Consistently monitoring groundwater quality (GWQ) is essential to reduce the risk of geochemical contaminants and ensure its suitability for agriculture and human consumption. The current investigation aims to assess groundwater (GW) acceptability in the Nand Samand catchment (NSC), utilizing the water quality index (WQI) and irrigation water quality indices (IWQIs) for domestic and irrigation purposes. To achieve this, GW samples were collected from 95 open wells which were located spatially in the catchment, during the pre-monsoon (PRM) and post-monsoon (POM) seasons of 2019 and 2020, and subsequently analysed for 11 physico-chemical parameters. Electrical conductivity (EC) varied from 1.25 to 6.61 dS/m and 0.58 to 7.42 dS/m during the PRM and POM seasons, respectively. Total dissolved solids of the study area ranged between 180 and 1,180 (27%) to 1,180 and 2,180 (62%) during PRM 1,180 (63%) to 1,180–2,180 (31%) during POM, respectively. The study also computed the ‘sodium adsorption ratio’ (SAR) and ‘residual sodium carbonate’ (RSC) to estimate GW's appropriateness for agriculture, finding it suitable in most locations due to its balanced composition. Based on WQI, varying percentages of samples were classified as ‘good’ and ‘poor’ for potable water quality in both seasons.

  • Water quality index for drinking and irrigation indices for irrigation use were estimated.

  • Eleven water quality parameters were analysed for 95 sampling locations.

  • Electrical conductivity varies from 1.25 to 6.61 dS/m and 0.58 to 7.42 dS/m during pre-monsoon and post-monsoon.

  • Varying percentages of samples were categorized as ‘good’ and ‘poor’ for drinking water quality in both seasons and safe for irrigation use.

The accessibility to freshwater poses a significant challenge in the twenty-first century (Subba Rao et al. 2019). Groundwater (GW) is acknowledged as a crucial natural resource, playing a vital role in supplying clean and potable water across diverse climatic regions (Priya et al. 2022). Consequently, world's quarter of the irrigated farmland leans on GW for irrigation, with the majority, 75%, situated in Asia (Basunia et al. 2015). Many regions globally, particularly in rural settings, are solely dependent on wells and springs for their water supply. In the context of India, GW holds immense significance, with roughly 30% of the urban and 90% of the rural population even today relying on untreated water for domestic and irrigation purposes (Adimalla & Venkatayogi 2018; Adimalla & Li 2019). However, over the previous two decades, a noticeable increase in dependency on GW is found due to extensive growth in the agricultural sector, population expansion, and industrial development (Kant et al. 2018; Adimalla et al. 2020).

According to the WHO, approximately 80% of human illnesses are linked to water-related factors. In India, waterborne diseases have led to significant rates of illness and death (Singh & Hussian 2016). Consequently, experts specializing in GW have concentrated their efforts on evaluating GW appropriateness for various usages like drinking, residential, agricultural, and industrial purposes (Thapa et al. 2017; Adimalla 2019; Subba Rao et al. 2019). Global investigations have been undertaken to illustrate the quality of subterranean water, its fitness for consumption and farming, as well as the primary origins of pollutants (Alaya et al. 2014; Li et al. 2016; Dişli 2017; Adimalla et al. 2018; Dimple et al. 2022, 2023).

In terms of population size, India's largest economic sector is agriculture, which also produces the second-most food worldwide (Rane & Jayaraj 2021). GW is the most essential freshwater resource for subsistence, accounting for over 60% in irrigated agriculture, and hence plays an imperative part in India's total socio-economic stability (CGWB 2017a, 2017b). India ranks as the top global consumer of GW, with yearly abstractions of , or about one-fourth of the total global GW extraction annually (Murmu et al. 2019). Due to spiking GW demand by various sectors, India has seen a significant GW reduction in recent years (Selvakumar et al. 2018). According to the CGWB (2021) study, the nation's overall annually GW recharge is 436.15 billion cubic meters (BCM), annual obtainable GW is 397.62 BCM, yearly GW depletion is 244.92 BCM, and the current GW development stage is 61.6%. According to a World Bank (2016) study, if required efforts are not made, India is going to be water-stressed by 2025 and water-scarce by 2050. At present, India is confronting a grave water crisis, impacting around 100 million individuals who are directly affected by the nationwide water scarcity, while numerous major cities are facing severe water shortages. According to United Nations and NITI Ayog assessments, water demand will exceed available supply by 2030, and 40% of India's population would lack access to safe drinking water (Saha 2019).

Rajasthan stands as the largest state in the nation, encompassing a land area greater than the combined territories of 128 countries. Nevertheless, the state faces a critical water situation. Despite its extensive expanse, accounting for over 10% of the nation's total topographical extent and accommodating more than 5.5% of the population and 18.7% of the livestock, Rajasthan possesses access to only a mere 1.16% of the nation's overall surface water and 1.70% of its total GW (Sinha et al. 2018). The state's cumulative surface water availability reaches 29.78 BCM, distributed with 0.38 BCM in minor reservoirs, 1.71 BCM in medium and minor check dams, 4.22 BCM in major state dams, and 23.47 BCM in major interstate contract dams. Rajasthan's net dynamic fresh GW availability stands at approximately 11.99 BCM (Singh et al. 2021).

The WQI is a mathematical indicator utilized to determine the general superiority of water for a specific application use. It is termed as a score that represents the collective effects of several water quality parameters utilized in the computation of the WQI. At present, there are a number of water quality evaluation methods/models, including analytic hierarchy process (Pang et al. 2008), grey clustering (Yang & Yang 2017), fuzzy comprehensive evaluation (Wu et al. 2017), pair-comparison analysis (Qiu et al. 2008), etc. However, most of these evaluation methods can only show where the water body is polluted and not the degree of pollution. As a result, taking appropriate prevention and control measures based on the evaluation results is difficult (Li & Zhang 2008; Wu et al. 2017). The drawbacks of evaluating water quality just using parameters, however, can be efficiently solved using the WQI. To reflect the level of water quality, it combines various water quality criteria into a singular value. Due to its superiority in evaluating water quality, the WQI is frequently utilized (Chen et al. 2020). Several studies have highlighted the significance of WQI, which act as an indication for monitoring water quality and were first proposed by Horton (1965). Its integration with advanced technologies, such as the geographic information system (GIS), has made it incredibly useful for figuring out the distribution and temporal and spatial variability of different water quality indices (Dash & Kalamdhad 2021). Some are the studies that were conducted in the hard-rock region of Rajasthan for the assessment of GWQ for irrigation and drinking purposes: Dahiphale et al. (2019) assessed the GWQ in Jaisamand catchment water quality index (WQI). Kumar et al. (2018a) studied the ground water quality of the Upper Berach Riverbasin, originating in the hills of Udaipur and Rajasmand districts also, Machiwal et al. (2011) selected the Udaipur district as the study area for the GWQ assessment by developing groundwater quality index (GWQI) model. But not much of extensive research has been carried out till date in Nand Samand catchment (NSC) to assess the GWQ for drinking and irrigation purposes, the primary objective of this work was to assess the suitability of the available GW for agricultural and domestic use in NSC of Rajsamand and Udaipur districts, Rajasthan.

Seasonal influences on WQI could be revealed by mapping WQI and irrigation indices with their temporal behaviour. This dataset offers insights into the levels of water quality indicators within the surveyed region, aiding decision-makers in comprehending the GWQ status for diverse applications. Anions and cations are among the most common attributes in water resources, making ongoing monitoring essential. Water quality is paramount for both drinking and irrigation purposes. Clean drinking water is vital for human health, preventing waterborne diseases and long-term health issues caused by contaminants. In agriculture, water quality directly affects soil health and crop yield; poor-quality water can lead to soil degradation and reduced productivity. Monitoring, treatment, and sustainable practices are crucial for maintaining high water quality, ensuring safe drinking water, and promoting efficient irrigation methods that preserve soil fertility and ecosystem health. The WQI in the NSC catchment has not been evaluated for both drinking and irrigation purposes. Therefore, the current research study was undertaken to appraise the GWQ using different indices and to classify the NSC into different classes based on the indices' values. This research results may benefit the local population by serving as a gauge for the proper utilization of groundwater for both drinking and irrigation purposes. Because the topic is visually represented, even individuals without specialized knowledge can grasp it. Moreover, the paper explores various indices beyond the WQI and investigates their interrelationships. The paper is organized as follows: Section 2 provides an explanation of the proposed model's structure. Additionally, Section 3 delves into the analysis of the results conducted by the authors. In conclusion, Section 4 summarizes the key findings, acknowledges limitations, and outlines future research directions. This research plays a crucial role in optimizing science-based GW resource management. The study's outcomes can aid planners and decision-makers in selecting the most suitable GW management strategies for the studied area.

Investigation region

The Nand Samand (NS) Catchment encompasses two administrative divisions, Rajasamand and Udaipur, situated within a semi-arid ecological zone. The study area, sampling location, and elevation map are shown in Figure 1 and the flow chart of the study is shown in Figure 2, which also pinpoints specific sampling sites. Encompassing an area of 865.18 km2, the NSC exhibits altitudinal variations, with elevations ranging from approximately 570 m to a peak of 1,318 m above sea level. The climate varies from arid to semi-arid spectrum, as evidenced by an average annual precipitation of 615.34 mm recorded between 2006 and 2016 (CGWB 2017a, 2017b). The study region's land use and land cover can be categorized into five distinct classes: agricultural lands, shrub-covered areas, unproductive land, urbanized regions, and water reservoirs.
Figure 1

Study area sampling location and elevation map.

Figure 1

Study area sampling location and elevation map.

Close modal
Figure 2

Study flow diagram.

Figure 2

Study flow diagram.

Close modal

Analytical procedures

Standard methods for water analysis were used for all sample procedures and data processing. Samples were gathered in 500-ml polyethylene bottles and subsequently transported to the laboratories of AICRP (All India Co-ordinated Research Project on Integrated Farming Systems) on Irrigation Water Management CTAE (College of Technology and Engineering) and Soil Science laboratory of RCA (Rajasthan College of Agriculture), Udaipur. All water samples underwent analysis following the standard methods for water examination (APHA 1995, 2005; Adimalla et al. 2018). Also, magnesium (by calculation), calcium (ethylenediaminetetraacetic acid (EDTA) titrimetric), and chloride (titrimetric) were measured using the titration method (APHA 1995, 2005). Sodium (Na) and potassium (K) concentrations were assessed using flame photometric analysis. pH and EC levels were estimated utilizing a pH meter and an EC meter, respectively. Subsequently, the obtained analytical data were employed within a GIS system to generate numerical representations of parameter distributions across space. The inverse distance weight (IDW) method was employed to create spatial maps depicting the distribution of water quality indicators and the WQI. All analyses were conducted using Excel 2010 and ArcGIS 10.1 software tools. The classes of water quality index are presented in Table 1.

Table 1

The water quality range based on the weighted arithmetic WQI method (Nag and Das, 2017)

WQI rangeClass
0–25 Very good 
26–50 Good 
51–75 Poor 
76–100 Very poor 
Greater than 100 Restricted for drinking purposes 
WQI rangeClass
0–25 Very good 
26–50 Good 
51–75 Poor 
76–100 Very poor 
Greater than 100 Restricted for drinking purposes 

Methods for data processing and classification

Irrigation quality indices

Water samples were assessed for their overall irrigation water quality using indices like SAR (Richards 1954) and RSC (Eaton 1950) as given in the following equations, respectively:
formula
(1)
formula
(2)
where Na+, Ca2+, Mg2+ denote soluble sodium, calcium, and magnesium concentration in milliequivalents/liter (meq/l).

Drinking WQI

The WQI serves as a substantial and distinctive indicator in the assessment of water quality and its appropriateness for human consumption. It amalgamates multiple water quality parameters to offer both the general public and policymaker's insights into water quality conditions. Among various methodologies, the effectiveness of the WQI approach has been notably evident, playing a key role in enabling the efficient management of water resources (Dash & Kalamdhad 2021). The drinking water guideline employed in this study conforms to the guidelines set forth by the WHO in 2011 and BIS (2012) specifications for drinking water presented in Table 2.

Table 2

Water parameters criteria for drinking use

ParameterIndian standard (Ramakrishnalah et al. 2009; BIS 2012; Adimalla et al. 2018)
WHO (2011) 
mg/lmeq/lmg/lmeq/l
pH 6.5–8.5 – 6.5–8.5 – 
Ca2+ 75–200 3.75–10 300 3.75–10 
Mg2+ 30–100 2.5–8.33 30 2.47 
 – – 200 8.70 
K+ 12 – 10 0.26 
 200–400 4.17–8.33 200 4.16 
Cl 250–1,000 7.04–28.16 250 7.05 
TDS <1,500 – 500  
 120    
ParameterIndian standard (Ramakrishnalah et al. 2009; BIS 2012; Adimalla et al. 2018)
WHO (2011) 
mg/lmeq/lmg/lmeq/l
pH 6.5–8.5 – 6.5–8.5 – 
Ca2+ 75–200 3.75–10 300 3.75–10 
Mg2+ 30–100 2.5–8.33 30 2.47 
 – – 200 8.70 
K+ 12 – 10 0.26 
 200–400 4.17–8.33 200 4.16 
Cl 250–1,000 7.04–28.16 250 7.05 
TDS <1,500 – 500  
 120    

The main objective of computing the WQI was to offer a thorough evaluation of the suitability of GW for drinking. The computation of the WQI employed the weighted arithmetic index method, which was originally introduced by Brown et al. (1970) and subsequently adopted by other researchers (e.g. Dwivedi & Pathak 2007; Sakizadeh et al. 2016; Nag & Das 2017; Roy et al. 2018). The WQI was computed as follows:

WQI is computed by using the following equations:
formula
(3)
formula
(4)
where in pure water (i.e. 0 for all parameters except pH which has a neutral value of 7) and is the unit weight of parameter, which is calculated
by:
formula
(5)
formula
(6)

The primary factors in determining water quality are quantitative assessments of several physico-chemical parameters. Statistical values for the various parameters for both seasons are given in Table 3. Following a comparison with suggested standard values, the acceptability of the collected water samples for irrigation and drinking was separately examined. Results showed that the standard deviation (SD) and coefficient of variation (CV) for the pH were 0.59 and 0.08, respectively during the pre-monsoon (PRM) and the same were 0.39 and 0.05 during the post-monsoon (POM) season.

Table 3

Statistical data of water quality parameters of the study area

ParametersPRM
POM
Min.Max.MeanSDCVMin.Max.MeanSDCV
pH 6.12 8.14 7.11 0.59 0.08 6.36 8.36 7.29 0.39 0.05 
EC 1.25 6.61 2.51 1.03 0.41 0.58 7.42 1.77 0.83 0.47 
TDS 200 4,400 1,580 706.46 0.45 180 3,417.74 1,166.61 451 0.39 
Ca2+ 2.43 11.49 4.88 1.58 0.32 1.27 17.48 3.69 2.12 0.57 
Mg2+ 1.23 22.97 4.87 3.27 0.67 0.00 8.62 2.33 1.33 0.57 
 1.71 17.41 4.20 2.45 0.58 0.23 7.46 2.90 1.53 0.53 
K+ 0.22 3.42 1.31 0.74 0.56 0.00 1.29 0.23 0.33 1.44 
 0.76 6.70 4.25 1.22 0.29 1.58 7.46 5.26 1.27 0.24 
 0.00 1.52 0.07 0.24 3.58 0.00 1.30 0.06 0.22 3.96 
Cl 2.97 20.68 5.08 2.62 0.52 1.73 15.73 4.50 3.07 0.68 
 1.62 12.72 4.42 1.74 0.39 0.00 16.55 2.95 2.33 0.79 
ParametersPRM
POM
Min.Max.MeanSDCVMin.Max.MeanSDCV
pH 6.12 8.14 7.11 0.59 0.08 6.36 8.36 7.29 0.39 0.05 
EC 1.25 6.61 2.51 1.03 0.41 0.58 7.42 1.77 0.83 0.47 
TDS 200 4,400 1,580 706.46 0.45 180 3,417.74 1,166.61 451 0.39 
Ca2+ 2.43 11.49 4.88 1.58 0.32 1.27 17.48 3.69 2.12 0.57 
Mg2+ 1.23 22.97 4.87 3.27 0.67 0.00 8.62 2.33 1.33 0.57 
 1.71 17.41 4.20 2.45 0.58 0.23 7.46 2.90 1.53 0.53 
K+ 0.22 3.42 1.31 0.74 0.56 0.00 1.29 0.23 0.33 1.44 
 0.76 6.70 4.25 1.22 0.29 1.58 7.46 5.26 1.27 0.24 
 0.00 1.52 0.07 0.24 3.58 0.00 1.30 0.06 0.22 3.96 
Cl 2.97 20.68 5.08 2.62 0.52 1.73 15.73 4.50 3.07 0.68 
 1.62 12.72 4.42 1.74 0.39 0.00 16.55 2.95 2.33 0.79 

Note: All parameter units are in meq/l except (pH, EC in dS/m, TDS in ppm). Min. (Minimum), Max. (Maximum).

General water quality

The pH parameter signifies the inverse logarithmic representation of hydrogen ion activity, measured on a scale that spans from 0 to 14. A pH below 7 indicates acidity, while a pH above 7 indicates alkalinity, and a pH of 7 signifies neutrality. Hence, pH serves as an indicator of water's acidic or alkaline nature (Bhat et al. 2018). The spatial distribution map displays the fluctuations in hydrochemical data across different seasons (as depicted in Figure 3(a) and 3(b). The largest proportion of the study area (83.06%) registers pH values ranging from 6.5 to 8.5 during the PRM period, and 82.26% during the POM season (Table 4).
Table 4

pH range and area covered in the study region

SeasonsStudy areaArea Area (%)
PRM <6.5 44.29 5.12 
6.5–8.5 718.64 83.06 
>8.5 102.25 11.82 
POM <6.5 2.46 0.28 
6.5–8.5 711.67 82.26 
>8.5 151.04 17.46 
SeasonsStudy areaArea Area (%)
PRM <6.5 44.29 5.12 
6.5–8.5 718.64 83.06 
>8.5 102.25 11.82 
POM <6.5 2.46 0.28 
6.5–8.5 711.67 82.26 
>8.5 151.04 17.46 
Figure 3

pH spatial distribution map for the pre-monsoon (a) and post-monsoon (b) season.

Figure 3

pH spatial distribution map for the pre-monsoon (a) and post-monsoon (b) season.

Close modal

Hydrogen ion (pH) levels of GW within the NSC exhibit a range between 6.12 and 8.14, with an average of 7.11 during the PRM period. Similarly, during the POM period, the pH ranges from 6.36 to 8.36, with an average of 7.29 (refer to Table 4). In the PRM season, approximately 83.06% of the catchment area falls within the pH range of 6.5–8.5, while 11.82% registers a pH above 8.5 (as depicted in Figure 3(a). In the POM season, the respective proportions are 82.26 and 17.46% for the same pH ranges (illustrated in Figure 3(b). A breakdown of pH ranges and coverage for both seasons can be found in Table 4. This trend of pH variation is consistent with findings by Dahiphale (2015), Kumar et al. (2018b), and Yadav & Singh (2018) in regions characterized by hard-rock terrain. A similar observation was reported by Adimalla (2019) within the study area, where around 80% of GW samples exhibited alkaline pH levels ranging from 7.3 to 8.6, with an average value of 7.96. Furthermore, more than 90% of the collected samples remained within the recommended pH limits of 6.5–8.5, as specified by WHO (2017).

The quantification of total dissolved solids (TDS) relies on the measurement of electrical conductivity (EC) within the given water medium. The presence of elevated salt content in water significantly impacts its suitability for irrigation purposes (Dolma et al. 2020). The measurement of TDS, serving as a quality gauge, revealed values ranging from 200 to 4,400 mg/l, with an average of 1,580 mg/l during the PRM period. Similarly, during the POM period, the range was 180–3,417.74 mg/l, with an average of 1,166.61 mg/l (refer to Table 5). As demonstrated in Figure 4(a) and 4(b), the majority of the study area during both PRM and POM periods maintains a concentration of TDS well within the acceptable range (500–1,500 mg/l). The elevated levels of TDS in GW might be attributed to the presence of bicarbonates, carbonates, sulphates, chlorides, and calcium (Bhat et al. 2018). Further details regarding the TDS ranges and area coverage for both seasons can be found in Table 5. These findings closely align with the study conducted by Naruka & Sharma (2017) on Rajasthan's Rajasamand Lake, where reported TDS values ranged from 293 to 386 mg/l. Indian standards recommend a desirable TDS limit of 500 mg/l for drinking water, a limit that the observed TDS values remained below. The lowest recorded value (293 mg/l) occurred during the 2014 monsoon, while the highest (386 mg/l) was observed in the winter of the same year. Sinha et al. (2018) also conducted research in the Wakal River basin of Rajasthan, noting that TDS concentrations in the majority of the study area fell within the permissible range (500–1,500 mg/l) during both PRM and POM periods. Similar conclusions were drawn by Kumar et al. (2018a) in their study of the Upper Bearch River basin in Udaipur, Rajasthan. Dissolved solids in water represent minerals that have undergone dissolution. Normally, natural water contains dissolved solids at concentrations lower than 500 mg/l. However, when water surpasses this threshold, it becomes unsuitable for both drinking and various industrial applications. Consequently, the cumulative concentration of dissolved minerals stands as a universally applicable indicator for evaluating the overall suitability of water for diverse purposes (Balakrishnan et al. 2011).
Table 5

TDS range and area covered in the study region

SeasonsTDS rangeStudy area Area (%)
PRM 180–1,180 230 27 
1,180–2,180 535 62 
2,180–3,180 49 
3,180–4,180 30.18 3.47 
4,180–5,180 21 2.43 
POM 180–1,180 543 63 
1,180–2,180 269 31 
2,180–3,180 33 
3,180–4.180 20.18 
SeasonsTDS rangeStudy area Area (%)
PRM 180–1,180 230 27 
1,180–2,180 535 62 
2,180–3,180 49 
3,180–4,180 30.18 3.47 
4,180–5,180 21 2.43 
POM 180–1,180 543 63 
1,180–2,180 269 31 
2,180–3,180 33 
3,180–4.180 20.18 
Figure 4

TDS spatial variability map for pre-monsoon (a) and post-monsoon (b) season.

Figure 4

TDS spatial variability map for pre-monsoon (a) and post-monsoon (b) season.

Close modal
EC is a widely employed parameter to signify the collective concentration of ionized constituents present in natural water. The fluctuations in EC are influenced by factors such as the concentration and ionization level of components, as well as temperature effects (Sarathbabu & John 2015). EC serves as a dependable gauge for quantifying the total amount of dissolved mineral salts in water, making it a common choice for evaluating salinity concerns relevant to agricultural irrigation (Kadyampakeni et al. 2018). The EC displays a range from 1.25 to 6.61 dS/m, with an average of 2.51 dS/m during the PRM period, and from 0.58 to 7.42 dS/m, with an average of 1.77 dS/m during the POM period (as outlined in Table 6). Figure 5(a) and 5(b) visually illustrate that around 35.27 and 64.73% of the NSC area exhibit EC values ranging from 0.75 to 2.25 dS/m and above 2.25 dS/m, respectively, during the PRM period. In the POM period, the respective proportions are 90.59 and 9.28% within the same EC ranges. Detailed classification of EC ranges and area coverage for both seasons is provided in Table 6. Notably, mean values of TDS and EC were higher in the PRM period compared to the POM period. Similar findings were reported by Tyagi et al. (2014) for GW samples from Pauri Garhwal district and by Kumar et al. (2018a) for the Upper Berach River basin. EC exhibited variations from 0.38 to 7.62 dS/m, with an average of 2.35 dS/m during the PRM period, and from 0.28 to 5.54 dS/m, with an average of 1.56 dS/m during the POM period, respectively.
Table 6

EC range and area covered in the study region

SeasonsStudy area
EC(dS/m) rangeArea Area (%)
PRM 0.75–2.25 305.15 35.27 
>2.25 560.03 64.73 
POM 0.25–0.75 1.13 0.13 
0.75–2.25 783.78 90.59 
>2.25 80.27 9.28 
SeasonsStudy area
EC(dS/m) rangeArea Area (%)
PRM 0.75–2.25 305.15 35.27 
>2.25 560.03 64.73 
POM 0.25–0.75 1.13 0.13 
0.75–2.25 783.78 90.59 
>2.25 80.27 9.28 
Figure 5

EC spatial distribution map. Pre-monsoon (a) and post-monsoon (b).

Figure 5

EC spatial distribution map. Pre-monsoon (a) and post-monsoon (b).

Close modal

Within the category of positively charged ions (cations), the concentrations of calcium (Ca2+), magnesium (Mg2+), sodium (Na+), and potassium (K+) ions exhibited a range of 2.43 to 11.49, 1.23 to 22.97, 1.71 to 17.41, and 0.22 to 3.42 meq/l, respectively, with average values of 4.88, 4.87, 4.20, and 1.31 meq/l during the PRM period. In the POM period, the concentrations of these ions varied from 1.27 to 17.48, 0.23 to 7.46, 0.00 to 8.62, and 0.00 to 1.29 meq/l, respectively, yielding average values of 3.69, 2.90, 2.33, and 0.23 meq/l (as presented in Table 3). Notably, sodium and calcium emerged as the most prevalent cations, closely followed by magnesium and potassium during both PRM and POM periods for both years. It is worth noting that while sodium and potassium occur naturally in GW, their levels can also be influenced by household and industrial waste inputs (Garg et al. 2009).

Sulphate, a component found in various minerals, is naturally present in water. BIS (2012) standard designates an acceptable level of 200 mg/l and a maximum permissible level of 400 mg/l for sulphate. Water alkalinity is primarily attributed to bicarbonate and carbonate ions. As per the WHO (2003) guidelines, chloride levels in water stem from both natural and human-related sources. Elevated chloride concentrations can lead to increased metal concentrations in water due to corrosion. According to BIS (2012), the acceptable chloride level is 250 mg/l, while the maximum permissible limit is 1,000 mg/l.

For negatively charged ions (anions), such as chloride (Cl), sulphate (), bicarbonate (), and carbonate (), the dissolved concentrations ranged from 2.97 to 20.68, 1.62 to 12.72, 0.76 to 6.70, and 0.00 to 1.52 meq/l, respectively, during the PRM period. In the POM period, these anions exhibited ranges of 1.58 to 7.46, 1.73 to 15.73, 0.00 to 16.55, and 0.00 to 1.30 meq/l, respectively, with mean values of 5.26, 4.50, 2.95, and 0.06 meq/l (as displayed in Table 3). Across both PRM and POM periods, chloride and bicarbonate stood out as the most prevalent anions among the major constituents (, Cl, , and ), followed by sulphate and carbonate, respectively. It is noteworthy that, with the exception of a few samples, the majority of GW samples from the study area fell below the acceptable concentration threshold for sulphate.

Calcium (): the spatial distribution maps (as illustrated in Figure 6(a) and 6(b)) depict that the largest portion of the area conforms to the acceptable range of calcium (Ca2+) content (3.75–10 meq/l) for drinking purposes during the PRM period. The calcium ranges across both PRM and POM seasons, along with the corresponding area coverage, are detailed in Table 7. The figures also indicate that during both PRM and POM periods, 72.57 and 92.30% of the study area, respectively, fall within the permissible Ca2+ limit. A study conducted within Rajasthan's JaiSamand catchment area (Dahiphale et al. 2019) similarly documented that the maximum extent of the region adhered to the acceptable Ca2+ content range (3.75–10 meq/l) for drinking purposes during the PRM period.
Table 7

Calcium range and area covered in the study region

Season (meq/l) rangeStudy area Area (%)
PRM 1.27–5.31 627.82 72.57 
5.31–9.35 230.30 26.82 
9.35–13.39 7.05 0.82 
POM 2.97–7.36 798.60 92.30 
7.36–11.76 58.29 6.74 
11.76–16.16 6.23 0.72 
16.16–20.68 2.05 0.24 
Season (meq/l) rangeStudy area Area (%)
PRM 1.27–5.31 627.82 72.57 
5.31–9.35 230.30 26.82 
9.35–13.39 7.05 0.82 
POM 2.97–7.36 798.60 92.30 
7.36–11.76 58.29 6.74 
11.76–16.16 6.23 0.72 
16.16–20.68 2.05 0.24 
Figure 6

Calcium spatial variability map for pre-monsoon (a) and post-monsoon (b) season.

Figure 6

Calcium spatial variability map for pre-monsoon (a) and post-monsoon (b) season.

Close modal
Magnesium (Mg2+) is an omnipresent element present in all natural water sources and ranks among the most prevalent elements in the Earth's crust. Its primary sources encompass mafic minerals like amphiboles, olivine, and pyroxenes, as well as materials such as dolomite, magnesite, and clay minerals. The geographical distribution maps for the study region are visualized in Figure 7(a) and 7(b). Detailed magnesium ranges for both PRM and POM periods, along with the corresponding area distribution, are tabulated in Table 8. In the PRM period, approximately 84.92% of the area adheres to the allowable magnesium limit (2.5–8.33 meq/l), and during the POM period, this proportion rises to 97.80%. Conversely, only 5.94% of the PRM area and 0.22% of the POM area exceed the permissible magnesium limit.
Table 8

Magnesium range and area covered in the study region

Season (meq/l) rangeArea Area (%)
PRM 1.23–6.63 769.31 88.92 
6.63–12.03 87.68 10.13 
12.03–17.43 6.27 0.72 
17.43–22.07 1.91 0.22 
POM 0–1.23 16.12 1.86 
1.23–6.63 846.12 97.80 
6.63–12.03 1.94 0.22 
Season (meq/l) rangeArea Area (%)
PRM 1.23–6.63 769.31 88.92 
6.63–12.03 87.68 10.13 
12.03–17.43 6.27 0.72 
17.43–22.07 1.91 0.22 
POM 0–1.23 16.12 1.86 
1.23–6.63 846.12 97.80 
6.63–12.03 1.94 0.22 
Figure 7

Magnesium spatial variability map for pre-monsoon (a) and post-monsoon (b) season.

Figure 7

Magnesium spatial variability map for pre-monsoon (a) and post-monsoon (b) season.

Close modal
For potassium (K+) and carbonate () ions, the majority of the study area exhibited negligible presence during both seasons. Visual representations of the spatial variability are showcased in Figure 8(a) and 8(b) for Potassium, Figure 9(a) and 9(b) for carbonate, and Figure 10(a) and 10(b) for sodium. Comprehensive potassium, carbonate, and sodium data ranges for the two seasons and respective area coverage are outlined in Table 9. Comparable results were documented by Sinha et al. (2018) in the context of the Wakal River basin of Rajasthan.
Table 9

Sodium, potassium, carbonate range, and area covered in the study region

Season (meq/l) rangeArea Area (%)
PRM <4.32 610.19 70.53 
4.32–6.92 224.61 25.96 
6.92–9.52 18.03 2.08 
9.52–12.12 6.64 0.77 
12.12–14.73 4.16 0.48 
>14.73 1.55 0.18 
POM <4.32 812.44 93.90 
4.32–6.92 52.15 6.03 
6.92–9.52 0.59 0.07 
(meq/l) range
PRM 0.22–1.28 453.50 52.42 
1.28–2.34 396.28 45.80 
2.34–3.42 15.40 1.78 
POM 0–1.22 540.42 62.46 
0.22–1.28 324.58 37.52 
1.28–2.34 0.17 0.02 
(meq/l) range
PRM 0–0.08 650.46 75.18 
0.08–0.16 114.17 13.20 
>0.16 100.55 11.62 
POM 0–0.07 588.13 67.98 
0.07–0.15 205.94 23.80 
>0.15 71.10 8.22 
Season (meq/l) rangeArea Area (%)
PRM <4.32 610.19 70.53 
4.32–6.92 224.61 25.96 
6.92–9.52 18.03 2.08 
9.52–12.12 6.64 0.77 
12.12–14.73 4.16 0.48 
>14.73 1.55 0.18 
POM <4.32 812.44 93.90 
4.32–6.92 52.15 6.03 
6.92–9.52 0.59 0.07 
(meq/l) range
PRM 0.22–1.28 453.50 52.42 
1.28–2.34 396.28 45.80 
2.34–3.42 15.40 1.78 
POM 0–1.22 540.42 62.46 
0.22–1.28 324.58 37.52 
1.28–2.34 0.17 0.02 
(meq/l) range
PRM 0–0.08 650.46 75.18 
0.08–0.16 114.17 13.20 
>0.16 100.55 11.62 
POM 0–0.07 588.13 67.98 
0.07–0.15 205.94 23.80 
>0.15 71.10 8.22 
Figure 8

Potassium spatial variability map. Pre-monsoon (a) and post-monsoon (b).

Figure 8

Potassium spatial variability map. Pre-monsoon (a) and post-monsoon (b).

Close modal
Figure 9

Carbonate spatial distribution map. Pre-monsoon (a) and post-monsoon (b).

Figure 9

Carbonate spatial distribution map. Pre-monsoon (a) and post-monsoon (b).

Close modal
Figure 10

Sodium spatial variability map. Pre-monsoon (a) and post-monsoon (b).

Figure 10

Sodium spatial variability map. Pre-monsoon (a) and post-monsoon (b).

Close modal
Figure 11(a) and 11(b) additionally highlight that around 19.41% of the catchment area maintains bicarbonate () concentrations below 3.93 meq/l (as specified by WHO 2004) during the PRM period, while roughly 14.45% of the area does so during the POM period. The bicarbonate range for both seasons, along with the corresponding area distribution, can be found in Table 10. Notably, bicarbonate levels are higher during the POM season, indicative of carbonate weathering processes.
Table 10

Bicarbonate, chloride, sulphate range, and area covered in the study region

SeasonParameterArea Area (%)
 (meq/l) range   
PRM 0–1.58 3.01 0.35 
1.58–3.06 46.53 5.38 
3.06–4.53 519.25 60.02 
4.53–5.99 287.68 33.25 
5.99–7.46 8.71 1.01 
POM 1.58–3.06 3.86 0.45 
3.06–4.53 121.17 14.00 
4.53–5.99 622.08 71.90 
5.99–7.46 118.08 13.65 
 (meq/l) range   
PRM 2.97–7.36 818.12 94.56 
7.36–11.76 40.56 4.69 
11.76–16.16 5.34 0.62 
16.16–20.66 1.15 0.13 
POM 0–2.97 142.26 16.44 
2.97–7.36 666.56 77.04 
7.36–11.76 50.07 5.79 
11.76–16.16 6.28 0.73 
 (meq/l) range   
PRM <3.83 222.07 25.67 
3.83–6.04 594.47 68.71 
6.04–8.25 42.43 4.90 
8.25–10.47 4.68 0.54 
>10.47 1.52 0.18 
POM <3.30 591.37 68.35 
3.30–6.6 258.26 29.85 
6.6–9.89 9.45 1.09 
9.89–13.19 3.94 0.46 
>13.19 2.17 0.25 
SeasonParameterArea Area (%)
 (meq/l) range   
PRM 0–1.58 3.01 0.35 
1.58–3.06 46.53 5.38 
3.06–4.53 519.25 60.02 
4.53–5.99 287.68 33.25 
5.99–7.46 8.71 1.01 
POM 1.58–3.06 3.86 0.45 
3.06–4.53 121.17 14.00 
4.53–5.99 622.08 71.90 
5.99–7.46 118.08 13.65 
 (meq/l) range   
PRM 2.97–7.36 818.12 94.56 
7.36–11.76 40.56 4.69 
11.76–16.16 5.34 0.62 
16.16–20.66 1.15 0.13 
POM 0–2.97 142.26 16.44 
2.97–7.36 666.56 77.04 
7.36–11.76 50.07 5.79 
11.76–16.16 6.28 0.73 
 (meq/l) range   
PRM <3.83 222.07 25.67 
3.83–6.04 594.47 68.71 
6.04–8.25 42.43 4.90 
8.25–10.47 4.68 0.54 
>10.47 1.52 0.18 
POM <3.30 591.37 68.35 
3.30–6.6 258.26 29.85 
6.6–9.89 9.45 1.09 
9.89–13.19 3.94 0.46 
>13.19 2.17 0.25 
Figure 11

Bicarbonate spatial distribution map. Pre-monsoon (a) and post-monsoon (b).

Figure 11

Bicarbonate spatial distribution map. Pre-monsoon (a) and post-monsoon (b).

Close modal
The spatial distribution pattern of chloride (Cl) in the study region is graphically depicted in Figure 12(a) and 12(b). The chloride range for both PRM and POM periods, along with the respective area coverage, is documented in Table 10. Approximately 95% of the NSC adheres to the permissible chloride range (5.64–16.93 meq/l) during the PRM period, while around 19.72% do so in the POM period. Similarly, for sulphate () ions, about 73.61% of the area adhere to the permissible range (4.16–8.33 meq/l) during the PRM period, while 30.94% of the area does so in the POM period. Visualizations of the spatial distribution for sulphate ions are presented in Figure 13(a) and 13(b). Detailed sulphate data ranges for both seasons and corresponding area distribution are presented in Table 10.
Figure 12

Chloride spatial variability map. Pre-monsoon (a) and post-monsoon (b).

Figure 12

Chloride spatial variability map. Pre-monsoon (a) and post-monsoon (b).

Close modal
Figure 13

Sulphatespatial variability map. Pre-monsoon (a) and post-monsoon (b).

Figure 13

Sulphatespatial variability map. Pre-monsoon (a) and post-monsoon (b).

Close modal

Statistical analysis

Pearson correlation matrix

Through the application of statistical analysis, it becomes feasible to establish relationships and fluctuations among the physico-chemical properties and ion concentrations present in GW samples. This analytical approach aids in interpreting data and comprehending the interactions that underlie them (Meireles et al. 2010). The correlation matrix for the 11 physico-chemical parameters is presented in Tables 11 and 12 for both the seasons, respectively. The correlation matrix serves to highlight substantial correlation coefficients, which point to several meaningful hydrochemical relationships.

Table 11

Correlation matrix for different water quality parameters in PRM

VariablespHECTDS
pH 1           
 0.24* 1          
 −0.05 −0.141 1         
 0.146 0.146 −0.26** 1        
EC 0.161 0.26* −0.36** 0.61** 1       
TDS 0.142 0.25* −0.23* 0.51** 0.88** 1      
 0.078 0.083 −0.06 0.09 0.23* 0.34** 1     
 −0.024 0.080 −0.05 −0.03 0.20* 0.31** 0.68** 1    
 −0.23* 0.04 −0.04 −0.11 0.09 0.27** 0.45** 0.47** 1   
 0.097 0.019 0.090 0.235* 0.106 0.123 −0.015 −0.046 −0.205* 1  
 0.028 −0.041 −0.204* −0.082 −0.099 −0.087 0.063 0.058 0.009 0.058 1 
VariablespHECTDS
pH 1           
 0.24* 1          
 −0.05 −0.141 1         
 0.146 0.146 −0.26** 1        
EC 0.161 0.26* −0.36** 0.61** 1       
TDS 0.142 0.25* −0.23* 0.51** 0.88** 1      
 0.078 0.083 −0.06 0.09 0.23* 0.34** 1     
 −0.024 0.080 −0.05 −0.03 0.20* 0.31** 0.68** 1    
 −0.23* 0.04 −0.04 −0.11 0.09 0.27** 0.45** 0.47** 1   
 0.097 0.019 0.090 0.235* 0.106 0.123 −0.015 −0.046 −0.205* 1  
 0.028 −0.041 −0.204* −0.082 −0.099 −0.087 0.063 0.058 0.009 0.058 1 

**Correlation is significant at the p < 0.01 level (two-tailed), *Correlation is significant at the p < 0.05 level (two-tailed). The highlighted bold values shows the significant correlation between the variables.

Table 12

Correlation matrix for different water quality parameters in POM

VariablespHECTDS
pH 1           
 0.20* 1          
 0.005 0.014 1         
 −0.053 0.102 0.175 1        
EC −0.14 −0.21* 0.25* 0.42** 1       
TDS −0.07 −0.04 0.44** 0.48** 0.54** 1      
 −0.03 −0.02 0.45** 0.53** 0.55** 0.91** 1     
 −0.11 −0.098 0.21* 0.40** 0.49** 0.79** 0.83** 1    
 −0.19 −0.23* 0.14 0.26* 0.45** 0.51** 0.47** 0.51** 1   
 −0.149 −0.15 0.126 0.08 0.125 0.08 −0.003 0.067 0.33** 1  
 0.043 −0.042 0.015 −0.108 0.026 −0.146 −0.056 −0.108 −0.151 −0.023 1 
VariablespHECTDS
pH 1           
 0.20* 1          
 0.005 0.014 1         
 −0.053 0.102 0.175 1        
EC −0.14 −0.21* 0.25* 0.42** 1       
TDS −0.07 −0.04 0.44** 0.48** 0.54** 1      
 −0.03 −0.02 0.45** 0.53** 0.55** 0.91** 1     
 −0.11 −0.098 0.21* 0.40** 0.49** 0.79** 0.83** 1    
 −0.19 −0.23* 0.14 0.26* 0.45** 0.51** 0.47** 0.51** 1   
 −0.149 −0.15 0.126 0.08 0.125 0.08 −0.003 0.067 0.33** 1  
 0.043 −0.042 0.015 −0.108 0.026 −0.146 −0.056 −0.108 −0.151 −0.023 1 

**Correlation is significant at the p < 0.01 level (two-tailed), *Correlation is significant at the p < 0.05 level (two-tailed). The highlighted bold values shows the significant correlation between the variables.

During both seasons, EC and TDS demonstrate a notable positive correlation with Na+, , Cl, and Mg2+. The existence of sodium in the study area significantly influences TDS and EC levels. Particularly during the pre- and POM periods, EC exhibits a highly significant and positive correlation with TDS. This correlation also extends to sulphate, Cl, Mg2+, and Ca2+ during the PRM period. A distinct correlation between Cl and Mg2+ suggests that hydrogeochemical processes involving water–rock interaction and mineral dissolution may play a central role in shaping GW chemistry within the catchment (Feng et al. 2020). Furthermore, a pronounced correlation observed between Cl and Ca2+ appears less attributable to recognized salinization processes and is more likely associated with secondary mechanisms such as ionic exchange. This phenomenon becomes particularly evident in highly salinized water, where Cl concentrations are elevated (Alaya et al. 2014). Conversely, no significant relation is observed between Ca2+ and , indicating that calcite may not be the primary source of Ca2+.

During the POM season, EC and TDS exhibit a notably significant and positive correlation with Ca2+, Mg2+, Na+, Cl, and parameters and the presence of these parameters in the research area significantly impacts the levels of TDS and EC. In this season, chloride and sulphate also exhibit significant and positive correlations with calcium, magnesium, sodium, and potassium. Additionally, chloride demonstrates significant and positive correlations with both sulphate and potassium. Conversely, during the PRM and POM periods, pH, , and display weak or negative correlations with the majority of variables under consideration.

WQI for drinking purpose

A mathematical formula is employed to depict the WQI, illustrating the interconnectedness of each GWQ parameter with the others. The calculation of the WQI utilized the weighted arithmetic index method as proposed by Brown et al. (1970), as shown in Table 13. In the PRM period, as depicted in Figure 14(a), the study area revealed specific categories: 1.27 km2 (0.15% area) had water of excellent quality, 79.43 km2 (9.18% area) showed good-quality water, 381.47 km2 (44.09% area) exhibited poor-quality GW, and 403.01 km2 (46.58% area) demonstrated very poor-quality GW for drinking purposes. The POM period, illustrated in Figure 14(b), highlighted the following: 0.51 km2 (0.06% area) exhibited excellent quality water, 80.13 km2 (9.26% area) presented good-quality water, 644.80 km2 (74.53% area) displayed poor-quality GW, 127.53 km2 (14.74% area) showed very poor GWQ, and 12.21 km2 (1.41% area) had unsuitable GW for drinking purposes. Dahiphale et al. (2019) conducted a study on the Jaisamand catchment in Rajasthan, generating WQI maps for PRM and POM periods. Their findings indicated that 22.40% of the area had good-quality water in the PRM period, while 40.30% had good-quality GW for drinking purposes in both PRM and POM seasons. In a study of Udaipur, India's hard-rock hilly terrain, Machiwal et al. (2011) produced a GWQI map, revealing generally good GWQ. Prajapati & Bilas (2018) conducted research in Varanasi district, Uttar Pradesh, India, where WQI results indicated that a significant portion of drinking water samples fell within the ‘good’-quality category, with WQI values ranging from 28 to 65, particularly in the central, eastern, and southern city areas. These studies collectively underscore the convergence of findings within this research context.
Table 13

Unit weight of physico-chemical parameters

ParameterUnit weight
EC (μS/cm) 0.021 
TDS (mg/l) 0.010 
(mg/l) 0.069 
(mg/l) 0.173 
pH (on scale) 0.611 
(mg/l) 0.021 
(mg/l) 0.026 
Alkalinity (mg/l) 0.043 
(mg/l) 0.026 
 1.00 
ParameterUnit weight
EC (μS/cm) 0.021 
TDS (mg/l) 0.010 
(mg/l) 0.069 
(mg/l) 0.173 
pH (on scale) 0.611 
(mg/l) 0.021 
(mg/l) 0.026 
Alkalinity (mg/l) 0.043 
(mg/l) 0.026 
 1.00 
Figure 14

Spatial variability map of WQI ((a) and (b)). Pre-monsoon (a) and post-monsoon (b).

Figure 14

Spatial variability map of WQI ((a) and (b)). Pre-monsoon (a) and post-monsoon (b).

Close modal

GWQ for irrigation suitability

The SAR and RSC are commonly utilized as significant indicators of the sodium levels in irrigation water or soil solution (Feng et al. 2020). The main goal of analysing agricultural water is to evaluate its impact on the soil and, subsequently, on the cultivated crops (Adamu 2013). The inadequate quality of irrigation water is a significant global environmental concern (Millennium Ecosystem Assessment 2005), whereas poor-quality irrigation water adversely affects soil quality and crop production (Kadyampakeni et al. 2018). For sensitive fruits, the permissible threshold for the SAR in irrigation water should not surpass 4. On the other hand, for general crops and forages, a range of 8–18 is commonly considered acceptable and beneficial (NTAC 1968).

In the present study, values of water samples were found under excellent class during both seasons, respectively (Table 14). Barua et al. (2021) reported comparable findings for the Dakshin Dinajpur district of West Bengal, India. The study area exhibited SAR values ranging from 0 to 5, signifying that all the water had low SAR and was, therefore, suitable for irrigation purposes. Similarly, Acharya et al. (2018) noted that all the samples of the study were in the excellent class for the irrigation use. Kumar et al. (2018b) conducted a study and revealed the SAR (sodium adsorption ratio) values of water samples from various villages. During the PRM period, the SAR values were categorized as excellent, good, and unsuitable for 85, 9, and 1 village(s), respectively. In the POM period, the SAR values for 93 villages were classified as excellent, with only 2 villages, Prakashpura and Bijna, falling into the good and doubtful categories, respectively, for irrigation purposes. In agricultural contexts, RSC is commonly employed to assess the potential adverse impact of carbonate and bicarbonate on water quality. Consistent utilization of water with elevated RSC levels can result in leaf burning in plants and diminish crop yields (Ramesh & Elango 2012; Bhat et al. 2018). The data presented in Table 14 clearly show that the RSC data for all the water samples in the research area are below 1.25, except for 1 sample in the PRM period. This indicates that the entire study area remains within the safe threshold for irrigation use, in both the season. RSC in GW varies from −29.24 to 1.50 and −22.32 to 3.33 during and POM period, respectively, also from Table 15 (Figure 15(a) and 15(b)) it is evident that 99.92 and 94.47% of the area of the study are in safe zone during both seasons, respectively. A negative RSC value suggests that the accumulation of sodium is improbable because there is an excess of calcium and magnesium available for precipitation as carbonates. On the other side, a positive RSC value indicates the possibility of sodium build-up in the soil. Nag & Das (2017) reported comparable results, indicating that in the study area, during the PRM period, the majority of GW samples were categorized as safe. However, during the POM period, 50% of the GW samples were classified as unsuitable for use. Furthermore, in their study, Dahiphale et al. (2019) found that the RSC data for all the samples in the catchment were below 1.25. The dominant cations present in the catchment were Ca, Na, and Mg. This suggests that the entire catchment remained within the safe class for irrigation purposes in both the periods. Moreover, an elevated presence of Ca2+ and Mg2+ in GW can negatively impact soil quality, leading to a reduction in crop productivity. Notably, magnesium has a more pronounced adverse effect on crop yields compared to calcium (Adimalla et al. 2018).
Table 14

Ratings of groundwater samples for irrigation suitability using irrigation indices

SARRemark on qualityPRMPOM
<10  0.891–8.384 (all 95 samples) 0.149–4.415 (all 95 samples) 
10–18  
18–26  
Greater than 26  
RSC Water class   
<1.25 Good −29.24–0.56 (94 samples) −22.33 to 1.21 (73 samples) 
1.25–2.5 Doubtful 1.50 (1 sample) 1.28–2.17 (14 samples) 
>2.5 Unsuitable Nil 2.56–3.33 (8 Unsuitable) 
SARRemark on qualityPRMPOM
<10  0.891–8.384 (all 95 samples) 0.149–4.415 (all 95 samples) 
10–18  
18–26  
Greater than 26  
RSC Water class   
<1.25 Good −29.24–0.56 (94 samples) −22.33 to 1.21 (73 samples) 
1.25–2.5 Doubtful 1.50 (1 sample) 1.28–2.17 (14 samples) 
>2.5 Unsuitable Nil 2.56–3.33 (8 Unsuitable) 
Table 15

Groundwater suitability for irrigation on the basis of RSC index

ClassArea (km2)
% Area
PRMPOMPRMPOM
(a) Good 864.5 817.3 99.92 94.47 
(b) Moderately suitable 0.72 43.05 0.08 4.98 
(c) Unsuitable  4.80  0.55 
ClassArea (km2)
% Area
PRMPOMPRMPOM
(a) Good 864.5 817.3 99.92 94.47 
(b) Moderately suitable 0.72 43.05 0.08 4.98 
(c) Unsuitable  4.80  0.55 
Figure 15

RSC spatial distribution map for pre-monsoon (a) and post-monsoon (b) season.

Figure 15

RSC spatial distribution map for pre-monsoon (a) and post-monsoon (b) season.

Close modal

Assessing the GWQ for irrigation is extremely critical in regions with semi-arid and arid climate worldwide, especially in developing nations such as India. The findings of hydrogeochemical investigations conducted in the predominantly rocky semi-arid area of NSC were assessed by comparing with the WHO and BIS standards. The objective of the present study was to evaluate the suitability of water quality for drinking and irrigation usage. The pH was within the recommended limits of 6.5–8.5 during the PRM (83.06%) and POM (82.26%) seasons, respectively. A strong correlation coefficient was found between EC and TDS during PRM (r = 0.88) and POM (r = 0.90), respectively. The evaluation of the comprehensive water quality was done by utilizing the WQI, revealing that a higher number of water samples exhibited good and poor quality during the POM compared to the PRM season. The computed irrigation indices suggested that the GW in the NSC is good for agricultural use. Furthermore, concerning irrigation, it's feasible to identify appropriate areas for various crops based on the tolerance limits. However, the study's limitation is the sampling of 95 samples during both PRM and POM seasons in the entire catchment. There is a need for further investigation to determine the key physical, chemical, and biological indicators for monitoring GWQ. Additionally, to optimize the efficient utilization of GW in the examined area, it is strongly advisable to carry out a comprehensive study focused on allocating its usage across different sectors.

Dimple is thankful to the INSPIRE (DST-INSPIRE-Fellowship), Government of India and Soil and Water Engineering Department, CTAE, MPUAT, Udaipur, Rajasthan, India.

D. conceptualized the whole article, developed the methodology selection, and carried out formal analysis. P. K. S. overviewed the manuscript and methodology improvement. M. K. contributed to drafting the manuscript. K. K. Y. reviewed and edited the article. S.R.B. contributed to reviewing the draft. J.R. rendered support in data analysis and edited the article. A. participated in the manuscript language editing. All the authors have contributed significantly to this research work. All authors read and approved the final manuscript.

No external fund was received to carry out this work.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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