Agricultural Water Management 142 (2014) 38–46

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

Evaluation of water movement and water losses in a
direct-seeded-rice ﬁeld experiment using Hydrus-1DYong Li a,b,∗ , Jirka Šimůnek c,∗ , Longfei Jing b , Zhentin Zhang b , Lixiao Ni a,baMinistry of Education Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Hohai University, Nanjing 210098, China
College of Environment, Hohai University, Nanjing 210098, Chinac
Department of Environmental Sciences, University of California Riverside, Riverside, CA 92521, USAba r t i c l e

i n f oArticle history:
Received 11 November 2013
Received in revised form 9 April 2014
Accepted 27 April 2014
Keywords:
Direct-seeded-rice
Water ﬂow
Water loss
Hydrus-1D
Taihu Lake Basina b s t r a c t
In the recent decade, increasing costs of labor, water, and fertilizers around the world led to a change
in the method of crop establishment from traditional transplanted rice (TPR) to direct-seeded rice (DSR)
and to a substantial rise in the DSR-managed area. Since water management in areas with DSR is quite
different from those with TPR, vertical water movement and water and nutrient losses during the crop
season may be different as well. Water ﬂow and water losses in a DSR ﬁeld in the Taihu Lake Basin ofeast China were monitored and evaluated using Hydrus-1D during two seasons with different rainfalls
and irrigation managements. While during the 2008 season, irrigation accounted for 57% of the total
water input (TWI), during the 2009 season, it accounted for only 32%. Due to large rainfall during thewet, 2009 rice season, surface runoff accounted for about 17.0% of TWI. During the much drier 2008
rice season with higher irrigation inputs, surface runoff (4.6% of TWI) could be controlled much better.
Modeled evapotranspiration during the 2008 and 2009 seasons accounted for 54.6% and 44.6% of TWIs,
respectively. Measured and simulated results indicate that water leaching (approximately 42.7% and34.9% of TWIs in the 2008 and 2009 seasons, respectively) was the main path of water loss from theDSR ﬁelds, which implies that frequent irrigation increases water leaching. The plough sole layer playeda major buffering role for water ﬂow during both dry and wet seasons. Water productivities evaluatedfrom TWIs during the 2008 and 2009 seasons were 0.71 and 0.59 kg/m3 , respectively; they were 1.30 and
1.33 kg/m3 when evaluated from modeled evapotranspiration ﬂuxes. Pressure heads and vertical ﬂuxes
simulated using Hydrus-1D matched measured data well. The Hydrus-1D can be used to simulate waterﬂow and water balance in the DSR ﬁelds.
© 2014 Elsevier B.V. All rights reserved.

1. Introduction
Rice is the most widely consumed staple food for a large partof the human population, especially in Asia, providing more than
one ﬁfth of the calories consumed by humans worldwide (Smith,1998). In many Asian countries, rice accounts for more than 70%
of human caloric intake. China is the world’s largest rice producer,
accounting for 30% of the total world production, followed by India(22%), Indonesia (9%), and Bangladesh (7%) (Kögel-Knabner et al.,2010). The traditional method of rice cultivation in the world used
to be transplanting rice (TPR), which ensured a steady yield during∗ Corresponding authors at: College of Environment, Hohai University, Nanjing
210098, Jiangsu, China. Tel.: +86 13952041995.
E-mail addresses: liyonghh@hhu.edu.cn, liyonghh@163.com (Y. Li),
Jiri.Simunek@ucr.edu (J. Šimůnek).
http://dx.doi.org/10.1016/j.agwat.2014.04.021
0378-3774/© 2014 Elsevier B.V. All rights reserved.

the long history of mankind (Cabangon et al., 2002; Chen et al.,2009).
However, in all climatic zones, human labor represents morethan 50% of the cost of TPR farming (Weerakoon et al., 2011),
followed by the cost of other inputs such as water (Cabangon et al.,
2002; Choudhury et al., 2007) and fertilizers (Pandey and Velasco,
1999; Farooq et al., 2011). With the development of rice cultivation
science and the requirements of different climatic zones, manyother methods of rice cultivation gradually emerged, such as dryor wet direct rice seeding (Chen et al., 2009). Direct-seeded rice(DSR), which is cultivated by directly broadcasting seeds onto the
topsoil of paddy ﬁelds without needing to raise and transplantseedlings, provides an opportunity to save both labor and time
(Chen et al., 2009). This farming method also enables earlier
crop establishment, providing an opportunity to make betteruse of early season rainfall, while increasing crop intensiﬁcation
in some rice-based systems (Tuong et al., 2000). Moreover, thedevelopment of early-maturing varieties and improved nutrientY. Li et al. / Agricultural Water Management 142 (2014) 38–46

management techniques, along with increased availability ofchemical weed control methods, have encouraged many farmers
in Asia to switch from transplanted to direct-seeded rice culture(Gupta and Seth, 2007; Farooq et al., 2011).
During the last two decades, the change in the method of crop
establishment from manual transplanting of seedlings to directseeding has occurred in many Asian countries in response to risingproduction costs, especially those of labor and water (Rao et al.,2007; Chen et al., 2009). In the Taihu Lake Basin (TLB) of east China,
many farmers have accepted the cultivation of DSR, although theaverage yield of DSR is not as stable and is still slightly lower thanthat of TPR (Zhang et al., 2009). The area with DSR cultivation has
rapidly increased and has already exceeded 50% of the total farmland in many TLB regions (Zhang et al., 2009). In accordance withthis development trend, the direct-seeding approach will likely
continue to remain popular in the TLB.
Water ﬂow in paddy ﬁelds with cultivated rice involves theinteraction of very complex processes, and their observation andevaluation under ﬁeld conditions is relatively difﬁcult, costly, andtime consuming. Therefore, a large number of scientists increasingly use computer models to study the complex processes in thesoil and to provide management and planning guidance. Hydrus-1Dand Hydrus (2D/3D) (Šimůnek et al., 2008) are numerical models
that have often been used by many researchers to simulate waterﬂow in agricultural ﬁelds with different crops and various irrigation
schemes (Kandelous et al., 2012; Ramos et al., 2012; Siyal et al.,2012), including TPR ﬁelds (Phogat et al., 2010; Sutanto et al., 2012).
However, the Hydrus-1D model has not yet been used for simulating water ﬂow in DSR ﬁelds. Compared to traditional TPR, DSR
requires different water management, which provides rice witha different growth environment, particularly during its seedlingstage (Chen et al., 2009). During the ﬁrst two weeks after seeding,rather than being ﬂooded as with TPR, the top soil only needs tokeep sufﬁcient moisture to allow for seed germination (Cabangon
et al., 2002). As a result, the root mass of DSR is distributed shallower than that of traditional TPR, which consequently produces
different vertical proﬁles of the water content (Naklang et al., 1996;
Yadav et al., 2007). Furthermore, compared to TPR, DSR prefers analternative drying and wetting soil environment during the middlelate season when multiple smaller irrigations can beneﬁt both theplant growth and deeper root growth (Chen et al., 2009). This water
management produces distinctly different characteristics of thewater ﬂow regime and water losses from DSR ﬁelds compared to
TPR ﬁelds. In this study, ﬁeld observations in a DSR ﬁeld in the
TLB during two consecutive rice-growing seasons (2008–2009) areevaluated using Hydrus-1D, and the main characteristics of thewater ﬂow regime and water losses are discussed.
2. Material and methods
2.1. Field experimental data
2.1.1. Site description
The agricultural land in the Taihu Lake Basin is used for very
intensive production of the rice crop. The basin area, which is39

located south of the Yangtze River, is approximately 36,900 km2 ,with rice ﬁelds accounting for about 34.8% of this area. Rotationsof rice with either wheat or rape are the most popular cultivating
modes in this region. The basin has a subtropical monsoon climate
with average annual rainfall of 1181 mm, 60% of which occurs from
May through September. The annual PAN evaporation from thewater surface is approximately 822 mm, and the average annual air
temperature is 15–17 ◦ C. The study site is in the Dangyang region(31◦ 56 N, 119◦ 43 E), upstream of Taihu Lake (the third largest
fresh water lake in China). The dominant soil type in this regionis classiﬁed as a hydromorphic paddy soil, and the parent material
is a lacustrine deposit. The physical and chemical properties of thesoil at the site are listed in Table 1.
2.1.2. Experimental design
In our experiments, the variety of rice used for the DSR cultivation was Wuxiangjing 14 (lowland rice), a type of Japonica rice that is
predominantly cultivated in the Dangyang region. The observations
were carried out in the same ﬁeld during two growth seasons. Afterthe mechanical ﬁeld preparation, the seeds were evenly broadcasted by hand on the soil surface at 75 kg/ha, without prior soaking,on June 8th in 2008 and June 11th in 2009. After seeding, theﬁelds were irrigated until the surface soil was saturated (without
ﬂooding). The harvest dates were on November 1st in 2008 andNovember 5th in 2009. The total growing periods during these twoyears were thus 147 and 149 days, respectively.
The water management in the DSR ﬁeld followed instructions
from the local Agricultural Technical Guidance Station and drewfrom the farmers’ own experience. The amount and distributionof both rainfall and irrigation during these two rice-growing seasons are shown in Fig. 1. During the 2009 season, the total rainfallof 97.4 cm was higher than the long-term average (which is about65.0 cm) and occurred mainly during the ﬁrst three months of thecrop growth. During the 2008 season, the total rainfall of only about
54.3 cm was lower than the long-term average. During the 2008
and 2009 seasons, 14 and 9 irrigations (each 5–6 cm) were applied,
resulting in total water depths of 72 and 46 cm, respectively. To
prevent long ﬂooding and anaerobic conditions and to control riceinsects and weeds, rice ﬁelds need to be naturally dried for about
5–10 days during the season, depending on the rice and soil conditions. The soil drying stage in the 2008 season was between August
7th and 12th, while in the 2009 season it was between August 14thand 20th.
2.1.3. Measurements
The daily climate data, including temperature, wind speed,
humidity, and daylight hours, were obtained from an adjacent agrometeorological station (about 5 km south) in the Dangyang region.
The amount of irrigation water was measured using ﬂow meters at
inlets, and the ﬂooding water depth was recorded every two days
at several points in the ﬁeld. The total amounts of surface runoff atoutlets were measured by ﬂow meters to be 5.2 and 22.5 cm during
the 2008 and 2009 seasons, respectively (Fig. 1). Before rice seeding, self-made ﬂux lysimeters for measuring vertical water ﬂuxes(Wang et al., 2001) were installed at a depth of 60 cm below the soil

Table 1
Physical and chemical properties of the paddy soil in the Taihu Lake Basin.
Depth (cm)

0–20
20–40
40–60
60–80
80–100
100–120

Soil particle size distribution (%)
Sand (>0.05 mm)

Silt (0.002–0.05 mm)

Clay (<0.002 mm)

7.01
0.02
18.53
14.44
14.25
11.32

73.70
81.01
72.83
81.64
81.59
80.30

19.29
18.97
8.64
3.91
4.16
8.38

Bulk density (g/cm3 )Organic matter (%)

pH (H2 O)

1.42
1.56
1.51
1.43
1.43
1.43

0.51
0.54
0.37
0.35
0.33
0.33

6.65
6.85
6.85
6.80
6.80
6.85

40

Y. Li et al. / Agricultural Water Management 142 (2014) 38–46

Fig. 1. Distribution of rainfall/irrigation and measured surface runoff in the direct-seeded-rice experimental ﬁeld in the Taihu Lake Basin during the (a) 2008 and (b) 2009seasons. DAS—days after seeding.

surface. Each device consisted of an open-cylinder (25 cm in diameter × 12 cm in height) that was connected to a glass bottle. The
top surface of an open-cylinder was welded using stainless steel
ribbons to avoid soil falling down into the cylinder. Leached waterthat was collected in the cylinder freely ﬂowed into the outside
glass bottle through a ﬂexible tube. The water volume in the bottle
was measured once every two weeks.
The groundwater table was observed once a week at an observation point near the experimental ﬁeld. The recorded data show thatthe groundwater table slightly ﬂuctuated, but remained arounda depth of 120 cm (between 117.7 and 121.8 cm) below the soil
surface.
Piezoelectric tube tensiometers (0–100.00 kPa) were installedat ﬁve observation points in the ﬁeld to measure pressure heads
one month before rice seeding. At each observation point, ﬁve tensiometers were installed at depths of 20, 40, 60, 80, and 100 cm
below the soil surface (each of the ﬁve tensiometers was installed
on a different vertical line to avoid mutual interference). The pressure head values were recorded every two days. The averaged datafrom each of the depths at the ﬁve observation points were used
for model simulation.
2.2. Hydrus-1D model
2.2.1. Model description
The one-dimensional Hydrus-1D computer program (Šimůnek
et al., 2008) was selected to simulate water movement in theexperimental ﬁeld. Since the ﬂow in the soil proﬁle between thesoil surface (the ponding layer) and groundwater is predominantly in the vertical direction, and horizontal ﬂow in the ponding
surface layer, such as surface runoff and/or irrigation, can be
accounted for using boundary conditions, there was no need touse a model such as Hydrus (2D/3D) that would consider multiple dimensions. The Hydrus-1D program numerically solves theRichards equation for saturated and unsaturated water ﬂow andthe convection-dispersion equations for heat and solute transport.
The governing one-dimensional water ﬂow equation for a partiallysaturated porous medium is described using the modiﬁed form of
the Richards equation, under the assumptions that the air phase
plays an insigniﬁcant role in the liquid ﬂow process and that waterﬂow due to thermal gradients can be neglected:

 

∂
∂
K
=
∂t
∂z



∂h
+1
∂z

−S

(1)

where h is the water pressure head (cm),  is the volumetric watercontent (cm3 /cm3 ), t is time (day), z is the spatial coordinate (cm),

K is the unsaturated hydraulic conductivity function (cm/day), andS is the sink term in the ﬂow equation (cm3 /cm3 /day) accounting
for root water uptake.
The soil water retention, (h), and hydraulic conductivity, K(h),functions according to van Genuchten (1980), are given as
S − r(h) = r +(h) = Sn m[1 + (˛h) ]
h≥0





1/m

K(h) = Ks Sel 1 − 1 − Se

h<0

m 2

(2a)
(2b)
(3)

here  S is the saturated water content (cm3 /cm3 );  r is the residual water content (cm3 /cm3 ); m, ˛ and n are empirical shape factors
in the water retention function, where m = 1 − 1/n; Ks is the saturated hydraulic conductivity (cm/day) measured using soil column
experiments; l is the shape factor in the hydraulic conductivity
function; and Se is the relative saturation, which is deﬁned as follows:Se = − rS − r(4)

2.2.2. Hydrus-1D input parameters2.2.2.1. Estimation of soil hydraulic parameters. The van Genuchten
soil hydraulic parameters,  r ,  S , m, ˛, and n (Table 2), which
are required by the Hydrus-1D model, were estimated using the
RETC software by ﬁtting retention data, (h), measured using thepressure plate apparatus. The pore connectivity parameter (l) was
assumed equal to an average value (0.5) for many soils.
2.2.2.2. Estimation of potential ET. To simulate the inﬂuence of soil
water on transpiration (root water uptake), Hydrus-1D requires
potential evaporation (the upper boundary condition) and potential transpiration (a sink term in the Richards equation) ﬂuxes
to be speciﬁed as separate input values at a daily time step. The
Penman–Monteith equation (Allen et al., 1998) was used for calculating the reference crop evapotranspiration (ET0 ) from available
climatic, crop, and soil parameters. The reference evapotranspiration, ET0 , and the crop coefﬁcient, KC , were used to determine thepotential crop evapotranspiration ETC under normal conditions as(Allen et al., 1998):
ETC = ET0 × KC

(5)

For the crop, which only partly covers the soil surface, ETP is
divided into potential evaporation, EP , and potential transpiration,
TP . This partitioning was achieved using the crop leaf area index

Y. Li et al. / Agricultural Water Management 142 (2014) 38–46

41

Table 2
Optimized values of the residual water content,  r , the saturated water content,  S , van Genuchten’s shape parameters, ˛, n, and m, and the saturated hydraulic conductivity,Ks .
Depth (cm)Soil type r (cm3 /cm3 ) S (cm3 /cm3 )˛ (cm)nm

Ks (cm/day)

0–20
20–40
40–60
60–80
80–100
100–120

Silt loam
Silt loam
Silt loam
Silt
Silt
Silt

0.132
0.075
0.074
0.043
0.083
0.054

0.505
0.449
0.497
0.501
0.530
0.501

0.00950.01250.00410.00520.00240.0037

1.415
1.113
1.841
1.349
1.826
1.424

0.293
0.102
0.457
0.259
0.452
0.298

0.767
0.523
0.748
1.196
0.696
0.711

Table 3
Leaf area index (LAI) of direct-seeded-rice at various growth stages during the 2008
and 2009 seasons (DAS—days after seeding).
DAS/year

30

60

90

120

147

2008
2009

2.9
2.8

5.2
5.0

7.2
6.8

7.2
6.9

3.4
3.3

as given by Belmans et al. (1983), which is a function of the cropdevelopment stage:
EP = ETC × e−Kgr ×LAI
TP = ETC − EP(6)here Kgr is an extension coefﬁcient for global solar radiation; itsvalue was taken as 0.3 for the rice crop (Phogat et al., 2010). Measured values of LAI at various growth stages in two continuous
seasons are given in Table 3. Estimated values of EP and TP were thenused as input parameters in the Hydrus-1D simulations (Fig. 2).
2.2.2.3. Root water uptake functions. Root growth was simulatedusing measured rooting depths during the growing season as
input. The method to consider water stress was used to determine root water uptake. Parameter values optimized by Singh et al.
(2003) for rice crops (h1 = 100 cm, h2 = 55 cm, h3 (high) = −160 cm,
h3 (low) = −250 cm, and h4 = −15,000 cm) were used to parameterize the water stress response function proposed by Feddes et al.
(1978) and Homaee et al. (2002). Parameters h1 through h4 represent different pressure head values, which affect root water uptake
in the soil. The water uptake is assumed to be zero for h > h1 .
For h < h4 (the wilting point pressure head), water uptake is also
assumed to be zero. Water uptake is considered optimal between
pressure heads h2 and h3 , whereas for pressure heads between h3
and h4 (or h1 and h2 ), water uptake linearly decreases (or increases)
with h.

2.2.3. Initial and boundary conditions
The initial conditions were deﬁned using the measured pressure
head distribution. The soil surface was subjected to the atmosphereboundary condition (BC) with speciﬁed values of precipitation,
irrigation, and evaporation. Furthermore, to reﬂect changing water
management conditions during the season, during the ﬁrst two
weeks and during the soil-drying stage, the upper boundary condition was set equal to an “Atmospheric BC with Surface Runoff”.
During the second stage (14–35 days) and during remaining growthstages, the upper boundary condition was set equal to an “Atmospheric BC with a Surface Layer” (with a maximum of 6 cm and10 cm, respectively). The Hydrus-1D model was modiﬁed so that it
could accommodate these dynamic changes in the surface boundary condition. Potential values of Ep and Tp , and irrigation andrainfall ﬂuxes were used to represent the atmospheric boundary
condition. Based on the observed data of the groundwater table, theconstant pressure head BC was considered at the bottom boundary.
2.3. Statistical tests
A paired t-test was used to test the difference between themeans (x̄) of measured and simulated values:dtcal = s 1/n1 + 1/n2

(7a)d = x1 − x2 , andd = x1 − x2 ,

and

s=

n1 s12 + n2 s22
n1 + n2 − 2(7b)where n is the number of comparable paired points, s is the standard
deviation of the mean, subscripts 1 and 2 indicate measured andsimulated values, and tcal is the calculated t value.

Fig. 2. Potential values of evaporation, Ep , transpiration, Tp , and evapotranspiration, ETp , estimated using the Penman–Monteith equation during the (a) 2008 and (b) 2009seasons.

42

Y. Li et al. / Agricultural Water Management 142 (2014) 38–46

Fig. 3. Simulated and observed pressure head values during the (a) 2008 and (b) 2009 seasons. 0–65 DAS and 66–147 DAS in the 2008 season were used for Hydrus-1D
calibration and validation, respectively. Positive pressure head values were not observed by tensiometers.

The root mean square error (RMSE) was also calculated to compare the measured and simulated values:
RMSE = 1
2(Mi − Si )
N
N

(8)

i=1

where Mi and Si are measured and simulated values for an output
variable (e.g., pressure heads, water ﬂuxes), and N is the number ofobservations.
3. Results and discussion
3.1. Hydrus-1D model calibration and validation
Sutanto et al. (2012) has previously validated that the Hydrus1D model is a good and reliable tool to simulate water ﬂow
and solute transport in the TPR ﬁeld. Here, we will evaluate
the effectiveness of Hydrus-1D for the DSR conditions. Using thesoil hydraulic parameters estimated by RETC (Table 2) as initial estimates, the observed values of the pressure heads at soil
depths of 20, 40, 60, 80, and 100 cm during 0–65 DAS (Days
After Seeding) in the 2008 season were used to further calibrate(ﬁne-tune) these parameters (i.e.,  r ,  S , ˛, and n). The correspondence between observed and simulated pressure heads during thiscalibration period was very good, with r = 0.97 (n = 135, tcal = 4.1,
RMSE = 2.61 cm). The calibrated parameters were then used to simulate the remaining data from the 2008 season, i.e., during 66–147
DAS, to validate the model. The correspondence between observed
and simulated pressure heads during this validation period was
similarly good, with r = 0.95 (n = 158, tcal = 9.3, RMSE = 2.86 cm). The
results during both calibration and validation time-periods of the2008 season are presented in Fig. 3a, indicating a good match ofobserved data. Fig. 3b shows the measured and simulated pressure heads during the 2009 rice season (n = 288, r = 0.94, tcal = 1.9,
RMSE = 9.54 cm).
Due to frequent rainfall in 2009, most pressure head values at a
depth of 20 cm were positive during the ﬁrst half of the season,
as opposed to the 2008 season’s values. During the second halfof the2008 season, frequent irrigations produced more frequentchanges between drying and wetting conditions in the soil. During
the soil drying stage, pressure heads at a depth of 20 cm reached theminimum values of −184.1 cm and −206.9 cm during the 2008 and2009 seasons, respectively. The pressure heads at a depth of 60 cm
were mostly between 0 and −57.2 cm, and their peaks lagged about
1–2 days behind those at a depth of 20 cm.

Fig. 4. Observed and simulated ﬂooding water depths during the 2008 and 2009seasons.

Percolation ﬂuxes can also be used to compare model simulations with observed values. While total simulated percolation at
a depth of 60 cm before 56 DAS in the 2008 season was 21.8 cm(the average ﬂux of 0.389 cm/day), the observed value was 23.3 cm(the average ﬂux of 0.416 cm/day). For the entire growing season of
2008, total percolation at a 60-cm depth simulated by Hydrus-1D
was 53.9 cm (the average ﬂux of 0.367 cm/day), and the observed
value was 55.6 cm (the average ﬂux of 0.378 cm/day). The correspondence between measured and simulated percolation ﬂuxes
during both early and entire growing periods is very good. The
difference between simulated and observed values may be caused
by macro-pores, earthworm burrows, roots, soil cracks, and lateralleakage (Akay et al., 2008; Janssen and Lennartz, 2009; Sander andGerke, 2009; Wang et al., 2011).
3.2. Flooding water depths and surface runoff
Observed values of the ﬂooding water depth can also be a good
indicator for evaluating the effectiveness of Hydrus-1D for simulating conditions in DSR ﬁelds, especially its ability to correctly
handle the upper boundary conditions. Measured and simulated
ﬂooding water depths are depicted in Fig. 4, which shows a good
match between measured and simulated values (n = 87, r = 0.98,
tcal = 16.4, RMSE = 0.56 cm) when using the calibrated soil hydraulicparameters. Overall, the measured data were slightly lower thanY. Li et al. / Agricultural Water Management 142 (2014) 38–46

43

water uptake substantially declined. The cumulative root water
uptake only slowly increased during the initial 30 days, then quickly
increased and ultimately reached 51.4 and 48.2 cm during theentire growing seasons of 2008 and 2009, respectively. Actual
evaporation rates (not shown) simulated using Hydrus-1D and estimated potential evaporation rates (Fig. 2) were 17.4 and 15.8 cm
during the 2008 and 2009 seasons. Most evaporation occurred during the early part of the season when the crop cover was still
relatively small. Similar results have been reported in the literature. For example, Choudhury et al. (2007) estimated the seasonal
DSR evapotranspiration loss in dry-seeded rice, on a ﬂat land withrow spacing of 20-cm in New Delhi, India, to be 55.6–56.0 cm during 2001 and 2002. Sudhir-Yadav et al. (2011) reported that theestimated ET values in intermittently irrigated DSR ﬁelds were49.7–70.7 cm during the 2008 and 2009 seasons in Punjab, India.
Fig. 5. Simulated actual and cumulative root water uptake during the 2008 and2009 seasons.

simulated values. As expected, larger differences in ﬂooding water
depths between the 2008 and 2009 seasons were mainly foundduring the ﬁrst half of the season. The frequent intensive rainfall inthe early 2009 season caused the water depth to reach the maximum depth of 10 cm within several days. On the other hand, during
the 2008 season, the water depth was always below the maximum
controlled depth, because the water input was mainly determinedby irrigation. According to simulated values, ﬂooding water depths
between 0 and 5 cm and between 5 and 10 cm during the 2008 season were 90 and 7 days, respectively, while during the 2009 season
the corresponding values were 72 and 26 days, respectively. Overall, rainfall was sufﬁciently utilized by the rice crop during the 2009season.
Although DSR could effectively use early rainfall, a large amountof water was still lost from ﬁelds as surface runoff. During the 2009season, most rainfall occurred during the early part of the season,
resulting in ﬁve surface runoff events. The biggest surface runoff
event (6.6 cm) was observed on 20 DAS, just after continuous intensive precipitation (Fig. 1). During the 2008 season, only two surface
runoff events were observed. To prevent long-ﬂooded and anaerobic conditions in 2009, the ﬁeld was artiﬁcially drained on 65 DAS.
Simulated and observed total surface runoff during the 2009 season was 24.4 and 22.5 cm, respectively, which was approximately
four times more than during the 2008 season, when simulated andobserved runoff was 5.8 and 5.2 cm, respectively. The difference
between simulated and observed surface runoff can be attributed
to several small surface runoff events simulated by Hydrus-1D thatwere not observed in the ﬁeld. This could also be due to preferential
ﬂow caused by macropores and cracks, which was not consideredin the simulation, and which would increase water inﬁltration andreduce surface drainage to a certain extent (Tsubo et al., 2007; Garg
et al., 2009; Patil et al., 2011).
3.3. Evapotranspiration
Phogat et al. (2010) showed that the Hydrus-1D model can
describe root water uptake of transplanted rice very well. In theirsimulations of water and salt movement in rice ﬁelds, they showedthat simulated root water uptake closely corresponded with crop
performance in their experiment. Fig. 5 shows daily root water
uptake by DSR during the 2008 and 2009 seasons, as simulatedusing Hydrus-1D. Root water uptake was initially almost zero
during the seeds’ germination stage. Root water uptake then gradually increased after about 10 DAS, reﬂecting the crop growth, andreached its maximum values between 90 and 120 DAS. The maximum daily root water uptake rate was approximately 0.6 cm/day
around 100 DAS. During later stages of crop growth, daily root

3.4. Soil water movement and changes in soil storage
Dynamic changes in water contents of the soil proﬁle of riceﬁelds depend on many natural (e.g., rainfall and evaporation)
and artiﬁcial (e.g., irrigation) factors. Differences in soil hydraulicproperties (e.g., retention or hydraulic conductivity) between different soil layers cause characteristic water content proﬁles. While
the plough sole layer was often saturated, unsaturated conditions
occurred in the subsoil below the plough sole layer (Chen and Liu,
2002; Garg et al., 2009). This is because the subsoil is often more
permeable than the plough sole layer and its hydraulic conductivity is 1.5–2.3 (or more) times higher than that of the plough solelayer. Actually, the plough sole layer often plays an obstructing or
buffering role on vertical water ﬂow (Liu et al., 2005; Filipović et al.,2013). The low hydraulic conductivity of the plough sole controls
the vertical water movement during both drying and wetting inrice ﬁelds.
Simulation results indicated that the soil water storage inthe upper 60 cm of the soil proﬁle throughout the 2008 season
decreased 1.2 cm, while during the 2009 season, it decreased byabout 2.3 cm. The change of soil water storage predominantly
depended on the initial water content and water contents at
harvest.
3.5. Water leaching
Downward leaching represented the largest loss of waterfrom the rice ﬁeld. Leaching predominantly depended on the soil
hydraulic conductivity of individual soil layers and the overallpressure head gradient. As shown in Fig. 6, leaching closely corresponded with precipitation and irrigation events. Due to early
intensive rainfalls, relatively continuous high leaching rates weremainly observed during 30–60 DAS. During the later growth stages
when the water input was predominately provided by irrigation,leaching was relatively regular and reﬂected particular irrigation
events. The maximum leaching rate (0.71 cm/day) at a depth of
60 cm occurred on 42 DAS during the 2008 rice season, immediately after an intensive rainfall (10.06 cm) on 41 DAS. During the2009 season, the maximum leaching rate (0.76 cm/day) at a depthof 60 cm occurred on 48 DAS after a continuous intensive rainfall(12.25 cm on 46 DAS and 6.39 cm on 48 DAS). Chen and Liu (2002)
reported similar results when an increase in the ﬂooding water
depth from 6 to 16 cm produced a 1.5 fold increase in the inﬁltration
rate in a paddy ﬁeld.
As shown in Fig. 6, the differences in leaching rates between
two seasons were mainly caused mainly by different water
managements. While the water input in the 2008 season was
predominately accomplished by irrigation, in the 2009 season, it
was mainly by rainfall (Fig. 1). Average simulated ﬂux rates at a
depth of 60 cm before 66 DAS in the 2008 season and before 72

44

Y. Li et al. / Agricultural Water Management 142 (2014) 38–46

Fig. 6. Actual and cumulative water ﬂuxes at the soil surface and at a depth of 60 cm during (a) 2008 and (b) 2009 seasons.
Table 4
Measured and simulated (using Hydrus-1D) water balance components (cm) in the 60 cm soil proﬁle, and water productivities (WP) (kg/m3 ) for the direct-seeded-rice cropin the Taihu Lake Basin.
Growing season
2008
2009

Simulated
Measured
Simulated
Measured

R

I

SR

ET

SS

L

54.3

72.0

97.4

46.0

−5.8
−5.2
−24.4
−22.5

−68.9
NA
−64.0
NA

−1.2
−1.5
−2.3
−3.1

−53.9
−55.6
−50.1
−52.9





−3.5
–
2.7
–

WPI

WPIR

WPET

1.25

0.71

1.85

0.59

1.30
–
1.33
–

R—rainfall, I—irrigation, SR—surface runoff, ET—evapotranspiration, SS—soil storage, L—leaching,—total water balance error, WPI —the ratio of grain yield to the amountof irrigation water, WPIR —the ratio of grain yield to the amount of irrigation water plus rainfall, WPET —the ratio of grain yield to crop ET.

DAS in the 2009 season were 0.36 and 0.41 cm/day, respectively,
while those during the entire 2008 and 2009 seasons were 0.37
and 0.34 cm/day, respectively. More frequent irrigations in the late2008 season resulted in more water leaching compared to the late2009 season. Similarly, frequent and intensive rainfall during theearly half season of 2009 resulted in much more water leaching. The
leaching rate in paddy ﬁeld soils in the area is about 0.3–0.5 cm/day,which is considered to be an optimal leaching rate for good rice
growth in this region (Xu et al., 1998). Soils with a higher leaching rate are considered “water-wasting” soils, while soils with alower leaching rate often have Eh too low for rice growth (Shanet al., 2005). Lian et al. (2003) also reported that the water leaching rates measured by lysimeters in adjacent paddy ﬁelds were
between 0.49–0.56 cm/day.
3.6. Water balance and productivity
The measured and simulated components of the water balanceof the upper 60 cm of the soil proﬁle are presented in Table 4. The
total measured water inputs during the 2008 and 2009 seasons
were 126.3 and 143.4 cm, respectively. Simulated evapotranspiration during the 2008 and 2009 seasons accounted for 54.6% and44.6% of corresponding volumes of water input, respectively, whileleaching at a depth of 60 cm accounted for approximately 42.7%
and 34.9%, respectively. The total water balance errors were −3.5
and 2.7 cm during the 2008 and 2009 seasons, respectively, which
account for about 2.8% and 1.9% of the total water input. This indicates that the Hydrus-1D model is a good tool for simulating thewater balance components in the DSR ﬁelds.
The average grain yields in the 2008 and 2009 seasons were8980 kg/ha and 8530 kg/ha, respectively. The grain yield of DSR isgreatly affected by local climate and water and fertilizer management. Water productivity (WP), also known as water use efﬁciency,
is a measure that characterizes the crop production per unit ofwater used. Three measures of water productivity were computed:
irrigation water productivity (WPI )—the ratio of grain yield to theamount of irrigation water; input water productivity (WPIR )—the
ratio of grain yield to the amount of irrigation water plus rainfall;and evapotranspiration water productivity (WPET )—the ratio of
grain yield to crop ET (Phogat et al., 2010; Sudhir-Yadav et al.,2011). The ET value for these calculations was taken as the sumof cumulated root water uptake and evaporation during the entire
season, as simulated by the Hydrus-1D model. Measured seasonalvalues of precipitation and irrigation were used in the above
calculations as the total water input (IR). Using this deﬁnition, theWPs in the 2008 and 2009 seasons are listed in Table 4.
The differences between WPI and WPIR values in the 2008
and 2009 seasons can be attributed mostly to different amountsof irrigation water. Sudhir-Yadav et al. (2011) estimated the
WPI for intermittently and daily irrigated DSR ﬁelds in India
to be 0.7–1.6 kg/m3 and 0.2–0.3 kg/m3 , respectively. The WPIR sin our experiments are close to a value (0.67 kg/m3 ) reported
by Ginigaddara and Ranamukhaarachchi (2009) in Thailand for
DSR with one week irrigation, followed by three weeks without
irrigation. Ye et al. (2013) reported that the WPIR s of intermittently irrigated TPR in the Taihu Lake Basin were between 0.48
and 1.06 kg/m3 for different fertilizer managements, with averagevalues of 0.88 and 0.77 kg/m3 during the 2010 and 2011 seasons,respectively. The WPET s in our experiments fall in the range of globally measured values for rice (0.6–1.6 kg/m3 ), and are higher than
the average value (1.09 kg/m3 ) (Zwart and Bastiaanssen, 2004) but
slightly lower than the median value of 1.52 kg/m3 (ranging from
0.89–2.03 kg/m3 ) reported by Roost et al. (2008) for TPR of theZhanghe Irrigation District in Central China. The evaluated waterproductivities indicated that DSR in the 2008 season had a better water use efﬁciency compared to DSR in the 2009 season, andthat the DSR in the 2009 season sufﬁciently used rainfall. Similarwater productivities in the two seasons, with respect to the simulated evapotranspiration, signiﬁed that the crop yields were closely
associated with the root water uptake.
4. Conclusions
Water ﬂow and water losses in a DSR ﬁeld in the Taihu Lake
Basin of east China were monitored and evaluated using Hydrus1D during two seasons with different rainfalls and irrigation

Y. Li et al. / Agricultural Water Management 142 (2014) 38–46

managements. Frequent and intensive rainfall during the 2009season resulted in long periods of ﬂooding and more surface runoff.
The plough sole layer, with a relatively low hydraulic conductivity,
played a buffering role during both drying and wetting processes
in the DSR ﬁelds and controlled the vertical water movement.
Water leaching was the main path of water loss from the DSR
ﬁelds, independent of whether the water input was mainly byirrigation or precipitation.
Water productivities evaluated based on the irrigation and total
water inputs explained the different water use efﬁciency of DSR in
the 2008 and 2009 seasons. On the other hand, water productivities
with respect to evapotranspiration were numerically similar in thetwo seasons due to similar grain yields and evapotranspiration.
The Hydrus-1D model simulations of pressure heads andpercolation ﬂuxes matched the observed data obtained fromdirect-seeded-rice ﬁelds in the Taihu Lake Basin, China fairly
well. Differences between the observed and Hydrus-1D simulatedmeans of pressure heads and percolation ﬂuxes as tested by thepaired t test were not found to be signiﬁcant at p = 0.05. Low
values of RMSE also indicated the applicability of Hydrus-1D tosimulate percolation ﬂuxes and pressure heads in the DSR ﬁelds.
Hydrus-1D was able to analyze the most important processes, suchas inﬁltration, surface runoff, root water uptake, and leaching.
Hydrus-1D, despite the considerable demand on input data, has
proven to be an effective tool for evaluating various water andsolute ﬂuxes in agricultural ﬁelds with different crops and various
irrigation schemes (e.g., Kandelous et al., 2012; Ramos et al., 2012;
Siyal et al., 2012). In this manuscript, we have demonstrated that
Hydrus-1D is a similarly effective tool for evaluating water ﬂuxes
and associated processes in direct-seeded rice ﬁelds. After proper
calibration and validation, Hydrus-1D should be considered a useful
tool for establishing sound irrigation policies and for managementof irrigation in regions with similar climatic conditions and similarcrops.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (nos.: 51079048 and 40601050), the Ministry of
Education Key Laboratory of Efﬁcient Irrigation-Drainage and Agricultural Soil-Water Environment in Southern China (no.: IPA004),
the Ministry of Education Key Laboratory of Integrated Regulation
and Resource Development on Shallow Lakes (no.: 2008KJ003), and
The China Scholarship Council.
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