Agricultural Water Management 197 (2018) 67–78

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

Numerical modeling of soil water dynamics in subsurface drained
paddies with midseason drainage or alternate wetting and drying
management
Abdullah Darzi-Naftchali a,∗ , Fatemeh Karandish b , Jiří Šimůnek caWater Engineering Department, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
Water Engineering Department, University of Zabol, Zabol, Iranc
Department of Environmental Sciences, University of California, Riverside, CA 92521, USAba r t i c l e

i n f oArticle history:
Received 25 May 2017
Received in revised form 15 October 2017
Accepted 24 November 2017
Keywords:
Water balance
Water table
Transpiration efﬁciency
Water use efﬁciency
HYDRUSa b s t r a c t
As a supplemental practice to land consolidation projects, subsurface drainage systems have been
installed in paddy ﬁelds to allow for crop diversiﬁcation and to improve the overall productivity of paddy
soils. The HYDRUS (2D/3D) model was applied to investigate the combined effects of different subsurface
drainage systems and water management strategies on water balance, groundwater table, transpiration
efﬁciency, and water use efﬁciency in paddy ﬁelds. Field experiments were conducted during four rice
growing seasons (2011, 2012, 2014, and 2015) at the subsurface-drained paddies of the Sari Agricultural
Sciences and Natural Resources University in northern Iran. Midseason drainage (MSD) management
was applied in 2011 and 2012, while alternate wetting and drying (AWD) management was adopted in2014 and 2015. The model performance was evaluated using the model efﬁciency (EF), root mean squareerror (RMSE), normalized root mean square error (NRMSE), and mean bias error (MBE) measures. The
model had strong predictive capabilities for simulating the mid-drain water table depth for both water
managements. Under MSD and AWD, daily evapotranspiration rates varied from 4.9 to 6.2 mm d−1 and4.9 to 5.9 mm d−1 , respectively. Drainage losses were higher under AWD than MSD, while the reverse
order occurred for percolation losses. Compared with MSD, AWD improved the transpiration and water
use efﬁciency of rice in the presence of subsurface drainage. Being capable of describing complex effects
of AWD and MSD strategies in subsurface-drained paddy ﬁelds, the HYDRUS (2D/3D) model can serve asa practical tool for optimizing water productivity in these ﬁelds.
© 2017 Elsevier B.V. All rights reserved.

1. Introduction
The positive effects of drying paddy ﬁelds during non-stressperiods of the rice growing season were demonstrated in multiple studies (Goto et al., 2000; Liu et al., 2013; Darzi-Naftchali and
Shahnazari, 2014). Midseason drainage (MSD) is a water management practice that is traditionally carried out during the maximum
tillering stage of rice to control non-productive tillers and toconsequently increase rice production. Removing toxic elements(Bouman et al., 2007) and providing better conditions for nutrient
uptake are additional beneﬁts of such management, resulting inincreased grain yield and increased nitrogen use efﬁciency (DarziNaftchali and Shahnazari, 2014). Alternate wetting and drying∗ Corresponding author.
E-mail address: abdullahdarzi@yahoo.com (A. Darzi-Naftchali).
https://doi.org/10.1016/j.agwat.2017.11.017
0378-3774/© 2017 Elsevier B.V. All rights reserved.

(AWD) is another water management practice in rice cultivation
systems that has been reported to improve rice yield while decreasing water consumption (Chu et al., 2014; Ye et al., 2013).
Paddy soils represent a complex hydrological system. Heavy texture and the presence of a much less permeable soil layer (hardpan)
below a plow pan are special characteristics of paddy soils that
reduce the drying rate of soil during non-irrigation periods. Evapotranspiration (ET) and percolation represent two main mechanismsthat result in the loss of moisture in the upper soil zone. Percolation losses are generally low in puddled, poorly drained paddy
soils. Under the AWD practice, the soil water regime of paddy
ﬁelds is transformed from being fully saturated to being alternately
saturated and unsaturated (Tan et al., 2014). Subsurface drainage
can accelerate water table drawdown and provide better aeration during drying periods. Under such conditions, experimentallyquantifying all water balance components is difﬁcult, expensive,
and time consuming. On the other hand, computer-aided analytical

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tools and models may prove helpful in assessing the consequencesof different water management strategies in a quick and efﬁcientmanner (Yao et al., 2014). Numerical computer models can incorporate descriptions of many key processes and transformations in
paddy soils to predict their overall systemic behavior. LEACHM(Wagenet and Hutson, 1989), DRAINMOD (Skaggs, 1978), SWAP
(Van Dam et al., 1997), DSSAT (Hoogenboom et al., 2014), AquaCrop(Steduto et al., 2009), and HYDRUS (Šimůnek et al., 2008) are among
the different simulation models developed for evaluating various
soil-water-atmosphere-plant interactions.
Having both the ﬂexibility to accommodate different types ofboundary conditions for water ﬂow and solute transport calculations and the capability to simultaneously consider root uptakeof water and nutrients, the HYDRUS model can simulate soil
water dynamics in paddy ﬁelds under different water management practices (Li et al., 2015; Hammecker et al., 2012). HYDRUS
use is also greatly facilitated by its sophisticated, graphical, userfriendly interface. For example, Janssen and Lennartz (2009)
applied HYDRUS-2D to quantify water ﬂuxes through bunds in aterraced paddy landscape. Garg et al. (2009) determined a soil water
regime involving preferential ﬂow in a multi-layer paddy soil using
HYDRUS-1D. Using HYDRUS-1D, Tan et al. (2014) simulated thesoil water regime and nitrogen fate in paddy ﬁelds under different
water managements and concluded that the model can properly
simulate water ﬂow in a multi-layered paddy soil when the saturated hydraulic conductivity of a plow pan is inversely estimated.
The results of a companion study on water and nitrogen fate inpaddy ﬁelds demonstrated that HYDRUS-1D can be used to assess

and improve the water and nitrogen management for sustainable
rice production (Tan et al., 2015). Li et al. (2014, 2015) reported thatHYDRUS-1D can be used to simulate water ﬂow, nitrogen transport
and transformations, and water and nitrogen balances in directseeded-rice ﬁelds. In a simulation study, Ebrahimian and Noory(2015) evaluated water ﬂow under various drain depths and spacings in a hypothetical paddy ﬁeld using HYDRUS-2D and concluded
that the model is a robust tool for designing subsurface drainage forpaddy ﬁelds.
Integrating AWD and MSD strategies with subsurface drainage
alters the water and nutrient regimes of the paddy ﬁelds andsigniﬁcantly increases the complexity of the system. Under suchcircumstances, precise knowledge of all the components of waterbalance is essential for increasing water productivity in paddy
ﬁelds, especially because irrigated rice is the leading consumer ofwater in the agricultural sector (Satyanarayana et al., 2007). In viewof the above-mentioned- earlier studies, no research has been yet
conducted on the analysis of soil water dynamics under the AWD
and MSD strategies in subsurface drained paddy ﬁelds using the
HYDRUS (2D/3D) model. Therefore, the ﬁrst main objective of thisstudy was to use collected experimental data involving water tabledrawdown and water balance components in subsurface drained
paddy ﬁelds under different water management strategies for theassessment of the capability of the HYDRUS model for describing
these processes. The second main objective was to use the calibrated HYDRUS model to evaluate the combined effects of different
subsurface drainage systems and water management strategies on

Fig. 1. Location of the study area in the Mazandaran province (top right) of Iran (top left) and the layout of the drainage systems (bottom).

A. Darzi-Naftchali et al. / Agricultural Water Management 197 (2018) 67–78

water balance, the groundwater table, transpiration efﬁciency, andwater use efﬁciency in paddy ﬁelds.
2. Materials and methods
2.1. Study site
A ﬁeld study was conducted during four rice growing seasons(2011, 2012, 2014, and 2015) at the 4.5 ha consolidated paddy ﬁeld
at the Sari Agricultural Sciences and Natural Resources University
in the Mazandaran province of northern Iran (Fig. 1). The area islocated in the coastal zone of the eastern part of the Caspian Sea.
The climate of the region is alternatively inﬂuenced by cold Arctic
air, humid temperate air from the Atlantic Ocean, dry and cold air
associated with Siberian high pressure zones, and Mediterranean
warm air. Minimum and maximum daily air temperatures recordedat the study site during 4 growing seasons were 10 and 37.4 ◦ C.
Average air temperatures during the 2011, 2012, 2014, and 2015
growing seasons were 25.3, 25.1, 26.2, and 27.3 ◦ C, respectively.
Total rainfalls during these growing seasons were 136.6, 73.7, 86.4,
and 88.6 mm, respectively. Fig. 2 displays daily values of minimum
and maximum temperatures along with daily rainfall during thegrowing seasons. The soil on the site is silty clay and clay to a depthof 300 cm. The saturated hydraulic conductivities in different layersof the soil proﬁle are very low.
Eleven PVC corrugated drain pipes (100 m long, with an outside
diameter of 100 mm) were installed at the study site in June–July of2011 at depths of 0.65 and 0.9 m and spacings of 15 and 30 m. Fourdifferent subsurface drainage systems were analyzed by installingdrains at different depths (Dx , where subscript x indicates a drain
depth) and spacings (Ly , where subscript y indicates a drain spacing): D0.9 L30 , D0.65 L30 , and D0.65 L15 . The last drainage system,

69

denoted as Bilevel, has a drain spacing of 15 m and alternate drain
depths of 0.65 and 0.9 m. A solid plastic pipe was used to connect the lateral lines with an open channel as a collector drain at a
depth of 1.2 m. Further details about the experimental design canbe found in Darzi-Naftchali et al. (2013). Fig. 1 shows the locationof the research ﬁeld in the country and the layout of the drainage
systems in the research ﬁeld.
2.2. Field management
All agricultural operations except for water management followed the conventional practices of local growers in the study area.
To decrease percolation losses of water and nutrients during rice
growing periods, the ﬂooded paddy plots were puddled during
land preparation. Basal fertilizers were applied before rice transplanting as follows: 140 kg ha−1 triple superphosphate in 2011;
100 kg ha−1 triple superphosphate, 100 kg ha−1 potassium sulfate,and 80 kg ha−1 urea in 2014; and 50 kg ha−1 triple superphosphate,
50 kg ha−1 potassium sulfate, and 100 kg ha−1 urea in 2015. No
basal fertilizers were applied in 2012. Rice cultivation was carriedout during four cropping seasons: from July 21 to October 10 in2011, from May 28 to August 11 in 2012, from May 10 to August 5
in 2014, and from June 4 to August 28 in 2015. During rice growing
periods, urea was broadcasted as follows: 90 kg ha−1 at 8 days after
transplanting (DAT) in 2011, 90 kg ha−1 at 9 DAT in 2012, 80 and
30 kg ha−1 at 13 and 31 DAT in 2014, respectively, and 50 kg ha−1
at 12 DAT in 2015. The growers in the region adjust fertilization
based on their other activities in the crop rotation, which may resultin minor differences in the amount of applied fertilizer between
different growing seasons (Darzi-Naftchali et al., 2017).
While the usual water management practice for local riceﬁelds is continuous ﬂooding as it helps control weeds and pests

Fig. 2. Daily values of rainfall, minimum and maximum temperatures (left), irrigation depth (Irr), and potential evapotranspiration (ET0 ) (right) during the four growing
seasons (DAT – days after transplanting).

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Table 1
Physical properties and hydraulic parameters of different soil layers in the study area.
Soil depth (cm)Clay (%)

Silt (%)

Sand (%)

Soil texture␳b (g cm−3 )rs␣ (cm−1 )nlKs (cm d−1 )

0–30
30–60
60–90
90–120
120–150
150–200

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

Silty Clay
Silty Clay
Silty Clay
Silty Clay
Silty Clay
Clay

1.34
1.40
1.37
1.36
1.37
1.38

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

r and ␪s are the residual and saturated water contents, respectively, Ks is the saturated hydraulic conductivity, ␳b is bulk density and ␣, n, and l are the shape factors in thevan Genuchten-Mualem model (van Genuchten, 1980).

(Darzi-Naftchali et al., 2013), two types of water managements
were followed during this experiment: midseason drainage (MSD)
and alternative wetting and drying (AWD). Under both managements, the plots were continuously ﬂooded (with about 5 cm ofstanding water) during 3–4 weeks after transplanting, and suitableconditions for harvesting were created by end-season drainage several days before the harvest. The plots were ﬂooded except during
the drainage periods.
The MSD water management was followed in 2011 and 2012
with the following sequence of operations: ﬂooding, MSD (on 25
DAT for a 7 day period), re-ﬂooding, and end-season drainage management. AWD water management was adopted in 2014 and 2015.
Two periods of drainage were conducted during the vegetative
growth stage (as a midseason drainage). The plots were drained
during the time periods of 25–34 and 43–47 DAT in 2014 and
28–32 and 39–43 DAT in 2015. Each drainage period was ended
when the formation of small cracks on the soil surface was visuallyobserved. Temporal variations of irrigation are presented in Fig. 2
for all growing seasons.

was determined as a ratio of grain yield per total evapotranspiration.
2.4. Numerical modeling approach
HYDRUS (2D/3D) (Šimůnek et al., 2008, 2016) is powerful software for simulating transient, two- or three-dimensional
movement of water and nutrients in soils for a wide range of boundary conditions, irregular boundaries, and soil heterogeneities.
Water ﬂow in soils is described using the Richards equation as
follows:

∂
∂
=
∂t
∂x



Kx

∂h
∂x



+

∂
∂z



Kz

∂h
∂z



−

Before crop cultivation, soil samples were taken from each treatment plot every 30 cm to a depth of 200 cm. Soil properties were
determined on these soil samples. Soil water contents at 14 different pressure heads (from 0 to 16 bar) were measured in thelaboratory using a pressure plate apparatus. The van GenuchtenMualem model (van Genuchten, 1980) was then ﬁtted to theobserved retention curves using the RETC model. Table 1 provides
hydraulic parameters and soil physical properties for different soil
layers in the study area.
Measurements of water table depths were manually made in theobservation wells that were dug midway between drains each day
during drainage periods. The water table proﬁle between shallow
and deep drains in the Bilevel drainage system was determined in
2014. To do this, additional observation wells were dug at drain
trenches and 1, 2.5, and 5 m apart from deep and shallow drains.
Subsurface drainage discharge was daily measured during drainage
periods, and the drains were plugged the rest of the time. Duringﬂooding periods in 2014, standing water levels were daily recordedat three locations in different plots.
In 2014 and 2015, the total dry matter (TDM) production andleaf area index (LAI) were determined at 16, 28, 39, 50, 60, and
67 DAT. In addition, grain and biological yields of rice were determined at harvest each year. The HYDRUS (2D/3D) model was used
to determine the water balance components under different water
management strategies and drainage systems. Due to the lack of
direct measurements of transpiration (T), this term was estimatedby the model from the weather data and crop LAI. For all samplingsand drainage systems, transpiration efﬁciency (TE) expressed ingrams of biomass per liter (kg) of transpired water was calculated
as a mean of three replicated measurements of TDM and the corresponding cumulative transpiration. The water use efﬁciency (WUE)(1)

where ␪ is the volumetric soil water content (SWC) [L3 L−3 ], K is
the unsaturated hydraulic conductivity [LT−1 ], h is the soil waterpressure head [L], x is the lateral coordinate [L], z is the vertical coordinate (positive downwards), t is time [T], and WU(h, r, z) denotes
root water uptake [T−1 ]. WU is computed as follows:
WU (h, x, z) =  (h) RDF (x, z) WTpot

2.3. Measurements∂k− WU (h, x, z)∂z(2)

where (h) is the soil water stress function (dimensionless) of
Feddes et al. (1978), RDF is the normalized 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. In the present study, the root distribution was assumed
to be uniform in time (which is a restriction of HYDRUS-2D). The
soil hydraulic properties were modeled using the van GenuchtenMualem constitutive relationships (van Genuchten, 1980).
The two-dimensional transport domain was a rectangle 200 cm
deep (the upper depth of the impermeable layer) and either 30 m
wide for the D0.9 L30 and D0.65 L30 drainage systems, or 15 m wide
for the D0.65 L15 and Bilevel drainage systems. The transport domain
was discretized using unstructured, triangular, ﬁnite element mesh(FEM). A non-uniform FEM was generated by HYDRUS-2D withﬁnite element sizes gradually increasing with distance from thedrains. Six soil horizons with different soil hydraulic properties
were deﬁned for the 0–30 cm, 30–60 cm, 60–90 cm, 90–120 cm,
120–150 cm, and 150–200 cm soil depths (Table 1). The soil layer
below the 200 cm depth was considered to be impermeable (DarziNaftchali et al., 2013).
The measured pressure head distribution was applied to deﬁne
the initial conditions for ﬂow simulations. For the drainage periods,
the time-variable pressure head boundary condition was appliedat the top of the transport domain to represent different water
managements at the ﬁeld. For the rest of the simulation periods,
the atmospheric boundary condition was deﬁned at the top of thetransport domain. A seepage face boundary condition was used torepresent the drains during the drainage periods. All other remaining boundaries were assigned a no-ﬂow boundary condition.
The interactions between soil and atmosphere were describedusing the atmospheric time-variable boundary condition and measured meteorological data. Potential evapotranspiration (ETp ) was
calculated using the FAO56 Penman–Monteith method (Allen et al.,A. Darzi-Naftchali et al. / Agricultural Water Management 197 (2018) 67–78

71

Fig. 3. Temporal variations of water depths (WD) midway between drains during the 2014 growing season for the four drainage systems (a: Bilevel, b: D65 L15 , c: D65 L30 , d:
D90 L30 ) and the AWD water management. D1, D2, and D3 denote the ﬁrst (25–34 DAT), second (43–47 DAT), and third (64–71 DAT) drainage periods, respectively. Statisticalmeasures evaluating the model performance are also provided.

1998), while crop evapotranspiration (ETc ) was calculated by multiplying ETp by the crop coefﬁcients. Using the measured leaf areaindex (LAI) for each treatment, ETc was divided into evaporationand transpiration by using Eq. (5) (Belmans et al., 1983):
Ep = ETc × e−Kgr ×LAI
Tp = ETc − Ep(3)where Ep is potential evaporation [LT−1 ], Tp is potential transpiration [LT−1 ], ETc is crop evapotranspiration [LT−1 ], and Kgr is anextension coefﬁcient for global solar radiation [–]. Kgr for rice was
set to 0.3 based on Phogat et al. (2010). Estimated values of Ep and
Tp were used as input parameters in HYDRUS-2D.
An additional calibration process was carried out for all treatments using measured temporal variations of water depths (WD)during the ﬁrst and second drainage periods in 2014. During thecalibration process, the saturated hydraulic conductivity (Ks ), theresidual soil water content (␪r ), and the saturated soil water content (␪s ) were optimized using the inverse analysis of HYDRUS-2D

Fig. 4. Temporal variations of water depths (WD) midway between drains during the 2015 growing season for the four drainage systems (a: Bilevel, b: D65 L15 , c: D65 L30 , d:
D90 L30 ) and the AWD water management. D1, D2, and D3 denote the ﬁrst (25–34 DAT), second (43–47 DAT), and third (64–71 DAT) drainage periods, respectively. Statisticalmeasures evaluating the model performance are also provided.

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Fig. 5. Temporal variations of drain discharge during the 2014 growing season for the four drainage systems. D1 (left), D2 (middle), and D3 (bottom) denote the ﬁrst (25–34
DAT), second (43–47 DAT), and third (64–71 DAT) drainage periods, respectively.

and measured WDs, while the shape parameters ␣, l, and n in thevan Genuchten-Mualem model (van Genuchten, 1980) were kept
equal to values obtained by RETC. Using the calibrated values of
Ks , ␪r , and ␪s , HYDRUS-2D was then validated using temporal variations of WDs during the third drainage period in 2014 and theother drainage periods in 2011, 2012, and 2015.
2.5. Criteria indices
The root mean square error (RMSE), the normalized root meansquare error (NRMSE), the mean bias error (MBE) and the model
efﬁciency (EF) were calculated to compare the predicted andobserved data (Parchami-Araghi et al., 2013).
3. Results and discussion
3.1. Calibration and validation
Figs. 3 and 4 show the simulated and observed daily water
depths (WDs) along with the model performance criteria for different drainage systems, the AWD water management, and thedrainage periods of the 2014 and 2015 growing seasons, respectively. The HYDRUS (2D/3D) model slightly over-predicted WDs
during the calibration and validation drainage periods in 2014
(Fig. 3), while it closely matched the observed data during drainage
periods in 2015 (Fig. 4). The correlation coefﬁcients for the 2015
validation period were even higher than for the 2014 calibration
period. The average observed WDs for the Bilevel, D0.65 L15 , D0.65 L30 ,and D0.9 L30 drainage systems in 2015 were −50.5, −31.1, −32.9,
and −39.9 cm, respectively, while the corresponding simulated values were −50.4, −32.3, −33.1, and −40.1 cm, respectively. The
model performance criteria indicate the strong predictive capability of the model. EF, RMSE, NRSME, and MBE ranged from 0.91–1,

0.2–6.4 cm, 15.7–498.6%, and 0.14–5.5 cm, respectively, across different drainage systems and the 2014 and 2015 growing seasons.
The efﬁciency of the HYDRUS-1D model for simulating soil watercontents under the continuous ﬂooding and AWD water management systems in traditional paddy ﬁelds was also demonstrated by
Tan et al. (2015). During the irrigation periods, WDs were generally between 1 and 5 cm on the soil surface. WD ﬂuctuations during
these periods were mainly due to variations in irrigation amounts,
evapotranspiration and crop water demand, as well as percolation.
Regardless of soil characteristics, percolation losses are dependent
on the depth of ponded water (Khepar et al., 2000), the water table
depth (Kampen, 1970), and puddling (Garg et al., 2009). Tan et al.
(2014) reported lower deep percolation under AWD than undercontinuous ﬂooding in an undrained paddy ﬁeld. However, conducting AWD in subsurface drained paddy ﬁelds may enhance crack
formation and reduce the positive effects of puddling due to rapid
lowering of the water table during drainage periods, which may
result in increased percolation losses.
The calibrated model was then applied to simulate WDs for the
MSD water management strategy (ﬂooding – MSD – re-ﬂooding –end-season drainage) during the 2011 and 2012 growing seasons(data not shown). The correspondence between observed and simulated WDs during the MSD periods of these two growing seasons
was good, indicating that the calibrated model is well suited for theexperimental ﬁeld.
Fig. 5 shows temporal variations of simulated and observed
drain discharges (q) for different drainage systems for the drainage
periods of the 2014 growing season. The HYDRUS (2D/3D) modelslightly under-predicted q during the ﬁrst drainage period (D1)and overestimated it during the last days of the third drainage
period (D3) for the Bilevel system. The simulated discharge generally closely matched the observed data for the other drainage
systems in 2014. In addition, a visual inspection of scatter plots

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73

Fig. 6. Scatter plots between observed and HYDRUS-2D-simulated drain discharge (q) during the 2011 (top 2 rows), 2012 (middle 2 rows), and 2015 (bottom 2 rows) growing
seasons for the four drainage systems (left to right) and the AWD water management. D1, D2 and D3 denote ﬁrst, second, and third drainage periods, respectively.

in Fig. 6, which compares observed and HYDRUS-2D-estimated q
during the other growing seasons (2011, 2012, and 2015), clearly
indicates the high potential of the HYDRUS-2D modeling. Correlation coefﬁcients varied in the range of 0.60-0.99 across different
drainage systems, which indicates the strong predictive capabilityof the model.

3.2. Water table analysis
The ability of HYDRUS (2D/3D) to simulate the spatial watertable proﬁle was evaluated by comparing simulated and observed
water table depths between the shallow and deep drains in the
Bilevel drainage system. Temporal and spatial variations of WDs
in the Bilevel treatment during three drainage periods in 2014 are

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A. Darzi-Naftchali et al. / Agricultural Water Management 197 (2018) 67–78

Fig. 7. Temporal variations of water depths (WD) at different points between shallow and deep drains in the Bilevel drainage system. “a” denotes the shallow drain, “b”, “c”,
“d”, “e”, “f”, “g”, and “h” are 1, 2.5, 5, 7.5, 10, 12.5, and 14 m away from the shallow drain, respectively, and “i” denotes the deep drain. D1, D2, and D3 denote the ﬁrst (25–34
DAT), second (43–47 DAT), and third (64–71 DAT) drainage periods, respectively.

presented in Figs. 7 and 8, respectively. The accuracy of the predictions was low at the trenches and in the vicinity of the drains.
Soil properties in the vicinity of the drains are inﬂuenced by drain
installations and faster draining of this area than the remaining area
between drains. Due to faster soil drying during drainage periods,
the surface soil layer above drains tends to develop cracks and otherpreferential ﬂow paths. The cracks may originate in the backﬁlled
trench and expand further with time. Less accurate predictions in
the vicinity of the drains indicate crack development and consequently, different ﬂow patterns and abrupt water table drops inthis region.
The simulated water table depths corresponded well to the measured water table depths at distances larger than 1 m from theshallow and deep drains, where the water table falls more grad-ually. Different responses of the water table proﬁle to drainage are
related to speciﬁc ﬂow patterns in paddy ﬁelds. Puddling, a traditional practice in paddy ﬁelds for controlling percolation losses,
causes the formation of a less conductive soil layer (a hardpan)
below a plow pan (Darzi-Naftchali et al., 2013). This layer causes
water to horizontally ﬂow from between the drains to the backﬁlled trench in a surface soil layer and then vertically into the drains(Ogino and Ota, 2007). Simulated water table proﬁles clearly indicate that HYDRUS (2D/3D) is a suitable tool for predicting watertable ﬂuctuations after irrigation or rainfall even in the presenceof crop. This capability makes the model applicable to the assessment of different water table management strategies during therice growing season.

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Fig. 8. Spatial variations of average WDs during three drainage periods in the Bilevel drainage system in 2014.

3.3. Water balance analysis
Since the model performed as well during the validation period
as during the calibration period, the calibrated model was further
used to analyze various water balance components under the AWD
and MSD water management strategies (Table 2). The total measured input of water during the four growing seasons ranged from677.3 to 786.7 mm. Water requirements of rice were reported to
be 700–1500 mm (Guerra et al., 1998) and 935–1211 mm (Thakur
et al., 2014) for a medium-duration rice variety (130–135 days). In
our study, since an early-maturing rice variety was planted, thecrop growth duration from transplanting to harvest in different
growing seasons was less than 90 days, and the water consumption
was correspondingly lower. The total irrigation volumes in 2011,2012, 2014, and 2015 were 618, 669, 591, and 698 mm, respectively.
Differences in water consumption between different growing seasons were related more to variations in growth conditions than towater management strategies. About 18.1, 9.5, 12.8, and 11.3% of
the input water was supplied by rainfall in 2011, 2012, 2014, and2015, respectively. Due to delayed transplanting in 2015, temperatures and sunshine durations in 2014 were lower than those in2015, resulting in less evaporation demand in 2014. The main partof the water input was used by ET, which varied between 423 and512 mm. Average daily ET under MSD and AWD varied from 4.9
to 6.2 mm d−1 and from 4.9 to 5.9 mm d−1 , respectively, indicatingthat there were no considerable differences between the two water
management strategies. The differences between ET values in dif-ferent growing seasons and drainage systems could be attributed
to the differences in temperature and moisture conditions (Xie andCui, 2011). Simulated daily ET values are comparable with published values of 4–7 mm d−1 (e.g., De Datta, 1981) or seasonal ET
of 458–483 mm under aerobic rice culture reported by Xue et al.
(2008).
Under both management strategies, ET increased for drainage
systems with higher intensity (i.e., deeper depths and smaller distances between drains), and thus the minimum and maximum ET
were obtained for D0.65 L30 and Bilevel, the least and most intensive
drainage systems, respectively. Increased ET was probably due toenhanced root activity and root development as a result of a fasterdecrease in the water table depth during drainage periods undermore intense drainage systems. A deeper water table causes roots
to grow vertically into deeper soil horizons, whereas in wetter systems, roots grow more horizontally (Mishra et al., 1997). Deeper
roots increase water extraction from the soil proﬁle during drying
periods. Moreover, the crop water demand increased after the ﬁrst
drainage period due to the increased canopy cover. Due to the heavy
textured soils at the study site and a short duration of draining periods, soil drying did not reach the threshold limit to negatively affectthe crop. On the other hand, it seems that the moderate water stressincreased the root activity to capture any available soil moisture.
Rijal et al. (2012) reported 16 and 7% higher ET for corn and soybean grown in subsurface drained ﬁelds than in un-drained ﬁelds
in 2009 and 2010, respectively.

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A. Darzi-Naftchali et al. / Agricultural Water Management 197 (2018) 67–78

Table 2Components of the water balance (WBC) (in mm) during the 2011, 2012, 2014, and 2015 growing seasons for different drainage systems.
Year

WBC

Drainage systems
D0.65 L15

D0.65 L30

D0.90 L30

Bilevel

2015

P+I
ET
E
T
Drainage
S
DP

786.7
498
233
265
15.4
101.3
172

786.7
449
213
237
9.7
96.0
232

786.7
545
187
267
10.8
9.9
221

786.7
509
236
273
16.2
106.5
155

2014

P+I
ET
E
T
Drainage
S
DP

677.3
481
243
238
43.2
72.2
80.9

677.3
434
223
211
10.0
77.3
156

677.3
438
224
215
57.0
68.3
114

677.3
492
263
229
91.1
59.7
34.5

2012

P+I
ET
E
T
Drainage
S
DP

773
469
237
232
23.3
73.7
207

773
423
218
206
7.7
73.3
269

773
427
218
209
7.8
73.2
265

773
479
256
223
28.4
66.6
199

2011

P+I
ET
E
T
Drainage
S
DP

755
502
253
248
15.1
90.9
147

755
452
233
220
6.1
91.9
205

755
457
233
224
3.1
89.9
205

755
512
273
239
7.4
83.6
152

P – precipitation, I – irrigation, ET – evapotranspiration, E – evaporation, T – transpiration,  S – change in storage, DP – deep percolation.

Table 3
Total biomass, yield, transpiration (T), evapotranspiration (ET), transpiration efﬁciency (TE), and water use efﬁciency (WUE) for different drainage systems and for the 2011,2012, 2014, and 2015 growing seasons.
Year

Parameter

DAT

D0.65 L15

D0.65 L30

D0.90 L30

Bilevel2015

T growing season (mm)ET growing season (mm)Yield (Mg ha−1 )TE (g kg−1 )

–
–
–
16
28
39
50
60
67
–
–

265
498
4.9
4.6
4.3
5.1
5.3
3.9
2.8
1.8
0.98

237
449
5.0
4.6
3.3
3.8
4.6
4.4
4.1
2.1
1.11

267
454
4.0
5.3
5.3
4.6
4.5
3.8
3.3
1.5
0.89

273
509
5.0
4.6
3.4
3.8
4.5
4.4
4.2
1.8
0.99

TE harvest (g kg−1 )WUE (kg m−3 )

–
–
–
16
28
39
50
60
67
–
–

238
481
5.0
4.0
4.8
6.2
6.8
6.3
5.2
2.1
1.04

211
434
4.6
3.5
4.3
5.9
7.2
7.5
6.2
2.2
1.07

215
438
4.3
4.4
5.6
7.2
7.6
6.6
5
2.0
0.97

229
492
5.3
4.7
5.1
6.3
6.8
6.4
5.3
2.3
1.07

2012

T growing season (mm)ET growing season (mm)Yield (Mg ha−1 )TE harvest (g kg−1 )WUE (kg m−3 )

–
–
–
–
–

232
469
3.3
1.41
0.70

206
423
3.7
1.81
0.88

209
427
3.9
1.88
0.92

223
479
4.2
1.87
0.87

2011

T growing season (mm)ET growing season (mm)Yield (Mg ha−1 )TE harvest (g kg−1 )WUE (kg m−3 )

–
–
–
–
–

248
502
3.0
1.21
0.60

220
452
3.5
1.60
0.78

224
457
3.9
1.74
0.85

239
512
4.2
1.78
0.83

TE harvest (g kg−1 )WUE (kg m−3 )2014

T growing season (mm)ET growing season (mm)Yield (Mg ha−1 )TE (g kg−1 )A. Darzi-Naftchali et al. / Agricultural Water Management 197 (2018) 67–78

Seasonally, only 0.04–13.6% of the supplied water in different drainage systems was lost to drainage. Simulated drainage
losses were higher under the AWD (9.7–91.1 mm) than the MSD(3.1–28.4 mm) strategy. Higher drainage in 2014, compared to2015, was due to the occurrence of 64.3 mm rainfall during the ﬁrst
drying period and a longer duration of the drainage period. Suchnon-beneﬁcial losses should be reduced through suitable measuresduring drying periods. In different growing seasons, minimum andmaximum drainage losses occurred in the D0.65 L30 and Bilevel
drainage systems, except in 2011.
A large part of input water was lost through deep percolation,deﬁned here as ﬂow below the root zone, accounting for 12–35%
across the growing seasons and drainage systems. Simulated percolation losses were higher under MSD than AWD. On average,
percolation losses ranged from 1.8 to 3.1 mm d−1 under MSD andfrom 0.4 to 2.7 mm d−1 under AWD. These values are in the range ofthe 1–5 mm d−1 percolation losses reported by Bouman and Tuong(2001). Generally, as expected, the minimum and maximum percolation losses occurred in the D0.65 L30 amd Bilevel drainage systems,
respectively. Total non-productive outﬂows through drainage andpercolation varied from 159 to 277 mm for MSD and from 124
to 232 mm for AWD. Simulated total losses under both management strategies (1.4–3.2 mm d−1 ) are lower than percolation losses
reported for conventional paddies, 4.3–5.0 mm d−1 (Aimrun et al.,2010), 3.6 mm d−1 (Chen and Liu, 2002), and 5.21 mm d−1 (Pathak
et al., 2004). The simulation results indicate that a suitable subsurface drainage system can be selected based on the optimum balance
between percolation and drainage losses and a desired water management strategy. As nitrogen and phosphorus fertilizers are lostfrom paddy ﬁelds mainly through these routes of water losses, thereduction of percolation and drainage could be an effective measure
to increase the nutrient use efﬁciency.
3.4. Transpiration and water use efﬁciency
Transpiration, ET, grain yield, transpiration efﬁciency (TE), andwater use efﬁciency (WUE) for different drainage systems under
the MSD and AWD strategies are presented in Table 3. There were
no obvious trends for T under the different drainage systems. The
T to ET ratio is a useful index to understand the crop behavior
in response to the deﬁcit water stress. Transpiration constituted
47–59% and 45–49% of ET under the AWD and MSD strategies,
respectively, across the growing seasons and drainage systems,
indicating a more plant stress to drying under MSD than AWD. It
was reported that T accounts for 50–90% of total ET during a growing
season in wetlands (Bachand et al., 2013). However, Bouman et al.
(2005) reported lower T under AWD than under full ﬂooding. The
predicted range of T was low (0.2–0.6 mm d−1 ) at the early stageof growth and reached its maximum (5.1–6.2 mm d−1 ) at 50 and
60 DAT (data not shown). The maximum simulated T values are inagreement with the corresponding values of 5–7 mm d−1 reported
by Tomar and O’Toole (1980) for heading time under ﬂooded conditions.
The transpiration efﬁciency gradually increased in 2014 until
50 and 60 DAT and then decreased until harvest. Except for TE at
16 DAT, a similar trend occurred in 2015 under all drainage systems, except for D0.9 L30 . High TE at 16 DAT in 2015 was mainly
attributed to the higher production of dry matter than during thesame period in 2014. Additionally, climatic conditions have a considerable effect on CO2 and water vapor diffusion processes at theleaf surface (Haefele et al., 2009), which may inﬂuence the T rate.
The seasonal average of TE ranged from 3.8–5.5 g kg−1 , with higher
TEs in 2014. Higher TE in 2014 was a possible reason for higher grain
yield in all drainage systems except in D0.65 L30 where a higher yield
was produced in 2015. Under different drainage systems, TE at harvest was higher under AWD than MSD, except for D0.9 L30 . However,77

TE at harvest is not a helpful index to address differences in grain
yield, as higher grain yield was observed under AWD in various
drainage systems. Plants under a water stress likely transpire in
the early hours of the morning and close their stomata later in theday (Vadez et al., 2014).
Yield is reported as a function of the quantity of water extracted
from the soil to support T, TE, and the conversion of biomass into
grains via the harvest index (Vadez and Ratnakumar, 2016). No
study has looked at the TE dynamics in rice under both the MSD and
AWD strategies in relation to a drying stress. However, predicted
TEs are in agreement with some published values: 2.3–5.9 g kg−1
(Yoshida, 1975) and 2.5–5.4 g kg−1 (Impa et al., 2005). Since T is
the only productive water consumption, assessing the TE dynamics
should help in ﬁnding the optimum duration and timing of drying
periods under the AWD and MSD strategies. In addition, the WUE
could be enhanced by increasing T. The WUE responded differently
to different water managements in different drainage systems.
The D0.65 L30 and D0.9 L30 drainage systems produced the highest
WUE under the AWD and MSD strategies, respectively. Moreover,a higher WUE was obtained under AWD (0.89–1.11 kg m−3 ) thanunder MSD (0.65–0.92 kg m−3 ).
4. Conclusions
This experimental and numerical study was carried out toanalyze complex consequences of the AWD and MSD water management strategies in subsurface drained paddy ﬁelds. The HYDRUS(2D/3D) model performed well during both the calibration and validation phases for both water management strategies, as its results
closely matched the observed drainage discharges and water table
depths. The model predicted well the water table proﬁle between
two semi and bilevel subsurface drains except in the immediate
vicinity of the drains. A higher intensity of the subsurface drainage
systems increased both ET and drainage losses while decreasing deep percolation losses. 20.2–27.8% and 25.7–41% of supplied
water were lost through drainage and percolation under the AWD
and MSD strategies, respectively, indicating a better response of
subsurface drained paddy ﬁelds to AWD from the water conservation viewpoint. During the study period, the maximum losses ofwater occurred in to D0.90 L30 , followed by D0.65 L30 , D0.65 L15 , andBilevel. Simulation results indicated that there were no considerable differences between ET values under MSD and AWD while
the WUE was slightly higher under the AWD strategy. Both transpiration, as the only productive water use in paddy ﬁelds, andTE were higher under AWD than under MSD. Under the MSD
and AWD practices, the maximum TEs and WUEs were found in
D0.90 L30 and D0.65 L30 , respectively. The results of this study indicated that the HYDRUS (2D/3D) model is well suited for analyzing
soil water dynamics in paddy ﬁelds equipped with different subsurface drainage systems.
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