Agricultural Water Management 213 (2019) 809–820

Contents lists available at ScienceDirect

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

A comparison of the HYDRUS (2D/3D) and SALTMED models to investigate
the influence of various water-saving irrigation strategies on the maize
water footprint

T

Fatemeh Karandisha, , Jiří Šimůnekb
⁎ab

Water Engineering Department, University of Zabol, Zabol, Iran
Department of Environmental Sciences, University of California Riverside, Riverside, CA 92521, USA

ARTICLE INFOABSTRACT

Keywords:
Crop growth
Water consumption
Water footprint
Consumptive WF
Degradative WF
Deficit irrigation
Partial root-zone drying

In this comparative research, we applied the HYDRUS (2D/3D) and SALTMED models to investigate the influence of various water-saving irrigation strategies on maize water footprints (WFs). The models were first calibrated and validated based on data collected in a two-year field investigation under five water-saving irrigation
treatments: full irrigation (FI), partial root-zone drying at water deficit levels of 55% (PRD55) and 75% (PRD75),
and deficit irrigation at the same levels (DI55 and DI75). While the SALTMED model performed well whensimulating crop growth parameters, with absolute relative error (|RE|) of 3.5–12%, the HYDRUS (2D/3D) model
was more accurate when simulating soil water and solute transport, with the normalized values of the root meansquare error (nRMSEs, 6.7–31.8%) and the mean bias error (nMBEs, 7.7–34.3%) lower than by SALTMED. Thisbetter performance of HYDRUS (2D/3D) resulted in 0.6–3.0% and 5.3–30.2% lower values of estimated consumptive and degradative WFs, respectively, compared to values estimated by SALTMED. While no considerable
differences were observed among various irrigation treatments regarding their consumptive WFs for the maize
production, PRD75 may represent a safer option under the water crisis, since its grey WF was 17.1–77.2% lower
than those estimated for the other water-saving irrigation treatments. This WF reduction was accompanied by aninsignificant reduction in crop yield and improved N uptake. Based on our results, while HYDRUS (2D/3D)
provides more reliable results, both the HYDRUS (2D/3D) and SALTMED models may be applied for the evaluation of new targets implemented for achieving sustainable agriculture in water-scarce regions.

1. Introduction
Water and agricultural authorities indicate that the scarcity of fresh
water is a major worldwide concern (Karandish and Šimůnek, 2017)
which threatens food security. Irrigated agriculture increased rapidly
throughout the world to satisfy the increasing demand for food (Yaoet al., 2017). In many countries, agriculture is the biggest fresh water
user, accounting for over 90% of diverted water. According to the Foodand Agriculture Organization (FAO) of the United Nations (AQUASTAT,
2016), in Iran in 2004, nearly 68% of total renewable water resourceswere used, of which 92.2% was allocated to the agricultural sector,
while the municipal and industrial sectors consumed only 6.6% and1.2%, respectively (AQUASTAT, 2016). Therefore, efficient water
management in the agricultural sector may represent the main source offresh water across the country.
Adapting water-saving irrigation strategies such as deficit irrigation⁎(DI) or partial root zone drying (PRD), in which crops receive less irrigation water during their growing season (Karandish and Šimůnek,2016a), may be a rational decision to cope with fresh water scarcity.
Many researchers have investigated economic and environmental consequences of applying DI (Stone, 2003; Klocke et al., 2004; Payero
et al., 2006) or PRD (Dry et al., 2000; Kang and Zhang, 2004; Kirda
et al., 2004; Tang et al., 2005; Shao et al., 2008; Karandish andŠimůnek, 2016a,b). They mostly concluded that while significant economic losses may be expected for agricultural crops under DI, PRD may
produce water savings without a significant decrease in yields.
Various indicators have been developed over the past decades toaddress the profitability of new water-saving irrigation strategies.
Among these, the water footprint (WF) index is known as the mostcomprehensive indicator. The WF is a multi-dimensional index ofhuman appropriation of freshwater resources, which could facilitate aproper assessment of patterns of consumption, production, and trade as

Corresponding author.
E-mail addresses: F.Karandish@uoz.ac.ir, Karandish_h@yahoo.com (F. Karandish).

https://doi.org/10.1016/j.agwat.2018.11.023
Received 22 October 2018; Received in revised form 20 November 2018; Accepted 22 November 20180378-3774/ © 2018 Elsevier B.V. All rights reserved.

Agricultural Water Management 213 (2019) 809–820

F. Karandish, J. Šimůnek

a function of water consumption (Hoekstra et al., 2011; Hoekstra, 2013,2017). One of the most highlighted advantages of the WF index is that it
is possible to apply it for evaluating such appropriation at various
spatial (i.e., for example, at the field, basin, national, or even global
scale) and temporal scales. Such assessment may be carried out for anindividual process, or for a number of processes, involved in the production and/or consumption of a product.
Previous researchers have mainly focused on the WF assessment at
the national or global scales (e.g., Chukalla et al., 2017, 2018;
Karandish and Hoekstra, 2017; Lee et al., 2017; Zhuo et al., 2016a,b;
Yoo et al., 2016a, b; Chukalla et al., 2015; Mekonnen and Hoekstra,
2014, 2015, 2016; Hoekstra, 2013), while its field-scale application hasreceived less attention. Additionally, only few researchers have estimated the WF related to the crop production based on data collectedduring field experiments, although it is expected that such assessment
would produce more realistic results.
Since field experiments are very time consuming and expensive, alarge number of the mathematical/conceptual models for simulatingsoil-water-crop relationships have been developed that can, after
proper validation, be used in scenario assessment. The modeling approach, although considered to be a useful alternative to field investigations, always involves uncertainties, the range of which depends
on many factors, including embedded governing equations and formulas for estimating target parameters. Hence, employing the best
available model may produce more reliable results for the policy makers when developing new targets for achieving sustainable agriculture.
While there are numerous crop-growth simulation models, only a few ofthem are suitable to simulate PRD conditions. Among these, theSALTMED model of Ragab et al. (2005) has proven to be highly accurate (Ragab et al., 2005; Pulvento et al., 2013; Ragab et al., 2015;
Ragab, 2015; Pulvento et al., 2015a; Hassanli et al., 2016; Afzal et al.,2016; Abdelraouf and Ragab, 2018). Additionally, Karandish andŠimůnek (2016a,b, 2017) demonstrated that soil water and solute dynamics under PRD conditions is very well captured also by the HYDRUS
model (Šimůnek et al., 2008, 2016).
Hence, in the current research, we carried out a comparative analysis to address the following objectives: (i) to evaluate the performanceof the SALTMED model for simulating soil-water-crop relationships
under various water-saving irrigation strategies, (ii) to compare themaize consumptive and degradative water footprints estimated using
HYDRUS (2D/3D) and SALTMED, and finally, (iii) to propose the mostappropriate water-saving irrigation strategy for the maize production in
Iran while both water and environmental issues are taken in consideration.

Fig. 1. Horizontal locations of laterals, drippers, and plants in the experimental
field, and vertical locations of TDR probes in the maize root zone. A rectangulardomain of 75*80 cm was considered in the HYDRUS and SALTMED models
since the maximum rooting depth was measured to be 80 cm during both
cropping cycles.

Thereafter, five 100 cm long TDR probes (Trime FM; IMKO; Germany)
were installed in each treatment (i.e., 25 TDR probes were installed in
the study area; 5 probes * 5 treatments) for continuous monitoring of
the soil water content (SWC) during both growing seasons.
Fig. 1.
With a 75 × 20 cm crop row and crop spacing, maize single-crosshybrid 704 was sown 5 cm deep, between and parallel to drip lines, on
May 26 both in 2010 and 2011. On May 26 in 2010 and 2011, 150 kg
ha−1 triple superphosphate was applied to the field. On June 12, 2010
and June 5, 2011, 65 kg ha−1 urea and 50 kg ha-1 potassium sulfate
were applied via irrigation water (fertigation). In addition, these fertilizers were also applied on July 19, 2010 and July 9, 2011, at rates of
135 kg ha−1 and 100 kg ha-1 for urea and potassium sulfate, respectively.
Maize was irrigated using the surface drip irrigation system every
other day. The irrigation water quality was measured weekly; theelectrical conductivity of irrigation water (ECiw) was in the range of
0.8–1.5 dS m−1, with averages of 1 dS m-1 and 1.1 dS m−1 during theentire cropping cycles of 2010 and 2011, respectively.
For each irrigation event, the irrigation water depth for the FI
treatment was calculated as follows:[In ]FI =2. Materials and methodsm
i=1{[ FCi( BIi )FI ] Di}(1)

where [In ]FI is the net irrigation depth (mm) of the nth irrigation eventfor the FI treatment, FCi is the volumetric SWC at field capacity (FC,cm3 cm−3) of the ith soil layer, ( BIi )FI is the volumetric SWC of the ithsoil layer before irrigation (cm3 cm−3) in the FI treatment, Di is the soil
layer thickness (mm), i is the soil layer, and m refers to the number ofsoil layers down to a specific soil depth, for which [In ]FI is calculated.
All treatments received the same amount of irrigation water during
the first 55 days after sowing (DAS) in 2010 and during 45 DAS in 2011.
Irrigation treatments were implemented during 55–107 DAS in 2010
and during 45–110 DAS in 2011, during which the PRD55 and DI55
treatments received 55% of the FI treatment’s irrigation amount at eachirrigation event, while the PRD75 and DI75 treatments receive 75%.
While in the FI and DI treatments, both drip lines were operated simultaneously, in the PRD treatments during the PRD period, to ensure
partial root-zone drying, just one of the drip lines was operated while
the other one was not during each irrigation event. Only half of the rootzone was thus irrigated during the PRD period, while irrigation shifted
between the two sides of the plants each week.
Prior to the onset of the irrigation treatments (i.e., on 55 DAS in2010 and 45 DAS in 2011) and then once a week (i.e., on 60, 66, 72, 78,
84, 90, 96, 102, and 106 DAS in 2010 and 52, 58, 64, 70, 76, 82, 88, 94,

2.1. Field experiment
Filed data were collected during a two-year experiment (2010 and2011) in an 825 m2 (15 × 55 m) maize field at the Sari Agricultural
Sciences and Natural Resources University (SANRU: 36.3◦ N, 53.04◦ E;
15 m below sea level). Sandy clay loam and clay loam textures wereobserved in the 0–20 and 20–100 cm soil depths, respectively. Daily
weather data were recorded at the weather station near the experimental field. The field trial was carried out using a complete block
design. Irrigation treatments consisted of full irrigation (FI), two partial
root-zone drying (PRD) treatments (PRD75 and PRD55), and two deficit
irrigation (DI) treatments (DI75 and DI55) in three replicates. Eachtreatment occupied a total area of 165 m2 (15 × 11 m), and each replicate of a specific treatment occupied an area of 55 m2 (5 × 11 m).
Before sowing, soil samples were collected every 20 cm down to the
80 cm soil depth for the analysis of soil chemical and physical properties. The field was then equipped with a surface drip irrigation system.
Having emitters 20 cm apart and an emitter discharge rate of 2 L hr−1,
drip lines were placed on the soil surface 75 cm apart (Fig. 1).
810

Agricultural Water Management 213 (2019) 809–820

F. Karandish, J. Šimůnek

100, 106, and 110 DAS in 2011), soil samples were collected every
20 cm vertically to a depth of 80 cm and at five equal horizontal distances between two drip lines for each treatment. Soil samples wereanalyzed for their total nitrogen (TN) and NO3−-N concentrations. At
the same dates, three crops per plot (i.e., each plot is considered as one
replicate of a treatment) were harvested for determining total crop N
uptake, total wet and dry biomass, and the leaf area index (LAI). Allconsidered soil and crop properties, as well as maize grain yield, werealso determined at harvest (107 DAS in 2010 and 110 DAS in 2011). A
detailed description of various measurements is provided in Karandishand Šimůnek (2018).
Soil physical properties, including soil texture, volumetric soil watercontents at field capacity, a permanent wilting point, sand, silt, and clay
contents, soil organic matter, and initial soil water and soil salinity
contents were measured at soil samples collected during the growing
season. Retention curves were measured for two soil horizons. For thispurpose, soil samples were taken for each treatment in three replicatesevery 20 cm to a depth of 80 cm before crop sowing using a 2-in ID
augur. SWCs at 11 different pressure heads were measured in the laboratory at each sample using a pressure plate apparatus. The van
Genuchten (1980) model parameters were then fitted to observed retention data.

osmotic pressure head for which Smax (t ) is reduced by half, and a (t ) is aweighting coefficient, which accounts for differential responses of acrop to matric and osmotic stresses, which may be defined as 50 (t ) ,h50 (t )where h50 (t ) is the matric pressure head for which Smax (t ) is reduced by
half.
There are two options in the SALTMED model for simulating crop
yield. One can calculate crop yield either using the relative yield index(RY) or more precisely using the crop growth status. In the second
approach, which was employed in our study, crop yield is obtained by
calculating the daily biomass production ( q ) and the harvest index(HI) (Exkersten and Jansson, 1991). On a daily scale, the q is calculated as follows:q = NANA = A R
A = E × I × f (Temp) × f (T ) × f (Leaf

2.2.1. Model description
The SALTMED model (Ragab, 2015) is a physically based holistic
model, which includes the following key processes: evapotranspiration,
crop water uptake, water and solute transport under different initialand boundary conditions, drainage, and the relationship between crop
yield and water uptake (Ragab et al., 2005; Ragab, 2015).
Potential evapotranspiration is estimated using the FAO-PenmanMonteith equation (Allen et al., 1998):
900

0.408(Rn G) + ( T+273 ) × U2 × (es+ (1 + 0.34U2)

AY =

ea )

t

(2)

Smax (t )5/3L for z(z ) =

25
×
12L

(1

0.0 for z > L

(6)

Kx

h
h
+
Kz
x
z
z
Dxx

x

k
z

Sw

c
c
+ Dxz
+
x
z
z

Dzz

(7)

c
c
+ Dzx
z
x

Sc

(8)

where is the volumetric SWC (L L ), K is the unsaturated hydraulicconductivity function (LT-1), h is the soil water pressure head (L), x is
the lateral coordinate, z is the vertical coordinate (positive downwards), t is time (T), Sw denotes root water uptake (T-1), c is the soluteconcentration in the liquid phase (ML−3), qx and qz are the componentsof the volumetric flux density (LT-1), Dxx, Dzz, and Dxz are the components of the dispersion tensor (L2T-1), and Sc is the sink term (nutrient
uptake, ML−3T-1).
h and
K relationThe soil hydraulic properties (i.e., theships) are in the SALTMED model described using the analytical functions of van Genuchten (1980):

(h) =

r +

( s
r)
(1 + | h|n )m

K (h) = Ks Kr (h) = Ks Se1/2 1(z , t )) for 0.2L < z

q × HI

3 −3

(1

1

Se m )m

2

(9)
3 −3

where r is the residual soil water content (L L ), s is the saturated
soil water content (L3L−3), Ks is the saturated hydraulic conductivity(LT-1), Kr is the relative hydraulic conductivity (-), (L-1) and n (-) areshape factors, m = 1 1/ n , and Se is the normalized volumetric soil
water content.
While the SALTMED model can consider subsurface drainage forboth horizontally or vertically installed tile drains, this option is not
used in this study. A more detailed description of the model is provided
in Ragab et al. (2005, 2015) and Ragab (2015).

0.2L
z
L

x

qc
qx c
+ z
x
z

(3)

3

a (t ) h +1+
50 (t )=c=t

where K cb is the basal crop coefficient and K e is the evaporation coefficient (Allen et at., 1998). The product K cb × ETO represents croptranspiration and K e × ETO bare soil evaporation. The crop coefficient isthen defined as K c = K cb + K e .
Actual root water uptake is then estimated using the approach developed by Cardon and Letey (1992), who determined root water uptake (S(z,t), mm d−1) as:S (z , t ) =whole cropping cycle

The Richards equation is used in the SALTMED model for simulating
two-dimensional water flow in the soil, while the nutrient transport issimulated using the diffusion-dispersion-convection equation (Hillel,1977):

where Δ is the slope of the vapor pressure curve (kPa OC−1), Rn is the
net radiation at the crop surface (MJ m−2 d−1), γ is the psychrometricconstant (kPa OC−1), T is the average air temperature (OC), U2 is theaverage wind speed (m s−1) at a 2-m height, G is the soil heat flux
density (MJ m−2 d−1), es is the saturation vapor pressure (kPa), and ea
is the actual vapor pressure (kPa).
Based on calculated ETO, crop evapotranspiration (ETc, mm) is thencalculated as follows (Allen et al., 1998):

ETc = (K cb + K e ) × ETO(5)

where q is increased crop biomass, NA is the net assimilation rate, A isthe assimilation rate, R is the respiration loss, E is the photosynthesis
efficiency (g dry matter MJ−1), I is the radiation input, Rs is solar radiation (MJ m-2 d−1), k is the extinction coefficient (k 0.6), LAI is theleaf area index, and f(temp), f(T), and f(Leaf-N) are stress factors related
to heat stress, transpiration stress, and leaf N content, respectively.
Based on daily calculated q , the AY at the end of the growing season,
subject to existing stresses (i.e., salinity, water, or nutrient stresses), canthen be calculated as follows:

2.2. The SALTMED model

ETO =

N)

e l × LAI )I = Rs (1L(4)

where Smax (t ) is maximum potential root water uptake at time t anddepth z, z is the vertical depth, (z , t ) is the depth-time dependent
fraction of the total root mass, L is the maximum rooting depth, is theosmotic pressure head, h is the matric pressure head, 50 (t ) is the811Agricultural Water Management 213 (2019) 809–820

F. Karandish, J. Šimůnek

2.2.2. Data requirements
The main inputs of the SALTMED model include: (i) meteorological
data, (ii) soil and crop properties, (iii) model parameters, (iv) and waterand nutrient management data. These input data may be directly
measured or may be obtained from the SALTMED database, which
provides default values for more than 200 plant species and 40 different
soil types.
The soil hydraulic parameters for the SALTMED model were discussed above. Since the SALTMED model requires the pore size distribution index , we estimated this parameter as = nm based on thefitted n and m in the van Genuchten (1980) model. Soil solute transportparameters were taken from Karandish and Šimůnek (2017).
Crop parameters, including the cropping calendar (i.e., sowing andharvesting dates, and duration of different growth stages), crop height,
rooting depth, LAI values, crop yield, and HI (harvest index) were takenfrom field-based measurements. Crop coefficients (Kc) were taken from
Karandish and Šimůnek (2016a). Ke values were taken from the FAOIrrigation and Drainage paper no 56 (Allen et al., 1998), and Kcb values
were then estimated as Kcb=Kc-Ke. Crop growth parameters were takenfrom the model database, except for the harvest index (i.e., the HI values), which was directly measured in the field. The initial estimates ofwater uptake parameters were also taken from FAO-56 (Allen et al.,1998). These parameters were then fine-tuned during the calibration
process, which is described in the following section. Water and nutrient
management data, including the number and dates of irrigation events,
irrigation water depths, irrigation water quality, the number and datesof fertilization events, fertilizers types, and fertilization rates werebased on field measurement data.

2.4. Comparison of the two models
While the SALTMED and HYDRUS (2D/3D) models use similar
equations for simulating water and solute transport in soils, they use
different approaches to calculate the effects of the matric and osmotic
stresses on root water uptake. SALTMED uses an additive function
while HYDRUS (2D/3D) allows users to choose different options. While
crop yield can only be obtained in HYDRUS as the ratio (RY) of actualand potential crop evapotranspiration, it is expected to be described
more accurately in SALTMED, which uses multiple crop parameters tosimulate crop yield during the cropping cycle. As described in Section
2.2, there is an option in SALTMED to obtain actual crop yield based oncrop growth parameters rather than based only on RY. The HYDRUS(2D/3D) model, on the other hand, cannot simulate the crop growth
status and only simulates actual evaporation and transpiration. As aresult, differences can be expected in crop yields and crop water consumptions simulated using the two models.
2.5. Criteria indices
The accuracy of both models was evaluated using selected criteria
indices, including the normalized root mean square error (nRMSE), thenormalized mean bias error (nMBE), and the relative error (RE), as
defined by the following equations:
Pi )2

(Oi
n

nRMSE =
(Oi

RE =

Pi )
n

nMBE =

2.2.3. Calibration and validation process
In the calibration process, the SALTMED model was first run using
the initial measured/default values of soil and crop parameters.
Thereafter, relevant model parameters (both soil and crop parameters)
were adjusted to obtain the best agreement between measured and simulated data for the 2010 growing season, including crop yield,
aboveground biomass, LAIs, soil water content, soil electrical conductivity, and soil NO3− content. The following parameters were finetuned during the calibration process using a trial-and-error approach:(i) crop parameters including Ke, Kcb, a crop fraction cover (fc), 50 , andphotosynthesis efficiency, and (ii) soil hydraulic parameters includingKs, s , the pore size distribution index , and the air-entry value.
Data collected during the 2011 growing season for all treatments
were then used to validate the SALTMED model. In the validation
process, the SALTMED model was run with calibrated soil and cropparameters, while the accuracy of model predictions was evaluated forcrop yield, aboveground biomass, LAIs, soil water content, soil electrical conductivity, and soil NO3− content.

(10)

Ōi

(11)

Ōi

(Pi

Oi )
Oi

× 100%(12)

where Pi and Oi are predicted and observed data, respectively, Ōi is theaverage of the observed data, and n is the number of observations. We
use the normalized criteria indices instead of the absolute indices tobetter evaluate the models performance. While the nRMSE reflects themodel accuracy, the nMBE and RE characterize the bias provided by the
SALTMED and/or HYDRUS (2D/3D) models.
2.6. WF accounting
The water footprint (WF) related to the crop production consists of
two main components: the consumptive WF, including blue (WFblue)and green (WFgreen) WFs, and the degradative grey WF (WFgrey). The
WFblue is estimated by dividing blue evapotranspiration (ETblue,
m3 ha−1) by crop yield (Y, t ha−1), while the WFgreen is calculated by
dividing green evapotranspiration (ETgreen, m3 ha−1) by Y:

WFgreen (m3 t 1) =2.3. The HYDRUS (2D/3D) model

WFblue (m3 t 1) =The HYDRUS (2D/3D) model (Šimůnek et al., 2008, 2016) is a
powerful numerical model that can simulate soil water and solute dynamics under various irrigation treatments and for different initial andboundary conditions. Karandish and Šimůnek (2016a, b, 2017, 2018)
employed this model to simulate soil water and solute dynamics under
different irrigation treatments during the 2010 and 2011 growing
seasons. They provided detailed information on the modeling approach
including the model description and governing equations, data requirements by the model, and how these data were collected during thecurrent field investigation. Additionally, they provided details aboutmodel calibration and validation. Hence, detailed information aboutthe HYDRUS modeling can be found in Karandish and Šimůnek (2016a,b, 2017, 2018).

ETgreenY
ETblueY(13)

where ETblue is a part of total evapotranspiration, which is supplied
from blue water (i.e., blue water refers to water supplied to crops fromsurface and/or groundwater resources during the cropping cycle), andETgreen is a part of crop evapotranspiration, which is supplied fromgreen water (i.e., green water refers to water supplied due to effective
precipitation stored in the rooting zone during the cropping cycle).
Crop evapotranspiration (ET) is thus equal to ETblue+ETgreen. ET was
estimated by the SALTMED and/or HYDRUS(2D/3D) models.
The following soil water balance was adopted to calculate ETblue andETgreen:S[t ] = S[t 1] + P[t ] + I[t ] + CR[t ]RO[t ]ET[t ]DP[t ](14)

where S[t ] and S[t 1] are soil water storages at the end of days t and t-1,
812

Agricultural Water Management 213 (2019) 809–820

F. Karandish, J. Šimůnek

respectively, P[t ] is precipitation on day t, I[t ] is irrigation on day t, CR[t ]is capillary rise from groundwater, RO[t ] is surface runoff, ET[t ] is evapotranspiration, and DP[t ] is deep percolation. All terms are in mm.
Following Allen et al. (1998), the capillary rise is set equal to zero since
the groundwater table was more than one meter below the rooting zone
during the entire cropping cycle of both years. For each day, the relative
contributions of P and I to P+I were used to calculate the green andblue fractions of RO. The fractions of green and blue water in the soil
water storage over time were calculated following Chukalla et al.
(2015); Zhuo et al. (2016a), Karandish and Hoekstra (2017); Karandish
et al. (2018). This method is based on the assumption that the storage ofgreen water in the soil increases when rainfall infiltrates into the soil
and that the storage of blue water increases when precipitation infiltrates. The fractions of green and blue water in the total soil water
storage at the end of the previous day were used to calculate the fractions of green and blue ET and DP on day t.
The degradative grey WF (WFGrey, m3 t−1) is the volume of freshwater required to assimilate the pollutant loads to freshwater bodies tothe ambient water quality standard. The degradative grey WF (m3 t−1),which is related to surplus N loads to water bodies, was estimated using
the procedure introduced by Hoekstra et al. (2011):

WFGrey(m3t 1) =AR(Cmax Cnat ) Y

Table 1
The measured soil and crop properties used in the SALTMED model.
Type

Crop properties

Parameter*

Growing season

Sowing date
HarvestRooting depth

Maximum(cm)
Minimum(cm)
Potential crop yield (t ha-1)**
Crop
Kc-inicoefficient(Kc)***
Kc-midKc-lateLength ofcropping cycle(15)

Soil properties

where is the leaching-runoff fraction, AR is the chemical application
rate to the agricultural soils (kg ha−1 y−1) (i.e., AR is the pollutant
load to freshwater bodies), Cmax and Cnat are, respectively, the ambient
water quality standard (i.e., the maximum allowable concentration in
kg m-3) and its natural background concentration in a receiving body
(kg m-3), and Y is crop yield (kg ha−1). A maximum acceptable N
concentration of 50 mg nitrate l-1 (or 11.3 mg N l-1) was adopted in thisstudy based on the EU Nitrates Directive (Monteny, 2001) and Cnat was
set to 1.5 mg l-1 (Mekonnen and Hoekstra, 2015). AR was estimatedusing either the HYDRUS (2D/3D) or SALTMED modeling, resulting in
the model-simulated WFGrey, or using field data, resulting in the measured WFGrey. To calculate the measured grey WF, AR for a specifictreatment was set to residual N in the rooting zone at the end of thegrowing cycle (Frank et al., 2013). As described in Section 2.1, residual
N at harvest was measured for all treatments and for both cropping
cycles.

initial stage
developing
stage
mid-season
stage
late-season
stage
The whole cropping cycle
Bulk density (gr cm3)
Soil texture
Maximum soil depth (cm)Soil water content at saturation(%)
Water content at field capacity(%)
Water content at wilting point
(%)

2010

2011

May 26
September 9
80

May 26
September 12
80

15

15

7.0
Ke = 0.32
Kcb = 0.20
Ke = 0.11
Kcb = 1.15
Ke = 0.26
Kcb = 0.19
22
22

6.6
Ke = 0.51
Kcb = 0.15
Ke = 0.11
Kcb = 1.13
Ke = 0.31
Kcb = 0.23
22
22

38

38

25

28

107
1.4
Sandy clay
loam
80
0.47

110
1.4
Sandy clay
loam
80
0.47

30

30

15

15

* All parameters reported in this Table are measured values.
** Potential crop yield was determined for the FI treatment.
*** Kc values are set to those reported by Karandish and Šimunek (2016a).

During the validation period (i.e., the 2011 growing season),nRMSEs and nMBEs varied in the range of 0.7–5.8% and 1–11.5%, respectively, which is an indication that the SALTMED model can capture
well both temporal and spatial variations of SWCs under different
treatments. Such results are also supported by other researchers (e.g.,
Hassanli et al., 2016; Afzal et al., 2016; Ragab et al., 2015; Fghire et al.,2015; Rameshwaran et al., 2015; Pulvento et al., 2013, 2015a,b).

3. Results and discussion
First, we will discuss the capability of the two models (SALTMED
first, followed by HYDRUS (2D/3D) (2D/3D)) to represent the collected
experimental data. After the calibration and validation of the twomodels, we will use them to evaluate WF components for different
treatments and to find out the best water-saving irrigation treatment forsustainable maize production in the study area (Table 1).

3.1.2. Soil salinity and N content
The results of the quantitative assessment summarized in Table 2
indicate a good agreement between the observed and SALTMED-simulated soil salinities, expressed using the electrical conductivity of thesoil solution (ECsw), both for the calibration and validation periods.
Simulated ECsw concentrations agreed well with observed values, withnRMSE = 2.4–11.3% and nMBE = 0.8–10.3% for the calibration period
and nRMSE = 2.2–11.1% and nMBE = 1.4–7.0% for the validation
period. A close match was also obtained between the observed and simulated soil NO3− contents for various treatments and different soil
layers. Simulated soil NO3− contents agreed well with observed values,
with nRMSEs ranging from 3.5 to 13.2% and 2.5–12.7% in 2010 and2011, respectively, and nMBEs ranging from -9.9–11.5% and-3.8–11.7% in 2010 and 2011, respectively. Hence, the SALTMED
model is capable of capturing the spatial and temporal trends in ECsw
and soil NO3− contents well.

3.1. The SALTMED model efficiency
3.1.1. Soil water content
Based on the criteria indices presented in Table 2, the SALTMED
model was capable of simulating SWCs during the calibration periodwith relatively high accuracy. Based on the nMBE values reported inTable 2, SWCs were underestimated by 0.9–14.2%. Except for the FI
treatment, SWCs were better estimated for three top soil layers (i.e.,
0–60 cm) than for the 60–80 cm soil depth. For the FI treatment, large
differences between measured and simulated SWCs at the 60–80 cm soil
depth may be explained by the fact that during individual irrigation
events water infiltrated deeper into the soil than for the other watersaving irrigation treatments. In addition, more accurate results wereobtained when simulating SWCs under the DI treatment, likely due tolower SWC variations as a result of limited irrigation and root water
uptake3.1.3. Crop N uptake
A comparison between the observed and model-simulated crop N
uptake also indicates that reliable results were provided by the
SALTMED model. Simulated crop N uptake was close to measured values, with nRMSEs ranging from 5.1 to 6.4% and 2.5–6.7% during the813Agricultural Water Management 213 (2019) 809–820

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Table 2
Criteria indices (nRMSE, nMBE) comparing the measured and SALTMED-simulated soil data during the calibration (the 2010 growing season) and validation (the2011 growing season) periods for various irrigation treatments (FI, DI55, DI75, PRD55, and PRD75).
PeriodCalibration
periodParameter

Soil watercontent
Soil salinity*

Soil NO3
content

Validation
periodCrop N uptake
Soil watercontent
Soil salinity*

Soil NO3
content
Crop N uptake

Depth0-20 cm
20-40 cm
40-60 cm
60-80 cm
0-20 cm
20-40 cm
40-60 cm
60-80 cm
0-20 cm
20-40 cm
40-60 cm
60-80 cm
0-20 cm
20-40 cm
40-60 cm
60-80 cm
0-20 cm
20-40 cm
40-60 cm
60-80 cm
0-20 cm
20-40 cm
40-60 cm
60-80 cm

FI

PRD75

PRD55

DI75

DI55

nRMSE (%)

nMBE (%)

nRMSE (%)

nMBE (%)

nRMSE (%)

nMBE (%)

nRMSE (%)

nMBE (%)

nRMSE (%)

nMBE (%)

2.0
1.9
1.5
0.5
8.6
4.0
9.5
7.4
6.4
7.3
5.1
7.1
6.1
2.7
0.8
0.9
0.7
7.1
3.4
8.2
5.7
4.1
5.5
9.2
11.1
2.5

2.9
2.7
1.7
0.9
3.4
1.5
3.6
2.7
2.6
4.7
−0.3
0.9
−3.6
2.2
2.6
1.6
1.3
3.1
1.4
3.5
2.2
1.8
−0.3
−3.8
10.1
1.2

2.4
2.1
1.6
5.2
11.3
5.1
10.7
6.7
4.7
4.6
4.2
10.4
5.3
2.5
2.1
2.0
1.9
6.5
2.9
5.9
3.3
5.3
7.0
4.1
12.7
4.9

3.0
2.6
2.2
14.2
10.3
4.5
9.2
6.0
1.5
3.0
−2.4
3.0
−3.4
4.1
2.5
2.9
2.1
4.3
2.0
4.0
2.2
2.0
4.5
0.9
11.7
1.5

2.7
2.3
2.0
13.1
7.2
3.0
2.9
2.4
3.9
3.6
5.7
10.2
6.4
1.1
2.0
1.5
1.2
5.7
2.4
3.0
2.2
2.5
4.6
7.3
12.7
6.5

2.7
2.4
1.7
12.3
3.6
1.5
2.6
2.1
1.0
0.6
5.2
3.0
−3.8
1.4
2.3
1.8
1.5
4.6
1.9
2.6
1.9
−1.5
−1.0
5.3
11.7
5.1

1.2
1.1
1.1
3.7
8.9
3.7
7.7
7.3
11.5
5.5
13.2
10.2
6.1
3.1
0.9
1.0
0.8
11.1
5.4
10.9
9.0
3.7
3.5
5.3
12.6
5.7

1.5
1.5
1.5
5.9
2.7
1.2
2.6
2.4
−9.9
3.8
11.5
3.0
−3.2
6.7
1.4
1.5
1.0
7.0
3.4
6.7
5.6
−2.7
−0.3
−2.2
11.7
4.3

2.1
1.8
1.3
4.9
5.3
2.5
7.1
5.0
4.9
3.5
8.0
10.4
5.1
5.8
1.2
1.0
1.3
7.2
3.5
9.6
6.0
3.8
4.5
9.7
12.7
6.7

2.3
2.3
1.5
6.9
1.8
0.8
2.3
1.6
2.4
0.8
7.2
3.0
−2.0
11.5
1.4
1.4
1.7
2.8
1.4
3.8
2.5
−2.9
−1.0
7.6
11.7
2.6

2010 and 2011 cropping cycles, respectively. While crop N uptake was
generally overestimated during the calibration period, with nMBEs
ranging from -3.8% to -2%, it was generally underestimated during thevalidation period, with nMBEs ranging from 1.2% to 5.1%.

With REs ranging from 3.5 to 8.3% in 2010 and 3.6–7.9% in 2011, theSALTMED model is well capable of simulating maize crop yield.
Hassanli et al. (2016) reported REs in the range of 0.9–24.7% whensimulating maize crop yield under various water and saline stresstreatments. REs in our study are also within the range of those reportedby Ragab et al. (2005) (|RE| = 0 21.5%, with an average of 5.7%), by
Razzaghi et al. (2011) for quinoa seed yield (|RE| = 0.8 2.2%, with anaverage of 1.5%), by Kaya et al. (2015) for quinoa yield(|RE| = 1.2 12.6%, with an average of 6.1%), and by Hirich et al.
(2012) for simulating corn yield (|RE| = 0 29.1%, with an average of
13.8%).
The SALTMED-simulated values underestimated measured total
biomass at harvest by 5.7–12% and 4.3–10% in 2010 and 2011, respectively. The model performed better for the water stress treatments
compared to the FI treatment. A close match was also obtained between
the observed and model-simulated LAImax, with |RE| ranging from 4.6 to
9.1% and 4–9% in the 2010 and 2011 growing seasons.

3.1.4. Aboveground biomass and LAI
In addition to simulating soil water and solute dynamics, a reliable
description of the crop response to applied treatments is also importantwhen applying the SALTMED model. Hence, we assessed the capabilityof the SALTMED model to capture temporal variations in the aboveground biomass (DM) and leaf area index (LAI) for various treatments
during the 2010 (the calibration period) and 2011 (the validation
period) growing seasons. Although SALTMED in general underestimated DM by 3–14% in 2010 and 4–14% in 2011, simulated DM
was highly correlated with observed values, with R2 ranging from 0.96
to 0.99 (Fig. 2). With respect to the average bias evaluated over thewhole cropping cycle, SALMED simulated DM best for the PRD55
treatment and worst for the FI treatment during both calibration andvalidation periods.
An inspection of the time-series of observed and model-simulated
LAIs (Fig. 3) shows that LAI is usually underestimated by 3–10% during
the 2010 growing season and by 2–18% in 2011. However, a visualinspection of scatter plots in Fig. 3, which compares the observed andSALTMED-simulated LAIs, clearly indicates the high potential of the
SALTMED modeling. Notice the high values of R2 in Fig. 3 (i.e.,
R2 = 0.95-0.99). In general, better results were obtained for the PRD
treatments when simulating temporal variations of LAIs. Nevertheless,the paired t-test analysis for the statistical comparison of the observed
and SALTMED-simulated data demonstrated no significant difference
between the observed and model-simulated DMs and LAIs, during the2010 and 2011 cropping cycles.

3.2. The HYDRUS (2D/3D) model efficiency
3.2.1. Soil water and solute dynamics
Based on the results reported in our previous papers (Karandish andŠimůnek, 2016a, b, 2017, 2018), and the nRMSE and nMBE values reported in Table 4, the HYDRUS (2D/3D) model capable of simulating
soil water and solute dynamics, with nRMSE = 0.4–11.5% andnMBE = 0.7–12.5% for the soil water content, nRMSE = 1.8–10.9%
and nMBE=-6.8–10% for the soil NO3− content, nRMSE = 2.2–5.9%
and nMBE=-3.1–4.5% for crop N uptake, and nRMSE = 2.1–9.1% andnMBE = 0.7–8.2% for soil ECsw. Most earlier studies demonstrated thehigh capability of the HYDRUS (2D/3D) model to describe soil waterand solute dynamics for various initial and boundary conditions (e.g.,
Cote et al., 2003; Gärdenäs et al., 2005; Assouline et al., 2006; Hanson
et al., 2006; Ajdary et al., 2007; Crevoisier et al., 2008; Siyal andSkaggs, 2009; Mubarak, 2009; Li and Liu, 2011; Ramos et al., 2011,2012; Tafteh and Sepaskhah, 2012; Phogat et al., 2013, 2014; Zenget al., 2014; Mguidiche et al., 2015; Karandish and Šimůnek, 2016a,

3.1.5. Yield, total biomass, and maximum LAI
Table 3 shows the measured and SALTMED-simulated crop yield,
total biomass, and maximum LAI, and the corresponding relative errors(RE) for different treatments in the 2010 and 2011 growing seasons.
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Fig. 2. Temporal variation of the observed and SALTMED-simulated aboveground biomass (DM, gr plant−1) for various treatments (FI, DI55, DI75, PRD55, and PRD75)
during the calibration (i.e., the 2010 growing season) and validation (i.e., the 2011 growing season) period. DAP – days after planting.

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Fig. 3. Temporal variation of the observed and the SALTMED-simulated leaf area index (LAI) for various treatments (FI, DI55, DI75, PRD55, and PRD75) during thecalibration (i.e., the 2010 growing season) and validation (i.e., the 2011 growing season) periods. DAP – days after planting.

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Table 3The observed and SALTMED-simulated crop yield, total biomass, and maximum leaf area index (LAI) for various treatments (FI, DI55, DI75, PRD55, and PRD75) in the2010 and 2011 cropping cycles. RE – relative maximum error.
Year

2010

2011

Treatment

FI
PRD75
PRD55
DI75
DI55
FI
PRD75
PRD55
DI75
DI55

Crop yield (t ha−1)
Observed

Simulated

7
6.9
3.3
5.8
3.2
6.6
6.2
5.7
5.3
4.9

6.4
6.6
3.2
5.4
3.0
6.1
6.0
5.5
5.0
4.7

Total biomass (t ha−1)

RE (%)

−8.3
−4.2
−3.5
−6.3
−5.1
−7.9
−3.9
−3.6
−5.5
−4.9

RE (%)

Observed

Simulated

25.7
21.8
14.6
17.8
14
19
18.7
16
16.5
15.2

22.6
20.1
13.8
16.4
13.1
17.1
17.4
15.3
15
14

2016b, 2017, 2018).
Crop yield. Since the HYDRUS (2D/3D) model doesn’t simulate crop
yield directly, we estimated crop yield based on the ratio of actual andpotential evapotranspiration, since there is general belief among scientists that crop yield is linearly correlated with crop transpiration(Payero et al., 2006; Klocke et al., 2004; Stone, 2003). Table 5 shows
the observed and HYDRUS (2D/3D)-simulated crop yields for different
treatments, as well as the corresponding REs. Absolute REs (i.e., |RE|)
range from 2.2 to 15.6% and 4.9–13.8% in 2010 and 2011, respectively.

Maximum LAI

−12
−7.9
−5.7
−7.6
−6.3
−10
−6.8
−4.3
−9.1
−8.2

RE (%)

Observed

Simulated

4.4
4.09
3.27
3.68
3.26
3.6
3.54
3.3
3.06
2.85

4
3.85
3.12
3.42
3.07
3.28
3.35
3.17
2.84
2.66

−9.1
−6
−4.6
−7
−5.7
−9
−5.4
−4
−7.3
−6.6

Table 5The observed and HYDRUS-simulated crop yield and the corresponding relative
errors (RE) for various treatments (FI, DI55, DI75, PRD55, and PRD75) in the 2010
and 2011 cropping cycles.
Year

Treatment

2010

3.3. The comparison of the models

FI
PRD75
PRD55
DI75
DI55
FI
PRD75
PRD55
DI75
DI55

2011

The HYDRUS (2D/3D) model performed better than the SALTMED
model when simulating soil water and solute dynamics under definedtreatments, with 11.6–31.1% lower nRMSE and 11.7–31.7% lower
nMBE when simulating soil water contents, 13.7–31.8% lower nRMSE
and 14.3–34.3% lower nMBE when simulating soil NO3− contents,

Crop yield (t ha−1)

RE (%)

Observed

Simulated

7
6.9
3.3
5.8
3.2
6.6
6.2
5.7
5.3
4.9

7.0
6.8
3.8
6.4
3.7
6.6
6.5
5.4
6.0
5.4

0.0
−2.2
15.2
10.1
15.6
0.0
5.4
−4.9
13.8
10.9

Table 4
Criteria indices (nRMSE, nMBE) comparing the measured and HYDRUS-simulated soil and crop data during the calibration (the 2010 growing season) and validation(the 2011 growing season) periods for various irrigation treatments (FI, DI55, DI75, PRD55, and PRD75).
PeriodCalibration
periodParameter

Soil watercontent
Soil salinity*

Soil NO3
content

Validation
periodCrop N uptake
Soil watercontent
Soil salinity*

Soil NO3
content
Crop N uptake

Depth0-20 cm
20-40 cm
40-60 cm
60-80 cm
0-20 cm
20-40 cm
40-60 cm
60-80 cm
0-20 cm
20-40 cm
40-60 cm
60-80 cm
0-20 cm
20-40 cm
40-60 cm
60-80 cm
0-20 cm
20-40 cm
40-60 cm
60-80 cm
0-20 cm
20-40 cm
40-60 cm
60-80 cm

FI

PRD75

PRD55

DI75

DI55

nRMSE (%)

nMBE (%)

nRMSE (%)

nMBE (%)

nRMSE (%)

nMBE (%)

nRMSE (%)

nMBE (%)

nRMSE (%)

nMBE (%)

1.4
1.5
1.3
0.4
7.0
3.5
8.2
6.5
4.7
5.5
4.2
6.0
5.0
2.0
0.6
0.8
0.6
5.6
2.8
6.6
5.2
3.0
4.2
7.1
9.5
2.2

2.0
2.1
1.4
0.7
2.6
1.3
3.1
2.4
1.7
3.4
−0.3
0.8
−3.0
1.5
2.0
1.3
1.1
2.2
1.1
2.7
2.0
1.2
−0.2
−2.8
8.6
1.0

1.7
1.6
1.3
4.6
9.0
4.5
9.1
5.8
3.3
3.4
3.4
8.6
4.3
1.8
1.6
1.6
1.6
4.9
2.3
4.6
2.9
3.6
5.1
3.1
10.9
4.3

2.1
2.0
1.8
12.5
8.2
3.9
7.9
5.2
1.1
2.2
−1.9
2.5
−2.8
3.0
2.0
2.4
1.8
3.3
1.6
3.1
2.1
1.5
3.4
0.7
10.0
1.3

1.9
1.7
1.7
11.5
5.7
2.5
2.4
2.1
2.8
2.7
4.6
8.6
5.3
0.8
1.6
1.3
1.0
4.3
1.9
2.3
2.0
1.8
3.4
5.5
10.8
5.7

1.9
1.9
1.5
10.9
2.9
1.3
2.2
1.8
0.7
0.4
4.2
2.5
−3.1
1.0
1.8
1.5
1.3
3.4
1.5
2.1
1.7
−1.1
−0.7
4.0
10.0
4.5

0.9
0.9
0.9
3.3
7.1
3.2
6.6
6.3
8.2
4.2
10.8
8.6
5.1
2.3
0.7
0.9
0.7
8.6
4.2
8.6
8.3
2.7
2.6
4.1
10.8
5.1

1.1
1.1
1.3
5.2
2.1
1.0
2.2
2.1
−6.8
2.8
9.3
2.5
−2.6
4.8
1.1
1.2
0.9
5.2
2.5
5.2
5.0
−1.8
−0.2
−1.6
10.0
3.8

1.4
1.4
1.1
4.3
4.2
2.2
6.0
4.3
3.4
2.6
6.5
8.6
4.2
4.2
0.9
0.8
1.1
5.3
2.7
7.5
5.5
2.6
3.2
7.3
10.8
5.9

1.6
1.8
1.3
6.1
1.4
0.7
2.0
1.4
1.7
0.6
5.9
2.5
−1.7
8.4
1.1
1.2
1.5
2.1
1.1
3.0
2.2
−2.1
−0.7
5.7
10.0
2.3

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Table 6
Field-based calculated and model-simulated consumptive WF and degradative grey WF for various treatments in 2010 and 2011 cropping cycles.
Year

2010

2011

Treatment

FI
PRD75
PRD55
DI75
DI55
FI
PRD75
PRD55
DI75
DI55

HYDRUS (2D/3D)

SALTMED

Field-based

Crop yield(t ha−1)ETa(mm)Consumptive WF(m3 t−1)

Grey WF(m3 t−1)

Crop yield(t ha−1)ETa(mm)Consumptive WF(m3 t−1)

Grey WF(m3 t−1)

Grey WF(m3 t−1)

7
6.8
3.8
6.4
3.7
6.6
6.5
5.4
6
5.4

650.3
627.2
535.9
593.2
531.5
572.5
566.7
470.3
523.3
471.2

929
926
1411
929
1438
871
871
872
873
870

398
466
1339
691
1529
296
357
1110
950
1518

6.4
6.6
3.2
5.4
3
6.1
6
5.5
5
4.7

604.4
616.1
462.1
518.1
450.5
534
524.3
475.7
447.8
414.2

942
932
1451
953
1483
879
880
866
894
889

453
492
1631
830
1880
388
472
1339
1362
1883

321
385
1391
686
1690
325
411
1164
1174
1728

10.8–17.8% lower nRMSE and 11.0–18.0% lower nMBE when simulating crop N uptake, and 6.7–25.5% lower nRMSE and 7.7–28.1%
lower nMBE when simulating soil ECsw. Such differences may originate
from the fact that the HYDRUS (2D/3D) and SALTMED models use
different types of boundary conditions for water flow and solutetransport.
However, the SALTMED-simulated yields are more accurate compared to those simulated by HYDRUS. The |RE| obtained by the
HYDRUS (2D/3D) model were generally 1.3–11.7% higher compared tothose obtained by the SALTMED model, except for the PRD75 treatment
in 2010, for which the |RE| by the HYDRUS (2D/3D) model was 2%
smaller than that obtained by the SALTMED model. Better performanceof the SALTMED model in simulating crop yield is related to the embedded driving equations and formulas for estimating crop yield(Hassanli et al., 2016; Oster et al., 2012). In fact, yield estimations
based on the crop growth parameters rather than RY provided better
results for the SATMED-simulated yields.

Such an increase is mainly due to a significant reduction in crop yieldunder the PRD55 and DI55 treatments in 2010 rather than the associatedreduction in the crop water consumption. Nearly similar consumptive
WFs estimated for the other treatments may be justified by the fact thatthe negative consequences of reduced yield under the water stress arecompensated by the positive consequences of the reduced crop water
consumption.
Based on the estimated grey WF, which is an indicator of adverse
environmental effects of human activities and water quality management, the PRD application may be safer than DI when water resources
are limited in the study area. A unit of maize under PRD75 and PRD55 is
produced with 32.6–65.3% and 12.4–32.7% fewer pollutant loads tofreshwater bodies than under DI75 and DI55, respectively. The smallest
grey WFs can always be found under FI, followed by PRD75. The grey
WF under PRD75 was slightly higher than under the FI treatment, accounting for 8.6–26.7%, which may be attributed to lower yield and N
water uptake under PRD75. Nevertheless, the PRD75 treatment seems tobe the safest water-saving irrigation strategy in the study area, since theestimated grey WF for PRD75 was 17.1–77.2% lower than those estimated for the other water-saving irrigation treatments.

3.4. WF accounting
Table 6 shows the consumptive and degradative grey WFs estimatedusing the results of the HYDRUS (2D/3D) and SALTMED models forvarious treatments in 2010 and 2011. The HYDRUS (2D/3D)-estimatedconsumptive WFs are 0.3–3.2% lower than those estimated by the
SALTMED model. The consumptive WF is influenced by the yield andcrop water consumption (Karandish and Hoekstra, 2017; Karandish andŠimůnek, 2018). Any increase in crop yield and/or reduction in cropwater consumption may reduce the consumptive WF related to crop
production. Table 6 indicates that compared to the SALTMED predictions, HYDRUS (2D/3D) simulated a lower crop yield reduction than
the reduction in the crop water consumption, which resulted in lower
consumptive WFs. Crop yield and water consumption estimated by
HYDRUS (2D/3D) are 3–23.3% and 1.8–18% lower, respectively, thanthose estimated by SALTMED.
The HYDRUS (2D/3D)-estimated grey WFs were 5.3–30.2% lower
than those estimated using SALTMED, which may be associated withthe higher HYDRUS (2D/3D)-estimated yield (Table 5). Table 6 also
shows that the HYDRUS (2D/3D)-estimated grey WFs provided a closer
match to those calculated based on field measurements. While for
HYDRUS (2D/3D), |RE| ranged from 0.7 to 24.2%, for SALTMED it
ranged from 8.9 to 41.2%. Such results may be associated with a better
estimation of crop N uptake, and consequently, soil N residual at harvest by the HYDRUS (2D/3D) model (Table 4).
Except for the PRD55 and DI55 in 2010, no considerable differenceswere observed in estimated consumptive WFs for various treatments. In
the absence of the water stress, 906.5 ± 35.5 m3 of water is required to
produce a unit (ton) of maize in the study area. Applying water stress
may produce a relative change of -0.3%–54.7% in the estimated consumptive WF, with a particular increase under PRD55 and DI55 in 2010.

4. Conclusions
Using data collected during a two-year field investigation in a surface drip-irrigated maize field, we compared the performance of the
SALTMED and HYDRUS (2D/3D) models in simulating water and solutedynamics, maize growth, and maize consumptive and degradative WFs
under various water-saving irrigation strategies, including full irrigation (FI), partial root-zone drying (PRD), and deficit irrigation (DI). The
consumptive and degradative WFs estimated by HYDRUS (2D/3D) were0.6–3% and 5.3–30.2%, respectively, lower than those estimated by the
SALTMED model. However, the grey WFs simulated by HYDRUS (2D/
3D) were in better agreement with those estimated using the fieldcollected data, particularly due to the more accurate estimation of soil
N dynamics. While the gross blue water consumption is 25 or 45%
lower for considered water-saving irrigation strategies (DI75 and PRD75
or DI50 and PRD50, respectively), the corresponding reduction in maizegrain yield under the water stress resulted in insignificant differences inthe estimated maize consumptive WFs among various treatments.
Regarding the grey WF, which is an indicator of the negative environmental impact of human activities and water quality management,
PRD75 produced better results than the other water-saving irrigation
treatments, suggesting that sustainable agriculture may be easierachieved under PRD75. Reliable estimates of both consumptive anddegradative grey WFs related to the maize production for various
treatments by the HYDRUS (2D/3D) and SALTMED models indicatethat there is an alternative approach to the labor- and time-consuming
field investigations. Nevertheless, more accurate results are expected tobe achieved when employing the HYDRUS (2D/3D) model.
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Acknowledgment

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Fatemeh Karandish would like to appreciate the support of
University of Zabol for carrying out this research under the grant
number ‘UOZ_GR_9618_4’.
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