Agricultural Water Management 283 (2023) 108303

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

Optimizing the strategies of mulched brackish drip irrigation under ashallow water table in Xinjiang, China, using HYDRUS-3D
Leilei Guo a, b, Zaimin Wang a, *, Jirka Šimůnek c, Yujiang He d, Rizwan Muhamma aaCollege of Environment & Civil Engineering, Chengdu University of Technology, Chengdu 610059, Sichuan, China
Sichuan Water Conservancy College, Chengdu 611231, Sichuan, Chinac
Department of Environmental Sciences, University of California, Riverside, CA, USAdInstitute of Hydrogeology and Environmental Geology, CAGS, Zhonghua Street 268, Shijiazhuang, Hebei Province 050061, Chinab

A R T I C L E I N F O

A B S T R A C T

Handling editor - Dr. B.E. Clothier

Optimizing mulched drip irrigation (MDI) with brackish water, which would benefit both soil and cotton yields,
is crucial for arid areas lacking fresh water (FW) resources and having shallow brackish groundwater (BW). The
HYDRUS-3D model, simulating three-dimensional soil water and salinity movement, was used to understandbetter MDI’s effects on the soil environment with shallow groundwater and optimize its operations. Optimized
irrigation water amounts varied from 478.5 to 669.9 mm when salt concentrations of irrigation water were 1.01
g/L for FW and 3.0 g/L for BW, reflecting local surface water and groundwater qualities. Irrigation water con­sumption was evaluated and compared with literature results. The results showed that increasing irrigation
frequency reduces water stress more than increasing irrigation water amounts. The low salinity area under thedripper expanded with an increase in the irrigation water amount and decreased with an increase in watersalinity. Cotton water and salt stress could be avoided when 66 mm of freshwater was used at the seedling stage
when the soil salinity threshold Ce1 was 2.58 g/L, and 412.9 mm of brackish water was used during the othergrowth stages when Ce2 was 6.62 g/L. Irrigation intervals of 3 days during the flowering and boll-setting stages
and 6 days during the other growth stages were recommended. Compared with earlier published results, 1190
m3/ha of surface water and 495 m3/ha of groundwater could be saved in Xinjiang when the above-recommended
irrigation strategy is used. Finally, HYDRUS-3D is a valuable and efficient tool for evaluating farmland water andsalinity management.

Keywords:
Mulched brackish drip irrigation
HYDRUS-3D
Soil water and salt movement
Water and salt stress
Optimize irrigation scheme

1. Introduction
Saving water and improving water use efficiency are essential goals
for avoiding regional ecological degradation in dry regions (Zhang et al.,2016; Hu et al., 2020; Wang et al., 2022). Mulched drip irrigation (MDI)with brackish water, which is used widely in cotton fields, is one of thecritical technologies to solve the shortage of fresh water in Southern
Xinjiang, Northwest China (Hu et al., 2020; Tan et al., 2018; Qi et al.,2018; Liu et al., 2012). However, several studies have shown that theaccumulation of salts in the root zone when saline water is used for
irrigation inhibits cotton root growth and reduces fertilizers uptake andcrop yield, as well as the irrigation water use efficiency (Wang et al.,2018; Feike et al., 2017; Leogrande et al., 2016; Min et al., 2014; Kang
et al., 2010; Chen et al., 2010; Bowman et al., 2007). Hence, it isessential to explore proper MDI strategies that benefit both the soil

environment and cotton yields.
Field experiments have been widely used to research the effects ofirrigation on crop growth. The experimental results have shown thatfilm mulching, drip tapes, fertilization, and irrigation water quantityand quality were the most important factors (Li et al., 2018; Chen et al.,2018; Min et al., 2017; Wang et al., 2014). Complete film mulching cansignificantly reduce salt accumulation in the top 0–70 cm soil profile
compared to partial film mulching by further retarding the soil salts’
upward movement (Qi et al., 2018). While drip irrigation with partial
film mulching can save water, there is still an unavoidable soil-water
loss and excessive salt accumulation in the bare strips due to soil
evaporation (Tan et al., 2017). Both soil water content and salinity
increased significantly when irrigation water salinity increased (Zhang
et al., 2015; Chen et al., 2018). Salt accumulation occurs mainly at thesoil surface within the top 0–10 cm and on the bare surface between* Corresponding author.
E-mail address: wangzaimin13@cdut.cn (Z. Wang).
https://doi.org/10.1016/j.agwat.2023.108303
Received 5 February 2023; Received in revised form 31 March 2023; Accepted 1 April 2023
Available online 5 April 2023
0378-3774/© 2023 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

L. Guo et al.

Agricultural Water Management 283 (2023) 108303

mulching films. No salinization occurs within the root zone (40–60 cm
soil depth), where abundant lateral roots germinate and extend hori­zontally (Wang et al., 2019; Li et al., 2015). Additionally, using double
irrigation tapes instead of a single tape can further reduce salt accu­
mulation in the root zone without a yield decrease when the same irri­
gation water quality is used (Liu et al., 2012). Li et al. (2018) suggested
that smaller distances between drippers, with short but more frequentirrigation cycles, would be much more helpful in satisfying temporal andspatial plant water demands.
Cotton biomass, nitrogen (N) uptake, and yield increased with anincrease in the irrigation and N application amounts (Min et al., 2017;
Singh et al., 2010; Shenker et al., 2003). However, excessive salinity
could cause osmotic stress, inhibiting N uptake and causing nutritional
imbalance and specific ion toxicity (Munns and Tester, 2008; Aslamet al., 1984). Moreover, salt accumulation in the root zone reduces
evapotranspiration (ET) (Jiang et al., 2012). Under such conditions,leaching soil salts with fresh water is necessary. The flood leaching isalways carried out in the winter and spring following the local Xinjiang
traditional cotton irrigation patterns when there is no plastic mulching(Hu et al., 2012; Wang et al., 2014, 2022) and when the flood irrigation
can leach soil salts more uniformly. Using both brackish and freshwa­
ters, rotational or intermittent irrigations were superior ways to leachsoil salts (Zeng et al., 2014). Heng et al. (2018) confirmed that
sub-surface tile drainage combined with drip irrigation significantly
increased the desalination rate of the 0–200 cm soil profile and main­tained the mean soil salt content below 10 g/kg.
Numerical modeling is a practical approach to predicting soil waterand solute distributions in drip-irrigated fields. The HYDRUS software is
one of the most powerful simulating tools widely used for this purpose(Šimůnek et al., 2008, 2012, 2016). For example, Grecco et al. (2019)used HYDRUS-2D to simulate water flow and potassium movement incontainers packed with tropical soil irrigated by a subsurface drip andcultivated with sugarcane. Mekala et al. (2016) and Guo et al. (2022)
simulated the nitrogen dynamics in variably saturated soils using
HYDRUS-2D. Qi et al. (2018) used the HYDRUS-2D model to explore soil
water flow and salt transport in field experiments to understand pro­
cesses under MDI better. Chen et al. (2018) evaluated two-dimensional
distributions of soil water contents and soil EC1:5 (the electrical con­ductivity of the 1–5 soil/water suspension) during different crop growthstages under mulched drip irrigation with fresh and brackish waters.
However, there were some differences between the two-dimensional(2D) model and the 2D cross-sections of the three-dimensional (3D)
model. Karandish et al. (2019) stated that a 3D model could achieve
higher accuracy when simulating soil water and solute transport in fieldsoils with a drip irrigation system than a 2D model. Honari et al. (2017)
found that soil water content values simulated using HYDRUS-3D werehighly consistent with the experimental observations. Finally, Li et al.
(2018) simulated individual drippers as nodal discharge sources using
the 3D model and could effectively describe the overlap of wetting fronts
along the drip lines.
The use of HYDRUS (2D/3D) as an excellent and accurate tool for
irrigation management simulations is justified by available literature(see an extensive list in Šimůnek et al., 2016). Nevertheless, using the 3D
module of HYDRUS (2D/3D) for comprehensive evaluation of environ­mental impacts, plant water and salinity stresses, and irrigation watersavings of various irrigation management schemes has been relatively
rare. Hence, this study aims to use HYDRUS-3D to simulate
three-dimensional soil water and salinity movement for MDI withdifferent irrigation water amounts and salinities. Irrigation water con­sumption is also simulated and compared with the results from theliterature. The two objectives of this study are thus as follows. First, toevaluate three-year 3D soil water movement and salt transport fordifferent modeling scenarios; Second, to assess various MDI schemes interms of their impacts on the soil environment and irrigation watersavings while considering the cotton soil water and salinity stressthresholds. This study contributes to optimizing MDI strategies for arid

regions, which are much needed for regional sustainability.
2. Materials and methods
2.1. Field experiments
The experiments were carried out in a cotton field between June and
September 2010. The experimental site is located in the Tarim Basin, in
the arid southern part of Xinjiang, Northwest China (41º35′ 14′ ’N,
86º10′ 24′ ’E). The area is classified as having a continental desert climate
with annual precipitation of 53.3–62.7 mm and annual evaporation of
2273–2788 mm. The average elevation is 901 m above the mean sea
level, and the average annual minimum and maximum temperatures
range from 5 ◦ C to 18 ◦ C. Freshwater (FW) used for irrigation comesfrom the Peacock River, whose mean salinity is 1.01 g/L. Brackish water(BW) used to irrigate cotton has a salinity between 2.98 and 4.19 g/L,
with the mean salinity of 3.0 g / L. Following traditional cotton irriga­
tion patterns, brackish water was used for drip irrigation. The actual
amount of irrigation water was 420 mm and the irrigation frequency
was set every six days 18 times (irrigation #1–18). The irrigation water
quantities were 9.35 mm (# 1–2), 19 mm (# 3–5), 36 mm (# 6–10),
24.1 mm (# 11–16), and 9.75 (# 17–18) from the seedling to bollopening stages. The soil profile consisted of four soil layers of sandy
loam and sand, the properties of which are shown in Table 1.
The drip irrigation experiments with brackish water and mulchedsoil surface were conducted in one randomly selected cotton plot of thesize of 10 m × 24.8 m = 248 m2 (Fig. 1a). Seventeen drip lines were
installed in a pattern of “one mulch strip, two drip lines, and four croprows” (Fig. 1b). Two irrigation lines were installed for four cotton rows.
The soil surface was mulched with degradable plastic sheeting. The
distance between the drip lines was 55 cm, and the drip spacing was 30
cm. The cotton plants along a narrow row were spaced 10 cm apart. The
non-mulched area of the bare zone was 40 cm. This pattern was expected
to provide irrigation water for roots and reduce deep percolation (Liet al., 2015).
2.2. Measurements
The soil volumetric water content (θ), soil salinity (EC1:5), cotton leaf
area index (LAI), and the groundwater table depth were measured in theexperimental field (Fig. 1 a-d). The hourly weather parameters, such as
air temperature, air pressure, air humidity, solar radiation, and windspeed and direction, were recorded using a Davis wireless Vantage Pro2
weather station (Davis Instruments, California, USA). The soil watercontent was measured in different depths using the L520 Neutron Probe(Institute for Application of Atomic Energy, JAAS, Nanjing, China) in thewide, narrow, and bare zone (Fig. 1c). The soil water samples wereextracted using a Rhizon Soil Moisture Sampler (Eijkelkamp Agrisearch
Equipment Co., Giesbeek, the Netherlands) and their EC1:5 wasmeasured using a conductivity meter (DDS-307, INESA Scientific In­
strument Co., Ltd., Shanghai, China) in the on-site laboratory. The LAI of
cotton during all growth stages was measured using a WDY-500A Microelectron Leaf Area Instrument (Harbin Optical Instrument Factory,
Harbin, China). The groundwater table depth was monitored using theelectronic digital water level gauges and was 1.37～2.47 m (Fig. 1d).
Table 1
Soil properties of the four soil layers.

2

Depth
(cm)

Bulk density (g/
cm3)

Clay
(%)

Silt
(%)

Sand
(%)

Soil type

0–30
30–60
60–90
90–140
140–160

1.70
1.65
1.60
1.80
1.77

4.9
5.0
5.1
3.9
2.4

16.4
16.8
17.2
19.9
7.1

77.7
77.9
76.7
75.8
89.7

Sandy
loam
Sand

L. Guo et al.

Agricultural Water Management 283 (2023) 108303

Fig. 1. The cotton field with a monitoring well (a), the conceptual 3D model of mulched drip irrigation with boundary conditions (b), locations of different
measurements (c), and groundwater table depth (d).

The cotton root distributions at different growth stages were
measured by soil sampling (Table 2). The spatial cotton root distribu­
tions were sampled at a regular grid and measured using DT-SCAN(Wang et al., 2014). Root distribution parameters, i.e., temporal varia­
tions in root-zone depths and widths, were input into the HYDRUS
model for various cotton growth stages.

where ET0 is evapotranspiration of the reference grass (mm/day); Rn isthe net radiation at the crop surface (MJ/(m2d)), G is the soil heat flux
density (MJ/(m2d)), T is the air temperature at a 2-m height (◦ C); μ2 is
the wind speed at a 2-m height (m/s), es is the saturation vapor pressure(kPa), ea is the actual vapor pressure (kPa), Δ is the slope of the vapor
pressure curve (kPa/◦ C), and γ is the psychrometric constant (kPa/◦ C).
The difference between field crop evapotranspiration, ETc, andreference grass evapotranspiration, ET0, can be integrated into a single
crop coefficient Kc or separated into two coefficients, i.e., a basal crop
coefficient Kcb and a soil evaporation coefficient Ke:

2.3. ETc under mulched drip irrigation
Except for dripper fluxes, the surface fluxes were calculated using
meteorological data. The water fluxes at drippers, i.e., the irrigation
amounts, were determined considering potential evapotranspiration
rates calculated using the Penman-Monteith equation. The standard
FAO Penman-Monteith equation (Eq. 16) was used to calculate thereference evapotranspiration rate, ET0, from meteorological data (Allenet al., 1998).
ET0 =

900
0.408Δ × (Rn − G) + γ × T+273
× μ2 × (es − ea )Δ + γ(1 + 0.34μ2 )Table 2The maximum depths and widths of cotton roots.
2010–630

2010–711

2010–720

2010–729

Maximum lateral width(cm)
Maximum depth (cm)

6.65

14.1

32.75

33.44

10–20

20–30

25–40

35–50

(2)

Kc=Kcb+Ke(3)

where ETc is cotton evapotranspiration (mm/day), Kc is the crop coef­
ficient for cotton (dimensionless), and ET0 is reference crop evapo­transpiration (mm/day). The dual crop coefficient approach isappropriate for irrigation scheduling of high-frequency water applica­
tions. The basal crop coefficient Kcb describes plant transpiration, andthe soil water evaporation coefficient Ke describes evaporation from thesoil surface.
The crop coefficients for conditions without mulch are given by FAO(Allen et al., 1998). The Kc coefficient increases gradually during theearly and middle growth stages, reaches the maximum during thevigorous growth stages, and then decreases slowly in the later growthstages. It is larger under arid conditions when the agricultural crop has alarge leaf area and roughness height. Kc may be larger than one sinceevaporation from the soil surface is considerable when the soil is wet due

(1)

Date

ETc=Kc•ET03

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Agricultural Water Management 283 (2023) 108303

to irrigation or rain. Areas with a more arid climate and larger windspeeds will have higher values of Kc.
Many scholars have researched mulched conditions (e.g., Battikhi
and Hill, 1988). The crop coefficient is reduced due to a decreased cropwater demand under mulched drip irrigation. Soil evaporation frombare zones gradually declines during later cotton growth stages, and theinfluence of the mulch is thus steadily reduced. As a result, Kc is slowly
approaching the conditions without mulch. The recommended values of
Kc for our experimental field were 0.13 (early) and 0.27 (late) during theseedling stage, 0.91 during the budding stage, 1.15 (early) and 1.08(late) during the flowering and boll-setting stage, 0.61 (early) and 0.27
(late) during the boll-opening stage, respectively.
Evaporation from the exposed soil mainly depends on the watercontent in the surface soil. When the soil surface is wet (following rain or
irrigation), the soil evaporation reduction coefficient Kr is 1. When soil
water that can be evaporated from the topsoil is depleted, Kr decreases
and becomes 0 (FAO Irrigation and Drainage Paper No. 56):
Kr =TEW − De,i− 1
forDe,i− 1 > REW
TEW − REW

(

(

)

(

)

(7)

where S is the root water uptake rate (day− 1). A water stress response
function (to account for saturation stress on root water uptake) sug­gested by Feddes et al. (1978) and the Threshold-and-Slope Model (toaccount for salinity stress) of Maas (1990) were considered in thesimulations.
The soil salt transport is described using the three-dimensional
advection-dispersion equation (Šimůnek et al., 2018):
(
)
∂(θc)
∂
∂c
∂qi c
− Scs
(8)
=
θDij
+
∂xi
∂t
∂xj
∂xi
where c is the total concentration (g/L), Dij are the components of thedispersion coefficient tensor (cm2/d), qi is the i-th component of thevolumetric fluid flux (cm/d), and cs is the concentration of the sink term
due to root water uptake (g/L).
The solution of Eqs. (7) and (8) requires the knowledge of the initial
distribution of soil water pressure heads and solute concentrations
within the flow domain. The soil water pressure head h and total con­centration c of soil salts measured on July 15th, 2010 (one day before theirrigation day), are used as the initial conditions. The initial conditions
were given as follows:(4)

where Kr is the evaporation reduction coefficient, which depends on thesoil water depletion (a cumulative depth of evaporation) from thetopsoil layer, De,i-1 is the cumulative depth of evaporation (depletion)from the soil surface layer at the end of the day i-1 (mm), TEW is the
maximum cumulative depth of evaporation from the soil surface layer
when Kr = 0 (mm), and REW is the cumulative depth of readily evap­orable water (mm). The amount of water that can be depleted byevaporation during a complete drying cycle can be estimated as follows:
TEW = 1000(θFC − 0.5θWP )Ze

)

∂θ ∂
∂h
∂
∂h
∂
∂h
∂K
=
K
+
K
+
K
+
− S
∂y
∂z
∂t ∂x
∂x
∂y
∂z
∂z

h(x, y, z, 0) = hi (x, y, z)(0 ≤ x ≤ 150, 0 ≤ y ≤ 90, − 160 ≤ z ≤ 0)(9)c(x, y, z, 0) = ci (x, y, z)(0 ≤ x ≤ 150, 0 ≤ y ≤ 90, − 160 ≤ z ≤ 0)

(10)

The boundary conditions are shown in Fig. 1b. Four different
boundary conditions are used for water flow: time-variable flux, timevariable pressure head, atmospheric, and no flux boundary conditions.
A third-type Cauchy boundary condition (with different indexes) is used
for soil salt transport along all domain boundaries. The time-variable
flux boundary conditions are used for the drippers (Var.Fl1) and thesoil surface under the mulch (except for the drippers) (Var.Fl2). The baresoil on the sides of the transport domain was represented using an at­mospheric boundary condition. We designed the maximum dripper
water amount based on the volume, which could saturate the root zone
soil (0 – 50 cm), so irrigation impact on the groundwater level was
negligible. Therefore, a time-variable pressure head specifying the
groundwater table position was assigned along the bottom boundary(Var.H1). No-flow boundary conditions were used on the sides of thetransport domain. The specific boundary conditions are as follows(Šimůnek et al., 2018):(i) No flux:(5)

where θFC is the soil water content at field capacity (cm3/cm3), θWP is thesoil water content at the wilting point (cm3/cm3), Ze is the depth of thesurface soil layer that is subject to drying by way of evaporation, 0.10 ~0.15 m. We use θFC = 0.271, θWP = 0.051, Ze = 0.15, and thus TEW= 36.8 cm (Wang et al., 2022). The cumulative depth of readily evap­orable water was recommended as REW = 20 cm. The cotton evapo­transpiration rate ETc, measured precipitation, and soil evaporationfrom unmulched soil surface are shown in the supplementary electronic
file.
2.4. 3-D conceptual model
The HYDRUS (2D/3D) software (Šimůnek et al., 2018) was selected
for the simulations to evaluate the effects of various irrigation man­agement systems. We refer to the HYDRUS technical manual (Šimůnek
et al., 2018) for a detailed description of the governing equations
describing variably saturated flow using the Richards equation, solutetransport using the advection-dispersion equation, and root water up­
take, as well as various initial and boundary conditions that can be
implemented. The conceptual model used for the calculations is shownin Fig. 1b. The transport domain was 150 cm × 90 cm× 160 cm. The
surface soil area associated with transpiration was 150 × 90 = 13,
500 cm2. The governing water flow equation is given by the following
modified form of the Richards equation (Šimůnek et al., 2018):
[ (
)]
∂θ
∂
∂h
K K Aij− S(6)+ K Aiz=∂t ∂xi∂xj

qx = 0, qy = 0; (x = 0 or 150; y = 0 or 90; − 160 ≤ z ≤ 0 )(11)(ii) Atmospheric:− K(h)(Kxz

∂h
∂h
∂h
+ Kyz + Kzz + Kzz ) = qa (t)(0 ≤ x ≤ 20, 130 ≤ x∂z∂y∂z≤ 150, 0 ≤ y ≤ 90, z = 0)

(12)(iii) Time-variable flux Var.Fl1:
K(h)(Kxz∂h∂h∂h+ Kyz + Kzz + Kzz ) = qd (t) (x = 47.5 or ​ 102.5; ​ y∂z∂y∂z= 15, 45, or ​ 75; ​ z = 0)(13)(iv) Time-variable flux Var.Fl2:

where h is the soil water pressure head (cm), θ is the volumetric watercontent (cm3/cm3), xi and xj are the spatial coordinates (cm) (i, j = 1, 2,
3, x1=x, x2=y, x3=z), t is time (day), K Aij are the components of thedimensionless anisotropy tensor KA; K is the unsaturated hydraulicconductivity (cm/day); and S is root water uptake (day− 1). The threedimensional version of Eq. (1) for an isotropic medium is as follows:− K(h)(Kxz

∂h
∂h
∂h
+ Kyz + Kzz + Kzz ) = qm (t)
∂z
∂y
∂z

(20 ≤ x ≤ 130, x ∕= 47.5 and 102.5; 0 ≤ y ≤ 90, y ∕= 15, 45, and 75; z
= 0)
(14)
4

L. Guo et al.

Agricultural Water Management 283 (2023) 108303(v) Time-variable pressure head Var.H1:h(x, y, z, t) = hg (t)(0 ≤ x ≤ 150, 0 ≤ y ≤ 90, z = − 160)

narrow, and wide zone) of the cotton field. The θ at all three locations
decreased due to root water uptake as follows: flowering and boll-setting> seedling > the mean value > budding > boll-opening (Fig. 2). The θ
increased gradually with soil depth due to the effects of groundwater.
The θ during the flowering and boll-setting stages in the narrow andwide zones were higher than in the bare zone, indicating that mulch wasbeneficial in conserving soil water. The C values increased with depth in
the narrow and wide zones’ upper layer, then decreased with depth in
the lower part of the soil profile. The C peaked at a depth of 30–40 cm,
mainly due to the root uptake water. The C values in the three zones
decreased as follows: bare > wide > narrow. The C values visibly fluc­tuated in both wide and bare zones, but varied only slightly in the
narrow zone, indicating that soil salt distributed more uniformly
because of an abundant water supply under irrigation drippers.

(15)(iv) Solute transport boundary conditions:
(
)
∂c
∂c
∂c
− θ Dxz + Dyz + Dzz+ qc = qa ca (0 ≤ x ≤ 20, 130 ≤ x ≤ 150, 0 ≤ y∂x∂y∂z≤ 90, z = 0)
(16)
(
)
∂c
∂c
∂c
− θ Dxz + Dyz + Dzz+ qc = qd cd (x = 47.5or102.5; y∂x∂y∂z= 15, 45, or75; z = 0)

(17)

(
)
∂c
∂c
∂c
− θ Dxz + Dyz + Dzz+ qc = qm cm∂x∂y∂z(20≤x≤130, x∕=47⋅5 and 102⋅5; 0≤y≤90, y∕=15, 45, and 75; z=0)(18)c(x, y, z, t) = cg (x, y, z)(0 ≤ x ≤ 150, 0 ≤ y ≤ 90, z = − 160)

(19)

3.2. Model validation
The soil hydraulic properties were described using the van
Genuchten-Mualem model (van Genuchten, 1980; van Genuchten andLeij, 1991). The hysteresis model of Lenhard et al. (Lenhard et al., 1991;
Lenhard and Parker, 1992), which avoids the pumping effect duringmultiple drying and wetting cycles, was used. Initial soil hydraulic pa­
rameters were adopted from the literature (Li et al., 2018; Guo et al.,2022). The longitudinal and transverse dispersivities, DL and DT, wereselected based on the DL/DT ratio for different textural classes given in
the literature (Table 4). The initial diffusion coefficient was set equal tothe average value found in the literature (1.60 cm2/day) (Table 6)(Kadyampakeni et al., 2018; Mazloomi and Jalali, 2019; Yang et al.,2017). The root water uptake parameters of the water stress response
function were based on the literature values and were set at − 10 cm,− 25 cm, − 200 cm, − 800 cm, − 1000 cm, 0.5 cm/day, and
0.1 cm/day. (Feddes et al., 1978; Wang et al., 2014; Li et al., 2018). A
database of suggested values for different plants for the threshold-slope
salinity stress model is provided by Maas (1990). The salinity thresholdand slope values for cotton were suggested to be 15.4 dS/m and 2.6,
respectively. We converted the threshold value to the concentration
units as 13.24 g/L.
The HYDRUS simulation results were obtained using the adjusted
soil hydraulic parameters listed in Table 5. The soil parameters for waterflow and solute transport were calibrated using the observed values ofsoil water contents and salt concentrations at multiple locations. The
final diffusion coefficient was set to 1.58 cm2/day. Fig. 3 showsobserved and simulated water content distributions at various depths in
the narrow zone for the experimental irrigation scenario, while similarplots for C. It shows a generally good agreement between observed andcalculated water contents. The simulated values showed much morevariation versus time in response to irrigation than the observed values
due to the lower measuring accuracy of field water content. Similarly, 3D simulation results showed more obvious fluctuations than 1-D (Wanget al., 2014) and 2-D (Li et al., 2018; Guo et al., 2022) modeling results(data not shown). Observed and simulated C values showed similarvariations during the crop growth stage. The model could better display

where qx and qy are water fluxes in the x and y direction (cm/day); t is
time (day); K(h) is the unsaturated hydraulic conductivity function (cm/day); qa, qd, and qm are water fluxes (cm/day) at the bare soil surface,under drippers, and in the mulched zone (except for the drippers),
respectively; hg is the groundwater table position (cm). qm was set to
0 during irrigation events and to qa during non-irrigation. ca, cd, and cm
are liquid phase concentrations (g/L) for the bare soil surface, drippers,
and the mulched zone (except for the drippers), respectively. cd is equal
to the salt concentration of irrigation water during irrigation events and0 when no irrigation occurred. The flux concentration for the mulchedarea (no drippers) and the bare zone was 0.
2.5. Modeling scenarios
Numerical simulations of soil water flow and salt transport werecarried out using HYDRUS (Šimůnek et al., 2016, 2018). In our simu­
lations, we considered the irrigation water concentrations to be 3.0 g/L(brackish water, BW) and 1.01 g/L (freshwater, FW). There was nowater flux under the mulched zone except for the drippers. Table 3
provides information about irrigation scheduling (qd). The total brackish
irrigation water amounts were as follows: 1.0 Q = 478.5 mm, 1.2 Q= 574.2 mm, and 1.4 Q = 669.9 mm, where Q is the reference irrigation
amount. While freshwater (FW) was used at the beginning of theexperiment (1st May) to wash out the salts, brackish water was used for
subsequent irrigations.
3. Experimental results and validation
3.1. Measured soil water contents and salt concentrations
The average soil water contents (θ) and soil salt concentration (C) for
different growth stages were calculated for various locations (bare,
Table 3Irrigation scheduling considered in the model.
Irrigation order1

2–5

30 daysFrequency: irrigation every 6 days

Growth stage

Washing salt

Seedling stage/

Water quantity (mm)

1.0 Q
1.2 Q
1.4 Q

40FW
40FW
40FW

16.5BW
19.8BW
23.1BW

6–8
Budding stage

27.5BW
33.0BW
38.5BW

9–12

13–15

Flowering and boll-setting stage
Early

Late

49.5BW
59.4BW
69.3BW

33.0BW
39.6BW
46.2BW*FW: freshwater; BW: brackish water.
5

16–17

Total brackish irrigation water amount(mm)

Boll-opening stage

16.5BW
19.8BW
23.1BW

478.5
574.2
669.9

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Agricultural Water Management 283 (2023) 108303

Fig. 2. Variations in soil water contents and salt concentrations during different growth stages (seedling, budding, flowering and boll-setting, and boll-opening) at
various locations of the cotton field (bare zone – left, narrow zone – middle, and wide zone – right).
Table 4The literature reference values of DL/DT.
DL/DT
(cm)

soils

Bulk density(g/cm3)

Clay
(%)

Silt
(%)

Sand
(%)

References

8.00
20.00

Sandy loam
Loamy sand
Sand
Clay loam
Sandy - sandy clay loam
Silty clay loam
Sandy loam
Clay

1.46–1.54
1.68
1.94–2.04
1.20
1.31
1.21–1.59
1.52–1.65
1.49
1.40
1.64
1.50
1.60–1.61

0.5
8.0
2.0–3.0

45.1–46.8
10
5.0–10.0

46.0–54.4
82.0
87.0–93.0

Lyu et al. (2019)
Singh et al. (2019)

6.6–9.2

50.7–62.9> 50
27.9–42.7

20.0
21.3

51.0
29.4

29.0
49.3

13.6–17.6

34.6–36.6

47.8–51.8

4.0

25.0

71.0

0.02–1.64
1.73
5.00–10.00
1.52
0.63–1.87/0.22–0.44
0.57
5.00/0.50
0.60
10.00/1.00
0.25–0.98

Sand
Loam
Loam
Clay soil
Sandy loam

soil water content and salinity dynamics during irrigation events.
Overall, simulated values of the soil water content and the C matchedthe observed values well Table 6.
To better evaluate the accuracy of the model simulations, thefollowing statistical performance indexes were calculated: the root meansquare error (RMSE), the Willmott index (d), the Nash–Sutcliffe model
efficiency coefficient (NSE), and the coefficients of determination (R2),
involving the values of θ and C observed and those estimated by themodel (Moriasi et al., 2015; Arraes et al., 2019; Al-Mayahi et al., 2020;
Wen et al., 2022) (Table 7). The d values were 0.852 and 0.970, indi­
cating that the model’s accuracy is sufficient (Table 7).

Archundia et al. (2019)
Waleeittikul et al. (2019)
Yang et al. (2017)
Jha et al. (2017)
Pal et al. (2014)
Wang et al. (2017)
El-Nesr et al. (2014)
Ranjbar et al. (2019)
Inosako et al. (2019)
Masipan et al. (2016)

4. Modeling results and discussion
To better understand the soil water and salt concentration dynamics,
we display below θ and salt concentrations in the cross-sectional views
through the 3-D transport domain.
4.1. Soil water distributions
Fig. 4 shows the simulated θ values for the 1.0 Q (=478.5 mm)
scenario during three years. The θ values in the soil profile’s upper layer(0–50 cm) fluctuated but changed only slightly in the deeper layer(50–160 cm). Due to restricted evaporation, the θ values under the6

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Agricultural Water Management 283 (2023) 108303

Table 5The soil hydraulic parameters and solute dispersion coefficients of different soil layers.
Initial parameters

Optimized parameters

Depth (cm)θrθsα (cm− 1)n

Ks cm/day)lDL (cm)DT (cm)

0–30
30–60
50–90
60–140
140–160
0–30
30–60
60–90
90–140
140–160

0.03810.050
0.054
0.03370.0441
0.051
0.045
0.054
0.034
0.044

0.36
0.37
0.38
0.39
0.34
0.36
0.37
0.38
0.39
0.34

0.022
0.024
0.005
0.013
0.026
0.011
0.010
0.005
0.013
0.026

2.06
2.40
2.62
2.56
2.42
2.06
2.22
2.62
2.56
2.42

36.1
33.5
32.4
31.3
42.1
23.5
21.2
22.4
24.7
25.5

0.5
0.5
0.5
0.5
0.5
0.05
0.05
0.05
0.05
0.05

200
200
200
200
200
38
39
37
53
31

5
5
5
5
5
4.8
5.3
5.2
4.2
3.5

Fig. 3. The comparison of the measured and simulated soil water contents (θ) and salt concentrations (C) at different depths in the narrow zone.

mulched zone were higher than those under the bare zone. The θ values
in the root zone (within depths of 0–50 cm) reached 0.35–0.4 one day
after irrigation with FW and gradually decreased until the next irrigation
event. Five days (35th, 59th, 77th, 101st, 119th) after irrigation with BW
during cotton growth stages, the θ values in the top soil layer (within
depths of 0–10 cm) of the bare zone decreased to 0.05 − 0.15, and at
depths of 10–50 cm to 0.15 − 0.203, which was lower than field ca­
pacity (0.271). When irrigation was terminated, the θ values in the rootzone (within depths of 0–50 cm) dropped to 0.10–0.15.
Fig. 5 shows the temporal and spatial variations in simulated θ one
day after irrigation for different irrigation scenarios with varying
amounts of irrigation during growth stages when six ellipsoids

Table 6
Molecular diffusion coefficients for selected solutes.
Diffusion coefficient (cm2/day)

SoluteReferences

1.70
2.32
1.65
0.86
1.63
1.30
1.52
1.64

Potassium
N-NO3-, N-NH4+
NitrogenPhenol
NaCl
Urea
NH+-N
NO-3-N

Grecco et al. (2019)
Lyu et al. (2019)
Jha et al. (2017)
Pal et al. (2014)
Wang et al. (2017)
Ranjbar et al. (2019)

Table 7
Statistical comparison of simulated and observed values.
RMSE

θ
C

d

R2

NSE

Actual Value

OptimalValue

Actual Value

OptimalValue

Actual Value

OptimalValue

Actual Value

Optimal
Value

0.037
0.579

0.0

0.852
0.970

1.0

0.405
0.890

1.0

0.53660.88961.0

7

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Agricultural Water Management 283 (2023) 108303

Fig. 4. Simulated θ during three years (at different growth stages) in the 1.0 Q (478.5 mm) scenario.

Fig. 5. Simulated θ on the 1st day after irrigation events with different irrigation water amounts for different irrigation scenarios (1.0 Q – top, 1.2 Q – middle, and 1.4
Q – bottom).

(θ ≥ 0.34) with a dripper at the center were formed (Murat, 2020). The
ellipsoids expand wider and deeper for larger irrigation water amounts,
i.e., for the 1.2 Q and 1.4 Q scenarios. For the 1.0 Q scenario, theellipsoid expanded 4 cm horizontally and 2 cm vertically in the seedlingstage (16.5 mm), which is in the same range as in the boll-opening stage.
In the budding stage (27.5 mm), six mutually independent ellipsoids(about 9 cm horizontally and 16 cm vertically) were formed under two
drip lines. In the late flowering and boll-setting stage (33.0 mm), theellipsoids expanded 14 cm horizontally and 20 vertically. The ellipsoids
under two drip lines overlapped during the early flowering andboll-setting stage (49.5 mm). The overlapped ellipsoids under each drip
line were observed during the budding stage for scenario 1.4Q when theirrigation amount was 38.5 mm. The ellipsoidal regions were the largest
at the early flowering and boll-setting stages, and the soil was almost
saturated. The ellipsoids expanded beyond the X-direction boundary
when the irrigation water amount was higher than 59.4 mm, whichmeans that the ellipsoids of two adjacent conceptual models overlapped.
The 3-D results could well show the spatial distribution of soil water.

4.2. Soil salts distributions
The spatial and temporal variations of C for the 1.0Q scenario areshown in Fig. 6. The salts concentrations decreased during the washingsalt stage and increased during the cotton growth stages. Soil salts wereleached with FW during the washing salt stage, which provided a lowsalt environment for planted cotton seedlings. Low salinity (< 2.8 g/L)
regions 17 cm deep and 13 cm wide (radially) with a dripper at thecenter were formed. During the seedling stage, the green region (withsalinities between 3.00 and 3.44 g/L) increased to a depth of 100 cm.
The low salinity green region disappeared during the budding stage, andthe C values reached 3.44–6.62 g/L. The salinity values increased atlater stages, especially under the bare zone (96th day, 114th day, 126th
day). Salt accumulation occurred at the soil profile’s depths of
50 − 160 cm, and the C values increased from 3.44 to 5.30 g/L. Soilsalinity fluctuations mainly occurred at upper depths of 0–50 cm due toevaporation, irrigation, and root water uptake.

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Agricultural Water Management 283 (2023) 108303

Fig. 6. Simulated salt concentrations during different growth stages in the 1.0 Q (478.5 mm) scenario.

4.3. Water stress analysis

demand could not be fully met even in the 1.4 Q scenario.
Therefore, we adjusted irrigations to one event per 3 days during theearly and late flowering and boll-setting stages and split the total irri­
gation amount into two. Fig. 8 shows the variations in θ before and after
adjusting irrigation amounts during the flowering and boll-setting
stages. The optimized water amount of one irrigation event was
reduced to 24.8 mm and 16.5 mm during the early and late floweringand boll-setting stages, respectively, with the total irrigation amount of
the two events the same as in the 1.0 Q scenario (49.6 mm and33.0 mm). The θ values in the root zone were higher than 0.203 during
both the early and late flowering and boll-setting stages. Hence, the soil
water could meet the cotton water demand.

The appropriate strategy of drip irrigation with brackish water andmulching should fulfill the cotton water demand without salt stresswhile minimizing the impact on the soil environment and water re­sources. The Regulated Deficit Irrigation index (RDI) was used to eval­uate whether the soil water meets the water requirements of cotton(Meng, 2008). The RDI and cotton root length (RL) during the growth
period are given in Table 8. The water stress moisture contents deter­
mined using RDI for cotton were 0.136 in the seedling stage, 0.163 in thebudding stage, 0.203 in the flowering and boll-setting stage, and 0.136
in the boll-opening stage.
Fig. 7 shows the variations in θ under the root zone (within depths of
0–50 cm). The θ values fluctuated from 0.174 to 0.20 on the 6th day after
irrigation during the seedling stage in the 1.0 Q scenario. These values
were higher than 0.136 and thus met the cotton water demand, similarly
to during the budding and boll-opening stages (Fig. 7a). The θ values
fluctuated from 0.171 to 0.020 during the early flowering and bollsetting stages in the 1.0 Q scenario and were lower than 0.203 on the
6th day after irrigation. The θ values fluctuated from 0.168 to 0.20
during the late flowering and boll-setting stages in the 1.0 Q scenario
and were lower than 0.203 on the 6th day after irrigation. These values
were lower than 0.203 and thus could not meet the cotton water de­
mand. Fig. 7b shows that all θ values were lower than 0.271 on the 3rd
day and lower than 0.203 on the 4th day after irrigation during theflowering and boll-setting stage in the 1.4 Q scenario. The cotton water

4.4. Salt stress and soil environment analysis
The reported soil salinity threshold for cotton is ECe = 7.7 dS/m (Minet al., 2017). Beltrán et al. (1999) and Maas et al. (1977) suggested thatthe threshold value (for the saturation extract electric conductivity) at
the seedling stage is ECe1 = 3.0 dS/m. Soil salinity levels were inferred
based on the measured values of salt concentrations C and electricalconductivities of the 1:5 soil/water suspension, and it was calculatedusing C = 0.8598 ×EC (Chen et al., 2018; Slavich and Petterson, 1993;
Wang et al., 2014). Hence, the threshold value in the C units wascalculated to be Ce1 = 2.58 g/L and Ce2 = 6.62 g/L. ECe values were used
to classify soil salinization stresses into mild ( 2–4 dS/m), moderate (4–8
dS/m), severe (8 − 15 dS/m), and extremely severe (> 15 dS/m). (Akçaet al., 2020; Fitzpatrick, 1980). Similarly, we calculated the soil salinity
C classifications as 1.72 – 3.44 g /L, 3.44 – 6.88 g /L, 6.88 – 12.90 g /L,> 12.90 g /L.
Fig. 9 shows the variations in C under the root zone (at depths of
0–50 cm) on the 1st day after drip irrigation at different growth stages
for the 1.0 Q and 1.4 Q scenarios (Y = 45 cm). The C values at 0 − 50 cm
depths under the mulched zone were below the Ce2 limit in both 1.0 Q
and 1.4 Q scenarios. Meanwhile, the soil salinization stresses were mildand moderate.
Soil salinization stress is mild in the low-salinity (2.23–3.44 g/L)
region at depths of 0 − 100 cm when FW is used for irrigation (Fig. 6).
Soil salinization stress is mild (3.19–3.44 g/L) at depths of 0 − 80 cm
when BW is used for irrigation, which would fail to meet the re­quirements of Ce1 = 2.58 g/L during the seedling stages (Fig. 9). Soilsalinization stress was moderate (4.02–6.20 g/L ≤ 6.62 g/L) at depths of
0 − 50 cm under the mulched zone during 55 − 126 days. Hence, weTable 8
Cotton-appropriate RDIs and root depths during various growth periods.

Sowing stage
Seedling stage
Budding stage
Flowering andboll-setting
stage
Boll-opening
stage

Sensitivity to
water
shortage

RDI (
Meng,
2008)

Water stressmoisturecontent

Time
(day)

RL
(cm)

/
Light/
Moderate
Light

/
≥ 50%
FC
≥ 60%
FC
≥ 75%
FC

/
0.136

0–10
10–45

/
15

0.163

45–70

25

0.203

70–120

45

≥ 50%
FC

0.136

120–200

15

Adequatewater supply
Moderate

FC (Field Capacity) = 0.271;
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Agricultural Water Management 283 (2023) 108303

Fig. 7. Variations of θ under the root zone (within depths of 0–50 cm): a) 6th day after irrigation during different growth stages in the 1.0 Q scenario and b) from 2th
to 4th day after irrigation during the flowering and boll-setting stages in 1.4 Q scenario.

changed irrigation from brackish water to fresh water (1.01 g/L) for
irrigation events 2 − 4. Fig. 10 shows the variations in salinity before
and after irrigation adjustment during the seedling stage. The C values in
the root zone under mulch were lower than 2.58 g/L during the seedlingstage, which meets the salt stress demand.

salinities were simulated for different irrigation water amounts and sa­
linities using HYDRUS-3D. The 3-D model could better describe thespatial distributions of soil water contents and soil salts. Due torestricted evaporation, the soil volumetric water content (θ) in themulched zone was higher than in the bare zone. Variations in soil watercontents in the root zone (within the depth of 0 − 50 cm) due to irri­
gation events were apparent, and less so in the deeper layers. The soil
water content response to irrigation events lasted about three days. The
soil salinity substantially decreased after freshwater was applied during
the washing salt stage and then gradually increased after irrigation withbrackish water during the cotton growth stages. The highest soil salt
concentration (C) values were observed in the upper layer of the barezone.
The cotton drought tolerance values in terms of θ were calculated tobe 0.136, 0.163, 0.203, 0.136 during the seedling, budding, floweringand boll-setting, boll-opening stages, respectively. The soil salinity
should not exceed the soil salinity threshold Ce1 = 2.579 g/L during theseedling stage and Ce2 = 6.620 g/L during later growth stages. To avoid
water and salt stresses, we suggest that 132 mm of freshwater irrigation
be used during the seedling stage and 412.9 mm of brackish water
irrigation during the other growth stages. The first freshwater irrigation
amount was 40 mm. After sowing and mulching, 33.0 mm of fresh water(FW) was used to avoid the salt stress in the root zone’s 0–15 cm depth
during the seedling growth stage. An additional 27.5 mm, 16.5 mm,
24.8 mm, and 16.5 mm of brackish groundwater (BW) were used during
the budding, boll-opening, early and late flowering, and boll-setting
stages, respectively. The irrigation interval of 3 days was recom­
mended during the flowering and boll-setting stages and 6 days during
the other stages. Compared with the earlier literature results, this irri­
gation strategy could save 26.18 × 108 m3 of surface (fresh) water and10.89 × 108 m3 of (brackish) groundwater in Xinjiang, considering thecotton farmland was about 2.2 × 106 ha. HYDRUS-3D is an efficient tool
for assessing and optimizing farmland water and salinity management.
Salt migration processes were simulated for three years for condi­
tions with a shallow groundwater table. The gradually increasing
groundwater use resulted in a groundwater table decline. Considering
the interactions between saturated and unsaturated zones and related
mechanisms is very important. Under such conditions, the water andsolute transport characteristics in the unsaturated-saturated zone would

4.5. Evaluation of water resources
We recommend an optimized irrigation scheme (Table 9) based on
the results discussed in Sections 4.3 and 4.4, which is as follows: Freshwater is applied in May of each year (1 − 30 days, when counting fromMay 1), and the water amount is set to 40 mm. After sowing andinstalling the plastic mulch, drip irrigation with fresh water is applied
again to avoid the salt stress in the topsoil (at depths of 0–15 cm) during
the seedling stage (31 − 54 days), and the water amount is set to33.0 mm. Brackish water is used during the later growth stages. The
water amounts are set to 27.5 mm and 16.5 mm for the budding andboll− opening stages, respectively. During the flowering and boll− set­ting stages, the irrigation frequency is set to 3 days (73 − 114 days) to
avoid water stress, and the water amounts are set to 24.8 mm during theearly stage and 16.5 mm during the late stage.
To optimize the water-saving irrigation system, we compared andanalyzed previously published irrigation schemes and our recommended
optimal scheme (Fig. 11). We calculated the theoretical volume of saved
water using the irrigation area demand for 2.2 × 106 ha of cotton fields
in the Xinjiang Province (China Statistical Yearbook, 2018). Wang et al.
(2014), Chen et al. (2014), Hu et al. (2020), and Guo et al. (2022)
showed that field MDI experiments required 49.5 × 108 m3 of surface
freshwater. Wang et al. (2014) used 101.7 × 108 m3 of brackish
groundwater, Chen et al. (2014) 121.44 × 108 m3, Hu et al. (2020)
110 × 108 m3, and Guo et al. (2022) 125.84 × 108 m3. Our recom­
mended irrigation strategy could save 26.18 × 108 m3 of surface fresh­water and 10.89 × 108 m3, 30.60 × 108 m3, 19.16 × 108 m3, and35.00 × 108 m3 of brackish groundwater compared to the field MDI
studies discussed above, respectively.
5. Conclusions
Three-dimensional distributions of soil water contents and soil
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Agricultural Water Management 283 (2023) 108303

Fig. 8. Variations in θ before (one irrigation on the 1st day) and after adjusting (two irrigations on the 1st and 4th day) irrigation during the early (top) and late(bottom) flowering and boll-setting stages. The profile with water stress is highlighted.

Fig. 9. Variations in C on the 1st day after drip irrigation during different growth stages in the 1.0 Q and 1.4 Q scenarios (Y = 45 cm).

be more complicated and computationally intensive, which would be
worthy of further research and discussion. Similarly, this irrigation
strategy also needs to consider macroscopic mechanisms mutuallyaffecting the soil environment and crops to manage agricultural water
usage. The model also did not consider the scale effects on soil salt

migration and accumulation.
Declaration of Competing Interest
The authors declare that they have no known competing financial
11

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Agricultural Water Management 283 (2023) 108303

Fig. 10. Variations in soil salinity after irrigation adjustment (from brackish water to fresh water) during the seedling stage.
Table 9Optimal irrigation scheme based on the model.
Irrigation event
Growth stage

1
Washingsalt

2–5
Seedling
stage

6–8
Budding
stage

Date

5/01–5/30

6/01–6/24

6/25–7/12

Time/days
Irrigation water quality
Irrigation frequency (d)Irrigation water amount
(mm)

1–30
F
/
40

31–54
F
6
16.5

55–72
B
6
27.5

9–16
17–22
Flowering and bollsetting stage
early

late

7/13–8/
05
73–96
B
3
24.8

8/06–8/23
97–114
B
3
16.5

23–24
Boll-opening
stage

Total freshwateramount(mm)

Total brackish wateramount(mm)

8/24–9/04

106

412.9

115–126B
6
16.5

Fig. 11. Comparison of recommended (literature) and optimized (this study) water requirements for freshwater (FW) and brackish water (BW) per unit area (leftaxis) and the entire region (right axis). The numerator (denominator) number refers to the left (right) axis.

interests or personal relationships that could have appeared to influence
the work reported in this paper.

Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.agwat.2023.108303.

Data Availability
Data will be made available on request.

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Acknowledgments
We sincerely appreciate Yujiang He from CAGS for providing
constructive comments on our work. This work was supported by the
National Natural Science Foundation of China [Grant No. 41702259],[Grant No. 42272306], and the Doctoral Fund of Technological Inno­
vation R & D project [2021SCSZYD-03].

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