letey et al.
2011; Isidoro and Grattan 2011). Data collected from thewheat-cultivated land of the Fars Province indicate that the
average wheat yield increases by about 900 kg ha-1 inthose years, for which annual rainfall is 50 % above its
long-term mean (Cheraghi and Rasouli 2008). Saline water
was also successfully used in the high-rainfall ([550 mm)

Irrig Sci (2013) 31:1009–1024

1000

14

RainfallECiw= 10 dS/m

12

ECiw= 6 dS/m
ECiw= 4 dS/m
ECiw= 8 dS/m

800

10
600

8

-1

ECe=6.9 dS m

6400

Rainfall (mm)-1

Average Root-zone Salinity (dS m )Fig. 10 Temporal changes insimulated root-zone salinity
when irrigation waters with
ECiw of 2, 4, 6, and 8 dS m-1
are used

1021

4
200
2

agro-ecological zone of India. Guidelines for utilizing
saline water in these areas indicate that allowable ECiw isabout 1.8 times larger than that in low-rainfall (\350 mm)
regions (Minhas et al. 1998).
Another scenario that was evaluated involved the entire
33-year rainfall series (Fig. 10). Simulations that consider
applications of irrigation waters of different salinities for theentire 33-year series of meteorological data should provide
the best insight into variations of soil salinity over the longterm. If irrigation waters with an ECiw of 4, 6, 8, and 10 dS
m-1 are used for the entire period of 33 years (from 1978 to2011), the ECe seasonal-average root-zone salinity will
range from 2.3 to 4.6, 3.9 to 7.0, 4.6 to 9.3, and 5.35 to
11.6 dS m-1, with mean values of 3.9, 5.8, 7.7, and 9.7 dS
m-1, respectively (Fig. 10). The salinity threshold value of
6.9 dS m-1 for wheat (Wahedi 1995) is never reached overthe entire analyzed time period when irrigation water with an
ECiw of 4 dS m-1 is used. When irrigation water with an
ECiw of 6 dS m-1 is used, maximum seasonal soil salinity is
maintained below 6.95 dS m-1, which translates into a yieldof over 99.7 % of the optimal production.
Simulations further show that irrigation water with an
ECiw of 8 dS m-1 can be safely used only in above-average
rainfall years. Annual rainfall of 365 mm or more wasfound to be adequate to keep soil salinity below thethreshold value for wheat. For irrigation water with an
ECiw of 8 dS m-1, an appreciable yield loss of more than
10 % would occur in only 5 years. The maximum yield
reduction of about 13 % would presumably occur in the
2007–2008 year, which had the lowest amount of annual
rainfall on record. The use of irrigation water with asalinity of 10 dS m-1 would increase soil salinity above thethreshold yield level in all years except in the 2 years

2010-20112008-20092006-2007

2004-2005

2002-2003

2000-2001

1998-1999

1996-1997

1994-1995

1992-1993

1990-1991

1988-1989

1986-1987(1986–1987 and 1992–1993) with very high annual rainfall(449 and 491 mm). The buildup in soil salinity would
cause a loss in the wheat yield ranging from 3 to 26 %.
Results of the probability analysis for the entire time
period of 33 years (Fig. 11) illustrate that all seasonalaverage root-zone salinities are below the threshold valueof 6.9 when irrigation waters with salinities of 4 and 6 dS
m-1 are used. On the other hand, there are only seven and2 years when the seasonal-average root-zone salinity is
below 6.9 dS m-1 when irrigation waters with an ECiw of 8
and 10 dS m-1 are used, respectively. Given these longterm simulation results and taking into account all otherfactors that may potentially impact the crop yield, using
irrigation water of 6 dS m-1 as a threshold ECiw value for
irrigation water can be considered to be a conservative
choice for cultivating wheat in the Sarvestan area.
100
80

P( ECe< ECt)

1984-1985

1982-1983

1980-1981

0

1978-1979

060

ECiw= 4 dS/m
ECiw= 6 dS/m

40

ECiw= 8 dS/m
ECiw= 10 dS/m

20
0
0

2

4

6

8

10

12

14

16

Average Root-zone Salinity (dS m -1)

Fig. 11 The probability distribution function of the root-zone salinity(ECe) being smaller than the wheat yield threshold (ECt) obtained bysimulating the 33-year series with irrigation waters of an ECiw of 4, 6,
8, and 10 dS m-1

123

1022

160

10

8

Rainfall (2005-2006)

Rainfall (2010-2011)

Soil Salinity (2005-2006)

Soil Salinity (2010-2011)

140
120
100

6

80
4

60

Rainfall (mm)Average Root-zone Salinity(dS m-1)

Fig. 12 Simulated averageroot-zone salinity during 2 year
(2005–2006 and 2010–2011)
with similar amounts but
different distributions of annual
rainfall

Irrig Sci (2013) 31:1009–1024

40
2
20
0

0
1 Nov

1 Des

1 Jan

1 Feb

1 Mar

1 Apr

1 May

Month

Results of the long-term simulations for a given irrigation water salinity (Fig. 10) showed certain variability insoil salinity not only for different amounts of annual
rainfall, but also for the same amounts of annual rainfall
when rainfall was differently distributed during the year.
To test this phenomenon, simulations for 2 years(2005–2006 and 2010–2011) with similar rainfall andirrigation water with salinity of 6 dS m-1 were carried out
and compared in Fig. 12. Simulated results showed that themean seasonal soil salinity was lower (5.1 dS m-1) in
2010–2011 than in 2005–2006 (5.9 dS m-1). Rainfall that
occurred in a short period of the winter of 2010–2011 wasmore effective in reducing the salinity of soil in the rootzone than a similar amount of rainfall distributed
throughout the entire year of 2005–2006. Moreover,changes in soil salinity during the wheat growing season in
2010–2011 were not consistent with those in 2005–2006.
In the early wheat growth stage of 2005–2006, soil salinity
was lower than in 2010–2011. On the other hand, after
February of 2011, soil salinity dropped dramatically andstayed continuously below levels reached in 2005–2006.
These results suggest that while winter-concentrated
rainfall has an important influence on seasonal root-zone
salinity, initial soil salinity may be sufficiently reduced bylate autumn rainfall. High initial (during wheat germination) soil salinity is one of the limiting factors for wheat
production in the Fars Province of Iran (Cheraghi andRasouli 2008). Autumn rainfall would thus be very effective for conditions in which the initial level of soil salinity
is so high that it could adversely influence the wheatestablishment. Research conducted in different parts of theFars Province has shown that 30–50 mm of rain would be
sufficient to flush salts out of the top 30 cm depth (Cheraghi and Rasouli 2008). In the study area, in 28 years of thestudied time period (33 years), rainfall occurred in lateautumn. The amount of rainfall varied between 12 and343 mm from November 23 to December 23, a123

recommended planting date for wheat in the Fars Province.
Meteorological data illustrate that in 24 years, rainfall wasat least 40 mm during the optimum planting time period. In
other words, rainfall is an important potential salt-leaching
factor in the early stages of wheat growth in 73 % of years.
Hence, irrigation with saline water should be delayed after
December 23, because rainfall may improve conditions for
germination and ensure low salinity in the root zone during
the planting month. If there is not enough rainfall to leachaccumulated salts, non-saline water may need to be applied
to leach salts out of the root zone during standestablishment.

Conclusions
Numerical simulations with the UNSATCHEM model
were used to evaluate the effects of saline water used for
irrigation of wheat on root-zone salinity and wheat yield.
Based on temporal changes in root-zone salinity simulated
for different modes of conjunctive water use, the following
ranking in the order of decreasing preference (in terms ofyield) was obtained: alternate 3LS:3HS, LS:HS, Mix (1:1),
and alternate HS:LS. Optimal planning of conjunctive
irrigation water use should attempt to delay the salinity
stress to later growth stages as much as possible and toalways keep it within permissible limits.
The balance of salt fluxes and salt storage in the soil
profile was affected by the quantity and quality of irrigationwater, the quantity of rainfall, and by dissolution/precipitation reactions. Rainfall had a significant effect on soil
desalinization. In addition, precipitation reactions removed
salts from the soil solution and lowered the solution concentrations in the root zone, especially when less irrigationwater was used.
The maximum irrigation water ECiw that can be used tomaintain soil salinity below the wheat yield threshold of

Irrig Sci (2013) 31:1009–1024

6.9 dS m-1 throughout the growing season was calculated
after taking into account different rates of rainfall. Maximum salinity of irrigation water for a sustainable production of wheat was 6.21 and 6.29 dS m-1 for sub-average
and average rainfall years, respectively. For above-average
rainfall years, saline water with an ECiw of up to 8.31 dS
m-1 can be applied without any reduction in wheat yield.
For irrigating wheat on a sustainable basis, long-term
simulations indicate that it is possible to use saline waterwith an ECiw of 6.29 dS m-1. Similar types of simulations
as these can be used to find a suitable quality of irrigationwater for different salinity-sensitive crops. This modelingapproach can be valuable not only for policy and decision
makers, but also for irrigation managers. Given theseresults and taking into account other factors that canpotentially impact crop yield, the use of 6 dS m-1 as thethreshold ECiw value for irrigation water can be considereda conservative value for wheat in the study area.
Rainfall distribution also plays a major role in determining the salinity of the soil in the root zone. The winterconcentrated rainfall is more effective in reducing soil
salinity than a similar amount of rainfall distributed
throughout the autumn, winter, and spring seasons.
Autumn rainfall would be very effective in reducing thepotentially high initial soil salinity level to levels thatwould not adversely influence wheat establishment.
Results of this study can help us predict the salinity ofwater, which can be used on a sustainable and long-termbasis for irrigating a clay loam soil. Moreover, the management option for minimizing root-zone salinity during
the establishment stage by considering the effects of rainfall was evaluated. There are different climatic zones in theFars Province, in which weather, water quality, and soil
conditions may vary. The long-term simulations can beeffectively used to establish safe salinity limits for irrigation waters for different climatic zones.

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