Simulation on Agricultural Non-point Source Pollution Based on SWAT Model inDahuofang Reservoir Catchment, ChinaWei YANG University of Tsukuba *Yoshiro HIGANO University of Tsukuba
Takeshi MIZUNOYA University of Tsukuba
1. Introduction
Non-point source pollution is gaining concern increasingly due to the wide range, randomness anddifficult control. China has paid attention to the agricultural non-point source pollution (ANSP),
especially for the pollution prevention in the important reservoirs and water source regions andundertaken some correlated researches. Currently, the integration of remote sensing, geography
information systems, global positioning systems (3S) and non-point source pollution mechanism modelis the main method for ANSP research. There are some representative large-scale continuous distributed
mechanistic models such as soil and water assessment tool (SWAT), agricultural non-point source(AGNPS) model and so on. These models can be used to simulate mechanisms and processes of nonpoint source pollution accurately, which are important methods to promulgate the spatial and temporal
distribution of pollution and to identify pivotal source areas (Fenghua et al., 2006). SWAT model has
become one of the most widely used non-point source pollution model because of its reasonablestructure, high efficiency of operation, and user-friendly (Gessese et al., 2008). According to domestic
and international researches, SWAT model was used in the fields of hydrological simulation, water
resources assessment, non-point source pollution and nutrient migration and others (Rostamian et al.,2008; Santhi et al., 2001; Lei, 2007)
Dahuofang reservoir is an important drinking water source of Liaoning province, which was listed as
one of nine national water sources. It is a typical valley reservoir, which provides domestic water andindustrial water for 7 cities such as Shengyang, Daliang Fushun and so on. The total capacity ofreservoir is 21.87 ×108 m3; the area of basin is 5347km2. There are there main rivers in study area
named Hun River, Suzi River and She River as shown in Fig.1. In recent years, the ANSP isincreasingly serious due to the using of large amount of fertilizer and pesticide and discharge ofabundant livestock manure and rural domestic wastewater. The exceeding standard rate of annual
average value of total nitrogen (TN) and total phosphorus (TP) are 100% and 85.7%. In previous studies,
researchers only focused on planting pollution based on SWAT, this study also considers livestock andrural life simultaneously. The results of the research will provide basic support to the establishment of
the early-warning platform, and the formulation of controlling technologies and management policies of
the ANSP.

Fig.1. Study area location and meteorological, hydrologic, water quality monitoring sites
2. Methodology
The load of TN and TP discharged from livestock and poultry breeding and rural life was estimated byusing export coefficient method, the data was input SWAR model, and then making a comprehensive
analysis of the load of pollutants discharged from planting, livestock and poultry breeding and rural lifeand pollution law.
2.1 Export coefficient method
Export coefficient method, proposed in the early 1970s, was mainly used to study the relationshipbetween land use and eutrophication of lakes. Johnes also considered livestock, population and otherfactors (Johnes, 1996). This model has been further improved and was widely used. Equation as fallow:nL   Ei Ai I i   P(1)i 1

Where: L is the loss amount of pollutant (kg), Ei is the output coefficient of pollution source i, Ai is thearea of land use i, the amount of livestock i, or the population, Ii is the input load from pollution source i,
P is the load of pollutants input from rain.
According to ‘National Drinking Water Source Protection Plan’, the export coefficients of TN
discharged from night soil and domestic wastewater for one person are 0.83 and 1.83 kg•a-1, and TP is0.08 and 0.16 kg•a-1. The coefficients of livestock breeding were determined based on statistic datafrom ‘Animal Health Supervision Authority’. The TN export coefficients of unit cattle, pigs, sheep andchickens are 10.58、0.57、0.91and 0.03 kg•a-1, TP is 0.31、0.14、0.05 and 0.005 kg•a-1.

The study area was divided into 12 sub-regions according to the spatial distribution of rural populationand livestock and poultry farms, pollutants output port were established in each sub-region. The output
load of pollutants in each sub-region was dispersed based on the rainfall of 12 months. Finally, thetemporal and spatial discretization results were input SWAT model.
2.2 SWAT model
SWAT is watershed scale simulation model, which is developed by Dr. Jeff Arnold for the USDA
Agricultural Research Service (Neitsch et al, 2002). Spatial data include DEM resolution ratio30m×30m, land use and soil type map scale 1:50000, and water system map. Attribute data mainly consist ofmeteorological data and soil data. The monthly data of precipitation was collected from Qingyuan,
Zhangdang and Xinbin meteorological station from 1986 to 2009, which is used to establish the weather
generation routines. The daily data of precipitation and air temperature was collected respectively from
14 precipitation monitoring sites and 3 meteorological monitoring sites from 2001 to 2009 as shown inFig.1. The data used to construct soil attribute data base was collected from ‘Fushun Soil’.
SWAT model extracted water system within watershed based on DEM. 45 sub-watersheds were formedby means of the fixed area of catchment and outlet of the watershed as shown in Fig.1. According to the6% threshold of land use and the 12% threshold soil type, the whole watershed was divided into 277
hydrological response units (HRU), and then loading meteorological data and the results ofdiscretization. Initial simulation was implemented in SWAT VIEW interface.
Relative error δ, determination coefficient r2 and Nash-Suttcliffe coefficient Ens were used to evaluate
the applicability of model and to calibrate SWAT model. Equations are as follow:
Qm  Qo
100%Qo

(2)



2

 n

  Q o ,i  Q o Q m ,i  Q m 
r 2  n i 1n22
 Q o,i  Q o  Q m ,i  Q mi 1(3)i 1n（Q  Q ）2

Ens  1  i1no,im,i (Q  Q )i 1o,i(4)2oWhere: Qo,i is measured value, Qm,i is simulation value,average simulation value.

Qo is the average measured value, and Qm is theIn this study, runoff and sediment were calibrated firstly, then nitrogen and phosphorus. The order oftime calibration is annually, monthly and daily calibration. The date of runoff and sediment used tocalibrate and verify was collected form Nanzhangdang, Zhanbei, and Beikouqian hydrology monitoringsites. The simulation values of nitrogen and phosphorus were calibrated by using the data from Taigou,

Gulou and Beizahmu water quality monitoring sites. The time for model preheating is 2005, forcalibration is from 2006 to 2007, for verification is from 2008 to 2009. The spatial distribution ofhydrology and water quality monitoring sites was showed in Fig.1. The results of calibration andverification were showed in Table 1 and Fig.2.
Table 1 Evaluation on the results of calibration and validation of runoff, sediment, TN and TP

Calibrationperiod

Verification
periodItem
Runoff (m3/s)
Load of sediment (t)Load of TN (t)Load of TP (t)Runoff (m3/s)
Load of sediment (t)Load of TN (t)Load of TP (t)Rainfall

Measured valueof runoff

Average monthly
measured value
31.12
5069.23181.62
10.02
21.19
3142.23
153.34
7.85

Average monthly
simulation value
29.53
4756.39194.1611.46
22.63
3619.35
159.46
8.52

δ%

Ens

r2

-5.13
-6.17
6.09
14.37
6.81
15.18
3.99
8.58

0.85
0.91
0.66
0.69
0.84
0.69
0.67
0.73

0.86
0.920.71
0.68
0.85
0.74
0.69
0.68

Simulation valueof runoff
0200501001501501002002505030002006 2006 2007 2007 2008 2008 2009 2009-01-07-01-07-01-07-01-07
Month350

Load of sediment/10 3 t

Simulation
periodFig.2 Simulated and observed value of monthly runoff, sediment, TN and TP
3. Results and analysis
According to Table 1, the simulation index δ, Ens and r2 all reached the requirement. The simulation
effect of runoff was good, Ens>0.80，r2>0.80. The simulation effect of sediment in calibration period isbetter than in verification period. The simulation index of TN and TP are all higher than 0.65. The
applicability of SWAT in this study reached the standard.
3.1 Temporal analysis of ANSPThe load of sediment, TN and TP that flowed into reservoir were showed in Table 2. The load ofsediment, TN and TP in 2007 was larger than other years with the maximum rainfall 850mm. The load

of TN that flowed into reservoir was decreased from 2155.83t to1685.15t from 2006 to 2009. The
annual average load of sediment, TN and TP can be calculated as 82.65×103 t, 1873.49 t and 81.97 t.
Table 2 Annual variation of ANSP input the reservoir from 2006 to 2009
YearRainfall/mm

Runoff/106m3

Load of sediment/103t

Load of TN/t

Load of TP/t

200620072008
2009
Average

850.40639.60714.40694.60724.751416.811032.431034.76875.511089.88108.22
71.95
84.16
66.29
82.65

2155.83
1878.32
1774.67
1685.15
1873.49

95.79
77.38
81.11
73.60
81.97

The monthly load of pollutants was compared with water quantity that flowed into reservoir, as shownin Fig.3. Determination coefficient r2 of sediment, TN and TP with water quantity input reservoir was0.94, 0.89 and 0.88. Generation and migration of Sediment were closely related with surfacehydrological cycle, which were strongly impacted by the spatial distribution of rainfall and runoff. The
generation of ANSP and the migration of the pollutants are substantially influenced by the runoff,
precipitation and the generation of sediment. The loss of sediment, nitrogen and phosphorus was up to
the maximum in July and August each year, accounting for 67.91%, 42.64% and 44.42% of the annual
loss amount respectively, because of the strength of rainfall and soil erosion increasing, sedimenttransport capacity was increased by large runoff.

Fig.3 Variation of the load of sediment, TN and TP with water quantity input reservoir
4.1 Spatial analysis of ANSPThe study area was divided into four sub-basins to analyze the contribution rate of pollutants to thereservoir as shown in Table 3. The data in Table 3 was the annual average value from 2006 to 2009.
According to Table 3, there was 522.63×106 m3 water that flowed into reservoir from Hun River basin,
as the biggest contributor, also Hun River basin has the greatest contribution rate of the ANSP, thecontribute rate of pollutants was more than 40%, followed by Suzi River basin and She River basin, The
small river basins around the reservoir have the least contribution rate. Hun River basin as the biggest
contributor has a large population with biggest output value of farming and livestock breeding.
Table 3 Contribution rate of water quantity and ANSP from different basins
Item
Hun River basin
Suzi River basin
She River basin
Small river basins
TotalWaterquantity(106m3)522.63482.1152.72
32.421089.88

CR of Waterquantity
(%)
47.95
44.23
4.85
2.97
100

Load ofsediment(103t)
40.77
35.18
3.22
3.48
82.65

CR of Loadof sediment
(%)
49.33
42.57
3.88
4.22
100

Load ofTN(t)966.28802.35
83.75
21.11
1873.49

CR of
Load ofTN (%)
51.58
42.83
4.46
1.13
100

Load ofTP(t)
43.37
33.82
3.05
1.73
81.97

CR of
Load ofTN (%)
52.91
41.26
3.72
2.10
100

4. Conclusion
According to the results of calibration and verification based on the monitoring data of hydrology andwater quality, the r2 of monthly runoff and sediment was higher than 0.74, the Ens was higher than 0.69.
The SWAT model after being calibrated can be used in this study area. By simulation of SWAT model
total amount of TN, TP and average sediment load that flows into Dahuofang Reservoir can becalculated as 1873.49t, 81.97t and 82.65×103t. The generation and migration of ANSP was substantiallyinfluenced by the rainfall and runoff. The loss of sediment, TN and TP was up to the maximum in Julyand August each year, accounting for 67.91%, 42.64% and 44.42% of the annual loss amountrespectively. Spatially, Hun River basin contributed the greatest of the ANSP, followed by Suzi Riverbasin and She River basin; the small river basins around the reservoir have the least contribution rate.
A comprehensive command of the source, generation, distribution and inflow of the ANSP in the study
area and the dynamite change of precipitation can be acquired. The results of the research will provide
basic support to the establishment of the early-warning platform, the conservation of the water quality inthe water source region and ecological security.
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