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Model development for nutrient loading from paddy rice fields
Agricultural Water Management 62 (2003) 1–17
Model development for nutrient loading from paddy rice fields Sang-Ok Chunga,*, Hyeon-Soo Kimb, Jin Soo Kimc a
Department of Agricultural Engineering, Kyungpook National University, Taegu 702-701, South Korea b Rural Research Institute, KARICO, Ansan 425-170, South Korea c Department of Agricultural Engineering, Chungbuk National University, Cheongju 361-763, South Korea Accepted 6 January 2003
Abstract The objective of this study was to develop a model groundwater loading effects of agricultural management systems in paddy fields (GLEAMS-PADDY) to predict nutrient loading to surface waters from paddy rice fields. This model was developed by modifying the GLEAMS model that is used for uplands. The GLEAMS-PADDY model is composed of hydrology and chemical sub-models. In the hydrology sub-model of the GLEAMS-PADDY model, the ponded depth routing method was used to handle ponded water condition of paddy fields. With the chemical sub-model, the soil was assumed to be saturated, and the soil profile in the root zone was divided into oxidized and reduced zones. Field experiments were performed at the Soro region, Chungbuk Province of South Korea, from May to September 1999 and 2000. Field data included rainfall amount, irrigation water input, drainage water output, and percolation. Concentrations of total nitrogen (T-N) and total phosphorus (T-P) in irrigation water, rain water, ponded water, surface drainage water, and percolated water were analyzed. The coefficient of determination (r2) and root mean square error (RMSE) between observed and model predicted surface drainage volume relative to the 1:1 line were 0.66 and 66.6 mm, respectively. The r2 and RMSE between observed and predicted T-N concentrations in the ponded water were 0.74 and 1.43 mg l1, respectively. The same statistics for T-P were 0.64 and 0.03 mg l1, respectively. The observed and predicted total T-N loadings from surface drainage were 72.4 and 72.6 kg ha1, respectively. The observed and predicted total T-P loadings were 1.70 and 1.69 kg ha1, respectively. The r2 and RMSE between observed and predicted T-N concentrations in the surface drainage water were 0.79 and 2.71 mg l1, respectively. The same statistics for T-P were 0.73 and 0.04 mg l1, respectively. Comparisons of observed and model predicted water balance components, nutrient concentrations, and loading rates exhibited reasonably good agreement. Hence, the GREAMS-PADDY model can be * Corresponding author. Tel.: þ82-53-950-5734; fax: þ82-53-950-6752. E-mail address: [email protected] (S.-O. Chung).
0378-3774/03/$ – see front matter # 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0378-3774(03)00078-7
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used to predict nutrient loading from paddy fields and to develop BMPs in paddy rice culture to reduce surface water pollution from paddy field drainage water. # 2003 Elsevier Science B.V. All rights reserved. Keywords: Non-point source pollution; Paddy rice; Runoff; Nutrient loading; Model; GLEAMS-PADDY
1. Introduction The total agricultural land area in Korea is 1,899,000 ha, of which 1,153,000 ha are paddy rice fields, which have a saturated root zone and a ponded soil surface. The other agricultural lands are upland fields. A total of 1,083,000 ha of paddy fields were cultivated in Korea in 2001. The growing season for rice (Oryza sativa) in Korea is from April to October. Transplanting is the most popular method of rice cultivation. However, about 10% of the total paddy field area is directly seeded every year. Non-point source pollution from paddy fields is a great concern in Korea. In particular, N and P from agricultural land cause eutrophication in lakes and streams. Previous studies have demonstrated that farmers tend to over-fertilize paddy fields in Korea. The recommended application rate for N is 110 kg ha1, while the actual application rate is 159 kg ha1. The recommended application rate for P is 48 kg ha1, while the actual application rate is 78 kg ha1. The management of drainage water quality for agricultural non-point source pollution has received much attention during the last three decades. Werner and Wodsak (1995) studied the role of non-point nutrient sources in water in Germany. In FL, USA, agricultural best management practices (BMPs) were developed to reduce non-point source pollution of Lake Okeechobee (Anderson and Flaig, 1995). Non-point source pollution from paddy fields is one of the major problems in water quality management in Korea. One of the most frequently used models in agricultural drainage water quality is GLEAMS. The GLEAMS was developed by Leonard et al. (1987), and is modified from the CREAMS (Knisel, 1980) to include the plant root zone. Many studies have used GLEAMS, and include modifications and improvements to the model (Leonard and Knisel, 1989; Leonard et al., 1990; Knisel and Turtola, 1999). Alexander (1988) developed the agricultural drainage and pesticide transport (ADAPT) model, which incorporates subsurface drainage and irrigation parts of the DRAINMOD into the GLEAMS. Chung et al. (1992) improved the ADAPT model by modifying the hydrology sub-model. Yoon et al. (1994) applied GLEAMS to predict nutrient losses from land application of poultry litter. Bakhsh et al. (2000) predicted NO3–N losses through subsurface drainage using GLEAMS, and Bakhsh and Kanwar (2001) simulated tillage effects on non-point source pollution from agricultural lands using GLEAMS. Tucker et al. (2000a,b) and Stone et al. (2001) coupled GLEAMS and Riparian Ecosystem Management Model (REMM) to model the riparian ecosystem management. Recently, GLEAMS version 3.0 was developed by Knisel and Davis (2000) to address Y2K software problems and to expand the model’s pesticide database. In this study, GLEAMS version 3.0 was modified to develop the GLEAMS-PADDY model, which is then used to predict nutrient loading to surface water from paddy rice fields. The developed
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model was validated using field data. The GLEAMS-PADDY model can be used to evaluate BMPs in paddy fields, with the goal of identifying practices that are sustainable, and have minimal water pollution effects in rural areas.
2. Model description The GLEAMS model has three sub-models: hydrology, erosion/sediment and chemicals. The chemical sub-model has two parts: pesticides and nutrients. In the GLEAMS-PADDY model, the hydrology and chemical sub-models were modified to take into account the ponded paddy field conditions. In a paddy field, which has a nearly level soil surface with ponded water, and is usually surrounded by about 20 cm high berms, soil erosion hardly occurs. Therefore, erosion/sediment is very small and occurs mostly before transplanting. 2.1. Hydrology In upland fields water is supplied onto the soil surface through either rainfall or irrigation. Water then flows out in the form of surface runoff, infiltration/percolation, and evapotranspiration (ET) with no inundation. On the other hand, in paddy fields, water is ponded on the soil surface. The water balance equation in a paddy field (Fig. 1) is: DS ¼ ðR þ G1 þ DR1 Þ ðET þ G2 þ DR2 Þ
(1)
where R is the rainfall (cm), ET the evapotranspiration (cm), DR1 and DR2 surface water inflow and outflow (cm), respectively, G1 the subsurface inflow, G2 the lateral and vertical subsurface outflow (cm), and DS the change in storage of the control volume (cm). In GLEAMS, surface runoff is computed using the soil conservation service (SCS) curve number method for upland fields. In the GLEAMS-PADDY model, however, the ponded water depth routing method is used to determine daily ponded depth on the soil surface.
Fig. 1. Schematics of water balance components in a paddy field.
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Ponded water depth in a paddy plot can be determined by: Wd ¼ ðWd1 þ Rd þ IRd Þ ðFd þ ETd þ DRd Þ
(2)
where W is the ponded depth (cm), R the rainfall (cm), IR the irrigation amount (cm), F the infiltration (cm), ET the evapotranspiration (cm), DR the surface drainage (cm), and the subscript d date. Surface drainage is controlled by the outlet height of the paddy plot and determined by: DRd ¼ Wd OHd ; DRd ¼ 0:0;
if Wd > OHd if Wd OHd
(3)
where DR is surface drainage (cm), W the ponded depth (cm), OH the outlet height (cm), and the subscript d date. Ponded water above the outlet sill is drained. The outlet height is an important management parameter in maintaining the optimum ponded depth for rice culture. In GLEAMS, the potential ET is computed using the Penman–Monteith method. In the GLEAMS-PADDY model, any appropriate method can be used. Based on a preliminary study, the FAO corrected Blaney–Criddle method (Allen and Pruitt, 1992) was selected to compute the potential ET in the GLEAMS-PADDY model. Soil water percolates to a lower layer when the water content of a layer is greater than the field capacity. In paddy fields, soil layers in the root zone are assumed to be saturated because of the ponded soil surface. Therefore, in the GLEAMS-PADDY model, soil water percolates based on Darcy’s law. 2.2. Erosion/sediment Since paddy plots are surrounded by berms, sediment transport is much smaller than that in upland fields. Therefore, in the GLEAMS-PADDY model, erosion/sediment from paddy fields is ignored. 2.3. Chemicals In GLEAMS-PADDY, the chemical sub-model presently addresses only nutrients. The N and P, the main causes of eutrophication in the lakes and streams of Korea, are included in the model. Considering ponded conditions, the root zone soil layer is divided into two parts, a 1 cm thick surface oxidized layer, and reduced layers below. The oxidized and reduced conditions are important factors controlling chemical reactions of the nutrients. The N and P turnover processes in GLEAMS for upland fields are detailed in Knisel (1993). The N and P turnover processes in reduced state in the saturated soil layer of paddy fields are different from those in the unsaturated condition in the upland fields. Ammonification and nitrification do not occur, and denitrification occurs in N turnover in the saturated soil layer. Mineralization of P does not occur in the saturated soil layer. 2.4. Nitrogen Nitrogen input into paddy fields occurs through chemical fertilizer application, rainfall, and irrigation water. Nitrogen output occurs through surface drainage, infiltration/percola-
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tion, plant uptake, and denitrification. Nitrogen is divided into ammonia N and nitrate N. The daily nitrogen balance equation is as follows: NO3d ¼ ðNO3d1 þ NITd þ RFNOd þ IRNOd Þ ðDNId þ RONO3d þ PERCNOd þ UPNOd Þ
(4)
NH4d ¼ ðNH4d1 þ FENHd þ MNd Þ ðNITd þ RONH4d þ PERCNHd þ SEDNHd þ UPNHd þ VOLNd Þ 1
(5) 1
where NO3 is the nitrate–N (kg ha ), NIT the nitrification (kg ha ), and RFNO and IRNO the NO3–N dissolved in rainfall and irrigation water, respectively (kg ha1), DNI the denitrification (kg ha1), RONO3 and PERCNO are surface runoff and percolated NO3–N, respectively (kg ha1), UPNO the plant uptake NO3–N (kg ha1), NH4 the ammonia–N (kg ha1), FENH the fertilized NH4–N (kg ha1), MN the ammonification (kg ha1), NIT the nitrification from NH4–N (kg ha1), RONH4 and PERCNH the surface runoff and percolated NH4–N, respectively (kg ha1), SEDNH the surface runoff NH4–N by erosion (kg ha1), UPNH the plant uptake NH4–N (kg ha1), VOLN the volatilization (kg ha1), and the subscript d date. It is assumed that dissolved N in rainfall and irrigation water is NO3–N and that N loss by erosion and volatilization occurs in NH4–N. The N concentration in the water phase in the 1 cm thick surface oxidized soil layer is given by (Knisel, 1993): Cw ¼
Cav b 1 þ bKd
(6)
where Cw is N concentration in the water phase in the surface soil layer, Cav the available N concentration in the surface soil layer, b the extraction coefficient, and Kd the partitioning coefficient. The N concentration in the surface layer available for runoff and infiltration, Cav is defined by (Knisel, 1993): ðF AbstÞ Cav ¼ C1 exp (7) Kd1 ð1 POR1 =2:65Þ þ POR1 where C1 is N mass divided by soil mass (m g1), F the infiltration (cm), Abst the initial abstraction (cm), Kd1 the partitioning coefficient in the surface soil layer, and POR1 the porosity of the surface soil layer. Soluble ammonia concentration of water in the surface soil layer is obtained from Eqs. (6) and (7) as follows (Knisel, 1993): AMON1 103 ðF AbstÞ CNH4 W ¼ exp CNHKD1 ð1 POR1 =2:65Þ þ POR1 SOILMS1 b (8) 1 þ bCNHKD1 where CNH4W is NH4–N concentration dissolved in the surface layer soil water (mg l1), AMON1 the NH4–N in the surface soil layer (kg ha1), SOILMS1 the soil mass in the
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surface soil layer (t ha1), CNHKD1 the partitioning coefficient in the surface soil layer, and other symbols are as previously defined. The distribution coefficient of NH4–N is a function of the clay content of the soil: CNHKDi ¼ 1:34 þ 0:083 CLi
(9)
where CNHKD is the distribution coefficient, CL the clay content (%), and the subscript i the soil layer number. The NO3–N concentration in ponded water is calculated by taking a weighed average of NO3–N concentrations in ponded water of the previous day, soil water in the surface soil layer, irrigation water, and rainfall. The NH4–N concentration in ponded water is calculated by diluting the ponded water concentration with irrigation and rainwater, since no NH4–N exists in the irrigation or rainwater. The concentrations of NO3–N and NH4–N in the ponded water are calculated by: ðPNO3 Wd1 Wd1 Þ þ ðCNO3 Wd POR DÞ PNO3 Wd ¼ PNH4 Wd ¼
þðCNO3 IRd IRd Þ þ ðCNO3 Rd Rd Þ Wd1 þ ðPOR DÞ þ IRd þ Rd ðPNH4 Wd1 Wd1 Þ þ ðCNH4 Wd POR DÞ Wd1 þ ðPOR DÞ þ IRd þ Rd
(10) (11)
where PNO3W and PNH4W are concentrations of NO3–N and NH4–N in the ponded water, respectively (mg l1), CNO3W and CNH4W the concentrations of NO3–N and NH4–N in the soil water of the surface soil layer, respectively (mg l1), POR the porosity of the surface soil layer, D the thickness of the surface soil layer (cm), W the ponded depth (cm), IR the irrigation (cm), R the rainfall (cm), and the subscript d date. Nitrogen loading by surface drainage can be calculated by: RONO3 ¼ 0:1 ðPNO3 WÞ ðDRÞ
(12)
RONH3 ¼ 0:1 ðPNH3 WÞ ðDRÞ
(13)
where RONO3 and RONH4 are loadings of NO3–N and NH4–N, respectively (kg ha1), PNO3W and PNH4W the concentrations of NO3–N and NH4–N in the ponded water, respectively (mg l1). Total N loading by surface drainage is the sum of the NO3–N and NH4–N loads. 2.5. Phosphorus Phosphorus input to paddy fields occurs through chemical fertilizer application and irrigation water, while output occurs through surface drainage and plant uptake. The daily phosphorus balance equation is given by: TPd ¼ ðTPd1 þ FEPd þ MNd Þ ðROLPd þ PERCLPd þ SEDLPd þ UPPd Þ 1
1
(14)
where TP is total P (kg ha ), FEP the fertilized P (kg ha ), MN the immobilized P (kg ha1), ROLP the surface-drained P (kg ha1), PERCLP the percolated P (kg ha1), SEDLP the P outflow by sediment (kg ha1), UPP the plant uptake P (kg ha1), and the subscript d date.
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The P concentration in ponded water is calculated in the same manner as NH4–N: PPLABWd ¼
ðPPLABWd1 Wd1 Þ þ ðCPLABWd POR1 DÞ Wd1 þ ðPOR1 DÞ þ IRd þ Rd
(15)
where PPLABW is P concentration in ponded water (mg l1), CPLABW the P concentration in water of the surface soil layer (mg l1), POR1 the porosity of the surface soil layer, D the thickness of the surface soil layer (cm), W the ponded depth (cm), IR the irrigation (cm), R the rainfall (cm), and the subscript d date.
3. Model validation The GLEAMS-PADDY model was validated using field data. Water and nutrient balance components were calculated by the model, after which the observed and model predicted outputs were compared. To run the model, both a parameter file and a weather data file are required. The parameter file of the hydrology sub-model includes field size, rooting depth, leaf area index, soil texture, soil water properties, type of crop, planting date, and harvesting date. The parameter file of the nutrient sub-model includes N concentration in rainfall and irrigation water, P concentration in irrigation water, and date and amount of fertilizer application. The output files have water and nutrient balance components. Since parameter values are readily available from field experiments, model calibration is not necessary. Sensitivity analysis of the parameters was not performed in this study. 3.1. Field experiments Field experiments were performed in the Soro region, Chungbuk Province of South Korea, from May to September in 1999 and 2000. This region is 51 ha in area with 100 m 100 m standard sized paddy plots (Fig. 2). The USDA soil classification indicates loamy soil in this area. The rice was transplanted in early May and harvested in early October. Field data included rainfall amount, irrigation water input, drainage water output, and percolation rate. Irrigation water was diverted from a stream. Flow rates were obtained by using a stage–discharge relationship and hourly records from water level loggers. Infiltration rings with 20 cm diameter were installed down to 30 cm deep. PVC pipes (75 mm diameter) with perforations near the bottom were buried to 1 m deep, to collect percolated water samples. Sampling bottles and a bailer were used to sample surface water and percolated water, respectively. Concentrations of T-N and T-P in the irrigation water, rain water, ponded water, surface drainage water, and percolated water were analyzed. In addition, fertilizer application dates and amounts were observed. Flow measurements (at locations with no level logger) and water sample collections were made at 5-day intervals from mid-May to mid-June, when nutrient concentrations varied significantly, and at 10-day intervals during other periods. The T-N and T-P concentrations in samples were determined using the ultraviolet
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Fig. 2. Layout and locations of the field observation point.
spectrophotometric method and ascorbic acid reduction spectrophotometric method, respectively. Field-applied fertilizer amounts (Table 1) were observed at the site. Nitrogen was applied at three growth stages: pre-transplanting, tillering, and panicle. The total amount of N applied was 164.5 kg ha1 in 1999, and 179.6 kg ha1 in 2000. Phosphorus was applied once, in the amounts of 21.4 kg ha1 in 1999 and 23.5 kg ha1 in 2000. Table 1 Fertilizer application rate (kg ha1) Growth stage and application method
Timea
Nitrogen
Phosphorus
1999
2000
1999
2000
21.4
23.5
21.4
23.5
Pre transplanting (incorporated)
May/E
40.6
55.6
Tillering (broadcasted)
May/L June/E June/M
32.5 50.8
35.4
July/M August/E
29.0 11.6
24.4 12.0
164.5
179.6
Panicle (broadcasted) Total a
E: early, M: middle, L: late.
52.2
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Table 2 Observed nutrient concentrations in the rainfall (mm, mg l1) Year
Date
Rainfall
T-N
T-P
1999
5/26 6/16 6/23 7/28 7/30 8/3 9/5 9/10
0.7 67.3 106.7 39.4 17.0 35.2 62.7 71.7
1.78 1.65 1.63 0.64 0.68 0.16 0.32 0.13
0.008 0.017 0.017 0.009 0.011 0.011 0.028 0.006
Total/mean
400.7
0.89
0.015
15.5 20.3 0.7 25.4 53.8 6.8 31.1 18.5
1.38 0.87 1.71 0.45 0.51 0.82 0.21 0.92
0.039 0.013 0.080 0.047 0.015 0.003 0.006 0.036
172.1
0.86
0.030
0.88
0.101
2000
5/26 6/26 7/15 7/20 8/4 8/21 8/27 9/8 Total/mean
Mean
The amount of rainfall in 2000 was much smaller than that in 1999. T-N concentrations in rainfall (Table 2) ranged between 0.13 and 1.78 mg l1, and rainfall T-P concentrations were below 0.05 mg l1. Weighted average concentrations were 0.88 mg l1 T-N, and 0.101 mg l1 T-P. Nutrient concentrations in the irrigation and drainage water were high in the early stage of the experiment (Table 3) and decreased as time passed. The high concentrations in the irrigation water were caused by the water source (stream water), which was significantly affected by nutrient runoff from paddy fields in upstream basins during the fertilizing period. The high concentrations in the drainage water in May and June were affected by the outflow of the ponded water with high concentrations due to nature of irrigation water and fertilization. 3.2. Hydrology Surface drainage from paddy fields causes pollution of streams and lakes in Korea, and therefore is an important attribute to model. A comparison of observed and model predicted surface drainage was made. Total observed and predicted surface drainage amounts were 1927 and 1970 mm, respectively, in 1999 and 1858 and 2045 mm, respectively, in 2000. Data were distributed on both sides of the 1:1 line in a scatter plot of observed versus predicted surface drainage for each 10-day period of the experiments (Fig. 3). Statistical analysis indicates r2 and RMSE values of 0.66 and 66.6 mm, respectively.
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Table 3 Observed nutrient concentrations in the irrigation and drainage water (mg l1) Year
1999
a b
Date
5/5 5/14 5/20 5/25 5/31 6/5 6/10 6/15 6/25 7/5 7/15 7/26 8/5 8/16 8/28 9/9 Meanb
T-N
T-P
Irrigation water
Drainage water
Irrigation water
Drainage water
3.87 2.86 4.77 3.13 2.42 3.36 5.92 2.23 3.15 1.63 0.76 2.44 –a 2.59 1.72 1.80 2.78
5.10 3.92 3.47 1.95 9.38 11.66 8.65 4.16 2.82 1.06 0.84 1.85 2.12 0.88 1.10 1.12 2.35
0.18 0.09 0.20 0.13 0.07 0.07 0.06 0.06 0.13 0.05 0.05 0.08 –a 0.05 0.07 0.12 0.10
0.28 0.16 0.13 0.09 0.13 0.08 0.06 0.09 0.10 0.04 0.05 0.03 0.03 0.04 0.06 0.09 0.09
Not observed. Flow weighted mean.
Fig. 3. Comparison of the observed and predicted surface drainage for each 10-day period.
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3.3. Nutrients Comparisons were made of observed and predicted nutrient concentrations and loadings in ponded water, surface drainage water, and percolated water. Observed and predicted nutrient loadings below the root zone were very small. Model predicted T-N concentrations were higher than observed values in ponded water (Fig. 4). The r2 and RMSE values were 0.74 and 1.43 mg l1, respectively, for T-N concentration, and 0.64 and 0.03 mg l1, respectively, for T-P concentration. Model predicted T-N concentrations in ponded water were higher than observed values in 1999, while the model predicted T-N concentrations agreed with observed values in 2000 (Fig. 5). A comparison of observed and predicted nutrient concentrations in surface drainage water in 1999 also was made. The observed T-N and T-P concentrations in surface drainage water were 3.76 and 0.09 mg l1, respectively. The r2 and RMSE values for T-N were 0.79 and 2.71 mg l1, respectively, and those for T-P were 0.73 and 0.04 mg l1, respectively. Model predicted T-N and T-P concentrations are higher than observed concentrations (Figs. 6 and 7). A comparison of observed and predicted nutrient loading from surface drainage in 1999 also was made. Concerning water pollution, the total nutrient loading is more important than nutrient concentration of the surface drainage water. Observed and predicted T-N loadings from the surface drainage were 72.4 and 72.6 kg ha1, respectively, and corresponding T-P loadings were 1.70 and 1.69 kg ha1, respectively. The r2 and RMSE between observed and predicted T-N concentrations in the surface drainage water were 0.79 and 2.71 mg l1, respectively. The corresponding statistics for T-P were 0.73 and 0.04 mg l1, respectively. These loadings are among the main causes of eutrophication in lakes and rivers downstream, and may also cause some health problems for water that is in contact with people (for example, if the pollution causes toxic algal blooms or contributes organic precursors for trihalomethane development in drinking water). In particular, high concentrations of T-P are linked to these problems. The comparisons of observed and model predicted water balance and nutrient components indicate reasonably good agreement. Hence, the GREAMS-PADDY model can be used to predict nutrient loading from paddy fields.
4. Summary and conclusions The objective of this study was to develop a GLEAMS-PADDY model to predict nutrient loading to surface waters from paddy rice fields. The model was developed by modifying the GLEAMS model of uplands. The GLEAMS-PADDY model is composed of hydrology and chemical sub-models. In the hydrology sub-model of the GLEAMS-PADDY model, the ponded depth routing method was used to handle the ponded water condition of paddy fields. In the chemical submodel, the soil was assumed to be saturated, and the soil profile in the root zone was divided into oxidized and reduced zones.
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Fig. 4. Comparison of the observed and predicted nutrient concentrations in the ponded water.
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Fig. 5. Comparison of the observed and predicted daily nutrient concentrations in the ponded water.
Field data from the Soro region, Chungbuk Province of the South Korea were collected from May to September 1999 and 2000, and used for model validation. Field data included rainfall amount, irrigation water input, drainage water output, and percolation rate. Concentrations of T-N and T-P in the irrigation water, rain water, ponded water, surface drainage water, and percolated water were analyzed. The results obtained from this study are as follows: 1. The r2 and RMSE between the observed and model predicted surface drainage water were 0.66 and 66.6 mm, respectively. 2. The r2 and RMSE between observed and predicted T-N concentrations in ponded water were 0.74 and 1.43 mg l1, respectively. The corresponding statistics for T-P were 0.64 and 0.03 mg l1, respectively. 3. The observed T-N and T-P concentrations in surface drainage water were 3.76 and 0.09 mg l1, respectively. The r2 and RMSE values for T-N were 0.79
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Fig. 6. Comparison of the observed and predicted nutrient concentrations in the surface drainage water.
and 2.71 mg l1, respectively, and those for T-P were 0.73 and 0.04 mg l1, respectively. 4. The observed and model predicted T-N loadings from the surface drainage in 1999 were 72.4 and 72.6 kg ha1, respectively. The observed and model predicted total T-P
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Fig. 7. Comparison of the observed and predicted daily nutrient concentrations in the surface drainage water.
loadings from the surface drainage in 1999 were 1.70 and 1.69 kg ha1, respectively. The r2 and RMSE between the observed and predicted T-N concentrations in the surface drainage water were 0.79 and 2.71 mg l1, respectively. The corresponding statistics for T-P were 0.73 and 0.04 mg l1, respectively.
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5. The observed and predicted nutrient loading below the root zone was very small. The comparisons of observed and model predicted water balance components and nutrient concentrations indicated reasonably good agreement. Hence, the GREAMSPADDY model can be used to predict nutrient loading from paddy fields. This model can be applied in developing BMPs in paddy rice culture and can help to reduce water pollution from paddy field drainage water. By using this model farmers can reduce fertilizer application and can pick the best timing of the fertilizer application with respect to rainfall events. In addition, farmers can identify the best ponded water depth management technique based on the model simulation of various scenarios.
Acknowledgements This research was funded in part by the Ministry of Agriculture and Forestry through the Agricultural Research and Development Promotion Center, Seoul, Korea. References Alexander, C., 1988. ADAPT-A model to simulate pesticide movement into drain tiles. M.S. Thesis. Agricultural Engineering Department, The Ohio State University, Columbus, OH. Allen, R.G., Pruitt, W.O., 1992. FAO-24 reference evapotranspiration factors. Am. Soc. Civil Eng. J. Irrig. Drain. Eng. 117, 758–773. Anderson, D.L., Flaig, E.G., 1995. Agricultural best management practices and surface water imporvement and management. Water Sci. Technol. 31, 109–116. Bakhsh, A., Kanwar, R.S., Jaynes, D.B., Colvin, T.S., Ahuja, L.R., 2000. Prediction of NO3–N losses with subsurface drainage water from manured and UAN-fertilized plots using GLEAMS. Trans. Am. Soc. Agric. Eng. 43, 69–78. Bakhsh, A., Kanwar, R.S., 2001. Simulating tillage effects on non-point source pollution from agricultural lands using GLEAMS. Trans. Am. Soc. Agric. Eng. 44, 891–898. Chung, S.O., Ward, A.D., Schalk, C.W., 1992. Evaluation of the hydrologic component of the ADAPT water management model. Trans. Am. Soc. Agric. Eng. 35, 571–579. Knisel, W.G. (Ed.), 1980. CREAMS: A field scale model for chemicals, runoff, and erosion from agricultural management systems. USDA Conservation Research Report No. 26. Knisel, W.G. (Ed.), 1993. GLEAMS: groundwater loading effects of agricultural management system. University of Georgia, Coastal Plain Experiment Station, Bio. Agric. Eng. UGA-CPES-BAED Publication No. 5. Knisel, W.G., Davis, F.M., 2000. GLEAMS Version 3.0 User Manual. USDA-ARS-SEWRL, Tifton, Georgia. Pub. No. SEWRL-WGK/FMD-050199. Knisel, W.G., Turtola, E., 1999. GLEAMS model application on a heavy clay soil in Finland. Agric. Water Manage. 43, 285–309. Leonard, R.A., Knisel, W.G., 1989. Groundwater loadings by controlled-release pesticides: a GLEAMS simulation. Trans. Am. Soc. Agric. Eng. 32, 1915–1922. Leonard, R.A., Knisel, W.G., Davis, F.M., Johnson, A.W., 1990. Validating GLEAMS with field data for fenamiphos and its metabolties. Am. Soc. Civil Eng. J. Irrig. Drain. Eng. 116, 24–35. Leonard, R.A., Knisel, W.G., Still, D.A., 1987. GLEAMS: groundwater loading effects of agricultural management systems. Trans. Am. Soc. Agric. Eng. 30, 1403–1418. Stone, K.C., Gerwig, B.K., Williams, R.G., Watts, D.W., Novak, J.M., 2001. Using GLEAMS and REMM to estimate nutrient movement from a spray field and through a riparian forest. Trans. Am. Soc. Agric. Eng. 44, 505–512.
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Tucker, M.A., Thomas, D.L., Bosch, D.D., Vellidis, G., 2000a. GIS-based coupling of GLEAMS and REMM hydrology. I. Development and sensitivities. Trans. Am. Soc. Agric. Eng. 43, 1525–1534. Tucker, M.A., Thomas, D.L., Bosch, D.D., Vellidis, G., 2000b. GIS-based coupling of GLEAMS and REMM hydrology. II. Field test results. Trans. Am. Soc. Agric. Eng. 43, 1535–1544. Werner, W., Wodsak, H.P., 1995. The role of non-point nutrient sources in water pollution-present situation, countermeasures, outlook. Water Sci. Technol. 31, 87–97. Yoon, K.S., Yoo, K.H., Wood, C.W., Hall, B.M., 1994. Application of GLEAMS to predict nutrient losses from land application of poultry litter. Trans. Am. Soc. Agric. Eng. 37, 453–459.
Model development for nutrient loading from paddy rice fields Sang-Ok Chunga,*, Hyeon-Soo Kimb, Jin Soo Kimc a
Department of Agricultural Engineering, Kyungpook National University, Taegu 702-701, South Korea b Rural Research Institute, KARICO, Ansan 425-170, South Korea c Department of Agricultural Engineering, Chungbuk National University, Cheongju 361-763, South Korea Accepted 6 January 2003
Abstract The objective of this study was to develop a model groundwater loading effects of agricultural management systems in paddy fields (GLEAMS-PADDY) to predict nutrient loading to surface waters from paddy rice fields. This model was developed by modifying the GLEAMS model that is used for uplands. The GLEAMS-PADDY model is composed of hydrology and chemical sub-models. In the hydrology sub-model of the GLEAMS-PADDY model, the ponded depth routing method was used to handle ponded water condition of paddy fields. With the chemical sub-model, the soil was assumed to be saturated, and the soil profile in the root zone was divided into oxidized and reduced zones. Field experiments were performed at the Soro region, Chungbuk Province of South Korea, from May to September 1999 and 2000. Field data included rainfall amount, irrigation water input, drainage water output, and percolation. Concentrations of total nitrogen (T-N) and total phosphorus (T-P) in irrigation water, rain water, ponded water, surface drainage water, and percolated water were analyzed. The coefficient of determination (r2) and root mean square error (RMSE) between observed and model predicted surface drainage volume relative to the 1:1 line were 0.66 and 66.6 mm, respectively. The r2 and RMSE between observed and predicted T-N concentrations in the ponded water were 0.74 and 1.43 mg l1, respectively. The same statistics for T-P were 0.64 and 0.03 mg l1, respectively. The observed and predicted total T-N loadings from surface drainage were 72.4 and 72.6 kg ha1, respectively. The observed and predicted total T-P loadings were 1.70 and 1.69 kg ha1, respectively. The r2 and RMSE between observed and predicted T-N concentrations in the surface drainage water were 0.79 and 2.71 mg l1, respectively. The same statistics for T-P were 0.73 and 0.04 mg l1, respectively. Comparisons of observed and model predicted water balance components, nutrient concentrations, and loading rates exhibited reasonably good agreement. Hence, the GREAMS-PADDY model can be * Corresponding author. Tel.: þ82-53-950-5734; fax: þ82-53-950-6752. E-mail address: [email protected] (S.-O. Chung).
0378-3774/03/$ – see front matter # 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0378-3774(03)00078-7
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used to predict nutrient loading from paddy fields and to develop BMPs in paddy rice culture to reduce surface water pollution from paddy field drainage water. # 2003 Elsevier Science B.V. All rights reserved. Keywords: Non-point source pollution; Paddy rice; Runoff; Nutrient loading; Model; GLEAMS-PADDY
1. Introduction The total agricultural land area in Korea is 1,899,000 ha, of which 1,153,000 ha are paddy rice fields, which have a saturated root zone and a ponded soil surface. The other agricultural lands are upland fields. A total of 1,083,000 ha of paddy fields were cultivated in Korea in 2001. The growing season for rice (Oryza sativa) in Korea is from April to October. Transplanting is the most popular method of rice cultivation. However, about 10% of the total paddy field area is directly seeded every year. Non-point source pollution from paddy fields is a great concern in Korea. In particular, N and P from agricultural land cause eutrophication in lakes and streams. Previous studies have demonstrated that farmers tend to over-fertilize paddy fields in Korea. The recommended application rate for N is 110 kg ha1, while the actual application rate is 159 kg ha1. The recommended application rate for P is 48 kg ha1, while the actual application rate is 78 kg ha1. The management of drainage water quality for agricultural non-point source pollution has received much attention during the last three decades. Werner and Wodsak (1995) studied the role of non-point nutrient sources in water in Germany. In FL, USA, agricultural best management practices (BMPs) were developed to reduce non-point source pollution of Lake Okeechobee (Anderson and Flaig, 1995). Non-point source pollution from paddy fields is one of the major problems in water quality management in Korea. One of the most frequently used models in agricultural drainage water quality is GLEAMS. The GLEAMS was developed by Leonard et al. (1987), and is modified from the CREAMS (Knisel, 1980) to include the plant root zone. Many studies have used GLEAMS, and include modifications and improvements to the model (Leonard and Knisel, 1989; Leonard et al., 1990; Knisel and Turtola, 1999). Alexander (1988) developed the agricultural drainage and pesticide transport (ADAPT) model, which incorporates subsurface drainage and irrigation parts of the DRAINMOD into the GLEAMS. Chung et al. (1992) improved the ADAPT model by modifying the hydrology sub-model. Yoon et al. (1994) applied GLEAMS to predict nutrient losses from land application of poultry litter. Bakhsh et al. (2000) predicted NO3–N losses through subsurface drainage using GLEAMS, and Bakhsh and Kanwar (2001) simulated tillage effects on non-point source pollution from agricultural lands using GLEAMS. Tucker et al. (2000a,b) and Stone et al. (2001) coupled GLEAMS and Riparian Ecosystem Management Model (REMM) to model the riparian ecosystem management. Recently, GLEAMS version 3.0 was developed by Knisel and Davis (2000) to address Y2K software problems and to expand the model’s pesticide database. In this study, GLEAMS version 3.0 was modified to develop the GLEAMS-PADDY model, which is then used to predict nutrient loading to surface water from paddy rice fields. The developed
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model was validated using field data. The GLEAMS-PADDY model can be used to evaluate BMPs in paddy fields, with the goal of identifying practices that are sustainable, and have minimal water pollution effects in rural areas.
2. Model description The GLEAMS model has three sub-models: hydrology, erosion/sediment and chemicals. The chemical sub-model has two parts: pesticides and nutrients. In the GLEAMS-PADDY model, the hydrology and chemical sub-models were modified to take into account the ponded paddy field conditions. In a paddy field, which has a nearly level soil surface with ponded water, and is usually surrounded by about 20 cm high berms, soil erosion hardly occurs. Therefore, erosion/sediment is very small and occurs mostly before transplanting. 2.1. Hydrology In upland fields water is supplied onto the soil surface through either rainfall or irrigation. Water then flows out in the form of surface runoff, infiltration/percolation, and evapotranspiration (ET) with no inundation. On the other hand, in paddy fields, water is ponded on the soil surface. The water balance equation in a paddy field (Fig. 1) is: DS ¼ ðR þ G1 þ DR1 Þ ðET þ G2 þ DR2 Þ
(1)
where R is the rainfall (cm), ET the evapotranspiration (cm), DR1 and DR2 surface water inflow and outflow (cm), respectively, G1 the subsurface inflow, G2 the lateral and vertical subsurface outflow (cm), and DS the change in storage of the control volume (cm). In GLEAMS, surface runoff is computed using the soil conservation service (SCS) curve number method for upland fields. In the GLEAMS-PADDY model, however, the ponded water depth routing method is used to determine daily ponded depth on the soil surface.
Fig. 1. Schematics of water balance components in a paddy field.
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Ponded water depth in a paddy plot can be determined by: Wd ¼ ðWd1 þ Rd þ IRd Þ ðFd þ ETd þ DRd Þ
(2)
where W is the ponded depth (cm), R the rainfall (cm), IR the irrigation amount (cm), F the infiltration (cm), ET the evapotranspiration (cm), DR the surface drainage (cm), and the subscript d date. Surface drainage is controlled by the outlet height of the paddy plot and determined by: DRd ¼ Wd OHd ; DRd ¼ 0:0;
if Wd > OHd if Wd OHd
(3)
where DR is surface drainage (cm), W the ponded depth (cm), OH the outlet height (cm), and the subscript d date. Ponded water above the outlet sill is drained. The outlet height is an important management parameter in maintaining the optimum ponded depth for rice culture. In GLEAMS, the potential ET is computed using the Penman–Monteith method. In the GLEAMS-PADDY model, any appropriate method can be used. Based on a preliminary study, the FAO corrected Blaney–Criddle method (Allen and Pruitt, 1992) was selected to compute the potential ET in the GLEAMS-PADDY model. Soil water percolates to a lower layer when the water content of a layer is greater than the field capacity. In paddy fields, soil layers in the root zone are assumed to be saturated because of the ponded soil surface. Therefore, in the GLEAMS-PADDY model, soil water percolates based on Darcy’s law. 2.2. Erosion/sediment Since paddy plots are surrounded by berms, sediment transport is much smaller than that in upland fields. Therefore, in the GLEAMS-PADDY model, erosion/sediment from paddy fields is ignored. 2.3. Chemicals In GLEAMS-PADDY, the chemical sub-model presently addresses only nutrients. The N and P, the main causes of eutrophication in the lakes and streams of Korea, are included in the model. Considering ponded conditions, the root zone soil layer is divided into two parts, a 1 cm thick surface oxidized layer, and reduced layers below. The oxidized and reduced conditions are important factors controlling chemical reactions of the nutrients. The N and P turnover processes in GLEAMS for upland fields are detailed in Knisel (1993). The N and P turnover processes in reduced state in the saturated soil layer of paddy fields are different from those in the unsaturated condition in the upland fields. Ammonification and nitrification do not occur, and denitrification occurs in N turnover in the saturated soil layer. Mineralization of P does not occur in the saturated soil layer. 2.4. Nitrogen Nitrogen input into paddy fields occurs through chemical fertilizer application, rainfall, and irrigation water. Nitrogen output occurs through surface drainage, infiltration/percola-
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tion, plant uptake, and denitrification. Nitrogen is divided into ammonia N and nitrate N. The daily nitrogen balance equation is as follows: NO3d ¼ ðNO3d1 þ NITd þ RFNOd þ IRNOd Þ ðDNId þ RONO3d þ PERCNOd þ UPNOd Þ
(4)
NH4d ¼ ðNH4d1 þ FENHd þ MNd Þ ðNITd þ RONH4d þ PERCNHd þ SEDNHd þ UPNHd þ VOLNd Þ 1
(5) 1
where NO3 is the nitrate–N (kg ha ), NIT the nitrification (kg ha ), and RFNO and IRNO the NO3–N dissolved in rainfall and irrigation water, respectively (kg ha1), DNI the denitrification (kg ha1), RONO3 and PERCNO are surface runoff and percolated NO3–N, respectively (kg ha1), UPNO the plant uptake NO3–N (kg ha1), NH4 the ammonia–N (kg ha1), FENH the fertilized NH4–N (kg ha1), MN the ammonification (kg ha1), NIT the nitrification from NH4–N (kg ha1), RONH4 and PERCNH the surface runoff and percolated NH4–N, respectively (kg ha1), SEDNH the surface runoff NH4–N by erosion (kg ha1), UPNH the plant uptake NH4–N (kg ha1), VOLN the volatilization (kg ha1), and the subscript d date. It is assumed that dissolved N in rainfall and irrigation water is NO3–N and that N loss by erosion and volatilization occurs in NH4–N. The N concentration in the water phase in the 1 cm thick surface oxidized soil layer is given by (Knisel, 1993): Cw ¼
Cav b 1 þ bKd
(6)
where Cw is N concentration in the water phase in the surface soil layer, Cav the available N concentration in the surface soil layer, b the extraction coefficient, and Kd the partitioning coefficient. The N concentration in the surface layer available for runoff and infiltration, Cav is defined by (Knisel, 1993): ðF AbstÞ Cav ¼ C1 exp (7) Kd1 ð1 POR1 =2:65Þ þ POR1 where C1 is N mass divided by soil mass (m g1), F the infiltration (cm), Abst the initial abstraction (cm), Kd1 the partitioning coefficient in the surface soil layer, and POR1 the porosity of the surface soil layer. Soluble ammonia concentration of water in the surface soil layer is obtained from Eqs. (6) and (7) as follows (Knisel, 1993): AMON1 103 ðF AbstÞ CNH4 W ¼ exp CNHKD1 ð1 POR1 =2:65Þ þ POR1 SOILMS1 b (8) 1 þ bCNHKD1 where CNH4W is NH4–N concentration dissolved in the surface layer soil water (mg l1), AMON1 the NH4–N in the surface soil layer (kg ha1), SOILMS1 the soil mass in the
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surface soil layer (t ha1), CNHKD1 the partitioning coefficient in the surface soil layer, and other symbols are as previously defined. The distribution coefficient of NH4–N is a function of the clay content of the soil: CNHKDi ¼ 1:34 þ 0:083 CLi
(9)
where CNHKD is the distribution coefficient, CL the clay content (%), and the subscript i the soil layer number. The NO3–N concentration in ponded water is calculated by taking a weighed average of NO3–N concentrations in ponded water of the previous day, soil water in the surface soil layer, irrigation water, and rainfall. The NH4–N concentration in ponded water is calculated by diluting the ponded water concentration with irrigation and rainwater, since no NH4–N exists in the irrigation or rainwater. The concentrations of NO3–N and NH4–N in the ponded water are calculated by: ðPNO3 Wd1 Wd1 Þ þ ðCNO3 Wd POR DÞ PNO3 Wd ¼ PNH4 Wd ¼
þðCNO3 IRd IRd Þ þ ðCNO3 Rd Rd Þ Wd1 þ ðPOR DÞ þ IRd þ Rd ðPNH4 Wd1 Wd1 Þ þ ðCNH4 Wd POR DÞ Wd1 þ ðPOR DÞ þ IRd þ Rd
(10) (11)
where PNO3W and PNH4W are concentrations of NO3–N and NH4–N in the ponded water, respectively (mg l1), CNO3W and CNH4W the concentrations of NO3–N and NH4–N in the soil water of the surface soil layer, respectively (mg l1), POR the porosity of the surface soil layer, D the thickness of the surface soil layer (cm), W the ponded depth (cm), IR the irrigation (cm), R the rainfall (cm), and the subscript d date. Nitrogen loading by surface drainage can be calculated by: RONO3 ¼ 0:1 ðPNO3 WÞ ðDRÞ
(12)
RONH3 ¼ 0:1 ðPNH3 WÞ ðDRÞ
(13)
where RONO3 and RONH4 are loadings of NO3–N and NH4–N, respectively (kg ha1), PNO3W and PNH4W the concentrations of NO3–N and NH4–N in the ponded water, respectively (mg l1). Total N loading by surface drainage is the sum of the NO3–N and NH4–N loads. 2.5. Phosphorus Phosphorus input to paddy fields occurs through chemical fertilizer application and irrigation water, while output occurs through surface drainage and plant uptake. The daily phosphorus balance equation is given by: TPd ¼ ðTPd1 þ FEPd þ MNd Þ ðROLPd þ PERCLPd þ SEDLPd þ UPPd Þ 1
1
(14)
where TP is total P (kg ha ), FEP the fertilized P (kg ha ), MN the immobilized P (kg ha1), ROLP the surface-drained P (kg ha1), PERCLP the percolated P (kg ha1), SEDLP the P outflow by sediment (kg ha1), UPP the plant uptake P (kg ha1), and the subscript d date.
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The P concentration in ponded water is calculated in the same manner as NH4–N: PPLABWd ¼
ðPPLABWd1 Wd1 Þ þ ðCPLABWd POR1 DÞ Wd1 þ ðPOR1 DÞ þ IRd þ Rd
(15)
where PPLABW is P concentration in ponded water (mg l1), CPLABW the P concentration in water of the surface soil layer (mg l1), POR1 the porosity of the surface soil layer, D the thickness of the surface soil layer (cm), W the ponded depth (cm), IR the irrigation (cm), R the rainfall (cm), and the subscript d date.
3. Model validation The GLEAMS-PADDY model was validated using field data. Water and nutrient balance components were calculated by the model, after which the observed and model predicted outputs were compared. To run the model, both a parameter file and a weather data file are required. The parameter file of the hydrology sub-model includes field size, rooting depth, leaf area index, soil texture, soil water properties, type of crop, planting date, and harvesting date. The parameter file of the nutrient sub-model includes N concentration in rainfall and irrigation water, P concentration in irrigation water, and date and amount of fertilizer application. The output files have water and nutrient balance components. Since parameter values are readily available from field experiments, model calibration is not necessary. Sensitivity analysis of the parameters was not performed in this study. 3.1. Field experiments Field experiments were performed in the Soro region, Chungbuk Province of South Korea, from May to September in 1999 and 2000. This region is 51 ha in area with 100 m 100 m standard sized paddy plots (Fig. 2). The USDA soil classification indicates loamy soil in this area. The rice was transplanted in early May and harvested in early October. Field data included rainfall amount, irrigation water input, drainage water output, and percolation rate. Irrigation water was diverted from a stream. Flow rates were obtained by using a stage–discharge relationship and hourly records from water level loggers. Infiltration rings with 20 cm diameter were installed down to 30 cm deep. PVC pipes (75 mm diameter) with perforations near the bottom were buried to 1 m deep, to collect percolated water samples. Sampling bottles and a bailer were used to sample surface water and percolated water, respectively. Concentrations of T-N and T-P in the irrigation water, rain water, ponded water, surface drainage water, and percolated water were analyzed. In addition, fertilizer application dates and amounts were observed. Flow measurements (at locations with no level logger) and water sample collections were made at 5-day intervals from mid-May to mid-June, when nutrient concentrations varied significantly, and at 10-day intervals during other periods. The T-N and T-P concentrations in samples were determined using the ultraviolet
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Fig. 2. Layout and locations of the field observation point.
spectrophotometric method and ascorbic acid reduction spectrophotometric method, respectively. Field-applied fertilizer amounts (Table 1) were observed at the site. Nitrogen was applied at three growth stages: pre-transplanting, tillering, and panicle. The total amount of N applied was 164.5 kg ha1 in 1999, and 179.6 kg ha1 in 2000. Phosphorus was applied once, in the amounts of 21.4 kg ha1 in 1999 and 23.5 kg ha1 in 2000. Table 1 Fertilizer application rate (kg ha1) Growth stage and application method
Timea
Nitrogen
Phosphorus
1999
2000
1999
2000
21.4
23.5
21.4
23.5
Pre transplanting (incorporated)
May/E
40.6
55.6
Tillering (broadcasted)
May/L June/E June/M
32.5 50.8
35.4
July/M August/E
29.0 11.6
24.4 12.0
164.5
179.6
Panicle (broadcasted) Total a
E: early, M: middle, L: late.
52.2
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Table 2 Observed nutrient concentrations in the rainfall (mm, mg l1) Year
Date
Rainfall
T-N
T-P
1999
5/26 6/16 6/23 7/28 7/30 8/3 9/5 9/10
0.7 67.3 106.7 39.4 17.0 35.2 62.7 71.7
1.78 1.65 1.63 0.64 0.68 0.16 0.32 0.13
0.008 0.017 0.017 0.009 0.011 0.011 0.028 0.006
Total/mean
400.7
0.89
0.015
15.5 20.3 0.7 25.4 53.8 6.8 31.1 18.5
1.38 0.87 1.71 0.45 0.51 0.82 0.21 0.92
0.039 0.013 0.080 0.047 0.015 0.003 0.006 0.036
172.1
0.86
0.030
0.88
0.101
2000
5/26 6/26 7/15 7/20 8/4 8/21 8/27 9/8 Total/mean
Mean
The amount of rainfall in 2000 was much smaller than that in 1999. T-N concentrations in rainfall (Table 2) ranged between 0.13 and 1.78 mg l1, and rainfall T-P concentrations were below 0.05 mg l1. Weighted average concentrations were 0.88 mg l1 T-N, and 0.101 mg l1 T-P. Nutrient concentrations in the irrigation and drainage water were high in the early stage of the experiment (Table 3) and decreased as time passed. The high concentrations in the irrigation water were caused by the water source (stream water), which was significantly affected by nutrient runoff from paddy fields in upstream basins during the fertilizing period. The high concentrations in the drainage water in May and June were affected by the outflow of the ponded water with high concentrations due to nature of irrigation water and fertilization. 3.2. Hydrology Surface drainage from paddy fields causes pollution of streams and lakes in Korea, and therefore is an important attribute to model. A comparison of observed and model predicted surface drainage was made. Total observed and predicted surface drainage amounts were 1927 and 1970 mm, respectively, in 1999 and 1858 and 2045 mm, respectively, in 2000. Data were distributed on both sides of the 1:1 line in a scatter plot of observed versus predicted surface drainage for each 10-day period of the experiments (Fig. 3). Statistical analysis indicates r2 and RMSE values of 0.66 and 66.6 mm, respectively.
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Table 3 Observed nutrient concentrations in the irrigation and drainage water (mg l1) Year
1999
a b
Date
5/5 5/14 5/20 5/25 5/31 6/5 6/10 6/15 6/25 7/5 7/15 7/26 8/5 8/16 8/28 9/9 Meanb
T-N
T-P
Irrigation water
Drainage water
Irrigation water
Drainage water
3.87 2.86 4.77 3.13 2.42 3.36 5.92 2.23 3.15 1.63 0.76 2.44 –a 2.59 1.72 1.80 2.78
5.10 3.92 3.47 1.95 9.38 11.66 8.65 4.16 2.82 1.06 0.84 1.85 2.12 0.88 1.10 1.12 2.35
0.18 0.09 0.20 0.13 0.07 0.07 0.06 0.06 0.13 0.05 0.05 0.08 –a 0.05 0.07 0.12 0.10
0.28 0.16 0.13 0.09 0.13 0.08 0.06 0.09 0.10 0.04 0.05 0.03 0.03 0.04 0.06 0.09 0.09
Not observed. Flow weighted mean.
Fig. 3. Comparison of the observed and predicted surface drainage for each 10-day period.
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3.3. Nutrients Comparisons were made of observed and predicted nutrient concentrations and loadings in ponded water, surface drainage water, and percolated water. Observed and predicted nutrient loadings below the root zone were very small. Model predicted T-N concentrations were higher than observed values in ponded water (Fig. 4). The r2 and RMSE values were 0.74 and 1.43 mg l1, respectively, for T-N concentration, and 0.64 and 0.03 mg l1, respectively, for T-P concentration. Model predicted T-N concentrations in ponded water were higher than observed values in 1999, while the model predicted T-N concentrations agreed with observed values in 2000 (Fig. 5). A comparison of observed and predicted nutrient concentrations in surface drainage water in 1999 also was made. The observed T-N and T-P concentrations in surface drainage water were 3.76 and 0.09 mg l1, respectively. The r2 and RMSE values for T-N were 0.79 and 2.71 mg l1, respectively, and those for T-P were 0.73 and 0.04 mg l1, respectively. Model predicted T-N and T-P concentrations are higher than observed concentrations (Figs. 6 and 7). A comparison of observed and predicted nutrient loading from surface drainage in 1999 also was made. Concerning water pollution, the total nutrient loading is more important than nutrient concentration of the surface drainage water. Observed and predicted T-N loadings from the surface drainage were 72.4 and 72.6 kg ha1, respectively, and corresponding T-P loadings were 1.70 and 1.69 kg ha1, respectively. The r2 and RMSE between observed and predicted T-N concentrations in the surface drainage water were 0.79 and 2.71 mg l1, respectively. The corresponding statistics for T-P were 0.73 and 0.04 mg l1, respectively. These loadings are among the main causes of eutrophication in lakes and rivers downstream, and may also cause some health problems for water that is in contact with people (for example, if the pollution causes toxic algal blooms or contributes organic precursors for trihalomethane development in drinking water). In particular, high concentrations of T-P are linked to these problems. The comparisons of observed and model predicted water balance and nutrient components indicate reasonably good agreement. Hence, the GREAMS-PADDY model can be used to predict nutrient loading from paddy fields.
4. Summary and conclusions The objective of this study was to develop a GLEAMS-PADDY model to predict nutrient loading to surface waters from paddy rice fields. The model was developed by modifying the GLEAMS model of uplands. The GLEAMS-PADDY model is composed of hydrology and chemical sub-models. In the hydrology sub-model of the GLEAMS-PADDY model, the ponded depth routing method was used to handle the ponded water condition of paddy fields. In the chemical submodel, the soil was assumed to be saturated, and the soil profile in the root zone was divided into oxidized and reduced zones.
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Fig. 4. Comparison of the observed and predicted nutrient concentrations in the ponded water.
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Fig. 5. Comparison of the observed and predicted daily nutrient concentrations in the ponded water.
Field data from the Soro region, Chungbuk Province of the South Korea were collected from May to September 1999 and 2000, and used for model validation. Field data included rainfall amount, irrigation water input, drainage water output, and percolation rate. Concentrations of T-N and T-P in the irrigation water, rain water, ponded water, surface drainage water, and percolated water were analyzed. The results obtained from this study are as follows: 1. The r2 and RMSE between the observed and model predicted surface drainage water were 0.66 and 66.6 mm, respectively. 2. The r2 and RMSE between observed and predicted T-N concentrations in ponded water were 0.74 and 1.43 mg l1, respectively. The corresponding statistics for T-P were 0.64 and 0.03 mg l1, respectively. 3. The observed T-N and T-P concentrations in surface drainage water were 3.76 and 0.09 mg l1, respectively. The r2 and RMSE values for T-N were 0.79
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Fig. 6. Comparison of the observed and predicted nutrient concentrations in the surface drainage water.
and 2.71 mg l1, respectively, and those for T-P were 0.73 and 0.04 mg l1, respectively. 4. The observed and model predicted T-N loadings from the surface drainage in 1999 were 72.4 and 72.6 kg ha1, respectively. The observed and model predicted total T-P
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Fig. 7. Comparison of the observed and predicted daily nutrient concentrations in the surface drainage water.
loadings from the surface drainage in 1999 were 1.70 and 1.69 kg ha1, respectively. The r2 and RMSE between the observed and predicted T-N concentrations in the surface drainage water were 0.79 and 2.71 mg l1, respectively. The corresponding statistics for T-P were 0.73 and 0.04 mg l1, respectively.
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5. The observed and predicted nutrient loading below the root zone was very small. The comparisons of observed and model predicted water balance components and nutrient concentrations indicated reasonably good agreement. Hence, the GREAMSPADDY model can be used to predict nutrient loading from paddy fields. This model can be applied in developing BMPs in paddy rice culture and can help to reduce water pollution from paddy field drainage water. By using this model farmers can reduce fertilizer application and can pick the best timing of the fertilizer application with respect to rainfall events. In addition, farmers can identify the best ponded water depth management technique based on the model simulation of various scenarios.
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