Introduction

Water is an invaluable natural resource, which is essential for the survival of life on the planet Earth. It has a significant impact on the socioeconomic, industrial, and agricultural developments of society (Bouslah et al. 2017). Water is useful to keep all ecosystems (terrestrial, aquatic, and human) and environmental conditions healthy, alive, and sustainable (Adimalla and Venkatayogi 2018; Adimalla and Taloor 2020). It occurs as surface water and groundwater. Groundwater is estimated to meet nearly 40% of the global needs of water for food production and 30% for drinking (Amiri et al. 2021; Chowdhury et al. 2021). Water demand has increased due to its use for a variety of purposes such as urbanization, agriculture, industry, and improving living standards. Currently, an increasing trend in groundwater utilization has been observed due to several reasons such as climate change, incessant population growth, and inadequate precipitation (Li et al. 2013; IPCC Report 2021; Verma 2021). It has been found that groundwater is at an alarming stage of water crisis for cities, towns, and rural habitations in almost all parts of the world (Jasrotia et al. 2019; Adimalla and Taloor 2020; Amiri et al. 2021). Nearly one-third of the world’s population uses groundwater for drinking purposes (UNEP 1999). Around 50% of water demands in urban and nearly 85% in rural regions are being fulfilled by groundwater (World Bank 2010). Globally, 65% of groundwater is used for drinking, 20% for agriculture, and 15% for industry and mining purposes (Saeid et al. 2018). The study of groundwater quality is helpful in determining the source rocks and minerals that interact with aquifers, groundwater level fluctuations, and recharge as well as discharge of groundwater (Adimalla 2020; Reddy et al. 2020). Recently, the geographic information system (GIS) has become a vibrant tool in hydrogeological studies especially in groundwater quality modeling, due to its ability to store, analyze, manipulate, and visualize spatial data (Rao and Latha 2019). Many studies have been conducted to evaluate the groundwater quality using GIS (Singh et al. 2017; Jasrotia et al. 2019; Taloor et al. 2020, 2021; Ram et al. 2021).

India is one of the largest populated and agriculture dependent countries of the world. As a result, it needs a huge amount of water for various purposes. As groundwater is easily available even during the summer months, therefore, its demands and needs are growing exponentially (Taloor et al. 2020, 2021). Thus, groundwater crises can be seen in many parts of India, with varying scales and intensities depending on the different intervals of a year (Kumar et al. 2005, 2021; Jain et al. 2007; Rodell et al. 2009; Singh et al. 2017; Jasrotia et al. 2019; Taloor et al. 2020). In recent times, the increasing population along with other activities notably, agriculture, industrialization, and urbanization have augmented the demand of water to such an extent that it has adversely affected its quality and quantity (Howard and Bartram 2003; Taloor et al. 2020; Verma 2021). Rapid population growth, an ever-increasing economy, and various anthropogenic activities not only increased the demand for groundwater, but also polluted and degraded its quality (Chowdhury et al. 2021; Wang and Li 2021; Zhao et al. 2021). Groundwater quality has been rapidly deteriorating in various parts of the country, particularly in states of Punjab, Haryana, Rajasthan, Gujarat, Madhya Pradesh, Odisha, West Bengal, Karnataka, Andhra Pradesh, and Telangana, which in turn, making it unsuitable for various uses (Bajaj et al. 2011; Hundal 2011; MacDonald et al. 2016; Lapworth et al. 2017; Singh et al. 2017; Mukherjee et al. 2019; Haque et al. 2020; Khan et al. 2020). As a consequence, the deteriorated groundwater causes many diseases such as fluorosis, arsenicosis, haemochromatosis, bronchitis, cancer, and many other chronic diseases in various parts of the country (e.g., Mukherjee et al. 2019).

It is critical to investigate the groundwater quality of any region to determine its suitability for drinking, agricultural, and industrial purposes (Todd 1976; Tatawat and Chandel 2008). In India, detailed investigations of groundwater quality have been conducted in various parts of the country (Singh et al. 2008; Afroza et al. 2009; Dar et al. 2011; Vahab et al. 2015; Li et al. 2016; Disli 2017; Adimalla and Venkatayogi 2018; Adimalla et al. 2018, 2022; Patil et al. 2020; Karunanidhi et al. 2021). According to Faten et al. (2016), the groundwater quality deteriorates due to natural processes like rock–water interaction, evaporation, and anthropogenic influences such as industrial pollution, excess use of fertilizers, and human waste. Various researchers in the Union Territory (UT) of Jammu and Kashmir, Northern India, have been working on the groundwater, spring water, and river water quality in various parts of the UT and found that the water is polluted naturally and anthropogenically in some areas, particularly in Jammu, Doda, and Srinagar regions (Dar et al. 2011; Jeelani et al. 2014; Yaseen et al. 2015; Chandan 2017; Haq et al. 2017; Lone et al. 2020; Murtaza et al. 2020; Dar et al. 2020; Taloor et al. 2020, 2021). Furthermore, deteriorated groundwater quality has been reported in the Kathua District’s neighboring regions, which is posing a serious threat to the local population’s health, economic development, and social prosperity (Kanwar and Khanna 2014). While examining the literature, it is found that the Kathua region of the Jammu and Kashmir is largely neglected, and a few studies have been carried out in the evaluation of groundwater resources of the region (Pir 2020; Taloor et al. 2020). Currently, the groundwater demand has increased in the study area for various purposes such as domestic, agricultural, livestock and industrial (Pir 2020; Taloor et al. 2020). The primary objectives of the present study are to: (1) evaluate the overall quality and understand the hydrogeochemical characteristics; (2) know the hydrogeochemical facies and evolution of groundwater using Piper and Gibbs diagrams; (3) develop groundwater quality indices for drinking purposes by considering Groundwater Quality Index (GWQI) method; and (4) examine the groundwater quality for irrigation, and industrial purposes using various indicators such as Wilcox diagram, United State Soil Laboratory Staff (USSLS) diagram, permeability index (PI), magnesium hazard (MH), and corrosivity ratio (CR). The results obtained from this study would prove useful for the policy makers and planners to design suitable plans and scientific techniques for sustainable development and management of groundwater in the study area.

Study area

The study area is situated in the foothill zone of the Himalayan mountains chain, and part of the Indo-Gangetic plains in the Kathua region of the UT of Jammu and Kashmir, India (Fig. 1). It lies between the latitude 32° 16′ to 32° 55′ N and longitude 75° 06′ to 75° 54′ E. The climate varies with altitude, ranging from subtropical to moist temperate. The winter temperature ranges from 0.9 to 21 °C and summer temperature from 27 to 47 °C, respectively (Jasrotia and Kumar 2014; DDC Report 2019; Pir 2020). The average rainfall is about 1116 mm. There are numerous ephemerals and small perennial streams that originate from the northern mountainous region and flow toward the south-western direction. The Ravi is the perennial river and its tributaries such as Ujh, Tarnah, and Bein, and some seasonal streams (locally known as khad) flow in the area. It has been observed that structure and lithology play a vital role in the evolution of different types of drainage system in the area (Pir 2020; Taloor et al. 2020). The hydrogeological conditions are complex due to highly dissected hills, topographical barriers, hydrological boundaries, and varied geological units (CGWB 1997, 2013; Kanwar and Khanna 2014).

Fig. 1
figure 1

Geological map of the study area showing the location of the groundwater samples. Inset is the map of India showing study area, highlighted by red color (redrawn and modified after Karunakaran and Ranga Rao 1976)

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Geological setting

During the Early Cenozoic, some 55–35 million years ago, the Tethys Ocean closed due to the collision of the Indian plate with the Eurasian plate, forming the Himalayan Mountain range (Gansser 1964; Najman 2006; Valdiya 2016; Verma et al. 2016; An et al. 2021). Subsequently, the sedimentations of the Subathu, Murree, and Siwalik basins took place in the foreland basin situated toward the south of the rising Himalaya, and later, these basins got uplifted, folded, tilted, and faulted due to post-collisional Himalayan orogenic movements (Najman and Garzanti 2000; Kumar et al. 2003; Valdiya 2016; Shah 2018; Prashanth et al. 2021, 2022). The rocks and sediments of the Siwalik Group and the Kandi and Sirowal formations dominantly cover the study area.

Stratigraphically, the Siwalik Group is divided into three subgroups: Lower, Middle, and Upper. The Lower Siwalik Subgroup is composed red mudstone, fine- to medium-grained sandstone, and green sandstone of the Middle Miocene age (Nanda 2015; Fig. 1). The overlying Middle Siwalik Subgroup is made of medium to coarse-grained sandstone and subordinate gray brown mudstone of the Upper Miocene age (Karunakaran and Ranga Rao 1976; Nanda 2015). The topmost Upper Siwalik Subgroup comprises conglomerate, coarse-grained sandstone, and pink gray mudstone of Pliocene to middle Pleistocene age (Ranga Rao et al. 1988; Jasrotia et al. 2019). A large portion of the study area is covered by the rocks of the Upper Siwalik Subgroup. The Upper Siwalik Subgroup of the Jammu region is subdivided into the three formations such as Parmandal Sandstone, Nagrota Silt, and Boulder Conglomerates (Agarwal et al. 1993; Taloor et al. 2020). The study area is represented by two significant geological formations such as Kandi and Sirowal, which are spread over the southwestern most part of the study area. The Kandi Formation comprises boulders, cobbles, pebbles, and coarse sands. The Sirowal Formation includes fine to coarse sands with clay and silt.

Material and methods

For the present study, a total of 75 groundwater samples were collected using a random sampling technique in October 2020 during the post-monsoon period from various tube wells and dug wells of the study area. Water samples were collected in 2-L polythene bottles, and before sample collection, these bottles were pre-washed, then soaked with 1:1 diluted HCl solution, and further, washed twice with distilled water. The bottles were filled with water samples and subsequently sealed with double plastic caps to avoid evaporation. The in situ parameters such as temperature, pH, electrical conductivity (EC), and total dissolved solids (TDS) were measured in the field at the time of sample collection by portable digital meters such as pH, EC, and TDS meter. Further, samples were examined in the laboratory for water quality metrics and chemical parameters using standard methods as suggested by the American Public Health Association (APHA 1998), and the Manual of Pollution Control Board, New Delhi (MPCB 1997). The total hardness (TH), calcium (Ca2+), and magnesium (Mg2+) were determined by the titrimetric method by following the standard ethylene diaminetriacetic acid (ETDA) method, sodium (Na+), and potassium (K+) by flame photometer and bicarbonate (HCO3−), and chloride (Cl−) by titration method. The fluoride ion concentration was obtained with an atomic absorption photometer. Sulfate and nitrate ion concentration was estimated by the gravimetric method and using a flame photometer. All the values were represented in milligram per liter (mg/L). The analyzed hydrochemical data (Table 1) were plotted on Piper and Gibbs diagrams to understand hydrogeochemical facies, hydrochemistry, and water types, and processes that control the geochemistry of water, respectively (Piper 1944; Schoeller 1967). The salinity and sodium hazard diagrams were also prepared for determining the suitability of groundwater for irrigation uses.

Table 1 Chemical analysis of the groundwater samples of the study area. All values are in mg/L except pH and EC. WT stands for well types, T/W for tube well and D/W for Dug well
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To determine the reliability of the water quality analysis for major cations and anions, the ion balance error (IBE) or charge balance error (CBE) technique was performed (Freeze and Cherry 1979). It is proposed that the IBE should be less than ± 5% to consider an analysis is valid, and if it is greater than ± 5%, results are not to be acceptable and required to investigate the reason of error (Freeze and Cherry 1979; Hounslow 1995; Naidu et al. 2021). The error percentage in cations and anions ion balance was calculated by using the following equation (Hem 1991; Freeze and Cherry 1979; Ansari and Umar 2019; Naidu et al. 2021):

$${\text{IBE}}/{\text{CBE}} = \frac{{\sum {\text{cations }} - \sum {\text{anions}}}}{{\sum {\text{cations }} + \sum {\text{anions }}}} \times 100$$
(1)

All the groundwater samples in the current study area are within the limits of 5%, indicating a valid water quality analysis. Table 1 demonstrates the data for the analyzed physicochemical parameters of the samples, and Table 2 shows a statistical summary of the data.

Table 2 Descriptive statistics of the physicochemical parameters of groundwater in the Kathua region, Jammu and Kashmir, Northern India
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GIS has emerged as a significant tool for performing numerous spatial operations that are useful for various decision-making processes over the last few decades (Burrough et al. 1998; Jasrotia et al. 2019; Taloor et al. 2020). GIS-based outputs relating to hydrochemical data of groundwater in the form of spatial analysis/distribution have been extensively used all around the globe for making groundwater assessment, development, and management (Singh et al. 2017; Khan et al. 2020; Taloor et al. 2020). In the present study, sample location data of all the groundwater samples obtained by the global positioning system (GPS) was mapped in ARC GIS 10.4 environment. The spatial analysis had been performed to generate various distribution maps of groundwater quality index (GWQI), electricity conductivity (EC), sodium percentage (Na%), sodium adsorption ratio (SAR), magnesium hazard (MH), and corrosivity ratio (CR) by spatial interpolation technique using inverse distance weighted (IDW) method (Latha and Rao 2012; Rao and Latha 2019; Adimalla and Taloor 2020).

Result and discussion

Groundwater chemistry

To determine groundwater suitability for drinking, evocative statistics are computed and compared with the WHO (2011, 2017) and BIS (2012) drinking water quality standards (Table 2). The pH value ranges from 6.45 to 7.73, with a mean value of 6.95 indicating a slightly acidic to alkaline nature of groundwater. The EC value of groundwater samples ranges from 80 to 1500 μS/cm, with a mean of 498 μS/cm. The higher EC value indicates high salinity and mineral content in groundwater with low runoff, high infiltration, and discharges water type (Subba Rao et al. 2012; Ravikumar and Somashekar 2017). Conversely, the low EC value is generally associated with high elevated topography, high runoff, low infiltration and recharge water type, and low salt enrichment. Moreover, the groundwater can be classified as: type I, if the concentration of salts is low (< 1500 μS/cm), type II, if the salts show medium enrichment (1500 and 3000 μS/cm), and type III, if the salts enrichment is high (> 3000 μS/cm) (Subba Rao et al. 2012). Almost all groundwater samples of the study area fall under the type I (i.e., water with low salt enrichment) except one sample, which falls under type II (i.e., medium salt enrichment). The TDS in water comprises all inorganic salts that demonstrate the water salinity and its suitability for human consumption (WHO 2011, 2017). The TDS in groundwater samples ranges from 51.46 to 964.80 mg/L, with a mean value of 317.97 mg/L (Table 2). As a result, almost all of the samples are below the acceptable limit (500 mg/L), except two samples that are above the desirable limits, but within the permissible limits. It is commonly assumed that if 99% of samples fall within the acceptable limit and only 1% falls outside the permissible limit, the groundwater can be considered desirable for drinking and irrigation purposes (Davis and Dewiest 1966; Sawyer and McCarty 1967). The total hardness (TH) in groundwater water is caused by the presence of calcium and magnesium and other metal ions (Razowska-Jaworek 2014). Jain et al. (2010) stated that intake of higher concentrations of water with TH (> 300 mg/L) may cause health issues like kidney problems. Hence, hard water is unsuitable for domestic purposes. According to Sawyer and McCarty (1967) classification, 11 samples fall under the very hard class and other samples fall under the hard water category of the TH (Table 3). Therefore, it is found that groundwater in most parts of the study area is suitable for drinking, irrigation, and agriculture purposes.

Table 3 Total hardness value of the samples (Sawyer and McCarthy 1967)
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Cation chemistry

The concentration of calcium (Ca2+) cation in groundwater is an important component of groundwater chemistry because it aids in the growth of bones and teeth. The Ca2+ concentration in the analyzed samples shows that it varies from 9.0 to 140 mg/L, with an average of 66 mg/L (Table 2). Its comparison with WHO (2017) and BIS (2012) standards indicates that Ca2+ concentration is within the desirable limit. The calcium-bearing minerals (e.g., plagioclase, amphibole, and pyroxene) and rocks (e.g., limestone, dolomite, and shale) commonly make groundwater enriched with calcium. Additionally, the presence of carbon dioxide in the soil zone and ion exchange are other sources from which calcium comes into the groundwater (Hem 1991; Subba Rao 2018). Similarly, in the study area, most of these minerals and rocks serve as a source of calcium to the groundwater. Commonly, magnesium occurs in the natural water in association with calcium and it may also be derived from geogenic (seawater, ferro-magnesium minerals, and ion exchange) or anthropogenic (e.g., mining activities, and industrial wastage) sources. Its concentration in the study area is found between 0.84 and 283.08 mg/L, with an average of 25.86 mg/L. Its comparison with WHO (2017) and BIS (2012) indicates that the groundwater is found to be above the permissible limit (Table 2). The Na+ concentration varies from 1.59 to 238.27 mg/L, with an average of 31.40 mg/L, and its comparison with WHO (2017) and BIS (2012) standards clearly shows that the Na+ concentration is above the permissible limit (Table 2). Zhang et al. (2019) reported that high Na+ concentration in groundwater is possibly due to cation exchange and persistent evaporation. The K+ concentration varies from 0.68 to 392.70 mg/L, with an average of 11.13 mg/L. It is found that K+ concentration in majority of the samples fall within the permissible limits, and in a very few samples, it is found to be above the permissible limits prescribed by the WHO (2017) and BIS (2012) standards (Table 2). It was found that the order of abundance of cations was Ca2+ > Na+ > Mg2+ > K+ in the study area.

Anion chemistry

Bicarbonate (HCO3−) is the most important chemical component that occurs in natural water. Generally, bicarbonate is derived from weathering of the silicate rocks, but also comes from primary carbonate and calcareous rocks (Chandan 2017), and helps to produce alkaline nature to the groundwater (Ram et al. 2021). Its concentration in the area ranges from 123 to 1500 mg/L, with an average of 287 mg/L, which falls well within the permissible limit (WHO 2011, 2017). The chloride (Cl−) is an important anion found in the groundwater, and it may be derived from various sources like leaching, weathering of different minerals, and also from some anthropogenic sources. Its high concentration in groundwater gives a salty taste and effect on human health by contributing to kidney stones (Mohamed et al. 2019). In the study area, Cl− concentration varies from 4.99 to 173.90 mg/L, with an average of 26.60 mg/L, which indicates that the groundwater falls within the permissible limits and is suitable for consumption (BIS 2012; WHO 2017). Sulfate (SO42−) can be derived from geogenic as well as anthropogenic sources, and its high concentration makes groundwater unsuitable for use. The geogenic source of sulfate is resulting from carbonate sedimentary rocks rich in gypsum mineral (Magesh et al. 2013). Its concentration varies from 0 to 297.07 mg/L, with an average of 48.74 mg/L, indicating that they fall within the permissible limit according to prescribed standards (BIS 2012; WHO 2017; Table 2). The results show the dominance of anions as HCO3− > SO42− > Cl− > NO3− in the study area.

The iron is one of the most important natural trace elements associated with fluoride minerals and is responsible for the high concentration of fluoride ions in the groundwater (Handa 1975; Wenzel and Blum 1992; Subramani et al. 2005; Adimalla and Venkatayogi 2017). The higher concentration of fluoride is mainly due to the dissolution of fluoride in groundwater derived from fluoride-bearing minerals and controlled by several factors such as source rocks, depth of wells, residential period, and favorable environments for the upward rise of deep-seated groundwater (Adimalla et al. 2020). The fluoride concentration in the groundwater samples varies from 0 to 2 mg/L, with an average of 0.17 mg/L, indicating that most of the samples of the study area fall within the permissible limit, except two samples that fall above the permissible limit (WHO 2011, 2017; BIS 2012). The moderately high fluoride concentration found in the two samples is probably due to the geogenic processes such as leaching, weathering, and ion exchange. Billings et al. (2004) viewed that consuming fluoride-rich groundwater (> 1.50 mg/L) usually causes dental fluorosis. Nowadays, nitrate (NO3−) concentration is considered a major groundwater pollutant especially in those areas where intensive agriculture activities, industrialization, and increasing population growth were noticed. The source of nitrate may be either geogenic or anthropogenic. The agriculture run-off with the use of different fertilizers, leakage from pipes, and septic tanks in human settlement zones are the main anthropogenic sources of nitrate in the groundwater (Dolma et al. 2015; Zhang et al. 2019; Adimalla and Qian 2019). The high concentration of nitrate in groundwater causes health problems that lead to Methaemoglobinemia (Blue baby) disease, gastric problems, and cancer (Comly 1945; Gilly et al. 1984; Subramani et al. 2005; Adimalla et al. 2020). In the study area, nitrate concentration varies from 0 to 100 mg/L, with an average of 13.28 mg/L, and a majority of the samples fall within the desirable limit, except three samples, which fall above the desirable limit (WHO 2011, 2017; BIS 2012; Table 2).

Groundwater quality index

Groundwater quality index (GWQI) is used to measure the overall qualitative nature of water and to fix spatial boundaries between various zones of the GWQI (Yadav et al. 2010; Adimalla and Qian 2019; Verma et al. 2020). The computation of the groundwater quality data for drinking purposes was carried out and compared with the recommended drinking water standard of BIS (2012) for the calculation of the GWQI. Twelve important physicochemical parameters, namely pH, TDS, TH, Ca2+, Na+, K+, Mg2+, SO42−, HCO3−, Cl−, NO3−, and F−, were considered for calculating GWQI for the current study (Table 4).

Table 4 Relative weight of hydrochemical parameters
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The following steps were used to determine GWQI:

  • Step 1 Each physicochemical parameter has been assigned a weight (wi), which is ranging from 2 to 5, and is based on inferences drawn from earlier studies (Singh et al. 2017; Taloor et al. 2020; Verma et al. 2020; Naidu et al. 2021). An assigned weight of 2 shows the least significant parameter, whereas 5 indicates a highly significant parameter (Table 3).

  • Step 2 The relative weight (Wi) for all the twelve parameters, whose assigned weight ranges from 2 to 5, was calculated by using the following equation:

    $$Wi = \frac{{{\text{w}}i}}{{\mathop \sum \nolimits_{i = 1}^{n} wi}}$$
    (2)

    where “wi” is the weight and “Wi” is the relative weight of each parameter, and “n” is the number of parameters as shown in Table 4.

  • Step 3 Based on BIS (2012) standards, a quality rating scale (qi) for each parameter was computed by using the following equation:

    $$qi = \frac{Ci}{{Si}} \times 100$$
    (3)

    where “\(Ci\)” is the concentration of each chemical parameter of each groundwater sample in milligram per liter (mg/L) and “\(Si\)” is the Indian standard water guidelines (BIS 2012) for each chemical parameter.

  • Step 4 The groundwater quality subindex (SIi) for each chemical parameter was calculated by using the following equation:

    $${\text{SI}}_{i} = {\text{ W}}_{i} q_{i}$$
    (4)

    where “SIi” is the groundwater quality subindex of the ith parameter, “Wi” is the relative weight of each parameter, and “qi” is the quality rating scale based on the concentration of ith parameters.

Finally, the whole GWQI was calculated by using the following equation:

$${\text{GWQI}} = \mathop \sum \limits_{i = 1}^{n} {\text{SI}}i$$
(5)

where “Sli” is a subindex of ith parameters and “n” is the number of the parameters.

The groundwater samples (n = 75) and their GWQI values are shown in Table 5. The calculated GWQI values range from 30.28 to 318.01, with an average value of 59.90. The GWQI was classified into five types: excellent water type, if it ranges less than 50; good water type, if it ranges between 50 and 100; poor water type, if it ranges from 100 to 200; very poor water type, if it ranges between 200 and 300, and water as unsuitable for drinking, if the GWQI is greater than 300 (Adimalla et al. 2020; Verma et al. 2020). In the study area, at one location namely, Sukrala (sample no. 60) had yielded a GWQI value of 318.01, which indicates that its water is not suitable for drinking (Table 5). The spatial map of GWQI was prepared to depict the spatial distribution of various zones of GWQI (Fig. 2).

Table 5 Groundwater quality classification based on GWQI range (after Verma et al. 2020)
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Fig. 2
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Spatial distribution of the groundwater quality index (GWQI)

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Correlation analysis

The Pearson’s correlation analysis was performed to know the interrelationship between the water quality parameters. For correlation analysis, thirteen parameters were computed for the correlation matrices and the correlation coefficient (r) is presented in Table 6. If the “r” value is between + 1 and –1, it shows a perfect linear relationship (Meena et al. 2016; Lakshmi et al. 2021). If the “r” value lies between ± 0.8 to ± 1.0, it shows a strong correlation. If the “r” value is ± 0.5 to ± 0.8, it shows a moderate relationship. If the “r” value is ± 0.0 to ± 0.5, in this case, a weaker relationship occurs between water quality parameters. Further, as shown in Table 6, a strong positive correlation exists between EC and TDS (0.984); TH (0.871); Mg2+ and HCO3− (0.883); TDS and TH (0.870); HCO3− (0.840); and TH and Mg2+ (0.833), showing a major impact on the quality of groundwater compared to other ions and physical parameters. This strong positive correlation shows the influence of weathering process, mineral precipitation, mineral dissolution, and rock–water interaction on the groundwater. Likewise, Subramani et al. (2005) observed that a strong correlation between TH and Mg2+ indicates an enriched carbon dissolution mechanism in a rock–water interface.

Table 6 Pearson’s classification showing the coefficient of groundwater quality
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Hydrogeochemical facies

The term hydrogeochemical facies refers to the chemical character of groundwater solutions found in hydrogeochemical systems (Back 1966). Establishing facies are useful not only for understanding the similarities and relationships among different ions present in an aquifer’s groundwater, but also for understanding the influence of chemical processes operating in a lithological framework between minerals and groundwater (Back 1966). Numerous techniques, such as graphical and statistical analysis, are used to interpret groundwater hydrogeochemistry, but Piper and Gibbs diagrams are commonly used to establish hydrogeochemical facies (Back 1966; Adimalla et al. 2020; Verma et al. 2020).

Piper diagram depicts hydrogeochemical characteristics of ions (anions and cations) in an aquifer system (Piper 1944, 1953). In this diagram, the cations (Ca2+, Mg2+, Na+, K+) and anions (Cl−, HCO3−, SO42−) concentration were plotted to know the overall geochemical character and water quality. This diagram is represented by six fields: Ca2+-HCO3− type, Na+-Cl− type, Ca2+-Mg2+-Cl− type, Ca2+-Na+-HCO3− type, Ca2+-Cl− type, and Na+-HCO3− type (Piper 1944, 1953). In the present study, the plotted sample data show that water samples fall in the four fields, among which 90% of samples fall in the Ca2+-Mg2+-HCO3− facies, and the rest of the samples fall in other facies (Fig. 3). The Ca2+-Mg2+-HCO3− water type shows that the Ca2+ and Mg2+ are major cations and the HCO3− is the major anion. Thus, the results illustrate that the hydrochemistry of the groundwater is dominated by alkaline earth metals (Ca2+, Mg2+) and weak acids (HCO3−).

Fig. 3
figure 3

Piper Trilinear diagram showing geochemical classification of groundwater

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Gibbs diagram is used to elucidate the processes and mechanisms controlling the water chemistry and to understand the relationships between water chemistry and aquifer lithologies (Gibbs 1970). Further, Gibbs (1970) argued that atmospheric precipitation, rock weathering, and evaporation-crystallization processes largely control the global groundwater chemistry. Thus, Gibbs’s diagram contains three fields such as precipitation dominance, rock dominance, and evaporation dominance for finding the mechanism controlling water chemistry. In Gibbs diagram, ratio I (Na+  + K+ /Na+  + K+  + Ca2+) for cations and ratio II (Cl−/(Cl− + HCO3−) for anions are shown, where the concentration of ions was represented in meq/L. The ratio I represents the ratio of major cations, and ratio II shows that the ratio of major anions of the water samples was plotted against the relative values of the TDS to determine the mechanism controlling the composition of the groundwater (Fig. 4).

Fig. 4
figure 4

Gibbs plot showing the rock–water interaction

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The majority of samples are found in the rock dominance or rock–water interaction dominance zone indicating that the groundwater samples are of bicarbonate type (Zhang et al. 2020). This type of dominance commonly occurs in the hard-rock terrains having high temperature and low rainfall where slow water infiltration rapidly increases the ionic concentration in the groundwater (Subba Rao et al. 2012; Adimalla et al. 2018; Chowdhury et al. 2021). Furthermore, Rao and Latha (2019) observed that the rock–water interaction zone is dominated by rock weathering, secondary carbonate mineral dissolution, precipitation, and the process of ion exchange between water and clay-rich minerals. The scatter plot between HCO3− + SO42−, and Ca2+  + Mg2+ values shows that rock–water interaction is typically associated with carbonate weathering because most of the samples fall within the carbonate weathering region (Fig. 5). It may be noted that some samples are falling in the silicate weathering region (Fig. 5). Finally, it was inferred that rock dominance is the most effective controlling mechanism for knowing the hydrochemistry of the study area, which comprises various geological units.

Fig. 5
figure 5

Scatter plot between Ca2+ + Mg2+ and HCO3− + SO42− ionic concentrations of the groundwater samples

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Irrigation groundwater quality

Groundwater suitability for irrigation purpose depends on the concentration of dissolved ions present in it. Excessive ion concentration can affect the soil, plant growth, and agriculture productivity (Wilcox 1955; Jasrotia et al. 2019; Mandal et al. 2019; Xu et al. 2019; Snousy et al. 2021). Groundwater is the main source of water for irrigation in the study area, and recently increased agriculture activities as well as excessive use of chemical fertilizer, pesticides, and livestock waste have a negative impact on groundwater quality (e.g., CGWB 2013; Jasrotia et al. 2018). Apart from groundwater, surface water resources have also been utilized in some parts of the study area for agricultural purposes. These regions are mostly located along rivers or streams fed by melting water from different Himalayan glaciers while the remaining areas use groundwater as a main source of water for irrigation purpose (CGWB 1997). To assess the groundwater suitability for irrigation purpose, the important groundwater quality parameters: electrical conductivity, sodium percentage, sodium adsorption ratio, permeability index, and magnesium hazard, were analyzed.

Electrical conductivity and sodium percentage

Electrical conductivity (EC) measures the concentration of dissolved salts in groundwater and signifies salinity hazard to crops. Groundwater with high salinity is unsuitable for plants and poses a salinity hazard. Based on EC values, Ayers and Westcott (1985) classified groundwater into three categories: excellent (EC less than 700 µS/cm), good (EC ranges between 700 and 3000 µS/cm), and fair (EC more than 3000 µS/cm). Water with a low EC has a significant impact on crop productivity. In the study area, a small variation in the EC was observed with minimum and maximum values of 80 µS/cm and 1500 µS/cm, respectively, with an average value of 498 µS/cm. According to Ayers and Westcott (1985) classification, all samples fall under the excellent to good category, and hence, groundwater is suitable for the growth of crops.

The sodium content of groundwater can be used to determine its quality for irrigation (Wilcox 1955). As sodium reacts with soil and reduces its permeability and texture, a high sodium percentage in water causes soil degradation (Karanth 1987; Naidu 2021). The sodium percentage was calculated by using the following equation:

$${\text{Na}}^{ + } {\text{\% }} = \frac{{{\text{Na}}^{ + } + {\text{ K}}^{ + } }}{{{\text{Ca}}^{2 + } + {\text{ Mg}}^{2 + } + {\text{Na}}^{ + } + {\text{ K}}^{ + } }} \times 100$$
(6)

All cation concentrations are expressed in meq/L.

In the study area, the Na+% values fall between 4 and 88%, with a mean of 19%. According to BIS (2012), the water with Na+% up to 60% is recommended fit for irrigation and above it is considered as unsafe. There are only three samples that have yielded Na+% more than 60%.

Wilcox diagram

Wilcox (1955) classified the water for irrigation purposes based on EC and Na+%. He plotted values of EC and Na+% on a diagram, popularly known as Wilcox diagram and classified the water into five types as: class I: excellent to good, class II: good to permissible, class III: permissible to doubtful, class IV: doubtful to unsuitable, and class V: unsuitable. This diagram indicates that water quality decreases with the increase of EC and Na+% concentrations (Wilcox 1955). The values of EC and Na+% were plotted on the Wilcox diagram (Fig. 6). The Wilcox plot depicts that 98% of samples fall under the excellent to good category and one sample falls under the good to permissible limit (Fig. 6).

Fig. 6
figure 6

Wilcox diagram showing groundwater quality in relation to sodium percentage verses electrical conductivity

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United state soil laboratory staff diagram

The United State Soil Laboratory Staff (USSLS) diagram given by Richard (1954) shows 16 zones of water suitability for irrigation purposes. The presence of a higher concentration of sodium in water affects the soil characteristics and reduces its permeability (Adimalla et al. 2018). The most significant parameters like sodium and salinity hazard favor water usability for agricultural purposes (Table 7). The salinity hazard (C) is classified into 4 subzones: very low salinity (C1: 250 μS/cm), medium salinity (C2: 250 to 750 μS/cm), high salinity (C3: 750 to 2250 μS/cm), and very high salinity (C4: above 2250 μS/cm). Similarly, the sodium hazard is classified into 4 subzones: low sodium hazard (S1: < 10), medium sodium hazard (S2: 10–18), high sodium hazard (S3:18–26), and very high sodium hazard (S4: > 26). In the study area, the majority of the samples fall under the C1 S1 category of the USSLS diagram, indicating low conductivity and very low sodium hazard, specifying that the groundwater of the study area is suitable for irrigation (Fig. 7). For USSLS diagram, the sodium adsorption ratio (SAR) was calculated by using the following equation:

$${\text{SAR}} = { }\frac{{{\text{Na}}^{ + } }}{{\sqrt {\left( {{\text{Ca}}^{2 + } + {\text{Mg}}^{2 + } } \right)/2} }}$$
(7)
Table 7 Classification of groundwater for irrigation purposes based on the SAR and EC (Adimalla et al. 2020)
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Fig. 7
figure 7

Graph showing groundwater quality in relation to salinity and sodium hazards (after US Salinity Laboratory 1954)

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All ion concentrations are expressed in meq/L.

Permeability index

The long-term use of irrigation water affects soil permeability. Therefore, values of the permeability index (PI) were used to classify the quality of groundwater for irrigation. The PI was calculated based on the following equation (Doneen 1964):

$${\text{PI}} = \frac{{{\text{Na}}^{ + } + { }\sqrt {{\text{HCO}}_{3}^{-} } }}{{{\text{Ca}}^{2 + } { } + {\text{ Mg}}^{2 + } + {\text{ Na}}^{ + } }} \times 100$$
(8)

where all cation and anion concentrations are expressed in meq/L.

The groundwater was classified into three classes based on the PI: class I, class II, and class III. The groundwaters of class I and II contain 75% or more permeability and are considered suitable for irrigation, whereas the groundwater of class III holds a maximum of 25% permeability and is regarded as unsuitable for irrigation (Doneen 1964). According to Rao and Latha (2019), the presence of an excess concentration of ions such as Na+, Ca2+, Mg2+, and HCO3− can reduce the permeability and affect the overall soil composition. The majority of samples are classified as class I and II, indicating that groundwater is suitable for irrigation (Fig. 8).

Fig. 8
figure 8

Showing the Doneen’s classification for irrigation water based on permeability index

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Magnesium hazard

Magnesium hazard (MH) is used to evaluate groundwater suitability for irrigation by determining the concentration of Ca2+ over Mg2+ (Ragunath 1987). Calcium and magnesium are essential nutrients for crops and commonly occur in the soil. Their high value in water increases the pH of the soil (Joshi et al. 2009). Excess quantity of Mg2+ ions in irrigation water has adverse impacts on soil quality (makes soil alkaline) and also reduces crop production (Snousy et al. 2021). The magnesium adsorption ratio (MAR) was calculated by using the following equation (Szabolcs and Darab 1964):

$${\text{MAR}} = \frac{{{\text{Mg}}^{2 + } }}{{\left( {{\text{Ca}}^{2 + } + {\text{ Mg}}^{2 + } } \right)}} \times 100$$
(9)

All values of ions are expressed in meq/L.

Groundwaters with a MAR value of 50 or less are classified as suitable for irrigation and those groundwaters with a value of more than 50 are considered unsuitable and regarded as risky for irrigation because they can pose severe impacts on crop yields (Szabolcs and Darab 1964; Paliwal 1972). In the present study, 81% (61 samples) of groundwater samples had a MAR value less than 50 and were found to be suitable for irrigation. The remaining 19% (14 samples) had yielded MAR values more than 50 and were considered to be unsuitable for irrigation. The spatial distribution of EC, SAR, MH, and Na+% of the study area, is shown in Fig. 9.

Fig. 9
figure 9

Maps showing spatial distribution of a EC, b SAR, c MAR and d Na%

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Industrial groundwater quality

The groundwater utilization for industrial purposes requires a distinct water quality. As a result, every industry has its own set of water quality requirements. According to the AWWA (1971) standard, the groundwater quality in the study area appears suitable for industrial uses. The industrial sector of the study area has seen a tremendous growth in the last two decades as a result of the establishment of numerous new industries (DES Report 2017; DDC Report 2019). Accordingly, an attempt was made to know the groundwater quality for industrial uses. Poor groundwater quality can promote incrustation (formation of calcareous deposits on the metal surface) and corrosion (an electrochemical action in which metal transform into oxide/rust) activities, both of which can have serious adverse impacts on industries, particularly where metals are used (Subba Rao 2018).

Corrosivity ratio (CR) deals with the susceptibility of groundwater to corrosion and is used to determine whether water is safe to transport through metallic pipes. It is shown as the ratio of alkaline earth metals to saline salts in groundwater and is calculated by using the following equation (Ryner 1944; Raman 1985; Jasrotia et al. 2019):

$$CR = \frac{{\left( {\frac{{{\text{Cl}}^{ - } }}{35.5}} \right) + 2 \times \left( {\frac{{{\text{SO}}_{4}^{2 - } }}{96}} \right)}}{{2\left( {\frac{{{\text{CO}}_{3}^{2 - } + {\text{ HCO}}_{3}^{ - } }}{100}} \right)}}$$
(10)

where all the ion concentrations are expressed in meq/L.

The effects of corrosivity on metallic pipes had been thoroughly investigated. Groundwater with a CR less than one is considered safe for its transport in any kind of metallic pipes, and water with a CR more than one is considered to be unsafe (Ryner 1944; Raman 1985; Jasrotia et al. 2019). Corrosion affects the hydraulic capacity of metallic pipes (Rao and Latha 2019). The calculated values of groundwater samples show that 65 samples have a CR less than one and are thus, safe for their transport through pipes, whereas 10 samples have a CR of more than one, and are, therefore, considered to be unsafe. The spatial distribution of CR is shown in Fig. 10.

Fig. 10
figure 10

Map showing spatial distribution of the CR

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The general water quality criteria of Anon (1986) for industrial purposes are followed to determine the groundwater suitability for industrial uses because it deals with both incrustation and corrosion (Table 8). According to this standard, incrustation can occur if groundwater yields HCO3− and SO42− more than 400 mg/L and 100 mg/L, respectively. Corrosion can occur if the pH of the groundwater is less than 7 with TDS more than 1000 mg/L or the Cl− more than 500 mg/L. The results obtained show that groundwater quality causes incrustation in 21.3% of the samples, out of which 12% of samples develop incrustation due to high HCO3−, and 9.3% of the samples due to SO42,− whereas 53% of the samples develop corrosion due to less pH (Table 8).

Table 8 Groundwater water quality for industrial purposes (Anon 1986)
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Conclusion

The present study was conducted in the Kathua region of Jammu and Kashmir, Northern India, where the majority of people rely on groundwater for a variety of purposes (domestic, irrigation, and industrial). The obtained hydrochemical results were compared with the WHO (2017) and BIS (2012) water guidelines. It is found that groundwater is soft to hard, excellent to good, and alkaline in nature. The Ca+ is the dominant cation and HCO3− is the dominant anion in groundwater. In the order of abundance, Ca2+ > Na+ > Mg2+ > K+ are the dominant cations, and HCO3− > SO42− > Cl− > NO3− are the dominant anions. The higher HCO3− concentration and less pH (< 8.8) indicate that the chemical weathering had been operating in the area. According to the WHO (2017) and BIS (2012) water guidelines, the groundwater falls within the permissible limits and is good for drinking and irrigation purposes. Groundwater quality index shows that the groundwater is suitable for drinking except at one location. The Piper diagram reveals that nearly 90% of samples fall in the Ca2+-Mg2+-HCO3− facies and groundwater is alkaline in nature and is good for drinking. The Gibbs diagram indicates that the hydrochemistry of the study area is largely influenced by rock–water interaction and varied lithologies are a dominant factor that controls its water composition.

The electrical conductivity and sodium percentage indicate that the groundwater is suitable for irrigation, but three samples have a sodium percentage of more than 60%, and are not suitable for crop growth. According to the Wilcox and USSLS diagrams, the majority of groundwater is suitable for irrigation. The MAR indicates that only groundwater of 14 samples (19%) is unsuitable for irrigation. The corrosivity ratio implies that groundwater of 65 samples is safe to transport through metallic pipes, and for the remaining 10 samples is considered unsafe for industrial purposes. Groundwater, on the other hand, can cause incrustation and corrosion in some parts of the area. Finally, it is found that the groundwater in most parts of the study area is suitable for drinking, irrigation and industrial uses.