Estimation of non-point source pollution loads with flux method in Danjiangkou Reservoir area, China

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1 Water Science and Engineering 2017, 10(2): 134e142 HOSTED BY Available online at Water Science and Engineering journal homepage: Estimation of non-point source pollution loads with flux method in Danjiangkou Reservoir area, China Xiao-kang Xin a,b, *, Wei Yin b, Ke-feng Li a a State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu , China b Changjiang Water Resources Protection Institute, Changjiang Water Resources Commission, Wuhan , China Received 1 June 2016; accepted 18 October 2016 Available online 31 May 2017 Abstract The estimation of non-point source pollution loads into the Danjiangkou Reservoir is highly significant to environmental protection in the watershed. In order to overcome the drawbacks of traditional watershed numerical models, a base flow separation method was established coupled with a digital filtering method and a flux method. The digital filtering method has been used to separate the base flows of the Hanjiang, Tianhe, Duhe, Danjiang, Laoguan, and Qihe rivers. Based on daily discharge, base flow, and pollutant concentration data, the flux method was used to calculate the point source pollution load and non-point source pollution load. The results show that: (1) In the year 2013, the total inflow of the six rivers mentioned above accounted for 95.9% of the total inflow to the Danjiangkou Reservoir. The total pollution loads of chemical oxygen demand (COD Mn ) and total phosphorus (TP) from the six rivers were t and t, respectively, and the non-point source pollution loads were t and t, respectively, indicating that the non-point source pollution is a major factor (with a contribution rate of 68.4% for COD Mn and 82.9% for TP). (2) The Hanjiang River is the most significant contributor of pollution loads to the Danjiangkou Reservoir, and its COD Mn and TP contribution rates reached 79.3% and 83.2%, respectively. The Duhe River took the second place. (3) Non-point source pollution mainly occurred in the wet season in 2013, accounting for 80.8% and 90.9% of the total pollution loads of COD Mn and TP, respectively. It is concluded that the emphasis of pollution control should be placed on non-point source pollution Hohai University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Danjiangkou Reservoir; Non-point source pollution; Base flow separation; Digital filtering method (DFM); Flux method 1. Introduction The Danjiangkou Reservoir plays an important role in optimal allocation of water resources in China because of the Middle Route Project of the South-to-North Water Diversion Project (Ma et al., 2014), and the protection of its water resources has received widespread attention from all over the world (Li et al., 2009). Over the past 20 years, local governments around the Danjiangkou Reservoir have gradually This work was supported by the National Key Research and Development Program of China (Grants No. 2016YFC and 2016YFC ). * Corresponding author. address: xin.xiaokang@163.com (Xiao-kang Xin). Peer review under responsibility of Hohai University. closed down or relocated the point sources of pollution distributed around the reservoir, in order to ensure that clean water is transferred (Xin et al., 2015). Non-point source pollution has become a dominant factor influencing the water quality of the Danjiangkou Reservoir (Wang et al., 2011; Li et al., 2014). Plenty of research has been conducted on the problem of non-point source pollution in the Danjiangkou Reservoir (Zhao and Hu, 2007; Jiang et al., 2010; Huang et al., 2012). In the Danjiangkou Watershed, the non-point sources mainly include losses of pesticides and fertilizers, livestock wastes, and rural domestic wastewater (Zhao and Hu, 2007). The process of non-point source pollution is accompanied by rainfall (water and soil losses), and the contribution rate of non-point source pollution is about 75% (Yin et al., 2011) / 2017 Hohai University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( creativecommons.org/licenses/by-nc-nd/4.0/).

2 Xiao-kang Xin et al. / Water Science and Engineering 2017, 10(2): 134e The estimation of non-point source pollution loads into the Danjiangkou Reservoir is highly significant to the determination of the pollution control scheme and environmental management planning. Researchers have adopted the export coefficient method (Fang et al., 2011), annualized agricultural non-point source (AnnAGNPS) model (Polyakov et al., 2007; Tong, 2008), and soil and water assessment tool (SWAT) model (Qiao et al., 2013) to study the non-point source pollution load in the Danjiangkou Reservoir area. However, most of the models mentioned above require complete digital elevation model (DEM) data, land use data, soil attribute data, meteorological data, and hydrological data. The model establishment process is very complicated and difficult (Hong et al., 2012; Chowdary and Adiga, 2001), so the current research scope is extremely limited and does not cover the whole watershed. Based on the features of clear physical conception, easy data acquisition, and dependable accuracy, the base flow separation of non-point source pollution load estimation models in hydrology has received widespread attention in recent years (Li et al., 2010; Zhao, 2010). Base flow separation is a hydrological method that can separate the river base flow, which is relatively stable, from the surface flow, which is the fluctuant part of the total flow. The point source pollution load and the natural background load are stable and show little change throughout a year. They can be represented by the base flow flux. The non-point source pollution load along with the rainfall and surface flow rushes into rivers, with a characteristic of volatility, and can be represented by the surface runoff flux. According to the daily discharge data of six main tributaries of the Danjiangkou Reservoir, the Hanjiang, Tianhe, Duhe, Danjiang, Laoguan, and Qihe rivers, in the hydrological year 2013, this study sought, first, to separate the base flow from the total flow using the digital filtering method (DFM), then to calculate the non-point source pollution loads using the flux method based on monthly water quality monitoring data from 2013, and, finally, to investigate the spatial and temporal characteristics of pollution loads. 2. Study area and basic data 2.1. Study area The Danjiangkou Reservoir is located in the upper reaches of the Hanjiang River and at the junction of northwest Hubei Province, southwest Henan Province, and southeast Shaanxi Province. The watershed is in the transition zone of the Daba Mountains, Qinling Mountains, and Jianghan Plain. The Danjiangkou Reservoir is a large reservoir with a variety of functions, such as water supply, flood control, power generation, and navigation. The first construction stage of the Danjiangkou Reservoir began in September 1958 and was completed at the end of The normal water level was 157 m and the dead water level was 139 m. The total water capacity was m 3, the dead storage was m 3, and the reservoir surface area was 745 km 2 at the normal water level. In order to implement the Middle Route Project of the South-to-North Water Diversion Project, a dam-heightening project was carried out in 2005, and the normal water level increased to 170 m, with a total water capacity of m 3. After the wet season in 2014, there were about m 3 of water (the annual average value) transferred to Beijing City, Tianjin City, and Henan Province. The area of the watershed upstream of the Danjiangkou Dam is over km 2, with an average annual inflow of m 3 /s and an annual runoff of m 3. There are seven counties in the reservoir watershed: Xixia, Xichuan, Yunxi, Yunxian, Danjiangkou, Maojian, and Zhangwan (Fig. 1). There are approximately 200 tributaries around the reservoir. The sum of the drainage areas of the sixteen largest tributaries account for 95% of the total drainage area. Information about these sixteen tributaries is given in Table 1. Only six tributaries have been equipped with hydrological monitoring stations: the Hanjiang River (Baihe Station), the Tianhe River (Jiajiafang Station), the Duhe River (Zhushan Station), the Danjiang River (Jingziguan Station), the Qihe River (Xiping Station), and the Laoguan River (Xixia Station) (Fig. 1). The sum of the drainage areas of these six tributaries is km 2, accounting for 90.9% of the total watershed area of the upper Danjiangkou Dam, and the sum of the annual discharge is m 3 /s, accounting for 95.9% of the total discharge. Therefore, the analysis of the non-point source pollution loads of the six main tributaries is representative Basic data Hydrological data According to the daily hydrological data measured in 2013 in the six main tributaries, the monthly average discharges were calculated and are listed in Table 2. The discharge was mainly concentrated in the wet season (from June to September), accounting for 73.4% of the annual discharge. The discharge in the dry season (from December to March) accounted for only 10.5%, and the discharge in the normal season (October, November, and April) accounted for 16.1%. From the point of view of the tributaries (Table 2), the annual discharge of the Hanjiang River is the largest ( m 3 /s, accounting for 78.3% of the total annual discharge of the six tributaries), and followed by the Duhe River ( m 3 /s, accounting for 16.2%), while the other four small tributaries contribute a small part of the total discharge (37.61 m 3 /s, accounting for 5.5%). The representative hydrological data from 2013 were analyzed. The annual discharges of the six tributaries in 2013 are significantly less than the average annual discharge. According to the Pearson type III hydrological frequency analysis method, the hydrological frequency of the Hanjiang River in 2013 was 75.3%, and the hydrological frequencies of the Tianhe River, Duhe river, Danjiang River, Qihe River, and Laoguan River were 80.3%, 83.3%, 90.5%, 90.3%, and 96.5%, respectively. Therefore, it was a dry year in 2013 for the first three tributaries and an extreme dry year for the last three tributaries. The anomaly ratio is the difference between the measured value and the annual average, so the monthly anomaly ratio can reflect the degree of

3 136 Xiao-kang Xin et al. / Water Science and Engineering 2017, 10(2): 134e142 Fig. 1. Locations of hydrological and water quality monitoring stations in Danjiangkou Reservoir area. wetness and dryness. The flow anomaly ratios of the six tributaries for different months in 2013 are shown in Table 3. Overall, the inflow of the Danjiangkou Reservoir was less in 2013 than in a normal year Water quality data Nineteen water quality indices were monitored at all the monitoring stations of the sixteen tributaries, including dissolved oxygen (DO), chemical oxygen demand (CODMn), five-day biochemical oxygen demand (BOD5), total phosphorus (TP), ammonia nitrogen (NH3-N), sulfide (S2 ), cyanide (CN ), fluorine ion (F ), hexavalent chromium (Cr6þ), arsenic (As), mercury (Hg), selenium (Se), copper (Cu), lead (Pb), cadmium (Cd), zinc (Zn), volatile phenols, and oils. The monitoring frequency was once per month. The month-tomonth variation trend graphs for typical indices, the CODMn and TP concentrations, in 2013 are shown in Fig. 2. Non-point source pollutants are often discharged into rivers with rainfall runoff. Therefore, in general, pollutant concentration is higher during the wet season than during the dry season or normal season, indicating that the river water quality is significantly affected by the non-point source pollution. The Tianhe River flows through Yunxi County and the Laoguan River flows through Xixia and Xichuan counties. Because these three counties are more industrialized and the proportions of point source pollution and non-point source pollution are equal, the correlations between concentrations and discharge are less apparent. The other four tributaries are rural rivers, and the Table 1 Hydrological parameters for sixteen large tributaries within study area. Tributary Water quality station Hydrological station Length (km) Drainage area (km2) Mean annual discharge (m3/s) Hanjiang River Tianhe River Duhe River Shending River Jianghe River Sihe River Guanshan River Jianhe River Langhe River Danjiang River Qihe River Taohe River Laoguan River Quyuan River Jiangjun River Taogou River S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 Baihe Jiajiafang Zhushan Jingziguan Xiping Xixia

4 Xiao-kang Xin et al. / Water Science and Engineering 2017, 10(2): 134e Table 2 Monthly average discharges of six main tributaries in Month Discharge (m 3 /s) Total discharge Hanjiang River Tianhe River Duhe River Danjiang River Qihe River Laoguan River (m 3 /s) January February March April May June July August September October November December Annual Proportion of total discharge (%) Table 3 Flow anomaly ratio of six main tributaries for different months in Tributary Anomaly ratio (%) January February March April May June July August September October November December Hanjiang River Tianhe River Duhe River Danjiang River Qihe River Laoguan River contamination is mainly caused by agricultural non-point source pollution. As we can see from the relationships between the COD Mn and TP concentrations and discharge Q (Fig. 3), COD Mn and TP concentrations have strong positive correlations with the discharge, with the coefficient of determination R 2 ranging from 0.39 to 0.78 and 0.51 to 0.80, respectively. Therefore, we drew an inference that the COD Mn and TP concentrations in the Danjiangkou Reservoir are significantly influenced by non-point source pollution. 3. Research methods 3.1. Digital filtering method Base flow separation methods separate the base flow from the total flow. Due to different interpretations of base flow, there are different theories and methods of separation. The most commonly used methods are the linear segmentation method, slash segmentation method, hydrological modeling method, water balance method of Kalinin, environmental isotope method, and digital filtering method (Chen et al., 2006; Ni et al., 2005). The digital filtering method is a popular method used to separate highfrequency signals from low-frequency signals in the field of digital signal analysis and processing. In the hydrological course of the rainfall-runoff process, surface flow responds to rainfall and changes rapidly, representing the highfrequency signal, while the base flow changes slowly with rainfall and represents the low-frequency signal. Therefore, separating the base flow from the total flow is just like separating the low-frequency signal in signal processing (Eckhardt, 2005). Fig. 2. Monthly change of COD Mn and TP concentrations of six main tributaries in 2013.

5 138 Xiao-kang Xin et al. / Water Science and Engineering 2017, 10(2): 134e142 Fig. 3. Correlation between typical pollution indices and flow rate. In the field of hydrology, the digital filtering method was first used by Lyne and Hollick (1979), and the core of the method is represented by the filter equation: q j ¼ bq j 1 þ 1 þ b Qj Q j 1 ð1þ a where q j is the filtered flow on the jth day (q j and q j 1 are surface flow); Q j is the total flow on the jth day; and a and b are the filter parameters. After the surface flow is obtained, the base flow is equal to the total flow minus the surface flow. There have been no good ways to validate filter parameters until now. Nathan and Mcmahon (1990) compared the results of the digital filtering method and the manual segmentation method, and proposed that the reference values of a and b were 2.0 and 0.925, respectively Flux method After completing the base flow separation, the total pollution load, point source pollution load, and non-point source pollution load can be estimated with the flux method. On the basis of division of river base flow and surface flow, we consider that the pollution load transported by the base flow to be the sum of the natural background load and the point source pollution load (hereafter referred to as the point source pollution load). The pollution load transported by the surface flow is regarded as the non-point source pollution load. The above-mentioned concept can be rewritten as Eq. (2): W t ¼ W p þ W np ¼! t Cp ðtþq p ðtþþc np ðtþq np ðtþ dt ð2þ 0 where W t is the total pollution loads transported in the river channel; W p is the point source pollution load; W np is the nonpoint source pollution load; t is time; C p ðtþ is the point source pollutant concentration at time t, represented by the monitoring data of water quality in the dry season (in this study, C p ðtþ was approximately replaced by the mean concentration from December to March); Q p ðtþ is the base flow at time t; C np ðtþ is the non-point source pollutant concentration at time t, which can be represented by the mean concentration from May to October; and Q np ðtþ is the surface flow at time t. Due to the lack of continuous water quality monitoring data, the integral equation Eq. (2) should be transformed into the discrete equation Eq. (3): W t ¼ Xn C pi Q pi Dt þ Xn C npi Q npi Dt where C pi is the point source pollutant concentration in the ith month, Q pi is the average base flow in the ith month, C npi is the non-point source pollutant concentration in the ith month, Q npi is the surface flow in the ith month, n is the number of months in a year, and Dt is the period of the ith month. At first, we should calculate the total pollution load with the monthly flow data and pollutant concentration data with Eq. (4): W t ¼ Xn C i Q i Dt where C i is the pollutant concentration monitored in the ith month, since the water quality data monitoring frequency was once per month in this study; and Q i is the average discharge in the ith month. Thus, the non-point source pollution loads can be expressed as W np ¼ W t W p, which can be rewritten as X n C npi Q npi Dt ¼ Xn C i Q i Dt Xn C pi Q pi Dt Eq. (5) is the non-point source pollution load computation formula based on the base flow separation method, in which the non-point source pollution load equals the total pollution load minus the point source pollution load, while the point source pollution load is estimated by the product of the base flow and pollutant concentration during the dry season. 4. Results and discussion 4.1. River base flow separation With the help of daily flow data from the six main tributaries around the Danjiangkou Reservoir, the base flow and ð3þ ð4þ ð5þ

6 Xiao-kang Xin et al. / Water Science and Engineering 2017, 10(2): 134e surface flow, which were calculated with the digital filtering method, are listed in Table 4. In 2013, the average base flows of the Hanjiang River, Tianhe River, Duhe River, Danjiang River, Qihe River, and Laoguan River were , 4.90, 37.79, 7.71, 1.54, and 2.04 m 3 /s, respectively. The ratios of total runoff taken up by the base flow varied from 30.1% to 52.1%. The profiles of the total flow and base flow can be seen in Fig Pollution load calculation results and pollution structure interpretation The established method was used to calculate the point source pollution loads and non-point source pollution loads of the six main tributaries, and the results are shown in Table 5. The total pollution loads of COD Mn and TP transported by the six main tributaries were t and t, of Table 4 Base flows of main tributaries around Danjiangkou Reservoir. Tributary Hydrological station Average total discharge (m 3 /s) Total runoff (10 8 m 3 ) Average base flow (m 3 /s) Baseflow volume (10 8 m 3 ) Hanjiang River Baihe Tianhe River Jiajiafang Duhe River Zhushan Danjiang River Jingziguan Qihe River Xiping Laoguan River Xixia Surface runoff (10 8 m 3 ) Fig. 4. Profiles of total flow and base flow for six main tributaries in 2013.

7 140 Xiao-kang Xin et al. / Water Science and Engineering 2017, 10(2): 134e142 Table 5 Pollution loads of main tributaries in Danjiangkou Reservoir area. Tributary Pollution load of COD Mn (10 3 t) Proportion of non-point Point source Non-point source Total source pollution load of COD Mn (%) Pollution load of TP (10 3 t) Point source Non-point source Hanjiang River Tianhe River Duhe River Danjiang River Qihe River Laoguan River Sum Total Proportion of non-point source pollution load of TP (%) which about tofcod Mn and toftp came from the non-point source pollution, reaching 68.4% and 82.9% of the total, respectively. The calculated results agreed with the results of the export coefficient method, which shows that the non-point source pollution load of TP was t (Fang et al., 2011). This nearly agrees with the results from the SWAT model, which shows that the non-point source pollution load of TP was t (Qiao et al., 2013). The total COD Mn loads of the six tributaries were , , , , , and t, respectively, and the proportions of the nonpoint source pollution load varied from 65.7% to 80.0%. The total TP loads of the six tributaries were , , , , , and t, respectively, and the proportions of the nonpoint source pollution load varied from 50.0% to 83.8%. These results are consistent with the calculation results of non-point source pollution loads in other basins in China (Li and Huang, 2005; Hao et al., 2004). Therefore, we can draw a conclusion that non-point source pollution is the dominant factor in the water quality in the Danjiangkou Reservoir. In terms of different pollution indicators, COD Mn has a strong relationship with the decentralized rural domestic sewage and TP is closely associated with the application of pesticides and chemical fertilizers (Qiao et al., 2013) Distribution characteristics of pollution loads Spatial distribution characteristics Table 6 shows the contribution rates of the six tributaries to total pollution loads and non-point source pollution loads. The Table 6 Contribution rates of each tributary to total and non-point source pollution loads. Tributary Contribution rate to COD Mn pollution loads (%) Contribution rate to TP pollution loads (%) Total Non-point source Total Non-point source Hanjiang River Tianhe River Duhe River Danjiang River Qihe River Laoguan River contribution rates of tributaries to the total COD Mn pollution loads are ranked in the following descending order: the Hanjiang River, Duhe River, Danjiang River, Laoguan River, Tianhe River, and Qihe River; and those of COD Mn non-point source pollution loads are ranked in the following descending order: the Hanjiang River, Duhe River, Danjiang River, Tianhe River, Laoguan River, and Qihe River. As we can see, the contribution rate of the Hanjiang River to the total COD Mn pollution loads was 79.3% and the contribution rate to the nonpoint source COD Mn pollution loads was 78%, demonstrating that the contribution of the Hanjiang River to the pollution loads of the Danjiangkou Reservoir is the most significant, followed by that of the Duhe River. The contribution rate of the Duhe River to the total COD Mn pollution loads was 14.1%, and the contribution rate to the non-point source COD Mn pollution loads was 15.0%. Similarly, the contribution rates of tributaries to total TP pollution loads are ranked in the following descending order: the Hanjiang River, Duhe River, Danjiang River, Tianhe River, Laoguan River, and Qihe River, and the order of the contribution rates of tributaries to non-point source pollution loads is the same. The contribution rates of the Hanjiang River, followed by the Duhe River, occupy a dominant position as well, with 83.2% and 84.1% for the total pollution loads and non-point source pollution loads, respectively. This phenomenon is strongly related to the inflows of the tributaries: the proportion of discharge from the Hanjiang River was 78.4% in 2013, and that of the Duhe River was 16.1%, while there were no significant differences among the COD Mn and TP concentrations of the six main tributaries. The Danjiangkou Watershed can be divided into two parts: the Han Part, which is located in Shaanxi Province and includes the Hanjiang, Tianhe, and Duhe rivers; and the Dan Part, which is mainly located in Hubei Province and Henan Province and contains the other three tributaries. Further analysis shows that non-point source pollution loads from the Dan Part are t for COD Mn and t for TP, while those from the Han Part are t for COD Mn and t for TP. This spatial distribution characteristic shows that non-point source pollution loads of the Danjiangkou Reservoir come mainly from Shaanxi Province, followed by Hubei Province, and the contribution rate of Henan Province is relatively small.

8 Xiao-kang Xin et al. / Water Science and Engineering 2017, 10(2): 134e Table 7 Temporal distribution characteristics of pollution loads. Water period COD Mn TP Total load (10 3 t) Proportion of total load (%) Non-point source load (10 3 t) Proportion of non-point source load (%) Total load (10 3 t) Proportion of total load (%) Non-point source load (10 3 t) Wet season Normal season Dry season Proportion of non-point source load (%) The emission intensity of COD Mn in the Dan Part is 0.52 t/(km 2 $year) and that of TP is 0.02 t/(km 2 $year), while the emission intensity of COD Mn in the Han Part is 0.16 t/(km 2 $year) and that of TP is t/(km 2 $year). Therefore, the emission intensity of non-point sources from the Han Part is larger than that from the Dan Part. This characteristic is consistent with the area of farmland and the rural population distribution in the water source area of the Middle Route Project of the South-to-North Water Diversion Project: according to the statistics yearbooks of Shaanxi Province, Hubei Province, and Henan Province, the rural populations are , , and in Shaanxi Province, Hubei Province, and Henan Province, respectively; and the farmland area is ha for Shaanxi Province, ha for Hubei Province, and ha for Henan Province Temporal distribution characteristics Table 7 shows the distribution of pollution loads in different water periods. As we can see, both the total pollution loads and non-point source pollution loads of COD Mn and TP are ranked in the following descending order: the wet season, the normal season, and the dry season. During the wet season, the total pollution load of COD Mn from the six main tributaries is t, accounting for 78.3%; and the non-point source pollution load is t, accounting for 80.8%. The total pollution load of TP from the six main tributaries is t, accounting for 87.7%; and the non-point source pollution load is t, accounting for 90.9%. These results further prove that the pollution in the Danjiangkou Reservoir mainly comes from non-point sources, and the nonpoint source pollution is accompanied by rainfall. 5. Conclusions (1) Non-point source pollution is the most important factor in the water quality of the Danjiangkou Reservoir. A simple and feasible base flow separation method coupled with the digital filtering method and flux method was established to separate the point and non-point source pollution loads. It was verified to be accurate and effective. (2) There is a strong correlation between the COD Mn and TP concentrations and the discharges of main tributaries. Therefore, the established method can be used to calculate the pollution loads of these two indicators, and the calculation results show that about t of COD Mn and toftpwere transported into the Danjiangkou Reservoir through the six main tributaries in However, there were t COD Mn and t TP coming from the non-point source pollution, and the contribution proportions were as high as 68.4% and 82.9%, respectively. These results agree with the results from the export coefficient method and the SWAT model. (3) The spatial distribution characteristics of pollution loads show that the non-point source pollution loads from the Dan Part are t for COD Mn and t for TP, while those from the Han Part are t and t, respectively. The non-point source pollutant emission intensity of the Han Part is larger than that of the Dan Part. The Hanjiang and Duhe rivers have the largest non-point source pollution loads, so more pollution control emphasis should be paid to the Hanjiang Basin and Duhe Basin. 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