Abstract

In addition to its environmental effects, Karoon River plays an important role in the neighboring people's lives. In recent years, its salinity has been raised due to low flow rates and increased loading of pollutants. One of the practical issues in this regard is the management of salinity sources considering different applicable scenarios. The aim of this study is evaluating these management scenarios by removal and reduction of point source pollutants loading. The hydrodynamics and salinity of this study, from Molasani to Farsiat stations, were simulated using MIKE11 model. Continuing the existing conditions revealed that loading pollutant sources will lead to an increase in salinity longitudinal profile, where Ahvaz and farmlands are located. The outcome of the removal scenario shows that eliminating agricultural sources in the dry season and the industrial and urban sources in the wet season dramatically results in river salinity reduction upstream and downstream of Ahvaz. Also, results expressed that upstream boundary reduction shows influence at the end of the study area with an average salinity reduction of 35.10 and 26.10%, in the wet and dry seasons, respectively. The simulation results of combined scenarios show that managing the loading of pollutant sources through the upstream boundary is of significant importance.

INTRODUCTION

Existing municipal, industrial and agricultural pollutant sources in rivers and their interactions with other polluted water bodies have affected their water quality (Karamouz et al. 2004; Li et al. 2016, 2017; Wu et al. 2017). One of the primary indicators to measure river water quality is related to the salinity which represents the concentration of dissolved ions (,,, , , , and ) in water. The salinity is increased through loading point and non-point pollutant sources into the river (Cañedo-Argüelles et al. 2013). Salinity discharge into the river undergoes variations while it is moving downstream. As salinity passes 1,560 μS/cm, water becomes less useful as it is no longer potable for human consumption (WHO 1997). Above 4,688 μS/cm, it is no longer suitable for most municipal or agricultural uses, therefore irrigation with such high salinity water causes problems for most major crops (FAO 1994; Li et al. 2018).

Managing and reducing pollution in rivers are important tasks in the planning and operation of water bodies. For endangered and polluted rivers, quality management focuses on proper protection and operation of the rivers. In both quality enhancement and salinity reduction of rivers, water requires exerting management practices to control pollutant sources (Somlyódy et al. 1998; McBride 2002; Tri et al. 2013). Quality simulation of the water in rivers under different management scenarios not only helps to make operational decisions and salinity control projects, but also facilitates bringing the quality of river water to the required standards (Prairie et al. 2005). Hence any management strategy to be implemented should be already simulated in the form of management scenarios and also its effectiveness has to be evaluated as well (Sharma & Kansal 2013; Tri et al. 2013).

Karoon River is located in the southwest of Iran and discharges into the Persian Gulf (Figure 1). Its water salinity is significantly affected by input consumption and pollutant sources. Considering that the salinity of Karoon River has increased in recent years due to loading different pollutant sources, its ecosystem has been threatened consequently through polluting sources creating an environmental dilemma. Nowadays, water is unusable for neighboring towns for several months of the year (Karamouz et al. 2004; Afkhami et al. 2007). Research shows that the salinity increasing agent of Karoon River encompasses: flow reduction with illegal withdrawal in the upstream, constructing multiple dams, loading agricultural drainage, industrial and domestic effluents and saline lands along the river (Ghadiri 2016).

Figure 1

(a) Location of Karoon River (Kashefipour & Zahiri 2010), (b) Karoon River network and study area, (c) main channel Karoon river, cross-sections (d) at chainage 0 km, (e) at chainage 60 km and (f) at chainage 105 km.

Figure 1

(a) Location of Karoon River (Kashefipour & Zahiri 2010), (b) Karoon River network and study area, (c) main channel Karoon river, cross-sections (d) at chainage 0 km, (e) at chainage 60 km and (f) at chainage 105 km.

In recent years, numerous researches have dealt with investigating and managing sources causing water salinity increase in rivers. Yu et al. (2014), in a study of salinity distribution in Brisbane River with MIKE11 model, pointed out that saline tidal inflow alongside the river increases between 750 and 1,000 μS/cm. The salinity front is shifted to 5 km down the flooding condition by managing flooding situations and fresh and saline water crossing. Jordan River salinity changes affected by the removal of either surface-groundwater flows, or differential pollutant sources using mass balance techniques, showed that river salinity reduces as sources of salty springs in the northern part of the river are eliminated. Imposing a 20–80% discharge reduction of groundwater infiltration into the river, river salinity decreases from 3,125 to 3,687.5 μS/cm, respectively. In the southern part of the river, surface runoff removal gives rise to a salinity increase, while removal of salinity sources leads to its reduction (Farber et al. 2005). Afkhami et al. (2007) examined water quality management of Karoon and Dez rivers with pollution reduction scenarios as direct and indirect projects funded in agriculture, domestic and industry. The results showed that management approaches accompanied with indirect projects are much better for improving river water quality objectives and optimizing project costs. Kerachian & Karamouz (2005), in research of removing or reducing seasonal pollutant loads discharging into Karoon River to meet quality standards, cited that appropriate evaporation ponds could be employed for optimizing loading-reduction policies of seasonal pollutants and managing riverine system. Investigating Colorado River salinity showed that the main factors causing a salinity increase in the river are drainage water of agricultural fields and its salinity decreases by the removal of these sources (Prairie et al. 2005). Salinity intrusion in Mekong River estuary using MIKE11 model was simulated under management scenarios where the impact of floods and the least flow from upstream in the river dredging condition are examined. The results showed that river salinity has diminished in times of flooding but as the upstream flow reduces, it sharply increases in the estuary. As dredging makes changes in the bed slope and the status of tidal waves, the amount of salinity intrusion decreases, so the greatest dredging-induced impact in reducing salinity intrusion takes place in the dry season (Tri et al. 2014).

To improve the quality and reduce salinity of Karoon River several rehabilitation programs, such as the completion of Ahvaz water treatment plant, transferring agro-industrial and agricultural drainages to adjacent wetlands and salinity management of Gotvand Dam reservoir by the Iranian water authorities, have been proposed and implemented (Iran Ministry of Power 2016). Conversely, in terms of river salinity reduction, it has been less addressed how to manage pollutant sources in different seasons. This study is aimed at inspecting management strategies to reduce the salinity of Karoon River under various scenarios of point-source pollutant loading removal and reduction within Mollasani to Farsiat using MIKE11 model. In this study, existing conditions and assessing the impact of each management scenario temporally and spatially, as well as evaluating its salinity reduction percentage compared to the existing conditions at the selected stations, have been examined.

METHODOLOGY

Study area

Karoon River is located in the southwest of Iran and it is the longest with a length of 900 km (Figure 1(a)) in Iran. Its basin area (67,000 km2) is located between longitudes 48°15′ and 52°30′ E, latitude 30°17′ and 33°49′ N (Naddafi et al. 2007). The main branch of Karoon River originates from the Zagros Mountains. It discharges into the Persian Gulf after passing the Khuzestan plain (Figure 1(b)) (Karamouz et al. 2004). This river supplies water for several cities and villages, several industries including steel, oil, petrochemical, sugarcane, paper and cement industries, as well as intensely agricultural areas along the river. In return, river salinity increased due to the discharging agricultural, domestic and industrial wastewaters (Afkhami et al. 2007; Naddafi et al. 2007). The study reach along the river was about 105 km between Molasani and Farsiat stations where major cities such as Molasani, Veys, Sheyban and Ahvaz, as well as intensive water withdrawal for agricultural lands and sugar cane agro-industries, are located (Figure 1(b)). This is one of the most critical reaches due to the high population density and riverbank multiple industries (Kerachian & Karamouz 2005).

Numerical simulation

MIKE11, which is developed by the Danish Hydraulic Institute (DHI), is a one-dimensional modeling tool for investigating rivers, irrigation canals and other inland water systems. To investigate the salinity distribution variations along the river, a hydrodynamic module (HD) coupled with an advection–dispersion module (AD) have been employed in this study. The HD, based on the dynamic wave principles, solves the equations of conservation of continuity and momentum (Saint-Venant equation), as defined in Equations (1) and (2) (DHI Water and Environment 2012): 
formula
(1)
 
formula
(2)
where Q is discharge, A is the cross-sectional area, q is the lateral inflow, h is the water level, If is the flow resistance term, f is the momentum force, w is the density of water, g is the acceleration due to gravity and is the momentum distribution coefficient (DHI Water and Environment 2012). The AD implies the equation of mass conservation of a dissolved or suspended material, such as salinity. The equation is defined as follows: 
formula
(3)
in which C is the salinity concentration, D is the dispersion coefficient, K is the linear decay coefficient, and C2 represents source/sink concentration of the substance (DHI Water and Environment 2012).

The main channel of Karoon river from Molasani (chainage 0 km (the chainages are the distance from the first site to a certain observation site), which is located at 48.87 E and 31.52 N) to Farsiat (chainage 105 km, which is located at 47.51 E and 31.17 S), is depicted in Figure 1(c) by a dotted solid line. Along this channel, 115 cross-sections were shown, at an approximate interval of 1,000 m, representing the transverse section of the flow area. Some typical cross-sections within the study reach are displayed in Figures 1(d)–1(f). The upstream river cross-sections are usually deep and narrow, for example the cross-section at chainage 0 km (Figure 1(d)). The cross-sections in the mid-river, such as chainage 60 km (Figure 2(e)), remain deep but become wider while moving downstream the river shows a reduction in both depth and width such as chainage 105 km (Figure 2(f)).

Figure 2

Comparison of the simulated and measured daily-averaged water levels at Farsiat station in 2011.

Figure 2

Comparison of the simulated and measured daily-averaged water levels at Farsiat station in 2011.

The river up- and down-stream discharges and salinity were measured on a daily and monthly basis, respectively.

The measured discharges and salinity concentrations were used as the upstream open boundary conditions at the entrances of the Karoon River (chainage 0 km), as shown in Figure 1(c). For river downstream (chainage 105 km) the daily measured discharges as open boundary conditions and salinity concentration as Zero Gradient were employed. Also, the input pollution sources were applied as point sources on a monthly average basis. The spatial and temporal steps were determined to be 350 m and 20 s, respectively. To guarantee the stability of the numerical simulation, the following Equation (4) has been considered: 
formula
(4)
where V is flow velocity, is the time step and is the spatial step.

Data and pollution sources

Khuzestan Water and Power Authority (KWPA) and Khuzestan Department of Environment (KDOE) are the main authorities of Karoon River water quality monitoring and supervision (KWPA, 2001). Hydrometric and quality data of Molasani, Ahvaz and Farsiat stations were collected from KWPA and wastewater discharge and output salinity of point sources were gathered from KDOE. Table 1 represents quantitative and qualitative characteristics corresponding to the most important sources of pollutants in the study area. Moreover, hydrodynamic data were obtained from Dezab Engineering Company (www.dezab.com).

Table 1

Average discharge and annual wastewater salinity of point-source pollutants within Molasani to Farsiat

Pollutant source Point source Chainage (m) Q (m3/s) EC (μS/cm) TDS (mg/L) Annual load (* kg) Relative contribution (%) 
Molasani Station (0 km) 
Molasani City P1 120 0.12 2,523.2 1,610.88 6.0961 1.50 
North East of Ahvaz Irrigation Network 1 P2 3,800 1.2 2,373.8 1,516.16 57.36095 14.12 
North East of Ahvaz Irrigation Network 2 P3 6,000 0.72 1,726.4 11.04 25.06037 6.17 
Ramin Thermal Power Plant P4 9,500 0.75 4282 8 2,743.68 64.89352 15.97 
Veys City P5 11,500 0.22 1,826 1,168 8.10349 1.99 
North East of Ahvaz Irrigation Network 3 P6 18,700 2.40 2,672.6 1,709.44 129,327.45 31.83 
North East of Ahvaz Irrigation Network 4 P7 34,600 0.48 3286 8 2,103.04 31,821.04 7.84 
City Sheyban P8 37,900 0.1 1,726.4 1,109.76 3,359.75 0.81 
Sugarcane Agro-industry Dehkhoda P9 42,100 0.1 19,206 12,294.4 3,877.16 0.95 
Ahvaz Sugar Factory P10 47,200 031 1,560.4 1,002.88 9,709.43 2.39 
Zargan Thermal Power Plant P11 48,600 0.1 3,602.2 2,307.84 7,423.56 1.83 
Ahwaz Wastewater 1 P12 58,100 1.13 2,606.2 1,671.68 59,307.86 14.60 
Ahvaz Station (60 km) 406.34 100 
Ahwaz Wastewater 2 P13 64,700 1.80 4,233 2,711.04 153,891.64 16.82 
Ahwaz Wastewater 3 P14 68,600 0.90 6,175.2 3,950.08 112,112.75 12.25 
Ahwaz Wastewater 4 P15 72,000 0.68 3,270.2 2,097.92 44,658.00 4.88 
Ahwaz wastewater treatment P16 81,000 0.69 4,681.2 2,993.92 65,147.22 7.12 
Industrial complex P17 83,100 0.69 3,867.8 2,477.97 54,209.39 5.92 
Local agricultural drainages P18 92,000 5.04 2,739 1,754.24 278,600.14 30.45 
Local agricultural drainages P19 97,500 3.73 2,739 1,754.24 206,349.99 22.55 
Farsiat Station (105 km) 914.97 100 
Pollutant source Point source Chainage (m) Q (m3/s) EC (μS/cm) TDS (mg/L) Annual load (* kg) Relative contribution (%) 
Molasani Station (0 km) 
Molasani City P1 120 0.12 2,523.2 1,610.88 6.0961 1.50 
North East of Ahvaz Irrigation Network 1 P2 3,800 1.2 2,373.8 1,516.16 57.36095 14.12 
North East of Ahvaz Irrigation Network 2 P3 6,000 0.72 1,726.4 11.04 25.06037 6.17 
Ramin Thermal Power Plant P4 9,500 0.75 4282 8 2,743.68 64.89352 15.97 
Veys City P5 11,500 0.22 1,826 1,168 8.10349 1.99 
North East of Ahvaz Irrigation Network 3 P6 18,700 2.40 2,672.6 1,709.44 129,327.45 31.83 
North East of Ahvaz Irrigation Network 4 P7 34,600 0.48 3286 8 2,103.04 31,821.04 7.84 
City Sheyban P8 37,900 0.1 1,726.4 1,109.76 3,359.75 0.81 
Sugarcane Agro-industry Dehkhoda P9 42,100 0.1 19,206 12,294.4 3,877.16 0.95 
Ahvaz Sugar Factory P10 47,200 031 1,560.4 1,002.88 9,709.43 2.39 
Zargan Thermal Power Plant P11 48,600 0.1 3,602.2 2,307.84 7,423.56 1.83 
Ahwaz Wastewater 1 P12 58,100 1.13 2,606.2 1,671.68 59,307.86 14.60 
Ahvaz Station (60 km) 406.34 100 
Ahwaz Wastewater 2 P13 64,700 1.80 4,233 2,711.04 153,891.64 16.82 
Ahwaz Wastewater 3 P14 68,600 0.90 6,175.2 3,950.08 112,112.75 12.25 
Ahwaz Wastewater 4 P15 72,000 0.68 3,270.2 2,097.92 44,658.00 4.88 
Ahwaz wastewater treatment P16 81,000 0.69 4,681.2 2,993.92 65,147.22 7.12 
Industrial complex P17 83,100 0.69 3,867.8 2,477.97 54,209.39 5.92 
Local agricultural drainages P18 92,000 5.04 2,739 1,754.24 278,600.14 30.45 
Local agricultural drainages P19 97,500 3.73 2,739 1,754.24 206,349.99 22.55 
Farsiat Station (105 km) 914.97 100 

In water quality management, specifying the contribution of various pollution loads of pollutant sources is one of the initial and primary requirements. In this study, the mass balance method (Farber et al. 2005; Holtzman et al. 2005) to determine the loading contribution of pollutant sources between Ahvaz upstream and downstream reaches has been applied. Calculations showed that in both reaches, agricultural pollutant sources greatly affect river salinity by a 65 and 61% increase, respectively. The loading contribution of each pollutant source is been given in detail in Table 1 in both reaches.

Management scenarios

Karoon water salinity in the given study area is important from two perspectives: (1) high-quality drinking water supply for nearby cities like Ahvaz and Molasani; (2) good-quality irrigation water supply for downstream intakes of Farsiat station. According to the World Health Organization (WHO), the desirable limit and its maximum acceptable water salinity for drinking water are 800 and 1,560 μs/cm, respectively (WHO 2008). On the other hand, the acceptable limit of irrigation water salinity is 2,000 μs/cm. Of course, this level for irrigation water salinity varies depending on the type and amount of yield loss, so its admissible limit for plants such as wheat and barley reaches 3,000 μs/cm (FAO 1994). Subsequently, management strategies such as removal and reduction of loading pollutant sources are addressed as simulated scenarios.

Removal scenario

This scenario assumes that all or parts of the point-source pollutants are removed and no discharging into the river is taking place.

Reduction scenario

This scenario was carried out in two modes, salinity reduction from upstream input boundary and point-source pollutants. In the case of reducing through the upstream, according to the advice of KWPA experts and upstream pollutant sources, reduction options of 200, 600, 1,000 and 1,400 salinity units were considered. Each of these options was expressed according to the average salinity in input boundary (3,332.2 μs/cm) as a percentage decrease of 8, 25, 43 and 60. In the case of point-source reduction however, two reduction options of 30 and 50% were performed (Iran Ministry of Power 2016).

Combined scenario

After observing the effect of each of salinity removal and reduction scenarios, four of the top scenarios that show the highest effect on salinity reduction were selected. The combined scenarios were simulated and evaluated afterwards. These scenarios include two scenarios: (a) (reduction 43% from upstream plus 50% reduction from point sources) and (b) (reduction 60% from upstream plus 30% reduction from point sources).

Model calibration and verification

In this study, Manning's roughness coefficient (n) was used to represent the bed resistance. The values of Manning's n ranging from 0.025 to 0.05 (Roshanfekr et al. 2008; Mohammadi & Kashefipour 2014) were tested and calibrated using the hydrodynamic model. Furthermore, within the AD, the dispersion coefficient which largely influences the dispersive transport term due to concentration gradients in MIKE11 is defined as: 
formula
(5)
where V is the magnitude of mean flow velocity in m/s, and a and b denote dispersion and exponent factor, respectively (DHI Water and Environment 2012).

The initial value of dispersion coefficient was calculated based on four distinct empirical equations, Fischer et al. (1979), Seo & Cheong (1998), Deng et al. (2001) and Kashefipour & Falconer (2002) (Table 2). The dispersion coefficient in the model was considered in two b modes, 0 and 1 for each of the relationships. If b is equal to 0, the dispersion coefficient is velocity-independent during the calculations and is equal to the dispersion factor. If b is not 0, the dispersion coefficient becomes velocity-dependent during the calculations and will assign a varying value. The decay coefficient takes 0 (k = 0) in the model, because salinity is a conservative pollutant (Kanda et al. 2015).

Table 2

Results of dispersion coefficients calibrated within the study area

Run Equation  a and b R2 RMSE (μs/cm) NRMSE (%) 
#1 Fischer et al. (1979)  301.19 a = 301.19; b = 0 0.52 0.44 28.06 
#2 a = 814.03; b = 1 0.61 0.37 23.59 
#3 Seo & Cheong (1998)  263.72 a = 263.72; b = 0 0.68 0.23 14.67 
#4 a = 712.76; b = 1 0.73 0.16 10.20 
#5 Deng et al. (2001)  276.83 a = 276.83; b = 0 0.84 0.31 19.77 
#6 a = 748.19; b = 1 0.89 0.25 15.94 
#7 Kashefipour & Falconer (2002)  103.31 a = 103.31; b = 0 0.85 0.20 12.75 
#8 a = 179.22; b = 1 0.93 0.14 8.93 
Run Equation  a and b R2 RMSE (μs/cm) NRMSE (%) 
#1 Fischer et al. (1979)  301.19 a = 301.19; b = 0 0.52 0.44 28.06 
#2 a = 814.03; b = 1 0.61 0.37 23.59 
#3 Seo & Cheong (1998)  263.72 a = 263.72; b = 0 0.68 0.23 14.67 
#4 a = 712.76; b = 1 0.73 0.16 10.20 
#5 Deng et al. (2001)  276.83 a = 276.83; b = 0 0.84 0.31 19.77 
#6 a = 748.19; b = 1 0.89 0.25 15.94 
#7 Kashefipour & Falconer (2002)  103.31 a = 103.31; b = 0 0.85 0.20 12.75 
#8 a = 179.22; b = 1 0.93 0.14 8.93 

It was found that the model with the value of 0.035 for Manning's n produced the most comparable simulated results in this study, compared to other values of Manning's n. A comparison of simulated and measured daily-averaged water levels in 2011 at chainage 105 km (Farsiat station) is shown in Figure 2.

The simulated water levels in Farsiat station generally matched the observed water levels by R2 of 0.91. The root mean square error (RMSE) and normalized root mean square error (NRMSE) between the simulated and measured water levels are 0.17 m and 4.13%, respectively.

The calibration results of dispersion coefficient based on the concentration of salinity in 2011 at Farsiat stations indicated that the Kashefipour and Falconer relationship in case of a = 179.22 and b = 1, with RMSE and NRMSE of respectively 32/14 μs/cm and 8/93%, shows better agreement. The simulated concentrations of salinity matched the observed concentrations of salinity with R2 of 0.93 (Table 2).

Based on the calibration results, the model performance with Manning's roughness coefficient (n) and dispersion coefficient (D) are verified using water level and salinity from February to September 2012 (Figure 3). Therefore, the model was verified and can accurately determine flow and water salinity parameters in rivers within the study area.

Figure 3

Comparison of the simulated and observed (a) water level and (b) salinity within the Farsiat station in 2012.

Figure 3

Comparison of the simulated and observed (a) water level and (b) salinity within the Farsiat station in 2012.

Existing condition study

Studies indicated that river flow has decreased in recent years (2005–2014) due to the drought and upstream excessive withdrawals and contrastingly the salinity in the river increased significantly (Figure 4(a)). A power relationship with R2 0.7281 was fitted between river flow and water salinity (Figure 4(b)).

Figure 4

Monthly-averaged river discharge rate and salinity: (a) during a period of last decade; (b) regression equation; (c) in 2014.

Figure 4

Monthly-averaged river discharge rate and salinity: (a) during a period of last decade; (b) regression equation; (c) in 2014.

The existing condition is investigating seasonal variation in river flow and its water salinity in 2014. Actually, the effectiveness of each of the scenarios was evaluated with respect to the existing condition. The existing condition in Ahvaz station shows the river flow increases (336 m3/s) in wet seasons (December–May), and comparatively its water salinity lowers; also, the flow decreases (237 m3/s) in dry seasons (June–November) and the salinity increases (Figure 4(c)). In addition to changing the flow (or flow variation), changes in the loading of pollutant sources, such as agricultural drainages, lead to salinity changes in different seasons (Zarei & Pourreza 2013; Ghadiri 2016).

Longitudinal profile of salinity changes

Due to point-source loading of pollutants, the river longitudinal salinity profile has encountered a stepwise increase (Figure 5). The effect of point source pollutants in any location differs because of the difference in discharge rate and salinity content of wastewater loading, so that the stepwise increase in salinity profiles observed by loading of Ahvaz city sewerage and also by return flow of agricultural fields often occurs 60 and 90 km from upstream. In the Molasani to Ahwaz segment, the salinity longitudinal profile variations increase moderately because of the distance and the amount of low-mass loading of pollutants. While in the Ahvaz to Farsiat segment, in addition to reducing the number of polluting sources, both the mass-loading amount and distance between polluting sources increases through which the salinity step increase in longitudinal profile is observed further.

Figure 5

Longitudinal profile of river salinity in existing conditions.

Figure 5

Longitudinal profile of river salinity in existing conditions.

The status of step increase varies according to the loading times. The step increase of salinity profile occurs in the months of May, June, July, August, September and October at chainages 20–90 km more than other months (November, December, January, February, March and April) (Figure 5). In these months, the peak of backflow from agricultural fields, especially salts leached from the soil profile, accompanied with surface runoff of Ahvaz northeast irrigation system drainages and Ahvaz southern farms, are discharged in the river with average salinity of 2,609 μs/cm and average drained water rate of 13.75 m3/s (Afkhami et al. 2007; Hosseinizare et al. 2016).

RESULTS AND DISCUSSION

Management strategies

Pollutants (dissolved ions) resulting in the salinity increase in rivers are usually conservative and self-purification processes and reaction and settlement do not impact remarkably in their mass reduction (Naseri & Kashefipour 2013). Therefore, management strategies such as the removal and reduction of loading pollutant sources are mainly recommended to reduce salinity in rivers (Farber et al. 2005). To evaluate the effect of management strategies and its effect on salinity variations, it is essential to simulate and assess any strategy as a management scenario.

Pollutant sources removal scenario

Table 3 represents the percentage of salinity reduction in the river undergone by removing pollutant sources against the existing conditions. The percentage of river water salinity reduction by applying the scenario of removing all pollutant sources at 60 km chainage (Ahvaz station) for both wet and dry seasons is 6.79 and 8.25%, respectively. At the end of the study area (Farsiat station), with increasing point-source pollutants, it is 16.27 and 20.12% for both wet and dry seasons, respectively. However, in the dry season, removing all point sources of pollution relative to the wet season greatly influences salinity reduction in the river. In the dry season the flow of river reduces and the loading pollutant sources, especially drained water from the farmlands, increases. Thus, the quality of water in the river is enhanced by their removal.

Table 3

The percentage of river salinity reduction due to different scenarios

  Percent reduction relative to the existing condition (%)
 
Management scenarios  Symbol Wet season Dry season Wet season Dry season 
Removal of point sources Removal of all R1 6.79 8.25 16.27 20.13 
Removal of agricultures R2 3.06 4.97 5.61 10.66 
Removal of sewages R3 2.48 1.05 6.99 6.65 
Removal of industrials R4 3.79 1.60 3.72 1.64 
Average  4.04 3.97 8.15 9.77 
Reduce point source 30% DU1 4.11 3.21 10.40 9.41 
50% DU2 5.86 5.67 14.11 14.98 
Average  4.98 4.44 12.26 12.19 
Reduction of upstream 8% DU3 10.01 6.95 9.12 6.33 
25% DU4 31.89 23.22 29.18 21.12 
43% DP1 47.85 35.24 43.82 32.04 
60% DP2 66.79 49.39 61.20 44.90 
Average  39.12 28.70 35.83 26.10 
Combined Reduction 43% from upstream +50% reduction from point sources (scenario a) DU3 + DP2 51.49 39.35 54.97 41.98 
Reduction 60% from upstream +30% reduction from point sources (scenario b) DU4 + DP1 67.92 51.61 67.41 52.51 
Average  59.71 45.48 61.19 47.25 
  Percent reduction relative to the existing condition (%)
 
Management scenarios  Symbol Wet season Dry season Wet season Dry season 
Removal of point sources Removal of all R1 6.79 8.25 16.27 20.13 
Removal of agricultures R2 3.06 4.97 5.61 10.66 
Removal of sewages R3 2.48 1.05 6.99 6.65 
Removal of industrials R4 3.79 1.60 3.72 1.64 
Average  4.04 3.97 8.15 9.77 
Reduce point source 30% DU1 4.11 3.21 10.40 9.41 
50% DU2 5.86 5.67 14.11 14.98 
Average  4.98 4.44 12.26 12.19 
Reduction of upstream 8% DU3 10.01 6.95 9.12 6.33 
25% DU4 31.89 23.22 29.18 21.12 
43% DP1 47.85 35.24 43.82 32.04 
60% DP2 66.79 49.39 61.20 44.90 
Average  39.12 28.70 35.83 26.10 
Combined Reduction 43% from upstream +50% reduction from point sources (scenario a) DU3 + DP2 51.49 39.35 54.97 41.98 
Reduction 60% from upstream +30% reduction from point sources (scenario b) DU4 + DP1 67.92 51.61 67.41 52.51 
Average  59.71 45.48 61.19 47.25 

According to the pollutant sources removal scenario, this scenario has relatively little effect on reducing the salinity in the river, and the removal of all pollutant sources in a river system is not feasible (Kerachian & Karamouz 2005). In the next step, the effect of pollutant sources reduction will be discussed.

Pollutant sources reduction scenario

The results show that the effect of either reducing point sources salinity or reduction through the river upstream in the wet season is better than the dry season. Maximum average salinity takes place in Ahvaz station (60 km) in the wet season at 39.12%, whereas this amount reaches 28.70% in the dry season (Table 3).

Water salinity reduction in the river varies depending on the essence of each scenario. As such, in the scenario of point pollutant sources reduction, water maximum average salinity in the river occurs at the end (Farsiat station); while in the reduction scenario through the upstream boundary of the river, this is achieved at the first section (Ahvaz station). Indeed, in the point pollutant sources scenario, salinity mass loading diminishes as the flowing length of river increases. While in the option of reducing through the upstream boundary of the river at the beginning of the range, due to high-velocity flow and severe dispersion impact of pollutants, the river salinity decreases (Naseri & Kashefipour 2013; Yu et al. 2014).

In the scenario of reducing point pollutant sources loading such as the removal scenario, the impact of reducing point sources was negligibly low in river salinity reduction, while the effect of an average reduction of 35.83 and 26.10 respectively in the wet and dry seasons at the upstream boundary of the study area was enhanced (Farsiat station).

Comparing different options of reduction scenarios show that 30 and 50% reduction options of point pollutant sources and 43 and 60% reductions at the upstream boundary have the greatest impact on reducing river salinity. However, these four options were selected for the combined scenarios.

Combined scenario

The simulation results related to each combined options showed that in both wet and dry seasons, scenario DU4 + DP1 (60% upstream salinity reduction plus 30% reduction of point sources) performs better in terms of reducing the salinity of the river. This scenario appears to surpass other options in reducing river salinity with an average salinity reduction of 60 and 46% in both wet and dry seasons, respectively. The results indicated that in order to reduce salinity in the study area, management of pollutant sources in the upstream is vitally important.

The results obtained from management scenarios show a significant reduction in the river discharge relative to the existing conditions. Consequently, the maximum salinity reduction occurs when agricultural resources removal takes place.

Comparing effectiveness of scenarios with standard salinity

The results of leading options concerning management scenarios were compared with admissible salinity limits of drinking and irrigation water (Figure 6). The results revealed that river water salinity takes the maximum desirable drinking limit by imposing all superior options in the wet season as well as containing allowable quality for drinking in DU4 + DP1 and Du4 scenarios (Figure 6(a)). In contrast, river water salinity exceeds the admissible limit in two given scenarios in the dry season and lies on the edge of the desired maximum (Figure 6(b)). Other scenarios were also non-potable and appropriate only for irrigation. The efficiency of the management scenarios can be improved by: (1) increasing the inflow, (2) reducing the loading of pollutant sources, especially agricultural sources, and (3) increasing velocity, transfer and dispersion processes (Yu et al. 2014).

Figure 6

Comparison of management scenarios and salinity standard for drinking and irrigation (a) wet season and (b) dry season.

Figure 6

Comparison of management scenarios and salinity standard for drinking and irrigation (a) wet season and (b) dry season.

CONCLUSIONS

The main objective of the present study is to evaluate variations of the salinity in the existing conditions and the effectiveness of management strategies to reduce water salinity in Karoon River (Mollasani to Farsiat). The numerical results showed that the salinity longitudinal profile faces a step increase caused by the loading of pollutants, and the maximum and minimum salinity changes correspond respectively to the months of August and December due to discharging point pollutant sources.

The results indicated that options of removing agricultural sources in the dry season, industrial sources in the wet season through the upstream of Ahvaz city, and domestic sources in the downstream of Ahvaz city influence the river salinity reduction effectively. The results associated with the removal of all pollutant sources along the river showed that this scenario reduces river salinity in the dry season more than in the wet season but due to high salinity inputting from the upstream boundary, no significant river salinity reduction by removing pollutant sources takes place.

The results demonstrated that the scenario of reducing pollutant sources through the upstream boundary, especially in the wet season, has the highest effect on river salinity reduction. Therefore, it is necessary to manage the pollutant sources from the upstream, meaning the need for the reduction of loading pollutant sources such as the salinity of Gotvand dam reservoir, drained water of Gotvand and Aqili irrigation network, effluent irrigation network, Karoon and Shuabia agro-industries, wastewater of Shushtar and Gotvand cities.

The results of combined scenarios also showed that a 30% reduction point sources scenario plus a 60% reduction from the upstream has the highest impact on river salinity reduction. Comparing the results of this scenario with salinity allowable standard limits represent the desired standard conditions for drinking and agricultural water in both wet and dry seasons.

This research showed that the salinity of rivers could be reduced by managing pollutant sources. Finally, it is recommended that their implementation feasibility be inspected individually as an optimization problem in different seasons.

REFERENCES

REFERENCES
Afkhami
M.
,
Shariat
M.
,
Jaafarzadeh
N.
,
Ghadiri
H.
&
Nabizadeh
R.
2007
Developing a water quality management model for Karun and Dez rivers
.
Environ. Health Sci. Eng.
4
(
2
),
99
106
.
Cañedo-Argüelles
M.
,
Kefford
B. J.
,
Piscart
C.
,
Prat
N.
,
Schäfer
R. B.
&
Schulz
C. J.
2013
Salinization of rivers: an urgent ecological issue
.
Environ. Pollut.
173
,
157
167
.
Deng
Z. Q.
,
Singh
V. P.
&
Bengstsson
L.
2001
Longitudinal dispersion coefficient in straight rivers
.
Hydraul. Eng.
127
(
11
),
919
927
.
DHI Water and Environment
2012
MIKE11, A Modeling System for Rivers and Channels
.
Reference Manual
,
Horsholm
,
Denmark
, p.
516
.
FAO (Food and Agricultural Organization of the United Nations)
1994
Water Quality for Agriculture, vol. 29
.
FAO
,
California
,
USA
.
Farber
E.
,
Vengosh
A.
,
Gavrieli
I.
,
Marie
A.
,
Bullen
T.
,
Mayer
B.
,
Holtzman
R.
,
Segal
M.
&
Shavit
U.
2005
Management scenarios for the Jordan River salinity crisis
.
Appl. Geochem.
20
,
2138
2153
.
Fischer
H. B.
,
List
E. J.
,
Koh
R. C. J.
,
Imberger
J.
&
Brooks
N. H.
1979
Mixing in Inland and Coastal Waters
.
Academic Press Inc.
,
San Diego
, p.
483
.
Ghadiri
H.
2016
Salinization of Karun River in Iran by shallow groundwater and seawater encroachment
.
Adv. Hydro-Sci. Eng.
4
,
1
9
.
Holtzman
R.
,
Shavit
U.
,
Segal-Rozenhaimer
M.
,
Gavrieli
I.
,
Farber
E.
&
Vengosh
A.
2005
Mixing processes along the Lower Jordan River
.
Environ. Qual.
34
,
897
906
.
Hosseinizare
N.
,
Gholami
A.
,
Panahpour
E.
&
Jafarnezady
A. R.
2016
Identifying and determining pollution load of agricultural pollutants in the catchment basin of Karun and Dez rivers
.
Irrig. Sci. Eng.
39
(
3
),
121
134
(in Persian)
.
Iran Ministry of Power
2016
The Decisions of the Twenty-Fifth Session of the Supreme Council for Water Iran
.
Ministry of Power
,
Tehran
,
Iran
, p.
1
(in Persian)
.
Kanda
E.
,
Kosgei
J.
&
Kipkorir
E.
2015
Simulation of organic carbon loading using MIKE 11 model: a case of River Nzoia, Kenya
.
Water Pract. Technol.
10
(
2
),
298
304
.
Karamouz
M.
,
Mahjouri
N.
&
Kerachian
R.
2004
River water quality zoning: a case study of Karoon and Dez river systems
.
Environ. Health Sci. Eng.
1
(
2
),
16
27
.
Kashefipour
S. M.
&
Falconer
R. A.
2002
Longitudinal dispersion coefficient in natural channels
.
Water Res.
36
,
1596
1608
.
Kashefipour
S. M.
&
Zahiri
J.
2010
Comparison of empirical equations application in the advection-dispersion equation (ADE) on sediment transport modeling
.
World Appl. Sci.
11
(
8
),
1015
1024
.
Kerachian
R.
&
Karamouz
M.
2005
Waste-load allocation model for seasonal river water quality management: application of sequential dynamics genetic algorithm
.
J. Scint. Iranica
12
(
2
),
117
130
.
KWPA
2001
An Assessment of Pollutants in Karoon River
.
A report prepared by the Water Quality Assessment section
.
Khuzestan Water and Power Authority, Ministry of Power
,
Ahwaz
,
Iran
.
Li
P.
,
Wu
J.
&
Qian
H.
2016
Preliminary assessment of hydraulic connectivity between river water and shallow groundwater and estimation of their transfer rate during dry season in the Shidi River, China
.
Environ. Earth Sci.
75
(
2
),
75
99
.
Li
P.
,
Wu
J.
,
Tian
R.
,
He
S.
,
He
X.
,
Xue
C.
&
Zhang
K.
2018
Geochemistry, hydraulic connectivity and quality appraisal of multilayered groundwater in the Hongdunzi coal mine, northwest China
.
Mine Water Environ.
37
(
2
),
222
237
.
McBride
G. B.
2002
Calculating stream reaeration coefficients from oxygen profiles
.
J. Environ. Eng.
128
(
4
),
384
386
.
Mohammadi
S.
&
Kashefipour
S. M.
2014
Numerical modeling of flow in riverine basins using an improved dynamic roughness coefficient
.
Water Resour.
41
(
4
),
412
420
.
Naddafi
K.
,
Honari
H.
&
Ahmadi
M.
2007
Water quality trend analysis for the Karoon River in Iran
.
Environ. Monit. Assess.
134
,
305
312
.
Naseri
M.
&
Kashefipour
S. M.
2013
Hydrodynamic simulation and qualitative parameters in the river system by using FASTER
.
J. Iran Water Res.
7
(
13
),
121
129
(in Persian)
.
Prairie
J. R.
,
Rajagopalan
B.
,
Fulp
T. J.
&
Zagona
E. A.
2005
Statistical nonparametric model for natural salt estimation
.
Environ. Eng.
131
(
1
),
130
138
.
Roshanfekr
A.
,
Kashefipour
S. M.
&
Jafarzadeh
N.
2008
Numerical modeling of heavy metals for riverine systems using a new approach to the source term in the ADE
.
J. Hydroinform.
10
(
3
),
245
255
.
Seo
I. W.
&
Cheong
T. S.
1998
Predicting longitudinal dispersion coefficient in natural streams
.
Hydraul. Eng.
124
,
25
32
.
Sharma
D.
&
Kansal
A.
2013
Assessment of river quality models: a review
.
Rev. Environ. Sci. Biotechnol.
12
,
285
311
.
Somlyódy
L.
,
Henze
M.
,
Koncsos
L.
,
Rauch
W.
,
Reichert
P.
,
Shanahan
P.
&
Vanrolleghem
P.
1998
River water quality modeling III, future of the Art
.
Water Sci. Technol.
38
(
11
),
253
260
.
Tri
D. Q.
,
Ching
C. Y.
&
Mishra
P. K.
2013
Numerical modeling in water quality management for rivers case study of the Day/Nhue river sub-basin
.
Vietnam. J. Earth Sci. Eng.
5
(
1
),
1111
1119
.
Tri
D. Q.
,
Don
N. C.
,
Ching
C. Y.
&
Mishra
P. K.
2014
Modeling the influence of river flow and salinity intrusion in the Mekong River Estuary, Vietnam
.
International Association of Lowland Technology (IALT)
16
(
1
),
14
25
.
WHO (World Health Organization)
1997
Guidelines for Drinking Water Quality: Surveillance and Control of Community Supplies
,
2nd edn
.
World Health Organization
,
Geneva
.
WHO (World Health Organization)
2008
Guidelines for Drinking Water Quality: Surveillance and Control of Community Supplies
,
2nd edn
.
World Health Organization
,
Geneva
.
Wu
J.
,
Xue
C.
,
Tian
R.
&
Wang
S.
2017
Lake water quality assessment: a case study of Shahu Lake in the semi-arid loess area of northwest China
.
Environ. Earth Sci.
76
,
217
232
.
Yu
Y.
,
Zhang
H.
&
Lemckert
C.
2014
Salinity and turbidity distributions in the Brisbane River estuary, Australia
.
Hydrology
519
,
3338
3352
.
Zarei
H.
&
Pourreza
M.
2013
Factor analysis of chemical composition in the Karoon River basin, southwest of Iran
.
Appl. Water Sci.
3
,
753
761
.