# Impact of Precipitation and Flow Rate Changes on the Water Quality of a Coastal River

Authors: Hassan Pourfallah Koushali; Reza Mastouri; Mohammad Reza Khaledian
Journal: Shock and Vibration
 (2021)

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## Abstract

This paper aims to investigate the effects of natural variables, including precipitation and flow rate, on the quality of the Zarjoub River in Guilan province, Iran. The new hydrological insight in this study is a lack of national research focused on the dual effects of rainfall and flow rate on river water quality in coastal areas along the Anzali Wetland. To investigate the effect of precipitation and flow rate on river water quality, nine water quality variables were monitored during the 10-year period. In this article, (a) the existence of trends and the best fitted models of water quality parameters and the discharge and precipitation were analysed using statistical techniques and (b) the relationships between concentration of constituents with the discharge and precipitation on the up-stream and middle station were also examined. Box plots, for explaining the distribution of a data collection, were used. The results showed the existence of trend of water quality parameters with river flow and rainfall. As presented in Section3, with increasing precipitation and flow rate, concentration of all constituents, except pH and SO₄2, decreased. On the contrary, the maximum amount of water quality elements was observed in low precipitations; therefore, the maximum concentration occurred in less than 15 mm precipitation. Simple regression was used to evaluate the discharge concentration and precipitation concentration. According to the correlation coefficient (r), the relationship between concentration and precipitation is weaker than (0.238) discharge concentration (0.699). The results further showed climate change and river water quality to be related.


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## Body

## 1. Introduction

Zarjoub is one of the rivers polluting Anzali Wetland, known as a habitat for migratory birds. This swampy area is the value of an officially registered wetland under the Ramsar Convention. Unfortunately, the wetland is declining in terms of the environment due to unsustainable human activities and infrastructure development. Water quality analysis as well as possible monitoring changes in water quality support decisions to manage coastal water resources. The water quality of creeks and rivers is very sensitive to human influences and natural processes, affecting their use for various agricultural, drinking, and industrial purposes [1–3]. Climate and seasonal changes lead to biological activity in the catchment area that affects the water quality of rivers [4–6]. Some researchers also have studied the effect of meteorological variables on water resource management [7, 8].The river water quality is changed due to chemical properties, unlike seawater (due to chemical and biological interactions) [9–20]. Furthermore, many previous studies have suggested several strategies for improving water quality in different parts of the world [21–23].Some studies have shown that meteorological (air, rainfall, solar radiation, and humidity) and hydrological (water discharge and groundwater percentage) parameters affect flow water temperature [24–29]. Also, temperature affects the creek’s physical, biological, and chemical properties, including the levels of dissolvable oxygen in water, plant photosynthesis, and animal metabolism. River water quality can be affected by human activities, such as urban and rural land-use change [30–34], precipitation, and streamflow [35–37]. Several studies have investigated the impact of climate change on river water quality [38–40]. Evaluation of long-term changes in river water quality can help identify the most important factors affecting water quality as a suitable method for determining any environmental changes over time [22, 41]. Many other studies have focused on the effect of temperature rise on water quality [42–46]. In their study, Rueda et al. [47] and Chorus and Bartrum [48] have revealed that heavy rainfall transports 80% and 400% of the average annual inflow of nitrogen and phosphorus to rivers. Under changing weather conditions, variations in rainfall and its pattern constantly occur. Scientific studies using future precipitation scenarios have examined the impact of climate change on water quality [40, 49, 50]. Variations in surface runoff under climate changes lead to changes in the transfer of pollutants to waters [51, 52].From the above viewpoint, the observed relationship of rainfall and flow with water quality suggests the importance of meteorological conditions, especially rainfall, in river water quality management. Although these cases have been investigated in the literature, the lack of studies of rainfall and flow impact on the quality of rivers in coastal basins in different seasons and the importance of quality protection of rivers ending to Anzali Wetland shows the necessity of further investigation. This research determines statistically (1) the relationships between concentration of physicochemical parameters and the discharge and rainfall at Zarjoub River and (2) the existence of trends and the estimate of the best-fitted trend models.


## 2. Materials and Methods

### 2.1. Study Area

A flowchart is done to arrange and explain all the main activities. Figure1 shows the flowchart of research methodology.Figure 1
Research methodology flowchart.Zarjoub River is one of the tributaries of Pirbazar River, which originates from the heights of the Jokolbandan Mountains located in the south of Rasht; after passing the eastern side of Rasht under the name of “Siahroud River,” it finally flows into Anzali Wetland along the southern Caspian Sea which is one of the Ramsar sites in Iran. The environment of the wetland is deteriorating due to the entry of sewage and sediment from its catchment area. Measurements show that Anzali Wetland was much deeper in the past, but seems to be shallower due to sediment. The total amount of sediment entering the lagoon is estimated at approximately 400,000 tons per year. Zarjoub River transports large amounts of sediment to the lagoon and negatively affects the value of the system. The river length is about 41.30 km. The altitude of the study site varies from 2 to 750 meters above sea level, and the basin area is 162 square kilometers. The basin receives an average of 1300 to 1500 mm precipitation annually.


### 2.2. Water Quality, Flow, and Precipitation Data

Flow quality parameters were monitored by the Ministry of Energy of Iran on a monthly basis (2007–2017) in a period of 10 years. Presently, two hydrometric stations, one at the entrance to the plain (B) and the other in the middle section of the plain (W), are active (Figure2). The (W) station was established in 1967 with a period of 39 years of statistics. It is hydraulically located in a suitable position, but hydrologically, its flow is more than that of the watershed due to being located inside the plain and thus receiving surface flows of several creeks and returned agricultural water, as well as part of urban sewage and plain drainage; however, given the project objectives and as it is located at the city entrance, it has a good position. The (B) station was established in 1987 with statistical age of 19 years. The above two stations are located at an altitude of 4 and 40 meters above sea level, respectively.Figure 2
Study area and location of monitoring sites.This article does not intend to examine specific parameters of water quality; thus, in total, nine hydrochemical parameters, including electrical conductivity (EC), alkalinity (PH), total dissolved solids (TDS), sulfate (SO₄2−), calcium (Ca2+), magnesium (mg2+), sodium (Na+), chlorine (Cl−), and bicarbonate (HCO3−), were measured and analysed at monthly intervals in two hydrometric stations of the Zarjoub River basin over a 10-year period by Iran Water Resources Management Company (IWRMC). The daily flow and precipitation data were collected from IWRMC. To gain insight into hydrological and hydrochemical processes in precipitations, the water quality response was analysed when in coastal watersheds. In order to receive significant changes in river water quality response, we selected precipitations that amounted to more than 10 mm at least 12 hours before and after.Based on these criteria, 30 data collections were obtained about the water quality of the river for 30 rainfall events in the Zarjoub River coastal area. EC and pH were measured in the field by a portable multiparameter water quality meter. TDS were determined by gravimetric analysis. The dissolved water samples were shipped to the laboratories of IWRMC for analysis of Ca2+ and Mg2+ (by EDTA titration), Na+ (by flame photometer), SO₄2− (by barium chloride titration), HCO3− (by sulfuric acid titration), and Cl− (by silver nitrate titration).


### 2.3. Analysis Methods

Formal correlation analysis was used to investigate the relationship between flow and precipitation, as well as water quality in each station. An intense relationship between river chemistry and discharge at different spatial and temporal scales has been recognized [53]. Conventional methods and tests have adequate power since the data distribution is clear and the sample size is large in them; hence, they are preferred because, in this case, parametric methods are more accurate than nonparametric ones. Statistical analysis was performed for a 95% confidence level.Several models were proposed to explain the relationship between concentration discharge [54, 55]. For this research, the linear (Cij = a+bQj), the exponential (Cij = a exp (bQj)), the power (Cij = aQj^b), and the logarithmic (Cij = a+b ln (Qj)) models were used.Based on the correlation coefficient and the critical values for Pearson’sr, the existence of a significant relationship between variables was investigated, and data testing was performed homogeneity. The model producing the highest coefficient of determination (R2) was selected and used as the optimal model to investigate the impact of climate change on project water quality. Regression analysis in SPSS and correlation determination were performed using EXCEL software with supplement action.


## 2.1. Study Area

A flowchart is done to arrange and explain all the main activities. Figure1 shows the flowchart of research methodology.Figure 1
Research methodology flowchart.Zarjoub River is one of the tributaries of Pirbazar River, which originates from the heights of the Jokolbandan Mountains located in the south of Rasht; after passing the eastern side of Rasht under the name of “Siahroud River,” it finally flows into Anzali Wetland along the southern Caspian Sea which is one of the Ramsar sites in Iran. The environment of the wetland is deteriorating due to the entry of sewage and sediment from its catchment area. Measurements show that Anzali Wetland was much deeper in the past, but seems to be shallower due to sediment. The total amount of sediment entering the lagoon is estimated at approximately 400,000 tons per year. Zarjoub River transports large amounts of sediment to the lagoon and negatively affects the value of the system. The river length is about 41.30 km. The altitude of the study site varies from 2 to 750 meters above sea level, and the basin area is 162 square kilometers. The basin receives an average of 1300 to 1500 mm precipitation annually.


## 2.2. Water Quality, Flow, and Precipitation Data

Flow quality parameters were monitored by the Ministry of Energy of Iran on a monthly basis (2007–2017) in a period of 10 years. Presently, two hydrometric stations, one at the entrance to the plain (B) and the other in the middle section of the plain (W), are active (Figure2). The (W) station was established in 1967 with a period of 39 years of statistics. It is hydraulically located in a suitable position, but hydrologically, its flow is more than that of the watershed due to being located inside the plain and thus receiving surface flows of several creeks and returned agricultural water, as well as part of urban sewage and plain drainage; however, given the project objectives and as it is located at the city entrance, it has a good position. The (B) station was established in 1987 with statistical age of 19 years. The above two stations are located at an altitude of 4 and 40 meters above sea level, respectively.Figure 2
Study area and location of monitoring sites.This article does not intend to examine specific parameters of water quality; thus, in total, nine hydrochemical parameters, including electrical conductivity (EC), alkalinity (PH), total dissolved solids (TDS), sulfate (SO₄2−), calcium (Ca2+), magnesium (mg2+), sodium (Na+), chlorine (Cl−), and bicarbonate (HCO3−), were measured and analysed at monthly intervals in two hydrometric stations of the Zarjoub River basin over a 10-year period by Iran Water Resources Management Company (IWRMC). The daily flow and precipitation data were collected from IWRMC. To gain insight into hydrological and hydrochemical processes in precipitations, the water quality response was analysed when in coastal watersheds. In order to receive significant changes in river water quality response, we selected precipitations that amounted to more than 10 mm at least 12 hours before and after.Based on these criteria, 30 data collections were obtained about the water quality of the river for 30 rainfall events in the Zarjoub River coastal area. EC and pH were measured in the field by a portable multiparameter water quality meter. TDS were determined by gravimetric analysis. The dissolved water samples were shipped to the laboratories of IWRMC for analysis of Ca2+ and Mg2+ (by EDTA titration), Na+ (by flame photometer), SO₄2− (by barium chloride titration), HCO3− (by sulfuric acid titration), and Cl− (by silver nitrate titration).


## 2.3. Analysis Methods

Formal correlation analysis was used to investigate the relationship between flow and precipitation, as well as water quality in each station. An intense relationship between river chemistry and discharge at different spatial and temporal scales has been recognized [53]. Conventional methods and tests have adequate power since the data distribution is clear and the sample size is large in them; hence, they are preferred because, in this case, parametric methods are more accurate than nonparametric ones. Statistical analysis was performed for a 95% confidence level.Several models were proposed to explain the relationship between concentration discharge [54, 55]. For this research, the linear (Cij = a+bQj), the exponential (Cij = a exp (bQj)), the power (Cij = aQj^b), and the logarithmic (Cij = a+b ln (Qj)) models were used.Based on the correlation coefficient and the critical values for Pearson’sr, the existence of a significant relationship between variables was investigated, and data testing was performed homogeneity. The model producing the highest coefficient of determination (R2) was selected and used as the optimal model to investigate the impact of climate change on project water quality. Regression analysis in SPSS and correlation determination were performed using EXCEL software with supplement action.


## 3. Results and Discussion

### 3.1. Spatial Variability of the Water Quality Parameters

Some water quality variables show significant seasonal variations. For pH and Cl, at both the upstream and middle stations, the changes over time are relatively uniform. There is also variation in flow rate, rainfall, and concentration. These changes depend on the discharge and seasonality and the flow of domestic sewage into the river. Upstream fluctuations are greater than the midpoint. The ratio of the highest to the lowest concentration, flow rate, and rainfall for the upstream station are discharge (226.3 : 1), Cl− (25.5 : 1), Na+(17.01 : 1), Mg2+(17.2 : 1), SO₄2−(7.5 : 1), HCO3−(5.8 : 1), rainfall 5.1; 1), TDS (5.04 : 1), EC (5.03 : 1), Ca2+(3.3 : 1), and pH (1.2 : 1) and for the midstream station are discharge (57.8 : 1), Na+(20.8 : 1), Cl− (15.2 : 1), Mg2+(10.3 : 1), SO₄2−(8.1 : 1), EC (6.2 : 1), TDS (6.21 : 1), rainfall (5.7; 1), Ca2+(4.3 : 1), HCO3−(3.4 : 1), and pH (1.1 : 1). The predominant anions and cations in the water of the Zarjoub River are Cl− and Na+, respectively.Box-and-whisker diagram of measured parameters at the two monitoring stations were plotted during 2007–2017 (Figure3). The horizontal line inside the boxes represents the median and the upper and lower lines of the boxes indicate the 75 and 25% percentiles, respectively. The vertical lines indicate the minimum and maximum values for observations. The height of the box represents the variance among the 25 and 75% percentiles.Figure 3
Box-and-whisker plots for the measured parameters at the two monitoring stations B (upstream) and W (middle).Figure3 shows that, at both stations, there are large extremes for the variables Q, Na+, Mg2+, and Cl_. Extreme values of river water flow are due to flood conditions, but extreme values of other variables may be due to measurement errors in water samples.According to box plots, minimum, maximum, and median of parameters in midstream station are more than the upstream station because of the influx of contaminated streams resulting from municipal runoff and domestic sewage. Also, variances of all parameters and flow in the midstream station are more than the upstream station.


### 3.2. The Dependence of Water Quality on Discharge and Precipitation

In this section, to analyse the relationship between water quality and discharge, the relationship between all factors and discharge was statistically investigated. The flow regime is regarded as the “main variable” since it directly and indirectly affects the river ecosystem [56–58].One-way analysis of variance was used (Tables1 and 2) for statistical analysis of data. There was a correlation between flow and precipitation (r = 0.35); the relationship direction was positive, and the correlation intensity was moderate. Researchers have reported different ranges of correlation coefficients in earlier studies; however, some [59, 60] have reported moderate-to-severe dependence between rainfall and river flow, arguing that the correlation coefficient is approximately 0.50. The mild correlation coefficient between rainfall and river flow can be due to the entry of a constant amount of domestic wastewater into the river, which is not affected by the amount of rainfall.Table 1
Correlation coefficients (r) of water quality concentrations with the flow rate at the upstream station (B).
Appropriate equationPowerLogarithmicLinearExponentiallyEquation parameterY = 20.009X^0.0440.3610.1240.3510.351PrecipitationY = 0.966X^−0.539−0.88−0.804−0.471−0.632Na+Y = 0.708X^−0.399−0.803−0.779−0.428−0.490Mg2+Y = 2.029X^−0.184−0.750−0.735−0.435−0.48Ca2‏+Y = 0.969X^−0.522−0.815−0.764−0.447−0.554Cl−Y = 2.399X^−0.284−0.852−0.811−0.494−0.624HCO3−Y = 0.125ln (x)+7.232−0.233−0.399−0.138−0.063SO₄2−Y = 260.12X^−0.313−0.872−0.841−0.481−0.563TDSY = −0.076ln (x)+0.61660.3820.3890.3030.305pHY = 412.85X^−0.313−0.872−0.841−0.481−0.562ECCorrelation coefficients underlined are not statistically significant at the significance level of 5%.Table 2
Correlation coefficients (r) of water quality concentrations with the flow rate at the midstream station (W).
Appropriate equationPowerLogarithmicLinearExponentiallyEquation parameterY = 18.192X^0.040.3220.1050.0450.084PrecipitationY = 6.130X^−0.565−0.783−0.768−0.605−0.657Na+Y = 2.55X^−0.463−0.832−0.824−0.657−0.742Mg2+Y = −0.752ln (x)+4.535−0.805−0.813−0.667−0.691Ca2‏+Y = 6.424X^−0.548−0.806−0.775−0.615−0.715Cl−Y = −0.632ln (x)+3.966−0.769−0.794−0.642−0.646HCO3−Y = −0.539ln (x)+2.584−0.677−0.694−0.541−0.543SO₄2−Y = 868.41X^−0.388−0.83−0.825−0.652−0.709TDSY = 0.003X+7.2160.1220.1260.1300.126pHY = 1375.5X^−0.387−0.829−0.824−0.65−0.708ECCorrelation coefficients underlined are not statistically significant at the significance level of 5%.Figure4(a) and 4(b) show the best fitted to the data regression models. A strong correlation between Zarjoub streamflow and water quality was observed in most parameters, same with some other studies [61–63].Figure 4
(a) Correlation of qualitative parameters in different river discharges. (b) Correlation of qualitative parameters in different precipitations.
(a)(b)On upstream station, the relationship between Na+ and discharge has best correlation coefficient value (r = −0.88), followed by EC (r = –0.872) and then by the other TDS, HCO3−, and Cl− (r = –0.872), (r = –0.852), and (r = –0.815) and then cations Mg2+ amd Ca2+((r = –0.803) and (r = –0.750), while phosphate and pH show weaker relations.Among the nine studied water quality parameters, only one parameter, alkalinity, had a positive relationship with river flow. Eight other parameters had a negative relationship with river flow. This is a dilution effect of surface runoff, during periods of heavy runoff in the catchment. According to the analysis results in Tables1 and 2, a significant relationship was observed between all parameters and discharge except pH and SO₄2− (at the upstream station). The salinity dispersion in different discharges was determined, as seen in Figure 4; therefore, with increasing discharge, the amount of electrical conductivity decreased; the maximum EC was observed at discharges of less than 5 m^3/s. Some researchers have found seasonal differences in river water conductivity, generally due to a negative relationship with discharge volume [64, 65].The total concentration of soluble salts varies with the flow rate, being higher in low flow times. Amazonian water is rich in electrolytes and high electrical conductivity, and when caused by heavy rains, the amount of this parameter decreases [66].These results showed that the concentration of the parameters was primarily diluted by surface runoff produced by precipitation, same with the results of Budai and Clement [67] and Akbar et al. [68]. Overall, the water alkalinity is from limestone or dolomite in nature, which carries minerals through the rocks containing lime when rainwater passes over the Earth's surface. Therefore, the soil of the study area probably contained calcareous mode, which increased flow, leading to the alkalinity of water. Thus, it was expected to observe the positive correlation between the pH value and the flow. Higher pH values observed (in the research by [69]) show that the balance of carbon dioxide and bicarbonate carbonate is affected by changes in physicochemical conditions. The factors responsible for the change in pH values are the combined effect of increased rainfall, reduced temperature, and concentration of carbon dioxide in water due to the conversion of bicarbonate to carbonate [70–72].Hydrological changes play a vital role in the structural biodiversity in river ecosystems since they change habitat conditions in rivers and floodplains [73, 74]. In order to investigate the effects of rainfall on quantitative and qualitative changes of Zarjoub River water in the scale of each precipitation, 30 index precipitates (above 10 mm) were selected, and simultaneously, the average flow rate over a period and also the average values of water quality factors corresponding to the same precipitation were extracted. Regression models examined the relationship between precipitation and water quality parameters of stations in the study area of Zarjoub River. Based on the results in Tables 3 and 4, the qualitative parameter rainfall relationships show weaker correlation than that of qualitative parameter discharge. The correlation coefficients are low, r = −0.349 and r = −0.319, for the anion and cation HCO3− and Ca2+, respectively, and very low, r = 0.167 and r = 0.164, for Cl− and Na+.Table 3
Correlation coefficients (r) of water quality concentrations with the precipitation at the upstream station (B).
Appropriate equationPowerLogarithmicLinearExponentiallyEquation parameterY = 1.816X^−0.364−0.164−0.138−0.141−0.155Na+Y = 0.547e^−0.004−0.055−0.197−0.176−0.062Mg2+Y = 4.085X^−0.284−0.319−0.316−0.283−0.281Ca2+Y = 1.993X^−0.389−0.167−0.158−0.155−0.141Cl−Y = 6.715X^−0.421−0.349−0.326−0.307−0.322HCO3−Y = 0.248X^0.2250.1870.1260.1050.164SO₄2−Y = −77.31ln (x)+459.15−0.243−0.251−0.232−0.219TDSY = −0.302ln (x)+8.2600.2570.2590.2290.237pHY = 412.85X^−0.313−0.243−0.251−0.232−0.219ECCorrelation coefficients underlined are not statistically significant at the significance level of 5%.Table 4
Correlation coefficients (r) of water quality concentrations with the precipitation at the midstream station (W).
Appropriate equationPowerLogarithmicLinearExponentiallyEquation parameterY = −737ln (x)+4.514−0.055−0.152−0.138−0.045Na+Y = −0.004X+1.155−0.055−0.045−0.055−0.032Mg2+Y = −0.403ln (x)+4.030−0.110−0.145−0.145−0.105Ca2+Y = −0.013ln (x)+4.552−0.089−0.141−0.141−0.095Cl−Y = −0.013X+2.815−0.118−0.130−0.134−0.118HCO3−Y = 0.852X^0.0880.0550.0320.0320.032SO₄2−Y = −3.398X+484.51−0.077−0.138−0.138−0.084TDSY = 8.018X^−0.0330.2300.2300.1670.164pHY = −5.345X+768.6−0.071−0.138−0.138−0.077ECCorrelation coefficients underlined are not statistically significant at the significance level of 5%.t-test results of statistical significance of difference between mean values of physicochemical and precipitation. There was a significant difference between Ca2‏+, HCO3-, TDS, and EC concentrations at the upstream station, while other parameters were not significantly different (P > 0.05). As the results showed, in general, different water quality levels were observed in different amounts of rainfall; however, a limited number of the parameters showed a statistical difference with precipitation.Based on the results, the trend of changes in the amount of each qualitative element followed that of precipitation; hence, with increasing amount of precipitation, the concentration of most elements decreased (Figure4(b)), although the effect of precipitation on most elements was not statistically significant. As observed in Figure 4, the electrical conductivity with precipitation was logarithmic, decreasing by increasing the amount of precipitation per rainfall; hence, the highest EC level was observed in rainfalls below 15 mm. Generally, changes in rainfall and flow at both river stations affected TDS and EC significantly. Therefore, their management should be considered under the influence of climate change to prevent further water quality degradation in the future. The results here help government managers in their related planning, such as releasing treated wastewater effluent into the river and using fertilizers in the agricultural sector by considering prior metrologic conditions, especially precipitation, to prevent the destructive effects of high-water concentrations. Furthermore, given that the number of significant relationships of parameters with precipitation at upstream and middle stations is not the same, the spatial variation of the precipitation effect on water quality along a river is observed.


### 3.3. Investigation of Regression Models

According to Table5, in the relationship between discharge and quality parameters, 70%, 25%, 5%, and 5% of data fitting belonged to power, logarithmic, exponential, and linear models, respectively. However, in the relationship between precipitation and quality parameters, 45%, 30%, 5%, and 20% of data fitting belonged to power, logarithmic, exponential, and linear models, respectively. According to the present research results, the precipitation parameter (independent variable) in most cases has no significant relationship and the discharge parameter (independent variable) has a significant relationship with other qualitative parameters (dependent variables).Table 5
Regression candidate models for calculation of correlation coefficients (r).
PrecipitationStream flowStationType of modelType of modelSum of parametersParameters with significant differencesLinearLogarithmicExponentiallyPowerSum of parametersParameters with significant differencesLinearLogarithmicExponentiallyPowerUpstream station (B).10403661070208Midstream station (W).10043031080316A noteworthy point in the final results of fitting and examining the relationship between discharge and precipitation with water quality parameters in this river is the existence of 7 significant relationships between discharge and other parameters at the upstream station, including EC, TDS, -Cl, HCO3-, Ca+ 2, Mg+ 2, and Na+, as well as the highest correlation coefficient with Na + (0.88) and the lowest correlation coefficient with water pH (0.389) The number of significant relationships, in the middle station, reaches 8 by adding the sulfate parameter to the above qualitative parameters. The increase in the correlation coefficient of discharge with sulfate ion in the middle station compared to that of upstream shows the vital role of human factors and sewage and chemical fertilizer discharge in the river bank. Snowmelt runoff causes a strong seasonal flow during the spring in the catchment area. The seasonal discharge and inverse relationships with the variables Ca2+ and Mg2+, which have groundwater sources and Na+, which is mainly a point pollutant, controls the patterns.The tables show that the trends are not significant for each of the upstream and middle stations; however, the increase in flow is probably due to the overall increase in rainfall. Due to the general relationship between both rainfall and river flow with water quality, any slight change in rainfall, thus flow rate, can lead to significant changes in water quality parameters. Therefore, it can be concluded that rainfall may be the main factor in the water quality of the Zarjoub River. The results can provide important information needed to properly manage the Zarjoub River and ensure sustainable water resources for the industrial and agricultural sectors.


### 3.4. Comparison with Other Rivers in the Worldwide

The comparison study was performed with the water quality characteristics of other rivers in the world. In Table6, a comparison is introduced between data collected in the present study on both stations and data from Nestos River collected in 2006–2009, together with data collected from Nakdong River, South Korea, in 2008–2012 and Mayur River in 2017–2018.Table 6
Worldwide comparison of river water parameters.
Mayur River (c)Nakdong River (b)Nestos river (a)Zarjoub river (middle-streamZarjoub river (up-stream)Parameter218.00—6.001.630.490Na+24.36—5.700.800.480Mg2+50.26—43.202.691.60Ca2‏+240.77—5.701.720.52Cl−346.38—164.902.401.84HCO3−18.79—12.801.190.495SO₄2−935.92——351180.50TDS7.197.7–8.1—7.167.16PH1290126.9–773.6—557286.50ECValues are reported as average in mg/L, except for EC (μS/cm) and pH. ((a) Boskidis et al. [75]; (b) Jung et al. [76]; (c) Roy et al. [77]).Comparing the two stations related to the water quality of Zarjoub River, the concentrations of some parameters measured in the middle station such as salinity and suspended solids are higher than the concentrations measured upstream, and a result that shows the condition of the river due to the entry of sanitary sewage and urban runoff has changed. The concentrations of SO₄2- and Na+ in Nestos are ten and three times more than the concentrations in Zarjoub on middle station. This difference in concentrations can be attributed to heavy rainfall along the Zarjoub basin and dilution of pollutants. Table6 shows the parameters measured in Mayur. It is at a higher level than other rivers, which indicates the very poor environmental condition of the river, and as a result, more intense human activities in that area and the salinity of the river water due to its proximity to the sea.


## 3.1. Spatial Variability of the Water Quality Parameters

Some water quality variables show significant seasonal variations. For pH and Cl, at both the upstream and middle stations, the changes over time are relatively uniform. There is also variation in flow rate, rainfall, and concentration. These changes depend on the discharge and seasonality and the flow of domestic sewage into the river. Upstream fluctuations are greater than the midpoint. The ratio of the highest to the lowest concentration, flow rate, and rainfall for the upstream station are discharge (226.3 : 1), Cl− (25.5 : 1), Na+(17.01 : 1), Mg2+(17.2 : 1), SO₄2−(7.5 : 1), HCO3−(5.8 : 1), rainfall 5.1; 1), TDS (5.04 : 1), EC (5.03 : 1), Ca2+(3.3 : 1), and pH (1.2 : 1) and for the midstream station are discharge (57.8 : 1), Na+(20.8 : 1), Cl− (15.2 : 1), Mg2+(10.3 : 1), SO₄2−(8.1 : 1), EC (6.2 : 1), TDS (6.21 : 1), rainfall (5.7; 1), Ca2+(4.3 : 1), HCO3−(3.4 : 1), and pH (1.1 : 1). The predominant anions and cations in the water of the Zarjoub River are Cl− and Na+, respectively.Box-and-whisker diagram of measured parameters at the two monitoring stations were plotted during 2007–2017 (Figure3). The horizontal line inside the boxes represents the median and the upper and lower lines of the boxes indicate the 75 and 25% percentiles, respectively. The vertical lines indicate the minimum and maximum values for observations. The height of the box represents the variance among the 25 and 75% percentiles.Figure 3
Box-and-whisker plots for the measured parameters at the two monitoring stations B (upstream) and W (middle).Figure3 shows that, at both stations, there are large extremes for the variables Q, Na+, Mg2+, and Cl_. Extreme values of river water flow are due to flood conditions, but extreme values of other variables may be due to measurement errors in water samples.According to box plots, minimum, maximum, and median of parameters in midstream station are more than the upstream station because of the influx of contaminated streams resulting from municipal runoff and domestic sewage. Also, variances of all parameters and flow in the midstream station are more than the upstream station.


## 3.2. The Dependence of Water Quality on Discharge and Precipitation

In this section, to analyse the relationship between water quality and discharge, the relationship between all factors and discharge was statistically investigated. The flow regime is regarded as the “main variable” since it directly and indirectly affects the river ecosystem [56–58].One-way analysis of variance was used (Tables1 and 2) for statistical analysis of data. There was a correlation between flow and precipitation (r = 0.35); the relationship direction was positive, and the correlation intensity was moderate. Researchers have reported different ranges of correlation coefficients in earlier studies; however, some [59, 60] have reported moderate-to-severe dependence between rainfall and river flow, arguing that the correlation coefficient is approximately 0.50. The mild correlation coefficient between rainfall and river flow can be due to the entry of a constant amount of domestic wastewater into the river, which is not affected by the amount of rainfall.Table 1
Correlation coefficients (r) of water quality concentrations with the flow rate at the upstream station (B).
Appropriate equationPowerLogarithmicLinearExponentiallyEquation parameterY = 20.009X^0.0440.3610.1240.3510.351PrecipitationY = 0.966X^−0.539−0.88−0.804−0.471−0.632Na+Y = 0.708X^−0.399−0.803−0.779−0.428−0.490Mg2+Y = 2.029X^−0.184−0.750−0.735−0.435−0.48Ca2‏+Y = 0.969X^−0.522−0.815−0.764−0.447−0.554Cl−Y = 2.399X^−0.284−0.852−0.811−0.494−0.624HCO3−Y = 0.125ln (x)+7.232−0.233−0.399−0.138−0.063SO₄2−Y = 260.12X^−0.313−0.872−0.841−0.481−0.563TDSY = −0.076ln (x)+0.61660.3820.3890.3030.305pHY = 412.85X^−0.313−0.872−0.841−0.481−0.562ECCorrelation coefficients underlined are not statistically significant at the significance level of 5%.Table 2
Correlation coefficients (r) of water quality concentrations with the flow rate at the midstream station (W).
Appropriate equationPowerLogarithmicLinearExponentiallyEquation parameterY = 18.192X^0.040.3220.1050.0450.084PrecipitationY = 6.130X^−0.565−0.783−0.768−0.605−0.657Na+Y = 2.55X^−0.463−0.832−0.824−0.657−0.742Mg2+Y = −0.752ln (x)+4.535−0.805−0.813−0.667−0.691Ca2‏+Y = 6.424X^−0.548−0.806−0.775−0.615−0.715Cl−Y = −0.632ln (x)+3.966−0.769−0.794−0.642−0.646HCO3−Y = −0.539ln (x)+2.584−0.677−0.694−0.541−0.543SO₄2−Y = 868.41X^−0.388−0.83−0.825−0.652−0.709TDSY = 0.003X+7.2160.1220.1260.1300.126pHY = 1375.5X^−0.387−0.829−0.824−0.65−0.708ECCorrelation coefficients underlined are not statistically significant at the significance level of 5%.Figure4(a) and 4(b) show the best fitted to the data regression models. A strong correlation between Zarjoub streamflow and water quality was observed in most parameters, same with some other studies [61–63].Figure 4
(a) Correlation of qualitative parameters in different river discharges. (b) Correlation of qualitative parameters in different precipitations.
(a)(b)On upstream station, the relationship between Na+ and discharge has best correlation coefficient value (r = −0.88), followed by EC (r = –0.872) and then by the other TDS, HCO3−, and Cl− (r = –0.872), (r = –0.852), and (r = –0.815) and then cations Mg2+ amd Ca2+((r = –0.803) and (r = –0.750), while phosphate and pH show weaker relations.Among the nine studied water quality parameters, only one parameter, alkalinity, had a positive relationship with river flow. Eight other parameters had a negative relationship with river flow. This is a dilution effect of surface runoff, during periods of heavy runoff in the catchment. According to the analysis results in Tables1 and 2, a significant relationship was observed between all parameters and discharge except pH and SO₄2− (at the upstream station). The salinity dispersion in different discharges was determined, as seen in Figure 4; therefore, with increasing discharge, the amount of electrical conductivity decreased; the maximum EC was observed at discharges of less than 5 m^3/s. Some researchers have found seasonal differences in river water conductivity, generally due to a negative relationship with discharge volume [64, 65].The total concentration of soluble salts varies with the flow rate, being higher in low flow times. Amazonian water is rich in electrolytes and high electrical conductivity, and when caused by heavy rains, the amount of this parameter decreases [66].These results showed that the concentration of the parameters was primarily diluted by surface runoff produced by precipitation, same with the results of Budai and Clement [67] and Akbar et al. [68]. Overall, the water alkalinity is from limestone or dolomite in nature, which carries minerals through the rocks containing lime when rainwater passes over the Earth's surface. Therefore, the soil of the study area probably contained calcareous mode, which increased flow, leading to the alkalinity of water. Thus, it was expected to observe the positive correlation between the pH value and the flow. Higher pH values observed (in the research by [69]) show that the balance of carbon dioxide and bicarbonate carbonate is affected by changes in physicochemical conditions. The factors responsible for the change in pH values are the combined effect of increased rainfall, reduced temperature, and concentration of carbon dioxide in water due to the conversion of bicarbonate to carbonate [70–72].Hydrological changes play a vital role in the structural biodiversity in river ecosystems since they change habitat conditions in rivers and floodplains [73, 74]. In order to investigate the effects of rainfall on quantitative and qualitative changes of Zarjoub River water in the scale of each precipitation, 30 index precipitates (above 10 mm) were selected, and simultaneously, the average flow rate over a period and also the average values of water quality factors corresponding to the same precipitation were extracted. Regression models examined the relationship between precipitation and water quality parameters of stations in the study area of Zarjoub River. Based on the results in Tables 3 and 4, the qualitative parameter rainfall relationships show weaker correlation than that of qualitative parameter discharge. The correlation coefficients are low, r = −0.349 and r = −0.319, for the anion and cation HCO3− and Ca2+, respectively, and very low, r = 0.167 and r = 0.164, for Cl− and Na+.Table 3
Correlation coefficients (r) of water quality concentrations with the precipitation at the upstream station (B).
Appropriate equationPowerLogarithmicLinearExponentiallyEquation parameterY = 1.816X^−0.364−0.164−0.138−0.141−0.155Na+Y = 0.547e^−0.004−0.055−0.197−0.176−0.062Mg2+Y = 4.085X^−0.284−0.319−0.316−0.283−0.281Ca2+Y = 1.993X^−0.389−0.167−0.158−0.155−0.141Cl−Y = 6.715X^−0.421−0.349−0.326−0.307−0.322HCO3−Y = 0.248X^0.2250.1870.1260.1050.164SO₄2−Y = −77.31ln (x)+459.15−0.243−0.251−0.232−0.219TDSY = −0.302ln (x)+8.2600.2570.2590.2290.237pHY = 412.85X^−0.313−0.243−0.251−0.232−0.219ECCorrelation coefficients underlined are not statistically significant at the significance level of 5%.Table 4
Correlation coefficients (r) of water quality concentrations with the precipitation at the midstream station (W).
Appropriate equationPowerLogarithmicLinearExponentiallyEquation parameterY = −737ln (x)+4.514−0.055−0.152−0.138−0.045Na+Y = −0.004X+1.155−0.055−0.045−0.055−0.032Mg2+Y = −0.403ln (x)+4.030−0.110−0.145−0.145−0.105Ca2+Y = −0.013ln (x)+4.552−0.089−0.141−0.141−0.095Cl−Y = −0.013X+2.815−0.118−0.130−0.134−0.118HCO3−Y = 0.852X^0.0880.0550.0320.0320.032SO₄2−Y = −3.398X+484.51−0.077−0.138−0.138−0.084TDSY = 8.018X^−0.0330.2300.2300.1670.164pHY = −5.345X+768.6−0.071−0.138−0.138−0.077ECCorrelation coefficients underlined are not statistically significant at the significance level of 5%.t-test results of statistical significance of difference between mean values of physicochemical and precipitation. There was a significant difference between Ca2‏+, HCO3-, TDS, and EC concentrations at the upstream station, while other parameters were not significantly different (P > 0.05). As the results showed, in general, different water quality levels were observed in different amounts of rainfall; however, a limited number of the parameters showed a statistical difference with precipitation.Based on the results, the trend of changes in the amount of each qualitative element followed that of precipitation; hence, with increasing amount of precipitation, the concentration of most elements decreased (Figure4(b)), although the effect of precipitation on most elements was not statistically significant. As observed in Figure 4, the electrical conductivity with precipitation was logarithmic, decreasing by increasing the amount of precipitation per rainfall; hence, the highest EC level was observed in rainfalls below 15 mm. Generally, changes in rainfall and flow at both river stations affected TDS and EC significantly. Therefore, their management should be considered under the influence of climate change to prevent further water quality degradation in the future. The results here help government managers in their related planning, such as releasing treated wastewater effluent into the river and using fertilizers in the agricultural sector by considering prior metrologic conditions, especially precipitation, to prevent the destructive effects of high-water concentrations. Furthermore, given that the number of significant relationships of parameters with precipitation at upstream and middle stations is not the same, the spatial variation of the precipitation effect on water quality along a river is observed.


## 3.3. Investigation of Regression Models

According to Table5, in the relationship between discharge and quality parameters, 70%, 25%, 5%, and 5% of data fitting belonged to power, logarithmic, exponential, and linear models, respectively. However, in the relationship between precipitation and quality parameters, 45%, 30%, 5%, and 20% of data fitting belonged to power, logarithmic, exponential, and linear models, respectively. According to the present research results, the precipitation parameter (independent variable) in most cases has no significant relationship and the discharge parameter (independent variable) has a significant relationship with other qualitative parameters (dependent variables).Table 5
Regression candidate models for calculation of correlation coefficients (r).
PrecipitationStream flowStationType of modelType of modelSum of parametersParameters with significant differencesLinearLogarithmicExponentiallyPowerSum of parametersParameters with significant differencesLinearLogarithmicExponentiallyPowerUpstream station (B).10403661070208Midstream station (W).10043031080316A noteworthy point in the final results of fitting and examining the relationship between discharge and precipitation with water quality parameters in this river is the existence of 7 significant relationships between discharge and other parameters at the upstream station, including EC, TDS, -Cl, HCO3-, Ca+ 2, Mg+ 2, and Na+, as well as the highest correlation coefficient with Na + (0.88) and the lowest correlation coefficient with water pH (0.389) The number of significant relationships, in the middle station, reaches 8 by adding the sulfate parameter to the above qualitative parameters. The increase in the correlation coefficient of discharge with sulfate ion in the middle station compared to that of upstream shows the vital role of human factors and sewage and chemical fertilizer discharge in the river bank. Snowmelt runoff causes a strong seasonal flow during the spring in the catchment area. The seasonal discharge and inverse relationships with the variables Ca2+ and Mg2+, which have groundwater sources and Na+, which is mainly a point pollutant, controls the patterns.The tables show that the trends are not significant for each of the upstream and middle stations; however, the increase in flow is probably due to the overall increase in rainfall. Due to the general relationship between both rainfall and river flow with water quality, any slight change in rainfall, thus flow rate, can lead to significant changes in water quality parameters. Therefore, it can be concluded that rainfall may be the main factor in the water quality of the Zarjoub River. The results can provide important information needed to properly manage the Zarjoub River and ensure sustainable water resources for the industrial and agricultural sectors.


## 3.4. Comparison with Other Rivers in the Worldwide

The comparison study was performed with the water quality characteristics of other rivers in the world. In Table6, a comparison is introduced between data collected in the present study on both stations and data from Nestos River collected in 2006–2009, together with data collected from Nakdong River, South Korea, in 2008–2012 and Mayur River in 2017–2018.Table 6
Worldwide comparison of river water parameters.
Mayur River (c)Nakdong River (b)Nestos river (a)Zarjoub river (middle-streamZarjoub river (up-stream)Parameter218.00—6.001.630.490Na+24.36—5.700.800.480Mg2+50.26—43.202.691.60Ca2‏+240.77—5.701.720.52Cl−346.38—164.902.401.84HCO3−18.79—12.801.190.495SO₄2−935.92——351180.50TDS7.197.7–8.1—7.167.16PH1290126.9–773.6—557286.50ECValues are reported as average in mg/L, except for EC (μS/cm) and pH. ((a) Boskidis et al. [75]; (b) Jung et al. [76]; (c) Roy et al. [77]).Comparing the two stations related to the water quality of Zarjoub River, the concentrations of some parameters measured in the middle station such as salinity and suspended solids are higher than the concentrations measured upstream, and a result that shows the condition of the river due to the entry of sanitary sewage and urban runoff has changed. The concentrations of SO₄2- and Na+ in Nestos are ten and three times more than the concentrations in Zarjoub on middle station. This difference in concentrations can be attributed to heavy rainfall along the Zarjoub basin and dilution of pollutants. Table6 shows the parameters measured in Mayur. It is at a higher level than other rivers, which indicates the very poor environmental condition of the river, and as a result, more intense human activities in that area and the salinity of the river water due to its proximity to the sea.


## 4. Conclusion

Data of water quality variables and discharge and precipitation at the two station of the Zarjoub River in north of Iran were analysed using statistical methods and trend analysis. The monthly measured values of nine water quality variables (Na+, Ca2+, Mg2+, Cl−, HCO3−, SO₄2−, TDS, pH, and EC) over 10 years (2007–2017) are used for this analysis.The statistical analysis of abovementioned variables resulted that(1)
Negative correlation with precipitation and discharge was calculated from all water quality parameters except pH and SO₄2−.(2)
The concentration-rainfall relationships show weaker correlation than that of concentration discharge at all stations on the rivers.(3)
The maximum concentration of water quality elements is observed in low rainfalls so that the maximum concentration of these elements occurs in precipitations less than 15 mm.(4)
Only in the upstream station there is a significant relationship between bicarbonate, calcium, TDS and EC parameters with rainfall. Of course, significant differences in the concentrations of many water quality parameters were found among discharge.(5)
The logarithmic and the power models explain better the concentration-discharge and concentration-rainfall relationships.This paper shows a significant dependence on some water quality parameters (such as EC and TDS) under climate change. Therefore, more efficient management should be created to target and eliminate these pollutants in order to reduce the impact of climate change. In addition, the results show the importance of storm water runoff management because it carries pollutants and has a positive dependence on rainfall and flow. The results of this study confirm the relationship between rainfall and river water quality over the past decade and show the effective role of rainfall on water quality. Therefore, the change in rainfall pattern under climate change has a direct impact on river water quality [78].



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