Agricultural Water Management 148 (2015) 213–222

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

Evaluation of nitrogen balance in a direct-seeded-rice ﬁeld
experiment using Hydrus-1DYong Li a,b,∗ , Jirka Šimůnek c,∗ , Zhentin Zhang b , Longfei Jing 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 6 May 2014
Accepted 12 October 2014
Keywords:
Nitrogen balance
Nitrogen loss
Direct-seeded-riceHydrus-1D
Taihu Lake Basina b s t r a c t
Nitrogen (N) pollution is a global environmental problem that has greatly increased the risks of both theeutrophication of surface waters and contamination of ground waters. The majority of N pollution mainly
comes from agricultural ﬁelds, in particular during rice growing seasons. In recent years, a gradual shift
from the transplanting rice cultivation method to the direct seeding method has occurred, which resultsin different water and N losses from paddy ﬁelds and leads to distinct impacts on water environments. The
N transport and transformations in an experimental direct-seeded-rice (DSR) ﬁeld in the Taihu Lake Basinof east China were observed during two consecutive seasons, and simulated using Hydrus-1D model. The
observed crop N uptake, ammonia volatilization (AV), N concentrations in soil, and N leaching were used
to calibrate and validate the model parameters. The two most important inputs of N, i.e., fertilizationand mineralization, were considered in the simulations with 220 and 145.5 kg ha−1 in 2008 and 220
and 147.8 kg ha−1 in 2009, respectively. Ammonia volatilization and nitrate denitriﬁcation were the twodominant pathways of N loss, accounting for about 16.0% and 38.8% of the total N input (TNI), respectively.
Both nitriﬁcation and denitriﬁcation processes mainly occurred in the root zone. N leaching at 60 and
120 cm depths accounted for about 6.8% and 2.7% of TNI, respectively. The crop N uptake was 32.1% and30.8% of TNI during the 2008 and 2009 seasons, respectively, and ammonium was the predominant form
(74% of the total N uptake on average). Simulated N concentrations and ﬂuxes in soil matched well withthe corresponding observed data. Hydrus-1D could simulate the N transport and transformations in the
DSR ﬁeld, and could thus be a good tool for designing optimal fertilizer management practices in the
future.
© 2014 Elsevier B.V. All rights reserved.

1. Introduction
Nitrogen (N) contamination of surface and ground waters has
become a serious, global environmental problem (Chandna et al.,2011). The risk of groundwater contamination by N depends largelyon the N input to agricultural ﬁelds in the form of inorganic fertilizers and on its effective use by agricultural crops (Becker et al.,2007). Paddy ﬁelds represent a special environment for the transport and transformations of N species due to their semi-artiﬁcial,alternate drying, and wetting conditions (Lüdemann et al., 2000;
Ishii et al., 2011). Furthermore, diverse rice cultivation approaches∗ Corresponding author at: College of Environment, Hohai University, Nanjing,
Jiangsu 210098, China. Tel.: +86 02583787145.
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.2014.10.010
0378-3774/© 2014 Elsevier B.V. All rights reserved.

may produce different types of N losses from paddy ﬁelds (Brar and
Bhullar, 2013). The increasing cost and growing scarcity of labor,water, and fertilizers around the world has led to a conversion of
the rice farming practice from traditional transplanting to direct
seeding. There are three main types of direct-seeded-rice (DSR):
dry, wet, and water direct-seeding, when rice seeds (with or without prior soaking) are directly sown onto dry, wet, and ﬂooded
soil, respectively, after land preparation (dry, wet, zero, reduced,
or conventional tillage) (e.g., Farooq et al., 2011; Kumar and Ladha,2011). Many researchers have previously reported that the DSR has
different water management needs from TPR (Kumar and Ladha,
2011; Weerakoon et al., 2011). Additionally, some reports indicatethat in DSR ﬁelds, most N is applied during the early-to-middle
growth season (Naklang et al., 1996; Zhang and Wang, 2002; Yinet al., 2004; Brar and Bhullar, 2013), while in traditional TPR ﬁelds,
about 40–60% of the total N fertilizer is applied as a basal fertilizer (Naklang et al., 1996; Hayashi et al., 2007; Chen et al., 2009).

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Y. Li et al. / Agricultural Water Management 148 (2015) 213–222

Also, the distinct distribution of the root mass of DSR comparedto TPR signiﬁcantly inﬂuences the N fate and transport in paddy
soils (Naklang et al., 1996). As a result, this shift of rice cultivation method, as well as tillage method, could result in different
water and N losses from paddy ﬁelds, leading to different impacts
on surface and ground water environments.
Due to the dynamic nature of paddy ﬁelds, N transport andtransformation processes are complex and difﬁcult to understand. Therefore, a fully calibrated, mathematical model that could
describe these processes would be very useful. Although in recent
years, a large number of researchers successfully evaluated the
N balance in paddy ﬁelds using mathematical models, it is stilla challenge to model N processes in paddy ﬁelds in their overall
complexity. Some models focus mainly on N uptake and fate inﬂoodwater (Jeon et al., 2004), while others pay more attention to
the N transport in soils or describe the N cycle in soils without
considering the N transport (Chowdary et al., 2004; Antonopoulos,
2010). In particular, the rhizosphere dynamics of N uptake is very
complicated, since it may involve both passive and active uptake(Šimůnek and Hopmans, 2009), as well as other processes suchas N mineralization, nitriﬁcation–denitriﬁcation, and rhizosphereacidity (Singh et al., 2011; Li and Wang, 2013; Najaﬁ, 2013). Meanwhile, rice prefers ammonium (NH4 + –N) to nitrate (NO3 − –N) as asource of N (Ishii et al., 2011), and in the presence of both N species,
rice seedlings take up NH4 + –N faster than NO3 − –N (Sasakawa andYamamoto, 1978). However, in order to simplify the model and tolimit the number of required parameters, many studies made thecommon assumption that the root uptake of NH4 + –N and NO3 − –N
is strictly passive and other uptake mechanisms may be neglected(Hanson et al., 2006; Ravikumar et al., 2011; Ramos et al., 2012).
This simpliﬁcation may signiﬁcantly affect the modeling of NH4 + –N
uptake and subsequent reactions and N distribution in the soil proﬁle. Some studies also assumed homogeneous conditions in thesoil proﬁle and that the N transport and transformations could be
simulated considering only one set of parameters for the entire
soil proﬁle while neglecting different conditions, and consequently
different reaction parameters in different depths, due to changing
aerobic and anaerobic conditions and soil layering (Chowdary et al.,2004).
Hydrus-1D (Šimůnek et al., 2008), a software package for simulating water ﬂow and solute transport in soils, has been widelyused to analyze ﬂow and transport processes in TPR ﬁelds (Phogat
et al., 2010) as well as in many other applications (Šimůnek et al.,2008; Sutanto et al., 2012). However, its use in DSR ﬁelds has beenrather limited, especially with respect to simulating the N transport. In a companion paper, Li et al. (2014) successfully evaluatedthe use of Hydrus-1D to simulate water ﬂow and water balancein DSR ﬁelds. They considered processes such as irrigation, precipitation, evaporation, surface runoff, and downward leaching andthen quantiﬁed their contributions to the overall water balance.
They also discussed the water use efﬁciency and crop yield. As afollow-up to this previous study, this paper’s objective is to further
extend their analysis to describe the transport and transformationsof N in the same DSR ﬁeld. This additional analysis will consider
the processes such as the N transport, crop N uptake, ammonia

volatilization, nitriﬁcation–denitriﬁcation, and vertical N leaching.
We will also discuss the N balance in the experimental DSR ﬁeld.
2. Material and methods
2.1. Field experiments and measurements
2.1.1. Site description
The study site is located in the upstream region (31◦ 56 N,
119◦ 43 E) of the Taihu Lake Basin (TLB) in eastern China, which
is classiﬁed as a ﬂood plain because of frequent ﬂoodings of the
Yangtze River. The climate is subtropical with periodical monsoon
rains. Mean annual precipitation is 1181 mm (60% of which occurs
from May through September) and the mean annual temperature
is 15–17 ◦ C. Field experiments were conducted in this area during two consecutive seasons (2008 and 2009). The soil layers of
0–60 cm and 60–120 cm were classiﬁed as silt loam and silt, respectively. Physical and hydraulic soil properties were described indetail in Li et al. (2014). Here, we will mainly list soil chemical
properties (Table 1). The variety of rice used for DSR cultivation
was Wuxiangjing 14 (lowland rice), a type of Japonica rice, which is
predominantly cultivated in this region. The available N in the soil
was about 220 kg ha−1 .
2.1.2. Experimental design
Field experiments were carried out in a 1350 m2(13.5 m × 100 m) paddy ﬁeld, which was divided evenly into
three plots (each about 13.5 m × 33.3 m), with each plot further
subdivided into three sub-plots (each about 150 m2 ) for replicating
experiments. Five randomly selected sub-plots were used in thisstudy. Each subplot had its own inlet (for irrigation) and outlet(for drainage) points. Averages of all observed data were used incomparisons with corresponding simulated values.
Details about the water management, as well as about evaporation, surface runoff, and drainage ﬂuxes are described in Li et al.
(2014). Total rainfall was 54.3 and 97.4 cm during 2008 and 2009seasons, respectively, and 14 and 9 irrigations (each 5–6 cm) wereapplied, resulting in total irrigation water depths of 72 and 46 cm,
respectively. The seed germination stage for dry DSR lasted for
two weeks. To prevent long ﬂooding and anaerobic conditions, andto control weeds, paddy ﬁelds need to be dried for about 5–10
days during each season, depending on the crop and soil conditions. The soil drying stage in the 2008 season was between August
7th and 12th (61–66 days after the basal fertilizer application, orDAB), while in the 2009 season it was between August 14th and20th (65–71 DAB). The potential evapotranspiration, determinedusing the Penman–Monteith equation (Allen et al., 1998) and thecrop coefﬁcient, was divided into the potential evaporation andtranspiration using observed values of LAI (Li et al., 2014).
The fertilization management was guided both by recommendations from the local Agricultural Technical Guidance Station andfarmers’ own experiences. The total quantity of N in each season
was 220 kg N ha−1 , applied in ﬁve applications. The basal fertilizer (BF) was a compound fertilizer, consisting of about 15% of N,

Table 1
Selected physical and chemical properties of the soil in the experimental DSR ﬁeld in the Taihu Lake Basin.
Depth (cm)Texture

0–20
20–40
40–60
60–80
80–100
100–120

Silt loam

Silt

Bulk density (g cm−3 ) Organic matter (g kg−1 ) CEC (cmol kg−1 ) pH (H2 O) Total Nitrogen (g kg−1 ) NH4 + –N (mg kg−1 ) NO3 − –N (mg kg−1 )
1.42
1.56
1.51
1.43
1.43
1.43

5.1
5.4
3.7
3.5
3.3
3.3

14.6
13.7
10.7
11.3
9.4
9.1

6.65
6.85
6.85
6.80
6.80
6.85

1.54
0.62
0.34
0.31
0.24
0.22

4.6
3.1
2.4
1.2
1.3
1.4

6.2
5.8
4.7
5.1
4.3
3.4

Y. Li et al. / Agricultural Water Management 148 (2015) 213–222

215

Table 2Agricultural activities in the experimental DSR ﬁeld during the 2008 and 2009seasons.
2008

2009

Jun. 8Jun. 11

Jun. 8–21
Jun. 27
Jul. 13
Jul. 30
Aug. 7–12
Aug. 20

Jun. 11–24
Jun. 25
Jul. 12
Jul. 31
Aug. 13–19
Aug. 22

Nov. 1

Nov. 5Agricultural activities
Basal fertilizer (33 kg N ha−1 , 33 kg P ha−1 ,33 kg K ha−1 ) plowed in by machines within
5 cm top soil; Sowing by hand (75.0 kg ofseeds ha−1 ).
Germination stage
Seedling fertilizer (44 kg N ha−1 )Tillering fertilizer (55 kg N ha−1 )Jointing fertilizer (44 kg N ha−1 )Soil drying stage
Panicle fertilizer (44 kg N ha−1 , 12 kg P ha−1 ,12 kg K ha−1 )Harvestphosphorus, and potassium. During four additional applications,
fertilizer was top-dressed (TF) in the form of urea (the content of N
was about 46%) at the following rates: 44 (TF1 ), 55 (TF2 ), 44 (TF3 ),and 44 (TF4 ) kg N ha−1 . During land preparation, the BF was applied
without ﬂoodwater and plowed within 5 cm of the top soil by agricultural machinery, while each TF was evenly broadcasted by handin ﬂoodwater. Detailed agricultural activities are listed in Table 2.
2.1.3. Measurements and analysis
Samples of surface runoff water were collected hourly from theoutlet points when they were draining. The cumulative N loss due
to surface runoff was calculated by multiplying water ﬂux withcorresponding NH4 + –N and NO3 − –N concentrations. Soil solutions
were collected once a week throughout the growing season using
porous ceramic cups (PCCs, 60 mm long). The PCCs were installed
at 20, 40, 60, 80, and 100 cm depths before rice seeding. Each time
before sampling, the residual solution in each cup was cleansed
using an injector. The PCCs were then evacuated to about 80 kPa bya vacuum pump with a tensiometer. About 1.5 h later, the solutionsin the PCCs could be extracted for further analysis. The cumulative
N leaching at the 60 cm depth was determined by multiplying the N
concentrations with corresponding water ﬂuxes (Li et al., 2014) andintegrating over the entire season. All water samples and soil solutions were ﬁltered through a 0.2-␮m membrane ﬁlter. The NH4 + –N
and NO3 − –N concentrations were measured using standard colorimetric methods and ﬂow injection analysis.
Ammonia (NH3 ) volatilization (AV) was measured using the
H2 SO4 trap method (Sarkar et al., 1991; Kyaw et al., 2005). The
AV rate was observed daily after the fertilizer application and thentwice weekly thereafter. Samples of three replications were taken
for each morning or afternoon measurement. The total ammonia
emission during a season was calculated by integrating linearlyinterpolated gas emissions on sampling days.
Rice growth was observed on 15, 30, 45, 60, 75, 90, 105, 120
DAB, and on the harvest day. The plants were separated into grain,straw, and roots, and then oven dried at 70 ◦ C. The dried samples
were weighed, ground, and prepared for chemical analysis. Total
N (TN) content in the plant samples was determined by automatic
combustion in tin (Sn) capsules and analyzed using a stable isotope
mass spectrometer. Cumulative total N uptake was determined as
the product of the TN content of the rice tissue and the dry weight(Kyaw et al., 2005).
2.2. Hydrus-1D model
2.2.1. Model description
Hydrus-1D allows for simultaneously considering the fate andtransport of multiple solutes that are subject to ﬁrst-order degradation reactions. The ﬁrst-order decay chain of urea can be describedas follows (Hanson et al., 2006):

The following N transformation processes were considered
here: (1) hydrolysis of urea to NH4 + –N; (2) mineralization oforganic N to NH4 + –N; (3) ammonia volatilization; (4) nitriﬁcationof NH4 + –N to NO3 − –N; (54) denitriﬁcation of NO3 − N; (6) adsorption of NH4 + –N; and (7) rice root uptake of NH4 + –N and NO3 − –N.
Hydrolysis, nitriﬁcation, and denitriﬁcation are all considered asﬁrst-order reactions (Hanson et al., 2006), while mineralization isconsidered to be a zero-order process.
The partial differential equations governing one-dimensionaltransport of N involved in sequential ﬁrst-order decay chain reactions during transient water ﬂow in a variably-saturated rigid
porous medium are taken as (Šimůnek et al., 2008):
∂C1
∂
∂qC1
∂C1
=
(D1w
)−
− w,1 C1
∂t
∂z
∂z
∂z

(1)

∂C2
∂C2
∂S2
∂av g2
∂
∂
∂qC2
g ∂g2
+
+
=
(D2w
)+
(av D2
)−
∂t
∂t
∂t
∂z
∂z
∂z
∂z
∂z
− w,2 C2 + w,1 C1 + s,2  − ra,2

(2)

∂C3
∂qC3
∂C3
∂
=
(D3w
)−
− w,3 C3 + w,2 C2 − ra,3
∂t
∂z
∂z
∂z

(3)

where, C is the solute concentration in the liquid phase (mg L−1 );S is the solute concentration in the solid phase (mg g−1 ); g is thesolute concentration in the gas phase (mg L−1 );  is the volumetric water content (cm3 cm−3 );  is the dry bulk density (g cm−3 );q is the volumetric ﬂux density (cm day−1 ), w is the ﬁrst-order
rate constant for solute in the liquid phase (day−1 );  w is a similarﬁrst-order rate constant, providing connections between individual chain species;  s is a zero-order rate constant in the solid phase(day−1 ); ra is the root nutrient uptake (mg L−1 day−1 ); Dw is the dispersion coefﬁcient (cm2 day−1 ) for the liquid phase, and Dg is the
diffusion coefﬁcient (cm2 day−1 ) for the gas phase. The subscripts
1, 2, and 3 represent (NH2 )2 CO, NH4 + –N, and NO3 − –N, respectively.
Adsorption/desorption of NH4 + is considered to be an instantaneous reaction between the soil solution and the exchange sitesof the soil matrix (Nakasone et al., 2004). The adsorption isotherm
relating S2 and C2 is described using a linear equation of the form:
S2 = Kd,2 C2(4)

where, Kd,2 is the distribution coefﬁcient for NH4 + (L mg−1 ).
2.2.2. Input parameters
The input parameters for the solute transport part of Hydrus1D are required to characterize the three main sets of processes:
solute transport, solute reactions/transformations, and root solute
uptake. The solute transport parameters were considered using
the following values: the molecular diffusion coefﬁcients in freewater (Dw ) for NH4 + –N and NO3 − –N were 1.52 and 1.64 cm2 day−1 ,respectively, the molecular diffusion coefﬁcient in air (Da ) for NH3
was 18057.6 cm2 day−1 , the longitudinal dispersivity (12 cm) was
considered equal to one-tenth of the proﬁle depth (Phogat et al.,2012), and the Henry’s law constant (KH , at 25 ◦ C) for NH4 + –N was
2.95 × 10−4 (Renard et al., 2004). The distribution coefﬁcient (Kd )for NH4 + and for different soil layers is listed in Table 3, based onvalues for this region reported by Chen and Chiang (1963).
The N reaction parameters were initially taken from the literature (Chowdary et al., 2004) and then corrected for individual

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Y. Li et al. / Agricultural Water Management 148 (2015) 213–222

Table 3
Reaction parameters for N transformations obtained by model calibration (Kmi is the comprehensive production rate of NH4 + representing mineralization/immobilization;
Kd is the distribution coefﬁcient for NH4 + ; Kn is the nitriﬁcation rate; Kdn is the denitriﬁcation rate; and Kh is the urea hydrolysis rate).
Soil depth (cm)Km (day−1 )Kd (L mg−1 )Kn (day−1 )Kdn (day−1 )0 − 2020 − 40
40 − 60
60 − 80
80 − 100
100 − 1200.00450.0045
–
–
–
–

1.8
1.5
1.2
1.0
1.0
1.0

0.25
0.22
0.14
0.04
0.02
0.02

0.05
0.06
0.04
0.02
0.01
0.01

Kh (day−1 )0.74

soil layers to ﬁt observed data (Table 3). The urea hydrolysis
rate Kh was assumed equal to 0.74 day−1 in the top soil layer(0–20 cm). Mineralization (organic N→inorganic NH4 + –N) andimmobilization (inorganic NH4 + –N→organic N) are two important
N transformation processes that occur simultaneously in ﬂooded
soils (Chowdary et al., 2004). We assumed here that these twotransformations occurred only in the root zone (Rice and Smith,1984) (0–40 cm), and that they can be represented by a single comprehensive production rate for NH4 + –N Kmi of 0.0045 day−1 (Yanet al., 2006; Antonopoulos, 2010; Roberts et al., 2011). The nitriﬁcation and denitriﬁcation rates were initially assumed to be thesame in all soil layers (namely Kn = 0.2 day−1 and Kdn = 0.04 day−1 )and then adjusted for each layer according to observed data.
While some of these processes are temperature and water content
dependent, it is a common practice to neglect these dependences(Chowdary et al., 2004; Hanson et al., 2006).
The average daily N uptake rates were calculated using linear
interpolation for each time interval between two observed N crop
data (in the 2008 season). Unlimited passive uptake of NO3 − –N wasallowed in the root solute uptake model (Šimůnek and Hopmans,
2009) by specifying the maximum allowed uptake concentration
exceeding NO3 − –N concentrations that were registered in the rootzone. By considering active uptake in addition to passive uptake forNH4 + –N, contrary to considering only passive uptake for NO3 − –N,
we could ensure that NH4 + –N uptake accounts for approximately
75% of the total N uptake (Kirk and Kronzucker, 2005; Li et al., 2006).
The Michaelis–Menten constant for the active uptake of NH4 + wasassumed equal to 0.31 mg L−1 (Wang et al., 1994; Duan et al., 2007).

represented in the model by converting the amount of appliedurea into the boundary concentration using the known value of thesurface water layer (from the previous simulation of water ﬂow)
at the time of fertilizer application. The concentration ﬂuxes of N
for all four urea applications were calculated assuming a molar
mass and a number of atoms for each molecule.

2.2.3. Initial and boundary conditions
The initial conditions for water ﬂow were deﬁned using
the observed pressure heads. The initial ammonium and nitratecontents in the soil were speciﬁed in terms of N concentrations insoil water according to observed data. The initial concentration of
urea was calculated from the initial application of the basal fertilizer and the initial water content, while assuming that urea wasmixed within the surface 5-cm soil layer.
An atmospheric boundary condition with surface runoff (during
the ﬁrst two weeks and during the drying stage) or with a surface
layer (during the rest of the season), which needs to be reached
before surface runoff is initiated, was speciﬁed at the soil surface
for water ﬂow. The constant pressure head boundary condition
was considered at the bottom boundary, reﬂecting the positionof the groundwater table (Li et al., 2014). Root water uptake wascalculated using the potential evapotranspiration rate, calculatedusing the Penman–Monteith equation (Allen et al., 1998), a given
rooting depth and density, and the Feddes’ stress response function(Feddes et al., 1978). The top boundary for solute transport was set
as a ‘volatile’ boundary condition (Jury et al., 1983) with a stagnant
boundary layer of 2.5 cm (calibrated using observed AV data fromthe 2008 season). This boundary condition assumes that there isa stagnant boundary layer (air) at the top of the soil proﬁle andthat solute movement through this layer is by solute diffusionin air. A third-type boundary condition was used at both the top
and bottom boundaries. The top-dressed fertilizer application was

The calibration and validation of complex numerical models areoften complicated due to many parameters that need to be simultaneously determined. Since all parameters related to water ﬂow
have been previously reported (Li et al., 2014), only the parameters related to N transformations were calibrated and are discussed
here. The observed data during the 2008 season were used formodel calibration. First, the input of mineral N consists of two
pathways, i.e., applications of mineral fertilizer and the mineralization of organic N. The mineralization rate was thus determinedbased on fertilizer applications and soil organic N (Chowdary et al.,
2004; Li et al., 2011). Second, observed AV ﬂuxes and their cumulative quantity were used to calibrate the thickness of the stagnant
boundary layer for gas diffusion (2.5 cm). Third, the daily NH4 + –N
active uptake rates were calibrated based on observed crop N data.
Finally, observed concentrations of NH4 + –N and NO3 − –N at 20, 40,
60, and 80 cm depths and a leaching ﬂux at the 60 cm depth wereused to determine the nitriﬁcation and denitriﬁcation rates for eachsoil layer. After successful model calibration, observed data fromthe 2009 season were used for model validation. The r2 and RMSE
values were calculated to evaluate the effectiveness of the model
input (calibrated) parameters.
Fig. 1(a) and (b) shows that simulated N concentrations at
different depths during the 2008 season agreed with observed datawell, with r2 = 0.99 and RMSE = 0.13 mg L−1 (n = 87) for NH4 + –N,
and r2 = 0.98 and RMSE = 0.40 mg L−1 (n = 88) for NO3 − –N. Similar
results (Fig. 1(c) and (d)) were also obtained for the 2009 validation

2.3. Model evaluation
Simulated values of inorganic N (NH4 + –N and NO3 − –N) concentrations in the soil, cumulative N leaching ﬂuxes at 60 cm depth,
and the total N uptake by rice were compared with the observed
data. The correspondence between simulated and observed data
was evaluated using the coefﬁcient of correlation (r2 ) at p = 0.05
and the root mean square error (RMSE). The RMSE was calculated
as


 n
1
RMSE = (Si − Mi )2
n

(5)

1

where, Si and Mi are simulated and observed values, respectively,
and n is the number of values compared. The optimum values of r2
and RMSE are 1 and 0, respectively.
3. Results and discussion
3.1. Model calibration and validation

Y. Li et al. / Agricultural Water Management 148 (2015) 213–222

217

Fig. 1. Comparison of simulated (Sim) and observed (Obs) NH4 + –N (a, c) and NO3 − –N (b, d) concentrations at 20, 40, 60, and 80 cm depths during the 2008 (a, b) and 2009(c, d) seasons.

season, with r2 = 0.98 and RMSE = 0.17 mg L−1 (n = 88) for NH4 + –N,
and r2 = 0.98 and RMSE = 0.41 mg L−1 (n = 87) for NO3 − –N. The
calibration and validation results for N plant uptake and the AV
and N leaching ﬂuxes will be discussed in different sections below.
Overall, Fig. 1 shows that the ﬁnal set of parameters adopted for
Hydrus-1D simulations (listed in Table 3) were reasonable andeffective for simulating the N transport and transformations in the
DSR experimental ﬁeld.
3.2. Ammonia volatilization
The comparison of simulated and observed ammonia volatilization (AV) ﬂuxes from the experimental DSR ﬁeld during the two
seasons is shown in Fig. 2. Overall, AV increased and then rapidly
dropped after each fertilizer application. After 2 DAB, the AV ﬂuxes
reached peak values of about 1.7 and 2.0 kg ha−1 day−1 during the2008 and 2009 seasons, respectively, and quickly dropped within
7–10 days. This was due to high concentrations of NH4 +–N insurface soil with low water contents after BF was applied. Both simulated and observed data then showed lower peak values of the AV
rate in response to subsequent fertilizer applications (TFs). In particular, AV after the third topdressing of fertilizer (TF3 ) during the2009 season was low, partly due to a low concentration of NH4 +in ﬂoodwater (Fig. 3) and partly due to large N loss (4.8 kg ha−1 )by surface runoff (8.1 cm). On the other hand, the rapid decline in
AV after TF2 may be attributed to a higher absorption of fertilizer
N by the well-developed root system in the top soil layer at thisstage of crop growth (Schnier et al., 1990). This may also explain the
low NH4 + –N concentrations in ﬂoodwater after the TF2 application(Fig. 3).
Simulated peak values of AV were slightly lower than the corresponding observed data, but the ﬂuxes between peaks, as well as
the overall trend, matched very well with observed data. Meanwhile, observed data show that after each fertilizer application,

AV proceeded faster than simulated. The simulated cumulative AV
ﬂuxes were 63.4 and 58.6 kg ha−1 during the 2008 and 2009 seasons, respectively, with averages of 0.43 and 0.39 kg ha−1 day−1 ; theobserved data were 56.2 and 52.6 kg ha−1 , with averages of 0.38 and0.35 kg ha−1 day−1 , respectively. The difference between the two
seasons was mainly ascribed to the different AV ﬂuxes after theapplications of TF2 and TF3 that resulted from different NH4 + –N
concentrations within variable depths of ﬂoodwater.
When compared with other studies in this region, our simulated
and observed AV values fell within reported ranges. Cao et al. (2013)
reported that the total NH3 losses from the TPR ﬁelds were 51.6 and49.2 kg N ha−1 during the 2009 and 2010 seasons, which accounted
for 17.2% and 16.4% of the total N fertilizer, respectively. Zhu et al.
(2004) and Li et al. (2008) reported that the N losses through AV in
the TPR ﬁelds were about 18.6–38.7% and 9.6–33.7% of the total N
fertilizer, respectively. Zhang et al. (2011a,b) also reported that themean AV ﬂuxes ranged from 0.29 to 0.53 kg ha−1 day−1 in the DSR
ﬁelds of central China. Overall, the Hydrus-1D model produced agood correspondence between observed and simulated AV ﬂuxes
during the 2008 (n = 73, r2 = 0.73, RMSE = 0.26 kg ha−1 day−1 ) and2009 (n = 62, r2 = 0.78, RMSE = 0.24 kg ha−1 day−1 ) seasons.
3.3. Nitrogen concentrations in ﬂoodwater and its loss due tosurface runoff
Fig. 3 shows the observed NH4 + –N and NO3 − –N concentrations in ﬂoodwater during both seasons and compares them withsimulated concentrations in the soil surface observation point,
which represents the model ﬂoodwater. After the fertilizer wasapplied, the concentrations of NH4 + –N and NO3 − –N increased
and then quickly declined. Meanwhile, the NO3 − –N concentrations lagged behind those of NH4 + –N. During the 2008 season,
the N concentrations increased much less after the TF2 application than after other TFs, because subsequent surface runoff

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Y. Li et al. / Agricultural Water Management 148 (2015) 213–222

Fig. 2. Comparison of simulated and observed ammonia volatilization (AV) ﬂuxes and simulated cumulative AV ﬂuxes during the 2008 (a) and 2009 (b) seasons. The arrows
at the top indicate fertilizer applications.

removed large amounts of N (5.0 kg ha−1 ). During the 2009 season, the low concentrations after the TF3 application were ascribedto the relatively high depth of ﬂoodwater. Observed NH4 + –N
and NO3 − –N during the 2008 season were within the range of
0.13–16.30 mg L−1 (average of 3.42 mg L−1 ) and 0.79–13.11 mg L−1(average of 5.10 mg L−1 ), respectively; during the 2009 season, theyranged from 0.11 to 12.18 mg L−1 (average of 3.25 mg L−1 ) andfrom 0.64 to 18.32 mg L−1 (average of 4.80 mg L−1 ), respectively.
Simulated values matched the observed data with a relatively
low r2 = 0.18 and high RMSE = 3.5 mg L−1 (n = 117) for NH4 + –N andr2 = 0.49 and RMSE = 2.42 mg L−1 (n = 113) for NO3 − –N during thetwo seasons. However, the overall trend of simulated N concentrations matched the observed data well.
In China’s TLB, the N loss due to surface runoff usually occurs
in the early season because of intensive, typhoon-like rainfall. Bothseasons (2008 and 2009) had a very different distribution of precipitation, which resulted in signiﬁcantly different surface runoff
and associated N loss. The simulated surface runoff amounted to24.4 cm during the 2009 season, which was approximately fourtimes more than in the 2008 season (5.8 cm) (Li et al., 2014). The
simulated N losses due to surface runoff in the two seasons were10.3 and 17.0 kg ha−1 , respectively. Qiao et al. (2012) reported
that N losses due to surface runoff from TPR ﬁelds in this region
were 12.0 and 47.0 kg ha−1 (for the total N fertilizer application of
216 kg ha−1 ) during the 2008 and 2009 seasons, respectively. Zhao
et al. (2012) reported that N runoff from paddy soil (TPR) varied
from 2.65 to 21.8 kg N ha−1 during three seasons (2007–2009) inthis region.
The average concentrations of NH4 + –N + NO3 − –N in surface
runoff in 2008 and 2009 were 17.7 and 7.0 mg L−1 , respectively.
The major N loss (accounting for about 71% of the total runoffloss) in 2008 occurred during the second surface runoff event
just one day after the TF2 application when NH4 + –N and NO3 − –N

concentrations were still relatively high. While NH4 + –N andNO3 − –N concentrations in surface runoff were relatively low during the 2009 season, a large amount of surface runoff still removed
high levels of N (19.2 kg ha−1 ).
3.4. Crop nitrogen uptake
While NH4 + –N uptake by rice was simulated in Hydrus-1D asboth passive and active uptake, NO3 − –N was only simulated as apassive uptake (Šimůnek and Hopmans, 2009). As shown in Fig. 4,
the uptake process proceeded relatively slowly after sowing, gradually increased during seedling stages, and reached about 12.8
(in 2008 season) and 12.6 kg ha−1 (in 2009) on 30 DAB. After thegermination stage and through TFs applications, the crop grewquickly and absorbed large quantities of N. At later stages, theN uptake remained relatively stable, ultimately reaching 114.8(2008) and 112.1 kg ha−1 (2009). The average N uptake ﬂuxes wereapproximately 0.78 and 0.75 kg ha−1 day−1 throughout both seasons, respectively. Similar results were reported for DSR ﬁelds in
the Philippines (110–139 kg ha−1 (Mnguu, 2010); 105–118 kg ha−1
(Schnier et al., 1990)). Since the root mass of DSR is distributed
shallower and more evenly in the soil (Naklang et al., 1996; Yadav
et al., 2007) compared to TPR, most uptake of water and nutrients
occurred in the shallower soil layer.
In the two seasons, most N was absorbed in the NH4 + –N form,
which accounted for about 74% of total N uptake on average.
Zhang et al. (2011a,b) reported, based on hydroponic experimental
studies, that during the seedling stage, about 2/3 of rice N uptake
was in the NH4 + –N form. Kirk and Kronzucker (2005) reported
that lowland rice absorbs NH4 + –N (60–85%) as a major sourceof N. Li et al. (2006) also reported that during a 96-h hydroponic
experiment with the NH4 NO3 solution, the uptake of NH4 + –N
by rice roots accounted for approximately 63% of total N uptake.

Fig. 3. Comparison of simulated and observed nitrogen (NH4 + –N and NO3 − –N) concentrations in ﬂoodwater during the 2008 (a) and 2009 (b) seasons.

Y. Li et al. / Agricultural Water Management 148 (2015) 213–222

219

Fig. 4. Simulated cumulative nitrogen (NH4 + –N, NO3 − –N, and total) uptake and observed total nitrogen uptake during the 2008 (a) and 2009 (b) seasons.

Overall, simulated values of N uptake matched the observed datawell (2008 season: n = 9, r2 = 0.98, RMSE = 6.1 kg ha−1 ; 2009 season:n = 9, r2 = 0.98, RMSE = 3.3 kg ha−1 ), with only slight overestimation
during the late stage. This implies that the N uptake parameters inHydrus-1D were reasonable and could be adapted to simulate theN uptake process of DSR.
3.5. Nitrogen leaching
Fig. 5 shows the simulated cumulative N ﬂuxes at 0, 20, 40, and60 cm depths during the two seasons. Although rainfall, runoff, andirrigation were quite different between the two seasons (Li et al.,2014), vertical ﬂuxes showed similar trends. Virtually the entire
vertical ﬂux of NH4 + –N at the soil surface occurred before 90 DAB,
when surface fertilizer applications took place. On the other hand,cumulative vertical ﬂuxes of NH4 + –N at 20, 40, and 60 cm depths
continued to slowly increase later in the season. This was mainly
because there was an additional important source of NH4 + –N in
the top two soil layers: the mineralization of organic N. About 22.2
and 20.9 kg ha−1 of NH4 + –N inﬁltrated into the soil proﬁle during the 2008 and 2009 seasons, respectively. The total vertical ﬂux
of NH4 + –N at 40 cm fell to 9.4 (2008) and 9.1 kg ha−1 (2009), due
to various processes that removed NH4 + –N from the top soil layers, such as AV, root N uptake, and nitriﬁcation in the root zone
(Hou et al., 2007). Contrary to NH4 + –N, NO3 − –N ﬂuxes at the soil
surface were slightly lower than those at 20 cm. This was mainly
due to processes that increased NO3 − –N concentrations in the root
zone, which was mainly nitriﬁcation of NH4 + –N. While NO3 − –N
was absorbed by rice roots and denitriﬁed in the root zone, large
quantities of NO3 − –N remained in the soil and were quickly transported downward along with leaching water. The average total ﬂux
at 40 cm was approximately 37.5 kg ha−1 , which was almost similar
to that at the soil surface (45.0 kg ha−1 ).

Observed concentrations of NH4 + –N (0.20–0.62 mg L−1 ) andNO3 − –N (0.76–6.67 mg L−1 ) at 60 cm showed that NO3 − –N was
the dominant N form (on average 92.3% of the NH4 + –N + NO3 − –N)in percolating water during the two seasons. Similar results werereported by Zhang et al. (2011a,b) from DSR ﬁelds in Central China.
Kyaw et al. (2005) also found that NH4 + –N accounted for only about
3–5% of total N leaching at 35 cm in a TPR ﬁeld. The simulated cumulative N ﬂuxes at 60 cm agreed well with the observed data during
the two seasons with r2 = 0.98 and RMSE = 0.12 mg L−1 for NH4 + –N
(n = 20), and r2 = 0.97 and RMSE = 1.38 mg L−1 for NO3 − –N (n = 20).
N leaching is closely correlated with vertical water ﬂow. As
shown in Fig. 6, the N leaching distributions at the bottomof the soil proﬁle (120 cm) are similar to the corresponding
water ﬂux distributions (Li et al., 2014). During the 2008 season, the N leaching mainly occurred in middle-to-late season,
especially after the soil drying stage, with frequent irrigation
events. During the 2009 season, relatively high and continuous leaching occurred during 40–65 DAB before the soil-drying
stage, with subsequent intermittent N leaching events corresponding to irrigation events. The leaching ﬂuxes of NH4 + –N during
the two seasons were relatively stable and remained within
0–0.017 kg ha−1 day−1 (average of 0.007 kg ha−1 day−1 ). Reduced
oxygen in the rhizosphere for long periods of time prevented
nitriﬁcation of NH4 + –N and leaching of this form of N (Kirket al., 1994). Note that NH4 + –N, contrary to NO3 − –N, is adsorbed
to soil and thus its leaching is signiﬁcantly retarded. Contrary
to NH4 + –N ﬂuxes, NO3 − –N ﬂuxes widely and frequently varied
along with the water ﬂux, and fell in a relatively wide rangeof 0.002–0.180 kg ha−1 day−1 (average of 0.063 kg ha−1 day−1 )in the 2008 season and 0.001–0.168 kg ha−1 day−1 (average of
0.057 kg ha−1 day−1 ) in the 2009 season.
The simulated cumulative N leaching at 120 cm was 10.3 and9.5 kg ha−1 during the 2008 and 2009 seasons, respectively, with

Fig. 5. Simulated cumulative nitrogen ﬂuxes at 0, 20, 40, and 60 cm depths below the soil surface and observed cumulative nitrogen ﬂuxes at the 60 cm depth during the2008 (a) and 2009 (b) seasons.

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Y. Li et al. / Agricultural Water Management 148 (2015) 213–222

Fig. 6. Simulated nitrogen leaching ﬂuxes (thin dotted lines) and cumulative ﬂuxes (thick solid lines) at the bottom of the soil proﬁle (120 cm) during the 2008 (a) and 2009(b) seasons.

NO3 − –N accounting for most of it (about 90%). Most NH4 + –N, produced by urea hydrolysis and mineralization, was nitriﬁed into
NO3 − –N or absorbed directly by rice roots; NO3 − –N, if not denitriﬁed, was easily vertically transported along with soil water ﬂow.
These values were consistent with the results (a 9.3 kg ha−1 leaching loss with a 250 kg ha−1 N application) reported by Zhu et al.
(2000) in the same region. Zhao et al. (2012) estimated that the Nloads in percolation water at 100 cm in TPR ﬁelds were between
3.46 and 5.82 kg ha−1 during the 2007–2009 rice seasons in thisregion.
3.6. Nitriﬁcation and denitriﬁcation
Paddy soils are dynamic environments, and the transport of N
and its transformations in the soil proﬁle are very complex andvary in time depending on alternate wetting and drying conditions(Lüdemann et al., 2000; Ishii et al., 2011). Additionally, the croproots and microorganisms, variably distributed in the soil, significantly affect the N transformations and thus further complicate
N distribution in the soil. Nitriﬁcation and denitriﬁcation are two
important processes in paddy ﬁelds, both signiﬁcantly affected bymany soil environmental factors, such as the degree of saturation,oxygen concentrations, and temperature.
Table 4 lists the simulated nitriﬁcation and denitriﬁcation ineach soil layer during the two seasons. No visible differences were
found for both nitriﬁcation and denitriﬁcation among the 2008 and2009 seasons. However, signiﬁcant differences existed between
different soil layers. Nitriﬁcation (on average 90.7%) and denitriﬁcation (on average 69.0%) predominately occurred in the top twosoil layers (in the root zone) and decreased quickly in deeper layers. These are signiﬁcantly affected by the distribution of oxygen insoil. Oxygen concentrations in the soil usually show vertical gradients with soil depth (Lüdemann et al., 2000) and radial gradientswith distance from the root surface (Revsbech et al., 1999). A large
amount of the surface roots of DSR may be advantageous in obtaining oxygen from ﬂoodwater (Naklang et al., 1996). Zhang et al.
(2011a,b) also reported that the nitriﬁcation decreased and denitriﬁcation increased with soil depth (in 0–20 cm) in DSR experimentalﬁelds. When soil is subjected to aerobic–anaerobic cycles, nitrateconcentrations tend to increase during aerobic periods but thenrapidly decrease when ﬁelds are ﬂooded, with soil nitrate presumably lost due to denitriﬁcation (Becker et al., 2007; Linquist et al.,2011).
Overall, during the two seasons, the simulated nitriﬁcation anddenitriﬁcation averaged 1.4 and 1.0 kg N ha−1 day−1 , respectively.
Zhou et al. (2012) reported in-situ denitriﬁcation, directly determined in the drained-reﬂooded paddy soil (with TPR), ranging from0.05 to 10.35 kg N ha−1 day−1 (averaging 38.9% of applied N), andrates in the continuously ﬂooded paddy soil ranging from 0.05 to
3.18 kg N ha−1 day−1 (an average of 9.9% of applied N).

3.7. Nitrogen balance
The analysis of the N balance is very important for understanding how efﬁciently fertilizer is utilized by crops and how
much is lost due to various processes. The two dominant inputsof mineral N to paddy ﬁelds were from fertilizer applications andmineralization of organic N, which were considered in the model
simulations. Other N inputs, such as from dry or wet depositions
or from irrigation water, were neglected in the model. As listed inTable 5, simulated outputs of N from a 60 cm soil column mainly
include AV (accounting on average for 16.6% of the TNI), crop uptake(31.5%), and denitriﬁcation (38.8%). The main difference of N loss
between the two seasons was due to surface runoff. The othercomponents of N output were similar. Both AV and denitriﬁcation made a signiﬁcant contribution to the N loss from the DSR
ﬁeld, together accounting for about 55.5% of the TNI. N runoff andleaching were the two main pathways of N pollution from agricultural lands to water systems (Zhao et al., 2012). N leaching at the
60 cm depth and N runoff together accounted for 10.5% of TNI on
average. The soil N storage increased by 17.3 kg ha−1 on average,
with 77% due to NO3 − –N, which was similar to observed data. The
total N balance errors of simulations were less than 1.0% acrossthe two seasons. Overall, simulated N balance components using
Hydrus-1D matched corresponding observed data well.

Table 4
Simulated nitriﬁcation and denitriﬁcation (kg ha−1 ) in each soil layer in the DSR ﬁeld during the 2008 and 2009 seasons.
2008 season

Soil depth (cm)
Nitriﬁcation
0–20
20–40
40–60
60–80
80–100
100–120
Total

120.2
67.5
16.3
1.6
0.6
0.5
206.7

2009 season
Denitriﬁcation
48.2
57.5
30.4
9.9
3.7
2.6
152.3

Nitriﬁcation
116.6
69.7
16.6
1.7
0.6
0.5
205.7

Denitriﬁcation
47.7
58.9
31.4
10.4
4.0
2.9
155.3

Y. Li et al. / Agricultural Water Management 148 (2015) 213–222

221

Table 5Components of nitrogen balance (kg ha−1 ) in a 60-cm soil column of the direct-seeded-rice ﬁeld during the 2008 and 2009 seasons.
Riceseason

F

M

AV

D

SR

L

CU

SS

2008

Simulated
Observed

220

145.5

−63.4
−56.2

−136.1
–

−10.3
−9.2

−25.3
−27.2

−114.8−107.3

−17.6
−11.7

−2.0
–

2009

Simulated
Observed

220

147.8

−58.6
−52.6

−138.0
–

−17.0
−19.2

−24.7
−26.2

−112.1−104.2

−16.9
−11.9

−0.5
–

2009a
2009a

Simulated
Simulated

270
270

142.9
117.6

−74.9
−70.4

−176.2−166.2

−18.4
−16.6

−10.4
−10.1

−113.0−108.0

−17.4
−13.3

2.6
3.0

ı

F–fertilizer, M–mineralization, AV–ammonia volatilization, D–denitriﬁcation, SR–surface runoff, L–leaching, CU–crop uptake, SS–change in soil storage, ı–total nitrogenbalance error.
aHypothetical scenarios with different fertilizer management discussed in Section 3.8.

3.8. Alternative scenarios
It should be noted that the two evaluated seasons (2008 and2009) had very different rainfall and irrigation management (Liet al., 2014). Consequently, the upper boundary conditions forwater ﬂow substantially differed. However, since applied irrigations more or less balanced rainfalls and corresponding surface
runoff, subsurface ﬂow during the two seasons was relatively similar.
On the other hand, fertilizer managements were very similar
during the two seasons. To evaluate how the model would respondto different fertilizer managements, we repeated simulations forthe 2009 season with the following fertilizer applications (partly
based on the study of Li et al. (2010) during the 2007 season): 54,
54, 54, 54, 54, and 71, 71, 0, 67.5, 40.5 kg N ha−1 for the base and four
top-dressed fertilizer applications, respectively. The total applied
N in these two scenarios thus was 270 kg N ha−1 , compared to
220 kg N ha−1 in our study. For these two scenarios, the model predicted that the N loss due to AV ﬂuxes was 74.9 and 70.4 kg N ha−1
(Table 5), respectively, compared to 70.5 and 78.4 kg N ha−1 measured during the 2007 season, with slightly different ﬂow (rainfall,
irrigation) conditions (Li et al., 2010). These additional two hypothetical scenarios document that different fertilizer managements,
as well as climate conditions and water managements, inﬂuence
the quantity and pathways of nitrogen loss and that the calibrated
Hydrus-1D model can be a useful tool in quantifying these different
factors in hypothetical or future scenarios.

4. Conclusions
Nitrogen pollution derived from agricultural ﬁelds increased
the risks of surface water eutrophication and groundwater contamination, especially during the rice season. Cultivation of
direct-seeded-rice in many Asian countries induced a different
nitrogen fertilizer management in paddy ﬁelds compared to ﬁelds
with traditional transplanted rice, and thus consequently revealed
different nitrogen losses into surface water and groundwater environments. The nitrogen balance in a direct-seeded-rice ﬁeld in the
Taihu Lake Basin during two seasons (2008 and 2009) were simulated using Hydrus-1D with prior simulations of water ﬂow andwater balance as reported by Li et al. (2014). Simulated nitrogenconcentrations at different depths, ammonia volatilization ﬂuxes,
and nitrogen leaching ﬂuxes matched the observed values well.
This indicates that Hydrus-1D can be a useful tool for simulating
the nitrogen transport and transformation processes in a directseeded-rice ﬁeld.
Fertilizer applications and mineralization of organic N were
the two most important nitrogen inputs to the paddy ﬁeld in ourstudy. Ammonia volatilization (on average 16.0% of the total nitrogen input) is a signiﬁcant nitrogen loss process in DSR ﬁelds. The
loss of nitrogen due to surface runoff signiﬁcantly differed between

the 2008 (2.8% of TNI) and 2009 (4.6% of TNI) seasons, which had
different rainfall/irrigation distributions. The nitriﬁcation and denitriﬁcation processes occurred mainly in the root zone where avariable, oxidation-reduction environment alternated with drying
and wetting conditions. The analysis of the nitrogen balance during the two seasons shows that on average, about 35.5% of total
N is removed from the soil proﬁle by rice root uptake, about 2.7%
(at 120 cm) leaches to groundwater, and about 22.2% is lost due todenitriﬁcation.

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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