Received: 15 March 2019

Accepted: 24 July 2019

DOI: 10.1002/hyp.13556

RESEARCH ARTICLE

Nitrate subsurface transport and losses in response to its initial
distributions in sloped soils: An experimental and modellingstudy
Meixiang Xie1,2 | Jirka Šimůnek3| Zhanyu Zhang1,2| Pingcang Zhang4 |Jinxin Xu4 | Qingming Lin4
1
College of Water Conservancy andHydropower Engineering, Hohai University,
Nanjing, China

Abstract
Transport and losses of nitrate from sloped soils are closely linked to nitrogen fertil-2

College of Agricultural Engineering, Hohai
University, Nanjing, China
3
Department of Environmental Sciences,
University of California, Riverside, CA, USA
4

Department of Soil and Water Conservation,
Changjiang River Scientific Research Institute,Wuhan, China
Correspondence
Zhanyu Zhang, College of Water Conservancy
and Hydropower Engineering, Hohai
University, Nanjing 210098, China.
Email: zhanyu_hhu@163.com
Funding information
China Scholarship Council; Fundamental
Research Funds for the Central Universities,
Grant/Award Number: 2018B627X14;
National Natural Science Foundation of China,
Grant/Award Number: 51879071;
Postgraduate Research & Practice Innovation
Program of Jiangsu Province, Grant/Award
Number: KYCX18_0596; Priority Academic
Program Development of Jiangsu Higher
Education Institutions, Grant/Award Number:
YS111001; Changjiang River Scientific
Research Instituteizer management. Previous studies have always focused on different types of fertilizer applications and rarely analysed various initial nitrate distributions as a result of
nitrogen fertilizer applications. Under certain conditions, both subsurface lateral saturated flow and vertical leaching dominate nitrate losses. Soil tank experiments and
HYDRUS-2D modelling were used to better understand the subsurface nitrate transport and losses through lateral saturated flow and vertical leaching under various initial nitrate distributions. Low (L: 180 mg L−1), normal (N: 350 mg L−1), and high (H:
500 mg L−1) nitrate concentrations were used in five different distributions (NNNN,
NLLN, LHHL, LNLN, and HNHN) along the slope of the tank. The first two treatments(NNNN and NLLN) were analysed both experimentally and numerically. Experiments
were conducted under 12 rainfall events at intervals of 3 days. The HYDRUS-2D
model was calibrated and validated against the experimental data and demonstrated
good model performance. The other three treatments (LHHL, LNLN, and HNHN)
were investigated using the calibrated model. Nitrate concentrations in purple slopedsoils declined exponentially with time under intermittent rainfalls, predominantly inthe upper soil layers. Non-uniform initial nitrate distributions contributed to larger
differences between four locations along the slope in deeper soil layers. The nonuniform nitrate distribution either enhanced or reduced decreases in nitrate concentrations in areas with higher or lower initial nitrate concentrations, respectively.
Higher nitrate concentrations at the slope foot and along the slope were reduced
mainly by lateral flow and vertical leaching, respectively. Increasing nitrogen application rates increased subsurface nitrate losses. Mean subsurface lateral nitrate fluxes
were twice as large as mean vertical leaching nitrate fluxes. However, due to longer
leaching durations, total nitrate losses due to vertical leaching were comparable withthose due to lateral flow, which indicated comparable environmental risks to surface
waters and groundwater.
KEYWORDSHYDRUS-2D, initial nitrate concentration distributions, intermittent rainfall, sloped soils,
subsurface lateral flow, vertical leaching3282© 2019 John Wiley & Sons, Ltd.

wileyonlinelibrary.com/journal/hyp

Hydrological Processes. 2019;33:3282–3296.

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LI ET AL.

1 | I N T RO D UC T I O NWang et al. (2012) found that bromine losses due to run-off are prone
to happen when the solute is placed on the surface of the slope foot.
Prasertsak et al. (2002) reported that incorporating urea into the soil

Sloped soils occupy two thirds of the land area in China. As such, sloping farmlands play an important role in agricultural production (Xie,
Zhang, Zhang, Xu, & Lin, 2018). Specifically, purple sloped soils
account for more than 70% of the farmland in the south-west of
China (Ma et al., 2016). Due to its low cost, urea fertilizer is currently
the most favoured form of nitrogen fertilizer utilized in agricultural
production (Prasertsak et al., 2002). It is well known that excessive
fertilizer applications cause nutrient leaching and losses towards
groundwater. Nitrate loss from sloped soils has been increasingly recognized as a source of serious pollution in the water environment (Xieet al., 2018; Zhang, Tang, Gao, & Zepp, 2011).
Nitrate is usually lost from sloped soils after rainfalls mainly due tosubsurface outflow and also through leaching towards deep groundwater. A large number of studies have shown that subsurface flow
dominates nitrate losses from sloped soils (Bechmann, 2014; Jia, Lei,
Lei, Ye, & Zhao, 2007; Wang, Zhang, Lin, & Zepp, 2011; Zheng, Liu,
Zuo, Wang, & Nie, 2017). Because purple sloped soils were formed byrock weathering, subsurface lateral flow is abundant at the interface
between the soil and the bedrock, which have dramatically different
permeabilities (Baram et al., 2016; Tang et al., 2012). Hence, nitratedissolved in water is easily lost through subsurface lateral flow. Moreover, leaching towards deep zones also plays an important role innitrate losses (Baram et al., 2016; Filipovic, Toor, Ondrasek, &Kodesova, 2015; Kahl et al., 2007). However, studies of nitrate losses
from purple sloped soils rarely take into account vertical nitrateleaching to deep groundwater.
Spatial and temporal variabilities in NO3-N concentrations significantly affect nitrate transport and losses in soils. Filipovic et al. (2015)
reported that a large percentage of nitrate fertilizer can reach the bottom of the soil profile and leach into groundwater. Zhu et al. (2009)
found that the maximum NO3-N concentrations appeared in theupper soil layers at the foot of the purple soil hillslope. Nitrate transfer
in soils is dominated by the spatial variability of physical and chemicalsoil properties and by the spatial and temporal variabilities of waterand fertilizer applications (Baram et al., 2016).
Rainfall factors such as intensity and duration play a crucial role innitrate losses from sloped soils (Kleinman et al., 2006; Wu, Peng, Qiao,& Ma, 2018). Additionally, slope gradients (Komatsu et al., 2018; Wu
et al., 2018) and fertilizer management (Sweeney, Pierzynski, &Barnes, 2012) also have great impacts on nitrate losses. In the past,
researchers analysed effects of fertilizer management on nitrate losses

rather than spreading it on the surface reduced ammonia nitrogen
losses from 37.3% to 5.5% of applied nitrogen. These different nitrogen fertilizer application rates and placements cause different initial
nitrate distributions in soils, which in turn impact the nitrate transportand losses. Hence, different initial nitrate distributions as a result ofvarious fertilizer applications play a crucial role in nitrate losses. However, direct effects of initial nitrate spatial distributions on nitratetransport and losses are rarely addressed in the literature.
Subsurface nitrate transport and losses accompanied by subsurface water flow are complex processes (Zhu et al., 2009). It is still achallenge to fully understand the relation between subsurface nitratetransport and losses and subsurface water flow. Visual modelling software is a useful tool for visualizing and identifying the subsurface
nitrate transport subjected to various initial nitrate distributions.
HYDRUS-2D software has been widely used in the literature instudies of subsurface nitrogen dynamics and leaching in croplands(Doltra & Munoz, 2010; Karandish & Šimůnek, 2017; Salehi, Navabian,Varaki, & Pirmoradian, 2017). For example, Karandish and Šimůnek
(2017) highlighted that HYDRUS-2D was capable of simulating subsurface water and nitrogen dynamics under different irrigation scenarios. Also, Salehi et al. (2017) reported that HYDRUS-2D well assessed
the relationship between fertilizer applications and nitrate leaching in
subsurface controlled drainage for a physical model of paddy fields.
Because many other successful HYDRUS-2D applications have beenreported by Šimůnek, van Genuchten, and Šejna (2016), we use thismodelling software to simulate complex processes of the subsurface
nitrate transport and losses in sloped soils under various initial nitrate
distributions.
To improve our understanding and interpretation of subsurface
nitrate transport and losses under various nitrate distributions, we
conducted both laboratory experiments and numerical simulations
using the HYDRUS-2D modelling software. In particular, spatial andtemporal distributions of porewater NO3-N concentrations in purple
sloped soils and nitrogen losses through subsurface lateral saturated
flow and vertical leaching were evaluated. In particular, the effects ofwater flow and initial nitrate distributions on its transport and losses
were studied. Furthermore, relations between the subsurface nitratetransport and losses, and differences in NO3-N losses due to lateralsaturated flow and vertical leaching, were analysed to better understand subsurface NO3-N losses from sloped soils.

mainly by means of application rates (Delgado, Khosla, Bausch,
Westfall, & Inman, 2005; Russo, Tully, Palm, & Neill, 2017; Wang,

2 | MATERIALS AND METHODS

Ying, Yin, Zheng, & Cui, 2018), fertilizer types (Liu, Yang, Yang, & Zou,
2012; Nyamangara, Bergstrom, Piha, & Giller, 2003; Smith, Owens,
Leytem, & Warnemuende, 2007), placement locations (Prasertsak
et al., 2002; Wang, Ping, Pan, & Shen, 2012), and fertilizer application

2.1 | Laboratory experiments
2.1.1 | Experimental set-up and design

techniques (Ke et al., 2018; Zhao & Shao, 2002). Russo et al. (2017)

Two 200-cm-long, 50-cm-deep, and 50-cm-wide steel soil tanks werereported that porewater nitrate concentrations and leaching fluxes

used for laboratory experiments (Figure 1). At the bottom of one tank,are highest at locations with highest applications of nitrogen fertilizer.

a 10-cm-deep cement layer was set up to imitate aquitard bedrock

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LI ET AL.

nitrogen content of the collected soil was 34.6, 2.86, and 1,107 mg
kg−1 for ammonia (NH4-N), nitrate (NO3-N), and total nitrogen (TN),respectively.
Observations of soil water contents and porewater nitrate concentrations in soils were set up at four positions along the slope at
depths of 5, 21, and 37 cm in the upper (U1, U2, U3, and U4), middle(M1, M2, M3, and M4), and bottom (D1, D2, D3, and D4) soil layers(Figure 1). Numbers 1, 2, 3, and 4 represent positions along thedirection of the slope 25, 75, 125, and 175 cm away from the bottomside, respectively (Figure 1). Water samples were collected by ceramic
cups that were linked using thin plastic pipes with effluent collecting
jars. A vacuum pump was used to enhance effluent. Specifically, only
the deep and middle layers were equipped with ceramic cups tocollect water samples because the suction in the upper layers was nothigh enough to make ceramic cups collect water effluent. As a result,porewater nitrate concentrations in the upper soil layers wereobtained only by model simulations. Additionally, each position was
equipped with a moisture sensor based on the frequency domainreflectometry principle with a 5% measurement error.
One day before applying rainfall events, different initial porewater
nitrate concentration distributions and similar soil water content (0.28
cm3 cm−3) distributions were obtained by sprinkling 40 L of waterwith different amounts of dissolved urea fertilizer. Specifically, 15 g ofurea was dissolved in water that was sprinkled uniformly on the soil
surface of one tank, achieving a uniform porewater nitrate concentraF I G U R E 1 Schematic illustration of the experimental set-up:(a) the side view of the tank, (b) the top view of the tank, and (c) thenumerical representation of the tank with the finite element mesh andboundary conditions

tion (350 mg L−1, denoted as a normal [N] initial concentration) distribution along the slope. This scenario was denoted as scenario NNNN.
For another soil tank, 7.5 and 5 g of urea was dissolved in two 20-L
volumes of water, which were then sprinkled at both ends and in theunder purple soils; 43.5 kg of cement was mixed with 130.4 kg of

middle of the sloped soil surface, respectively. As a result, normal andyellow sand and 36.1 kg of water and stirred well to get a 2 g cm−3

low (about 180 mg L−1, denoted as a low [L] initial concentration)

density. Cement was then paved over the bottom of the tank withconcentrations of NO3-N were distributed at positions 1 and 4, and 2

324 uniformly distributed drainage holes of 0.5 cm in diameter. Fiveand 3 (Figure 1), respectively. This scenario was denoted as scenario

litres of water were sprayed at the surface of the cement layer after it

NLLN. The other three scenarios, that is, LHHL, LNLN, and HNHN,

hardened. The soils were collected from the top 40-cm layer of thewere only simulated using the HYDRUS-2D model, where H0purple sloping farmland in the small Wangjiaqiao watershed (110 42
 0E, 31 5 N), close to the Yichang city in the Zigui County of the Hubei

represents areas with high initial concentrations (about 500 mg L−1,
denoted as a high [H] initial concentration).

Province, China. The purple sloping land is dominated by a subtropicalA stationary artificial rain device was located 9 m above the soil

monsoon climate with intensive rainfall during the summer from Junetank. Twelve rainfall events were applied during the laboratory experi-

to September. A slope of 10 , representing the most common

ment (Figure 2), according to the local climate conditions describedfarmland slopes of purple soils, was adjusted by a hydraulic driving

above. Specifically, 0.04, 0.1, and 0.18 cm min−1 were used as light,

device. An amino-plastic web was placed all around the soil tanks to

medium, and heavy rainfall intensities, respectively. All rainfall events

decrease boundary effects between the soil and the steel tank. The

lasted 60 min and were applied in 3-day intervals. Three light rainfalls

air-dried and sifted soil was then backfilled into the steel tank in eight

were applied first, followed by three medium rainfalls, and three

5-cm layers, and the surface of each layer was roughened to avoid

heavy rainfalls. Finally, the sequence of light, medium, and heavy

stratification of the interface. The same bulk density as that of the soil

rainfalls was applied.

collected in the study area (1.35 g cm−3) was achieved by packing

Run-off and subsurface flow collectors were installed at the67.5 kg of soil to each 5-cm-high layer. The particle size distribution

surface and the bottom at the foot of the soil tank (Figure 1),of the collected purple soil had the sand (>0.05 mm), silt (0.002–0.05

respectively. The subsurface flow collector was welded on the bottommm), and clay contents (<0.002 mm) of 54.7%, 40.2%, and 5.1%,

side of the tank next to the interface between the soil and cement

respectively, and it was classified as loam and Entisol according to thelayers (Figure 1). Both collectors were protected from rainfall to

United States Department of Agriculture taxonomy. Additionally, thegather flow only from the soil surface and subsurface.

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LI ET AL.

F I G U R E 2 Applied rainfall events,and simulated and observed run-off andlateral fluxes for the NNNN and NLLN
nitrate distribution treatments

2.1.2 | Measurements and analyses

equation in mixed form describing two-dimensional isothermal uniform Darcian flow of water in a variably saturated rigid porous

The run-off was collected into a container with graduation. The flux

medium. This equation assumes that the air phase plays an insignifi-(cm day−1) was calculated by dividing the discharge volume (cm3) bycant role in the liquid flow process. The governing equation for solutethe surface area of the tank (10,000 cm2) and time (day). Porewatertransport subject to physical and chemical equilibrium is thewas collected at observation points every 1 hr, and eight samples

convection–dispersion equation, which is applied here to simulate

were collected at each point per event. Porewater NO3-N concentra-

nitrate transport. The convection–dispersion equation considers thetions were obtained by averaging concentrations from individual sam-processes of convection, molecular diffusion, hydrodynamic disper-

ples. Leaching from the bottom of the soil tank was observed in very

sion, and denitrification of nitrate to nitrogen gas. The two governing

small discharge and was not collected. Subsurface flow was collected

equations are numerically solved using the finite element (FE) method.

using measuring cups, and all collected water samples were trans-

This method requires appropriate spatial and temporal discretization

ferred into clean polyethylene bottles and stored in a refrigerator atto prevent numerical oscillations and to achieve acceptable mass bal-

4 C. The nitrate concentration was determined by a discrete auto

ance errors (Šimůnek, van Genuchten, & Šejna, 2008). Boundaries ofanalyser (SmartChem 200, Alliance, France) within 48 hr. The lateral

the transport domain involving the purple soil were discretized using

nitrate flux (mg cm−1 day−1) was calculated by dividing the concentra-

4-cm FEs, whereas 8-cm FEs were used on all other boundaries. Intion (mg cm−3) by sampling duration (day) and an outflow area (cm2).

total, 2,509 triangular FEs with 1,219 FE nodes were used to dis-

The loss (mg) of NO3-N was calculated by multiplying the total watercretize the transport domain for all simulations. Initial, minimum, andflux (cm) with the mean nitrate flux (mg cm−1).

maximum time steps of 10−4, 10−5, and 5 days, respectively, wereused in all simulations.

2.2 | HYDRUS-2D modelling2.2.1 | Modelling set-up

2.2.2 | Input parameters

HYDRUS-2D (Šimůnek, van Genuchten & Šejna, 2016) was used in

The input parameters required by HYDRUS-2D include soil hydraulicthis study to analyse and interpret collected experimental data involv-

parameters characterizing flow properties of the porous material anding subsurface water flow and NO3-N transport in sloped soils. The

solute transport and reaction parameters characterizing nitrate trans-

governing equation for uniform water flow is the modified Richards

port and reaction properties (Table 1). The soil hydraulic parameters

TABLE 1

Optimized values of modelling parameters
Water flow parameter

Solute transport and reaction parameters

Material

Soil typeθr(cm3 cm−3)

θs(cm3 cm−3)α(cm−1)nKs(cm day−1)

L

DL
(cm)

DT
(cm)

Dw
(cm2 day−1)

Kdn(day−1)

Purple soil

Loam

0.026

0.413

0.0183

1.5

40

0.5

50

5

1.64

0.015

CementSand shale

0.001

0.069

0.0155

1.5

0.45

0.5

50

5

1.64

0.015

Abbreviations: θr, the residual water content; θs, the saturated water content; α and n, van Genuchten's shape parameters; Ks, the saturated hydraulicconductivity; L, the pore connectivity parameter; DL, the longitudinal dispersivity; DT, the transverse dispersivity; Dw, the molecular diffusion coefficient infree water; Kdn, the denitrification rate.

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LI ET AL.

(θr, θs, α, n, and Ks: the residual and saturated water contents, twoboundaries. The other water flow and solute transport boundaries

shape parameters, and the saturated hydraulic conductivity, respec-

were set as no flux boundary conditions. Figure 1c shows boundary

tively) for the van Genuchten (1980) functions and the statisticalconditions applied to the transport domain.

pore-size distribution model of Mualem (1976) were initially estimatedfor the purple soil using neural networks from textural information(Schaap, Leij, & van Genuchten, 1998). The hydraulic parameters for

2.3 | Model evaluation

the cement layer were obtained from Schneider, Baumgartl, Doley,

Simulated data for surface run-off and porewater nitrate concentra-

and Mulligan (2010). The pore connectivity parameter L was assumed

tions were compared with corresponding observed data from the lab-

to be 0.5 (Mualem, 1976). No hysteresis was considered in alloratory experiments. One set of soil tank experimental data (thesimulations. For the nitrate transport, the longitudinal dispersivity, DL

NNNN treatment in Table 2) was used to calibrate the input model(20 cm, the same value was considered for both soil and cement), wasparameters, and another set of observed data (the NLLN treatment ininitially set equal to one tenth of the travel distance; the transverse

Table 2) was used to validate the calibrated model. Three statisticaldispersivity, DT (2 cm), was assumed to be one tenth of DL; andmeasures were used to evaluate the model performance: thethe molecular diffusion coefficient in free water, Dw, and theNash–Sutcliffe model efficiency coefficient (NSE), the root mean

denitrification rate, Kdn, for NO3-N were 1.64 cm2 day−1 andsquare error (RMSE), and the coefficient of determination (R2) at

0.015 day−1, respectively (Li et al., 2015).

p = .05. NSE and RMSE were calculated asPnðMi − Si Þ2
NSE = 1− Pn1 ,
 2
1 Mi − M

2.2.3 | Initial and boundary conditions

ð1Þ

The initial water content was assumed to be distributed uniformly inthe soil tank after water infiltration at the soil surface. The average ini3

−3

RMSE =

tial water content of the soil (0.28 cm cm ) was obtained by soil

rﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1 XnðS −Mi Þ2 ,
1 i
n

ð2Þ

moisture sensors. The initial water content in the cement representing
the bedrock was set equal to 0.05 cm3 cm−3, that is, the volume ofwhere Mi and Si are measured and simulated values, respectively, andwater applied to the cement divided by the volume of the cement.

n is the number of experimental data points. The R2 was calculated

The soil tank was divided into four sections centred around positions

internally by Excel (Microsoft Corp., WA, USA) using the trend line

1, 2, 3, and 4 (25, 75, 125, and 175 cm from the slope bottom). The

fitting. Optimal values for NSE, RMSE, and R2 are 1, 0, and 1,

initial nitrate content was assumed to be distributed uniformly in each

respectively. Specifically, satisfactory and good model performances

section (Table 2, N: normal; H: high; L: low). Timing of rainfalls andare obtained when NSE is larger than 0.5 and 0.65, respectively

their intensity were set as shown in Figure 2 according to actual(Wang et al., 2018).

applied rainfall events. Evaporation and transpiration fluxes were notconsidered in simulations. An atmospheric boundary condition was
assigned to the soil surface boundary, and time-dependent precipitation events were specified as discussed above. Actual surface flux andrun-off were calculated internally by the program. Considering the

3 | RESULTS
3.1 | Model calibration and validation

experimental set-up, the bottom boundary of the soil tank was set as

Measured run-off, subsurface lateral fluxes, and soil water contents at

a free drainage boundary condition, and a seepage face boundary con-observation positions were used to calibrate and validate the soil

dition (1 cm long) was assigned on the left side of the soil tank above

hydraulic parameters. Simulated run-off fluxes for the NNNN and

the interface between the soil and the cement layer. These boundary

NLLN treatments were identical because the same initial and bound-conditions represent vertical leaching and saturated lateral flow,

ary conditions were used for both treatments. A good agreement

respectively. A third-type boundary condition (a Cauchy boundary

between observed and simulated water fluxes was obtained (Figure 2,

condition) for solute transport was assigned to all inflow and outflow

NSE = 0.849, RMSE = 27.43 cm day−1, n = 48, R2 = 0.984). A similarly

TABLE 2

Initial nitrate concentration distributions along the slope for various treatments
Treatments

Nitrate distributions

Sections−1

Initial nitrate concentrations (mg L )

NNNN

NLLN

LHHL

LNLN

HNHN

1

350

350

180

180

500

2

350

180

500

350

350

3

350

180

500

180

500

4

350

350

180

350

350

Note. Sections 1, 2, 3, and 4 are centred around positions 25, 75, 125, and 175 cm from the slope foot along the slope direction, respectively.

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LI ET AL.

F I G U R E 3 Simulated and observed
soil water contents at positions M1, M2,
M3, M4, D1, D2, D3, and D4

good agreement was also obtained between observed and simulated

than during calibration, was also obtained during model validationwater contents at the observed positions (Figure 3, NSE = 0.763,

with NSE of 0.744 and RMSE of 35.6 mg L−1 (n = 96, R2 = 0.774,

3

−3

2

RMSE = 0.015 cm cm , n = 280, R = 0.729). Both water fluxes andFigure 4b) for porewater nitrate dynamics. Differences between simu-

water contents showed similar dynamics between simulated andlated and observed nitrate concentrations at position D1 were theobserved data.

main cause of lower simulation accuracy during validation than during

Porewater NO3-N concentrations observed in subsurface soil

calibration. It can be seen that simulated nitrate concentrations werelayers (M1, M2, M3, M4, D1, D2, D3, and D4) and lateral NO3-N

smaller than those observed at D1 for the NLLN treatment

fluxes for the uniform normal fertilizer distribution (the NNNN treat-(Figure 4b). These discrepancies between experimental and modelling

ment) were used to calibrate solute transport. Calibrated values of theresults may be caused by the combined effects of water flow andlongitudinal (DL) and transverse (DT) dispersivities were 50 and 5 cm

nitrate transport. In contrast, simulated lateral nitrate fluxes showed a(DT = DL/10), respectively, for both purple soil and cement layers. Invery good modelling performance with NSE of 0.881 and RMSE ofcomparison, the molecular diffusion coefficient in free water (Dw) and7.13 mg cm−1 day−1 (n = 156, R2 = 0.977, Figure 5).

the denitrification rate (Kdn) were not calibrated as the results were

Although HYDRUS-2D simulated decreasing trends in nitrate con-relatively insensitive to their values. NSE and RMSE of porewater

centrations and fluxes that were consistent with the experimental

nitrate dynamics for the calibrated model (for the NNNN treatment)

results, it could not fully capture the entire variability in the observed

are 0.833 and 39.2 mg L−1 (n = 96, R2 = 0.894, Figure 4a), respec-

data, as reflected by the RMSEs between observed and simulated

tively. NSE and RMSE of lateral nitrate fluxes for the calibrated model

data, which were on the order of 10% of C0. Despite these discrepan-

are 0.902 and 6.75 mg cm−1 day−1 (n = 156, R2 = 0.947, Figure 5),

cies, an overall relatively good description of observed surface run-respectively. These NSE (>0.75) showed very good model perfor-

off, subsurface fluxes, water contents, and nitrate concentrations

mance (Wang et al., 2018) despite a relatively large RMSE (about one

during both calibration and validation indicates that the HYDRUS-2D

tenth of C0). The largest differences between measured and simulated

model can be used with calibrated parameters (Table 1) to analyse

nitrate concentrations were found at D1 (Figure 4). Contrary to exper-

other experimental treatments, as well as other potential scenarios.

imental data that showed relatively large spatial differences in nitrate

Figure 4 shows a similarly decreasing trend in porewater nitrateconcentrations at the same depth after the third rainfall, simulated

concentrations at all observation points. Porewater nitrate concen-

results showed much less spatial variance in nitrate dynamics

trations at points subjected to normal fertilizer application dis-(Figure 4a). This can potentially be explained by the fact that observed

played a sharply decreasing, exponential trend during the firstsoil water contents at the slope foot decreased faster than simulated

5 days of the experiment. Nitrate concentrations tended to bevalues and that observed soil water contents displayed hysteretic

more stable in the range between 50 and 100 mg L−1 during later

behaviour (Figure 3). As a result, observed nitrate dynamics displayed

stages of the experiment. Discrepancies between simulated andthe largest decrease at D1 and the smallest at D4 (Figure 4a).

observed nitrate dynamics can be partly explained by differences

Concentrations observed in another soil tank with a non-uniform

between simulated and actual soil water contents (Figure 3).

initial nitrate distribution along the slope (the NLLN treatment) were

Figure 4 shows that simulated nitrate concentrations registeredthen used to validate the model for calibrated solute transport andsignificant, sharp declines immediately after rainfall, especiallyreaction parameters. Good model performance, although less good

during early stages of the experiment.

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LI ET AL.

F I G U R E 4 Simulated and observed
porewater NO3-N concentrations at D1,
D2, D3, and D4 for the (a) NNNN and(b) NLLN nitrate distribution treatments.
Results displayed in (a) were used formodel calibration and those displayed in(b) for model validation

F I G U R E 5 Simulated and observed lateral NO3-N fluxes for the NNNN and NLLN nitrate distribution treatments. Results displayed in black
were used for model calibration and those displayed in red for model validation

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LI ET AL.

F I G U R E 6 Simulated soil watercontents at 12 observation points andsubsurface lateral and vertical leachingwater fluxes

3.2 | Water fluxes

between positions in the NNNN treatment, during which initialconcentrations were the same everywhere. The largest variations

Figures 2 and 6 show that water losses due to run-off, subsurface
lateral flow, and vertical leaching occurred simultaneously, even if at
different levels, with run-off fluxes significantly larger than subsurface
and leaching fluxes. Specifically, rainfall played a greater positive roleon run-off, whereas soil water contents had stronger positive effects
on subsurface flow. During the first three low intensity rainfall events(0.04 cm min−1), run-off increased from 7.9 to 35.8 cm day−1, and soil
water contents increased from the initial value to the saturated
water content. Subsequent rainfall events (with moderate and highintensities) produced run-off, which was proportional to the rainfall
intensity, contrary to maximum subsurface lateral flow and verticalleaching, which were independent of the rainfall intensity. Numericalsimulations indicate that rainfall of 6 and 10.8 cm produced thelargest run-off fluxes of 132.2 and 247.5 cm day−1 (Figure 2),
respectively, compared with the maximum lateral flow of 2.8 cm−1dayand the maximum leaching of 0.4 cm day−1(Figure 6).

However, both processes of lateral flow and leaching lasted much

were obtained when two different initial concentrations were usedin the LHHL treatment. Overall, NO3-N concentrations displayed
exponential declining trends and almost instantaneous response to
rainfall events. Simulated average residual NO3-N concentrations
for all initial nitrate concentration distributions tended to be about
50 mg L−1. Relative residual NO3-N concentrations (C/C0) werelarger in locations with lower initial concentrations (C0; Figure 7).
Larger differences between relative decreases in nitrate concentrations at 12 observed positions occurred for treatments with nonuniform initial nitrate distributions (Figure 7b–e). In all treatments,
nitrate concentration decreases were larger in the upper soil layers.
However, the smallest differences between nitrate concentrationsin three soil layers were at the foot of the slope (D1, M1, and U1)
under the NNNN, NLLN, and HNHN treatments (Figure 7a,b,e),
whereas the largest differences were under the LHHL and LNLN
treatments (Figure 7c,d). Non-uniform initial nitrate distributions

longer (Figure 6) than run-off (Figure 2) for each rainfall. The major

contributed to larger differences between four locations along thewater loss from sloped soils was thus attributed to surface run-off,slope in the deeper soil layers.

followed by lateral flow, and vertical leaching. Lateral flow occurredNO3-N concentrations on the fifth day are used below to analyse

only when the soil became saturated at the interface between the soil

the decreasing trend in response to different initial nitrate spatial dis-and bedrock layers. Vertical leaching was a continuous process,tributions because nitrate concentrations on this day were in the mid-although at a relatively low flow rate. Leaching flow towards deep

dle stage of sharp declines (Figure 7). Relative NO3-N concentrations

before the(C/C0) with respect to the corresponding initial concentration (C0)soil reached full saturation, when its maximum values occurred. Soil

were about 0.56 at U2, U3, and U4 positions, 0.65 at U1, M1, M2,

water contents displayed similar dynamics as lateral flow and vertical

M3, and M4 positions, and 0.7 at D1, D2, D3, and D4 positions in theleaching, with corresponding maximum and minimum values occurring

NNNN treatment (Figure 7a). Non-uniform initial nitrate distributions

at the same time (Figure 6).

produced different decreasing trends. In the NLLN treatment, values

groundwater occurred at rates from 0.008 to 0.4 cm day−1of C/C0 were about 0.55 at U1 and U4, 0.6 at D1, M1, and M4, and

3.3 | NO3-N transfer in response to different initial
distributions
Figure 7 shows simulated temporal changes in relative nitrate con-0.68 at D4 and decreased faster in the initially normal domain(Figure 7b) than those in the uniform distribution treatment. In thesame soil layer, corresponding values were larger and decreased
slower in the initially low domain of the NLLN treatment. In the LHHL

centrations at 12 different locations of the sloped soils subjected

treatment, values of C/C0 were about 0.53 at U2 and U3, 0.6 at M2

to five various initial spatial distributions of porewater nitrate con-and M3, 0.65 at D3, and 0.69 at D2 with high initial nitrate concentra-

centrations. Nitrate concentrations showed the smallest variations

tions (Figure 7c). In the LNLN treatment, values of C/C0 were about

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LI ET AL.

F I G U R E 7 Simulated relative NO3-N concentrations (C/C0) at 12 observation positions for five different initial nitrate distribution treatments:(a) NNNN, (b) NLLN, (c) LHHL, (d) LNLN, and (e) HNHN. C0 is the initial concentration

0.51 at U2, 0.55 at U4, 0.58 at M2, 0.6 at D2 and M4, and 0.67 at D4with normal initial nitrate concentrations (Figure 7d), which is similar
to the NLLN treatment. In the HNHN treatment, values of C/C0 were0.77 and 0.70 at D2 and D4, respectively, with normal initial nitrateconcentrations and 0.66 and 0.64 at D1 and D3, respectively, withhigh initial nitrate concentrations (Figure 7e). These trends indicatethat non-uniform initial distributions contributed to slower and faster
nitrate concentration decreases in areas with lower and higher initial
nitrate concentrations, respectively, due to the lateral transfer ofnitrate between the two domains to compensate for lower initial
nitrate contents.
Regions with lower initial concentrations in the non-uniform distribution treatments registered significant increases in relative concentrations during early stages of the experiment as indicated by grey
dashed-line circles in Figure 7. Regions with low initial nitrate concentrations (180 mg L−1) registered the largest increases due to the

3.4 | NO3-N fluxes and losses due to subsurface flow
and vertical leaching
Figure 5 shows comparisons between observed and simulated lateral
NO3-N fluxes. Although both simulated and observed fluxes displayed
similar dynamics, differences between observed lateral NO3-N fluxes
for two different initial nitrate distributions were smaller than thosesimulated. Differences in measured lateral NO3-N fluxes between the
two treatments can be explained by the observed spatial variability inporewater nitrate concentrations (Figure 4). However, in general,
observed NO3-N fluxes for the NNNN treatment were larger thanthose for the NLLN treatment.
Figure 8 shows simulated subsurface lateral and vertical leaching
NO3-N fluxes for five different initial NO3-N distribution treatments.
NO3-N fluxes displayed gradually decreasing peak values during subsequent rainfall events except for the first two low intensity rainfalls.
NO3-N fluxes reached maximum peak values on the seventh daycompensation from regions with high initial nitrate concentrations(after three low intensity rainfalls) when for the first time water fluxes(Figure 7c). Increases in nitrate concentrations correlated positively

reached their maximum values. After then, peak values tended towith the soil depth. These increases tended to be prolonged withdecrease similarly as porewater nitrate concentrations in the soil.

small increments in the upper soil layers. Also, the compensation from

However, lateral nitrate fluxes under the LHHL treatment peaked onregions with normal initial concentrations was larger in the LNLN

the 10th day and after that tended to be equal to the peak values fortreatment than in the NLLN treatment, which indicates that the low

the NNNN and HNHN treatments. Overall, NO3-N fluxes correlated

concentration region at the foot of the slope obtained higher NO3-N

positively with corresponding water fluxes, as shown in Figure 6. As acompensation. Relative concentrations in regions with higher initial

result, peak values of NO3-N lateral fluxes were much larger thanconcentrations always decreased faster than in other regions. The

corresponding NO3-N leaching fluxes, both getting smaller after eachcompensation becomes smaller when the concentration difference

rainfall event. After each rainfall, leaching nitrate fluxes decreased

between neighbouring regions decreases. However, all NO3-N

slower than lateral nitrate fluxes. Between rainfall events, verticalconcentrations tended to decline with time after rainfall was initiated.

nitrate leaching continued (Figure 8b), whereas lateral nitrate fluxes

LI ET AL.

3291

F I G U R E 8 Simulated NO3-N lateralflow (a) and vertical leaching (b) fluxes forvarious initial nitrate distribution
treatments

tended to stop (Figure 8a). Vertical leaching NO3-N fluxes were more

70.0 mg cm−1 day−1 for the HNHN and LNLN treatments, respec-

persistent than lateral nitrate fluxes between two rainfall events

tively, whereas maximum leaching NO3-N fluxes were 24.5 and 16.5

(Figure 8), similarly as lateral and leaching water fluxes (Figure 6).

mg cm−1 day−1, respectively. Largest (2,766 mg for lateral flow and

Average NO3-N lateral fluxes ranged from 9.6 to 17.5 mg cm−1 day−1.
Average NO3-N leaching fluxes ranged from 5.6 to 8.8 mg cm−1
day−1. In other words, NO3-N lateral fluxes were about twice as large
as NO3-N leaching fluxes for the same initial nitrate distribution treatments (Figure 9). However, total NO3-N fluxes due to vertical leaching(238, 174, 241, 183, and 278 mg cm−1 for the NNNN, NLLN, LHHL,
LNLN, and HNHN treatments, respectively) were almost the same as
those due to subsurface lateral flow (216, 162, 210, 150, and 272 mg
cm−1 for the NNNN, NLLN, LHHL, LNLN, and HNHN treatments,
respectively; Figure 10).
In response to different initial nitrate distributions, NO3-N lateral
fluxes displayed larger differences than NO3-N leaching fluxes. Specifically, maximum subsurface lateral NO3-N fluxes during the second
light rainfall were 25.3 and 9.2 mg cm−1 day−1 for the HNHN and
LNLN treatments, respectively, whereas maximum leaching NO3-N
fluxes were 18.4 and 11.4 mg cm−1 day−1, respectively. On the other
hand, maximum subsurface lateral NO3-N fluxes were 172.7 andF I G U R E 9 Mean NO3-N lateral flow and vertical leaching fluxes
for various initial nitrate distribution treatments

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LI ET AL.

rainfalls when soil water contents increased to saturation. Sharp NO3N declines occurred especially during the first three rainfall events
when soil water contents gradually increased towards saturation.
Larger decreases in nitrate concentrations in the upper soil layers
were related to larger rainfall events and faster increases in the soil
water content towards saturation. Additionally, faster decreases ofobserved soil water content at the slope foot caused larger declines innitrate concentrations compared with simulated data. When the
numerical simulations were extended without applying additional rainfall, porewater NO3-N concentrations remained more or less constant,whereas soil water contents gradually decreased (due to verticalleaching). This indicates that soil water affected nitrate transport bymeans of elution as a response to rainfall.

F I G U R E 1 0 Total NO3-N losses due to lateral flow and verticalleaching for various initial nitrate distribution treatments

4.1.2 | Initial NO3-N distributions

1,304 mg for leaching) NO3-N losses were obtained for the HNHN

amounts of nitrogen fertilizer applied to soils (Li, Zhang, & Rao, 2004;

treatment. Smallest lateral (1,522 mg) and leaching (823 mg) NO3-N

Wang et al., 2010; Xu, Niu, Xu, & Wang, 2013). Xu et al. (2013)

losses occurred under the LNLN and NLLN initial nitrate distributions.

reported that nitrate concentrations in soils and leaching were related

It is noteworthy that mean and total nitrate losses were almost thesame for the NLLN and LNLN treatments (Figures 9 and 10), which
had the same initial amount of fertilizer in the tank. The minimum
NO3-N loss occurred for the treatment with the smallest amount ofapplied fertilizer.

Different initial nitrate distributions were the result of different

to initial conditions according to observed and simulated data. We
operated under the assumption that the initial nitrate distribution was
uniform in the vertical direction. Simulated different initial nitrate distributions caused different declines in nitrate concentrations, as
shown in Figure 7. Although there was least distinct spatial variationin NO3-N transport in sloped soils in the NNNN treatment, there
were significant differences in soils with non-uniform initial nitrate

4 | DISCUSSION

distributions. Nitrate concentrations increased at an early stage of theexperiments at locations with lower initial nitrate concentrations in

4.1 | Effects of different factors on NO3-N transportand losses

non-uniform treatments, especially in the deep soil layers. Also, nitrate

4.1.1 | Soil water

these locations compared with locations with higher initial nitrateconcentrations (both observed and simulated) declined slower atconcentrations (Figures 4b and 7). It can be seen that nitrate

Previous studies have highlighted the effects of rainfall intensity andredistributed from locations with higher initial concentrations to loca-

duration on subsurface NO3-N transport and losses from purple

tions with lower initial concentrations. More nitrate was then eluted

sloped soils (Jia et al., 2007; Xie, Zhang, Zhang, & Chen, 2018). Rain-from tank sections with higher initial nitrate contents due to subsur-

falls directly affect soil water contents as a result of infiltration.

face lateral flow and vertical leaching, producing more pollutions to

Nitrate is easily soluble in water and very mobile (Hamoudi &watershed and groundwater.

Belkacemi, 2013; Kanthle, Lenka, Lenka, & Tedia, 2016). In this study,

The first two peaks of lateral NO3-N fluxes (Figure 8) decreased in

all peak values of water and NO3-N fluxes displayed simultaneous

the following order for different treatments: HNHN > NNNN > NLLN

declines after saturated stages (Figures 6 and 8), indicating that soil

> LHHL > LNLN. A different order (HNHN > NNNN > LHHL > LNLN

water losses accompanied NO3-N losses. Similarly, Baram et al. (2016)> NLLN) was found for peak values of vertical NO3-N leaching fluxes.

reported that mobile water was responsible for most of the nitrate

Higher initial nitrate concentrations at the slope foot produced larger

transport in the deep vadose zone. Observed and simulated water

lateral NO3-N fluxes during each rainfall event, whereas verticalcontents in the middle and deep soil layers displayed the same dynam-

nitrate leaching fluxes were positively correlated with total amounts

ics (Figure 3). However, although simulated soil water contents at allof nitrate applied. Essentially, both NO3-N losses due to lateral flow

positions tended to consistently reach full saturation after rainfalls,

and vertical leaching depended on porewater nitrate concentrations

observed soil water contents displayed a hysteretic behaviour (notin the vicinity of the outflow boundary (the seepage face boundary

considered in simulations) at higher positions of the tank. Hence,for lateral flow and the free drainage boundary for vertical leaching)observed nitrate concentrations displayed spatial variations, whereas

where losses occur. Rapid declines of nitrate concentrations in loca-

simulated porewater NO3-N concentrations were more consistenttions with higher initial concentrations caused different nitrate distri-along the slope (Figure 4a). Figures 6 and 7 show that simulated

butions during subsequent rainfall events. As a result, peak values ofnitrate concentrations at observation points decreased sharply after

lateral NO3-N fluxes decreased in a different order for the NLLN and3293

LI ET AL.

LHHL treatments (LHHL > NLLN). Also, differences in peak values

the experiment were almost the same. Similarly, total nitrate fluxes

among all treatments decreased, especially between the NLLN anddue to lateral flow were almost the same as those due to vertical

LNLN treatments with the same initial total nitrate contents. Figures 9leaching (Figure 10). This occurred even though peak values of waterand 10 show that the mean and total NO3-N fluxes displayed theand nitrate fluxes for lateral flow (2.8 cm day−1 and 173 mg cm−1

same descending patterns for the five treatments (HNHN > NNNN >day−1, respectively) were much larger than those for vertical leaching

LHHL > NLLN > LNLN for lateral flow and HNHN > NNNN > LHHL >(0.44 cm day−1 and 24 mg cm−1 day−1, respectively). The nitrate loss

LNLN > NLLN for vertical leaching). Differences between the NLLN

loads due to lateral flow (2,199, 1,648, 2,140, 1,522, and 2,766 mg for

and LNLN treatments were negligible, similarly as between the NNNN

the NNNN, NLLN, LHHL, LNLN, and HNHN treatments, respectively)

and LHHL treatments (with similar initial nitrate contents). Overall,

were almost twice as those due to vertical leaching (1,123, 823,

treatments with higher nitrogen applications caused higher subsurface

1,128, 879, and 1,304 mg for the NNNN, NLLN, LHHL, LNLN, andnitrate losses. Wang et al. (2018) reported that soil nitrate leaching

HNHN treatments, respectively). This indicates that large amounts ofincreased rapidly with increasing nitrogen application rates. There isnitrate are transported both laterally downhill and vertically towards

thus a positive correlation between nitrate concentrations at the

groundwater, resulting in comparable environmental risks. Moreover,interface where losses occur, nitrate applications, and subsurface

nitrate losses due to subsurface lateral flow occur mostly duringnitrate losses.

rainfalls, whereas losses due to vertical leaching are incessant and less

The NO3-N transport in sloped soils with a uniform initial nitratevariable with time.

distribution showed the least spatial variability. However, spatial variabilities were significant for initially non-uniform distributions. The
spatial variability along the slope can be attributed to convection with

4.3 | Analysis of calibrated parameter values

soil water and dispersion due to concentration gradients. Larger

The saturated hydraulic conductivity is the most important parameterdecreases in nitrate concentrations due to water dilution in the upper

for numerical simulations of water flow. The calibrated Ks value of thesoil layers were found in this study. Similarly, Li and Liu (2011) found

low permeability cement layer was a hundred times smaller than Ks ofthat nitrate concentrations increased with soil depth by applying

the purple soil (Table 1). Similar Ks values measured on bedrock sam-nitrate solution in uniform soils. At first, non-uniform nitrate distribu-

ples were reported by Katsura, Kosugi, Yamamoto, and Mizuyama

tions produced faster declines in regions with higher initial nitrate(2006). Although the low permeability layer was thin and its water

concentrations and slower decreases in regions with lower initialstorage had little effect on the overall soil storage, its hydraulic con-nitrate concentrations. The effects of non-uniform distributions on

ductivity defined the leaching potential of the entire soil profile

NO3-N transfer were more pronounced at the deep soil layer(Baram et al., 2016). The calibrated Ks of the purple soil was smaller(Figure 7). We suggest that non-uniform applications should be taken

than those reported by others (Long, Liu, & Liu, 2015) for both culti-

into account in fertilizer management on sloped soils. Specifically,

vated and uncultivated purple sloping soils. Differences in Ks were

smaller amounts of nitrate fertilizers should be applied near the slope

found to be dependent on the study scale (Laine-Kaulio, Backnas,

foot and larger amounts upslope in order to reduce nitrate losses andincrease downslope fertility.

Karvonen, Koivusalo, & McDonnell, 2014). DL, which is related to thetravel distance of the solute, controlled the nitrate transport. Although
the thickness of the soil layer was four times larger than the thickness

4.2 | Differences in NO3-N losses due to lateral flow
and vertical leachingof the cement layer, the DL value of the soil layer was calibrated to beabout the same as that of the bedrock layer. As a result of the shortduration of the experiment, the effects of the denitrification rate on

Saturated subsurface flow was collected at the foot side of the slope.

nitrate decreases in the soil were insignificant during simulations.

This lateral flow occurred along an inclined bedrock that had asignificantly lower permeability than the overlaying soil (Allaire,
Roulier, & Cessna, 2009; Dusek, Vogel, Dohnal, & Gerke, 2012; Xie

5 | C O N CL U S I O N S

et al., 2018). In the HYDRUS-2D model, water leaves the saturated
part of the flow domain laterally through a seepage face.

Subsurface water and nitrate leaching play an important role in water

Although there are short-duration flux peaks (Figure 6) corresponding

and nitrogen losses from sloped soils. It is essential to understandwith rainfalls, lateral flow through this boundary stops when the soil

these losses due to subsurface lateral flow and vertical leaching andbecomes unsaturated (i.e., the pressure head becomes negative). In

how various initial nitrate distributions can impact subsurface nitrate

contrast, vertical leaching at the bottom of the bedrock wastransport in sloped soils. In this study, HYDRUS-2D was used to ana-

simulated using a free drainage boundary condition, which resulted inlyse the subsurface nitrate transport and losses from sloped soils fora continuous bottom flux equal to the unsaturated hydraulicfive different initial nitrate distributions. HYDRUS-2D performed wellconductivity corresponding to the pressure head at the domain

when compared with experimental data, proving to be a useful model-

bottom. The leaching flux is thus incessant, increasing visibly (butling tool for investigating water flow and solute transport in sloped

much less than for lateral flow) after intensive rainfalls (Figure 6). As asoils and for providing better interpretation of spatial and temporal

result, total lateral flow (9.3 cm) and vertical leaching (8.7 cm) during

dynamics of nitrate distributions and losses in such environments.

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LI ET AL.

Laboratory experiments combined with the numerical analysis
demonstrated that water flow was the main factor in washing NO3-N
away from the soil profile in response to rainfalls and that nitrate concentrations declined exponentially with time. Significant differenceswere revealed along the slope for non-uniform initial nitrate distributions. Coupled effects of elution by soil water flow and diffusion due
to concentration differences on the nitrate transport were observed.
Larger decreases in nitrate concentrations in the upper soil layers due
to water elution were observed, whereas the effects of non-uniform
distributions on NO3-N transfer were more pronounced in the deep
soil layer. Higher nitrate concentrations at the slope foot and along
the slope were washed away mainly due to subsurface saturated lateral flow and vertical leaching, respectively. Non-uniform fertilizer
applications at sloped soils thus need to be taken into account inorder to reduce nitrate losses and keep soil fertility. Moreover,increasing nitrogen application rates enhances subsurface nitratelosses. There is thus a positive correlation between nitrate concentrations at the interface where losses occur, nitrate applications, and subsurface nitrate losses. Peak values of subsurface NO3-N fluxes
revealed that nitrate losses due to subsurface lateral flow occurred
mostly during rainfall events whereas nitrate losses due to verticalleaching were more gradual and had longer duration during and after
rainfalls. Average nitrate fluxes due to lateral saturated flow weretwice as large as those due to vertical leaching. However, total NO3-N
fluxes due to vertical leaching (as a result of its longer duration) werecomparable with those due to lateral saturated flow. Both processes
thus represent comparable environmental risks to surface andgroundwater.

ACKNOWLEDGMENTS
This research was supported by the Changjiang River Scientific
Research Institute and was funded by the Postgraduate Research&

Practice

Innovation

Programof

Jiangsu

Province(GrantKYCX18_0596), Fundamental Research Funds for the Central Universities (Grant 2018B627X14), National Natural Science Foundation ofChina (Grant 51879071), Priority Academic Program Development of
Jiangsu Higher Education Institutions (Grant YS111001), and the
China Scholarship Council.

DATA AVAI LAB ILITY S TATEMENT
The data that support the findings of this study are available from thecorresponding author upon reasonable request.

ORCID
Jirka Šimůnek

https://orcid.org/0000-0002-0166-6563

Zhanyu Zhang

https://orcid.org/0000-0002-7744-6698

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How to cite this article: Xie M, Šimůnek J, Zhang Z, Zhang P,
Xu J, Lin Q. Nitrate subsurface transport and losses in
response to its initial distributions in sloped soils: An
experimental and modelling study. Hydrological Processes.
2019;33:3282–3296. https://doi.org/10.1002/hyp.13556