1 Introduction

Rivers form key role in sustainable socio-economic development of a country and account for 0.006% of the freshwater resources in the world [1, 2]. The Himalayan rivers are under immense stress due to exponential population growth, industrialization, agriculture and urbanization. These anthropogenic effects combined with uncertain effects of climate change raising serious concerns on rivers both in terms of quality and quantity [3,4,5]. The people of Himalayan region are dependent on river water and groundwater for drinking, irrigation and other domestic purposes [6] and some towns are major tourist destinations hosting large population during peak tourist season. The discharge of untreated or partly treated sewage in river and poor waste management practices especially along the bank is deteriorating quality of river which sequentially also affecting the ground water quality in adjoining areas [7] Water portability is significantly dependent on its quality which is determined and controlled by its geochemistry in natural conditions [8, 9] whereas pollution in water is also dependent on seasonal variation in precipitation, surface runoff, interflow, groundwater flow from bacterial contamination and toxic elements due to their origin from anthropogenic activities [10]. Developing countries like India are more prevalent to degradation of river water quality where millions of people reside on the river banks with poor sewerage system. Therefore, it is essential to monitor water quality as it is directly related with human health and approximately 21% of communicable diseases are water related as per World Bank estimation [11, 12].

Climate changes have laid impact as there is a profound glacier retreat which has resulted in fresh water scarcity in Indian Himalayan Region (IHR) [13]. Similarly unprecedented urbanization, increased tourism, changing land use pattern, waste disposal and agricultural runoff are the main casual factors other than natural factors affecting the water quality of rivers in the Himalayan region [6, 14]. Parts of Kullu district in Himachal Pradesh are facing the water shortage due to changing climatic conditions and other anthropogenic activities [15, 16].

Studies on surface and groundwater monitoring with respect to WQI has been done by various researchers [17,18,19]. Water chemistry is constrained by various hidden factors related to natural and anthropogenic influences that are hard to comprehend and decipher significant data using simple techniques [20, 21]. In this study physicochemical properties of 36 water samples collected in pre-monsoon and post-monsoon from different altitudinal sites were analyzed to compare with standards given by Bureau of Indian Standards (BIS) and World Health Organization (WHO) for drinking and domestic purposes and water quality index is generated using compound data set to evaluate and assess the disparity in overall quality among study sites and seasons.In addition to the WQI and hydro geochemical analysis, the multivariate statistical approaches were used to understand complex information grids of water quality for efficient management and identification of effective solutions to pollution problems [22, 23]. Therefore, the findings of study will provide significant information of surface water quality of river Parbati and will help to adopt quality aspect of water in a judicious and sustainable manner.

2 Study area

The study area lies in Parbati valley in Kullu district of Himachal Pradesh. River Parbati is the main flowing river draining an area of 1938 km2 in the valley and the basin extends between 31◦50’ N and 32◦05’ N latitude and 77◦05’ E and 77◦50’ E longitude (Fig. 1). It originates from Mantalai glacier at an altitude of 5200 m amsl and below Pin Parbati pass on the western slope of the Greater Himalaya and traverse down through complex topography into the River Beas at Bhunter (1096 m). River Parbati is fast flowing, several small tributaries join at almost every angle outlining dendritic pattern. However, the valley is tourist hotspot as it is rich in art and culture, white-capped snowy peaks, dense green forest, dazzling rivers, hot water springs and sacred places. The climate is generally cool and dry whereas annual rainfall is 1405 mm out of which 57% occurs from June to September [24].

Fig.1
figure 1

Location of the study area with altitude gradient

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2.1 Geology and hydrogeology

The area under investigation comprises of Precambrian meta sedimentary now uncovered in the profoundly eroded “Kullu Rampur window” under the crystalline thrust sheets [25]. The geology of the area has been divided into three units, Manikaran quartzites, green bed member and Bhalan member under Banjar formation which includes various high-grade gneiss and schist, pegmatite, migmatite, granite and quartz veins along with carbonaceous phyllite, quartzite and limestone bands [26]. The exposed rock types around Manikaran and Kasol are white to grayish well jointed quartzites which are known as Manikaran quartzites [27]. The probable sources of high radioactivity in the area are due to intrusive tourmaline, whereas hot water emergence is through highly jointed and fractured quartzites at Manikaran and Kasol [24].The geology of the Parbati basin is represented in Fig. 2.

Fig.2
figure 2

Geological map of Parbati Basin

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The Kullu district has two hydro geological units in the form of porous and fissured formations. Porous formation has unconsolidated sediments of terrace, valley fills and fluvial channel whereas fissured formations has semi-consolidated to consolidated sediments of sedimentary, metamorphic and igneous origin descending higher to lower altitude [14]. The Parbati basin has fissured formation as shown in Fig. 2. The porous structures of unconsolidated sediments form the potential aquifers. The occurrence of groundwater in the basin is confined to semi-confined. However, phreatic aquifers form major source of domestic and irrigation water usages in the valley.

2.2 Drainage pattern

The Drainage pattern of the River Parbati exhibits tree-like dendritic pattern which is developed over sedimentary rocks (Fig. 2) [28]. Additionally, other drainage patterns are also present in the basin. Herringbone type of drainage pattern at Marhigarh Nala is one of the patterns where tributaries meet the mainstream at right angle. The trellis drainage pattern representing soft rocks like phyllitic slates and are well developed in the terrace and lower reaches of the valley [29]. In the areas of the uniformly dipping rocks, streams are running parallel to each other forming sub parallel drainage pattern. The sampling location in the study area is represented in Fig. 3.

Fig.3
figure 3

Drainage pattern and sampling sites of the Parbati River Basin

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3 Materials and methods

A well approached Stratified sample technique was adopted to collect the surface water sample along the river stretch of the study area. Total of thirty-six water samples, eighteen samples during pre-monsoon (June 2019) and eighteen samples during post—monsoon (September 2019) were collected from river Pārbati and its tributaries. Samples were collected in1000 ml best quality, sealed shut polyethylene bottles with cover lock. Physical parameters like EC, pH, TDS were measured on the field after sample collection using portable water and soil analysis kit. Major cations (Ca2+, Mg2+, Na+, K+) and anions (HCO3−, Cl−, SO42−, NO3−) were analyzed by following standard procedure given by American Public Health Association [30]. The accuracy of the chemical analysis was checked by Charge Balance Error (CBE) [31].

$${\rm{Charge\; Balance \;Error }}\left( {{\rm{CBE}}} \right) = {{\left( {\sum {{\rm{Cations }}} - \sum {{\rm{Anions}}} } \right)} \mathord{\left/ {\vphantom {{\left( {\sum {{\rm{Cations }}} - \sum {{\rm{Anions}}} } \right)} {\left( {\sum {{\rm{Cations}}} + \sum {{\rm{Anions}}} } \right) \times 100\% }}} \right. \kern-\nulldelimiterspace} {\left( {\sum {{\rm{Cations}}} + \sum {{\rm{Anions}}} } \right) \times 100\% }}$$
(1)

Majority of the analyzed samples showed CBE around ± 10%

Calcium, Magnesium, Bicarbonates and Total hardness were determined using EDTA titrimetric method by APHA 2017. Sodium and Potassium using flame photometric method, Nitrate and Sulphate by UV spectrophotometric method respectively. Chloride was estimated through argentometric method and alkalinity with acid titration method. Mean values were calculated for each parameter to understand the seasonal variation as an indication of the precision of each parameter. Maps were prepared using ArcGIS 10.8 software and Piper trillinear, Durov were plotted using Grapher. The statistical software SPSS, Grapher, Aquachem and Microsoft Excel were employed for the calculations and data interpretation.

3.1 Appraisal of Water Quality Index (WQI)

WQI is an effective mathematical tool to evaluate the suitability of water for drinking purpose [32]. It is calculated by adopting the weighted arithmetical index method [33]. In the present study physiochemical parameters namely pH, EC, TDS, TH, Ca2+, Mg2+, Cl−, SO42−, NO3− were considered for computing WQI for river Parbati. The weighted arithmetic index method was adopted by many researchers [34, 35] and the equation used is,

$${\text{WQI }} = \sum {\text{qn}}Wn /Wn$$
(2)

where Wn = unit weight of nth parameters, qn = sub-index or quality rating for the ith parameter.

The calculation of WQI involves following steps (Eq. 3–5):

$$W_{n} = {\text{K}}/{\text{Sn}}$$
(3)

K, is the proportionality constant obtained from,

$${\text{K }} = \left[1/\left( {\sum {\text{ni}} = 0{ }1/Si} \right)\right][{1}/$$
(4)

where, Sn and Si are the BIS (Bureau of Indian Standards) standard values of the water quality parameter

$${\text{q}}_{{{\text{ni}}}} = {\text{Va }}{-}{\text{Vi}}/{\text{Vs }} - {\text{Vi }} \times { 1}00$$
(5)

qni is the quality rating of the ith parameter for a total of n water quality parameters.

Where, Va = value of the water quality parameter obtained from laboratory analysis, Vi = ideal value (for pH = 7 and 0 for other parameters) and Vs = BIS standard value of water quality parameters.

3.2 Evaluation of hydrogeochemical facies of surface water

To comprehend the hydro geochemical attributes of surface water, different plots were utilized specifically, Piper, Durov and Gibbs plot [36,37,38]. These plots represent the graphical relationship characterizing different geochemical marks in surface water samples. The Grapher 12.0 was used to prepare the Piper diagram and Durov, while Gibbs plot was prepared by Aquachem software.

3.3 Multivariate statistical analysis

Multivariate Statistical techniques have been used to organize and simplify datasets and characterize freshwater, marine water and sediment quality [39,40,41]. In recent years, water quality assessment has been widely done using multivariate statistical techniques [42, 43].

Correlation analysis is a technique which determines the correlation coefficient between variables. The relationship between two variables can be measured by the strength and significance of the variables. The strength is indicated by the correlation (r), whereas the significance is expressed in probability levels (p values). Larger the correlation coefficient, stronger the relationship, whereas smaller the p level, more significant the relationship.

Statistical extraction of linear relationship from a given set of variables is performed by applying Principal Component Analysis (PCA) technique [22]. PCA allows to gain insight into the data without significant loss of information in the process [44, 45]. Principal components generated during the analysis are arranged in such a manner that they correspond to decreasing contribution of variance, i.e., principal component 1 (PC1) explains the highest amount of variance in the original data [46, 47] classified the factor loadings as ‘‘strong’’, ‘‘moderate’’ and ‘‘weak’’, corresponding to absolute loading values of 0.75, 0.75–0.50 and 0.50–0.30, respectively. However, loading reflects the relative importance of a variable within the component and does not reflect the importance of the component itself [48].

Cluster analysis was performed to arrange large data set of data into groups on the basis of given set of characterstics. Cluster analysis identifies relatively homogeneous groups or cluster of sampling sites based on their similarities/ dissimilarities [49]. In this study dendogram was obtained by performing wards method using squared Euclidean distance as a measure of similarity.

The datasets adequacy for PCA was calculated by the Kaiser–Meyer–Olkin Test (KMO) and Bartlett’s test [50]. A KMO value of 0.5 is considered the smallest value acceptable and high values near to 1 indicates usefulness of PCA in the study.

4 Results and discussion

Statistical summary of river water samples analyzed for various physio-chemical parameters is given in Table 1 with permissible limits prescribed by BIS and WHO [51, 52]. Mean value for all physio-chemical parameters, except Ca2+, Na+, K+ and Cl−, were showing higher values during the pre-monsoon period than post-monsoon.; EC accounted for 100% within desirable limit with mean values 93.44 and 81.05 µS/cm during the period of investigation. TDS values were well within desirable limit in both seasons with mean values 58.8 and 51.06 mg/L. The average values of total hardness are 95.36 mg/L in pre-monsoon and 83.44 mg/L in post-monsoon, respectively. Classification of river water based on total hardness [53] and total dissolved solids [54] are shown in Table 2. Further during the lab analysis higher soluble concentration of Ca2+, Mg2+ and HCO3− ions was found which may contribute to increased hardness of river water samples. Magnesium accounted for 12.62 mg/L in pre-monsoon and 8.85 mg/L for post-monsoon representing samples within desirable limits as the calcium starts precipitating after super saturation has been attained the dissolved concentration of magnesium exceeds that of calcium. Weathering of sandstone and dolomite, in the study area, accounts for higher concentration of Mg2+ found during the analysis [55]. Mean value of HCO3− was more significant during pre-monsoon due to dissolution of carbonate rocks, weathering of feldspar by carbonic acids and oxidation of NO3− and SO42− with organic matter as compared to post-monsoon [56]. The parameters like Na+, K+, Cl−, NO3−, SO42− and Mg2+ were all within the desirable range. During the pre-monsoon season the ionic dominance pattern in the water is governed by cationic species in the order of Mg2+  > Ca2+ > Na+ and K+. However, during the post-monsoon season, such dominance of cations is exceeded by that of anionic species in the order of HCO3− > SO42− > NO3− > Cl−.

Table 1 Statistical summary of physicochemical parameters of Parbati River water
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Table 2 Classification of Parbati River water based on total hardness and total dissolved solids
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4.1 Hydrogeochemical evaluation

Hydrogeochemical facies layouts in the form of graphical representation aiming to provide the analogies, dissimilarities and interpretation of evolutionary trends and different type of water in a particular area. For studying the effects of mixing water within the different lithological frameworks and for understanding various geochemical processes the value of graphical representation techniques is of immense importance. Many researchers such as Piper, Chadha, Collins and Black have contributed to the concept of graphical representation of geochemical analysis of water [36, 57,58,59].

4.1.1 Piper trilinear diagram

Piper diagrams are widely applied to graphically study the sources of dissolved constituents in water samples [36]. The ionic concentration was plotted in piper diagram to characterize the hydrochemistry of surface water in the study area (Fig. 4a, b). Piper diagram shows that all the samples are in Ca2+- Mg2+-HCO3− facies belongs to temporary hardness and alkaline earth elements (Ca2+  + Mg2+) exceeding the alkali elements (Na+  + K+) where Ca2+ and Mg2+ are leading cations in the study area shown in Table 3. Further, it was found that the concentration of weak acids (CO32− + HCO3−), during both the seasons, is higher than the strong acids (SO42− and Cl−). This further indicates the presence of HCO3− as principal anion in the surface water. The Cation triangle shows that 16% samples are Ca2+ type, 56% in Mg2+ and remaining 28% falls in no dominant cation zone for pre monsoon (Fig. 3a), whereas in post monsoon 44% is dominating in Mg2+ zone, 33% in no dominating zone and 23% in Ca2+ type (Fig. 3b). The anion triangle exhibits that HCO3− (100%) is the dominant ion during pre-monsoon and post-monsoon (Table 3). Consequently, concentration of ions such as Na+, K+ and Cl−, SO42− is very low and thus insignificant in both the seasons. As all the samples fall within the field of Ca2+-Mg2+-HCO3− water type, we can infer that the surface water chemistry is controlled by leaching process of dolomites, limestones and gypsum.

Fig. 4
figure 4

Piper classification diagram illustrating the chemical composition of Parbati River water. a Pre monsoon. b Post monsoon

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Table 3 Parbati River water samples characterization based on Piper diagram
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4.1.2 Durov plot

Durov plot is a significant graphical structure that gives better data on the hydrochemical portrayal and possible geochemical processes (mixing, cation exchange, reverse ion exchange dissolution) influencing the water quality of the area. This diagram is a composite plot consisting of two ternary diagrams where the milli equivalents percentages of the cations of interest were plotted against that of anions of interest; sides form a central rectangular, binary plot of total cation vs. total anion concentrations [60]. This diagram is very useful in indicating the samples with similar chemical composition as well as determines a useful relationship among different water samples [61]. The Durov plot of the water samples indicates that there are mainly two geochemical processes that could affect the genesis of water in the study area (Fig. 4a, b). According to the classification of [61], 88% samples in Pre-monsoon and 86% in Post-monsoon belong to HCO3− and Mg2+ dominant cation, this water type indicates probable mixing, uncommon dissolution influences and reverse ion exchange process (Fig. 5a) whereas 12% samples in pre-monsoon and 14% samples in post-monsoon falls where HCO3− and Ca2+ dominant type of water, indicating the partial ion exchange processes (Fig. 5b). None of the data points lie in the lower-right side of the boomerang, where water composition is dominated by atmospheric precipitation process.

Fig. 5
figure 5

Durov diagram showing the hydrochemical facies in the Parbati River water. a Pre monsoon. b Post monsoon

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4.1.3 Gibbs plot

Hydrochemical processes such as precipitation, rock water interaction and evaporation are well interpreted through gibbs plot [36]. Gibbs demonstrated that if total dissolved solid is plotted against Na + K/ (Na + Ca + K), it would provide information on the mechanism controlling chemistry of water. Collectively, the chemistry of water is influenced by the following three main factors: (1) evaporation dominance; (2) precipitation dominance and (3) rock dominance. Figure 6 exhibits that all the surface water samples for pre-monsoon and post-monsoon fall in the precipitation dominance zone which indicates that water is mainly controlled by rock dominance however geochemical process such as precipitation -dissolution; oxidation -reduction and ion exchange are the main governing factors of water chemistry.

Fig. 6
figure 6

Mechanism controlling water chemistry in Parbati River (after Gibbs [38])

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The studies in other parts of Himalayan regions confirmed rock dominance as main factor for controlling ionic composition in water bodies [6, 62, 63]. In the study area due to long time rock water interaction, percolations and flow through the rocky lithology has resulted in high solute concentration which is significantly controlling the water quality of the area. However, during post monsoon there is minor influence of precipitation dominance and melting of ice in the region. This reflects that water chemistry is mainly controlled by interaction of rock formation with precipitation for both seasons in Parbati river.

4.2 Water Quality Index

The cumulative effect of various physio-chemical parameters governing the overall water quality is holistically represented in Water Quality Index (WQI). The weighted arithmetic index method [33] has been used for the calculation. WQI reveals variation in the water quality status related to suitability for human consumption. A perusal of Table 4 reveals that 22% water samples in pre-monsoon and 50% water samples in post monsoon season respectively fall in excellent class whereas 73% water samples in pre monsoon and 50% water samples in post-monsoon falling in good class. Water quality of the river water samples has WQI =  < 50 which represents the excellent to good water quality of analyzed river water samples (Table 5). Most of the sampling locations meets the requirement of good water quality except in Bhunter during pre-monsoon. The Bhunter town is situated on banks of river Beas, due to incessant growth of population, modern infrastructure and small industries the water quality of the town maybe influenced by household and industrial wastewater followed by agricultural run-off. Along the altitudinal gradient, most of the upstream segments have excellent to good water quality for both the seasons where as in downstream segments the influence of population and urbanization on water quality is deteriorating towards the lower altitudinal regions. The locations like Rudranag, Guwacha, Toshnalla and Grahan nalla have lesser change in their WQI values whereas Jiyah, Hathithan and Bhunter showed a maximum change in WQI values from pre-monsoon to post-monsoon indicating that runoff played important role in dilution of river pollutant in these areas.

Table 4 WQI categorization
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Table 5 WQI Calculation for individual water sampling locations
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4.3 Correlation matrix

The Pearson’s correlation matrix develops relationship among components that how well the variance of each component can be explained through correlation with each of other [64]. A common origin and similar distribution must be defined to correlated variables in order to interpret the coefficient correlation. The value near to -1 or 1 in correlation coefficient depicts the strongest negative or strongest positive relationship between two variables whereas values closest to 0 denotes no linear relationship between variables.

The Pearson’s correlation coefficient (r) was prepared to assess correlation among pH, EC, TDS, Hardness and various chemical constituents variables for surface water in pre-monsoon (June 2019) and post-monsoon (September 2019). Several parameters are found to be strong positive (r > 0.8) and positive correlated (r > 0.5–0.79). During pre-monsoon a strong positive correlation value exist between EC-TDS and TH- Mg2+- HCO3− whereas positive correlation observed in TH- Ca2+, Mg2+- HCO3−, Cl−-HCO3− and Na2+–HCO3− indicating that these chemical parameters are from similar sources and can be attributed to geogenic process, while water traveling over sedimentary and metamorphic rocks which dissolve calcium, magnesium, chlorides and carbonates in it [65] (Table 6a). The positive correlation indicated influence of one parameter over other. However, during post-monsoon showed strong positive correlation among EC-TDS, TH- Mg2+- Ca2+-HCO3− indicating to the dissolution of limestone with the incoming water sources whereas positive correlation between TH- K+-SO42− have distinctive resources (Table 6b).

Table 6 a: Matrix of correlation for water variables during pre-monsoon. b: Matrix of correlation for water variables during post-monsoon
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4.4 Principal Component Analysis (PCA)

Table 7 represents the results of Kaiser-Meyer-Olkin (KMO) and Barlett’s Test. The KMO test results were interpreted according to guiding rules [66], the value of 0.743 for Pre-Monsoon (PRM) and 0.718 value for Post-Monsoon (POM) indicates data adequacy for PCA. Another preliminary assumption test (Kolmogorov–Smirnov, KS, and Shapiro–Wilk, SW) was conducted to check the normality of dataset [67] using IBM SPSS Statistics 26. The significant value for KS and SW test must be greater than 0.05 (p > 0.05). Results of computed p-value based on KS and SW is presented in Table 8. All the dependent variables had p-value greater than 0.005, it was therefore concluded that the data used is statistically normally distributed.

Table 7 Kaiser–Meyer–Olkin and Barlett’s test
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Table 8 Results of the normality tests
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To distinguish the latent factors influencing the hydrochemistry, PCA was applied on analyzed parameters of surface water samples. Principal components (PCs) taken for interpretation having eigen values > 0.75 and additionally second level of interpretation are considered statistically significant [23, 68]. Table 9 shows the component loading factors, cumulative percentage and percentages of variance and communality explained by each principal component. Standardized datasets were employed for performing PCA and in varimax rotated component matrix only four PCs, whose eigen value is greater than 1, explained 81.17 and 77.64% of the total variance for pre-monsoon and post-monsoon period, respectively. A scree plot representing all the PCs which were extracted during PCA is shown in Fig. 7 (a & b).

Table 9 Varimax rotated matrix of analyzed water samples of Parbati River
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Fig.7
figure 7

Scree plot extraction principal component in Pre monsoon and Post monsoon. a Pre monsoon. b Post monsoon

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During the pre-monsoon period, PC1 explains approximately 1/3rd i.e., 30.35% of the total variance and has strong positive correlation value with Total Hardness, Mg2+ and HCO3− and a moderately strong positive correlation with Na+ and Cl− which reflects the role of lithogenic factors in influencing the water chemistry [69]. High-positive loading of Mg2+ and HCO3− with TH shows temporary hardness in the water. Strong correlation of HCO3− ions with alkali and alkaline earth metals indicated the natural weathering sources [69, 70]. PC2 (25.30% of total variance) has moderate positive correlation with Na+ and Cl− and strong negative correlation with EC and TDS. Different hydrogeochemical processes that contribute to enrich more mineralized water is due to the combinations of Na+, Cl−, HCO3−, SO4− ions. PC3 was strongly positive weighted on Ca2+ and K+ and moderate positive score on Na+ accounts for 16.37%. PC4 explains 9.15% of the total variance observed and has strong positive correlation with NO3− which points towards the role of agricultural runoff (NPK) along with the seepage of wastewater into the surface water bodies.

For post-monsoon period, again PC1 explains 1/3rd i.e., 34.32% of the total variance and has a strong positive loading with EC, TDS and TH and a moderately positive correlation with Ca2+, K+, HCO3− and Cl−. The high positive correlation of Mg2+ with total hardness shows temporary hardness [71] indicated that with the high concentration of Mg2+ ions the degree of water hardness increases. A high positive loading of Ca2+ and HCO3− is attributed to various natural processes such as- weathering of rock minerals (limestone and calcium carbonate bearing rocks) and to various ion-exchange processes taking place in the groundwater system [69]. PC2 exhibits 18.39% of the total variance and the values of Mg2+ show high loading and the concentration of HCO3− has moderate positive loading on the PC2 (Table 7). PC3 shows the 15.73% of the total variance with moderate positive loading on Na+, moderate negative score on NO3−. The significant inverse relationship between Na+ and NO3− indicates the diverse source of chemical origin [72]. PC4 explains 9.1% of the total variance observed and has strong positive correlation value with Na+ and moderately strong negative correlation with Ca2+.

4.5 Cluster analysis

Cluster analysis is applied to obtain common cluster of monitoring locations having relatively similar characterstics. Dendrogram representing the clusters rendered during the analysis are shown in Fig. 8. Three clusters were obtained from pre-monsoon and post-monsoon from the criteria of significant Square Euclidean distance measure of standardized data by applying a complete linkage method. In pre-monsoon cluster 1 consist of monitoring locations Nhyarathach, Kasol and Bhunter (1, 9, and 18). Cluster 2 consist of monitoring location Rudranag, Guwacha, Barshaini, Manikaran bridge, Manikaran NHPC, Grahan nalla, Katagla, Malana-Jari, Charod and Hathithan (2, 3, 5, 6, 7, 8, 10, 12, 15 and 17). Cluster 3 consist of Toshnalla, Sumaropa, Shat nalla, Shat –Parvati confluence and Jiyah(4, 11, 13, 14 and 16). In Post-monsoon cluster 1 consist of Nhyarathach, Rudranag, Guwacha, Tosh nalla, Barshaini, Katagla, Sumaropa, Malana-Jari, Charod and Jiyah(1, 2, 3, 4, 5, 10, 11, 12, 15 and 16). Cluster 2 consists of Malana-Jari and Kasol (9 and 12) and cluster 3 consist Manikaran bridge, Manikaran NHPC, Shat nalla, Bhunter confluence, Grahan nalla and Hathithan (6, 7, 8, 13, 17 and 18).

Fig.8
figure 8

Hierarchical dendrogram showing agglomerate cluster grouping

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For both the pre-monsoon as well as post-monsoon seasons three significant clusters were found. All clusters formed have high similarity levels indicating the overall variability of parameters of different sampling points is low within the cluster. However, no specific intra-clusters trend was found to be significant during the analysis.

In the Table 10, average value of each variable is given for all the clusters for both pre-monsoon and post-monsoon seasons. During the pre-monsoon season cluster 1 exhibits the highest level for TH, Mg2+, and HCO3−. Cluster 2 has lowest level of pH as well as SO4− and highest level of TDS. Cluster 3 has highest value for pH, SO4− and NO3− and least value for EC, TDS, TH, Mg2+ and HCO3−.

Table 10 Cluster Centroids for each parameter of analyzed water samples of Parbati River
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In the post-monsoon season, Cluster 1 exhibit lowest levels of EC, TDS, TH, Mg2+, Na+ and SO4−. On the other hand, cluster 2 has highest levels of pH and TH, however, the overall pH of water samples during the post-season is lower than the pre-monsoon season. Cluster 3 has highest value for EC, TDS, Mg, K, Na and NO3−.

5 Conclusion

Usefulness of multivariate statistical methods along with WQI and graphical representation techniques is well illustrated in the present study. In the present study such tools are employed for understanding the physio-chemical characterization of river water system of Parbati river basin. The physicochemical parameters of all the analyzed water samples are well within the desirable limits prescribed by BIS (2012) and WHO (2011). Therefore, the water quality is suitable for domestic purposes, except for the few locations where pH was beyond the permissible range in pre-monsoon season. Piper and Durov plot classified 100% of samples were of Ca2+- Mg2+-HCO3− water types indicating temporary hardness in both the seasons. Dominance of alkaline earth metals over that of alkalis and of weak acidic anions over that of strong acidic anions is well represented in the Piper cross plots. Moreover, Piper cross plots also highlighted the natural geochemical processes such as- weathering and dissolution of minerals. The results of Gibbs diagram indicated that the chemical composition of surface water in the Parbati river basin is strongly influenced by rock dominance, weathering of silicates in pre monsoon whereas dominance of rock is followed by precipitation in post monsoon season. The WQI shows maximum water samples were falling in good class followed by excellent in both the seasons. PCA and CA identifies the major factors influencing the surface water chemistry such as- rock-water interaction, ion exchange and leaching of parent materials as well as dominant anthropogenic factors like agri-runoff and domestic waste water runoff.