Agricultural Water Management 242 (2020) 106420

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

Dynamic assessment of the impacts of global warming on nitrate losses froma subsurface-drained rainfed-canola ﬁeld

T

Farzad Haghnazaria, Fatemeh Karandisha,b,*, Abdullah Darzi-Naftchalic, Jiří Šimůnekd
a

Water Engineering Department, University of Zabol, Zabol, IranTwente Water Centre, University of Twente, P.O. Box 217, 7500AE Enschede, the NetherlandscWater Engineering Department, Sari Agricultural Sciences and Natural Resources University, Sari, Irand
Department of Environmental Sciences, University of California Riverside, Riverside, CA 92521, USAb

A R T I C LE I N FO

A B S T R A C T

Keywords:
HYDRUS (2D/3D)
RCP scenarios
Climate change projections
Drainage ﬂux
Nitrate loss

The impact of global warming on water and nitrate losses from a rainfed-canola cropping system under various
artiﬁcial drainage systems was assessed using an integrated ﬁeld-modeling approach. Four subsurface drainage
systems with diﬀerent drain depths (Dx) and spacings (Ly), including D0.90L30, D0.65L30, D0.65L15, and Bilevel(with a drain spacing of 15 m and alternate drain depths of 0.65 and 0.90 m), were considered. The HYDRUS(2D/3D) model was ﬁrst calibrated and validated using data collected for all drainage systems during the
2015–2016 and 2016–2017 canola cropping cycles, respectively, and then applied to simulate water/nitratelosses for diﬀerent drainage systems under meteorological conditions predicted assuming expected future global
warming. Future weather data were downscaled from 20 general circulation models and four RCP scenarios forthe mid 21st century (for 2041–2070). The model capability of representing experimental ﬁeld data was evaluated using the mean bias error (MBE), the normalized root mean square error (nRMSE), and the model eﬃciency (EF). The HYDRUS (2D/3D) model provided reliable description of soil water contents (MBE=-0.5 % to
0.2 %, nRMSE = 0.005−0.034%, and EF = 0.73−0.99), drainage ﬂuxes (MBE= -21.7 × 10−3 to 24.9 × 10−3
mm d-1, nRMSE = 0.23−0.37%, and EF = 0.69−0.85), soil nitrate concentrations (MBE= -0.002 to 1.00 mg
cm−3, nRMSE = 0.08−0.18%, and EF = 0.51−0.88), and nitrate ﬂuxes (MBE= -0.97 to 0.72 mg cm-1 d-1,
nRMSE = 0.35−0.57%, and EF = 0.77−0.87). The modeling results indicate that climate change will cause anincrease of up to 148 % in average daily drainage ﬂuxes and up to 125 % in average daily nitrate ﬂuxes
compared to the base case. This will result in an increase of 4–125 % in seasonal nitrate losses from various
drainage systems, with the lowest and highest projections for the D0.65L15 and D0.65L30 systems, respectively. The
HYDRUS-simulated results indicate that the D0.65L15 system is environmentally safer than the other evaluateddrainage systems for predicted global warming conditions concerning water/nitrate losses.

1. Introduction
The need to feed the growing global population on the one hand,and increasing global scarcity of blue (fresh) water (WWAP, 2009;
Hoekstra et al., 2012) on the other hand, indicate that it is essential toexpand rainfed agriculture in the world. In this regard, humid regions
may be of higher importance since they receive a suﬃcient quantity of
precipitation to fully supply crop’s water requirements by green (fromrainfall) water (Shahsavari et al., 2019). However, expanding dry-landfarming in these regions may be restricted by waterlogging problems,
which occasionally occur after heavy rainfalls and, particularly, inheavy soils (Darzi-Naftchali et al., 2017). Waterlogging threatens theyear-round cropping, resulting in considerable areas either going out of⁎

production or experiencing reduced yields (Darzi-Naftchali et al.,2013). Under such circumstances, installing subsurface drainage systems may help in providing suitable conditions for winter-crops-based
cropping systems and consequently improving the annual productivityof these lands. Subsurface drainage systems can speed up the watertable drawdown and provide better aeration during the growing season.
While being beneﬁcial in terms of the water table control, installed
drainage systems may cause serious environmental challenges due totheir negative impacts on nutrient leaching. Previous researchers havedemonstrated that drainage systems may accelerate the leaching process of soil nutrients, and in particular of nitrogen (N) (Kalita et al.,2006; Furukawa et al., 2008; Zhang et al., 2007; Darzi-Naftchali et al.,2017), which is well known to be an essential crop nutrient aﬀecting

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

https://doi.org/10.1016/j.agwat.2020.106420
Received 12 January 2020; Received in revised form 26 July 2020; Accepted 27 July 20200378-3774/ © 2020 Elsevier B.V. All rights reserved.

Agricultural Water Management 242 (2020) 106420

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Fig. 1. The location of the study area in Iran (top-left) in the Mazandaran province (top-right), and the schematic of the drainage systems (bottom) (After DarziNaftchali et al., 2018).

crops produces excessive N losses after heavy rains (Karandish andŠimůnek, 2017). Although a few attempts have been made to investigate the eﬀects of climate change on water and N losses in agricultural systems (e.g., Singh et al., 2009; Wang et al., 2015; He et al.,2018; Jiang et al., 2020), a literature review reveals that no such study
has been carried out for subsurface-drained paddy ﬁelds under winterrainfed cropping. As such systems are in their infancy in northern Iran
paddy ﬁelds, a comprehensive study of their behavior would enable
policymakers to incorporate the eﬀects of inherent complexities of climate change on drainage activities to protect the local environment.
Therefore, we carried out a ﬁeld experiment to investigate waterand N losses from a drained ﬁeld cultivated with rainfed-canola andwith various subsurface drainage systems. Our research is novel since itis the ﬁrst study in which environmental hazards in rainfed agriculture
due to global warming are evaluated in terms of N losses from the ﬁeld.
To the best of our knowledge, our study assesses for the ﬁrst time suchhazards in ﬁelds with subsurface drainage systems. The N dynamics
under various climate change projections were assessed using the
HYDRUS (2D/3D) model (from now on referred to simply as HYDRUS),
which was ﬁrst calibrated and validated using the ﬁeld-collected data.
We used the HYDRUS model since its capability in capturing drainage
and N ﬂuxes has been previously conﬁrmed by many researchers (e.g.,Öztekin, 2002; Mirjat et al., 2014; Filipovic et al., 2014; Li et al., 2014;
Mguidiche et al., 2015; Karandish and Šimůnek, 2016; Mekala andNambi, 2016; Darzi-Naftchali et al., 2018; Matteau et al., 2019). The
main objectives of this research thus were (i) to calibrate and validate
HYDRUS using experimental drainage and nitrate ﬂuxes collected in a
subsurface-drained ﬁeld cultivated with rainfed canola, and (ii) to investigate the impact of climate change projections on key climatic
variables, drainage, and nitrate ﬂuxes.

crop growth and yield (Wienhold et al., 1995; Jia et al., 2014). The
design parameters of drainage systems, including the type of the systems and the drain depth and spacing, may aﬀect water or N losses fromagricultural lands (Christen and Skehan, 2001; Wahba and Christen,
2006; Jafari-Talukolaee et al., 2015, 2016; Darzi-Naftchali et al., 2017).
For certain drainage systems, the rate of water and N losses additionallyalso depends on ﬁeld management practices. Improving ﬁeld management practices may increase the water/nutrient holding capacity of thesoil and thus reduce water and N losses (Azooz and Arshad, 1996; De
Vita et al., 2007; Triplett and Dick, 2008; Udayasoorian et al., 2009;
Constantin et al., 2010; Mitchell et al., 2012; Liu et al., 2013; Phogat
et al., 2013; Chukalla et al., 2017).
Drainage ﬂuxes represent a dominant factor for water and N losses
from drained areas. Among various inﬂuencing factors, climatic variables, including precipitation (P) and potential evapotranspiration(ET0), play key roles in determining the rate of drainage ﬂuxes.
Increased surplus precipitation, deﬁned as P- ETc, may accelerate therate of water and N losses due to enhanced drainage ﬂuxes. Water andN losses from drained dry-farming lands may become a more importantconcern in the future when predicted global warming, which aﬀects
these climatic variables, takes place. Numerous researchers have demonstrated various ranges of both negative as well as positive projections of climate change into P and ET0 in diﬀerent parts of the world(Abbaspour et al., 2009; Dastoorani and Poormohammadi, 2012; Terink
et al., 2013; Agarwal et al., 2014; Kazemi-Rad and Mohammadi, 2015;
Karandish et al., 2017; Karandish and Mousavi, 2018; Adham et al.,2019; Darzi-Naftchali and Karandish, 2019). Through such investigations, earlier research has mainly focused on determining probableeconomic consequences of global warming on the agricultural sector
due to the climate change impact on crop yields and productivity underdiﬀerent future greenhouse gas emission scenarios (Karandish et al.,2016; Harmsen et al., 2009; Massah Bavani and Morid, 2005).
Predicting probable N losses under climate change conditions isessential because the byproduct of these losses is the pollution of water
resources (Karandish and Šimůnek, 2017) due to N leaching/drainage
from agricultural lands (Zhu et al., 2005; Thompson et al., 2007;
Dudley et al., 2008; Burow et al., 2010; Dahan et al., 2014; Karandish
et al., 2016; Darzi-Naftchali et al., 2017). This issue has higher importance in humid regions, where a low recovery of N fertilizers by

2. Materials and methods
2.1. Field trial
2.1.1. Study area and drainage system layout
The ﬁeld research was carried out during two canola growing seasons (2015−16 and 2016−17) in the subsurface drainage pilot of theSari Agricultural Sciences and Natural Resources University in the2Agricultural Water Management 242 (2020) 106420

F. Haghnazari, et al.

depth of 200 cm to determine soil physical and hydraulic properties andthe soil N-content. Soil water characteristic curves of the soil samples
were determined by measuring soil water contents at 14 diﬀerentpressure heads (varied in the range of 0–15 bars). After that, the RETC
model was applied to ﬁt the van Genuchten-Mualem model (vanGenuchten, 1980) to the observed retention data.
For each treatment, two suction samplers were buried at depths of
30 cm and 60 cm beneath the surface before crop cultivation and remained continuously at 30 kPa. The samplers had porous ceramic capsof 5 cm in diameter. Using the vacuum pump, the leachate was thencollected during the growing seasons (once every two weeks). Nitrateconcentrations of water samples were determined by spectrophotometer (DR-4000 HACH).
To determine daily variations in the soil water content (SWC)
during the cropping cycles, a 100-cm long TDR probe (Trime FM;
IMKO; Germany) was installed midway between drains in each subsurface drainage system (i.e., 4 TDR probes were installed in the study
area; 1 probe * 4 drainage systems). TDR probes were used once a day
to measure SWC at a 15-cm interval during both growing seasons. The
accuracy of all TDR sensors was evaluated by comparing TDR-measured
SWCs with corresponding values measured using the gravimetricmethod. TDR-measured SWCs agreed well with gravimetrically-measured SWCs with a coeﬃcient of determination of 95 %.
Water table depths were measured daily in observation wells thatwere dug out midway between adjacent drains in each drainage system.
Free drainage management was adopted during canola growing seasons. Subsurface drainage discharges (q) were measured daily for all
drainage systems as long as the ﬂow was observed at the outlet ofdrains. Drain ﬂuxes were measured by using partial ﬂumes at the outletof representative drains during drainage periods. The total water discharge for a particular day is the sum of discharges over 24 h. The
above values were converted to water depths by dividing them with theplot area. Drainage water samples were taken every 15 days and several
consecutive days after fertilization. Collected water samples were thenanalyzed to determine their nitrate concentrations.

Mazandaran province, Iran (SANRU: 36.3◦ N, 53.04◦ E; 15 m below sea
level) (Fig. 1). In 2011, this pilot was designed and implemented in a
4.5-ha consolidated paddy ﬁeld (Darzi-Naftchali et al., 2013, 2018;
Darzi-Naftchali and Ritzema, 2018), consisting of four subsurface
drainage systems with diﬀerent drain depths (Dx) and spacings (Ly),including D0.90L30, D0.65L30, D0.65L15, and Bilevel which has a drain
spacing of 15 m and alternate drain depths of 0.65 and 0.9 m (Fig. 1).
The ﬁeld consisted of consolidated paddy plots 100 m long and 30 m
wide. Considering the dimensions of the plots and the ﬁeld size, thedrainage systems were installed at a scale much larger than the laboratory scale. Drainage installation costs at such a scale were very
high and required a large area. Diﬀerent drainage systems were notreplicated due to limited resources and land. The drainage pipes were
installed so that the last drain line in each system acted as the ﬁrst drainline in the adjacent system. Accordingly, at least three drain lines wereconsidered in each drainage system. In addition, the monitoring lines(lines 2, 4, 5, 7, and 9 in Fig. 1) for measuring drain discharge andwater quality were selected to fully represent each drainage system byremoving the buﬀering lines. A detailed description of the drainage
systems can be found in Darzi-Naftchali et al. (2013, 2018).
Long term averages of annual precipitation, mean temperature, as
well as minimum and maximum temperatures recorded in the study
area, are 616 mm and 17.3 °C, −6 °C, and 38.9 °C, respectively (DarziNaftchali et al., 2018). The variation of climatic variables during the
2015–2016 (the calibration period) and 2016–2017 (the validation
period) growing seasons is shown in Fig. 2.

2.1.2. Cropping system and data collection
The area was under rice-canola cropping during 2011–2017, withrice as a major crop and canola as a winter crop. The data from the2015−16 and 2016−17 canola growing seasons were used in thepresent study. Canola was cultivated in the two cropping cycles on
October 3 in 2015 and on September 28 in 2016. Crops were harvested
on May 4, 2016, and May 20, 2017. All agricultural practices were thesame as the conventional practices of local farmers in the study area. In
the growing season of 2015–2016, 50 kg ha−1 of triple superphosphate
was applied before cultivation, and 50 kg ha−1 of urea (i.e., CO(NH2)2
with 46 % N) was applied 52 days after sowing (DAS). In the second
growing season, urea was applied at rates of 85 kg ha-1, 85 kg ha-1, and115 kg ha-1 at 24, 96, and 116 DASs, respectively. The growers in theregion adjust fertilization based on their activities in the crop rotation.
For example, remaining residues after the previous crop are consideredby the experienced growers (Darzi-Naftchali et al., 2017).
Before crop cultivation, soil samples were collected every 30 cm to a

2.2. Climate data under global warning
The projections of future climate data were downscaled for four RCP
scenarios (the Representative Concentration Pathways reported in the
5th IPCC report, IPCC, 2013): the RCP2.6 (a low greenhouse gases
emission scenario), RCP4.5 (an intermediate one), RCP6.5 (a high one),and RCP8.5 (a very high greenhouse gases emission scenario). For eachscenario, the projections of air temperature (minimum and maximum),Fig. 2. Climatic variables in the study area during the 2015-2016 (the calibration period) and 2016-2017 (the validation period) growing seasons.
3Agricultural Water Management 242 (2020) 106420

F. Haghnazari, et al.

Table 1
Selected GCMs (Global Climate Models) for the current study.
(Source: Miao et al. (2014).
Model

SourceACCESS 1.0
BCC-CSM1.1
BNU-ESM
CanESM
CCSM4
CESM1.0-BGC
CISRO-MK3
GFDL-ESM2G
GFDL-ESM2M
HadGEM2-CC
HadGEM2-ES
inmcm4
IPSL-CL5A-LR
IPSL-CL5A-MR
MIROC
MIROC-ESM
MPI-ESM-LR
MPI-ESM-MR
MRI-CGCM3
Nor-ESM1-M

Common Wealth Scientiﬁc and Industrial Research Organization and Bureau of Meteorology, Australia
Beijing Climate Center, China Meteorological Administration, China
Beijing Normal University, China
Canadian Center for climate modeling and analysis, Canada
National Center for Atmospheric Research (NCAR), USA
National Center for Atmospheric Research (NCAR), USA
Australian Common Wealth Scientiﬁc and Industrial Research Organization
Geophysical Fluid Dynamic Laboratory, USA
Geophysical Fluid Dynamic Laboratory, USA
Met oﬃce Hadley Center, UK
Met oﬃce Hadley Center, UK
Institute of Numerical Mathematics, Russian Academy of Sciences
Institute Pierre-Simon Laplace, FranceInstitute Pierre-Simon Laplace, France
Japan Agency for Marin-Earth Science and Technology, Atmosphere and Ocean Research Institute (University of Tokyo Japan)
Japan Agency for Marin-Earth Science and Technology, Atmosphere and Ocean Research Institute (University of Tokyo Japan)
Max Plank Institute for Meteorology (MPI-M), Germany
Max Plank Institute for Meteorology (MPI-M), Germany
Meteorological Research Institute of Japan
Norwegian Climate Center, Norway

ΔPi = (

PGCM , fut , i
PGCM , base, i)(1)

ΔTi = (TGCM , fut , i − TGCM , base, i )(2)wheres ΔPi is a dimensionless monthly change factor for a particularclimatic variable (p), PGCM , fut , i and PGCM , base, i are monthly averages of
future (simulated) and historical values of a considered climatic variable, respectively, ΔTi is a dimensionless monthly change factor for air
temperature, and TGCM , fut , i and TGCM , base, i are monthly averages of futureand historical values of air temperature, respectively. These change
factors were calculated at a monthly time scale (i = 1–12).
Using generated climatic variables, reference evapotranspiration(ET0) was calculated using the Penman-Monteith FAO-56 equation(PMF) (Allen et al., 1998) due to its global acceptability (Karandish
et al., 2017). ET0 was then used to estimate crop evapotranspiration as
explained below.

Fig. 3. The transport domain and boundary conditions considered in the
HYDRUS model for simulating water ﬂow and solute transport.

2.3. N dynamics under current and future conditions

precipitation, solar net radiation, wind speed, atmosphere pressure, andrelative humidity were generated using 20 diﬀerent GCM models(general circulation models, Table 1) for the future period of
2040–2070, and compared with the corresponding values obtained forthe base period (1975–2005).
The statistical downscaling approach was carried out based on the
widely used change factor method (Jones et al., 1997). For all climatic
variables, the change factor coeﬃcients, which determine the relationship between the current and future climatic variables, were
calculated based on Eq. 1 (Jones et al., 1997), except for air temperature, for which Eq. 2 was applied (Trzaska and Schnarr, 2014).

2.3.1. The HYDRUS model
2.3.1.1. Model description and governing equations. Soil water and N
dynamics were simulated using the HYDRUS (2D/3D) model (Šimůnek
et al., 2008, 2016). The HYDRUS program numerically solves theRichards equation for saturated-unsaturated water ﬂow:

∂Θ (h)
∂ ⎛
∂h
∂ ⎛
∂h
∂K (h)=
K (h) ⎞ +K (h) ⎞ −− WU (h, x , z )
∂t
∂x ⎝
∂x ⎠
∂z ⎝
∂z ⎠
∂z

(3)

3 −3

where Θ is the volumetric soil water content (SWC) [L L ], K is theunsaturated hydraulic conductivity [LT-1], h is the soil water pressure
head [L], x is the lateral coordinate [L], z is the vertical coordinate

Table 2
Soil properties, and soil hydraulic and solute transport parameters for the study area (i.e., the calibrated and validated data for soil water and solute transports are
obtained through HYDRUS modeling).
Soil depth (cm)
0−30
30−60
60−90
90−120120−150150−200

OM (%)

Clay (%)

Silt (%)

Sand (%)

θr

θs

α

n

l

Ks (cm d−1)

DL

DT

48.5
55.5
46.5
42.5
52
58.5

44.4
42
45.5
51.5
42
35.5

7
2.5
8
6
6
6

0.001
0.001
0.192
0.098
0.001
0.229

0.40
0.40
0.40
0.40
0.57
0.59

0.004
0.008
0.008
0.006
0.004
0.004

1.193
1.119
1.355
1.423
1.274
1.467

0.5
0.5
0.5
0.5
0.5
0.5

25.6
8.1
20.7
16.3
10.9
8.3

35.4
30.6
22.5
16.1
13.2
7.2

3.7
3.4
2.5
2.0
1.3
0.6

4

Agricultural Water Management 242 (2020) 106420

F. Haghnazari, et al.

Fig. 4. Observed and simulated drainage ﬂuxes during the 2015-2016 (left, calibration) and 2016-2017 (right, validation) growing seasons for diﬀerent drainage
systems (Bilevel, D0.65L15, D0.65L30, and D0.90L30).

neglected due to the following reasons. First, the lack of organic matter(OM) lmits the mineralization process in mineral soils (i.e., soils with
OM < 3% in the upper horizon, Huang et al., 2009 cronologically.), as
in our study, the lack of OM limits the mineralization process (Li et al.,
2003; Deenik, 2006; Wijanarko, 2015). Second, mineralization mainly
occurs in coarse-textured soils with low clay content, while it decreases
considerably as soil clay content increases (Karandish and Šimůnek,2017). Third, the abundance of micropores in high clay soils causes aphysical protection of OMs from being microbially decomposed andmineralized (Deenik, 2006). Our frequent measurements also showedthat NO3− concentration in the soil were much higher than NH4 concentrations (Darzi-Naftchali et al., 2016). Only the NO3− transport in
the soil was thus considered in this research, assuming that the input ofN-fertilizer in the form of urea was instantly nitriﬁed into NO3-. This issimilar to the assumptions made by many other researchers (e.g.,
Ajdary et al., 2007; Tafteh and Sepaskhah, 2012; Karandish andŠimůnek, 2017), who assumed that nitriﬁcation is faster than the otherprocesses, and nitrifying urea into NO3-takes only a few days (Havlin
et al., 2006). In addition, nitriﬁcation is reported to be stimulated inless-organic (OM < 3%), ﬁnely-textured soils with high clay content, allof which are the cases in the current research.
We also considered that root N uptake was strictly passive, which
has also been assumed in many other studies (e.g., Hanson et al., 2006;
Tafteh and Sepaskhah, 2012). According to this assumption, N uptake
can be calculated by multiplying the local soil N concentration and root
water uptake.

(positive downwards), t is time [T], and WU(h, x, z) is root water uptake[T-1], which is calculated as follows:

WU (h, x , z ) = γ (h) RDF (x , z ) WTpot(4)

where γ(h) is the soil water stress response function (dimensionless) of
Feddes et al. (1978), RDF is the normalized spatial root water uptake
distribution [L−2], Tpot is the potential transpiration rate [LT-1], and W
is the width of the soil surface [L] associated with the transpiration
process. The stress response function was obtained from the HYDRUS
database for maize (Šimůnek et al., 2011). Uniform root distribution
was assumed in each soil layer. The van Genuchten-Mualem
constitutive relationships were used to model soil hydraulicproperties (van Genuchten, 1980).
The transport of nitrate (NO3−) was modeled using the convectiondispersion equation embedded in the HYDRUS model. This equation isreported to be suitable for conservative solutes such as NO3−:

∂
∂c
∂c
∂θc
∂ ⎛
∂c
∂c
+ θDxz ⎞ +θDzz= ⎧ ⎛θDxx+ θDzx ⎞ ⎫
⎨
∂x
∂z ⎠
∂t
∂z ⎝
∂z
∂x ⎠ ⎬⎩ ∂x ⎝⎭∂qz c ⎞∂q c− ⎜⎛ x +⎟ − Sc
x
∂
∂z ⎠
⎝
−

(5)
-3

where c is the NO3 concentration in the liquid phase (ML ), qx and qz
are the components of the volumetric ﬂux density (LT-1), Dxx, Dzz, andDxz are the components of the dispersion tensor (L2T-1) (Bear, 1972), Sc
is a sink term, which generally includes local NO3− uptake (through apassive process), mineralization, microbial immobilization, and denitriﬁcation (ML-3T-1). The ﬁrst term on the right side of Eq. 3 describes
the solute ﬂux due to dispersion, the second term describes the solute
ﬂux due to convection with ﬂowing water, and the third term describes
nutrient uptake by roots.
Similarly, as in other earlier studies (e.g., Ajdary et al., 2007; Wanget al., 2010; Tafteh and Sepaskhah, 2012), mineralization gains were

2.3.1.2. Flow domain, and initial and boundary conditions. The
impermeable layer in the study area was located at a soil depth of
200 cm (Fig. 3). Hence, the 2D transport domain was deﬁned as arectangle with a depth of 200 cm and a width of 30 m for D0.9L30 andD0.65L30 drainage systems and 15 m for the D0.65L15 and Bi-level
5Agricultural Water Management 242 (2020) 106420

F. Haghnazari, et al.

Table 3Model performance criteria for diﬀerent drainage systems during calibration (2014-2015) and validation (2016-2017). MBE, nRMSE, and EF are the mean bias error,
the normalized root mean square error, and the model eﬃciency coeﬃcient, respectively.
Parameter

Solute Flux

Solute concentration

Treatment

BilevelD0.65L15
D0.65L30
D0.90L30
BilevelD0.65L15
D0.65L30
D0.90L30

Drainage ﬂux (q)Soil water content

BilevelD0.65L15
D0.65L30
D0.90L30
BilevelD0.65L15

D0.65L30

D0.90L30

Soil depth (cm)

–
–
–
–
0−30
30−60
0−30
30−60
0−30
30−60
0−30
30−60
–
–
–
–
0−30
30−60
60−90
0−30
30−60
60−90
0−30
30−60
60−90
0−30
30−60
60−90

Calibration period

Validation period

MBEa

nRMSE (ndb)EF (%)MBEa

nRMSE (ndb)EF (%)

0.463
0.630
0.568
−0.2660.001
0.001−0.0020.001−0.001
0.001
0.001
0.001
−0.26
−1.51
2.37
−21.7
0.002
−0.0010.000−0.0020.001
0.000−0.001
0.001
0.001
0.001
0.001
0.001

0.415
0.418
0.386
0.353
0.097
0.100
0.112
0.080
0.106
0.079
0.152
0.114
0.286
0.311
0.291
0.230
0.025
0.022
0.015
0.024
0.022
0.016
0.027
0.029
0.023
0.005
0.005
0.006

0.795
0.870
0.841
0.772
0.883
0.863
0.839
0.868
0.850
0.843
0.829
0.883
0.822
0.688
0.850
0.822
0.840
0.946
0.986
−0.016
0.864
0.864
0.826
0.790
0.967
0.726
0.838
0.795

0.718
−0.0080.354−0.972−0.001
0.001
0.001
0.001
−0.001−0.0010.001−0.001
24.9
−1.11
−1.96
0.39
−0.0010.001−0.005−0.0010.001−0.001−0.001−0.0010.001
0.001−0.001−0.001

0.570
0.498
0.395
0.436
0.091
0.120
0.079
0.184
0.136
0.132
0.169
0.128
0.251
0.370
0.247
0.259
0.034
0.029
0.033
0.014
0.018
0.018
0.031
0.032
0.018
0.006
0.008
0.008

0.806
0.772
0.846
0.769
0.842
0.868
0.844
0.513
0.808
0.778
0.833
0.798
0.787
0.719
0.716
0.778
0.851
0.945
0.959
0.745
0.916
0.872
0.848
0.815
0.962
0.764
0.772
0.902

a
MBE is described in mg cm−1 d−1 for solute ﬂuxes, in mg cm-3 for soil NO3 concentrations, in 10-3 mm d−1 for drainage ﬂuxes, and in cm3 cm3 for soil watercontents.
b
nd means “no dimension”.

assigned a no-ﬂow boundary condition.
The initial condition for solute transport simulations was deﬁned
using the measured soil NO3− content in diﬀerent soil layers. A thirdtype Cauchy boundary condition was used to describe the concentration
ﬂux at the top boundary and the drains. A Cauchy boundary condition
is automatically converted into a second-type Neumann boundary
condition during periods of drain outﬂow.

drainage systems. The drains are considered to be on the sides of thetransport domain. An unstructured triangular ﬁnite element mesh(FEM) was used to discretize the deﬁned transport domain. A nonuniform FEM was generated by HYDRUS with ﬁnite element sizes
gradually increasing with distance from the drains. The entire transportdomain was divided into six soil layers, i.e., 0−30 cm, 30−60 cm,
60−90 cm, 90−120 cm, 120−150 cm, and 150−200 cm soil depths,
for which diﬀerent soil properties were deﬁned. To represent the highpermeability of the backﬁlled drain trench (gravel), an additional soil
layer was deﬁned 10 cm around, 10 cm below, and 30 cm above thedrains. Measured soil water contents in diﬀerent soil layers wereconsidered to represent initial conditions for water ﬂow simulations.
The saturated soil water content was speciﬁed as an initial condition insoil layers below the water table to diﬀerentiate the saturated andunsaturated zones.
The atmospheric boundary condition, deﬁned using potential evaporation (EP), potential transpiration (TP), and precipitation (P), wasapplied at the top of the transport domain. The dual-crop coeﬃcientapproach (Allen et al., 1998) was adopted to separate crop evapotranspiration (ETC ) into EP and TP as follows:⎧ ETC = Kc × ET0 = EP + TP
EP = K e × ET0⎨⎩ TP = K cb × ET0

2.3.1.3. Calibration and validation. The experimental data were ﬁrst
used to calibrate soil hydraulic and solute transport parameters andthen to validate the HYDRUS model. These parameters were optimized
using the inverse solution option of HYDRUS. In this process, soil
hydraulic parameters, including the saturated hydraulic conductivity(Ks), the saturated soil water content (θs), the residual soil watercontent (θr), and solute transport parameters, including thelongitudinal (DL, L) and transverse (DT, L) dispersivities, were
optimized. Measured drain ﬂuxes (q) and NO3− concentrations in the
2015–2016 growing season were used to calibrate the HYDRUS model.
The calibrated parameters were then used to validate the model using
the same data collected in the 2016–2017 growing season. The
molecular diﬀusion coeﬃcient was always set equal to zero sincemolecular diﬀusion in soils can usually be neglected (Radcliﬀe andŠimůnek, 2010; Karandish and Šimůnek, 2017).

(6)
2.3.1.4. Correspondence criteria indices. The capability of the HYDRUS
model to simulate drainage water and nitrate ﬂuxes under diﬀerent
treatments was assessed using the mean bias error (MBE), thenormalized root mean square error (nRMSE), and the modeleﬃciency index (EF) as follows (Karandish and Šimůnek, 2016):

where KC is the crop coeﬃcient, K Cb is the basal crop coeﬃcient, and K e
is the evaporation coeﬃcient. The values of K Cb for diﬀerent crowing
seasons were taken from Allen et al. (1998): 0.3, 1.05, and 0.25 for the
initial, mid-, and late- growing stages of canola, respectively. The values of Ke were then estimated as 0.05, 0.1, and 0.1, for then mentioned
growing stages, respectively. A seepage face boundary condition wasused to represent the drains. All other remaining boundaries were

n

MBE =
6

∑i = 1 (Oi − Pi )n(7)

Agricultural Water Management 242 (2020) 106420

F. Haghnazari, et al.

Fig. 5. Scatter plots between the measured and simulated soil water contents (SWC, cm3 cm−3) at diﬀerent depths (0-30, 30-60, and 60-90 cm) during the 2015-2016(calibration; the ﬁrst and third columns) and 2016-2017 (validation; the second and fourth columns) growing seasons for diﬀerent drainage systems (Bilevel (topleft), D0.65L15 (bottom left), D0.65L30 (top right), and D0.90L30 (bottom right)).
0.5n(Pi − Oi )2]
i=1

{ [∑
1

nRMSE =

n

2016–2017 growing season). A comparative analysis was then carriedout between the simulated projections for the 2041–2070 period andthe base period. In this regard, a relative change in the consideredparameter (i.e., climatic variables, drainage ﬂux, NO3 ﬂux, or seasonal
N loss) was calculated as follows:

}

O

(8)

n

EF = 1 −∑i = 1 (Oi − Pi )2n∑i = 1 (Oi − Oi )2

(9)

RC =

where Pi and Oi are simulated and observed data, respectively, O and P
are the averages of observed and simulated data, respectively, and n is
the number of observations.

Xf − Xb
Xb

× 100%(10)

where RC is a relative change in the considered parameter (%), Xf and
Xb are values of the considered parameter in the future and base periods, respectively.

2.3.2. Scenario analysis
The calibrated and validated HYDRUS model was then applied toestimate the probable consequences of climate change projections on
drainage water (q) and NO3− ﬂuxes for diﬀerent drainage systems. The
climate projections were ﬁrst generated for the base period (i.e.,
1975–2005), and then for all 20 GCMs for four considered RCP scenarios over the 2040–2070 period. To obtain consistent comparisons,the initial and boundary conditions, as well as all agricultural practices,were considered to be the same as during the validation period (i.e., the3. Results and discussion
3.1. The HYDRUS(2D/3D) model eﬃciency
The calibrated soil hydraulic and solute transport parameters are
summarized in Table 2. Fig. 4 shows temporal variations of the observed and HYDRUS-simulated drain ﬂuxes (q) for diﬀerent drainage
7

Agricultural Water Management 242 (2020) 106420

F. Haghnazari, et al.

Fig. 6. Observed and simulated NO3− ﬂuxes during the 2015-2016 (left charts, the calibration period), and 2016-2017 (right charts, the validation period) growing
seasons for diﬀerent drainage systems (Bilevel, D0.65L15, D0.65L30, and D0.90L30).

Table 3 also shows good correspondence between the observed and
HYDRUS-simulated NO3 concentrations in diﬀerent soil layers during
both growing seasons (MBE=(-0.002)-(+0.001) mg cm−1 d−1, nRMSE= 7.9–18.4 %, EF = 0.51−0.88), which indicates that the calibrated
model is well suited to describe the ﬂow and transport processes observed in the experimental ﬁeld.
To ensure the capability of the HYDRUS model to simulate the solute transport adequately, we also compared temporal variations of theobserved and simulated NO3 ﬂuxes in diﬀerent drainage systems during
the calibration and validation periods (Fig. 6). While the HYDRUS
model either slightly underpredicted or overpredicted NO3 ﬂuxes
during diﬀerent periods, the simulated ﬂuxes overall closely matchedthe observed data for all drainage systems during both the calibrationand validation periods, with correlation eﬃciencies of 0.77−0.9 (datanot shown). The model performance criteria reported in Table 3 also
indicate the high potential of the HYDRUS model in capturing the NO3transport in diﬀerent drainage systems. MBE, nRMSE, and EF varied in
the range of (-97)-(0.72) mg cm−1 d−1, 35.5–57 %, and 0.77−0.87,
respectively, across diﬀerent drainage systems and during both growing
seasons. The model capability of simulating N dynamics under diﬀerent
drainage conditions is also supported by others (e.g., Mekala andNambi, 2016; Matteau et al., 2019).

systems during the calibration (2015–2016 growing season) and validation (2016–2017) periods. The model captured the temporal patternof drainage ﬂuxes q in both periods for all drainage systems well, eventhough diﬀerent drainage systems did not display the same patterns. In
the 2015–2016 growing season, the average observed drainage ﬂuxes q
for the Bilevel, D0.65L15, D0.65L30, and D0.90L30 drainage systems were
1.08, 0.97, 1.13, and 2.05 mm d−1, respectively, while the corresponding HYDRUS-simulated values were 1.07, 0.98, 1.12, and 2.07mm d−1, respectively. The observed and HYDRUS-simulated values
matched even better during the validation period.
The model performance criteria, summarized in Table 3, also indicate the strong predictive capability of the model. MBE, nRMSE, andEF ranged from -21.7 × 10−3 to 24.9 × 10−3 mm d-1, from 23 % to 37%, and from 0.69 to 0.85, respectively, among diﬀerent drainage systems and diﬀerent growing seasons. The eﬃciency of the HYDRUS
model to predict drainage ﬂuxes q is also supported by other researchers (e.g., Öztekin, 2002; Mirjat et al., 2014; Filipovic et al., 2014;
Li et al., 2014; Darzi-Naftchali et al., 2018; Matteau et al., 2019).
The quantitative assessment of the model performance, as well as
the visual inspection presented in Fig. 5, also shows good agreement
between the observed and HYDRUS-simulated soil water contents(SWCs) in diﬀerent soil layers during the calibration and validation
periods; with MBE, nRMSE, and EF ranging from -0.005 to 0.002 cm3
cm−3, from 5 to -3.4 %, and from 0.73 to 0.99, respectively. These
diﬀerences may be a consequence of the fact that the HYDRUS-simulated SWCs are compared with the measured SWCs, which are averaged
over a certain soil volume, in which the SWC gradient caused by irrigation/precipitation/drainage may not be linear (Karandish andŠimůnek, 2016; Mguidiche et al., 2015). The capability of the HYDRUS
model to predict SWC variations is also supported by other researchers(Ramos et al., 2012; Kandelous et al., 2012).

3.2. Global warming projections
3.2.1. Climatic variables
Fig. 7 shows the range of relative changes in diﬀerent climatic
variables for diﬀerent RCPs, along with the range of GCMs projections
for each scenario. Regardless of the type of the climatic variable, Fig. 7
shows a diﬀerent behavior of projected variables for diﬀerent GCMs
and RCP scenarios, which could be attributed to diﬀerent resolutions of8Agricultural Water Management 242 (2020) 106420

F. Haghnazari, et al.

Fig. 7. The range of relative changes in minimum (Tmin) and maximum (Tmax) temperatures, monthly precipitation, and monthly ET0 in the study area for diﬀerent
future RCP scenarios (i.e., 2041-2070) compared to the base period (i.e., 1975-2005). The lower and upper ends indicate the 5% and 95 % intervals of the uncertainty
ranges, and dots show the 50 % value.

projections of diﬀerent GCMs/RCP scenarios indicate the uncertainty inthe projections of future climatic variables. Quantifying these ranges of
uncertainties is essential since they provide more accurate insights fordeveloping rational plans to cope with plausible consequences of global
warming. Fig. 7 shows that the range of uncertainty arising from projections of diﬀerent GCMs for diﬀerent RCP scenarios is lower for Tmin
and Tmax, which indicates more similar predictions of temperaturescompared to water ﬂuxes, such as P and ET0. Such results are inagreement with those reported by Karandish et al. (2017).
Fig. 7 also demonstrates non-uniform temporal projections of climatic variables by diﬀerent RCP scenarios, which is also conﬁrmed byothers (e.g., Zickfeld et al., 2005; Girvetz et al., 2009; Harmsen et al.,2009; Agarwal et al., 2014; Adham et al., 2019). Precipitation projections seem to be less uncertain over the October-January period, whilesmaller uncertainties in the projections of other variables were observed in May.
Fig. 7 shows that compared to the base period, and based on the
average value obtained from all GCMs under four RCPs, Tmin may increase by 0.15–1.25 °C, with the lowest and highest increases in April

the ocean models for diﬀerent GCMs. Climate processes fully depend on
how GCMs simulate the extent of sea ice, sea surface temperature,surface heat, ocean heat transfer, and momentum ﬂuxes (CCSP, 2008;
Karandish et al., 2017; Karandish and Mousavi, 2018). Diﬀerent projections by diﬀerent GCMs could also be attributed to many other factors, which control the simulation process, such as the prognostic
variable for cloud characterization and the compatibility between theheat and water budgets of the atmospheric and ocean models (Randall
et al., 2007). However, these diﬀerences among RCP scenarios can be
attributed to the embedded assumptions in the socio-economic andenvironmental models for each scenario. Other researchers have also
indicated that future projections of climate variables depend on either
the RCP scenarios or the selected GCM (e.g., Adham et al., 2019; HajAmor et al., 2020).
Climate change projections showed both positive (increases) andnegative (decreases) changes in monthly average values of Tmax and
ET0, while monthly precipitation P is more likely to increase in the
future. Except for RCP2.6 and a few GCMs for RCP4.5, climate change
may cause an increase in Tmin in the study area. Diﬀerences among the9Agricultural Water Management 242 (2020) 106420

F. Haghnazari, et al.

Fig. 8. Temporal variations of drainage ﬂuxes for diﬀerent drainage systems (Bilevel, D0.65L15, D0.65L30, and D0.90L30) for the base period (1975-2005) and the 20412070 period for diﬀerent RCPs (data are provided based on the ensemble average of 20 GCMs for each RCP scenario).

the other probable negative eﬀects of global warming on crop growth.
During the 2041–2070 period, monthly P in the study area will always increase with the lowest increase occurring over the NovemberJanuary period, and the highest one over the March-May period(Fig. 7). The RCP8.5 scenario led to the highest increases in monthly
precipitation. Karandish et al. (2016) carried out a high-resolution assessment for entire Iran and reported that global warming might cause
the highest increase in P along the coast of the Caspian sea, where thestudy area is located. They believed that such an increase might be
attributed to an increase in air temperature, which consequently causes
a considerable increase in the atmospheric water-holding capacity. An
increase in P during wet seasons, which is in agreement with theﬁndings of other researchers (e.g., Agarwal et al., 2014; Adham et al.,2019), may cause water-logging challenges, indicating the necessity ofand October, respectively. Tmax may increase by 0.45–2.18 °C, except inApril and May, when Tmax decreases by 0.08 °C and 0.15 °C, respectively. Others have reported a general increase in both Tmin and Tmax
under climate change in the study area (e.g., Abbaspour et al., 2009;
Dastoorani and Poormohammadi, 2012; Kazemi-Rad and Mohammadi,
2015; Karandish et al., 2017). They also indicated a non-uniform pattern of temporal projections of global warming into air temperature,
which is in agreement with results in our research. An increase in Tminin autumn and winter may be beneﬁcial since it may provide favorable
conditions for early cultivation and, consequently, may reduce theduration of the cropping cycle since the crop’s thermal energy requirement may be supplied during a shorter period (Karandish et al.,2016). Such projections may further lead to lower crop water requirements if the positive eﬀect of the shortened cropping cycle goes beyond
10Agricultural Water Management 242 (2020) 106420

F. Haghnazari, et al.

simulation rather than later.
Based on the ensemble average of 20 GCMs for each RCP scenario,
average drainage ﬂuxes during the growing season were calculated foreach drainage system, and the results are summarized in Table 4.
Compared to the base period, the average drainage ﬂuxes over the
cropping cycle may increase by 112.3 % (80.6–148.3 %), 66.9 %(62.0–74.9 %), 87.5 % (61.8–110.2 %), and 25.1 % (12.0 %–42.1 %) for
the Bilevel, D0.65L15, D0.65L30, and D0.90L30 drainage systems, respectively. For all drainage systems, the RCP6.5 scenario leads to thehighest increase in the average drainage ﬂux over the cropping cycle,
while the RCP2.6 scenario leads to the lowest increase.

Table 4
Climate change eﬀects on average drainage ﬂuxes (q), average NO3 ﬂuxes, andseasonal NO3 losses under diﬀerent drainage systems during the growing season(ensemble averages for 20 GCMs for each scenario; the base period refers to the
1975-2005 period).
Treatment

Scenario

Drain ﬂux
(mm d−1)

Nitrate ﬂux (mgcm−1 d−1)

Seasonal NO3 loss
(kg ha−1)

BilevelBase period
RCP2.6
RCP4.5
RCP6.0
RCP8.5
Base period
RCP2.6
RCP4.5
RCP6.0
RCP8.5
Base period
RCP2.6
RCP4.5
RCP6.0
RCP8.5
Base period
RCP2.6
RCP4.5
RCP6.0
RCP8.5

0.95
1.72
2.11
2.36
1.89
0.95
1.54
1.60
1.66
1.54
1.12
1.98
2.25
2.35
1.81
2.61
2.94
3.50
3.71
2.93

5.7
8.8
10.0
9.8
9.5
4.8
5.7
6.0
5.8
5.7
6.2
10.7
13.4
13.8
10.5
13.3
13.8
17.0
17.6
14.4

10.7
16.3
18.6
18.3
17.8
18.0
21.3
22.3
21.7
21.3
11.5
19.9
25.0
25.8
19.7
24.8
25.8
31.7
32.9
26.9

D0.65L15

D0.65L30

D0.90L30

3.2.3. Nitrate ﬂux
Daily HYDRUS-simulated NO3 ﬂuxes for diﬀerent drainage systems
under future global warming projections (the ensemble average of 20
GCMs for each RCP scenario) are displayed in Fig. 9. The visual inspection shows a dramatic increase in NO3 ﬂuxes during the OctoberJanuary period when monthly precipitations are also relatively high.
This pattern is also a consequence of increased drainage ﬂuxes in thesefuture climate scenarios (Fig. 9). Fig. 9 also shows that during the
October-January period, the range of uncertainties arising from the
projections of diﬀerent RCPs is also higher than during the other periods. While the Bilevel and D0.65L30 drainage systems registered increased NO3 ﬂuxes during the entire growing cycle, the D0.65L15 andD0.90L30 drainage systems experienced a considerable decrease in NO3
ﬂuxes during the February-March period. More similar predicted NO3
ﬂuxes were obtained for all drainage systems during the FebruaryMarch period, indicating less uncertainty during this time. Such results
may be attributed to lower NO3 soil concentrations during this period
due to high crop N uptake during previous months (Darzi-Naftchali
et al., 2017; DeDatta, 1981).
Based on the ensemble average of 20 GCMs for each RCP scenario,
the minimum, maximum and average NO3 ﬂuxes during the growing
season were calculated for each drainage system, and results are summarized in Table 4. The average NO3 ﬂux over the cropping cycle may
increase by 53.3–74.8 %, 18.6–23.9 %, 71.3–124.9 %, and 4–32.6 % in
the Bilevel, D0.65L15, D0.65L30, and D0.90L30 drainage systems, respectively. For all drainage systems, the RCP4.5 and RCP6.5 scenarios may
result in the highest increase in the average NO3 ﬂux over the cropping
cycle.

installing costly drainage systems to mitigate further problems. An increase in P during seasons may also provide favorable conditions for thegrowth of weeds and pests and may enhance soil erosion, along with aconsiderable change in the soil available water (Enete and Amusa,
2010). Such consequences may reduce economic beneﬁts by restraining
the proper crop growth.
Fig. 7 shows that monthly ET0 may slightly increase by 2.4–10.3 %throughout October-March. The highest increase can be observed inautumn during the October-December period. However, monthly ET0 is
likely to be reduced by 0.6 % and 2.3 % in April and May, respectively.
Such a decrease can be a result of a small increase in monthly average
Tmax in the same period. The highest increase in ET0 can be observed inautumn. Having a key role in the hydrological cycle, an increase in ET0
may threaten agricultural sustainability since it may lead to a signiﬁcant increase in the agricultural demand for water beyond its sustainable availability in a region. Such an increase may cause a signiﬁcant reduction in water availability due to the overexploitation of
surface and groundwater resources (Terink et al., 2013). Other researchers also support a projected increase in ET0 in Iran (e.g., Terink
et al., 2013; Karandish and Mousavi, 2018; Darzi-Naftchali and
Karandish, 2019).

3.2.4. Seasonal nitrate losses
The seasonal NO3 losses for diﬀerent drainage systems were computed based on the ensemble average of 20 GCM projections for each
RCP scenario (Table 4). Regardless of the time (i.e., either during thebase or future periods), the lowest NO3 loss always occurred for theD0.65L15 drainage system, where drains are installed at a shallower
depth and at a smaller distance. On the other hand, the D0.90L30 drainage system always had the highest NO3 losses during the growing
seasons, indicating that NO3 losses increase when drains are installed atdeeper depths and larger distances (Cooke et al., 2002; Burchell, 2003;
Yoon et al., 2006). These results indicate the importance of selecting aproper drainage system when sustainable agriculture and a safe environment are the primary concern.
Table 4 shows that climate change may result in higher seasonal
NO3 losses for all drainage systems. Compared to the base period, theseincreases will be 66.8 % (53.3–74.8 %), 20.5 % (18.6–23.9 %), 96.8 %(71.3–124.9 %), and 18.2 % (4–32.6 %) for the Bilevel, D0.65L15,
D0.65L30, and D0.90L30 drainage systems, respectively. The lowest andthe highest increases in NO3 losses are expected to occur in the D0.90L30
and D0.65L30 drainage systems, respectively.
Increased NO3 losses due to climate change may be attributed either
to increased drain ﬂuxes or the ﬁeld management practices and, inparticular, to the N-fertilization management. Drain ﬂuxes can becontrolled by various factors, including climatic variables, especially P
and ET0, soil hydraulic properties, the design of the drainage system,

3.2.2. Drainage ﬂux
Daily HYDRUS-simulated drainage ﬂuxes for diﬀerent drainage
systems under future global warming projections (the ensemble averageof 20 GCMs for each RCP scenario) are displayed in Fig. 8. While daily
drain ﬂuxes due to climate change projections both decreased and increased in diﬀerent periods compared to the base scenario, they are
more likely to increase, especially during the October-January period.
Drainage ﬂuxes are highly dependent on climatic variables, and particularly on P and ET0. While an increase in ET0 may lead to a likely
reduction in drainage ﬂuxes due to an increase in crop water uptake,this positive eﬀect may be reversed by a negative impact of increased
precipitation on q.
The results in Fig. 8 indicate that the monthly green water surplus(GWS), deﬁned as GWS=P-ET0 for a particular month (Karandish andMousavi, 2018), will signiﬁcantly increase due to global warming and acorresponding increase in monthly precipitation, which will consequently enhance drainage ﬂuxes. Simulated drainage ﬂuxes for different drainage systems seem to be more uncertain early in the11Agricultural Water Management 242 (2020) 106420

F. Haghnazari, et al.

Fig. 9. Temporal variations of NO3− ﬂuxes for diﬀerent drainage systems (Bilevel, D0.65L15, D0.65L30, and D0.90L30) for the base period (1975-2005) and the 20412070 period for diﬀerent RCPs.

2009), organic mulches (Chukalla et al., 2015), or proper tillage practices (Liu et al., 2013). Organic mulching is also reported to have apositive eﬀect on reducing nutrient leaching in agricultural lands (DeVita et al., 2007; Mitchell et al., 2012), and is commonly proposed as aneﬀective measure to prevent water pollution under intensive agriculture (Azooz and Arshad, 1996; Triplett and Dick, 2008; Constantin
et al., 2010; Chukalla et al., 2017). In particular, organic mulching may
restrict N-leaching from the soil surface layer, where suﬃciently highand crop management. Hence, increased seasonal NO3 losses may bepartially attributed to increased drainage ﬂuxes resulting from a profound increase in surplus precipitation (i.e., P-ET0) due to global
warming (Table 4). While future changes in climate variables may be
inevitable, negative consequences of climate change projections on
drain ﬂuxes may be alleviated by improving the soil water holding
capacity. Such improvements may be achieved by applying variousorganic amendments, such as manure or gypsum (Udayasoorian et al.,12Agricultural Water Management 242 (2020) 106420

F. Haghnazari, et al.

the optimal management scenarios in rainfed lands under global
warming conditions. While simulating the eﬀects of climate change
projections on crop yield was not among the objectives of this research,such eﬀects may be of high importance when economic interests are
considered. Further research is thus needed to compare both economicand environmental consequences to obtain a full picture of the coming
future.

concentrations of N enable continued uptake by roots (Phogat et al.,2013).
Drain depths and spacings also aﬀect drain ﬂuxes (Wahba andChristen, 2006). Jafari-Talukolaee et al. (2015) reported that increasing
drain spacing might reduce drainage volumes in the subsurface-drained
paddy ﬁeld. The impact of installing either deep or shallow drainage
systems on drainage water quality/quantity in Southern Australian irrigated lands was investigated by Christen and Skehan (2001). Theirresults demonstrated that lower drainage volumes occur from drainage
systems with reduced drain depths and spacings. The long-term ﬁeld
investigation in the paddy ﬁeld in northern Iran indicated that shallow
drains were more eﬀective than deeper ones in controlling the watertable depth during the ﬁrst three years after the installation of subsurface drainage while the reverse trend was observed in the fourthyear (Jafari-Talukolaee et al., 2016). However, shallow drains may
produce serious environmental problems since they enhance leaching
and, consequently, may increase seasonal NO3 losses (Darzi-Naftchali
et al., 2017). Hence, drain depths and spacing need to be optimized
depending on the main agricultural or environmental concerns andfuture global warming projections.
N-fertilization management is among the most crucial ﬁeld practices, which signiﬁcantly aﬀect NO3 losses. Numerous researchers demonstrated the close relationship between the N-fertilizer application
rate/timing and N-leaching in agricultural lands (Mosier et al., 2002;
Gasser et al., 2002; Haverkort et al., 2003; Daudén and Quilez, 2004;
Alva et al., 2006; Barton and Colmer, 2006; Hutton et al., 2008; Weiet al., 2009; Jia et al., 2014; Karandish and Šimůnek, 2017). Besides, itis worth noting that the type of N-fertilizer can also aﬀect the Nleaching rate (Jia et al., 2014). On the other hand, N is an essential crop
nutrient, that profoundly aﬀects crop growth and yield (Wienhold et al.,
1995; Jia et al., 2014). An insuﬃcient N supply in soil may result insevere yield and economic losses (Wienhold et al., 1995; Jia et a1.,
2014). Therefore, further research should be carried out to ﬁnd out theenvironmentally/economically safer applications of N-fertilizers under
future global warming predictions.

Declaration of Competing Interest
The authors declare that they have no known competing ﬁnancial
interests or personal relationships that could have appeared to inﬂuence the work reported in this paper.
Acknowledgement
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”.
Appendix A. Supplementary data
Supplementary material related to this article can be found, in the
online version, at doi:https://doi.org/10.1016/j.agwat.2020.106420.
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Field experiments and modeling analyses involving a subsurfacedrained ﬁeld of rained-canola, as a winter crop, were carried out toevaluate the integrated inﬂuence of subsurface drainage and global
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