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Assessment of effects of best management practices on agricultural non-point source pollution in Xiangxi River watershed
Agricultural Water Management 117 (2013) 9–18
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Assessment of effects of best management practices on agricultural non-point source pollution in Xiangxi River watershed Ruimin Liu ∗ , Peipei Zhang, Xiujuan Wang, Yaxin Chen, Zhenyao Shen State Key Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
a r t i c l e
i n f o
Article history: Received 29 March 2012 Accepted 21 October 2012 Available online 3 December 2012 Key words: SWAT Agriculture non-point source pollution (ANSP) Best management practices (BMPs) Evaluation
a b s t r a c t Agricultural non-point source pollution (ANSP) is considered a major contributor to local water degradation in the Three Gorges Reservoir Area (TGRA) of China. The Xiangxi River, which is a first level anabranch of the Yangtze River, was selected for investigation of the effectiveness of selected best management measures (BMPs) to alleviate water pollution through analysis of several scenarios by SWAT (Soil and Water Assessment Tool). Specifically, changes in land use, fertilizer management and tillage management measures were simulated in SWAT because they were shown to be the primary factors influencing nonpoint source (NPS) pollution in the Xiangxi River watershed. The results revealed that when farmland was returned to forests, both runoff and NPS pollution loads showed a clear downward trend and the NPS pollution loads in the Xiangxi River watershed decreased by 20% or more when compared with the status of 2007. Furthermore, conservation tillage and contour farming can help reduce runoff by 15.99% and 9.16%, total nitrogen (TN) by 8.99% and 8%, and total phosphorus (TP) by 7% and 5%, respectively. Conservation tillage has a greater effect in controlling the losses of soil, water and nutrients than contour farming. Based on the fertilizer conditions of 2007, changing the fertilizer application resulted in little change in local runoff; however, for NPS pollution loads, various forms of nitrogen (N) and phosphorus (P) pollution loads were directly proportional to the amount of chemical fertilizer applied. Overall, the results of this study can facilitate development of environmental friendly land use plans by local managers, and enable farmers to manage agriculture and fertilizer more efficiently, ultimately achieve the goal of reduce water pollution. © 2012 Elsevier B.V. All rights reserved.
1. Introduction A significant amount of work has been conducted in the Three Gorges Reservoir Area (TGRA) of China to understand and control water degradation. These studies have primarily focused on nonpoint source (NPS) pollution control because it has been found to be the main source of surface water and groundwater pollution (Ongley et al., 2010; Wang et al., 2006; Lam et al., 2010; Shen et al., 2012a,b). Agriculture has been identified as the major contributor to NPS pollution of water resources (Lam et al., 2010). Some recent applications (Saleh and Du, 2004; Grizzetti et al., 2003; Akhavan et al., 2010) have employed SWAT to simulate agricultural non-point source pollution (ANSP) at the sub-basin and watershed scales. To control the adverse effects of agricultural management practices on the water quality of the study area, it is essential to identify the critical impact factors of ANSP. ANSP is generally thought to be closely related to changes in land use in watersheds (Ding, 2010;
∗ Corresponding author. Tel.: +86 10 58800829; fax: +86 10 58800829. E-mail address: [email protected] (R. Liu). 0378-3774/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agwat.2012.10.018
Zhang et al., 2011). Specifically, unreasonable land use and agricultural management will lead to soil erosion and excessive N and P losses with surface runoff, resulting in the formation of ANSP for a large area within one watershed (Hao et al., 2004). Moreover, fertilizer has significant effects on the outputs of TN and TP (Huang et al., 2010). To improve the water quality and quantity of a region, best management practices (BMPs) have been developed by western countries, especially the United States, since the 1960s (Logan, 1993). BMPs are structural or non-structural management practices that are designed to reduce the adverse effects of agricultural activities on water quality. Both domestic and foreign managers have paid a great deal of attention to the study of BMPs for protection of water quality, and BMPs have been shown to effectively reduce the NPS pollutant loads from agricultural areas (Maringanti et al., 2011; Panagopoulos et al., 2011a; Zhang and Zhang, 2011; Lam et al., 2011; Inamdar et al., 2001). There are many factors that influence the effects of BMPs on non-point source pollution, and regulatory enforcement of the implementation of BMPs has resulted in an urgent need for quantitative information regarding their effectiveness for ANSP. The effectiveness of BMPs for reduction of ANSP is difficult to monitor
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and evaluate, and monitoring of such changes would be costly and time consuming. Accordingly, a model that can forecast the effects of BMPs is needed. SWAT can simulate various management practices and their impacts on water and soil in large and complex watersheds over long periods of time in a watershed (Akhavan et al., 2010; Lam et al., 2011). Indeed, SWAT is one of the best available models for simulating the effectiveness of BMPs. Studies have indicated that a key strength of SWAT is a flexible framework allowing simulation of a wide variety of structural and nonstructural BMPs, such as conservation tillage, cover crops, application rate and timing of fertilizers, nutrient management, buffer strips, flood prevention structures, grass waterways, and parallel terraces (Gassman et al., 2007). For example, SWAT has been used to successfully simulate the sediment and nutrient concentrations of the Midnapore watershed in the west of India and Bangladesh, as well as to identify seriously polluted areas of the sub-basin and select 1 of 48 BMPs for implementation (Grizzetti et al., 2003; Behera and Panda, 2006). Since impoundment in 2003, the water quality and quantity of the Three Gorges Reservoir has changed, this has resulted in many researchers investigating the water pollution status and solution strategy of the region (Ongley et al., 2010; Wang et al., 2006; Lam et al., 2010; Shen et al., 2012a,b; Yang et al., 2011). The Xiangxi River, located 38 km upstream of the Three Gorges Dam, is the largest tributary of the Three Gorges Reservoir (TGR) in Hubei Province. The NPS pollution loads of the Xiangxi River watershed have basically increased annually since impoundment in 2003 (Liu and Wang, 2009). Considerable research including hydrological modeling as well as water quality and related ecological studies have been conducted in the Xiangxi River Watershed (Ye et al., 2009; HÃ Rmann et al., 2009; Li et al., 2009). The spatial and temporal contribution of ANSP in the Xiangxi River Watershed has been estimated (Liu and Wang, 2009). However, few of these studies have investigated the effectiveness of BMPs at controlling nonpoint source pollution of the Xiangxi River watershed. Therefore, the present study was conducted to identify the critical impact factors of the local ANSP and simulate the effectiveness of selected BMPs using SWAT, as well as to suggest effective and efficient management practices that would help reduce adverse water quality effects. Our specific goals were to assist the government with design of effective policies for planning of land use changes and to help farmers implement more reasonable agriculture management practices and more economical and effective fertilizer application techniques.
2. Materials and methods 2.1. Study area Xiangxi River is a tributary of the Yangtze River (Fig. 1), which is the longest river in China and third longest in the world. The river originates from the Shennongjia Forest region and is located between 110.47◦ and 111.13◦ E, and 30.96◦ and 31.67◦ N in the Hubei portion of the Three Gorges Reservoir. The river is 94 km long with a catchment area of 3099 km2 and its elevation ranges from 154 m to nearly 3000 m. Annual average precipitation observed from 1961 to 2004 was 1100 mm and ranges from 670 mm to 1700 mm with considerable spatial and temporal variability. The main rainfall season is from May to September, with a flood season from July to August. The annual mean runoff depth is 688 mm and the annual average temperature from 1961 to 2004 was 15.6 ◦ C (range 12–20 ◦ C). The study area is typical of northern subtropical mountainous landscapes. Approximately 70.9% of the area is covered by forests, 6.5% is farmland, 5.3% is water, 4.4% is wasteland and the remainder is residential land and transportation (Xu et al., 2010).
2.2. Development of the database The basic database for the Xiangxi River includes the digital elevation model (DEM), soil and land use maps, climate data and land management data (Table 1). All digital data were projected to the Albers conical equal area projection during data processing. The parameters of the projection were as follows: standard parallel latitudes, 25◦ N and 47◦ N; central meridian longitude, 105◦ E. 2.3. SWAT model The SWAT (Soil and Water Assessment Tool, version 2005) model (Arnold et al., 1998) is an advanced, physically based, widely distributed hydrological model that can be integrated with remote sensing (RS), geographic information system (GIS) and DEM techniques. SWAT is primarily used for meso-scale catchments, and the basic units are Hydrological Response Units (HRU; hydrotopes) based on different soils, land uses and slopes. SWAT has become an effective means of evaluating NPS water resource issues (flow, sediment, and nutrients) for a large variety of national and international water quality applications (Liu and Wang, 2009; Green and van Griensven, 2008; Plus et al., 2006). SWAT has also been widely applied for watershed scale studies dealing with water quantity and quality (Abbaspour et al., 2007). The major components of SWAT include hydrology, weather, plant growth, nutrients, pesticides, and agricultural management (Gassman et al., 2007). The details of all components can be found elsewhere (Arnold et al., 1998; Neitsch et al., 2001). Input data include weather variables, soil properties, topography, vegetation and land management practices occurring in the catchment (Neitsch et al., 2005a). SWAT can predict the impacts of long-term, point and non-point source pollution on water quality variables such as sediments, nutrients, and pesticide loads (Arnold et al., 1994). In recent years, SWAT has been widely used in ANSP studies in China (Ongley et al., 2010; Zhang et al., 2011). The present study only focused on the hydrological, nutrients and agricultural management components of the SWAT model. 2.4. Model evaluation In the model simulation process, the accuracy of the parameter selection determines the reliability of this model. There are many parameters involved in the SWAT model, and it is difficult to determine accurate values of each parameter; therefore, sensitivity analysis of SWAT is a precondition of parameter selection. At present, methods for sensitivity analysis of model parameters commonly include One factor At a Time (OAT), Fourier analysis, Monte Carlo Sampling, and Latin Hypercube Sampling (LHS). In this study, LHS (Vachaud and Chen, 2002) and OAT (Van Griensven et al., 2006) were combined to determine the parameter sensitivity, refer to the latest research progress at domestic and foreign (Morris, 1991). Parameter sensitivity discrimination formulas are shown in Eq. (1): SN =
n−1 (Yi+1 − Yi )/Y0 i=0
(Pi+1 − Pi )/100
/n
(1)
where SN is the sensitivity factor, Yi is the output value of the i-th run of the model output, and Yi+1 is the (i+1)-th output value. Y0 is the output value with parameters adjusted at the first time, Pi is the percentage change of the i-th model operating parameter value relative to the calibrated parameter values of (i−1)-th, and Pi+1 is the (i+1)-th percentage change. n is the number of model runs. More details of the method can be found in SWAT 2005 Advanced Workshop (Griensven, 2005).
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Fig. 1. Geographic location of the Xiangxi River and Xingshan County.
Table 1 Data sources and corresponding process. Data sources
Data
Processing
Landsat ETM+ 1999 and 2007 GPS ground data 1:1,000,000 district map of China 1:250,000 DEM (digital elevation map) data 1:1,000,000 The national soil data Xingshan meteorological data (1980–2007) Xingshan hydrological data (1980–2007) Changjiang water quality data (2000–2007) Land management data
Land use data, NDVI Ground reference data Vector data of district Vector data of watershed and stream network Spatial soil data Daily meteorological data Daily stream flow Water quality data (NO3 -N; NO2 -N; TN) Crop growth period and fertilization condition
Supervised classification Differential correction using base station network Digitization Hydrological model analysis Hydrological model analysis Collected from Xingshan Meteorological Station Collected from Xingshan Hydrological Station Collected from Changjiang Water Resources Committee Xingshan field survey
Model calibration is an adjustment of model parameters within recommended ranges to optimize the agreement between observed data and model simulation results (Tolson and Shoemaker, 2007). Because it is not feasible to include all parameters in the calibration procedure, a pre-selection and aggregation step was required. In this study, 24 parameters were selected for calibration for the Xiangxi River watershed (Table 2). The calibration and validation of SWAT were conducted using the latest developed calibration package, SWAT-CUP (SWAT Calibration and Uncertainty Programs). The programs include four transfer parameter methods, SUFI2 (Sequential Uncertainty Fitting version 2) (Abbaspour et al., 2007), GLUE (Generalized Likelihood Uncertainty
Estimation) (Beven and Binley, 1992), ParaSol (Parameter Solution) (Van Griensven and Meixner, 2006) and the MCMC (Markov Chain Monte Carlo) (Vrugt et al., 2003) algorithm. In our study, the SUFI2 algorithm was selected for application in the calibration and validation procedure. SUFI2 has a high efficiency when used for calibration and uncertainty quantification of large watersheds (Faramarzi et al., 2009; Yang et al., 2008). The rules of SUFI2 are adopted by the computer automatically according to certain preferred targets, after which a set of parameters for the system is selected to help achieve a multi-objective optimization. The specific usages can refer to SWAT-CUP Manual (Rostamian et al., 2008).
Table 2 Main controlling SWAT 2005 parameters selected for calibration of the Xiangxi River watershed. Parameters
Definition
ALPHA BF CANMX NPERCO CN2 ESCO SOL AWC USLE P GW QMN SOL NO3 SOL ORGN SOL ORGP
Base flow recession factor Maximum canopy index Nitrogen percolation coefficient SCS run off curve number for moisture condition II Soil evaporation compensation factor Available water capacity of the soil layer (mm/mm soil) USLE equation support practice (P) factor Threshold depth of water in shallow aquifer Initial NO3 concentration (mg/kg) in the soil layer Initial organic N concentration in surface soil layer (kg/ha) Initial organic P concentration in surface soil layer (kg/ha)
Limit value range Min 0 0 0.01 −0.56 0.01 0 0.1 0 0 0 0
Calibrated value Max 1 100 1 1.3 1 1 1 5000 100 10,000 100
0.34 34 0.62 −0.35 1.00 0.57 0.83 4684 50.85 133 395
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Fig. 2. Flow calibration and validation results, Xiangxi River watershed.
The performance of SWAT was evaluated using statistical analyses to determine the quality and reliability of the predictions when compared to the observed values. Summary statistics included the mean and standard deviation (SD), which were used to assess both the ability of SWAT to reproduce the distribution of observed data and the variability between observed and simulated data. The goodness-of-fit measures used were the coefficient of determination (R2 ; Eq. (2)) and the Nash–Sutcliffe efficiency (ENS ) value (Eq. (3)) (Nash and Sutcliffe, 1970). The R2 and ENS values are explained by Eqs. (2) and (3), respectively,
n 2
R =
i=1
n i=1
¯ (Oi − O)
n ENS =
2
¯ ¯ (Oi − O)(P i − P)
i=1
2
n
i=1 2
¯ − (Oi − O)
¯ (Pi − P)
n i=1 2
¯ (Oi − O)
2
(Pi − Oi )2
(2)
based on the observation data of 1980 (scenario 1), 1995 (scenario 2), 2000 (scenario 3), and 2007 (scenario 4). In addition, tillage management has been shown to be highly related to ANSP in many studies (Lam et al., 2011; Behera and Panda, 2006; Pandey et al., 2009). But the local tillage management practices had little changes, furthermore tillage management data of study area in the past decades had not been collected, so tillage manament will be directly selected as the BMPs without discussing its influence on ANSP. With the identified inflence factors, the scanarios of each identified factor will be setting based on the baseline conditions of 2007 to select BMPs. All the input data of SWAT were of 2007 except for the influence factor whose scenarios were discussed. The simulation period of identifying influence factors and evaluating the effectiveness of BMPs were from 2007/1/1 to 2007/12/31 in SWAT.
(3)
where n is the number of observations during the simulated period, Oi and Pi are the observed and predicted values at each compari¯ and P¯ are the arithmetic means of observed and son point i, and O predicted values. The ENS value was used to compare predicted values to the mean of the average monthly observed values for the sub-watershed, where a value of 1 indicates a perfect fit. The ENS describes the explained variance for the observed values over time that is accounted for by the SWAT model. The R2 was used to evaluate how accurately the model tracks the variations in the observed values. The difference between ENS and R2 is that ENS can interpret model performance for replicating individually observed values, while R2 cannot. 2.5. Representation of BMPs Based on previous literature review, BMPs have been proved to be an effective way to control ANSP. In order to obtain reasonable BMPs, it is necessary to identify the influence factors and evaluate the effectiveness of them. The impact factors of ANSP include land use, conservation tillage, cover crops, application rate and timing of fertilizers, nutrient management, buffer strips, flood prevention structures, grass waterways, and parallel terraces. However, it is time-consuming and unnecessary to simulate the effectives of all impact factors; therefore, it is essential to identify the critical impact factors. Based on the frequent changes in land use and large amount of fertilizer consumed in the study area, land use and fertilizer management was simulated using SWAT under different scenarios
3. Results and discussion 3.1. Model calibration and validation Xiangxi River watershed was divided into 37 sub-basins which include 320 hydrology response units (HRUs) with the ArcSWAT model before model calibration and validation. Based on these HRUs, the sensitivity analysis of parameters was carried out to calibrate in SWAT for the Xiangxi River watershed. The initial sensitivity analysis resulted in selection of parameters that were calibrated as shown in Table 2. This table includes SWAT model parameters considered in the process of calibration and determination of sensitivity statistics. The results of sensitivity analysis indicated that the parameters selected for estimation of stream flow and nutrient loads were sensitive for the Xiangxi River watershed. Sensitivity analysis was performed for flow, TN and TP of Xiangxi River watershed. Table 2 presents an overview of the 11 most sensitive SWAT2005 parameters. Actual parameter values employed in SWAT reflect the unique characteristics of the Xiangxi River watershed. Among the above parameters, SOL AWC and CANMX were the most identifiable parameters for the Xiangxi River watershed, compared with other studies (Shen et al., 2012a,b). This could be explained by the fact that SOL AWC represented soil moisture characteristics or plant available water. This parameter plays an important role in evaporation, which is associated with runoff (Burba and Verma, 2005). CANMX was highly related with the precipitation and soil conditions. The monthly flow and water quality data observed from 2002 to 2007 were used in the calibration and validation periods.
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Fig. 3. Water quality calibration and validation results based on monthly data – Xiangxi River watershed.
3.1.1. Flow calibration and validation The monthly monitoring data from 2002 to 2007 that were obtained from the Xingshan hydrologic station in the Xiangxi River watershed were selected as the input data for simulation. The data from 2002 to 2004 were used in the calibration phase and data from 2005 to 2007 were employed for model validation. Flow calibration and validation results of Xiangxi River watershed are shown in Fig. 2 (Liu and Wang, 2009). The results indicated that the model can be used to successfully simulate runoff in the Xiangxi River watershed and that the simulation results are credible.
3.1.2. Water quality calibration and validation TP, ammonia nitrogen (NH4 -N) and nitrate (NO3 -N) were selected as water quality indicators for calibration and validation based on common research purposes and existing water quality monitoring data. All nutrient data were obtained from the monitoring data collected at the Xingshan hydrologic station in the Xiangxi River watershed. The NH4 -N and NO3 -N were simulated using data from 2004 to 2007, while observed data from 2004 to 2005 were used in the calibration phase and data from 2006 to 2007 were used for the validation phase. NH4 -N was calculated as a difference (TN – NO3 -N – NO2 -N). As to TP calibration and validation, due to the
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Fig. 5. The relationship between fertilizer application amount and ANSP loads. Fig. 4. The relationship between ANSP and land use under different scenarios in SWAT.
Xiangxi River basin is the major phosphate producing area of China and many mines are located there, the TP loads produced by the phosphate mining was high. However, sampling and monitoring of this portion of the NPS is extremely complex and difficult; therefore, the precise statistics of the total pollution loads of mining in the basin cannot be determined. During the dry season, point source pollution is a large contributor to water degradation, while that of NPS is less; therefore, TP observed data from 2004 to 2005 during the dry season were used for calibration and from 2006 to 2007 were used for validation. The simulation and validation results are shown in Fig. 3. As shown in Fig. 3, the simulated NH4 -N and NO3 -N values were better than those of the TP when the SWAT model was employed, as indicated by most ENS and R2 values for NH4 -N and NO3 -N being larger than those for TP. The Nash–Sutcliffe coefficients during the validation period were 0.62 and 0.70 for NH4 -N and NO3 -N, respectively, but only 0.53 for the TP. Overall, although the simulated and measured values showed some error, they met the requirements for the model simulation, so the simulation accuracy is acceptable. As outlined above, the simulated and measured values of both flow and water quality showed good consistency. The values of ENS and R2 obtained were satisfactory during both the calibration and validation periods. The simulated water quality results were better than the simulated flow results. According to the results of water quality simulation of watershed models applied to other large domestic (Yang et al., 2008) and foreign (Rostamian et al., 2008; Panagopoulos et al., 2011b) basins, the simulation results obtained here are credible. Therefore, the SWAT model can be successfully applied to simulate the ANSP loads in the study area. 3.2. ANSP influence factors analysis 3.2.1. The influence of land use changes The specific data of input land use and simulation results can be seen in Fig. 4. As shown in Fig. 4, the change rule of ANSP loads is the same as the land use within the watershed, and contrary to that of
forest. When cropped area increases, the proportion of forest will be smaller, and NPS pollution loads will be greater. When land use changed from scenario 1 to scenario 2, the cropped area was reduced by 5.61% and the forest increased by 5.70%, resulting in a decrease in ANSP loads of 55.21%. Since then, TN and TP loads increased from scenario 2 to scenario 3 as a result of the increase in cropped area and decreased in forest in scenario 3. These results indicate that the ANSP loads are primarily generated by tillage in the Xiangxi River watershed. Furthermore, forests control the generation of ANSP because of their strong water-holding capacity and sediment reduction features.
3.2.2. The influence of fertilizer application To study the impact of chemical fertilizer application on ANSP, the fertilizer application amounts of 1980, 1995, 2000 and 2007 were assumed as scenario 1, scenario 2, scenario 3 and scenario 4, respectively. Other model parameters (such as land use data, meteorology data) remained the same as before, and different fertilizer applications were input into the model run. The simulation period was from 1/1/2007 to 31/12/2007. The fertilizer application from 1980 to 2007 shown in Table 3 was reported in the Statistical Yearbook of Xingshan and related references (Jia et al., 2006). The simulation results gave the NPS pollution generation under different fertilizer application scenarios (Table 3 and Fig. 5). As shown in Table 3 and Fig. 5, the ANSP loads increased significantly as the amount of fertilizer in the study area increased. From 1980 to 2007, the N and P fertilizer application rates increased from 26.87 kg/ha and 9.15 kg/ha in 1980 to 37.50 kg/ha and 12.50 kg/ha in 2007 (Table 3), respectively. The simulation results show that the TN and TP loads at the outlet of the Xiangxi River watershed increased from 750.69 Mg and 113.73 Mg in 1980 to 1126.04 Mg and 164.91 Mg in 2007 (Table 3), respectively. The increased rates of TN and TP were 50% and 45% (Fig. 5), respectively. Thus, the application amount of chemical fertilizer is another important factor of NPS pollution generation; accordingly, reducing the nutrient application amount will be an effective method of reducing NPS pollution in the study area.
Table 3 ANPS simulation results under different fertilizer application scenarios. Scenarios (t)
Scenario 1 (1980) Scenario 2 (1995) Scenario 3 (2000) Scenario 4 (2007)
Simulation results 3
−1
N
P
Flow (m s
26.87 32.74 33.52 37.50
9.15 10.09 10.45 12.50
31.73 31.73 31.73 31.73
)
Change rate (%) TN (t)
TP (t)
751 902 910 1126
114 132 133 165
Scenario 1–2 Scenario 2–3 Scenario 3–4 Scenario 1–4
Flow
TN
TP
0.00 0.00 0.00 0.00
20.18 0.92 23.67 50.00
15.65 0.93 24.23 45.00
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Table 4 ANSP loads simulated under land use in 2007 and scenario in SWAT. Changed land use
Actual land use (km2 ) Land use in scenario (km2 ) Difference value (km2 ) Change rate (%)
Simulated results
Tillage
Forest
Orchard
Grassland
Runoff (m3 s−1 )
NH4 -N (t)
NO3 -N (t)
TN (t)
TP (t)
134.98 86.56 −48.42 −35.87
733.45 748.99 15.54 2.12
8.63 41.51 32.88 381
24.18 24.18 0.00 0.00
46.51 36.74 −9.77 −21
30.19 22.34 −7.85 −26
939 707 −232 −24.69
988 745 −243 −24.59
140 111 −29 −20.61
3.3. Effective evaluation of best management practice scenarios According to the aforementioned results, changes in land use and fertilizer application management have a significant impact on the load of ANSP. In addition, tillage management has been shown to be highly related to ANSP in many studies. 3.3.1. Simulation results of changes in land use For land use management, this study simulated the following scenarios: (1) high slope arable land (>25◦ ) returned to forest; (2) high slope arable land of 15◦ –25◦ returned to orchards (both measures applied simultaneously). The actual land use of the Xiangxi River watershed in 2007 was used as the background to compare the simulation results among land use types. The spatial distribution of major changed landuse area in Xiangxi River watershed is shown in Fig. 6. The results of runoff, NH4 -N, NO3 -N, TN and TP loads simulated by the SWAT model under assumed land use scenario and actual land use in 2007 are shown in Table 4. When compared with the land use of 2007, the nutrients and runoff varied obviously under different land use. When forests and orchards increased and cropped area decreased, the TN and TP loads showed a large reduction in the Xiangxi River watershed. The TN and TP loads estimated by SWAT under changed land use were 744.70 Mg and 110.85 Mg, respectively. When compared with the landuse status of 2007, the TN and TP loads were 24.59% and 20.61% lower, respectively. Therefore, the decreasing ANSP loads were due to the increasing forests and decreasing cropped area. The simulation results indicate the following: (1) in the study area, the conversion of high slope cropland to forests can reduce runoff by 9.77%. This is likely because the surface runoff being calculated by the SCS equation and runoff is proportional to the CN2 value in SWAT model, and the CN2 value of forests is less than that of cropland, which resulted in reduced runoff in the study area. In addition, the soil water holding capacity increased as the amount of forests increased, which resulted in decreased runoff; (2) for NPS pollution loads, as the high slope arable land is reforested (>25◦ ) and tilled land is changed to orchard on high slope arable land of 15◦ –25◦ , nitrogen, phosphorus and other nutrients in the watershed showed a decreasing trend, and almost all were reduced by 20% or more. This may have occurred for several reasons. Specifically, the reduction in runoff caused nutrient loads to decline. Additionally, decreases in cropped area, which led to reductions in the amount of fertilizer applied, may be the essential reason for the reduction of nitrogen and phosphorus loads in Xiangxi River watershed. 3.3.2. Simulation results of tillage management measures Tillage management, which is a major management measure for the control of ANSP, was divided into two aspects: conservation tillage and contour farming, in this study. Both of them were applied to arable land high slope less than 15◦ of Xiangxi river watershed. (1) Conservation tillage. Conservation tillage is any method of soil cultivation that leaves the previous year’s crop residue on fields before and after planting the next crop, to reduce soil erosion and runoff. Conservation tillage methods include no-till, strip-till,
Table 5 Contour farming practice factor P for the Modified Universal Soil Loss Equation (MUSLE). Land slope (%) 1–2 3–5 6–8 9–12 13–16 17–20 21–25
Slope (◦ ) 0.6–1.1 1.7–2.9 3.4–4.6 5.1–6.8 7.4–9.1 9.7–11.3 11.9–14.0
P factor
Maximum slope length (m)
0.6 0.5 0.5 0.6 0.7 0.8 0.9
122 91 61 37 24 18 15
For terraces, use revised LS factor, loss from crop, same P as contouring factor; loss from terrace with graded channel outlet, contour P factor × 0.2, loss from terrace with underground outlet, contour P factor × 0.1. Source: Schwab et al. (1995), originally based on Wischmeier and Smith (1978).
ridge-till and mulch-till. Each method requires different types of specialized or modified equipment and adaptations in management. No-till is an important form of conservation tillage used in many foreign countries, such as US, UK, Brazil and Germany (Wang et al., 2003). The goal of no-till is minimal disturbance of the soil to maintain the residue level after harvest. In recent years, increasing researchers have studied the impact of no-till on crop production and NPS pollution (Inamdar et al., 2001; Pandey et al., 2009; Panagopoulos et al., 2011b), and no-till techniques have been largely promoted and improved in China. No-till can simplify production processes and reduce labor costs; therefore, it is useful agricultural technology with low input, high output and high efficiency. (2) Contour farming. Contour farming consists of performing field operations along the contour. These operations include plowing, planting, cultivating, and harvesting. Contour farming can prevent sheet and gully erosion, and protect gentle high slope fields against low and moderate intensity storm erosion. Studies (Guto et al., 2011; Ng et al., 2008) have shown that when the slope ranges from 3% to 8%, contour farming can effectively prevent soil erosion. However, as the slope increases, the ability of contour farming to prevent soil erosion decreases, and when the slope is greater than 25% almost no erosion is prevented by this technique. In this study, the representation of contour farming was very similar to that of a terrace. SWAT defined the support measure factor values for different slope arable lands under different contour farming conditions. The support practice factor (P-factor) values and the maximum slope length of contour farming are shown in Table 5. Land use data pertaining to different slopes in the Xiangxi River watershed are shown in Table 6. Table 6 Land use area of different slopes in the Xiangxi River watershed. Slope (%) 1–2 3–5 6–8 9–12 13–16 17–20 21–25
Slope (◦ ) 0.6–1.1 1.7–2.9 3.4–4.6 5.1–6.8 7.4–9.1 9.7–11.3 11.9–14.0
Area (km2 )
Percentage (%)
114.80 20.82 67.47 62.89 88.26 115.78 141.83
18.76 3.40 11.03 10.28 14.43 18.92 23.18
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Fig. 6. Spatial contribution of major landuse adjusted in SWAT of the Xiangxi River watershed (Till indicates tillage; Re-or indicates land use returned to orchard; Re-fo indicates land use returned to forest. The study area is the sub-basin of the Xiangxi River watershed).
The simulation period was from 2007/1/1 to 2007/12/31, and all the input data were of 2007 except for the tillage management data. The SWAT model can simulate the generation of NPS pollution under different tillage management strategies by adjusting the tillage management factors (Neitsch et al., 2005b; Ullrich and Volk, 2009) and using the current implementation of straight tillage of 2007 as a background. The simulation results are shown in Table 7. As shown in Table 7, NPS pollution can be effectively reduced by implementing farming management measures (whether conservation tillage or contour farming) on high slope arable land (<15◦ ) in the Xiangxi River watershed. Conservation tillage and contour farming can reduce runoff by 15.99% and 9.16%, respectively, when compared with straight tillage. Conservation tillage and contour farming also led to reductions in TN loads of 8.99% and 8% and decreases in TP loads of 5% and 7%, respectively. Overall, for arable land high slope less than 15◦ , farming management measures can effectively alleviate soil erosion and nutrient emissions in the study area. In addition, comparison of conservation tillage with contour farming clearly demonstrated that conservation tillage more effectively controls the loss of soil, water and nutrients.
3.3.3. Simulation results of fertilizer application measures Studies have shown that agricultural lands in many areas of China currently have a surplus of nutrients. In addition, it has been shown that 30% reductions in fertilizer application dose do not impact crop yield (Xing and Zhu, 2002).
In this study, the fertilizer amounts of four scenarios were formulated based on actual surveys and statistical data generated within the study area in 2007. Specifically, the fertilizer amounts were scenario 1 (0.5 times of 2007), scenario 2 (0.8 times of 2007), scenario 3 (1.3 times of 2007) and scenario 4 (1.5 times of 2007), respectively. In the process of employing the SWAT model to simulate the impacts of chemical fertilizers on NPS pollution, all agricultural land within the watershed was treated as tillage due to the limited accuracy of the land use data. Per hectare fertilizers of arable land were the mean value of the total fertilizer application amount. The input fertilizer scenarios and simulation results are shown in Table 8. As shown in Table 8, under different simulation scenarios of NPS pollution loads, various forms of nitrogen and phosphorus were proportional to the amount of chemical fertilizer. In scenarios 1 and 2, the NPS pollution loads decreased when compared with the actual conditions as the chemical fertilizer amount decreased. When the fertilizer amount decreased to 0.5 times of 2007 (scenario 1), the TN and TP loads of the NPS pollution decreased to 0.72 and 0.65 times that of 2007. However, in scenarios 3 and 4, as the fertilization increased, the NPS pollution loads increased significantly, especially for scenario 4, in which the fertilizer amount increased by 1.5 times and the TN and TP loads increased to 1.42 times of 2007. As the fertilizer application increases, there will be more nitrogen, phosphorus and sediment loss, especially for nutrients beyond the crop growth. The greater amount of fertilizer application led to the more nutrient loss. Accordingly, controlling
Table 7 NPS pollution simulation results under different tillage management. Tillage management
Runoff (m3 s−1 )
NH3 -N (t)
NO3 -N (t)
TN (t)
TP (t)
Straight tillage Conservation tillage Change rate (%) Contour tillage Change rate (%)
50.21 42.18 15.99 45.61 9.16
11.88 10.22 13.97 10 12.96
358 304 15.00 315 11.99
388.6 353.6 8.99 358 8.00
72.14 67.09 7.00 68.53 5.00
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Table 8 NPS pollution loads under current and four fertilization scenario conditions.
Nitrogen (t) Phosphate (t) Runoff (m3 s−1 ) NH3 -N (t) NO3 -N (t) TN (t) TP (t)
Actual
Scenario 1
Scenario 2
Scenario 3
Scenario 4
37.5 12.5 46.51 30 939 988 140
18.75 6.25 46.47 21.33 667 711.08 91
30.0 10.0 46.51 26.3 866 889 119
48.75 16.25 46.54 35.93 1137 1195 161
56.25 18.75 46.60 42 1343 1412 198
the amount of chemical fertilizer and improving fertilizer application efficiency are undisputed useful measures for alleviation of ANSP.
4. Conclusions The newest version of SWAT (SWAT2005) assisted by the AVSWATX integrated GIS environment was applied in the Xiangxi River watershed at Hubei Province. Both the calibration and validation results showed quite good agreement between the observed and simulated data based on ENS and R2 . The validation remains an issue due to the lack of field data, and it will be improved in the next work. This study identified the influence factors of ANSP in study area, and simulated the effectiveness of the proposed best management practices for controlling ANSP with the SWAT model. To simulate changing land use, the effects of returning arable land with slopes more than 25◦ to forest and that with slopes of 15◦ –25◦ to orchards was simulated. As farmland was returned to forests, runoff and NPS pollution loads showed a clear decreasing trend and the NPS pollution loads decreased by at least 20%. These findings indicated that implementation of a reasonable land use plan can effectively control NPS pollution within the catchment. Simulation of the tillage management practices revealed that conservation tillage and contour farming can help reduce runoff by 15.99% and 9.16% when compared with straight tillage in 2007. From the view of nutrients, conservation tillage and contour farming can reduce TN by 8.99% and 8.00% and reduce TP by 7.00% and 5.00%. In addition, conservation tillage more effectively controlled the losses of soil, water and nutrients than both conservation tillage and contour farming. However, various forms of nitrogen and phosphorus from ANSP were found to be directly proportional to the amount of chemical fertilizer. Overall, the results of this study indicate that making reasonable adjustments in land use, tillage management and fertilizer application within the current legal and practical restrictions considered in this study will lead to significant improvement of the water quality and agriculture production in the Xiangxi River watershed. Future studies should be conducted to analyze the cost-effectiveness of the management practices that have been taken into account in this study and shown to effectively alleviate ANSP to determine the best combination of these BMPs.
Acknowledgments The research was funded by the National Natural Science Foundation of China (Grant No. 41001352), the Fundamental Research Funds for Central Universities, the Open Research Foundation of Pearl River Hydraulic Research Institute, PRWRI (No. [2010] KJ01) and the Nonprofit Environment Protection Specific Project (No. 200709024).
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Assessment of effects of best management practices on agricultural non-point source pollution in Xiangxi River watershed Ruimin Liu ∗ , Peipei Zhang, Xiujuan Wang, Yaxin Chen, Zhenyao Shen State Key Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
a r t i c l e
i n f o
Article history: Received 29 March 2012 Accepted 21 October 2012 Available online 3 December 2012 Key words: SWAT Agriculture non-point source pollution (ANSP) Best management practices (BMPs) Evaluation
a b s t r a c t Agricultural non-point source pollution (ANSP) is considered a major contributor to local water degradation in the Three Gorges Reservoir Area (TGRA) of China. The Xiangxi River, which is a first level anabranch of the Yangtze River, was selected for investigation of the effectiveness of selected best management measures (BMPs) to alleviate water pollution through analysis of several scenarios by SWAT (Soil and Water Assessment Tool). Specifically, changes in land use, fertilizer management and tillage management measures were simulated in SWAT because they were shown to be the primary factors influencing nonpoint source (NPS) pollution in the Xiangxi River watershed. The results revealed that when farmland was returned to forests, both runoff and NPS pollution loads showed a clear downward trend and the NPS pollution loads in the Xiangxi River watershed decreased by 20% or more when compared with the status of 2007. Furthermore, conservation tillage and contour farming can help reduce runoff by 15.99% and 9.16%, total nitrogen (TN) by 8.99% and 8%, and total phosphorus (TP) by 7% and 5%, respectively. Conservation tillage has a greater effect in controlling the losses of soil, water and nutrients than contour farming. Based on the fertilizer conditions of 2007, changing the fertilizer application resulted in little change in local runoff; however, for NPS pollution loads, various forms of nitrogen (N) and phosphorus (P) pollution loads were directly proportional to the amount of chemical fertilizer applied. Overall, the results of this study can facilitate development of environmental friendly land use plans by local managers, and enable farmers to manage agriculture and fertilizer more efficiently, ultimately achieve the goal of reduce water pollution. © 2012 Elsevier B.V. All rights reserved.
1. Introduction A significant amount of work has been conducted in the Three Gorges Reservoir Area (TGRA) of China to understand and control water degradation. These studies have primarily focused on nonpoint source (NPS) pollution control because it has been found to be the main source of surface water and groundwater pollution (Ongley et al., 2010; Wang et al., 2006; Lam et al., 2010; Shen et al., 2012a,b). Agriculture has been identified as the major contributor to NPS pollution of water resources (Lam et al., 2010). Some recent applications (Saleh and Du, 2004; Grizzetti et al., 2003; Akhavan et al., 2010) have employed SWAT to simulate agricultural non-point source pollution (ANSP) at the sub-basin and watershed scales. To control the adverse effects of agricultural management practices on the water quality of the study area, it is essential to identify the critical impact factors of ANSP. ANSP is generally thought to be closely related to changes in land use in watersheds (Ding, 2010;
∗ Corresponding author. Tel.: +86 10 58800829; fax: +86 10 58800829. E-mail address: [email protected] (R. Liu). 0378-3774/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agwat.2012.10.018
Zhang et al., 2011). Specifically, unreasonable land use and agricultural management will lead to soil erosion and excessive N and P losses with surface runoff, resulting in the formation of ANSP for a large area within one watershed (Hao et al., 2004). Moreover, fertilizer has significant effects on the outputs of TN and TP (Huang et al., 2010). To improve the water quality and quantity of a region, best management practices (BMPs) have been developed by western countries, especially the United States, since the 1960s (Logan, 1993). BMPs are structural or non-structural management practices that are designed to reduce the adverse effects of agricultural activities on water quality. Both domestic and foreign managers have paid a great deal of attention to the study of BMPs for protection of water quality, and BMPs have been shown to effectively reduce the NPS pollutant loads from agricultural areas (Maringanti et al., 2011; Panagopoulos et al., 2011a; Zhang and Zhang, 2011; Lam et al., 2011; Inamdar et al., 2001). There are many factors that influence the effects of BMPs on non-point source pollution, and regulatory enforcement of the implementation of BMPs has resulted in an urgent need for quantitative information regarding their effectiveness for ANSP. The effectiveness of BMPs for reduction of ANSP is difficult to monitor
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and evaluate, and monitoring of such changes would be costly and time consuming. Accordingly, a model that can forecast the effects of BMPs is needed. SWAT can simulate various management practices and their impacts on water and soil in large and complex watersheds over long periods of time in a watershed (Akhavan et al., 2010; Lam et al., 2011). Indeed, SWAT is one of the best available models for simulating the effectiveness of BMPs. Studies have indicated that a key strength of SWAT is a flexible framework allowing simulation of a wide variety of structural and nonstructural BMPs, such as conservation tillage, cover crops, application rate and timing of fertilizers, nutrient management, buffer strips, flood prevention structures, grass waterways, and parallel terraces (Gassman et al., 2007). For example, SWAT has been used to successfully simulate the sediment and nutrient concentrations of the Midnapore watershed in the west of India and Bangladesh, as well as to identify seriously polluted areas of the sub-basin and select 1 of 48 BMPs for implementation (Grizzetti et al., 2003; Behera and Panda, 2006). Since impoundment in 2003, the water quality and quantity of the Three Gorges Reservoir has changed, this has resulted in many researchers investigating the water pollution status and solution strategy of the region (Ongley et al., 2010; Wang et al., 2006; Lam et al., 2010; Shen et al., 2012a,b; Yang et al., 2011). The Xiangxi River, located 38 km upstream of the Three Gorges Dam, is the largest tributary of the Three Gorges Reservoir (TGR) in Hubei Province. The NPS pollution loads of the Xiangxi River watershed have basically increased annually since impoundment in 2003 (Liu and Wang, 2009). Considerable research including hydrological modeling as well as water quality and related ecological studies have been conducted in the Xiangxi River Watershed (Ye et al., 2009; HÃ Rmann et al., 2009; Li et al., 2009). The spatial and temporal contribution of ANSP in the Xiangxi River Watershed has been estimated (Liu and Wang, 2009). However, few of these studies have investigated the effectiveness of BMPs at controlling nonpoint source pollution of the Xiangxi River watershed. Therefore, the present study was conducted to identify the critical impact factors of the local ANSP and simulate the effectiveness of selected BMPs using SWAT, as well as to suggest effective and efficient management practices that would help reduce adverse water quality effects. Our specific goals were to assist the government with design of effective policies for planning of land use changes and to help farmers implement more reasonable agriculture management practices and more economical and effective fertilizer application techniques.
2. Materials and methods 2.1. Study area Xiangxi River is a tributary of the Yangtze River (Fig. 1), which is the longest river in China and third longest in the world. The river originates from the Shennongjia Forest region and is located between 110.47◦ and 111.13◦ E, and 30.96◦ and 31.67◦ N in the Hubei portion of the Three Gorges Reservoir. The river is 94 km long with a catchment area of 3099 km2 and its elevation ranges from 154 m to nearly 3000 m. Annual average precipitation observed from 1961 to 2004 was 1100 mm and ranges from 670 mm to 1700 mm with considerable spatial and temporal variability. The main rainfall season is from May to September, with a flood season from July to August. The annual mean runoff depth is 688 mm and the annual average temperature from 1961 to 2004 was 15.6 ◦ C (range 12–20 ◦ C). The study area is typical of northern subtropical mountainous landscapes. Approximately 70.9% of the area is covered by forests, 6.5% is farmland, 5.3% is water, 4.4% is wasteland and the remainder is residential land and transportation (Xu et al., 2010).
2.2. Development of the database The basic database for the Xiangxi River includes the digital elevation model (DEM), soil and land use maps, climate data and land management data (Table 1). All digital data were projected to the Albers conical equal area projection during data processing. The parameters of the projection were as follows: standard parallel latitudes, 25◦ N and 47◦ N; central meridian longitude, 105◦ E. 2.3. SWAT model The SWAT (Soil and Water Assessment Tool, version 2005) model (Arnold et al., 1998) is an advanced, physically based, widely distributed hydrological model that can be integrated with remote sensing (RS), geographic information system (GIS) and DEM techniques. SWAT is primarily used for meso-scale catchments, and the basic units are Hydrological Response Units (HRU; hydrotopes) based on different soils, land uses and slopes. SWAT has become an effective means of evaluating NPS water resource issues (flow, sediment, and nutrients) for a large variety of national and international water quality applications (Liu and Wang, 2009; Green and van Griensven, 2008; Plus et al., 2006). SWAT has also been widely applied for watershed scale studies dealing with water quantity and quality (Abbaspour et al., 2007). The major components of SWAT include hydrology, weather, plant growth, nutrients, pesticides, and agricultural management (Gassman et al., 2007). The details of all components can be found elsewhere (Arnold et al., 1998; Neitsch et al., 2001). Input data include weather variables, soil properties, topography, vegetation and land management practices occurring in the catchment (Neitsch et al., 2005a). SWAT can predict the impacts of long-term, point and non-point source pollution on water quality variables such as sediments, nutrients, and pesticide loads (Arnold et al., 1994). In recent years, SWAT has been widely used in ANSP studies in China (Ongley et al., 2010; Zhang et al., 2011). The present study only focused on the hydrological, nutrients and agricultural management components of the SWAT model. 2.4. Model evaluation In the model simulation process, the accuracy of the parameter selection determines the reliability of this model. There are many parameters involved in the SWAT model, and it is difficult to determine accurate values of each parameter; therefore, sensitivity analysis of SWAT is a precondition of parameter selection. At present, methods for sensitivity analysis of model parameters commonly include One factor At a Time (OAT), Fourier analysis, Monte Carlo Sampling, and Latin Hypercube Sampling (LHS). In this study, LHS (Vachaud and Chen, 2002) and OAT (Van Griensven et al., 2006) were combined to determine the parameter sensitivity, refer to the latest research progress at domestic and foreign (Morris, 1991). Parameter sensitivity discrimination formulas are shown in Eq. (1): SN =
n−1 (Yi+1 − Yi )/Y0 i=0
(Pi+1 − Pi )/100
/n
(1)
where SN is the sensitivity factor, Yi is the output value of the i-th run of the model output, and Yi+1 is the (i+1)-th output value. Y0 is the output value with parameters adjusted at the first time, Pi is the percentage change of the i-th model operating parameter value relative to the calibrated parameter values of (i−1)-th, and Pi+1 is the (i+1)-th percentage change. n is the number of model runs. More details of the method can be found in SWAT 2005 Advanced Workshop (Griensven, 2005).
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Fig. 1. Geographic location of the Xiangxi River and Xingshan County.
Table 1 Data sources and corresponding process. Data sources
Data
Processing
Landsat ETM+ 1999 and 2007 GPS ground data 1:1,000,000 district map of China 1:250,000 DEM (digital elevation map) data 1:1,000,000 The national soil data Xingshan meteorological data (1980–2007) Xingshan hydrological data (1980–2007) Changjiang water quality data (2000–2007) Land management data
Land use data, NDVI Ground reference data Vector data of district Vector data of watershed and stream network Spatial soil data Daily meteorological data Daily stream flow Water quality data (NO3 -N; NO2 -N; TN) Crop growth period and fertilization condition
Supervised classification Differential correction using base station network Digitization Hydrological model analysis Hydrological model analysis Collected from Xingshan Meteorological Station Collected from Xingshan Hydrological Station Collected from Changjiang Water Resources Committee Xingshan field survey
Model calibration is an adjustment of model parameters within recommended ranges to optimize the agreement between observed data and model simulation results (Tolson and Shoemaker, 2007). Because it is not feasible to include all parameters in the calibration procedure, a pre-selection and aggregation step was required. In this study, 24 parameters were selected for calibration for the Xiangxi River watershed (Table 2). The calibration and validation of SWAT were conducted using the latest developed calibration package, SWAT-CUP (SWAT Calibration and Uncertainty Programs). The programs include four transfer parameter methods, SUFI2 (Sequential Uncertainty Fitting version 2) (Abbaspour et al., 2007), GLUE (Generalized Likelihood Uncertainty
Estimation) (Beven and Binley, 1992), ParaSol (Parameter Solution) (Van Griensven and Meixner, 2006) and the MCMC (Markov Chain Monte Carlo) (Vrugt et al., 2003) algorithm. In our study, the SUFI2 algorithm was selected for application in the calibration and validation procedure. SUFI2 has a high efficiency when used for calibration and uncertainty quantification of large watersheds (Faramarzi et al., 2009; Yang et al., 2008). The rules of SUFI2 are adopted by the computer automatically according to certain preferred targets, after which a set of parameters for the system is selected to help achieve a multi-objective optimization. The specific usages can refer to SWAT-CUP Manual (Rostamian et al., 2008).
Table 2 Main controlling SWAT 2005 parameters selected for calibration of the Xiangxi River watershed. Parameters
Definition
ALPHA BF CANMX NPERCO CN2 ESCO SOL AWC USLE P GW QMN SOL NO3 SOL ORGN SOL ORGP
Base flow recession factor Maximum canopy index Nitrogen percolation coefficient SCS run off curve number for moisture condition II Soil evaporation compensation factor Available water capacity of the soil layer (mm/mm soil) USLE equation support practice (P) factor Threshold depth of water in shallow aquifer Initial NO3 concentration (mg/kg) in the soil layer Initial organic N concentration in surface soil layer (kg/ha) Initial organic P concentration in surface soil layer (kg/ha)
Limit value range Min 0 0 0.01 −0.56 0.01 0 0.1 0 0 0 0
Calibrated value Max 1 100 1 1.3 1 1 1 5000 100 10,000 100
0.34 34 0.62 −0.35 1.00 0.57 0.83 4684 50.85 133 395
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Fig. 2. Flow calibration and validation results, Xiangxi River watershed.
The performance of SWAT was evaluated using statistical analyses to determine the quality and reliability of the predictions when compared to the observed values. Summary statistics included the mean and standard deviation (SD), which were used to assess both the ability of SWAT to reproduce the distribution of observed data and the variability between observed and simulated data. The goodness-of-fit measures used were the coefficient of determination (R2 ; Eq. (2)) and the Nash–Sutcliffe efficiency (ENS ) value (Eq. (3)) (Nash and Sutcliffe, 1970). The R2 and ENS values are explained by Eqs. (2) and (3), respectively,
n 2
R =
i=1
n i=1
¯ (Oi − O)
n ENS =
2
¯ ¯ (Oi − O)(P i − P)
i=1
2
n
i=1 2
¯ − (Oi − O)
¯ (Pi − P)
n i=1 2
¯ (Oi − O)
2
(Pi − Oi )2
(2)
based on the observation data of 1980 (scenario 1), 1995 (scenario 2), 2000 (scenario 3), and 2007 (scenario 4). In addition, tillage management has been shown to be highly related to ANSP in many studies (Lam et al., 2011; Behera and Panda, 2006; Pandey et al., 2009). But the local tillage management practices had little changes, furthermore tillage management data of study area in the past decades had not been collected, so tillage manament will be directly selected as the BMPs without discussing its influence on ANSP. With the identified inflence factors, the scanarios of each identified factor will be setting based on the baseline conditions of 2007 to select BMPs. All the input data of SWAT were of 2007 except for the influence factor whose scenarios were discussed. The simulation period of identifying influence factors and evaluating the effectiveness of BMPs were from 2007/1/1 to 2007/12/31 in SWAT.
(3)
where n is the number of observations during the simulated period, Oi and Pi are the observed and predicted values at each compari¯ and P¯ are the arithmetic means of observed and son point i, and O predicted values. The ENS value was used to compare predicted values to the mean of the average monthly observed values for the sub-watershed, where a value of 1 indicates a perfect fit. The ENS describes the explained variance for the observed values over time that is accounted for by the SWAT model. The R2 was used to evaluate how accurately the model tracks the variations in the observed values. The difference between ENS and R2 is that ENS can interpret model performance for replicating individually observed values, while R2 cannot. 2.5. Representation of BMPs Based on previous literature review, BMPs have been proved to be an effective way to control ANSP. In order to obtain reasonable BMPs, it is necessary to identify the influence factors and evaluate the effectiveness of them. The impact factors of ANSP include land use, conservation tillage, cover crops, application rate and timing of fertilizers, nutrient management, buffer strips, flood prevention structures, grass waterways, and parallel terraces. However, it is time-consuming and unnecessary to simulate the effectives of all impact factors; therefore, it is essential to identify the critical impact factors. Based on the frequent changes in land use and large amount of fertilizer consumed in the study area, land use and fertilizer management was simulated using SWAT under different scenarios
3. Results and discussion 3.1. Model calibration and validation Xiangxi River watershed was divided into 37 sub-basins which include 320 hydrology response units (HRUs) with the ArcSWAT model before model calibration and validation. Based on these HRUs, the sensitivity analysis of parameters was carried out to calibrate in SWAT for the Xiangxi River watershed. The initial sensitivity analysis resulted in selection of parameters that were calibrated as shown in Table 2. This table includes SWAT model parameters considered in the process of calibration and determination of sensitivity statistics. The results of sensitivity analysis indicated that the parameters selected for estimation of stream flow and nutrient loads were sensitive for the Xiangxi River watershed. Sensitivity analysis was performed for flow, TN and TP of Xiangxi River watershed. Table 2 presents an overview of the 11 most sensitive SWAT2005 parameters. Actual parameter values employed in SWAT reflect the unique characteristics of the Xiangxi River watershed. Among the above parameters, SOL AWC and CANMX were the most identifiable parameters for the Xiangxi River watershed, compared with other studies (Shen et al., 2012a,b). This could be explained by the fact that SOL AWC represented soil moisture characteristics or plant available water. This parameter plays an important role in evaporation, which is associated with runoff (Burba and Verma, 2005). CANMX was highly related with the precipitation and soil conditions. The monthly flow and water quality data observed from 2002 to 2007 were used in the calibration and validation periods.
R. Liu et al. / Agricultural Water Management 117 (2013) 9–18
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Fig. 3. Water quality calibration and validation results based on monthly data – Xiangxi River watershed.
3.1.1. Flow calibration and validation The monthly monitoring data from 2002 to 2007 that were obtained from the Xingshan hydrologic station in the Xiangxi River watershed were selected as the input data for simulation. The data from 2002 to 2004 were used in the calibration phase and data from 2005 to 2007 were employed for model validation. Flow calibration and validation results of Xiangxi River watershed are shown in Fig. 2 (Liu and Wang, 2009). The results indicated that the model can be used to successfully simulate runoff in the Xiangxi River watershed and that the simulation results are credible.
3.1.2. Water quality calibration and validation TP, ammonia nitrogen (NH4 -N) and nitrate (NO3 -N) were selected as water quality indicators for calibration and validation based on common research purposes and existing water quality monitoring data. All nutrient data were obtained from the monitoring data collected at the Xingshan hydrologic station in the Xiangxi River watershed. The NH4 -N and NO3 -N were simulated using data from 2004 to 2007, while observed data from 2004 to 2005 were used in the calibration phase and data from 2006 to 2007 were used for the validation phase. NH4 -N was calculated as a difference (TN – NO3 -N – NO2 -N). As to TP calibration and validation, due to the
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Fig. 5. The relationship between fertilizer application amount and ANSP loads. Fig. 4. The relationship between ANSP and land use under different scenarios in SWAT.
Xiangxi River basin is the major phosphate producing area of China and many mines are located there, the TP loads produced by the phosphate mining was high. However, sampling and monitoring of this portion of the NPS is extremely complex and difficult; therefore, the precise statistics of the total pollution loads of mining in the basin cannot be determined. During the dry season, point source pollution is a large contributor to water degradation, while that of NPS is less; therefore, TP observed data from 2004 to 2005 during the dry season were used for calibration and from 2006 to 2007 were used for validation. The simulation and validation results are shown in Fig. 3. As shown in Fig. 3, the simulated NH4 -N and NO3 -N values were better than those of the TP when the SWAT model was employed, as indicated by most ENS and R2 values for NH4 -N and NO3 -N being larger than those for TP. The Nash–Sutcliffe coefficients during the validation period were 0.62 and 0.70 for NH4 -N and NO3 -N, respectively, but only 0.53 for the TP. Overall, although the simulated and measured values showed some error, they met the requirements for the model simulation, so the simulation accuracy is acceptable. As outlined above, the simulated and measured values of both flow and water quality showed good consistency. The values of ENS and R2 obtained were satisfactory during both the calibration and validation periods. The simulated water quality results were better than the simulated flow results. According to the results of water quality simulation of watershed models applied to other large domestic (Yang et al., 2008) and foreign (Rostamian et al., 2008; Panagopoulos et al., 2011b) basins, the simulation results obtained here are credible. Therefore, the SWAT model can be successfully applied to simulate the ANSP loads in the study area. 3.2. ANSP influence factors analysis 3.2.1. The influence of land use changes The specific data of input land use and simulation results can be seen in Fig. 4. As shown in Fig. 4, the change rule of ANSP loads is the same as the land use within the watershed, and contrary to that of
forest. When cropped area increases, the proportion of forest will be smaller, and NPS pollution loads will be greater. When land use changed from scenario 1 to scenario 2, the cropped area was reduced by 5.61% and the forest increased by 5.70%, resulting in a decrease in ANSP loads of 55.21%. Since then, TN and TP loads increased from scenario 2 to scenario 3 as a result of the increase in cropped area and decreased in forest in scenario 3. These results indicate that the ANSP loads are primarily generated by tillage in the Xiangxi River watershed. Furthermore, forests control the generation of ANSP because of their strong water-holding capacity and sediment reduction features.
3.2.2. The influence of fertilizer application To study the impact of chemical fertilizer application on ANSP, the fertilizer application amounts of 1980, 1995, 2000 and 2007 were assumed as scenario 1, scenario 2, scenario 3 and scenario 4, respectively. Other model parameters (such as land use data, meteorology data) remained the same as before, and different fertilizer applications were input into the model run. The simulation period was from 1/1/2007 to 31/12/2007. The fertilizer application from 1980 to 2007 shown in Table 3 was reported in the Statistical Yearbook of Xingshan and related references (Jia et al., 2006). The simulation results gave the NPS pollution generation under different fertilizer application scenarios (Table 3 and Fig. 5). As shown in Table 3 and Fig. 5, the ANSP loads increased significantly as the amount of fertilizer in the study area increased. From 1980 to 2007, the N and P fertilizer application rates increased from 26.87 kg/ha and 9.15 kg/ha in 1980 to 37.50 kg/ha and 12.50 kg/ha in 2007 (Table 3), respectively. The simulation results show that the TN and TP loads at the outlet of the Xiangxi River watershed increased from 750.69 Mg and 113.73 Mg in 1980 to 1126.04 Mg and 164.91 Mg in 2007 (Table 3), respectively. The increased rates of TN and TP were 50% and 45% (Fig. 5), respectively. Thus, the application amount of chemical fertilizer is another important factor of NPS pollution generation; accordingly, reducing the nutrient application amount will be an effective method of reducing NPS pollution in the study area.
Table 3 ANPS simulation results under different fertilizer application scenarios. Scenarios (t)
Scenario 1 (1980) Scenario 2 (1995) Scenario 3 (2000) Scenario 4 (2007)
Simulation results 3
−1
N
P
Flow (m s
26.87 32.74 33.52 37.50
9.15 10.09 10.45 12.50
31.73 31.73 31.73 31.73
)
Change rate (%) TN (t)
TP (t)
751 902 910 1126
114 132 133 165
Scenario 1–2 Scenario 2–3 Scenario 3–4 Scenario 1–4
Flow
TN
TP
0.00 0.00 0.00 0.00
20.18 0.92 23.67 50.00
15.65 0.93 24.23 45.00
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Table 4 ANSP loads simulated under land use in 2007 and scenario in SWAT. Changed land use
Actual land use (km2 ) Land use in scenario (km2 ) Difference value (km2 ) Change rate (%)
Simulated results
Tillage
Forest
Orchard
Grassland
Runoff (m3 s−1 )
NH4 -N (t)
NO3 -N (t)
TN (t)
TP (t)
134.98 86.56 −48.42 −35.87
733.45 748.99 15.54 2.12
8.63 41.51 32.88 381
24.18 24.18 0.00 0.00
46.51 36.74 −9.77 −21
30.19 22.34 −7.85 −26
939 707 −232 −24.69
988 745 −243 −24.59
140 111 −29 −20.61
3.3. Effective evaluation of best management practice scenarios According to the aforementioned results, changes in land use and fertilizer application management have a significant impact on the load of ANSP. In addition, tillage management has been shown to be highly related to ANSP in many studies. 3.3.1. Simulation results of changes in land use For land use management, this study simulated the following scenarios: (1) high slope arable land (>25◦ ) returned to forest; (2) high slope arable land of 15◦ –25◦ returned to orchards (both measures applied simultaneously). The actual land use of the Xiangxi River watershed in 2007 was used as the background to compare the simulation results among land use types. The spatial distribution of major changed landuse area in Xiangxi River watershed is shown in Fig. 6. The results of runoff, NH4 -N, NO3 -N, TN and TP loads simulated by the SWAT model under assumed land use scenario and actual land use in 2007 are shown in Table 4. When compared with the land use of 2007, the nutrients and runoff varied obviously under different land use. When forests and orchards increased and cropped area decreased, the TN and TP loads showed a large reduction in the Xiangxi River watershed. The TN and TP loads estimated by SWAT under changed land use were 744.70 Mg and 110.85 Mg, respectively. When compared with the landuse status of 2007, the TN and TP loads were 24.59% and 20.61% lower, respectively. Therefore, the decreasing ANSP loads were due to the increasing forests and decreasing cropped area. The simulation results indicate the following: (1) in the study area, the conversion of high slope cropland to forests can reduce runoff by 9.77%. This is likely because the surface runoff being calculated by the SCS equation and runoff is proportional to the CN2 value in SWAT model, and the CN2 value of forests is less than that of cropland, which resulted in reduced runoff in the study area. In addition, the soil water holding capacity increased as the amount of forests increased, which resulted in decreased runoff; (2) for NPS pollution loads, as the high slope arable land is reforested (>25◦ ) and tilled land is changed to orchard on high slope arable land of 15◦ –25◦ , nitrogen, phosphorus and other nutrients in the watershed showed a decreasing trend, and almost all were reduced by 20% or more. This may have occurred for several reasons. Specifically, the reduction in runoff caused nutrient loads to decline. Additionally, decreases in cropped area, which led to reductions in the amount of fertilizer applied, may be the essential reason for the reduction of nitrogen and phosphorus loads in Xiangxi River watershed. 3.3.2. Simulation results of tillage management measures Tillage management, which is a major management measure for the control of ANSP, was divided into two aspects: conservation tillage and contour farming, in this study. Both of them were applied to arable land high slope less than 15◦ of Xiangxi river watershed. (1) Conservation tillage. Conservation tillage is any method of soil cultivation that leaves the previous year’s crop residue on fields before and after planting the next crop, to reduce soil erosion and runoff. Conservation tillage methods include no-till, strip-till,
Table 5 Contour farming practice factor P for the Modified Universal Soil Loss Equation (MUSLE). Land slope (%) 1–2 3–5 6–8 9–12 13–16 17–20 21–25
Slope (◦ ) 0.6–1.1 1.7–2.9 3.4–4.6 5.1–6.8 7.4–9.1 9.7–11.3 11.9–14.0
P factor
Maximum slope length (m)
0.6 0.5 0.5 0.6 0.7 0.8 0.9
122 91 61 37 24 18 15
For terraces, use revised LS factor, loss from crop, same P as contouring factor; loss from terrace with graded channel outlet, contour P factor × 0.2, loss from terrace with underground outlet, contour P factor × 0.1. Source: Schwab et al. (1995), originally based on Wischmeier and Smith (1978).
ridge-till and mulch-till. Each method requires different types of specialized or modified equipment and adaptations in management. No-till is an important form of conservation tillage used in many foreign countries, such as US, UK, Brazil and Germany (Wang et al., 2003). The goal of no-till is minimal disturbance of the soil to maintain the residue level after harvest. In recent years, increasing researchers have studied the impact of no-till on crop production and NPS pollution (Inamdar et al., 2001; Pandey et al., 2009; Panagopoulos et al., 2011b), and no-till techniques have been largely promoted and improved in China. No-till can simplify production processes and reduce labor costs; therefore, it is useful agricultural technology with low input, high output and high efficiency. (2) Contour farming. Contour farming consists of performing field operations along the contour. These operations include plowing, planting, cultivating, and harvesting. Contour farming can prevent sheet and gully erosion, and protect gentle high slope fields against low and moderate intensity storm erosion. Studies (Guto et al., 2011; Ng et al., 2008) have shown that when the slope ranges from 3% to 8%, contour farming can effectively prevent soil erosion. However, as the slope increases, the ability of contour farming to prevent soil erosion decreases, and when the slope is greater than 25% almost no erosion is prevented by this technique. In this study, the representation of contour farming was very similar to that of a terrace. SWAT defined the support measure factor values for different slope arable lands under different contour farming conditions. The support practice factor (P-factor) values and the maximum slope length of contour farming are shown in Table 5. Land use data pertaining to different slopes in the Xiangxi River watershed are shown in Table 6. Table 6 Land use area of different slopes in the Xiangxi River watershed. Slope (%) 1–2 3–5 6–8 9–12 13–16 17–20 21–25
Slope (◦ ) 0.6–1.1 1.7–2.9 3.4–4.6 5.1–6.8 7.4–9.1 9.7–11.3 11.9–14.0
Area (km2 )
Percentage (%)
114.80 20.82 67.47 62.89 88.26 115.78 141.83
18.76 3.40 11.03 10.28 14.43 18.92 23.18
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Fig. 6. Spatial contribution of major landuse adjusted in SWAT of the Xiangxi River watershed (Till indicates tillage; Re-or indicates land use returned to orchard; Re-fo indicates land use returned to forest. The study area is the sub-basin of the Xiangxi River watershed).
The simulation period was from 2007/1/1 to 2007/12/31, and all the input data were of 2007 except for the tillage management data. The SWAT model can simulate the generation of NPS pollution under different tillage management strategies by adjusting the tillage management factors (Neitsch et al., 2005b; Ullrich and Volk, 2009) and using the current implementation of straight tillage of 2007 as a background. The simulation results are shown in Table 7. As shown in Table 7, NPS pollution can be effectively reduced by implementing farming management measures (whether conservation tillage or contour farming) on high slope arable land (<15◦ ) in the Xiangxi River watershed. Conservation tillage and contour farming can reduce runoff by 15.99% and 9.16%, respectively, when compared with straight tillage. Conservation tillage and contour farming also led to reductions in TN loads of 8.99% and 8% and decreases in TP loads of 5% and 7%, respectively. Overall, for arable land high slope less than 15◦ , farming management measures can effectively alleviate soil erosion and nutrient emissions in the study area. In addition, comparison of conservation tillage with contour farming clearly demonstrated that conservation tillage more effectively controls the loss of soil, water and nutrients.
3.3.3. Simulation results of fertilizer application measures Studies have shown that agricultural lands in many areas of China currently have a surplus of nutrients. In addition, it has been shown that 30% reductions in fertilizer application dose do not impact crop yield (Xing and Zhu, 2002).
In this study, the fertilizer amounts of four scenarios were formulated based on actual surveys and statistical data generated within the study area in 2007. Specifically, the fertilizer amounts were scenario 1 (0.5 times of 2007), scenario 2 (0.8 times of 2007), scenario 3 (1.3 times of 2007) and scenario 4 (1.5 times of 2007), respectively. In the process of employing the SWAT model to simulate the impacts of chemical fertilizers on NPS pollution, all agricultural land within the watershed was treated as tillage due to the limited accuracy of the land use data. Per hectare fertilizers of arable land were the mean value of the total fertilizer application amount. The input fertilizer scenarios and simulation results are shown in Table 8. As shown in Table 8, under different simulation scenarios of NPS pollution loads, various forms of nitrogen and phosphorus were proportional to the amount of chemical fertilizer. In scenarios 1 and 2, the NPS pollution loads decreased when compared with the actual conditions as the chemical fertilizer amount decreased. When the fertilizer amount decreased to 0.5 times of 2007 (scenario 1), the TN and TP loads of the NPS pollution decreased to 0.72 and 0.65 times that of 2007. However, in scenarios 3 and 4, as the fertilization increased, the NPS pollution loads increased significantly, especially for scenario 4, in which the fertilizer amount increased by 1.5 times and the TN and TP loads increased to 1.42 times of 2007. As the fertilizer application increases, there will be more nitrogen, phosphorus and sediment loss, especially for nutrients beyond the crop growth. The greater amount of fertilizer application led to the more nutrient loss. Accordingly, controlling
Table 7 NPS pollution simulation results under different tillage management. Tillage management
Runoff (m3 s−1 )
NH3 -N (t)
NO3 -N (t)
TN (t)
TP (t)
Straight tillage Conservation tillage Change rate (%) Contour tillage Change rate (%)
50.21 42.18 15.99 45.61 9.16
11.88 10.22 13.97 10 12.96
358 304 15.00 315 11.99
388.6 353.6 8.99 358 8.00
72.14 67.09 7.00 68.53 5.00
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Table 8 NPS pollution loads under current and four fertilization scenario conditions.
Nitrogen (t) Phosphate (t) Runoff (m3 s−1 ) NH3 -N (t) NO3 -N (t) TN (t) TP (t)
Actual
Scenario 1
Scenario 2
Scenario 3
Scenario 4
37.5 12.5 46.51 30 939 988 140
18.75 6.25 46.47 21.33 667 711.08 91
30.0 10.0 46.51 26.3 866 889 119
48.75 16.25 46.54 35.93 1137 1195 161
56.25 18.75 46.60 42 1343 1412 198
the amount of chemical fertilizer and improving fertilizer application efficiency are undisputed useful measures for alleviation of ANSP.
4. Conclusions The newest version of SWAT (SWAT2005) assisted by the AVSWATX integrated GIS environment was applied in the Xiangxi River watershed at Hubei Province. Both the calibration and validation results showed quite good agreement between the observed and simulated data based on ENS and R2 . The validation remains an issue due to the lack of field data, and it will be improved in the next work. This study identified the influence factors of ANSP in study area, and simulated the effectiveness of the proposed best management practices for controlling ANSP with the SWAT model. To simulate changing land use, the effects of returning arable land with slopes more than 25◦ to forest and that with slopes of 15◦ –25◦ to orchards was simulated. As farmland was returned to forests, runoff and NPS pollution loads showed a clear decreasing trend and the NPS pollution loads decreased by at least 20%. These findings indicated that implementation of a reasonable land use plan can effectively control NPS pollution within the catchment. Simulation of the tillage management practices revealed that conservation tillage and contour farming can help reduce runoff by 15.99% and 9.16% when compared with straight tillage in 2007. From the view of nutrients, conservation tillage and contour farming can reduce TN by 8.99% and 8.00% and reduce TP by 7.00% and 5.00%. In addition, conservation tillage more effectively controlled the losses of soil, water and nutrients than both conservation tillage and contour farming. However, various forms of nitrogen and phosphorus from ANSP were found to be directly proportional to the amount of chemical fertilizer. Overall, the results of this study indicate that making reasonable adjustments in land use, tillage management and fertilizer application within the current legal and practical restrictions considered in this study will lead to significant improvement of the water quality and agriculture production in the Xiangxi River watershed. Future studies should be conducted to analyze the cost-effectiveness of the management practices that have been taken into account in this study and shown to effectively alleviate ANSP to determine the best combination of these BMPs.
Acknowledgments The research was funded by the National Natural Science Foundation of China (Grant No. 41001352), the Fundamental Research Funds for Central Universities, the Open Research Foundation of Pearl River Hydraulic Research Institute, PRWRI (No. [2010] KJ01) and the Nonprofit Environment Protection Specific Project (No. 200709024).
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