Abstract

Flow hydraulics and sediment transport influence the overall stability and sustainability of rivers. This work reports the flow hydraulics and water quality parameters of Damercherla and Wadapally, the two gauging stations on the Musi River, Hyderabad, India. The trend analysis of the flow rate of these two gauging stations was carried out using the Mann-Kendall test (MKT) and a revised innovative trend analysis (RITA) by considering parameters such as flow rate, total alkalinity, total hardness, electrical conductivity, chlorine, potassium, sulphate, and pH for the period of 1987–2018. Data was segregated to develop a monthly time series for each parameter. Apart from conductivity and hardness, the average value of all other water quality parameters at these two gauging sites were within the permissible limit of the Bureau of Indian Standards (BIS). The average conductivity value was 882 and 560 µS cm^-1, respectively, at the Damercherla and Wadapally gauging stations. The hardness of water at Damercherla and Wadapally was noted to be 239 and 156 mg L^-1, respectively, which was greater than the permissible limit of BIS, whereas the average value of alkalinity at Damercherla and Wadapally was 193 and 128 mg L^-1, respectively. RITA shows an insignificant discharge trend at Damercherla and a significant discharge trend at Wadapally. Similar trend results were observed for the MKT, too. The rate of decline of discharge is around 33 m³/s at Wadapally during the month of August. The changes observed in discharge at the studied sites on the Musi River may be due to unplanned urbanization and climate change, apart from other contributing factors.

1 Introduction

Flow rate plays an important role in maintaining the hydraulics of rivers (Verma et al. 2023). Alteration in the flow characteristics of rivers, in turn, influences the water quality of the rivers, which is crucial for the safety and sustenance of every form of life. Globally, though all countries face water scarcity, the situation worsens in developing and primarily populated countries like India. Globally, more than 4,850,000 deaths from diarrhoea each year are caused by the consumption of contaminated drinking water (Zhang et al. 2023). Therefore, it is a challenging task to maintain surface water quality for future generations in terms of sustainable development (Parween et al. 2022; Singh et al. 2025a). Several physicochemical and biological water quality parameters are assessed against the standards for the suitability of water for various purposes (Son et al. 2020; Khan et al. 2022a; Ewaid et al. 2020; Kolothumthodi and Pulikkal 2022). For instance, hard water is unfit for drinking, bathing, and washing, and leads to scaling in boilers used in industries (Kakati 2010; Singh and Kumar 2016; Bakalov 2023; Kolothumthodi and Pulikkal 2022). Similarly, high chloride concentration increases the conductivity and corrosiveness of water, harming aquatic organisms by interfering with osmoregulation (Shukla and Arya 2018). Surface water quality is closely related to human health, diseases, and mortality.

Ensuring clean and safe surface drinking water is a great challenge for any country's economic and social development and supports the nation's sustainable development. Lakes and rivers act as important drinking water sources around the world; therefore, the water quality of these sources plays a very important role in sustaining livelihoods. Achieving safe and affordable drinking water for all is the first target of the United Nations Development Goals by 2030 (Zhang et al. 2023). Surface water quality is vulnerable due to the dual stress of climate change and anthropogenic activities.

The world’s average temperature has increased due to an increase in atmospheric greenhouse gases (Ahmadi et al. 2018). A rise in global temperature is mainly due to increasing anthropogenic activities by about 0.74°C, and significant shifts in temperatures and weather patterns have been reported (Girma et al. 2020). Surface water quality parameters are subjected to frequent changes in response to climatic factors and human intervention Han et al. 2023; Rose et al. 2023; Gascuel-Odoux et al. 2022). In this regard, not only is testing the surface water important, but studying the temporal variation in these parameters is also essential. Globally, over 80% of domestic wastewater is released into water bodies without adequate treatment (Agarwal et al. 2022). Unplanned urbanization, rapid industrialization, anthropogenic activities, and migration of people are among the significant factors contributing to the contamination of surface water sources (Preisner 2020; Khan et al. 2022b). According to the World Health Organization (WHO), 844 million people globally (97 million in India) are away from accessing safe drinking water (Sharma et al. 2017; Agarwal et al. 2022).

A study on the Tai Lake Basin, China, by applying various trend analysis methods, asserts that innovative trend analysis (ITA) is a new graphical and revolutionary technique that can be used to evaluate the presence of possible trends, which is in congruence with other statistical approaches (Wu et al. 2022). The Huai River basin in China was studied by applying ITA, the Mann-Kendall test (MKT), and Sen’s slope test (SST) methods, wherein ITA was found to be a trustworthy, consistent, and effective method (Girma et al. 2020). Further, ITA provides a graphical representation of trend information, data, or knowledge, intended to present information quickly and clearly to better understand the results by detecting a sub-trend series. The annual precipitation and temperature at six different stations in the Huai River basin, China, using the MKT, SST, and ITA, were analysed, and it was found that annual precipitation had risen at only one station, whereas a slight decline was observed in the other stations (Girma et al. 2020). A sharp decreasing trend in annual temperature was observed at all the stations. The data for the seasonal and annual flow of the Yangtze River basin, China, using the MKT, SST, and ITA methods were reviewed (Umar et al. 2022). A significant increasing trend was found at the source of the Yangtze River, whereas in another area of study, a non-significant decreasing trend was reported. The surface water quality of Hunan province, located in the south of the Yangtze River, was analysed using the MKT, and found that certain water quality parameters of the Xiangjiang, Yuanjiang, and Lishui Rivers, and Dongting Lake showed an upward trend, whereas a few parameters of the Zijiang River and Dongting Lake showed a downward trend (Yin and Hu 2009). The variation of stream flow and sediment load between 1952 and 2011 at four gauging stations in the Yellow River basin, China, showed a significant decreasing trend, suggesting that the presence of a large number of reservoirs has altered the stream flow and sediment transportation (Zhao et al. 2014).

The 15 stations in the Mahaweli basin, Sri Lanka, from 1990 to 2019, using the MKT, SST, and ITA, were analysed (Pawar and Rathnayake 2022). The authors found the MKT and ITA methods to be reliable and can be used for water resource management. It was observed that there are some fluctuations in rainfall with decreasing and increasing trends. The ITA method does not have any hypothesis to evaluate trend analysis. The time series method gives a mathematical approach with dynamic water quality data and provides accurate periodic information regarding changes in trends (Luo et al. 2019). The data from the Red River Delta, Vietnam, was analysed to estimate the sea level between 1961 and 2020, using the MKT, SST, and ITA methods, compared the results, and found a significant rising trend (Nguyen et al. 2022). The trend analysis of the water quality of the Klang River, Kuala Lumpur, Malaysia, was carried out using monthly data from 1997 through 2007 (Othman and Eldin 2012). The time series were plotted and showed improvement in water quality. Twelve water quality parameters in the Nisava River, Serbia, were examined between 2000 and 2004, and some parameters showed a significant trend in monthly and seasonal series, whereas the annual series showed an insignificant trend (Gocic and Trajkovic 2013). The impact of rainstorms and agriculture runoff on the water quality of the Al-Wehda dam, Jordan, was investigated during 2006–2012 and found that the high algal growth could be due to the concentration of organic matter in peak spring, in addition to noticing a large variation in the concentration of water quality parameters in the lake (Al-Taani 2014). Analysis of the lower Mekong basin in Southeast Asia found that due to urban development, commercial and agricultural activities, large areas of forest were cut down, which adversely affected the natural resources (Lyon et al. 2017). They implemented an innovative approach to determine the impact of such large-scale changes on hydrological response.

Assessment of the Jhelum River basin in Kashmir, India, using the MKT, SST, and ITA, found a significant decrease in annual average rainfall and annual average discharge, whereas a trend for annual maximum rainfall and annual maximum discharge indicated a significant increase (Umar et al. 2022). Analysis of the discharge data and sediment load of two gauging stations on the Drava River between 1988 and 2017, using the MKT, ITA, and revised innovative trend analysis (RITA), proved that the trends in annual maximum discharge and annual minimum discharge are insignificant (Zakwan et al. 2022). A parametric and nonparametric test on rainfall data for the period 1950–2016 in Bihar, India, was conducted (Zakwan and Ara 2019). It was found that there is an overall decline in rainfall, which can impact the availability of surface water and standardized rainfall anomaly. These studies suggest that, though rivers have been instrumental in the development of civilization, globally, they are experiencing water quality and quantity alteration. Shortage of drinking water is still a problem both in urban and rural areas, even after 75 years of the independence of India (Agarwal et al. 2022). Water pollution in India has become a very sensitive and serious issue, as nearly 80% of the rivers in India are polluted (Sharma et al. 2017). Most of the cities in India do not have a fully developed sewage system, and hence, most of the sewage is discharged directly or indirectly into surface water through drains during and after the monsoon season (McCartney et al. 2008).

The Krishna River in the Deccan Plateau is the third-longest river in India. Hyderabad is in the Krishna basin and is one of the fastest-growing cities. The Musi River is one of the most important rivers flowing through the middle of Hyderabad, Telangana, India. The city's water demand has been associated with this river since the period of the Nizam rulers. Urbanization directly impacts the quantity and quality of surface water sources (Nayan et al. 2020). Migration of people from the nearby states in search of better job opportunities, health facilities, and studies, because better opportunities in metropolitan cities increased the water demand and caused a burden on sewage facilities. In the last three decades, river water quality has degraded daily because of uncontrolled development and expansion of the city. The malodorous nature of the Musi River shows the severity of pollution due to sewage disposal. According to the Central Pollution Control Board (CPCB), the Musi River is the sixth most polluted river in India (Ajay Kumar et al. 2023; Sujatha et al. 2020). The present study analyses the flow hydraulics and water quality parameters of two gauging sites, Damercherla and Wadapally, on the Musi River. The Musi River is the only river flowing through Hyderabad, an important and the fastest growing city of India. Therefore, it is vital to keep a track of the water quality and quantity of the Musi River. However, a comprehensive analysis of water quality and quantity has not been performed to the best knowledge of the authors. Hence, a trend analysis was performed to understand the current and future direction of flow rate and water quality of the Musi River. Further, RITA, a novel method to understand water quality, which also provides a visual understanding of temporal variation in water quality parameters, has been employed to accomplish the trend analysis. The trends obtained from the RITA were cross-checked with the MKT. Such studies are essential to understanding the changes in flow hydraulics, planning water supply, and monitoring water quality.

2 Materials and Methods

2.1 Study area

The Musi River originates in the Ananthagiri Hills, located in Vikarabad, flows east, turns south, and travels 240 km, and then joins the Krishna River at Wadapally, as shown in Figure 1. The Musi River has good discharge and quality upstream, but as the river flows from the middle of Hyderabad city downstream, it looks like a giant sewage drain (Ajay Kumar et al. 2023). The Musi River is a major tributary of the Krishna River, flowing through the Deccan plateau of Telangana, India. Musi River divides the historical city of Hyderabad into two parts, namely the old town and the new city (Sujatha et al. 2020).

Figure 1 Location of the Damaecherla and Wadapally gauging sites on the Musi River, Hyderabad, India.

In 1908, a devastating flood occurred on the Musi River, which razed over 80,000 houses, making most people homeless, and about 15,000 people died (Sujatha et al. 2020). After the flood, the Nizam of Hyderabad planned to construct a dam across the Musi River. The Osman Sagar and Himayathsagar reservoirs were constructed on the Musi River during the period of the Nizam. The primary purpose of building these dams across the Musi River was to fulfil the water demand for drinking, irrigation, and recreation, and, most importantly, to protect the twin cities, Hyderabad and Secunderabad, in Telangana state, India, from floods. These reservoirs are still used to meet the domestic water demand of Hyderabad city (Nayan et al. 2020). Hyderabad is the fourth largest metropolitan city in India, with a population of 7.7 million. Hyderabad is the sixth most populous city among all metropolitan cities of India. The area of the Hyderabad metropolitan city is 7305 km² and receives an annual mean rainfall of 800 mm. Maximum precipitation occurs in July, August, and September (Nayan et al. 2020). Damercherla and Wadapally are the two gauging sites located on the Musi River.

2.2 Data sources

The present study used the MKT and RITA methods to obtain the trend analysis for two gauging sites (Damercherla and Wadapally) on the Musi River. The water quality and discharge data of the sites were obtained from the Central Water Commission (CWC), Hyderabad, India. Water quality data for 32 years, 1987–2018, were used in the present analysis. The coordinates and catchment area of the Krishna River, the Musi River, and the gauging sites (Damercherla and Wadapally) of the Musi River, Hyderabad, India, are listed in Table 1. Water quality and discharge parameters of Damercherla and Wadapally were evaluated using the following methodology, shown in Figure 2.

Table 1 Details of the Krishna River, Musi River, and gauging sites on the Musi River, Hyderabad, India.

River/gauging sites	Latitude (N)	Longitude (E)	Catchment area (km2)
Krishna	15°44′11″	80°55′12″	258,948
Musi	17°22′0″	78°28′0″	12,005
Damercherla	16°42′25″	79°38′11″	11,105
Wadapally	16°40′39″	79°39′09″	11,255

Figure 2 Evaluation of water quality and discharge parameters at Damercherla and Wadapally gauging stations on the Musi River, Hyderabad, India.

2.3 Data processing

The data obtained from CWC was initially analysed for outliers and missing values. The analysis of the time series of all the parameters revealed 5 missing values and 7 outliers, from which 3 pH values had typo errors of a missing decimal point. The remaining outliers and missing values were corrected by the method of averages. Before performing the trend test, all the data sets were subjected to Von-Neumann (VN) independence, Wald-Wolfowitz’s (WW) stationarity, and Mann-Whitney’s (MW) homogeneity test.

Von-Neumann (VN) Test

Von-Neumann (VN) test statistics can be determined using Equation 1 (Zakwan et al. 2022):

(1)

Where:

xi	=	ith observation, and
	=	average of the series.

The test statistic is compared with the standard values for a given confidence level to declare the independence of data.

Wald-Wolfowitz’s (WW) stationarity test

Wald-Wolfowitz’s (WW) stationarity test statistics can be determined as follows (Zakwan et al. 2022).

(2)

(3)

(4)

Where:

R	=	observed number of runs in time series
	=	expected number of runs,
mr	=	rth moment of origin, and
s1, s2, s3, and s4	=	intermediate quantities, calculated using Equation 5:

(5)

The  statistics are compared with the standard values for a given confidence level to assure the stationarity of data.

Mann-Whitney’s (MW) homogeneity test

Mann-Whitney’s statistics are utilized to assure that all the data in the time series follow a single probability distribution (Zakwan et al. 2022). Initially, the data is divided into two equal halves such that p ≤ q (p+q = n), then the actual data set is ranked in ascending order, and finally, the sum of the ranks (R₁ and R₂) for the two halves is calculated (Zakwan et al. 2022). After that, the minimum of the following statistics is compared to the standard table for a given confidence level to determine the homogeneity of data (Equations 6 and 7).

(6)

(7)

Where:

R1 and R2	=	sum of rank of first and second half,
p and q	=	sample size of two independent series, and
U1 and U2	=	Mann-Whitney statistics associated with series 1 and series 2, respectively.

Initially the VN, WW, and MW test were performed for the complete time series of discharge and water quality parameters, but some of the time series presented non-stationarity, therefore, time separate time series for each month of each parameter were prepared. Considering 8 parameters, 12 months, and 2 gauging sites, 192 time series were prepared and again tested for stationarity, independence, and homogeneity. Around 5 time series were still not found to be independent; for these time series, a simple trend free pre-whitening was applied before using Mann-Kendall test.

2.4 Mann-Kendall test

The Mann-Kendall test (MKT) is a statistical test widely used for trend analysis. The null hypothesis H0 presumes that the time series is trendless, which is tested against the alternative hypothesis H1, which assumes a trend exists (Zakwan and Ara 2019). For Xi and Xj, two subsets of data, where i = 1,2, 3,…, n^-1, and j = i+1, i+2, i+3,…, n. The test statistics (S) can be calculated using Equations 8 and 9:

(8)

(9)

The variance (σ²) for the S-statistic is defined by Equation 10:

(10)

The standard test statistics Z_s is calculated using Equation 11:

(11)

If |Z_s| is greater than Z_α/2, where α represents the chosen significance level (5% with Z_0.025 = 1.96) then the null hypothesis is invalid implying that the trend is significant.

2.5 Innovative trend analysis

As per the innovative trend analysis (ITA), the available time series is divided into two halves, and the mean of both parts of the time series is calculated as  and  (Şen 2012). The two parts of the time series are then arranged in an ascending sequence. A graph is prepared with the first half of the time series on the x-axis and the second half series on the y-axis. The comparative position of the scatter point concerning the trendless 1:1 line defines the trend. The points above and below the trendless line provide increasing and decreasing trends, respectively, while the points lying on the 1:1 line represent no trend (Zakwan 2022). The significance of the trend can be determined based on the confidence limit. The trend slope is determined using Equation 12:

(12)

Critical trend (Z_s) can be calculated using Equation 13:

(13)

Where:

σs

=

standard deviation of trend values.

σ_s can be calculated using Equation 14:

(14)

Where:

ρY2Y1

=

cross-correlation coefficient of averages of two halves as indicated by Equation 15:

(15)

Where:

E(x) and E(y)	=	expected value of series 1 and series 2,
E(xy)	=	mean of the products of corresponding observations of two time-series, and
σx and σy	=	standard deviation of first and second series, respectively.

2.6 Revised innovative trend analysis

Several researchers observed that significant trends were identified, while the trends were insignificant due to the assumption of equal variance of the two halves of the series (σ_x=σ_y) in ITA (Chen et al. 2019; Serinaldi et al. 2020; Alashan 2020). In this regard, the confidence interval of innovative trend analysis was modified by removing the equity condition of the variance of two half of the series. So, Equation 14 can be modified as Equation 16:

(16)

Where:

ρxy

=

correlation coefficient between two halves of the time-series.

3 Results and Discussion

In the present work, trend, water quality, and quantity analysis were performed for the Damercherla and Wadapally gauging sites on the Musi River. Water quality parameters were assessed for the temporal variation of the Musi River during 1987–2018. The average and standard deviation of water quality parameters and discharge for the Damercherla and Wadapally gauging sites of the Musi River, India, from 1987–2018, are described in Table 2. As the rainfall was concentrated over four months (June–September), the region experienced large seasonal variation in the river discharge, which is expected to also indirectly impact the water quality. So, the discharge and water quality analysis was performed separately for all the months by developing monthly average time series from 1987–2018. This step proved to be very beneficial in reducing the variability and scatter in the data by reducing the coefficient of variation for the monthly time series as shown in Table 3.

Table 2 Average and standard deviation of water quality parameters and discharge data for Damercherla and Wadapally gauging sites of the Musi River, Hyderabad, India, from 1987 to 2018.

Parameters	Damercherla			Wadapally
	Mean	Standard deviation	Coefficient of variation	Mean	Standard deviation	Coefficient of variation
pH	8.1	0.24	0.03	8.1	0.29	0.04
Total hardness (mg L-1)	239	54.4	0.23	156	42.17	0.27
Electrical conductivity (µS cm-1)	882	273.7	0.31	553	189.83	0.34
Chlorine (mg L-1)	98.4	59.3	0.60	53.4	22.96	0.43
Potassium (mg L-1)	3.0	2.23	0.74	2.4	1.22	0.51
Sulphate (mg L-1)	69.4	42	0.61	56	57.82	1.03
Total alkalinity (mg L-1)	193	62.9	0.33	128	51.55	0.40
Discharge (m3 s-1)	32.87	50.07	1.52	569	1135	1.99

Table 3 Coefficient of variation of discharge and various water quality parameters at Damercherla and Wadapally gauging sites, Musi River, Hyderabad, India.

Months/ Parameter	Damercherla									Wadapally
	pH	TH	EC	Chlorine	Potassium	Sulphate	TA	Discharge		pH	TH	EC	Chlorine	Potassium	Sulphate	TA	Discharge
Jan	0.03	0.17	0.26	0.51	0.37	0.41	0.32	0.64		0.02	0.27	0.29	0.35	0.29	0.47	0.34	0.63
Feb	0.02	0.1	0.18	0.42	0.52	0.76	0.25	0.64		0.02	0.20	0.44	0.28	0.62	0.65	0.35	0.75
Mar	0.02	0.27	0.24	0.58	0.50	0.45	0.30	0.86		0.02	0.27	0.26	0.32	0.33	0.40	0.42	0.72
Apr	0.03	0.23	0.27	0.52	1.23	0.96	0.35	0.99		0.03	0.27	0.40	0.47	0.36	0.73	0.44	0.74
May	0.02	0.22	0.17	0.29	0.42	0.47	0.38	1.00		0.03	0.27	0.26	0.29	0.38	0.38	0.45	0.99
Jun	0.03	0.19	0.19	0.42	0.41	0.39	0.35	1.00		0.04	0.29	0.30	0.29	0.41	1.07	0.38	0.84
Jul	0.03	0.20	0.25	0.62	0.56	0.39	0.32	0.98		0.03	0.21	0.26	0.41	0.73	0.39	0.34	1.00
Aug	0.02	0.28	0.35	0.49	0.72	0.54	0.39	1.03		0.07	0.27	0.24	0.42	0.42	0.83	0.38	1.01
Sept	0.03	0.23	0.25	0.39	0.59	0.51	0.33	0.92		0.04	0.24	0.29	0.43	0.45	0.46	0.38	1.03
Oct	0.03	0.17	0.25	0.46	0.51	0.64	0.31	1.03		0.03	0.26	0.28	0.46	0.49	0.52	0.36	1.04
Nov	0.03	0.19	0.23	0.51	0.54	0.63	0.27	0.69		0.03	0.22	0.31	0.57	0.43	0.61	0.37	0.51
Dec	0.02	0.19	0.37	0.58	0.62	0.64	0.35	0.61		0.03	0.31	0.31	0.54	0.53	0.49	0.49	0.68

Trends of water quality and discharge parameters obtained from RITA and MKT at the Damercherla gauging site, Musi River, India are listed in Table 4. Plots of RITA obtained for the Damercherla gauging site for the parameters such as electrical conductivity in February, pH in March, chlorine in September, potassium and sulphate in January, alkalinity in December and hardness in September are shown in Figure 3. Based on RITA, electrical conductivity shows a significant positive trend in February, March, May, June, July, September, and October. High values of electrical conductivity indicate that there is a significant amount of salts and heavy metals dissolved in the water, a few of which can be harmful to aquatic life and humans, especially at high concentrations (Shah 2017). The average conductivity value at Damercherla is 882 µS cm^-1, higher than the permissible limit (500 µS cm^-1) as per BIS. A significant positive trend for electrical conductivity, in which all the points are above the 1:1 trendless line, indicating the conductivity is increasing at Dameracherla, and can be seen in Figure 3(a).

Table 4 Trends of water quality and discharge parameters obtained from revised innovative trend statistics and Mann-Kendall statistics at Damercherla gauging site, Musi River, Hyderabad, India.

Months/ Parameter	Revised innovative trend statistics									Mann-Kendall test statistics
	pH	TH	EC	Chlorine	Potassium	Sulphate	TA	Discharge		pH	TH	EC	Chlorine	Potassium	Sulphate	TA	Discharge
Jan	-0.66	2.35	1.80	-0.66	-0.66	2.70	9.22	0.68		-0.49	3.39	5.25	5.01	3.30	3.89	3.71	-0.54
Feb	-2.48	1.94	4.80	-2.48	-2.48	0.99	6.33	0.56		-1.29	4.96	4.67	3.54	3.66	5.45	3.88	-0.24
Mar	-0.79	0.88	2.05	1.80	-0.79	1.89	6.89	0.57		-0.68	2.92	5.17	5.16	2.48	4.38	3.37	-0.02
Apr	-0.82	0.51	1.23	-0.82	-0.82	0.83	3.63	-0.17		-0.96	0.83	3.94	3.88	1.92	5.25	2.87	0.47
May	-0.29	1.30	2.51	-0.29	-0.29	1.38	8.52	-0.33		0.59	2.25	5.37	4.30	1.63	3.75	3.32	-0.21
Jun	0.52	2.03	3.84	0.52	-0.52	2.93	3.09	1.17		0.33	2.81	5.90	4.51	3.51	3.57	1.46	1.57
Jul	0.38	3.46	3.56	0.38	0.38	1.90	5.20	-0.29		0.50	4.93	5.63	4.36	1.44	2.34	3.08	0.18
Aug	-3.25	0.81	1.88	-3.25	0.24	0.79	1.16	0.72		-1.87	2.35	3.76	4.70	1.95	1.09	0.89	0.21
Sept	-0.23	2.01	3.43	-0.23	1.67	1.57	2.42	0.31		-0.18	2.55	3.89	3.99	2.68	2.89	2.71	-0.70
Oct	-0.49	0.86	2.19	-0.49	1.53	1.91	1.56	0.47		-0.13	0.71	4.62	3.80	2.79	3.49	2.37	-0.15
Nov	0.30	0.34	1.24	0.30	1.03	1.58	1.31	0.34		0.15	1.20	4.28	4.35	1.61	5.14	2.71	-1.35
Dec	-0.31	1.32	1.10	-0.31	0.37	2.09	7.42	0.32		0.22	2.58	4.56	4.07	0.19	3.65	4.53	-0.02

Figure 3 Plots of revised innovative trend analysis for different water quality parameters at the Dameracherla gauging site: (a) Electrical conductivity in February; (b) pH in March; (c) Chloride in September; (d) Potassium in January; (e) Sulphate in January; (f) Total alkalinity in December; (g) Total hardness in September.

The pH does not show significant changes except in February and August. Figure 3(b) also validates an insignificant trend of pH, as all the points are close to the 1:1 trendless line, indicating no change in pH. For most of the months, a negative trend exists for the pH, indicating that water at Damercherla is attaining an acidic nature. The acidity of river water is due to the presence of organic acids in combination with a catchment source of sulphate. The fluctuation in pH may be due to anthropogenic human activity, which is usually related to activities such as discharging unsatisfactorily treated industrial effluent, chemical dumping, landfill leachate, etc. Acid rain is another main source of the decline in the pH of surface water. A pH value greater than 9.5 or less than 4.5 is unsuitable for most aquatic organisms. The change in pH will directly affect the breeding and reproduction of fish. The average value of pH at the Damercherla gauging station was 8.1, and as per BIS, the pH of surface water may vary between 6.5 and 8.5.

Figure 3(c) represents a significant positive trend of chlorine, as all the points are above the 1:1 trendless line, indicating that chlorine is increasing at Dameracherla. The average chlorine value in the water was 98.4 mg L^-1. An increase in the concentration of chlorine in river water affects all aquatic life, especially fish, as it causes gills to burn and gets absorbed into the bloodstream (Firat and Kargin 2010; Valleau et al. 2022). Potassium shows an insignificant trend throughout the year except for February. The insignificant positive trend of potassium at Dameracherla is depicted in Figure 3(d). The positive trend of potassium indicates the increase in its concentration in the Musi River. The average value of potassium at Damercherla is 3.0 mg L^-1, whereas its permissible limit as per BIS is 10 mg L^-1.

Sulphate shows an insignificant trend throughout the year except for January, June, and December. The sulphate concentration increased at the Damercherla; however, there were no significant changes throughout the year. The positive trend of sulphate at Dameracherla is shown in Figure 3(e). The main source of sulphate in stream water could be the discharge of untreated or partially treated industrial waste (tanneries, pulp mills, and textile mills). The fertilizers in agricultural land can also contribute to a higher sulphate concentration in water bodies through runoff (Singh et al. 2022). The average sulphate value at the Damercherla gauging site is 69.4 mg L^-1, whereas its permissible limit as per BIS is 250 mg L^-1. Higher sulphate concentration makes the water bitter and causes algal blooms as a result of eutrophication, which reduces the amount of dissolved oxygen in the water (Lamers et al. 1998; Qin et al. 2013). The trend of discharge at Dameracherla is shown in Figure 4, which indicates a significant decline in discharge. The slope of trends of various water quality parameters and discharge for both the gauging sites is presented in Table 6, while Table 7 reports the correlation coefficient of discharge with various water quality parameters such as pH, total hardness, etc.

Figure 4 Revised innovative trend analysis for monthly discharge at Damercherla gauging site: (a) February; (b) April; (c) May; and (d) July.

The trends of water quality and discharge parameters assessed from RITA and MKT at the Wadapally gauging site of the Musi River, India, are listed in Table 5. Based on RITA, conductivity shows a positive trend in January, March, May, June, July, September, and October. The average conductivity value is 560 µS cm^-1, slightly higher than the maximum permissible limit.

Table 5 Trends of water quality and discharge parameters obtained from revised innovative trend statistics and Mann-Kendall statistics at Wadapally gauging site, Musi River, Hyderabad, India.

Months/ Parameter	Revised innovative trend statistics									Mann-Kendall test statistics
	pH	TH	EC	Chloride	Potassium	Sulphate	TA	Discharge		pH	TH	EC	Chloride	Potassium	Sulphate	TA	Discharge
Jan	0.02	1.86	4.50	1.69	1.04	2.35	6.41	-2.21		-0.49	-0.56	4.22	4.01	3.67	4.30	4.01	3.24
Feb	0.60	3.33	1.13	7.12	0.76	1.24	4.17	-1.98		-1.29	0.74	3.67	5.29	4.49	3.82	4.23	2.79
Mar	0.04	4.06	2.06	2.14	2.17	1.66	3.63	-2.38		-0.68	-0.03	4.95	5.63	4.46	2.94	3.84	4.07
Apr	2.56	2.90	1.61	0.73	2.05	0.79	4.79	-3.23		-0.96	1.98	4.38	5.48	3.92	3.12	3.91	2.84
May	0.88	1.96	4.47	1.80	0.56	2.72	3.48	-1.73		0.59	0.39	3.49	4.05	4.36	0.99	4.10	3.16
Jun	-0.37	1.61	3.32	2.84	1.96	-0.04	5.01	-2.94		0.33	-0.64	3.06	4.20	4.91	2.13	2.35	2.87
Jul	0.48	5.21	4.76	1.39	0.66	1.37	6.97	-0.84		0.50	0.03	4.41	4.65	3.62	2.16	3.52	3.92
Aug	-0.20	1.11	1.7	2.84	1.70	0.72	3.53	-0.27		-1.87	0.80	1.41	3.97	2.97	3.61	2.72	3.13
Sept	-0.52	2.19	3.58	1.09	2.03	2.70	6.12	-0.37		-0.18	0.26	2.79	4.17	3.05	2.54	3.11	3.18
Oct	-0.64	0.78	2.42	1.08	1.47	1.97	7.07	-0.97		-0.13	0.00	1.57	3.62	2.64	2.66	4.78	4.04
Nov	-0.60	1.24	1.76	0.77	1.69	1.61	4.06	-3.67		0.15	-0.15	3.05	3.42	3.57	2.60	4.15	3.34
Dec	-0.61	1.28	1.17	0.76	0.38	2.49	2.78	-1.39		0.22	-0.67	3.28	4.43	3.70	0.73	5.01	4.56

NOTE: Values > 1.96 represent a significant trend.

Table 6 Slope of water quality and discharge parameters obtained from revised innovative trend at Damercherla and Wadapally gauging sites, Musi River, Hyderabad, India.

Months/ Parameter	Damercherla									Wadapally
	pH	TH	EC	Chlorine	Potassium	Sulphate	TA	Discharge		pH	TH	EC	Chlorine	Potassium	Sulphate	TA	Discharge
Jan	0.00	2.12	17.50	2.48	0.06	1.96	5.98	0.47		-0.02	3.40	12.12	1.04	0.06	1.61	4.88	-10.94
Feb	-0.01	2.25	14.79	1.79	0.07	3.34	4.79	0.14		0.00	2.20	16.88	0.98	0.07	2.04	4.32	-13.78
Mar	0.00	1.82	18.96	3.33	0.03	1.94	5.43	0.41		0.00	3.64	16.00	1.25	0.04	1.25	5.91	-15.47
Apr	-0.01	1.76	15.64	2.90	0.13	3.17	5.56	0.46		0.01	3.29	20.66	0.79	0.13	2.11	5.49	-14.49
May	0.00	2.49	12.89	2.39	0.03	1.56	6.34	-0.08		0.01	2.36	11.67	1.22	0.03	1.32	5.29	-8.88
Jun	0.01	2.05	18.55	4.43	0.13	1.59	4.57	0.11		0.00	2.17	16.18	1.70	0.12	0.19	4.93	-8.80
Jul	0.00	4.24	28.16	5.01	0.07	1.63	6.76	-0.70		0.00	1.78	11.40	1.61	0.07	1.29	4.19	-33.01
Aug	-0.01	2.44	22.21	3.79	0.04	2.01	4.31	0.62		0.00	1.62	12.60	1.11	0.04	2.59	3.71	-17.29
Sept	0.00	2.32	15.19	2.52	0.09	1.63	4.78	0.39		0.01	1.89	11.46	0.83	0.09	1.18	3.79	-7.59
Oct	0.00	0.75	13.66	2.67	0.02	2.66	4.76	1.84		0.01	1.86	9.27	0.71	0.02	2.17	3.93	-34.95
Nov	0.00	0.72	15.2	3.42	0.05	3.04	3.39	0.53		0.01	1.49	9.70	1.22	0.05	1.65	4.39	-10.78
Dec	0.00	1.47	25.39	2.85	0.02	2.28	7.06	0.22		0.01	2.24	15.25	1.31	0.02	1.89	5.50	-11.97

Table 7 Correlation coefficient of discharge with various water quality parameters at Damercherla and Wadapally gauging sites, Musi River, Hyderabad, India.

Months/ Parameter	Damercherla								Wadapally
	pH	TH	EC	Chlorine	Potassium	Sulphate	TA		pH	TH	EC	Chlorine	Potassium	Sulphate	TA
Jan	0.22	-0.29	-0.29	-0.33	-0.14	-0.16	0.11		0.03	-0.45	-0.41	-0.58	-0.26	-0.50	-0.50
Feb	-0.38	0.04	-0.12	-0.23	-0.18	-0.27	0.14		-0.31	-0.26	-0.46	-0.62	-0.37	-0.42	-0.31
Mar	-0.27	-0.21	-0.08	-0.06	0.00	0.03	-0.01		-0.05	-0.40	-0.59	-0.51	-0.49	-0.57	-0.49
Apr	0.01	-0.20	-0.31	-0.22	-0.13	-0.19	0.19		-0.24	-0.53	-0.62	-0.39	-0.45	-0.50	-0.47
May	0.11	-0.20	-0.25	-0.27	-0.33	-0.25	0.04		-0.36	-0.56	-0.69	-0.53	0.04	-0.52	-0.44
Jun	0.23	-0.04	0.05	0.18	0.24	0.11	0.23		0.12	-0.39	-0.59	-0.68	-0.36	0.26	-0.61
Jul	0.15	-0.25	-0.17	0.62	-0.25	-0.05	-0.18		-0.35	-0.09	-0.29	-0.25	-0.08	-0.10	-0.36
Aug	-0.28	0.20	-0.15	-0.06	-0.22	-0.28	0.10		0.07	-0.12	-0.24	-0.32	-0.07	-0.19	-0.10
Sept	0.21	0.02	-0.07	0.17	-0.21	-0.16	-0.02		-0.20	-0.42	-0.34	-0.33	-0.19	-0.54	-0.11
Oct	-0.04	0.01	0.05	0.07	0.12	-0.18	0.07		0.05	-0.18	-0.29	-0.35	-0.07	-0.22	-0.32
Nov	0.09	-0.25	-0.28	-0.31	-0.06	-0.28	0.17		-0.07	-0.47	-0.49	-0.49	-0.11	-0.61	-0.43
Dec	-0.29	-0.25	-0.24	-0.39	0.03	-0.34	-0.08		0.06	-0.41	-0.59	-0.46	-0.14	-0.61	-0.55

The trends of water quality and discharge parameters assessed from RITA and MKT at the Wadapally gauging site of the Musi River, India, are listed in Table 5. Based on RITA, conductivity shows a positive trend in January, March, May, June, July, September, and October. The average conductivity value is 560 µS cm^-1, slightly higher than the maximum permissible limit.

Figure 5(c) embodies the insignificant positive trend of chlorine at Wadapally. Chloride shows an insignificant trend throughout the year except in February, March, June, and August, whereas potassium shows an insignificant trend throughout the year except for March, April, June, and September. The insignificant positive trend of potassium at Wadapally is shown in Figure 5(d). Sulphate shows an insignificant trend throughout the year except in January, May, October, September, and December. The significant positive trend of sulphate at Wadapally can be seen in Figure 5(e). Alkalinity shows a significant positive trend throughout the year, and the average value is 128 mg L^-1, which is within the BIS limit. Figure 5(f) also demonstrates the significant positive trend of alkalinity at Wadapally. The total hardness reflects a positive trend for February, March, April, May, and September. The significant positive trend of hardness at Wadapally in Figure 5(g). The average value of the total hardness of water at Wadapally was 156 mg L^-1, which is within the BIS permissible limit. The average value of chloride, potassium, and sulphate was within the BIS limit.

Figure 5 Plots of revised innovative trend analysis obtained for Wadapally gauging site: (a) Electrical conductivity in January; (b) pH in October; (c) Chloride in November; (d) Potassium in January; (e) Sulphate in January (f) Total alkalinity in December; (g) Total hardness in September.

The significant positive trend of conductivity at Wadapally is evident in Figure 5(a). This indicates the presence of many dissolved salts at the Wadapally gauging site of the Musi River. Figure 5(b) shows an insignificant trend in pH. Although a slight positive trend and a slight negative trend were observed from January to May and from June to December, the pH does not show any significant changes throughout the year.

The trend of discharge at Wadapally is shown in Figure 6, which indicates a significant decline in discharge. This also causes water quality deterioration, as shown in Table 4. The trend analysis of water quality parameters at Wadapally was also examined through the MKT, and these results are included in Table 4. As per the MKT, pH shows an insignificant trend throughout the year, except for April, which implies a significant positive trend with a value of 1.98. Total hardness, conductivity, chloride, potassium, sulphate, and alkalinity show significant positive trends, and the discharge shows a significant decline. Most of the water quality parameters values are significant, leading to a degradation of water quality in Wadapally. The results of RITA are in close agreement with the results of the MKT. It is also observed that discharge decline is higher at Wadapally than at Dameracherla. Both the gauging sites show a degradation in water quality. The primary reason behind the deterioration of the water quality of the Musi River, Hyderabad, is the disposal of a large amount of untreated wastewater, discharge of industrial (carrying heavy metals) and hospital waste into the Musi River, and the presence of an open dump near the river (Ensink et al. 2010; Sekhar et al. 2005). Similar trend significance results were obtained for the MKT and RITA, as shown in Table 3 and Table 4. A comparison of MKT and RITA also reveals that the problem of showing the presence of over significance in the case of ITA can be overcome by using RITA, wherein the inequality of standard deviation of the two halves of the time series is considered. Further, the application of RITA provides an additional feature, a graphical presentation of trends over the MKT.

Figure 6 Revised innovative trend analysis monthly discharge graph obtained for Wadapally gauging site: (a) January; (b) August; (c) September; (d) December.

Table 7 shows a negative correlation of most of the water quality parameters with the discharge; however, the correlation of discharge with water quality parameters is not very strong. Specifically, during July to August, a negative correlation of water quality parameters with discharge can be observed from Table 7, which hints towards an improvement in water quality with increase in discharge. However, the dynamics of river water is governed by large number of factors such as land use pattern, sediment availability, runoff rate, anthropogenic activities, groundwater table, climate change, and socio-economic activities within watershed and deforestation. Therefore, it can be concluded that an increase in discharge partly contributes to water quality improvement, along with other factors.

The Telangana State Pollution Control Board (TSPCB) and Hyderabad Metropolitan Water Supply and Sewage Board (HMWS&SB) intend to invest a large amount in sewage treatment plants (STPs) to ensure proper treatment of sewage before it is discharged into the Musi River. However, continuous monitoring of these STPs should be done to check that the sewage matches Indian sewage discharge standards into rivers. Singh et al. (2025a) also pointed out that the implementation of environmental protection laws for water bodies failed to keep pace with the rate of urbanisation and socio-economic development leading to detrition of water bodies across India. In this regard, the government should understand the importance and take necessary action to discourage the discharge of untreated effluents into the river. Further, the state government may establish an organisation to restore the water quality and flood plains of the Musi River.

In the present study, temporal and seasonal variations in water quality and quantity were analysed. In general, most regions in the world are facing crises in the availability of good-quality water. In this regard, a trend analysis of water quality and quantity will not only provide insight into future trends in water quality but will also provide knowledge about the water quality parameters that are significantly increasing, thereby providing a chance to ponder the reason for the increase in those parameters and the measures required to control them. In general, it was observed that a negative trend in discharge (water quality) leads to a positive trend in chemical concentration, i.e., a decline in water quantity. Therefore, while performing water quality analysis of water bodies, water quantity (discharge) should also be considered. Secondly, the results of RITA are in good agreement with the results of the MKT. Further, the problem of the trend showing over significance, as reported in the case of ITA, is also solved by using RITA. The water quality parameters at both the gauging sites showed significant seasonal variation, i.e., the water quality parameters varied between monsoon and non-monsoon months. Similar seasonal variation was observed by Singh and Yadav (2025b; 2025c) for small surface water bodies in northern India.

4 Conclusion

The present work assessed various water quality and discharge parameters of the Damercherla and Wadapally gauging sites located on the Musi River, Hyderabad, India, from 1987–2018. Water quality parameters were compared with the Bureau of Indian Standards (BIS). At Damercherla, electrical conductivity, hardness, and alkalinity were observed to be above BIS limits, whereas at the Wadapally gauging site, all parameters were within the BIS permissible limit except the electrical conductivity, which is slightly higher than the permissible limit. Anthropogenic activities such as the discharge of domestic sewage, untreated industrial effluent, illegal drains attached, open dumps, and improper sewage treatment have increased the pollution load on the surface water. Further, temporal variation in water quality and discharge parameters of the Musi River was analysed using trend analysis. The Mann-Kendall test (MKT) and revised innovative trend analysis (RITA) were used to analyse the trend of various water quality parameters. Similar trends were observed for both RITA and MKT. The trend analysis validates the deterioration of the water quality of the Musi River over the years at the Damercherla and Wadapally gauging sites. In this regard, the government should take necessary action to continuously monitor the Musi River's water quality and force industry to treat effluent before discharge to protect the Musi River properly. Also, it is recommended that to reduce the pollution in the Musi river, floating plants such as Nelumbo nucifera (Lotus), Hydrilla Verticillata (Hydrilla), Water Lettuce, Duckweeds, and Water hyacinths, etc., should be grown. Phytoremediation techniques (biological method) are more cost-effective, simple, environmentally non-disruptive, and ecologically sound for reducing surface water pollution. The Telangana government may take further steps to establish an organisation to restore the water quality of the Musi River, India.

Data availability

Data is freely available on the Government of India website and can be obtained from the link provided in the text.

Acknowledgements

The authors thank the National Institute of Technology Mizoram for providing the necessary facilities.

References

1. Agarwal, S., S. Darbar, and S. Saha. 2022. “Challenges in Management of Domestic Wastewater for Sustainable Development.” In Urban Water Crisis and Management, 1st ed., Chapter 25, Vol. 6, 531–552. Elsevier Inc. https://doi.org/10.1016/B978-0-323-91838-1.00019-1
2. Ahmadi, F., M.N. Tahroudi, R. Mirabbasi, K. Khalili, and D. Jhajharia. 2018. “Spatiotemporal Trend and Abrupt Change Analysis of Temperature in Iran.” Meteorological Applications 25 (2): 314–321. https://doi.org/10.1002/met.1694
3. Ajay Kumar, M.C., P. Vinay Kumar, and D. Ugandhar. 2023. “Aseessment of Pollution and Its Influence of Outflow on the Downstream of Musi River, Hyderabad.” Dogo Rangsang Research Journal 13 (2, 1): 466–479. https://journal-dogorangsang.in/no_1_Online_23/89_feb.pdf
4. Al-Taani, A.A. 2014. “Trend Analysis in Water Quality of Al-Wehda Dam, North of Jordan.” Environmental Monitoring and Assessment 186, 6223–6239. https://doi.org/10.1007/s10661-014-3850-2
5. Alashan, S. 2020. “Testing and Improving Type 1 Error Performance of Şen’s Innovative Trend Analysis Method.” Theoretical and Applied Climatology 142 (3–4): 1015–1025. https://doi.org/10.1007/s00704-020-03363-5
6. Bakalov, I. 2023. “A New Approach in the Treatment of Boiler Water, EPRA.” International Journal of Research and Development ( IJRD ) 8, 1.  https://eprajournals.com/IJSR/article/9933/abstract
7. Chen, A., M. Wu, S. Wu, X. Sui, J. Wen, P. Wang, C. Lin, et al. 2019. “Bridging Gaps between Environmental Flows Theory and Practices in China.” Water Science and Engineering 12 (4): 284–292. https://doi.org/10.1016/j.wse.2019.12.002
8. Ensink, J.H.J., C.A. Scott, S. Brooker, and S. Cairncross. 2010. “Sewage Disposal in the Musi-River, India: Water Quality Remediation Through Irrigation Infrastructure.” Irrigation and Drainage Systems 24, 65–77. https://doi.org/10.1007/s10795-009-9088-4
9. Ewaid, S.H., S.A. Abed, N. Al-Ansari, and R.M. Salih. 2020. “Development and Evaluation of a Water Quality Index for the Iraqi Rivers.” Hydrology 7 (3): 67. https://doi.org/10.3390/hydrology7030067
10. Firat, Ö. and F. Kargin. 2010. “Biochemical Alterations Induced by Zn and Cd Individually or in Combination in the Serum of Oreochromis Niloticus.” Fish Physiology and Biochemistry 36, 647–653. https://doi.org/10.1007/s10695-009-9337-3
11. Gascuel-Odoux, C., O. Fovet, M. Faucheux, J. Salmon-Monviola, and L. Strohmenger. 2022. “How to Assess Water Quality Change in Temperate Headwater Catchments Ofwestern Europe under Climate Change: Examples and Perspectives.” Comptes Rendus Géoscience Sciences de La Planète 355, 399–409. https://doi.org/10.5802/crgeos.147
12. Girma, A., T. Qin, H. Wang, D. Yan, M. Gedefaw, A. Abiyu, and D. Batsuren. 2020. “Study on Recent Trends of Climate Variability Using Innovative Trend Analysis: The Case of the Upper Huai River Basin.” Polish Journal of Environmental Studies 29 (3): 2199–2210. https://doi.org/10.15244/pjoes/103448
13. Gocic, M. and S. Trajkovic. 2013. “Trend Analysis of Water Quality Parameters for the Nisava River.” Facta Universitatis - series: Architecture and Civil Engineering 11 (3): 199–210. https://doi.org/10.2298/FUACE1303199G
14. Han, Y., K. Zhang, Q. Lin, S. Huang, and X. Yang. 2023. “Assessing Lake Ecosystem Health from Disturbed Anthropogenic Landscapes: Spatial Patterns and Driving Mechanisms.” Ecological Indicators 147, 110007. https://doi.org/10.1016/j.ecolind.2023.110007
15. Kakati, S.S. 2010. “Assessment of Water Hardness Used for Domestic Purposes in Some Tribal Villages of Dhakuakhana Sub-Division, Lakhimpur District, Assam.” Nature Environment and Pollution Technology 9 (4): 749–753. https://neptjournal.com/upload-images/NL-20-17-(17)-B-156.pdf
16. Khan, I., M. Zakwan, and B. Mohanty. 2022a. “Water Quality Assessment for Sustainable Environmental Management.” ECS Transactions 107 (1): 10133–47. https://doi.org/10.1149/10701.10133ecst
17. Khan, I., M. Zakwan, A.K. Pulikkal, and R. Lalthazula. 2022b. “Impact of Unplanned Urbanization on Surface Water Quality of the Twin Cities of Telangana State, India.” Marine Pollution Bulletin 185 (A): 114324. https://doi.org/10.1016/j.marpolbul.2022.114324
18. Kolothumthodi, S.A. and A.K. Pulikkal. 2022. “Water Quality Assessment of Open Wells in Malappuram District, Kerala, India.” Aqua Water Infrastructure, Ecosystems and Society 71 (12): 1325–1331. https://doi.org/10.2166/aqua.2022.095
19. Lamers, L.P.M., H.B.M. Tomassen, and J.G.M. Roelofs. 1998. “Sulfate-Induced Eutrophication and Phytotoxicity in Freshwater Wetlands.” Environmental Science & Technology 32 (2): 199–205. https://doi.org/10.1021/es970362f
20. Luo, Y., Q. Chen, Y. Liu, X. Xie, and Q. Du. 2019. “Water Quality Prediction Analysis of Qingyi River Based on Time Series.” Edited by C.-H. Weng, R. Weerasinghe, and J. Wu. E3S Web of Conferences 118 (January 2011): 1–7. https://doi.org/10.1051/e3sconf/201911803005
21. Lyon, S.W., K. King, O. Polpanich, and G. Lacombe. 2017. “Assessing Hydrologic Changes across the Lower Mekong Basin.” Journal of Hydrology: Regional Studies 12, 303–314. https://doi.org/10.1016/j.ejrh.2017.06.007
22. McCartney, M., C. Scott, J. Ensink, B. Jiang, and T. Biggs. 2008. “Salinity Implications of Wastewater Irrigation in the Musi River Catchment in India.” Ceylon Journal of Science (Biological Sciences) 37 (1): 49–59. https://doi.org/10.4038/cjsbs.v37i1.495
23. Nayan, N.K., A. Das, A. Mukerji, T. Mazumder, and S. Bera. 2020. “Spatio-Temporal Dynamics of Water Resources of Hyderabad Metropolitan Area and Its Relationship with Urbanization.” Land Use Policy 99, 105010. https://doi.org/10.1016/j.landusepol.2020.105010
24. Nguyen, H.M., S. Ouillon, and V.D. Vu. 2022. “Sea Level Variation and Trend Analysis by Comparing Mann–Kendall Test and Innovative Trend Analysis in Front of the Red River Delta, Vietnam (1961–2020).” Water (Switzerland) 14 (11): 1709. https://doi.org/10.3390/w14111709
25. Othman, F. and M.E. Alaa Eldin. 2012. “Assessment of the Klang River Quality Using the Water Quality Indices” Advanced Materials Research 599, 237–240. https://doi.org/10.4028/www.scientific.net/AMR.599.237
26. Parween, S., N.A. Siddique, M.T.M. Diganta, A.I. Olbert, and M.G. Uddin. 2022. “Assessment of Urban River Water Quality using Modified NSF Water Quality Index Model at Siliguri City, West Bengal, India.” Environmental and Sustainability Indicators 16, 100202. https://doi.org/10.1016/j.indic.2022.100202
27. Pawar, U. and U. Rathnayake. 2022. “Spatiotemporal Rainfall Variability and Trend Analysis over Mahaweli Basin, Sri Lanka.” Arabian Journal of Geosciences 15, 370. https://doi.org/10.1007/s12517-022-09646-7
28. Preisner, M. 2020. “Surface Water Pollution by Untreated Municipal Wastewater Discharge Due to a Sewer Failure.” Environmental Processes 7, 767–80. https://doi.org/10.1007/s40710-020-00452-5
29. Qin, B.Q., G.G. Gao, G.W. Zhu, Y.L. Zhang, Y.Z. Song, X.M. Tang, Hai Xu, and J.M. Deng. 2013. “Lake Eutrophication and Its Ecosystem Response.” Chinese Science Bulletin 58, 961–970. https://doi.org/10.1007/s11434-012-5560-x
30. Rose, K.C., B. Bierwagen, S.D. Bridgham, D.M. Carlisle, C.P. Hawkins, N. Le Roy Poff, J.S. Read, et al. 2023. “Indicators of the Effects of Climate Change on Freshwater Ecosystems.” Climatic Change 176, 23. https://doi.org/10.1007/s10584-022-03457-1
31. Sekhar, C., N.S. Chary, C.T. Kamala, Shanker, and H. Frank. 2005. “Environmental Pathway and Risk Assessment Studies of the Musi River’s Heavy Metal Contamination - A Case Study.” Human and Ecological Risk Assessment 11 (6): 1217–1235. https://doi.org/10.1080/10807030500278594
32. Şen, Z. 2012. “Innovative Trend Analysis Methodology.” Journal of HydroLogic Engineering 17, 9. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000556
33. Serinaldi, F., F. Chebana, and C.G. Kilsby. 2020. “Dissecting Innovative Trend Analysis.” Stochastic Environmental Research and Risk Assessment 34, 733–754. https://doi.org/10.1007/s00477-020-01797-x
34. Shah, A.I. 2017. “Heavy Metal Impact on Aquatic Life and Human Health – an Over View.” In: IAIA17 Conference Proceedings | IA’s Contribution in Addressing Climate Change 37th Annual Conference of the International Association for Impact Assessment, 4–7 April 2017. https://conferences.iaia.org/2017/final-papers/Shah,%20Alkesh%20-%20Heavy%20Metal%20Impacto%20on%20Aquatic%20Life%20and%20Human%20Health.pdf
35. Sharma, R.K., M. Yadav, and R. Gupta. 2017. “Water Quality and Sustainability in India: Challenges and Opportunities.” Chemistry and Water 201, 183–205. https://doi.org/10.1016/B978-0-12-809330-6.00005-2
36. Shukla, M. and S. Arya. 2018. “Determination of Chloride Ion (Cl-) Concentration in Ganga River Water by Mohr Method at Kanpur, India.” Green Chemistry & Technology Letters 4 (1): 06–08. https://www.mendeley.com/catalogue/9682c12d-4dd3-315b-af9f-2c43ed86d505/
37. Singh, B.P. and Y. Kumar. 2016. “Physico-Chemical Characteristic of Hindon River , Uttar Pradesh (India).” Journal of Environmental Science, Toxicology and Food Technology 10 (12): 35–38. https://www.iosrjournals.org/iosr-jestft/papers/vol10-issue12/Version-4/F1012043538.pdf
38. Singh, P., A. Raj, and B. Yadav. 2022. "Impacts of Agriculture-Based Contaminants on Groundwater Quality." In: Yadav, B., Mohanty, M.P., Pandey, A., Singh, V.P., Singh, R.D. (eds), Sustainability of Water Resources. Water Science and Technology Library, vol 116. Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-031-13467-8_16
39. Singh, P., T. Nema, B. Yadav, A. Raj, and I. Özgen-Xian. 2025a. "Exploring the intersection of socioeconomic and environmental changes and their impact on India's lentic water systems." Environmental and Sustainability Indicators 26, 100703. https://doi.org/10.1016/j.indic.2025.100703
40. Singh, P. and B. Yadav. 2025b. "Spatiotemporal and vertical variability of water quality in lentic small water bodies: implications of varying rainfall and land use conditions." Environmental Science and Pollution Research 32, 22877–22893. https://doi.org/10.1007/s11356-024-34711-x
41. Singh, P. and B. Yadav. 2025c."Seasonal eutrophication in lentic small waterbodies: understanding nutrients-chlorophyll-a relationships and implications." Journal of Hazardous Materials Advances 17, 100563. https://doi.org/10.1016/j.hazadv.2024.100563
42. Son, C.T., N.T.H. Giang, T.P. Thao, N.H. Nui, N.T. Lam, and V.H. Cong. 2020. “Assessment of Cau River Water Quality Assessment Using a Combination of Water Quality and Pollution Indices.” Journal of Water Supply: Research and Technology-Aqua 69 (2): 160–172. https://doi.org/10.2166/aqua.2020.122
43. Sujatha, M., N.R. Chandana, and S.K. Kolli. 2020. "Assessment of Musi River Groundwater Quality in Rural and Urban Areas of Hyderabad, Telangana-(India)." International Journal of Applied Engineering Research 15 (9): 921–929. http://www.ripublication.com/ijaer20/ijaerv15n9_06.pdf
44. Umar, S., M.A. Lone, N.K. Goel, and M. Zakwan. 2022. "Trend analysis of hydro-meteorological parameters in the Jhelum River basin, North Western Himalayas." Theoretical and Applied Climatology 148, 1417–1428.
45. Valleau, R.E., M.P. Celis-Salgado, S.E. Arnott, A.M. Paterson, and J.P. Smol. 2022. “Assessing the Effect of Salinization (NaCl) on the Survival and Reproduction of Two Ubiquitous Cladocera Species (Bosmina Longirostris and Chydorus Brevilabris).” Water, Air, & Soil Pollution 233, 135. https://doi.org/10.1007/s11270-021-05482-9
46. Verma, R.K., A. Pandey, S. Verma, and S.K. Mishra. 2023. “A Review of Environmental Flow Assessment Studies in India with Implementation Enabling Factors and Constraints.” Ecohydrology and Hydrobiology 23 (4): 662–677. https://doi.org/10.1016/j.ecohyd.2023.06.006
47. Wu, S., W. Zhao, J. Yao, J. Jin, M. Zhang, and G. Jiang. 2022. “Precipitation Variations in the Tai Lake Basin from 1971 to 2018 Based on Innovative Trend Analysis.” Ecological Indicators 139 ,108868. https://doi.org/10.1016/j.ecolind.2022.108868
48. Yin, X. and G. Hu. 2009. “Trend Analysis of Surface Water Quality in Hunan Province.” In: 2009 International Conference on Environmental Science and Information Application Technology. Wuhan, China.154–157. https://doi.org/10.1109/ESIAT.2009.158
49. Zakwan, M. 2022. “Application of Excel Optimisation Tool in Solving and Teaching Water Resource Problems.” International Journal of Hydrology Science and Technology 14 (1): 63–74. https://doi.org/10.1504/IJHST.2022.123647
50. Zakwan, M. and Z. Ara. 2019. “Statistical Analysis of Rainfall in Bihar.” Sustainable Water Resources Management 5, 1781–1789. https://doi.org/10.1007/s40899-019-00340-3
51. Zakwan, M., Q.B. Pham, O. Bonacci, and B. Đurin. 2022. “Application of revised innovative trend analysis in lower Drava River." Arabian Journal of Geosciences 15, 758. https://doi.org/10.1007/s12517-022-09591-5
52. Zhang, Y., J. Deng, B. Qin, G. Zhu, Y. Zhang, E.Jeppesen, and Y. Tong. 2023. “Importance and Vulnerability of Lakes and Reservoirs Supporting Drinking Water in China.” Fundamental Research 3 (2): 265–273. https://doi.org/10.1016/j.fmre.2022.01.035
53. Zhao, G., X. Mu, A. Strehmel, and P. Tian. 2014. “Temporal Variation of Streamflow, Sediment Load and Their Relationship in the Yellow River Basin, China.” PLoS ONE 9 (3): e91048. https://doi.org/10.1371/journal.pone.0091048