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Article

Assessment of Nutrient Leaching in Flooded Paddy Rice Field Experiment Using Hydrus-1D

1
Department of Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
2
Faculty of Sustainable Agriculture, Universiti Malaysia Sabah (UMS), Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia
*
Author to whom correspondence should be addressed.
Water 2018, 10(6), 785; https://doi.org/10.3390/w10060785
Submission received: 26 March 2018 / Revised: 28 April 2018 / Accepted: 11 May 2018 / Published: 14 June 2018
(This article belongs to the Section Water, Agriculture and Aquaculture)

Abstract

:
Solute runoff and leaching are two direct pathways of nutrient pollution from paddy fields into water systems. Due to the dynamic nature of paddy fields, solute transport and transformation processes are complex and difficult to understand. Therefore, in this study, nitrogen (N) transport in flooded paddy rice fields with conventional irrigation (flooding irrigation) in the Tanjung Karang Rice Irrigation Scheme (TAKRIS), Sawah Sempadan, were observed and modelled using the Hydrus-1D numerical model during two consecutive rice growing seasons. Based on solute transport analysis results, it was observed that 50.3% to 48% of percolated N was accumulated in the top 40-cm soil layer, while 49.7% to 52% of leachate N was lost below the 40-cm soil layer (40–100 cm) during the off and main seasons, respectively. About 85% of N leaching loss was in the form of NO3. NO3 was absorbed by rice roots within 0–40 cm and the denitrified root zone; however, there was still a large quantity of NO3 which remained below the root zone, which was quickly transported downward along with the leachate water. The NH4+ concentration in subsurface water was lower than the NO3 concentration due to various processes that removed NH4+ from the topsoil layer (0–40 cm), such as ammonium volatilisation, nitrification, and plant uptake. The total leaching loss of N was 34.9 and 27.9 kg/ha during the off and main seasons, respectively. The simulated and observed water flow and nutrient leaching were in a good agreement (R2 = 0.98, RMSE = 0.24). The results showed that Hydrus-1D successfully simulated the solute movement under different soil depths during the study period.

1. Introduction

The nutrient concentration of surface and subsurface waters has changed into a dangerous worldwide environmental and ecological problem [1], which has tremendously accelerated the potential risks of the eutrophication of surface water and toxic contamination of groundwater. Most chemical fertiliser loads primarily originate from agricultural fields, especially during the rice growing seasons, in which a tremendous amount of fertilisers are utilised within agricultural crops. Urea is the one of the most commonly utilised N fertilisers in rice production systems [2]. However, it is essential to use an ideal quantity of N for optimum handling of rice production in arid and semiarid regions, considering that the implementation of a surplus amount of water can cause nitrogen draining under the root zone and results in economic loss for farmers.
Fertilisers are usually applied in Malaysia by conventional means. In paddy fields, the applied fertilisers, after dissolution, are not only transported over and infiltrate the soil, but are also diffused out and channelled in all possible directions due to the transverse variation of water velocity and depth [3]. Deterioration of water quality in streams and lakes continues to be a significant issue in many counties [4]. Causative factors include not only pollutants from various point sources, but also those from nonpoint sources. The understanding of nutrient/fertiliser transformation in paddy fields is limited due to complex interactions between the soil, water, and biomass [5,6], and the behaviour related to plant growth in paddy soils has been extensively studied [7]. Therefore, forecasting hydrological pathways and pollutants’ (nutrient and pesticides) behaviour in paddy soil appears to be crucial to define specific management practices controlling nonpoint source pollution and preserving water resources [8]. Additionally, due to the dynamic nature of paddy rice fields, N transport and transformation processes are complex and difficult to understand. Therefore, the modelling of nutrient dynamics in paddy fields becomes challenging as rice is a highly water- and N-demanding crop.
Several mathematical models are available to describe the water balance and behaviours of nutrients and pesticides in flooded rice fields. Some models describing the fate of nitrogen in rice fields focus on various processes that occur in floodwater, which include PADDY (Pesticide Paddy Field Model) [9], PCPF-1 (Pesticide Concentration in Paddy Field, v.1) [10], RICEWQ (Rice Water Quality) [11,12], and PADDIMOD [13], while others describe mass transport in flood water and the soil underneath. Chung et al. [14] developed the GLEAMS-PADDY model to describe nutrient loading in surface water and groundwater bodies. Chowdary et al. [15] developed and applied a simple model for assessing the concentration of nitrates in water percolating out of the flooded rice fields. Tournebize et al. [8] developed a coupled model (PCPF–SWMS) for simulating the fate and behaviour of pollutants in water and soil of paddy fields. In the GLEAMS-PADDY model [14], the N balance is separated into the NH4-N and NO3-N balances and applied to ponding water and underlying soil. PADDIMOD [13] describes the N balance as the total inorganic N, without focusing on the NH4-N and NO3-N balances separately.
The Hydrus-1D [16] numerical model is one of those that have been widely tested by many researchers to predict nutrient leaching in paddy fields under different irrigation and management practices [17,18,19,20,21]. Li et al. [22] used HYDRUS-1D to estimate the nitrogen transport in the paddy field. They reported that HYDRUS-1D can simulate N transformation in paddy fields and could be a potential tool for optimal fertiliser management practices. Furthermore, Tan et al. [23] used the HYDRUS-1D model to evaluate solute transport in paddy fields under different irrigation practices. They concluded that the HYDRUS-1D model can be an alternative system approach to enhance water and solute management for sustainable rice production. Dash et al. [24] used a HYDRUS-1D model to predict water and nitrate leaching from paddy fields under different fertilisation managements. Based on their simulated results, it was found that there was a good agreement between the HYDRUS-1D model simulation and field data.
However, no work yet has been carried out checking the accuracy of this model for simulating solute transport and leaching in flooded paddy rice fields in the Tanjung Karang rice irrigation scheme (TAKRIS). The scheme practices two cropping seasons to produce two crops of rice per annum, namely the “main season” (July to October) and the “off season” (January to April), which are also referred to as the “wet season” and the “dry season”, respectively. The main season crop is harvested in the month of October, whereas the irrigated off season crop is in the months of April and May. During these two seasons, farmers use substantial amounts of N to obtain high yields. However, all N applied is not taken up by rice plants as some of it may percolate below the root zone, which substantially contaminates the quality of groundwater. After applying fertilisers to the field, they dissolve, are transported over the land surface, and infiltrate the soil through irrigation water. In flooded paddy rice fields, the solute may be transported vertically with irrigation water; thus, the HYDRUS-1D model can appropriately demonstrate the distribution of fertilisers through the soil by one-dimensional flow [25]. The objective of this study is to model N leaching in flooded paddy rice fields at TAKRIS using the Hydrus-1D numerical model. Moreover, in the present study, the suitability of this model for simulating N leaching in paddy fields under flooding irrigation was evaluated.

2. Materials and Methods

2.1. Site Description

The study region relates to the Tanjung Karang rice irrigation scheme, which is located at 3°25′ ~3°45′ N latitude and 100°58′~101°15′ E longitude in the state of Selangor, Malaysia. It is one of the several irrigation compartments in Sawah Sempadan, consisting of 1468 lots, with the total area of about 2300 hectares divided into 24 blocks. BLOCK C in the Sawah Sempadan compartment had 86 individual farmers and was chosen as a research study area of the present study as shown in Figure 1. The only source for irrigation supply in Sawah Sempadan is the Berman River. Geographically, the study area is located at 3°28′09.63465″ N 101°13′26.48399″ E, with average altitude of 6.2 m above the mean sea level. The experimental plot has the soil texture of clay loam, while the texture of the soil surface ranges from clay loam to clay. The soil is classified as Jawa series and defined as clayey, mixed isohyperthermic sulfic tropaquept [26]. The physical and chemical properties of the soil at the site are listed in Table 1 and Table 2, respectively.

Experimental Design and Measurements

The study was conducted during two consecutive rice growing seasons (January–April 2017 and July–October 2017) in the Sawah Sempadan irrigation compartment at IADA Selangor. The experimental plot is 0.5 ha (5000 m2) in size. “BLOCK C” was chosen as the study area. After the land preparation, the seeds were evenly broadcasted by hand on the soil during the off and main seasons, respectively. After seeding, the field was irrigated until presaturation. The harvest dates were on April and October for the off and main seasons, respectively. Table 3 illustrates agricultural activities during both seasons. The total growing periods during these two seasons were 100 and 105 days, respectively. Soil samples were collected from the experimental plot to estimate the physical and chemical properties of the soil. Textural analyses were performed using a pipet method [27]. The bulk density was determined using core methods [28]. Saturated hydraulic conductivity was determined using the constant head method [29]. Soil organic carbon was estimated using Walkley and Black’s method [30]. Soil EC was measured by conductivity meter. Total nitrogen and extractable P and K were determined using the CHN-S method.
Ceramic porous cups at the depths of 20 cm, 40 cm, 60 cm, 80 cm, and 100 cm were installed at five different places to collect the amount of leachate water (solutes) to the subsurface as shown in Figure 2. The percolation rate was calculated by the difference between the bottom closed and opened lysimeters (Figure 3). In Sawah Sempadan, conventional flooding practice is generally adopted in rice paddy fields; therefore, during the entire experimental period, the irrigation and drainage of the experimental plot were monitored. Five porous pipes with leachate collectors were installed to obtain water samples from the subsurface soil in a paddy field. A one-litre plastic bottle was used to collect the samples of subsurface water. Leachate samples were collected on a weekly basis with vacuum hand pump. Additionally, leachate water samples were collected three times within the first 10 days of the fertiliser application. In this period (since the fertiliser application date), the concentration of nutrients varies very significantly. Finally, the samples were transported in a cooler with ice to the water quality laboratory lab, Faculty of Engineering, UPM, for further analysis. The concentrations of nitrate nitrogen (NO3-N) and ammonia nitrogen (NH4-N) in these water samples were analysed using a Spectrophotometer DR/890 colorimeter. The amounts of NO3-N and NH4-N loss by leaching during the rice growing season were determined by multiplying the total amount of water leachate measured weekly, and the concentration of these nutrients of water was collected in ceramic porous cups at 20 cm, 40 cm, 60 cm, 80 cm, and 100 cm, respectively.
In the experimental field, a Parshall RBC flume, drainage sensor, rain gauge, and water level recorder were installed as shown in Figure 4. The amount of precipitation was measured using a data-logging rain gauge. During both seasons, the total amount of rainfall was 47 and 21 cm, respectively. The highest rainfall occurred in the month of January during the off season (23 January 2017), which was 8.7 cm day−1. This area experiences a humid equatorial climate with bimodal rainfall patterns largely influenced by the southwest and northeast monsoons. Rainfall is strongly seasonal, with roughly 70% occurring between the months of October and January during the northeast monsoon, while dry months generally fall in February to March and June to August during the southwest monsoon period. However, rainfall distribution is unreliable from January to August; therefore, the crop has to rely to a large extent on irrigation for sustained yields [31,32]. There was very little variation in the day length and temperature in the area. High temperatures are experienced during the dry season, with mean temperature values ranging from 28 °C to 35 °C. Fairly high humidity is experienced, with an average of 77%, which is typical of tropical climates. The amounts of irrigation water and the flow rate were measured using a Parshall RBC flume with an MJK 7070 level sensor with a CR200X logger (SZ-CR200X/7070) whenever an irrigation event occurred. The total amount of irrigation supply was 69.4 and 68.9 cm for both seasons, respectively. During the experimental period, the field water level was maintained from 3 cm to 10 cm depth until one week before harvesting time and every drainage event. The water level was measured using an E-water level sensor. In addition, the irrigation water was reapplied to maintain the crop water requirement when there was no rainfall (dry period) and the water level fell below a maintained depth.
An MJK7060 level sensor (NB-CR200X/7060) was installed at the outlet point to measure the amount of drainage water whenever a runoff event occurs. A concrete sump (70 cm × 50 cm × 70 cm) was constructed inside the outlet point to install the level sensor (NB-CR200X/7060). The drainage data was downloaded on a weekly basis during the study period. The total amount of drainage was 43 and 39 cm for both seasons, respectively. Percolation rate was estimated using four lysimeters. Two open and two closed field lysimeters were installed to estimate deep percolation. In the open condition, the lysimeter gives percolation (P) plus evapotranspiration ET (P + ET), while the closed lysimeter estimates only ET. The percolation rate can be obtained from the subtraction. Finally, the average readings from the four lysimeters (Closed, Opened, and Closed and Opened) were recorded as the daily deep percolation rate. The total amount of percolation water was 19.9 cm for the off season and 17.3 cm for the main season, respectively.
After obtaining the N concentrations by laboratory tests, the solute losses from subsurface/leachate were calculated as follow [33]:
Subsurface   losses   ( kg   ha 1 ) = 0.01 × C × DP
where C = solute concentration (mg/L) and DP = deep percolation depth (mm).

2.2. Hydrus-1D Model

The HYDRUS-1D model described by Simunek et al. [16] was selected to simulate water flow and solute transport in the experimental field. The governing flow and transport equations were numerically solved using a Galerkin-type linear finite element scheme. It has been widely used in applications ranging from water flow to solute and heat transfer in vadose zones.

2.2.1. Model Parameters

Hydrus-1D requires four main sets of processes: Soil hydraulic parameters, solute parameters, solute reaction parameters, and root water uptake.

2.2.2. Estimation of Soil Hydraulic Parameters

The van Genuchten soil hydraulic parameters [34] θ r ,   θ s ,   k s ,   m ,   ,   and   l , which are required by the model, were estimated using the ROSSETA [35] software package provided by Hydrus-1D regarding soil texture. The pore connectivity (l) was assumed to be equal to 0.5 for many soils [16].

2.2.3. Nitrogen Transport and Transformations

The partial differential equations governing one-dimensional advective–dispersive N transport in variably saturated paddy soils were taken as [22]:
Urea (Total Nitrogen, TN):
θ c 1 t = z ( θ D 1 w c 1 z ) q c 1 z μ w , 1 θ c 1
Ammonium nitrate (NH4+-N):
θ c 2 t + ρ s 2 t + a v g 2 t = z ( θ D 2 w c 2 z ) + z ( a ν D 2 g g 2 z ) q c 2 z u w , 2 θ c 2 + u w , 1 θ c 1 + γ s , 2 ρ r a , 2
Nitrate (NO3-N):
θ c 3 t = z ( θ D 3 w c 3 z ) q c 3 z u w , 3 θ c 3 + u w , 2 θ c 2 r a , 3
where C = solute concentration in the liquid phase (mg L−1); S = solute concentration in the solid phase (mg g−1); g = solute concentration in the gas phase (mg L−1); θ = volumetric water content (cm3 cm−3); ρ = dry bulk density (g cm−3); q = volumetric flux density (cm day−1); u w = first-order rate constant for solute in the liquid phase (day−1); u w is the similar first-order rate constant, providing concentrations between individual chain species; γ s is the zero-order rate constant in the solid phase (day−1); r a is the root nutrient uptake (mgL−1 day−1); Dw is the dispersion coefficient for the liquid phase (cm2 day−1); and Dg is the diffusion coefficient (cm2 day−1) for the gas phase. The subscripts of 1, 2, and 3 represent the N species urea, NH4+-N, and NO3-N, respectively. The adsorption isotherm relating s2 and c2 was described using a linear equation of the form:
S2 = kd,2c2
where kd,2 is the distribution coefficient for NH4+-N (L mg−1).
N transformations considered in Equations (2)–(4) were hydrolysis, mineralisation, fixation, volatilisation, nitrification, and denitrification in paddy fields [15,16]. In the present study, hydrolysis, nitrification, and denitrification were all considered as first-order reactions [36], while mineralisation, which is the key N source for rice production [37], was considered as a zero-order process.

2.2.4. Solute Transport and Reaction/Transformation Parameters

For solute transport parameters, molecular diffusion coefficients in free water (Dw) for ammonium and nitrate were 1.5 and 1.64 cm2 day−1, respectively [22]; the molecular diffusion coefficient in air (Da) for NH3 was 18,057.6 cm2 day−1; and the longitudinal dispersity (DL) was equal to 12 cm [38]. It was difficult to measure solute transport and reaction parameters at the laboratory or field scale. Therefore, the values under different soil depths were obtained from published literature and listed in Table 4 [15,22]. Urea hydrolysis was assumed to be equal to 0.74 day−1. According to a previous work [15], mineralisation and immobilisation are the two important solute transformation processes that occur in flooded soils. The constant rate of mineralisation was reported to be 0.0045 day−1 [15,22], whereas the constant rate of nitrification from ammonium to nitrate varied between 0.02 and 0.25 day−1. The denitrification rate coefficient varied between 0.01 and 0.06 day−1. The distribution coefficient (Kd) for NH4+-N for different soil layers were taken from a previous work [39]. However, it was assumed that urea and nitrate were only present in the dissolved phase (Kd = 0 L mg−1) [40].

2.2.5. Initial and Boundary Conditions

For water flow analysis, the initial boundary condition was defined using the observed soil moisture content. The upper boundary condition was defined as the atmospheric boundary condition (BC) by assigning the values of precipitation, evaporation, and irrigation. Due to the saturated condition of the topsoil in the paddy field, a stagnant water depth of 10 cm was considered as the initial condition. The bottom boundary condition was assigned as the free drainage boundary condition since the water table was far below the root zone. For solute transport analysis, the initial concentrations of NO3 and NH4+ were applied as the initial boundary conditions of solute transport.

2.2.6. Model Evaluation Criteria

Checking the capability of the model to predict the parameters of water flow and solute transport in paddy field requires the evaluation of agreement between the Hydrus-1D-predicted NO3 and NH4+ values and the observed field data. In this regard, two statistical procedures were used: the coefficient of determination (R2) and the root mean square error (RMSE), which can be calculated as:
Regression coefficient:
R 2 = 1 i = 1 N ( O i P i ) 2 i = 1 N ( O i O ¯ i ) 2
Root mean square error:
RMSE = i = 1 n ( P i O i ) 2 n
where P is the predicted values, Oi is the observed values, and O ¯ is the mean of the observed values. The optimum values of R2 and RMSE are 1 and 0, respectively.

3. Results

3.1. Model Assessment

In the present study, the model was calibrated using the van Genuchten soil hydraulic parameters θ r ,   θ s ,   m ,   ,   and   l , as well as the coefficient of dispersivity of the soils with the measured field data of the daily concentration of NO3 and NH4+ at 20, 40, 60, 80, and 100 cm soil depths during the off season. During the calibration period, an inverse estimation was implemented for these soil hydraulic parameters. To decrease the uncertainties of parameter optimisation, the saturated hydraulic conductivity was selected for calibration. In solute transport, calibration and validation processes are very complex and often complicated due to various parameters that need to be examined. Therefore, in this current study, solute transport and reaction parameters that needed to be simultaneously determined were obtained from the literature [22] and listed in Table 4. After a successful calibration, the solute data of NO3 and NH4+ observed during the main season under different soil depths were utilised to validate the Hydrus-1D model. Table 5 illustrates the optimised values of the soil hydraulic parameters used during the validation period. The accuracy of the model was assessed in calibration and validation stages by statistical parameters. The relationship between simulated and measured data was evaluated using the coefficient of determination (R2) and the root mean square error (RMSE) as shown in Table 6.
As shown in Table 6, the simulated solute concentrations at different soil depths during the off season were in line with the observed field data. During the calibration period, the model had well predicted the solute concentration at the root zone (0–40 cm), with R2 = 0.84 and RMSE = 0.15 mg/L for NO3 and R2 = 0.70 and RMSE = 0.08 mg/L for NH4. Furthermore, it determined solute concentration below the root zone (60–100 cm) satisfactorily, with R2 = 0.60 and RMSE = 0.11 mg/L for NO3 and R2 = 0.67 and RMSE = 0.06 mg/L for NH4+. Similar results were obtained during the main season for the validation period: within the root zone, R2 = 0.68 and RMSE = 0.32 mg/L for NO3, and R2 = 0.71 and RMSE = 0.15 mg/L for NH4+; below the root zone, R2 = 0.59 and RMSE = 0.23 mg/L for NO3, and R2 = 0.69 and RMSE = 0.10 mg/L for NH4+.

3.2. Dynamic Characteristics of Solutes in Subsurface Water

Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10, Figure 11 and Figure 12 display the comparison between the observed and Hydrus-1D-simulated NO3 and NH4+ data under the 20, 40, 60, 80, and 100 cm soil depths during two consecutive rice growing seasons, respectively. From the information in Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10, it was apparent that within the 0–40 cm soil layer, the Hydrus-1D model accurately predicted the NO3 movement in the soil for both seasons. Moreover, the model satisfactorily simulated N movement below the root zone (60–100 cm), but not as well as that simulated within the root zone. The concentration within the root zone was higher compared to below the root zone due to the direct contact of the top surface layer with the chemical fertiliser applied and stagnating water depth.
Based on the simulated and observed data, the NH4+ concentrations within the root zone were slightly higher than those below the root zone. This may be due to nitrification of NH4+ to NO3 in subsurface water and plant consumption. The calibrated and validated results suggested that the model results of NH4+ at different soil depths mostly matched closely with observed data. Both observed and simulated NH4+ showed similar patterns during both seasons. The NH4+ within the root zone was lower compared to NO3 due to plant uptake. Since rice prefers NH4+ to NO3 as a source of N [5,40], in the presence of both, rice seeding can take up NH4+ faster than NO3 [22,41]. Also, nitrification of NH4+ to NO3 was very fast, explaining the lower concentration of NH4+ in the root zone compared to NO3. The nitrate-N concentrations in leachate water ranged from 1.2 to 6.5 mg/L during the rice growing periods. The mean concentrations of NO3-N at five different depths below the surface were 3.6, 3.3, 2.8, 2.7, and 2.4 mg/L, respectively.
The mean concentrations of ammonium-nitrogen in percolation water were 1.4, 1.3, 1.2, 1.0, and 1.0 mg/L at five different depths below the surface, respectively. It ranged from 0.7 to 2.1 mg/L during the experimental period. During both seasons, the concentration of NO3-N increased after the first fertiliser application and then started to decrease gradually. During the off season, the concentration of NO3-N increased from all leachate samples during the rice maturity stage (8–15 April 2017). The concentration of NO3-N in subsurface water at the depth of 20 cm was the highest among leachate water samples collected at different soil depths, which was due to the progress of nitrification under aerobic conditions at the root zone. It was revealed that two weeks before harvest, NO3 concentration at the 40-cm topsoil layer had remarkably increased for the off season. However, the declining of the NO3 concentration with increasing soil depth reveals that denitrification in the deeper soil layer could be an important pathway of nitrate loss [42]. At the same time, the NO3 concentration in leachate water declined during flooding condition periods due to the anaerobic soil environment for denitrification. The concentration of NH4+-N had slightly increased after the first, second, and third fertiliser applications, respectively. However, the concentration of NH4+-N in leachate water declined with increasing soil depth.

3.3. Leaching Losses of N in Paddy Fields

Figure 13 and Figure 14 present the Hydrus-1D-simulated cumulative NO3 and NH4+ fluxes at the bottom of the soil layer (100 cm) during two rice growing seasons. The daily NO3 and NH4+ fluxes below the root zone were 0.13 and 0.015 kg/ha during the off season and were 0.53 and 0.27 kg/ha during the main season, respectively. The leaching fluxes of NO3 were almost the same during both seasons.
The vertical fluxes of N during the two seasons showed a similar trend. The model predicted that the NO3 and NH4+ fluxes at the bottom 100 cm were 4.7 and 10.1 kg/ha and 1.7 and 5.1 kg/ha for the off and main seasons, respectively. The simulated results of N in the present study was somehow close to those reported previously [43], estimating the total N leaching at the 100-cm soil depth in transplanted paddy rice fields and found to be 5.82 kg/ha during the 2009 rice growing season. In the present study, the overall results of N leaching at the bottom soil were very close to the observed data, which was also similar to previous studies on nutrient leaching losses at the bottom soil layer [39,44]. Furthermore, Dash et al. [24] revealed that the performance of the Hydrus-1D model for simulating N fluxes at the 100-cm soil depth during the two seasons was highly satisfactory.
The total loss of NH4+ was less that of NO3, suggesting that NH4+ was easily adsorbed by soil particles and only small amount leached down to the root zone. However, the NH4+ loads ranged from 1.9 to 3.9 kg/ha, in which 44.4% to 55.6% of percolated NH4+ was accumulated below the 40-cm soil layer. The total vertical flux of NH4+ at the 40-cm soil layer was 5.4 kg/ha for the off season and 4.9 kg/ha for the main season, respectively. The reason for this was that there were various processes that removed NH4+ from the topsoil layers, such as ammonia volatilisation, N plant uptake, and nitrification in the root zone [45]. During heavy raining periods (off season) from mid-January to the end of February, the N leaching was high due to the flooding water conditions. The ratios of NH4+ to NO3 were 33.7% during the off season and 38% during the main season, respectively. Additionally, NO3 below 40 cm was higher due to the process that elevated the concentration of NO3 in the root zone, which was actually a nitrification of NH4+.

4. Discussion

Approximately 45.7% to 46.9% and 53.1 to 55.6% of NO3 was accumulated in the top 40 cm and the below-60 cm soil layers during the two seasons, respectively. Since paddy rice is a shallow-rooted crop, it can only absorb and uptake NO3 up to the 30–40 cm root zone. However, a large amount of NO3 remained below the root zone and was transported downward along with leachate water. The average quantity of NO3 ranged from 4.5 to 6.6 kg/ha, while the total vertical flux of NO3 was 29.3 kg/ha for the off season and 25.3 kg/ha for the main season, respectively. During the off season, the mean cumulative losses of NO3 and NH4+ in subsurface water (20–100 cm) were 2.7 and 1 kg/ha, respectively. During the main season, the mean cumulative losses of NO3 and NH4+ in subsurface water (20–100 cm) were 1.8 and 0.8 kg/ha, respectively. As the paddy plant is shallow-rooted and can take up nutrients from up to 40 cm, it was thus considered that there were nutrient losses below the root zone, that is, below 40 cm. In this regard, the mean cumulative losses of NO3 and NH4+ below 40 cm (40–100 cm) during the off season were 2.4 and 0.9 kg/ha, respectively. During the main season, the mean cumulative losses of NO3 and NH4+ below 40 cm (40–100 cm) were 1.6 and 0.7 kg/ha, respectively. Based on the experimental results, it can be stated that the cumulative losses of NO3 and NH4+ decreased with increasing soil depth.
The observed total leaching loss of N was 34.9 and 27.9 kg/ha during the off and main seasons, respectively. This resulting tendency was almost similar to a previous study [24], in which the authors estimated N leaching below a 120-cm depth of soil and found a total loss of 28.5 kg/ha. However, it was less than those reported by another group [23], who recorded a total N leaching loss of 13.5 kg/ha during a single rice growing season. This was also higher than that reported elsewhere [46], where it was found that 12.4 kg/ha of N was lost through leachate. Perhaps this difference was mainly due to different fertiliser application, irrigation methods, and types of soil. Furthermore, high N leaching loss could be resulted from the high hydraulic conductivity of the plow pan soil [8]. The ratios of the total amount of N loss via leaching to the applied amount of chemical fertiliser were 22% and 17.6% in the off and main seasons, respectively. Yoon et al. [41] discovered the ratios of N losses through the subsurface of the paddy field to be 9.8%, which was a little higher than those reported previously [39,40]. Almost 55% of N leaching losses occurred during early stages, and more than 80% of nitrogen losses were seen in the form of NO3. Similar results were reported by others [22,41]. In this study, N leaching losses through the subsurface were significant. During the rice cultivation period, the observed N loss of the soil layer below 100 cm was 4.8 kg/ha. This was similar to the result obtained by other authors [43], in which an N leaching loss below 100 cm of 3.46 kg/ha was found.

5. Conclusions

Based on solute transport analysis results, it was observed that about 50.3% to 48% of percolated N was accumulated in the top 40-cm soil layer, while 49.7% to 52% of leachate N was lost below the 40-cm soil layer (40–100 cm) during the off and main seasons, respectively. About 85% of N leaching loss was in the form of NO3. NO3 was absorbed by rice roots within the 0–40 cm and denitrificated root zone; however, there was still a large quantity of NO3 that remained below the root zone, which was quickly transported downward along with the leachate water. The NH4+ concentrations in subsurface water were lower than the N and NO3 concentrations due to various processes that removed NH4+ from the topsoil layer (0–40 cm), such as ammonium volatilisation, nitrification, and plant uptake.
A comparison between the Hydrus-1D outputs and observed solute data from the paddy field indicates that the model has made sufficiently accurate predictions. In all figures, it was clear that the outputs of the model were mostly very close to the observed field experiment. The results revealed that the model had accurately predicted solute fluxes within the root zone compared to the below root zone results for both seasons. On other hand, the results confirmed that as the soil depth in the experiment increases, the difference between the model-predicted and field-observed data increased. These differences may be due to the results of rapid movement of water under different depths, without sufficient time for reaction between the soil and the solutes [47]. Another important reason was that the time taken by the solutes to reach up to 80–100 cm soil depth may be more than those within the root zone. This was mainly because of the decrease in saturated hydraulic conductivity [44]. Due to the conventional overflooding of the soil in paddy fields during the rice growing period, the soil within the root zone dispersed more as hydraulic conductivity decreased. Previously, researchers [18,22] confirmed that the Hydrus-1D model could be a successful tool for predicting N transport and transformation under both transplanted paddy rice (TPR) and direct seeded rice field (DSR) conditions. In the present study, the overall results revealed that Hydrus-1D simulations were reasonable and effective for simulating the solute transport in flooded paddy rice experimental fields.

Author Contributions

A.A.M. and M.R.K. designed the experiment and wrote the paper, H.H.M. analyzed the data, M.A.M.S., M.A.b.M.Z., A.W. and H.b.C.M. contributed, material and numerical analysis.

Funding

This research was funded by Ministry of Science, Technology and Innovation (MOSTI) grant number “5450774” And Putra-Grant, grant number “9411100”.

Acknowledgments

The authors wish to thank the Ministry of Sciences, Technology and Innovation (MOSTI) and University Putra Malaysia (UPM) for sponsoring this research. This research is funded by the Ministry of Sciences, Technology and Innovation (MOSTI) under e-science grant number escience5450774 and Putra-Grant 9411100.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location of the experimental plot at the Tanjung Karang rice irrigation scheme (TAKRIS), Malaysia.
Figure 1. Location of the experimental plot at the Tanjung Karang rice irrigation scheme (TAKRIS), Malaysia.
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Figure 2. Porous cup soil water samplers installed at 20 cm, 40 cm, 60 cm, 80 cm, and 100 cm in a paddy field.
Figure 2. Porous cup soil water samplers installed at 20 cm, 40 cm, 60 cm, 80 cm, and 100 cm in a paddy field.
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Figure 3. Monitoring daily percolation rate using field lysimeters in the experimental plot.
Figure 3. Monitoring daily percolation rate using field lysimeters in the experimental plot.
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Figure 4. Layout of the equipment installation for the field investigation of water balance components in a paddy field.
Figure 4. Layout of the equipment installation for the field investigation of water balance components in a paddy field.
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Figure 5. Comparison between simulated (Sim) and observed (Obs) NO3 concentrations within the root zone (0–40 cm) during the off season.
Figure 5. Comparison between simulated (Sim) and observed (Obs) NO3 concentrations within the root zone (0–40 cm) during the off season.
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Figure 6. Comparison between simulated and observed NO3 concentrations below the root zone (60–100 cm) during the off season.
Figure 6. Comparison between simulated and observed NO3 concentrations below the root zone (60–100 cm) during the off season.
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Figure 7. Comparison between simulated and observed NH4+ concentrations within the root zone (0–40 cm) during the off season.
Figure 7. Comparison between simulated and observed NH4+ concentrations within the root zone (0–40 cm) during the off season.
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Figure 8. Comparison between simulated and observed NH4+ concentrations below the root zone (60–100 cm) during the off season.
Figure 8. Comparison between simulated and observed NH4+ concentrations below the root zone (60–100 cm) during the off season.
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Figure 9. Comparison between simulated and observed NO3 concentrations within the root zone (0–40 cm) during the main season.
Figure 9. Comparison between simulated and observed NO3 concentrations within the root zone (0–40 cm) during the main season.
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Figure 10. Comparison between simulated and observed NO3 concentrations below the root zone (60–100 cm) during the main season.
Figure 10. Comparison between simulated and observed NO3 concentrations below the root zone (60–100 cm) during the main season.
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Figure 11. Comparison between simulated and observed NH4+ concentrations within the root zone (0–40 cm) during the main season.
Figure 11. Comparison between simulated and observed NH4+ concentrations within the root zone (0–40 cm) during the main season.
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Figure 12. Comparison between simulated and observed NH4+ concentrations below the root zone (60–100 cm) during the main season.
Figure 12. Comparison between simulated and observed NH4+ concentrations below the root zone (60–100 cm) during the main season.
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Figure 13. Simulated cumulative leaching fluxes of NO3 and NH4+ at the bottom of the soil layer (100 cm) during the off season.
Figure 13. Simulated cumulative leaching fluxes of NO3 and NH4+ at the bottom of the soil layer (100 cm) during the off season.
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Figure 14. Simulated cumulative leaching fluxes of NO3 and NH4+ at the bottom of the soil layer (100 cm) during the main season.
Figure 14. Simulated cumulative leaching fluxes of NO3 and NH4+ at the bottom of the soil layer (100 cm) during the main season.
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Table 1. Physical properties of the paddy soil in the experimental field.
Table 1. Physical properties of the paddy soil in the experimental field.
Depth (cm)Textural ClassBulk Density (g cm−3)Saturated Hydraulic Conductivity Ks (cm d−1)
0–20Clay loam1.288.31
20–40Clay loam1.337.58
40–60Clay loam1.387.22
60–80Clay1.446.98
80–100Clay1.426.54
Table 2. Chemical properties of the paddy soil in the experimental field.
Table 2. Chemical properties of the paddy soil in the experimental field.
Depth (cm)pHEC (ms/cm)Organic Carbon (g kg −1)Total Nitrogen (%)Extractable P (ug/g)Extractable K (ug/g)
0–205.440.403.70.2713.7293.7
20–405.580.503.90.2412.18201
40–605.580.802.60.1711.95215
60–805.500.702.50.1711.56211
80–1005.500.502.40.1611.17222
Note: EC = electrical conductivity of the soil.
Table 3. Agricultural activities in the experimental field during two rice growing seasons.
Table 3. Agricultural activities in the experimental field during two rice growing seasons.
SeasonsDateAgricultural Activities
Off season15 Jan 2017Rice broadcasting
29 Jan 2017First fertiliser application; urea 40 kg/ha
12 Feb 2017Second fertiliser application; 17.5:15.5:10 NPK = 80 kg/ha
11 Mar 2017Third fertiliser application; 17:3:25:2 mgo, NPK + 2 mgo = 70 kg/ha
13 Apr 2017Field dried (drainage period)
17 Apr 2017Harvesting
Main season15 Jul 2017Rice broadcasting
25 Jul 2017First fertiliser application; urea 40 kg/ha
01 Aug 2017Second fertiliser application; 17.5:15.5:10 NPK = 80 kg/ha
16 Aug 2017Third fertiliser application; 17:3:25:2 mgo, NPK + 2 mgo = 70 kg/ha
14 Oct 2017Field dried (drainage period)
17 Oct 2017Harvesting
Note: N = Nitrogen; P = Phosphorous; K = Potassium and mgo = magnesium oxide.
Table 4. Solutes transported and reaction parameters obtained by model calibration [22].
Table 4. Solutes transported and reaction parameters obtained by model calibration [22].
Soil Depth (cm)km (day−1)Kd (L mg−1)Kn (day−1)Kdn (day−1)Kh (day−1)
0–200.00451.80.250.050.74
20–40-1.50.220.06-
40–60-1.20.140.04-
60–80-1.00.040.02-
80–100-1.00.020.01-
Note: km is the comprehensive production rate of NH4+-N that represents mineralisation; Kd is the distribution coefficient for NH4+-N; Kn is the nitrification rate; Kdn is the denitrification rate; and Kh is the urea hydrolysis rate.
Table 5. Optimised values of soil hydraulic parameters.
Table 5. Optimised values of soil hydraulic parameters.
Soil Depth (cm)Soil Type θ r (cm3 cm−3) θ s (cm) n LKs (cm day−1)
0–20Clay loam0.07920.44180.01581.41450.510.25
20–40Clay loam0.07920.44180.01581.41450.59.34
40–60Clay loam0.07920.44180.01581.41450.58.55
60–80Clay0.09820.45880.01501.25290.58.1
80–100Clay0.09820.45880.01501.25290.57.2
Table 6. Statistical parameters for the evaluation of the model.
Table 6. Statistical parameters for the evaluation of the model.
SeasonDepth (cm)Solute ParametersR2RMSE (mg/L)
Off season (calibration period)20–40NO30.840.15
60–1000.590.11
20–40NH4+0.700.08
60–1000.670.06
Main season (validation period)20–40NO30.680.32
60–1000.590.23
20–40NH4+0.710.15
60–1000.690.10

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Mo’allim, A.A.; Kamal, M.R.; Muhammed, H.H.; Mohd Soom, M.A.; Mohamed Zawawi, M.A.b.; Wayayok, A.; Che Man, H.B. Assessment of Nutrient Leaching in Flooded Paddy Rice Field Experiment Using Hydrus-1D. Water 2018, 10, 785. https://doi.org/10.3390/w10060785

AMA Style

Mo’allim AA, Kamal MR, Muhammed HH, Mohd Soom MA, Mohamed Zawawi MAb, Wayayok A, Che Man HB. Assessment of Nutrient Leaching in Flooded Paddy Rice Field Experiment Using Hydrus-1D. Water. 2018; 10(6):785. https://doi.org/10.3390/w10060785

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Mo’allim, Abdikani Abdullahi, Md Rowshon Kamal, Hadi Hamaaziz Muhammed, Mohd Amin Mohd Soom, Mohamed Azwan b. Mohamed Zawawi, Aimrun Wayayok, and Hasfalina Bt. Che Man. 2018. "Assessment of Nutrient Leaching in Flooded Paddy Rice Field Experiment Using Hydrus-1D" Water 10, no. 6: 785. https://doi.org/10.3390/w10060785

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