Ecological Indicators 32 (2013) 294–304
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Ecological Indicators
journal homepage: www.elsevier.com/locate/ecolind
Investigating the effects of point source and nonpoint source pollution
on the water quality of the East River (Dongjiang) in South China
Yiping Wu a,b,∗ , Ji Chen b,∗∗
a
ASRC Research and Technology Solutions, contractor to U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls,
SD 57198, USA
b
Department of Civil Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China
a r t i c l e
i n f o
Article history:
Received 10 December 2012
Received in revised form 27 March 2013
Accepted 7 April 2013
Keywords:
East River (Dongjiang)
Nitrogen
Phosphorus
Pollution source areas
SWAT
Water quality index
a b s t r a c t
Understanding the physical processes of point source (PS) and nonpoint source (NPS) pollution is critical
to evaluate river water quality and identify major pollutant sources in a watershed. In this study, we used
the physically-based hydrological/water quality model, Soil and Water Assessment Tool, to investigate
the influence of PS and NPS pollution on the water quality of the East River (Dongjiang in Chinese) in
southern China. Our results indicate that NPS pollution was the dominant contribution (>94%) to nutrient
loads except for mineral phosphorus (50%). A comprehensive Water Quality Index (WQI) computed using
eight key water quality variables demonstrates that water quality is better upstream than downstream
despite the higher level of ammonium nitrogen found in upstream waters. Also, the temporal (seasonal)
and spatial distributions of nutrient loads clearly indicate the critical time period (from late dry season
to early wet season) and pollution source areas within the basin (middle and downstream agricultural
lands), which resource managers can use to accomplish substantial reduction of NPS pollutant loadings.
Overall, this study helps our understanding of the relationship between human activities and pollutant
loads and further contributes to decision support for local watershed managers to protect water quality
in this region. In particular, the methods presented such as integrating WQI with watershed modeling
and identifying the critical time period and pollutions source areas can be valuable for other researchers
worldwide.
Published by Elsevier Ltd.
1. Introduction
Surface water impairment due to point source (PS) and nonpoint
source (NPS) pollution threatens the aquatic ecosystems and water
supply security. PS pollution mainly includes municipal sewage
discharges (from urban or highly residential areas) and industrial
wastewater loads (from a variety of manufacturers). NPS pollution occurs when rainfall, snowmelt water or irrigation water runs
over land, carrying and depositing pollutants into rivers, lakes,
and coastal waters. NPS pollution from agriculture is regarded as
the major cause of the surface water quality degradation and has
attracted growing public concern (Darradi et al., 2012; Hao et al.,
2004; Nasr et al., 2007; Ongley et al., 2010; Tang et al., 2011; Zhang
et al., 2008). Estimating NPS pollutant loads is challenging due
∗ Corresponding author at: ASRC Research and Technology Solutions, contractor
to U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS)
Center, Sioux Falls, SD 57198, USA.
∗∗ Corresponding author.
E-mail addresses: [email protected], [email protected] (Y. Wu),
[email protected] (J. Chen).
1470-160X/$ – see front matter. Published by Elsevier Ltd.
http://dx.doi.org/10.1016/j.ecolind.2013.04.002
to the complicated hydro-meteorological and bio-chemical processes and the spatial variability involved in the process of pollutant
transport and transformation (Ficklin et al., 2010; Luo et al., 2008;
Nikolaidis et al., 1998).
With intensive agricultural development, excessive utilization
of commercial inorganic fertilizers for raising crop yields has
become a major issue and has resulted in increased nutrient additions. The subsequent nutrient losses to stream water and estuaries
have caused eutrophication of many coastal and freshwater ecosystems around the world (Alexander et al., 2008; Cao and Zhu, 2000;
Carpenter et al., 1998; Nixon et al., 1995; Rabalais et al., 2002;
Schoch et al., 2009; Vitousek et al., 1997; Wu and Liu, 2012b).
Therefore, it is crucial to investigate the status of water pollution
by measuring and estimating nutrient loadings for environmental planning, management, and restoration. However, long-term
watershed water quality monitoring is costly and time consuming
(Santhi et al., 2001) and not applicable for predicting the potential
effects of future climate and land cover change scenarios. Practically, a reasonable numerical simulation of these complicated
terrestrial processes in a watershed would be a useful tool to
investigate the water quality status, to predict potential impacts
of climate and land cover changes, and to find optimal solutions to
Y. Wu, J. Chen / Ecological Indicators 32 (2013) 294–304
pollution problems (Borah and Bera, 2002; Ficklin et al., 2010; Liu
et al., 2008; Panagopoulos et al., 2011; Panagopoulos et al., 2012;
Wilson and Weng, 2011; Wu and Liu, 2012a; Wu et al., 2012a,c;
Zhang et al., 2011; Zhang and Zhang, 2011).
Rapid socioeconomic development in the East River (Dongjiang
in Chinese) Basin in southern China (especially the downstream
area), resulting from population growth and dramatic industrial
and agricultural development, has substantially increased water
demands and pollutant loadings to the river and its estuary (Wu and
Chen, 2013b; Wu et al., 2012b; Zhou et al., 2012). Although water
is relatively abundant in this region (Wu and Chen, 2013a), water
quality degradation is of wide concern. Therefore, evaluating the
regional water quality and identifying the critical pollution sources
are an urgently needed as the local society seeks for sustainable
development strategies.
To investigate the PS and NPS pollution processes of the East
River, we used the multi-disciplinary Soil and Water Assessment
Tool (SWAT) as the modeling approach. This study has four tasks:
(1) evaluate the water quality status at two major cross sections
along the river, (2) investigate the seasonal variations of nutrients due to hydro-meteorological forcing, (3) identify the critical
source areas of the NPS nutrient loadings, and (4) examine the
contributions by PS and NPS pollutant loadings at the watershed
scale.
2. Materials and methods
2.1. Model description
The SWAT model (version 2005) (Arnold et al., 1998; Neitsch
et al., 2005) used in this study was developed by the U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS) for
exploring the effects of climate and land management practices on
water, sediment, and agricultural chemical yields (Douglas-Mankin
et al., 2010; Gassman et al., 2007). This physically-based watershed scale model simulates the terrestrial hydrological cycle, plant
growth, soil erosion, sediment transport, and agricultural chemical yields on a daily time step (Arnold et al., 1998). Hydrological
Response Unit (HRU) (Flügel, 1996; Flügel, 1997) is the basic simulation unit and is defined as a lumped land area composed of a
unique land cover, soil properties, and slope in SWAT (Neitsch et al.,
2005).
For the land phase nutrient cycle, SWAT simulates the organic
and mineral nitrogen (N) and phosphorus (P) fractions by separating each nutrient into component pools. Then, N and P can increase
or decrease depending on their transformation and/or additions/losses occurring within each pool (Green and van Griensven,
2008; Neitsch et al., 2005). For the water phase nutrient cycle
(i.e., in-stream water quality transformation), the SWAT model
incorporates the QUAL2E algorithm (Brown and Barnwell, 1987)
to simulate constituent interactions and transformations (Neitsch
et al., 2005). Further details of nutrient cycles in the land phase
and transport as well as transformation in the water phase can
be found in the model’s theoretical documentation (Neitsch et al.,
2005).
2.2. Water quality evaluation formula
In order to evaluate water quality, which encompasses a number of pollutant species, an ecological approach should combine
physical, chemical, and biological constituents to reflect the quality
status (Chapman, 1996). Stambuk-Gilijanovic (2003) and Liou et al.
(2004) presented the water quality score for each indicator (i.e.,
water quality variables such as nitrate N (NO3 –N), ammonium N
(NH4 –N), biological oxygen demand (BOD), and dissolved oxygen
295
Table 1
Eight water quality variables used in the calculation of Water Quality Index.
No.
Parameters
Unit
Weighting
Source of method for score scaling
1
2
3
1
5
6
7
8
min-N
org-N
min-P
BOD
DO
SS
T
pH
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
◦
C
–
0.2
0.1
0.16
0.14
0.2
0.06
0.07
0.07
Stambuk-Gilijanovic (2003)
Stambuk-Gilijanovic (2003)
Stambuk-Gilijanovic (2003)
Liou et al. (2004)
Liou et al. (2004)
Liou et al. (2004)
Liou et al. (2004)
Liou et al. (2004)
(DO)). With these scales, a general and comprehensive Water
Quality Index (WQI) (Liou et al., 2004; Stambuk-Gilijanovic, 2003)
can be calculated as follows:
1
Wi · Qi
n
n
WQI =
(1)
i=1
where Wi is the weighting factor of the water quality variable i,
Qi is the related water quality score, and n is the number of water
quality variables. The objective of the WQI is to inform about the
quality status of a specific water body.
To evaluate the water quality status of the East River with
such a comprehensive WQI, we used eight water quality variables
including mineral N (min-N), organic N (org-N), mineral (min-P),
BOD, DO, suspended sediment (SS), temperature (T), and pH (see
Table 1) at LC (upstream) and BL (downstream) gaging stations.
After Stambuk-Gilijanovic (2003) and Liou et al. (2004), as shown in
Fig. A.1 (see Appendix A), water quality score for each variable (Qi )
is on a continuous scale from 0 to 100, where 100 represents perfect
water quality conditions while zero indicates poor conditions.
The first six variables are from the model simulations, whereas
the last two (T and pH) are from the water quality monitoring
data (GEPMC, 1991–1999). The weighting factors are referenced
from Stambuk-Gilijanovic (2003). Because the variable coliform is
unavailable in our simulation, its weighting factor of 0.16 is evenly
allocated to four other key variables (i.e., BOD, DO, total N (TN), and
min-P). Table 1 lists the final weighting factors for the eight water
quality variables.
2.3. Study area
The East River (Dongjiang in Chinese) is one of the three main
tributaries of the Pearl River (Zhujiang in Chinese), which is the
fourth largest river in terms of drainage area in China (Niu and
Chen, 2010). The East River Basin (see Fig. 1) lies between latitudes
22◦ 34 and 25◦ 12 N and longitude 113◦ 24 and 115◦ 53 E (Chen and
Wu, 2008). Originating in Xunwu county in Jiangxi province, the
East River flows from northeast to southwest and discharges into
the Pearl River delta with an average gradient of 0.39‰ (Jiang et al.,
2007). The East River is also the drinking water source for the areas
outside of the basin (e.g., Hong Kong, Shenzhen, Huangpu, and
Dayawan). The major downstream gage station, Boluo (noted as BL
hereafter), has a drainage area of 25,325 km2 , and upstream gage
station, Longchuan (noted as LC hereafter), has an area of 7699 km2
(PRWRC, 1987). The East River Basin is near the coast of the South
China Sea and located in a monsoon-dominant climate region with
considerable spatial and temporal variations of precipitation (Wu
and Chen, 2013a). The wet season occurs from April to September,
and the remainder of a year is the dry season. The average annual
total precipitation of the basin is 1800 mm/yr, and the annual discharge at Boluo is about 739 m3 /s (23.3 billion m3 /yr or 920 mm/yr)
(Chen and Wu, 2012).
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Y. Wu, J. Chen / Ecological Indicators 32 (2013) 294–304
Fig. 1. Point source pollution locations and two major gage stations, (Longchuan (noted as LC) and Boluo (noted as BL)) (a) and land uses (b) in the East River Basin in southern
China. Land use codes such as AGRR, FRST, PAST, URBN, and WATR refer to agriculture, forest, pasture/range, urban, and water, respectively.
Table 2
Typical sewage water quality data and values used in the East River Basin.
2.4. PS pollution data
The PS pollution data are the important inputs to SWAT. The PS
pollution includes the municipal and industrial loads. Fig. 1a shows
the names and locations of the 12 cities where PS pollutant loads
discharge to the East River.
2.4.1. Municipal PS pollution data
Because measured sewage quantity and quality data for these
cities (Fig. 1a) are unavailable, the required load data were estimated based on urban population and China’s typical sewage water
quality data (Table 2) (Xiao, 2002). According to Xiao (2002), the
average values of the middle and low levels can be used to represent
the sewage quality in southern China (see the second last column
in Table 2). According to Zhang and Jorgensen (2005), water consumption can be set to 200 L/d per capita considering the sufficient
water supply over the region. As a result, the pollutant loads per
capita can be derived (see the last column in Table 2).
Table 3 lists the collected urban population data in 1990 and
2000 (GLRO and GDPC, 2003; POCNSD, 1996; SCO and PSSCNSD,
2003) for these cities. The total municipal pollutant loads can
No.
Item
BOD
NH4 –N
org-N
TN
min-P
org-P
TP
1
2
3
4
5
6
7
Concentration (mg/L)
Values useda
High
Middle
Low
Concentration
(mg/L)
Loadb
(g/d/capita)
400
50
35
85
10
5
15
200
25
15
40
5
3
8
100
12
8
20
3
1
4
150
18.5
11.5
30
4
2
6
30
3.7
2.3
6
0.8
0.4
1.2
Note: a Values used in this study were equal to the mean of middle and low levels;
b
Wastewater load of 200 L/d/capita was used to estimate the pollutant loads.
be estimated by multiplying pollutant loads per capita by urban
population. As an example, Table 3 lists the total nitrogen (TN)
and total phosphorus (TP) loads for 1990 and 2000, and pollutant
load data for other years (1991–1999) were estimated using linear
interpolation. Because the continuous measurement of sewage
water is unavailable, the method we described above to estimate
pollutant loads are usually used for designing a wastewater
Table 3
Urban population and daily total nitrogen (TN) and total phosphorus (TP) loads for each city.
No.
City
Subbasin number
Population
1990
1
2
3
4
5
6
7
8
9
10
11
12
Xunwu
Dingnan
Heping
Longchuan
Lianping
Xinfeng
Heyuan
Zijin
Huidong
Huiyang
Huizhou
Boluo
25
26
4
39
9
29
16
33
23
24
37
38
Total
–
600,604
TN load (kg/d)
2000
1990
TP load (kg/d)
2000
1990
28,061
18,378
40,793
64,707
42,680
28,953
103,341
63,803
100,685
651,97
154,839
112,739
66,776
64,298
87,115
143,071
87,174
64,072
279,389
135,828
446,114
895,978
897,858
436,537
168
110
245
388
256
174
620
383
604
391
929
676
401
386
523
858
523
384
1676
815
2677
5376
5387
2619
42
28
61
97
64
43
155
96
151
98
232
169
3,091,704
3604
18,550
901
4638
2000
100
96
131
215
131
96
419
204
669
1344
1347
655
Y. Wu, J. Chen / Ecological Indicators 32 (2013) 294–304
Table 4
Industrial pollution loads for each city in 1992.
No.
City
1
2
3
4
5
6
7
8
9
10
11
12
Xunwu*
Dingnan*
Heping
Longchuan
Lianping
Xinfeng
Heyuan
Zijin
Huidong
Huiyang
Huizhou
Boluo
Total
–
Subbasin
number
25
26
4
39
9
29
16
33
23
24
37
38
9203
2.6. Model setup
BOD (kg/d)
NH3 –N (kg/d)
min-P (kg/d)
5.5
5.5
5.5
910
0
348
745
759
4174
178
360
1712
0
0
0
0
0
0
12.6
0
569
2.7
0
233
0
0
0
0
0
0
2.5
0
114
0.3
0
47
817
297
164
Note: * Load data were missing for Xunwu and Dingnan, and the same load as Heping
was assumed.
treatment plant in China (Xiao, 2002). Thus, our estimations are
assumed to be reasonable.
2.4.2. Industrial PS pollution data
It is also a challenge to monitor the industrial wastewater quantity and quality load for long periods of time in China. The published
industrial PS pollution monitoring data (BOD, NH4 –N, and min-P)
(see Table 4) are available for 1992 only and for only 10 cities in
the East River Basin (GEPMC, 1992). The loads for the other two
cities (Xunwu and Dingnan), were set to be the same with that of a
nearby city, Heping, considering the similar development levels of
these cities. It is worth noting that the industrial PS pollution loads
listed in Table 4 are used for the water quality simulation period of
1991 to 1999. Compared to the industrial loads (Table 4), the estimated municipal loads (Table 3) accounted for more than 80% of
the total PS load.
2.5. NPS pollution data
NPS pollution mainly results from agricultural practices (e.g.
fertilization), atmospheric deposition (e.g., N and P contained in
rainwater), and plant residue decomposition.
2.5.1. Agricultural practices
Through field investigation and literature (GLRO and GDPC,
2003), the land management over the agricultural land was set as
two crops per year and three periods of fertilization for each crop
season over the East River Basin. The general farming practices of
planting and harvesting for two-season crops were adopted from
Guangdong Crop Irrigation Estimation (Liang, 1999). According
to the agricultural survey in Guangdong in the 1990s, the averaged amount of total fertilizer applied was around 140 kg/ha/yr,
with 70 kg/ha for each crop season beginning on April and August,
respectively.
2.5.2. Atmospheric deposition
Rainwater contains nutrients, originating from air pollution.
Zhang and Jorgensen (2005) provided six classes of the N and P
concentrations in rainwater based on the industrial and husbandry
levels for the condition of 1-m precipitation per year. Because the
percentage of urban area in the East River Basin is only 1.4%, and
the forest area is more than 75%, the study area can be classified
as the lowest level (class VI) in terms of nutrient concentrations
(Zhang and Jorgensen, 2005). Then the N concentration in rainwater was estimated as 0.1 mg/L for the whole basin for the annual
precipitation of 1.8 m, and the P concentration was set to 0.005 mg/L
based on the ratio of N:P (20:1) in class VI.
The input data for driving the SWAT model include weather data,
topographic data, soil properties, and land use and land management information (Arnold et al., 2000; Neitsch et al., 2005). In this
study, the SRTM Digital Elevation Model (DEM) data with the 90-m
resolution (Jarvis et al., 2006) were adopted to delineate the East
River Basin. To parameterize the model, the land use data with
30-m resolution obtained from the Chinese Academy of Sciences
were used. The data indicate five major land use types including agriculture, forest, pasture, urban area, and water surface (see
Fig. 1b). According to Guangdong Soil (Guangdong Soil Survey Office
(GSSO), 1993), there are three major soil types, latosolic soil, red
soil, and paddy soil, in the East River Basin. We used the multiple Hydrological Response Unit (HRU) option, representing each
unique combination of land cover and soil type as an individual
HRU, to discretize the basin into 271 HRUs.
Daily precipitation, maximum and minimum air temperature, wind speed and relative humidity data from eight weather
stations were obtained from the National Climatic Data Share
Center of China (http://cdc.cma.gov.cn/home.do) (see Fig. 1).
Solar radiation data were from the National Centers for Environmental Prediction and Atmospheric Research (NCEP/NCAR)
(http://www.esrl.noaa.gov/psd/data/reanalysis/reanalysis.shtml).
In addition, PS pollution and NPS pollution as described previously
(see Sections 2.4 and 2.5) were included in the model setup.
2.7. Model calibration and validation
Streamflow. In previous studies (Chen and Wu, 2012; Wu
and Chen, 2013a), we evaluated the performance of SWAT in
simulating streamflow at BL in the East River Basin using an eightyear (1973–1980) period for calibration and another eight-year
(1981–1988) period for validation. Model evaluation shows that
the streamflow simulation performed well with the daily NashSutcliffe efficiency (NSE) being 0.84 for calibration and 0.82 for
validation; whereas monthly NSE values can reach 0.93 for calibration and 0.90 for validation (Chen and Wu, 2012).
Sediment. In another previous study (Wu and Chen, 2012), we
evaluated the model performance in simulating sediment at BL in
the East River Basin with the same calibration (1973–1980) and
validation (1981–1988) periods. Model evaluation shows that the
monthly sediment simulation was satisfactory with the NSE being
0.69 for calibration and 0.67 for validation (Wu and Chen, 2012).
Due to the unavailability of streamflow and sediment observations for the current study period (1991–1999), we did not
re-calibrate the model but used the same streamflow and sediment
parameters from our previous studies (Chen and Wu, 2012; Wu and
Chen, 2012, 2013a).
Water quality. In the current study, we used the pollutant load
data including NH4 –N, nitrite N (NO2 –N), NO3 –N, BOD, DO, and TP
irregularly monitored at LC and BL to calibrate and validate the
SWAT model for water quality simulation. It is noted that such
monthly observation data published by Guangdong Environmental Protection and Monitoring Center were sparsely available for
1991–1999 (GEPMC, 1991–1999). Results for evaluation of water
quality modeling are presented in Section 3.1.
3. Results
3.1. Model examination
As stated above (see Section 2.7), the daily streamflow and sediment data at the basin outlet (BL) covering 16 years (1973–1988)
have been used for model calibration (1973–1980) and validation
(1981–1988) in our previous studies (Chen and Wu, 2012; Wu and
Chen, 2012, 2013a). In this study, due to the availability of observed
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Y. Wu, J. Chen / Ecological Indicators 32 (2013) 294–304
Fig. 2. Comparison of monthly simulated and observed six water quality variables including NH4 –N, NO2 –N, NO3 –N, BOD, DO, and TP at two stations (LC and BL) for the
period of 1991–1999.
water quality data, we used the four-year (1991–1994) observed
pollutant load data for model calibration and the rest of the data
(1995–1999) for validation. Ten water quality-related parameters
in SWAT were calibrated (see Table 5) by comparing the six simulated water quality variables (i.e., NH4 –N, NO2 –N, NO3 –N, BOD, DO,
and Total P (TP)) with the observed variables. Fig. 2 shows the simulated and observed monthly water quality variables. Although the
number of observation data is limited, the visual comparison indicates that SWAT can provide acceptable estimations because the
model simulation can capture the seasonal variations of the water
quality variables with the reasonable ranges when compared to the
observed variables.
3.2. Water quality status
Fig. 3a shows the nine-year (1991–1999) average min-N (i.e.,
NO3 –N, NH4 –N, and NO2 –N), org-N, min-P, and organic P (org-P)
loads at LC and BL, and Fig. 3b shows the average SS, BOD, DO,
and WQI for the same time period. Except for SS, the other eight
water quality variables indicate the water quality upstream (i.e.,
LC) was better than downstream (i.e., BL). The comprehensive WQI
(Fig. 3b) at LC is 89, indicating the water quality at LC is generally
better than BL with an index of 80. The higher SS concentration
(see Fig. 3b) in the upstream location makes sense considering the
higher soil erosion level due to larger land slopes and sediment
Y. Wu, J. Chen / Ecological Indicators 32 (2013) 294–304
299
Table 5
Calibrated parameters in SWAT for the East River Basin.
Parameter
Description
Above LC
LC to BL
CMN
NPERCO
1
2
3
2
ˇN,1
ˇN,2
ˇN,3
ˇP,4
Rate factor for humus mineralization of active organic N
N percolation coefficient
Local algal settling rate in the reach (m/d)
Benthic source rate for dissolved P in the reach (mg P/m2 d)
Benthic source rate for NH4 –N in the reach (mg N/m2 d)
Oxygen re-aeration rate for Fickian diffusion in the reach (d−1 )
Rate constant for biological oxidation of NH4 to NO2 in the reach (d−1 )
Rate constant for biological oxidation of NO2 to NO3 in the reach (d−1 )
Rate constant for hydrolysis of organic N to NH4 in the reach (d−1 )
Rate constant for mineralization of organic P to dissolved P (d−1 )
0.0001
0.04
1.5
0.001
2.5
0.55
0.008
0.04
0.01
0.01
0.0002
0.01
1.5
0.001
0.001
0.3
0.04
1.0
0.003
0.008
Fig. 3. Annual average nutrients (a), SS, BOD, DO concentrations and Water Quality Index (WQI) (b) at two stations (LC and BL).
carrying capacity due to higher river velocity in the upstream area
compared to the downstream area.
According to the monthly variation range of water quality
variables (see Fig. 2) and China’s Environmental Water Quality
Standards for Surface Water (GB3838-2002) (EPAC, 2002), water
quality at LC can be rated as Class II (DO ≥ 6 mg/L, BOD ≤ 3 mg/L,
NH4 –N ≤ 0.5 mg/L, TN ≤ 0.5 mg/L, and TP ≤ 0.1 mg/L), indicating
that the water is qualified as a drinking water source in China. However, water quality at BL can only be rated as Class III (DO ≥ 5 mg/L,
BOD ≤ 4 mg/L, NH4 –N ≤ 1 mg/L, TN ≤ 1 mg/L, and TP ≤ 0.2 mg/L)
because the TP load is high.
3.3. Seasonal variation of in-stream nutrients
To investigate the seasonal variation of in-stream nutrients,
Fig. 4 (a for LC and b for BL) shows the nine-year average loads
(including NH4 –N, NO3 –N, and min-P) for each calendar month,
which were computed by aggregating monthly time series simulation data. From the figure, the higher level of NH4 –N concentration
occurs in the dry season (October–March next year) and the lower
level occurs in the wet season (April–September). NH4 –N was
mainly from the relatively constant industrial and municipal PS pollution loads. As a result, the highest NH4 –N concentration appeared
in January (about 0.18 mg/L for LC and 0.21 mg/L for BL) due to low
streamflow and the lowest NH4 –N concentration (about 0.12 mg/L
for LC and 0.08 mg/L) occurred in August due to high streamflow
(Fig. 4c). Comparison of Fig. 4a and b reveals that the NH4 –N concentration upstream (LC) during part of the wet season (i.e., June
through September) is greater than downstream (BL), and the seasonal variation at LC is less than that at BL. Conclusively, the dry
season is the critical period for PS NH4 –N pollution.
For NO3 –N concentration at LC and BL (see Fig. 4a and b), the
highest value appeared in April and the second highest in August,
whereas the low values occurred in June, July, and during the
dry season. NO3 –N load is mainly from NPS pollution (especially
due to fertilization on croplands). The practices of planting and
Fig. 4. Monthly annual average NH4 –N, NO3 –N, and min-P concentrations at LC (a)
and BL (b), and the basin monthly average precipitation and water yield close to
streamflow (c).
base fertilization (accounting for half of the total amount of fertilizer for a crop cycle season) were implemented for the first
season and the second season crops in April and August in the
East River Basin, respectively. This two-season crop cycle is one
reason that the two peak values of NO3 –N occur in these two
300
Y. Wu, J. Chen / Ecological Indicators 32 (2013) 294–304
Fig. 5. Spatial distribution of annual average nutrient loads on the Hydrological Response Unit (HRU) level.
months. Another reason can be the rinsing effect of rainfall at
the beginning of the wet season, which transports NO3 –N within
the soil by overland flow and subsurface lateral flow as precipitation gradually increases during March through April (Wu and
Chen, 2013a). Apart from the above two reasons, the less streamflow in April compared to August (see Fig. 4c), can explain why
the NO3 –N concentration is greater in April than in August. Therefore, the time period from the ending of the dry season (March)
to the beginning of the wet season (April) (see Fig. 4c) is critical
for managing NPS NO3 –N pollution, resulting from the planting,
fertilization, and the rapid increase of overland flow and lateral
flow.
Compared to min-N, the PS pollution may contribute more
to min-P load because of the low P content in fertilizer and low
mobility of soluble P in soil. Moreover, the active and stable mineral
P can only be transported by surface runoff when attaching to
sediments (Neitsch et al., 2005). This can explain why the high
level of min-P concentration occurred in the dry season (especially
January) with low streamflow and the low level of min-P concentration occurred in the wet season (especially September) with
high streamflow (see Fig. 4a–c). In other words, the variation of
min-P concentration was mainly influenced by streamflow. Fig. 4a
and b indicates a slight increase of min-P in April and August,
which is the result from the base fertilization that occurs in these
two months. Therefore, it can be inferred that the combination of
PS and NPS pollution resulted in the seasonal variation of min-P
concentration in the stream water, and the dry season is the critical
period for managing PS pollution.
Y. Wu, J. Chen / Ecological Indicators 32 (2013) 294–304
301
Fig. 6. Spatial distribution of annual average nutrient loads on the subbasin level.
Conclusively, Fig. 4 reveals that the water pollution in terms of
NH4 –N, NO3 –N, and min-P is greater in the late dry season and early
wet season (December to April).
3.4. Identification of critical NPS pollution areas
To identify the critical NPS nutrient loading areas in the basin,
Fig. 5 shows the simulated annual average NPS pollution nutrient
loads (i.e., NO3 –N, org-N, min-P, and org-P) at the HRU level. The
NO3 –N load can reach as high as 18 kg/ha/yr in the middle and
downstream agricultural lands, whereas org-N load can reach as
high as 133 kg/ha/yr for the largest org-N load in the same areas.
Similarly, the annual P load from agricultural lands shows the highest loading level (about 3.2 kg/ha/yr of min-P and 20 kg/ha/yr of
org-P).
In addition, the annual average NPS nutrient loads at the subbasin level are presented in Fig. 6. We found that subbasins 15, 17,
20, and 23 had the highest level of NO3 –N load (>8.4 kg/ha/yr) due
to the high percentage of agricultural land in those areas. The highest levels of org-N (>50 kg/ha/yr), min-P (>1.7 kg/ha/yr), and org-P
(>7.6 kg/ha/yr) loads were found in these four subbasins as well as
in subbasin 35.
To further investigate the relationship between nutrient loads
and land use, Fig. 7 shows the nutrient loads based on the three
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Y. Wu, J. Chen / Ecological Indicators 32 (2013) 294–304
4. Discussion
4.1. Water quality evaluation
Fig. 7. Land use-based nutrient loads due to NPS pollution.
major land uses (agriculture, pasture/range, and forest). From
this figure, the agricultural areas contributed the highest nutrient
loads including NO3 –N (8.2 kg/ha/yr), org-N (89.2 kg/ha/yr), minP (2.5 kg/ha/yr), and org-P (13 kg/ha/yr), whereas the lowest loads
were from the forest areas except for the lowest NO3 –N load which
was from the range/pasture areas. This phenomenon—NO3 –N load
from forest areas was higher than grasslands—may be due to the
large residue amount left on the ground and the resulting high
decomposition rate in forest areas. Apparently, NPS pollution is
closely linked to land use activities that determine the sources
and magnitudes of pollutant loadings to stream water. Therefore,
effective control and management of farming practices (i.e., best
management practices (BMPs)) would help reduce nutrient loads.
3.5. Contributions of PS and NPS pollution
Because both PS and NPS pollutions contribute to the chemical
loads at the outlet of the East River Basin (i.e., loading to the Pearl
River delta), we designed two scenarios (Scenario A: NPS only, and
Scenario B: both PS and NPS) to investigate their respective contributions to the nutrient loads. It is noted that the municipal PS
loads, as shown in Table 3, were estimated amounts due to the
lack of available observations. For the industrial PS load, the data
were relatively more reliable for 1992 only (see Section 2.4.2 for
details). However, a single year (1992) model simulation may not
be sufficient to represent the real situation when considering climate variability. Therefore, we analyzed the above two scenarios
using a three year (1991–1993) period as a trade-off for the accuracy of industrial PS data and long-term simulation. Table 6 shows
the three-year average nutrient loads at BL for these two scenarios. The data in Table 6 clearly indicates that NPS pollution played
a significant role (94–99%) in contributing to loads of min-N, orgN, and org-P; for min-P load, PS and NPS contributed equally. As
a result, NPS contributed 93.2 × 103 t/yr for TN and 9.8 × 103 t/yr
for TP, accounting for 97% and 94% of the total loads of TN and TP,
respectively.
Table 6
Annual average nutrient loads at the basin outlet (BL) under two scenarios (with
and without point source pollution loads).
Scenario
Description
min-N
min-P
org-N
org-P
TN
A
B
A/B
NPS only (103 t/yr)
PS and NPS (103 t/yr)
Percentage of NPS (%)
27.4
29.2
94
0.35
0.70
50
65.7
66.7
99
9.4
9.7
97
93.2 9.8
95.9 10.4
97
94
TP
The use of WQI allows one to categorize water quality as ‘good’
or ‘poor’ by converting the diverse physico-chemical and biological
variables into a single number in a simple, objective, and reproducible manner (House and Newsome, 1989). With such a number,
we can classify and compare the water quality situations among different places or along different time lines for a specific place. The
method of linking the WQI with watershed modeling, as shown
in our case study (see Section 3.2), is encouraging because this
approach can present both a spatially and temporally explicit evaluation of water quality for a given watershed. For example, the
upstream cross section, LC, with a WQI of 89 had better water
quality than the downstream cross section, BL, with a WQI of 80.
Using hydrological simulation results, a time-series of WQI for each
cross section from the headwater to the estuary of a river can be
derived. Therefore, this approach can be a useful and informative
tool for watershed managers and support water qulaity comparisons between different regions (such as Hong Kong and Taiwan or
another region).
It is worth noting that one problem with WQI is that it synthesizes into a single number, a complex reality where numerous
environmental variables have influence on water quality. Another
problem is that classification (‘good’ to ‘poor’) of water quality
depends on its applications (purposes) such as industrial uses or
drinking water supplies (Simoes et al., 2008). The first problem
involves how many variables and how much each variable weighs
during the WQI calculations, and the second problem refers to who
or what may use the water. As shown in our case study, we used
eight key variables with a range of weighting factors from 0.07 to
0.2 (see Table 1) after considering Liou’s et al. (2004) recommendations, which are intended for general purposes in a nearby region
(Taiwan). Apparently, the WQI calculation with certain variables
and weighting factors (i.e., the first problem) should be dependent on the water application purposes (i.e., the second problem).
Therefore, the application of WQI for water quality classification
and comparison need to be conducted under the same conditions.
Consequently, how to derive the reasonable and specific requirements corresponding to each application purpose needs further
development and studies.
4.2. Water pollution features
Analyzing the seasonal variation of nutrient loads (see Section 3.3) of the East River indicates that the dry season is the
critical period for PS NH4 –N and min-P pollution due to the relatively lower streamflow, while from the end of the dry season
to the beginning of the wet season is the critical period for NPS
NO3 –N pollution because of the agricultural practices and rinsing effect of overland and lateral flows. Further investigation of
spatially explicit nutrient loads, together with the analysis of land
use-based nutrient loads, can help identify critical pollution source
areas and land covers where low-cost conservation programs (i.e.,
filter strip) can be implemented to reduce pollutant loadings effectively (see Section 3.3). Furthermore, the dominant contribution
by NPS pollution implies that using BMPs in the critical pollution
source areas identified previously would be promising. However, PS
pollution loads cannot be ignored either, considering PS pollution
would likely increase accompanying local economic and population
growths. Thus, effective management and treatment of industrial
and municipal wastewater is another important approach to avoid
deterioration of water quality. Although these findings and implications are derived from our studies on a local river, both methods
Y. Wu, J. Chen / Ecological Indicators 32 (2013) 294–304
There may be some limitations when interpreting some of our
results because of the data availability problem. First, the data
about municipal sewage quantity and quality was estimated by
multiplying the suggested values by populations in Section 2.4. We
acknowledged the data derived are somewhat rough and may bring
some uncertainties to the results about the contribution of PS pollution in Section 3.5. However, estimating the data was the only
feasible approach to take, and one which has been documented and
widely applied for designing wastewater treatment plants in China.
Second, the observation data for the six water quality variables (i.e.,
NH4 –N, NO2 –N, NO3 –N, BOD, DO, and TP) are scarce, and this may
impede deriving the optimal parameters in Section 3.1. However, a
visual comparison of monthly simulated and observed water quality variables (see Fig. 2) supported that the model simulation fell
within a reasonable range. Third, the observation of org-N is not
available, although we used this variable in calculating the WQI in
Section 3.2. Thus, the assumption of reasonable estimation of org-N
may lead to some uncertainties in the comprehensive water quality
evaluation. Fortunately, such uncertainty would not be substantial
because of the low weighting factor of org-N (i.e., 10% as shown in
Table 1). Fourth, we used four variables, min-N, min-P, org-N, and
org-P, in identifying the critical pollutant source areas (see Fig. 5
and Fig. 6), respectively, in Section 3.4 and investigating the contributions of PS and NPS pollution in Section 3.5. However, the last two
variables were not validated due to the lack of observations. Nevertheless, our analyses can be justified because the original model
design of SWAT was to operate in large-scale ungagged basins with
little or no calibration efforts (Arnold et al., 1998; Srinivasan et al.,
2010). Therefore, the actual values and the marked critical source
areas based on org-N and org-P may, at worst, serve as a reference for other researchers and call for further validation. Finally,
conducting research in data-scarce areas can be challenging, and
scientists may need to work with whatever data can be obtained.
Although the SWAT model may support its application in these
kinds of areas, limitations and uncertainties should be stated with
their results to avoid over-interpretations.
This study was supported by Hong Kong RGC GRF projects
HKU 711008E and HKU710910E. Part of this work was performed
under the USGS contract G08PC91508. Any use of trade, firm,
or product names is for descriptive purposes only and does not
imply endorsement by the U.S. Government. We thank Dongsheng
Cheng for collecting/sharing the limited observation data. We thank
Naga Manohar Velpuri (Arctic Slope Research Corporation (ASRC)
Research and Technology Solutions, a contractor to USGS EROS) for
his comments on the early draft. We are grateful to Sandra Cooper
(USGS) for her careful reviews and editing. We also thank the editor
and the two anonymous reviewers for their constructive comments
and suggestions.
Appendix A.
According to Stambuk-Gilijanovic (2003) and Liou et al. (2004),
Fig. A.1 indicates the water quality score for the eight water quality variables including mineral nitrogen (min-N), organic nitrogen
(org-N), mineral phosphorus (min-P), biological oxygen demand
(BOD), dissolved oxygen (DO), suspended sediment (SS), temperature (T), and PH.
100
(b)
80
80
60
60
40
40
20
20
0
0
0
0.2
0.4
0.6
0.8
1
1.2
0
min-N (mg/L)
1
2
3
100
(c)
(d)
80
80
60
60
40
40
20
20
0
0
0
0.3
0.6
0.9
0
1.2
4
8
12
100
100
(e)
Score value
16
BOD (mg/L)
min-P (mg/L)
(f)
80
80
60
60
40
40
20
20
0
0
0
2
4
6
8
0
40
DO (mg/L)
100
80
120
SS (mg/L)
160
100
(h)
(g)
Score value
4
org-N (mg/L)
100
5. Conclusions
Based on our previous studies on streamflow and sediment
modeling using SWAT, we further investigated the water quality with detailed PS and NPS pollution in the East River Basin.
To evaluate the water quality status of the river mainstem, we
used a comprehensive Water Quality Index (WQI) involving eight
water quality variables at two major cross sections (LC for representing the upstream area and BL for the downstream area). The
investigation of the temporal distribution (seasonal variation) of
water quality disclosed that there are high levels of nutrient loads
in the late dry season and early wet season (i.e., from March to
April). We further presented the spatial distribution maps for the
NPS nutrient loads and the land use-based nutrient loadings to
identify the critical pollution source areas and land covers where
more attention and measures may be considered because of their
cost-effectiveness. Finally, we also examined the PS and NPS contributions to nutrient loads and found NPS pollution contributed
substantially to min-N, org-N, and org-P, whereas contributions
to min-P from the PS and NPS pollution loads are nearly equal.
Overall, our findings can provide valuable information for the local
decision-makers to identify the causes of water pollution, which
would be useful for protecting the water environment. In addition,
the methods we adopted can be useful for other researchers around
the world.
100
(a)
Score value
4.3. Limitations
Acknowledgments
Score value
and results can be informative and useful for nearby regions and
other researchers around the world.
303
80
80
60
60
40
40
20
20
0
0
0
10
20
T (°C)
30
40
50
0
3
PH
6
9
12
Fig. A.1. Water quality scores for each water quality variable (after StambukGilijanovic (2003) and Liou et al. (2004)).
304
Y. Wu, J. Chen / Ecological Indicators 32 (2013) 294–304
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