Introduction

Karst is a landscape where geomorphology and hydrology are largely governed by the dissolution of carbonate and/or evaporite rocks (Gutiérrez 2010; Frumkin 2013). These landscapes are greatly known for their beauty and high aesthetic value, exemplified by spectacular surface and subterranean geomorphology and complex hydrographic network (Ford and Williams 2007; Parise et al. 2018; Shah et al. 2018). They provide multiple benefits ranging from water resources, minerals, oil, natural gas, thermal energy, tourism, and information-related paleo-environment, thus are proved crucial in promoting the regional economy (Ford and Williams 2007; Goldscheider et al. 2010; He et al. 2019). These landscapes cover 15.2% of Earth’s continental surface, acting as home for 16.5% of the global population (Goldscheider et al. 2020) and supplying drinking water to nearly 10% of inhabitants around the globe (Stevanović 2019). In karst systems, groundwater and surface water coexist in a single dynamic system, where karst features provide hydraulic connections that allow an exchange of water between the surface and subsurface (Katz et al. 1997). However, these are typically challenging hydrogeological systems (Chu et al. 2017; Frumkin 2013), characterized by high heterogeneity and hydraulic anisotropy because of triple-permeability (Yu et al. 2021; Bonacci 2015; Bakalowicz 2005), which makes them highly sensitive to hydro-meteorological events (Jeelani et al. 2017a; Hartmann et al. 2014), as well as more vulnerable to geotechnical instability (Younos et al. 2019) and anthropogenic forcing (Chen et al. 2017; Goldscheider and Drew 2007; Perrin et al., 2004) than other aquifers (Vogelbacher et al. 2019; Ghasemizadeh et al. 2012). This presents major challenges in the theoretical and quantitative evaluation of water resources within karst basins (Stevanović 2019; Savio et al. 2019) and difficulties in defining their catchment boundaries (Gunn 2007; Parise 2016).

In India, karstified carbonate rocks are widely available and cover an area of ~ 8.8% (Goldscheider et al. 2020). They are largely found in the Himalayan regions such as Jammu & Kashmir (J&K), Himachal Pradesh (HP), and Uttarakhand (UTKD), where diverse surface and subsurface karst characteristics have resulted in unique landscapes (Shah et al. 2018). The karstic nature of carbonates is also apparent in other places in India (Fig. 1a; Dar et al. 2014a). They represent productive groundwater reservoirs and provide pristine drinking and irrigation water to about 35 million (even more) inhabitants in 106 districts (Dar et al. 2014a, b). Kashmir Valley, one of the largest karst regions on the Indian Subcontinent and the Indian part of the Himalayas, occupy 1100 km2 (even more) of karstified carbonate rocks, of which (631 km2) lie towards its southern fringe (Shah et al. 2018); however, the karstic nature of these carbonate units is difficult to reveal at most places, because Karewa sediments typically cover them. Groundwater in these karstic rocks is the main source of clean and fresh water for about > 40% of the local population for drinking (Jeelani 2008; Jeelani et al. 2017a), especially during summer when the demand is high for agro-horticulture and during winter when there is lean streamflow. The large karst springs are the prominent surface features of these karst landforms in the region, where few of them act as primary sources of River Jhelum (Coward et al. 1972). The development of parks and floating gardens at these spring outlets in the region labels the karst areas like state parks, featuring the region as a popular holiday destination and supporting the regional economy (Shah et al. 2018). Furthermore, the existence of some warm springs and their use for bathing and therapeutic purposes has gained more attraction (Jeelani 2004).

Fig. 1
figure 1

SRTM-DEM (30 m resolution) of the study area showing the location of sampling sites. Note: P1,2 and SP1-8 represent weather stations and karst springs, respectively. The SRTM-DEM was downloaded from USGS Earth Explorer

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Despite the tremendous societal benefits, the karst aquifers in India have been least studied. Few noteworthy works by Singh (1985), Sing and Dubey (1997), and Dubey et al (2006) in the Vindhyan basin; Shibasaki et al (1985), and Dar et al (2011) in the Cuddapah basin; Jeelani (2008), Coward et al (1972), Jeelani et al (2017a; Jeelani 2008, 2011), and Shah et al (2017, 2018) in the Kashmir Basin (Fig. 1a) has highlighted some specificities of karst aquifers in some selected catchments. However, the scientific understanding and observational dataset in most of the karst regions in the Indian Himalayas are still lacking. A British team of researchers explored many Himalayan areas, particularly the Valley of Kashmir, and outlined the karst features within carbonate rocks of the Triassic (Waltham 1972). In the Kashmir Valley, most of the recent studies (Jeelani et al. 2010, 2014, 2017a) have emphasized the delineation of groundwater catchments and recharge estimation. Only a few studies like Jeelani (2005, 2010), Jeelani et al. (2011), Bhat and Jeelani (2015), and Shah and Jeelani (2016) have evaluated the hydrochemical characteristics of groundwater and water quality indices in a few catchments. These studies considered carbonate dissolution the dominant mechanism controlling the ionic constituents in water, but this needs to be strengthened with more detailed studies.

In the study region, because of the low yield of alluvial and other porous aquifers, there is increasing stress on karst water reserves, both in flux and quality (Shah et al. 2017). At many locations within the region, the uncontrolled queries have posed a severe threat to the aquifer and the internal drainage conduits (Shah and Jeelani 2016), making groundwater more vulnerable to anthropogenic forcing. Since ionic responses in karst aquifers are more sensitive than in porous ones, updating knowledge and correct understanding of groundwater in karst aquifers is necessary for the effective management of water resources within these systems. Additionally, due to the strategic importance of karst groundwater for the entire Kashmir region, the continuous monitoring and assessment of groundwater from these karst aquifer systems have become critical. In this context, an investigation strategy was developed for assessing the groundwater in the karst aquifers of the region.

Stable water isotopes and solutes inspired a wide spectrum of interpretations of hydrological processes both at the surface and within the aquifers (Zhao et al. 2017; Jeelani et al. 2017a). Solutes (ions) in water provide meaningful insight into various geochemical processes under different flow conditions (Tang et al. 2021; Luo et al. 2018, and others), to identify the flow paths (Jeelani et al. 2014), and assess vulnerability (Shah and Jeelani 2016; Rashrash et al. 2015, and others), etc. Likewise, stable isotopes in water molecules provide the hydrological knowledge for small to large basins (Vreča and Kern 2020; Price et al. 2008). Assessment of recharge processes, estimation of residence time, mixing processes, etc., continues among the widespread benefits of stable water isotopes in hydrogeology (Shah et al. 2017; Fynn et al. 2016; McGuire et al. 2002; Clark and Fritz 1997, and others). Wang et al (2020), Jeelani et al (2010, 2017b) and Shah et al (2017), and others, have used these tracers to understand the recharge processes and residence time of groundwater within karstified catchments and have achieved meaningful results. This study aims to better understand (1) the solute acquisition mechanism and flow route of groundwater using solutes and (2) to gain insights into groundwater recharge processes through stable water isotopes in three mesoscale karstified catchments of the Indian Himalayas. The desired outcome of this study will be helpful in the implementation of drinking/irrigation water protection schemes in the karst areas and in improving the understanding of karst heterogeneity in the region.

Study area

Spatial extent and climate

Three representative mesoscale karstified catchments (Liddar, Kuthar, and Bringi) in the western Himalayas (Fig. 1) were chosen for the present study. These catchments lie between latitudes 33°16′ and 34°12′ N and longitudes 75°09′ and 75°23′ E and cover an area of ~ 2200 km2. The area exhibits a complex topography with an altitude ranging from 1600 to 5200 m a.s.l. The mountainous region is bestowed with abundant water reserves including glaciers, snowfields, lakes, streams, and springs. Experiencing temperate climate, the region receives an average (1964–2014) annual precipitation of 1240 mm at Pahalgam (P1: 34°01′ N and 75° 19′ E; at 2132 m a.s.l.), and 1010 mm at Kokernag (P2: 33°05′ N and 75°18′ E; at 1890 m a.s.l.) weather stations (Fig. 1b). The precipitation in the region is brought by dual monsoon systems, viz., western disturbances (> 71%), and Indian Summer Monsoons (< 29%) (Jeelani et al. 2017c). July witnesses the maximum average daily temperature (37 °C), whereas January, records the minimum average daily temperature (− 5 °C).

Hydrogeology

A complex series of sedimentary formations influenced by intrusions, structural features, and erosion provides the basic hydrogeology framework in the study region. The major hydrogeological units identified are Triassic Limestone, Panjal Trap, Quaternary Karewa, and Alluvium (Fig. 2b). The Triassic Limestone is about 850 m thick and covers a large area in the study region, consisting mainly of limestone and dolomitic limestone (Middlemiss 1910; Wadia 1975). The Triassic Limestone is highly karstified and acts as a major groundwater reservoir with a hydraulic conductivity of up to 1000 m day−1 (Jeelani 2007). Groundwater is discharged as perineal springs through Triassic limestone at a few to 3000 L s−1 (Jeelani et al. 2018a, b). Panjal Traps consists of up to 2500 m thickness of Middle Carboniferous to Permian basaltic and andesitic rocks (Middlemiss 1910; Bhat et al. 1981), with a hydraulic conductivity of 6 m day−1, making them a productive aquifer. Many perennial springs with a flow of 20 litters per second emerge from it (Jeelani 2007).

Fig. 2
figure 2

a Karst map of the study region, depicting hydrogeological, speleological, and karst geomorphological information. Note: SP1-8, C1-8 and alphabetical letters (A, B, C, – etc.) represent karst springs, caves, and local location names, respectively. b Map illustrating the hydrogeological framework of the study area (Jeelani 2008)

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Joints and fractures formed during periods of tectonic uplift have increased the secondary permeability in the Panjal Trap and tertiary permeability in the Triassic Limestone. The progressive dissolution along these geological structures has played a key role in determining the flow patterns in both formations, particularly in Triassic Limestones, where extensive conduit networks are developed (Shah et al. 2018). In the studied catchments, the incision caused by tectonic upheavals and climate dominates the base-level changes of the karst extent and determines the hydrological network (Shah et al. 2018). Liddar and the Bringi catchments are among the highly karstified catchments of the region (Shah et al. 2018). Figure 2a represents the karst map of the aforementioned catchments.

Karewa covers about 5000 km2, and comprise 2000 m thick unconsolidated fluvial-glaciolacustrine sediments which include, limestones and contain sand, clay, silt, and conglomerate of Late Neogene to a Quaternary period (Datta 1983; Dar et al. 2014b). Alluvial deposits within the studied region mainly cover alluvial tracts, flood plains, and river terraces of the adjoining streams and rivers. Both Karewa and Alluvium have lower hydraulic conductivities (0.1–5 and 1–10 m day−1, respectively) because of fine sand, silt, and clay (Jeelani 2008), and the springs emerging through these have reservoirs in Triassic Limestone and Panjal Traps, respectively (Jeelani 2005). In one sense, the fault between the Triassic Limestone, the alluvium, and the Panjal Traps and Karewa provide hydraulic connection, but on the other hand, the contrasting hydraulic conductivities of the Alluvium and Karewa block groundwater flow, which leads to the existence of springs at their contact (Fig. 2b).

Materials and methods

Sampling

Water samples (n = 233) were collected from karst springs in Liddar, Kuthar, and Bringi catchments in the western Himalayas from 2012 to 2014 (Fig. 1b) for hydrochemical and stable water isotope analysis. For the analysis of stable water isotopes, groundwater samples were taken fortnightly from karst springs located between 1600 and 1900 m a.s.l., in 50-mL high-density polyethylene (HDPE) bottles. While separate aliquots of filtered and unfiltered water samples were taken on a monthly basis from karst springs in 1-L HDPE bottles for ions. The samples were properly labelled and sent to respective laboratories for analysis. Additionally, hydrochemical data of one karst spring (SP8) was taken from published work (Jeelani et al. 2011).

Analytical technique

The stable isotope ratios of oxygen and hydrogen were measured at Physical Research Laboratory (PRL) Ahmedabad, using an isotope ratio mass spectrometer (Delta V plus) in a continuous flow mode, following the standard gas equilibration method (Maurya et al. 2009). For δ18O (or δ2H) measurements, 300 µl of water samples were equilibrated with CO2 (or 2H)) for 16 h (or 1 h), and the equilibrated CO2 (or 2H) gas was analyzed for isotopic ratio 18O/16O (Maurya et al. 2009). These results of the stable isotopes are reported in the standard d-notation (Coplen 1995), and defined as Vienna Standard Mean Ocean Water (VSMOW). The accuracy of measurements remains 0.1‰ for δ18O and 1‰ for δ2H to VSMOW. To evaluate the consistency, samples were analyzed in duplicate in each cycle of measurements.

The in situ parameters like pH, water temperature, and electric conductivity (EC) were measured by HACH (HQD Sension+), whereas major ion (Ca2+, Mg2+, Na+, K+, Fe2+, HCO3−, Cl−, NO3−, F−) analysis was carried out at Hydrogeology Laboratory, Department of Earth Sciences, University of Kashmir. Standard methods were adopted to analyze the dissolved chemical constituents (APHA, 2001). Ca2+, Mg2+ was analysed by EDTA titration; HCO3− was measured by HCl titration; Cl− by AgNO3 titration; Na+ and K+ by flame emission photometry; SO4− and NO3− by spectrophotometer; and Fe2+ and F− by the Colorimeter. During the analytical procedures, blanks and standards were run to check the methods' reliability. Analytical precision was within ± 6% for major ions.

Principal component analysis

To evaluate the factors regulating the water chemistry, the effect of host rock lithology on water chemistry principal component analysis (PCA) of hydrochemical data was performed using XLSTAT software (www.xlstat.com). The PCA is a useful tool to identify the processes contributing the groundwater chemistry and quality (Zakhem et al. 2017; Wu et al. 2014, 2020). It reduces a large data matrix to a new matrix with fewer dimensions (Liu et al. 2020; Li et al. 2019). In the present study, the hydrochemical data (Table 1) of the karst springs were standardized and subjected to the PCA to characterize the processes controlling the solute mechanism.

Table 1 The statistical summary including arithmetic means, minimum, maximum and standard deviation of ionic constituents of karst springs
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In the PCA, the similarity matrix (Eq. 1) was used to generate eigenvalues and scores for the data series. The significant principal components (PCs) were determined using the eigenvalue > 1 rule (Kaiser 1960) and Scree test (Cattell 1966). Based on scree classification, three principal components F1, F2, and F3, with eigenvalues > 1, were chosen to assess the factor loading and various variables (Ren et al. 2021). Accordingly, the first three principal components (PCs), accounting for the majority of the variation (> 60%), were used for interpretation, while the F1 and F2 components were considered for bi-plots. Earlier studies (Lee et al. 2003) have proposed the factor loading classification into strong (> 0.75), moderate (0.75–0.5), and weak classes (< 0.5), respectively.

$${\text{F}}_{jk} = {\text{ a}}_{j1} x_{k1} + \cdots + \cdots {\text{a}}_{jn} x_{kn}$$
(1)

Fjk represents the principal component value for object j on component k, aj1 is the loading of element 1 on component j, xk1 is the length of the score for variable 1 on item k, and n represents the entire amount of observed variables.

The saturation index (SI) of minerals was calculated using the PHREEQC geochemical software (Parkhurst and Appelo 2013) by comparing the chemical activities of dissolved ions in view of minerals (ion activity product, IAP) with their solubility product (Ksp). In the present study, considering the presence of Cl−, NO3−, and F− concentrations in spring waters due to anthropogenic forcing, the pollution index was calculated (Vinnarasi et al. 2021) using the below formula (in µ eq L−1).

$$\left\{ {\left( {{\text{Cl}}^{ - } + {\text{NO}}_{3}^{ - } } \right)/\left( {{\text{Cl}}^{ - } + {\text{NO}}_{3}^{ - } + {\text{F}}^{ - } } \right)} \right\} \times 100$$
(2)

To classify the studied karst spring and provide meaningful insights on the subsurface heterogeneity, ionic/isotopic and hydrologic parameters of low and high flow rates were linked to each other to evaluate the karst index below. Various researchers across the globe have validated the index to classify the springs (Atkinson 1977; López-Chicano et al. 2001; Vesper and White 2003; Heinz et al. 2009; Mustafa and Merkel 2015).

$$k_{i} = M \times 100$$
(3)
$$M = \mathop \sum \limits_{i = 1}^{n} k_{i} /n$$
(4)
$$k_{i} = \frac{{Q _{{{\text{minimum}}}} \times iQ_{{\text{ minimum}}} }}{{Q _{{{\text{maximum}}}} \times iQ_{{\text{ maximum}}} }}$$
(5)

where M is the arithmetic mean and ki represents the ratio of flow (Q) multiplied by their corresponding values for a specific ionic/isotopic parameter (i) of a spring under low and high flow periods, and n is the number of constituents/parameters. To understand the seasonal variation of solutes in the karst springs, a mass balance equation was used to estimate the dilution factor (Absar et al. 1991) with the assumption that X fraction of diluting water mixed 1−X pre-event (lean flow) water fraction to produce post-event (high flow) diluted water.

Results

Ionic composition of groundwater

The observed variation in the ionic constituents of the karst springs during the monitoring period is summarized in terms of minimum, maximum, mean, and standard deviation in Table 1. The spring water is alkaline in nature, with a pH ranging from 6.9 to 9.4. Water temperatures vary from 10 to 22 °C with springs (SP2 and SP3) in Liddar (SP1, SP4, SP5) and Bringi (SP6, SP7, SP8) catchments attaining temperatures below 17 °C, while springs in Kuthar (SP2, SP3) achieved higher temperatures over 17 °C. The PCA results showed maximum positive loading (0.90 and 0.87) for Ca2+ and Mg2+ followed by HCO3− (0.82) and Cl− (0.81) in the Liddar catchment. In the Kuthar catchment, the highest positive loading was found for HCO3− (0.95) followed by K+ (0.91) and Ca2+ (0.90). However, in the Bringi catchment, Ca2+ (0.77) followed by HCO3− (0.64) contributes to the spring waters' major proportion. The strong positive factor loading (except Cl− which shows moderate loading) on Na+, Ca2+, Mg2+, K+, and HCO3− with a variance of 51.65% is witnessed in the spring water ionic constituents in the Kuthar catchment with respect to the F1 component (Fig. 3). Similarly, strong positive loading on EC, SO4−, Cl−, HCO3−, Ca2+ and Mg2+ was seen in the Liddar catchment with a variance of 40.30% in the ionic constituents (Fig. 2). In contrast, the springs in the Bringi catchment displayed 36.08% variance with moderate to strong positive factor loading on HCO3−, Ca2+, and Mg2+ with respect to the F1 component (Fig. 3). The F2 component accounts for 23.81% and 16.02% of the total variance in the Liddar and the Kuthar catchments (Fig. 3), with negative loading on NO3−, EC, K+, Cl−, F−, and weak negative loading on the rest of the ionic constituents, respectively. While the F2 component in the Bringi catchment (Fig. 3) exhibited a 24.47% variance in ionic constituents with strong positive loading on K and moderate on Ca2+, Na+, HCO3−. There is positive loading on EC, Cl−, Mg2+, NO3−. Furthermore, there is strong positive loading on EC, Ca2+, Mg2+, Na+, K+, HCO3− and weak negative loading on others with respect to F3 (Table 2), accounting for 10.75% variance in ionic constituents in the Kuthar catchment. This variance is higher (12.8% with respect to F3) in the Bringi catchments with most of the negative loading except Mg2+, Cl−, NO3−, and F−. The springs in the Liddar catchment displayed intermediate variance (10.74%) with negative loading on EC, K2+, and Fe2+ and weak positive loadings on other ions. It is observed that F1 and F2 components mainly control the ionic composition of the karst spring in the studied region; therefore, principal components (F4….F12) have a lower variance (< 8%) and were not considered (Fig. 3).

Fig. 3
figure 3

Principal component analysis of karst springs showing the ionic variability and controlling factors

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Table 2 Factor loadings of principal components (F1 to F4) of Karst springs in Bringi, Kuthar, and Liddar catchments
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The saturation index (SI) results indicate that karst springs in each catchment are under-saturated with calcite and dolomite (refer to Table 1). Furthermore, it is observed that computed partial pressure pCO2 for the karst springs is slightly higher (10–3 × 4.1) to (10–1 × 2.5) than the atmospheric level (10–3.5). The results also show that solutes (here EC) exhibit a similar pattern in all the karst springs, a seasonality based on the flow patterns (Fig. 4), with dilution factors ranging from 27 to 53% in higher flow conditions.

Fig. 4
figure 4

Relationship between electrical conductivity (EC) and discharge (Q) of karst springs in the study region

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Stable water isotopes in karst springs

In the study region, karst springs showed wide variability in δ18O and δ2H (δ-values) at spatial and temporal scales (Table 3, Fig. 5). The δ18O values range from − 6.3 to − 10.4‰ while the δ2H values range from − 58 to − 38.3‰. A similar temporal pattern with no major shift in isotopic (or δ) characteristics of all the karst springs was observed (Fig. 5). Enriched isotopic values were observed in each catchment during the summer months, while depleted values were reported during the spring and autumn months. However, subtle differences in δ18O and δ2H of karst springs were noticed on a spatial scale (Fig. 5). It was observed that δ-values of karst springs do not vary as a function of altitude. Overall, statistically, there exists an insignificant correlation (R2: 0.11, p: 0.06) between the altitude of karst springs and their δ-values.

Table 3 The statistical summary including arithmetic means, minimum, maximum, and standard deviation of δ18O and δ2H and the d-excess (d) in the karst springs
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Fig. 5
figure 5

Spatio-temporal variance in stable water isotopes in karst springs

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The d-excess (d) also vary substantially, from 7.3 to 32.7‰ with an average of 19‰ (Table 3, Fig. 6a). Though there is a small spatial variation in the d of the karst springs, a distinct temporal pattern in isotope characteristics of the karst springs was observed in all the catchments (Fig. 6a). The d varied from 7.3 to 31‰ in the Liddar, 10.7–23.6‰ in the Bringi, and 8.4–32.7‰ in the Kuthar catchments. Overall, nearly 75% of groundwater samples showed d > 16‰, complementing its close link to winter precipitation. Jeelani et al. (2017c) reported that winter precipitation across the western Himalayas exhibits higher d (> 15‰). The δ18O versus δ2H regression plot (Fig. 6c) of all the karst springs showed a lower slope and lower intercept (collective: 6.9 (± 0.4) × δ18O + 9.7 (± 1.4); R2 = 0.85) compared to the global meteoric water line (GMWL) and the reported local meteoric water line (LMWL: by Jeelani et al. 2017c, 2018a). Despite a slight variance noted in their slopes (Liddar: 5.4 ± 0.11, Kuthar: 5.2 ± 0.21, and Bringi: 5 ± 0.17) and intercepts of the groundwater regression lines at a catchment level (Liddar: 10 ± 0.4, Kuthar: 7.8 ± 1.2, and Bringi: 9.8 ± 1), their relation with these two meteoric water lines shows that samples are overlapped and closely approached (Fig. 6b). The results further reveal that springs in the Liddar and Bringi catchments have higher amplitudes (1.9–3 and 1.5–3.3, respectively, for δ18O), close to precipitation (4–5.6) in those catchments. However, the springs in the Kuthar catchment have a lower amplitude (0.9–1.8), than precipitation in the catchment.

Fig. 6
figure 6

a D-excess vs δ18O of the karst spring, b and c shows the least square regression of δ2H verses δ18O of karst springs with respect to the LMWL and GMWL as collective and at catchment scale

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Discussion

Solute source and flow routes

Variance observed between the chemical parameters as the highest loading factors is usually used to identify the geochemical processes involved (Rao et al. 2007; Zakhem et al. 2017). In the study region, the higher positive loading on Ca2+, Mg2+, and HCO3− with respect to the F1 component evinces the natural mineralization and/or dissolution of hosted carbonate rocks (refer, Fig. 3, Li et al. 2016, 2018). Their studies (Bhat and Jeelani 2015) and Shah and Jeelani 2016) reported that carbonate weathering mainly contributes to the region's Ca2 + , Mg2 + , and HCO3- to groundwater. While moderate-to-weak loading on NO3−, Cl−, and F−, accounting for the 16–24% variance for F2, indicates the anthropogenic input (e.g., Esteller and Andreu 2005). Considering the geology of the studied region and the ionic analysis, the positive loading such as Ca2+, Mg2+, and HCO3− represent the natural processes by which spring water acquires its chemical signatures (e.g., Nesrine et al. 2015), whereas moderate-to-weak loadings on ionic concentrations such as NO3−, Cl− and F− represents anthropogenic forcing (Esteller and Andreu 2005; Dragon and Gorski 2015). Earlier studies (e.g., Shah et al. 2017; Bhat and Jeelani 2015; Jeelani et al. 2011; Jeelani 2010) have demonstrated the anthropogenic origin of these solutes in waters of the studied region. The anthropogenic contribution to NO3− and Cl− is evident from the higher NO3−/Na+ and Cl−/Na+ ratios (Fig. 7a), where some data points are shifted towards right above a 1:1 equiline. The NO3−/Na2+ and Cl−/Na2+ ratios are usually used to quantify the anthropogenic input (Kou et al. 2019). This study (based on Eq. 2 and Fig. 7a) suggests that anthropogenic forcing contributes to about 38% of pollution in these catchments, including 1–38% in the Bringi, 9–24% in the Kuthar, and 2–31% in the Liddar. Therefore, 50–88% of NO3−, F−, and Cl− ions contributed to karst springs are derived from anthropogenic forcing. Overuse of pesticides and fertilizers to horticultural plantations on karst ridges affects the water table through sensitive features on the surface (Shah and Jeelani 2016). In addition, the depressions/sinks along stream banks used for dumping (particularly in the Bringi catchment, where numerous vertical shafts and sinkholes have been found along the Bringi stream at places such as Adigam, Dewalgam, Gadol, Wandevalgam, etc.) remain underwater during periods of high flow, adding toxicity to the flow (Shah and Jeelani 2016; Jeelani et al. 2018b). Rasool et al. (2021) found an increasing trend in both horticultural and built-up practices throughout the last 30 years in some catchments, including that under study (Fig. 7b). Similarly, the strong positive loading of EC, Ca2+, Mg2+, Na+, K+, HCO3− with respect to F3, accounting for 10.75–12.8% of the variance in solutes, also suggests that geogenic processes are determining the chemistry of spring water. The geogenic sources of these solutes in the catchment waters have also been reported (e.g. Jeelani et al. 2011).

Fig. 7
figure 7

a Scatter plot showing the contribution from anthropogenic forcing and b trend analysis of horticulture and built-up from 1990 to 2017

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To further assess the geogenic factors controlling the spring water chemistry, hydrochemical data were plotted as, Ca2+ + Mg2+ verses HCO3− + SO4− (Fig. 8a), where most of the data points fall above the aquiline. This suggests the significant contribution of carbonate and silicate dissolution in contributing to the ionic composition of the karst springs (Li et al. 2016, 2018; Wei et al. 2021). Since the proportion of Ca2+ and Mg2+ to the groundwater environment is mainly by carbonate lithologies within the study region, the Mg2+/Ca2+ molar ratios are used to estimate the calcite and dolomite weathering input (Szramek et al. 2011; Shah and Jeelani 2016). The calculated values (0.09–1.1) reveal that spring water in the studied region exhibit a wide range and shows dominancy of calcite dissolution (77% of samples) over dolomite dissolution (33% of samples). In Ca2+ + Mg2+–HCO3−-SO4− versus Na+–Cl− plot (Fig. 8c), most data points are scattered patterns. However, they lie close to equiline with a slope of ~ 80% towards the positive trend, confirming that Ca2+, Mg2+, and Na+ are interrelated through ion exchange during carbonate and silicate weathering. The role of silicate weathering is further supported by the Na+  + K+/TZ+ ratio (Fig. 8b), ranging between 0.4 and 1.3. The values clearly infer the release of Na and K to the groundwater environment from silicate rocks (Das and Kaur 2001).

Fig. 8
figure 8

Scatter plots of a Ca2+ + Mg2+ verses HCO3− + SO4−, b Na+  + K+ verses TZ+, c Ca2+  + Mg2+–HCO3−–SO4 − verses Na+–Cl−, and d Na/Na+ + Ca2+ verses TDS showing the sources of major ionic constituents in the karst springs. While sub plot (Fig. 7e) shows the temporal pattern of HCO3− and F− in the karst springs

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Characterisation of Ca2+-HCO3− and Ca2+-Mg2+-HCO3− water types evinces that major ionic constituents in the spring waters show dominancy of alkaline earth water group over the alkalies (refer, Fig. 9, 8d, 10). The Ca2+–HCO3− and Ca2+–Mg2+–HCO3− water types indicate young recharging water (Ophori and Toth 1989). A similar facies pattern (Ca2+–HCO3−) in all the karst springs within the Bringi basin (refer, Fig. 9). However, more than one water type (Ca2+–HCO3− and Ca2+–Mg2+–HCO3−) in springs of Liddar and Kuthar catchments reveals preferential flow through multiple lithologies such as carbonates and silicates. The increasing concentration of F− and HCO3− in the karst springs with a downward direction from SP8 to SP6 in the Bringi, SP4 to SP1 in the Liddar, and from SP3 to SP2 in the Kuthar catchment (refer, Table 1, Fig. 8e), suggest the direction of the flow pattern/or recharging waters with each catchment. The inverse relationship between discharge and electrical conductivity (Fig. 4), and the dilution factor of 26–53%, suggest that new and relatively dilute water is being added to the aquifer in the spring and early summer seasons, hence causing strong dilution effects on the groundwater chemistry in each catchment. When the temperature increases, the increasing discharge associated with decreasing EC from March onwards (Fig. 4) validates the simultaneous addition of melt-water released from winter accumulated snowpacks to the aquifers. In contrast, the increase of EC from August/September to October results from higher altitude melt-water input from permanent snowfields/glaciers. EC values from November to February (Fig. 4) coincide with a gradual decrease in water level, thus suggesting a decrease in recharge. The undersaturation state of karst springs with respect to calcite and dolomite in most seasons of the year suggests that springs are being fed with an active recharge. The higher partial pressure pCO2 for the karst springs than the atmospheric level reveals that springs are not in equilibrium with the atmosphere, maybe due to significant proportions of CO2 values from subcutaneous karstic layers and groundwater in these karstified catchments is aggressive and capable of dissolving the host rock.

Fig. 9
figure 9

Piper trilinear plot showing the water types of karst springs in the study region

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Fig. 10
figure 10

Scatter plot showing the hydrogeochemical classification and processes of groundwater in the study region. Each zone illustrates the hydrogeochemical processes as, (1) alkaline earth exceeds alkali metals, (2) alkali metals earth exceeds alkaline earth, (3) weak acidic anions exceeds strong acidic anions, (4) strong acidic anions exceeds weak acidic anions, (5) alkaline earth, and weak acidic anions exceed both alkali metals and strong acidic anions. (6) alkaline earth exceeds alkali metals and strong acidic anions exceed weak acidic anions, (7) alkali earth metals exceed alkaline earth and strong acidic anions exceed weak acidic anions, and (8) alkali metals earth exceeds alkaline earth and weak acidic anions exceed strong acidic anions

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Recharge processes

Groundwater recharge processes provide first-hand information about the heterogeneity within the aquifer system and the nature of the flow paths with contaminants therein (Jung et al. 2020; Jeelani et al. 2017a; Blasch and Bryson 2007). In the study region, the distinct sinusoidal pattern is seen in the δ-values of karst springs can be viewed in context to recharge through differential flow routes and change in seasonal hydro-meteorological conditions at their respective recharge area. The insignificant correlation of the isotopic values of groundwater with altitude may be attributed to different flow paths followed by recharging waters (Jeelani et al. 2017b). Furthermore, in karstified catchments, the heterogeneous secondary permeability can result in a varied hydrographic network (Wang et al. 2020). The temporal pattern seen in δ-values of the karst springs indicates the mixing various recharging waters (Solder and Beisner 2020; Joshi et al. 2018). From March to May, the depleted isotopic signal in karst springs is attributed to winter precipitation, released as air temperatures increase (Fig. 5). The rise of the air temperature from March promotes widespread melting of accumulated winter snow, generating higher spring flows with lighter δ-values during these months. Meanwhile, the enriched δ-values in February, June, and July are attributed to rainfall in these months, with δ-values similar to summer rainfall in the recharging altitude. This is because that summer rainfall is distinctly enriched than winter precipitation over the Kashmir Himalayas (Jeelani and Deshpande 2017). The highly depleted yet very little variation in the isotopic signatures (Fig. 5) in the karst springs from August up to the end of October (Fig. 5), when the spring discharge is moderate, are evidence of recharge at higher elevations either from the glaciers and/or permanent snowfields. Since under-zero ambient temperatures occurred in most recharge catchments during the period of low flow between November and January, the stability of isotope values during these periods indicates water is being stored in the aquifer rather than flowing into it.

The lower slopes and lower intercept (collective catchments: 6.9 (± 0.4) × δ18O + 9.8 (± 1.4); R2 = 0.85) of karst springs (Fig. 6c) compared to GMWL and to the established LMWL of Jeelani et al (2017c), reveal evaporation of recharging water before it enters into the aquifer system (Qian et al. 2013, 2014). However, the small difference in slopes and intercepts of the karst springs than LMWL (Liddar: 5.8 ± 0.11 and 11 ± 0.4; Kuthar: 5.1 ± 0.21 and 7.8 ± 1.2; Bringi: 6.3 ± 0.17 and 9.8 ± 1) in each catchment (Fig. 6b), as well as overlapping/clustering of groundwater samples on LMWL, provides strong evidence of common source recharging waters to karst springs. This similarity is expected in well-karstified carbonate aquifers where high groundwater flow velocity occurs within the high-hydraulic-conductivity solution channels (e.g., flow like underground rivers). Since GMWL (Craig 1961) has a d-excess of 10‰ which contributes to the formation of meteoric water lines at distinct water vapor sources (Gat 1981). Therefore, d-excess (or d) introduced by Dansgaard (1964) is used as a diagnostic tool to provide accurate information on the amount of moisture/precipitation that has come from different sources to a particular location (Peng et al. 2004; Jeelani and Deshpande 2017), this is because that particular air-sea conditions result in a specific d-excess value (Gat 1981). Previous studies suggest that precipitation brought by westerly from October to May over the studied region is imprinted with a δ-depleted signal associated with higher d (> 16‰) (Jeelani et al. 2017c; Shah et al. 2017). While the precipitation brought by ISM between June and September months over the region is characterized by the higher δ-values with lower d (Jeelani et al. 2017a; Jeelani and Deshpande 2017). In karst springs, the depleted isotopic fingerprint along with higher d (> 16‰) (Fig. 6a) is similar to that of winter precipitation across the western Himalayas (e.g. Jeelani et al. 2021, 2018a; b; Lone et al. 2019), which infers winter precipitation (snow) as the predominant source of recharge to the karst springs in the studied catchments. In the winter months, precipitation mainly occurs as snow (Jeelani et al. 2017c), which is stored in snowpacks due to sub-zero temperatures (Jeelani et al. 2013; Shah et al. 2017). Melt-water released from these snowpacks enters the flow system largely from March onward due to increasing temperatures (Jeelani et al. 2018a, b). The reflection of the progressive δ-depleted signal and higher d in karst springs from March to May and from August to October (Figs. 5, 6a) represents the signatures of winter accumulated snow of higher elevations. Moreover, the delayed response of the δ-depleted signal of winter precipitation in karst springs also evinces snow as a predominant recharging source to karst springs. Shah et al. (2017) reported that delayed response of δ-depleted signal of winter precipitation is characteristic of catchment/ region where winter snow significantly contributes to the water resources. Their studies (Jeelani et al. 2013, 2017b) reported that snowmelt sourced from westerly’s contributes most to streamflow in studied catchments, making it reasonable to conclude that δ-depleted signals associated with higher d in karst springs reflect depleted winter snow signal. However, the temporal variation in spring flow rates (Fig. 4) results from temporally variable melting processes that govern the isotopic characteristics of karst springs. Furthermore, it minimizes the effect of enriched signatures of rainfall during the summer and autumn months, except during the significant rain events when δ-values are modified and shifted towards heavier isotopic values (e.g., June/July) (refer Fig. 5). These temporal patterns, such as the lagged effects of δ-depleted winter precipitation signal and the rapid response of δ-enriched spring and summer rainfall events, serve as indicators of hydrologic processes associated with karst aquifers in the studied region. Similarly, the subtle differences in the amplitude of the karst springs suggest different levels of heterogeneity of karst in each catchment. The higher amplitude of karst springs within Bringi, and Liddar catchments close precipitation coincides with the higher calculated ki values (26–59%) (Table 4), suggesting moderately to well-developed karstic aquifers in these catchments. While the lower amplitude (slow response) of springs to recharging events in the Kuthar catchment coincides with lower ki values (17–26%), indicating a slight to moderate karstic level. Shah et al (2018), in their study of karst mapping, observed a higher degree of karstification in the Bringi and Liddar catchments and lower in the Kuthar catchment. Considering the above, it can be deduced that in Liddar and Bringi catchments, the preferential flow may occur through conduits network and or/ shorter circuitous routes. Wang et al (2020) and Chu et al (2017) explained the rapid response of springs to input signals (higher amplitude close to precipitation) by high aquifer karstification in their studies in karstified catchments in China. However, the longer route at deeper levels of recharging water for springs within the Kuthar catchment can be revealed from their higher water temperatures (17–22 °C) than the spring located in the other two catchments (10–16 °C). Serianz et al. (2020) reported that springs with higher water temperatures (20–23 °C) indicate deep regional flow systems. This coincides with the findings of Shah et al. (2018), who found hypogene (deeper level) karstification in the Kuthar catchment. Since the degree of karstification of an aquifer controls the differences in the hydrogeological function of springs even in similar geological and climatic conditions (Mudarra and Andreo 2011), therefore, the calculated ki values provide first-hand information on the type of aquifer heterogeneity, which results in varied spring responses to the input signal in the studied region.

Table 4 Parameters used for classifying the karst springs within the study region
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Conclusion

In the present work, karst groundwater which plays a significant role in the water supply and economic development in the entire Kashmir Valley (India), was investigated using ionic constituents and stable water isotopes. The main conclusion is listed below.

  1. 1.

    The study suggested that solute concentrations in spring water are dominantly controlled by the natural mineralization of carbonate and silicate lithologies, which provide insights into preferential groundwater flow in the studied catchments. However, the presence of NO3−, Cl−, and F− in the spring waters validates the contribution of anthropogenic forcing within the region.

  2. 2.

    The study found that karst springs are under-saturated with respect to calcite and dolomite and achieve higher partial pressure pCO2, which suggests they are being fed by an active recharge process, and continued dissolution of the host rocks in most of the seasons. Furthermore, the inverse relationships between discharge and electrical conductivity suggest that new and relatively dilute water is being added to the aquifer in the spring and early summer seasons, which cause strong dilution effects on the solute concentration of karst springs within the region.

  3. 3.

    The study revealed that distinct seasonal patterns in the δ18O and δ2H of karst springs can be viewed in context to recharge through differential flow routes and changing seasonal hydro-meteorological conditions at their respective recharge area. The depleted isotopic values associated with higher d (> 16‰) in karst springs suggest that winter precipitation is the primary source of recharge; however, lower values of slope and intercept of spring water regression lines infer evaporation of recharging water before entering the aquifer system.

  4. 4.

    The results inferred that lagged effects of δ-depleted winter precipitation signal, reflected in spring flow from March to May, and the rapid response of δ-enriched spring and summer rainfall events can serve as indicators of hydrologic processes associated with karst aquifers in the studied region.

  5. 5.

    Moreover, higher ki values and flow-based seasonality of EC and isotopes in karst springs of Liddar and Bringi catchments indicated that groundwater flowed through well-developed drainage systems through large conduits. Conversely, the lower ki values and higher water temperatures in the Kuthar catchment springs insight hypogene karstification.

  6. 6.

    The study recommends that anthropogenic input be restricted in the recharge areas of the karst springs due to the highly karstified nature of the catchments studied. To that end, spreading knowledge about karst landscapes and their resources among the local people will prove effective in protecting the karst aquifers systems within the region.