Groundwater quality analysis using different water quality indices in the hard rock aquifer region in semi-arid environment

An area's groundwater has a particular chemical makeup, and this composition can alter depending on things like the contact of rocks with water, temperature, mineral dissolution, the interaction of soil with water, the length of the interaction, and other anthropogenic tasks (Bera & Das 2021). Irrigation plays a vital role in crop yield quantity and quality. Agricultural irrigation practices primarily aim to improve crop yields (Foster & Perry 2010). India is regarded as one of the top countries in the world for extracting the most groundwater (89%) for irrigation, 9% for home requirements, and the remaining 2% for other industrial practices (Margat & Van Der Gun 2013). That is why it is of the utmost significance to have a thorough understanding of the hydrochemistry of water to accurately calculate the quality of the groundwater, particularly in rural regions, because this impacts the groundwater's suitability for irrigation, home, and industrial uses (Biswas et al. 2020; Bera et al. 2021). Many author's studies (Aravinthasamy et al. 2021; Hedjal et al. 2018; Kadam et al. 2021; Maghrebi et al. 2021; Rao et al. 2021; Şener et al. 2021; Mukhopadhyay et al. 2022) have been carried out previously regarding hydrochemistry and groundwater usability for agricultural use. Barati (2018) studied the groundwater quality by using soft computing model. The quality of groundwater relies on the composition of recharge, subsurface medium, and the climate (Bose et al. 2023). Ferhati & Mitiche-Kettab (2021) identified the mineralisation origin and distinguish between the different categories of groundwater quality in several areas of the semi-arid basin of Hodna in central Algeria. The quality of the water used for irrigation has a direct impact on crop productivity as well as land deterioration. As a result, a comprehensive groundwater quality assessment is critical for agricultural water management, proper food production, and understanding its usefulness for various needs (Malash et al. 2000). Also, climate change can have significant impacts on water quality for irrigation purposes like changes in precipitation patterns, rising temperatures can increase the temperature of surface waters, and changes in precipitation patterns can also lead to changes in the salt content of water sources used for irrigation. In regions where rainfall decreases, the concentration of salts in irrigation water can increase, making the water unsuitable for the crop growth. Barbieri et al. (2023) studied the climate change and its effect on groundwater quality in regional carbonate aquifers of Central Italy.

The quantity and the type of salt in the irrigation water affect its quantity and quality. Increased salinity, decreased permeability, and exposure to highly harmful ions are the three most significant problems associated with a decline in water quality (Singh et al. 2018). Consequently, assessments of irrigation water quality are characterized by its physico-chemical parameters utilizing imitative methodologies as developed by the United States Salinity Laboratory (USSL 1954; Elsayed et al. 2020) and Wilcox Diagram (Wilcox 1955; Elsayed et al. 2020). These methods are appropriate and widely used to estimate water quality for irrigation purposes. The availability of water for irrigation purposes is contingent on many factors, including water quantity and quality. However, the quality of water is typically disregarded when assessing its amount. Irrigation water quality generally is characterized by total dissolved solids (TDS), main cations, and major anions. Salinity-reduced permeability and enhanced specific ion toxicity are the three most prominent worldwide problems linked with poor water quality (Singh et al. 2018). However, whether water quality is excellent or poor is determined by more than just physical and chemical characteristics (Pham 2017).

Rajasamand and Udaipur are the districts of the state of Rajasthan; India comes under the semi-arid area with a mean annual rainfall (2010–2018) of 722.7 and 746.9 mm (Anonymous 2019–2020). The state's average annual precipitation in 2016 was 695 mm (CGWB 2017). The state's total groundwater recharge is 12.24 BCM, and its total extractable groundwater resource is 11.07 BCM. The state's annual groundwater extraction is 16.63 BCM, with a 150.2% stage. Of 295 evaluation units (blocks), 203 (68.81%) were deemed ‘Overexploited’, 23 (7.8%) ‘Critical’, 29 (9.83%) ‘Semi-Critical’, 37 (12.54%) ‘Safe’, and 3 (1.02%) ‘Saline’ (CGWB 2021; Dimple et al. 2023a). Sodium adsorption ratio (SAR), electrical conductivity (EC), Na %, residual sodium carbonate (RSC), permeability index (PI), Kelly's ratio (KR), and SSP assessed the fitness of the groundwater for irrigation. Piper and Gibbs's classifications were used to identify geochemical facies and assess the overall geochemical processes. Some authors have previously conducted research on groundwater quality and management in Rajasthan (Machiwal et al. 2011; Kumar et al. 2018a, 2018b; Sinha et al. 2018; Dahiphale et al. 2019). Also, machine learning algorithms have been employed to study the quality of irrigation water in the Nand Samand (NS) catchment (Dimple et al. 2022a, 2023b). The primary goal of this research was to determine whether the accessible groundwater was suitable for irrigation use in the NS catchment which covers two districts of Rajasthan, which are Rajsamand and Udaipur. Chemical testing of the water samples will enable us to determine numerous water quality indices required for irrigation, including %Na, SAR, SSP, RSC, EC, PI, and KR. This study can aid policymakers and local farmers create a sustainable groundwater management system for various agricultural operations. There has not been much extensive research in the NS catchment to assess the geochemistry and efficacy of water from the NS catchment for irrigation purposes. Because the NS catchment is used for irrigation, current research identifies the hydrochemistry of this water and classifies it to determine its suitability for irrigation. To evaluate groundwater quality and identify the evolution of hydrogeochemical processes, methods such as charts, statistical analysis, and geochemical simulations are used. Because no such study has previously been conducted in the studied catchment, this study will aid in understanding the quality of groundwater used for agriculture and will aid in the management of groundwater resources in the studied region.

The current study aims to quantify the quality of groundwater used for irrigation. The goal of the current study is to promote awareness of the importance of groundwater quality for agriculture, strengthen agricultural water management, enhance water quality, sustain food production, and ensure sustainable and healthy economic and social growth. An attempt was made to comprehend the hydrogeochemical parameters to develop an irrigation water quality index in the NS catchment. A total of 95 groundwater samples were collected in pre-monsoon (PRM) and post-monsoon (POM) seasons of 2019 and 2020 and analysed for major cations and anions. This study will benefit the residents of the region because it will serve as a guideline for the best use of groundwater for irrigation. The study's diagrammatic representation makes it simple for the common man to understand. This study employs not only the irrigation water quality index but also various other hydrochemical characterizations of the region using various diagrams such as the Piper diagram, United States Salinity Laboratory (USSL) diagram, Gibbs, Wilcox, and Chadha diagram. This article has been formulated as follows: authors have discussed the importance of the groundwater, quality of groundwater of irrigation use, and literature review in the introduction. Then, Methodology section introduces the architecture of the proposed indices with a proper explanation of the subsection. In addition, the authors have presented and discussed the results of the study in Results and Discussion section. Finally, the authors have concluded this article with limitations and future scope in Conclusion section.

Ninety-five open-well samples were taken in PRM and POM seasons (2019 and 2020) from the study area to analyse the water sample's suitability for irrigation purposes in the research area. Samples were collected 4/5 min after the start of the pump and stored in polyethylene bottles of 500 ml. Each sample was collected in sterilized plastic bottles. Different physico-chemical factors such as ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, and were estimated as per standard water analysis methods (APHA 2005).

To calculate the usability of groundwater for agricultural uses, the following parameters (Table 1) of irrigation quality were calculated:

Table 1

Water quality indices for irrigation

Irrigation indices .	Abbreviation .	Formula .	Equation number .
Residual sodium carbonate	RSC		(1)
Sodium absorption ratio	SAR		(2)
Percentage sodium	%Na		(3)
Permeability index	PI		(4)
Soluble sodium percentage	SSP		(5)
Kelly ratio	KR		(6)

Irrigation indices .	Abbreviation .	Formula .	Equation number .
Residual sodium carbonate	RSC		(1)
Sodium absorption ratio	SAR		(2)
Percentage sodium	%Na		(3)
Permeability index	PI		(4)
Soluble sodium percentage	SSP		(5)
Kelly ratio	KR		(6)

Note: The ionic concentrations are in meq/l, and the %Na and PI are in percent. The Piper plot, USSL diagram, and Gibbs diagram were also used to predict irrigation water quality.

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Categorizing IWQ based on SSP, RSC, %Na, SAR, KR, EC, and PI (Table 2). The widely used graphical USSL (1954), Wilcox, Piper, Chadha approaches, as well as several others, i.e., SAR, RSC, %Na, SSP, KR, EC, and PI, were opted for a deep understanding of the groundwater chemistry, together with its usability for agricultural purposes. Usually, loss of water through evaporation and declining groundwater levels, particularly in the hot season, uplift chemical element concentrations in groundwater, affecting both plants and soils (Adimalla &Venkatayogi 2018). This is a significant concern in most semi-arid areas dominated by rocks. The main issue is the excessive sodium content in water, which causes alkaline soil formation, and high salt concentration, which causes saline soil formation (Dimple et al. 2021).

Table 2

Groundwater irrigation suitability range

Irrigation indices .	Water class .
Alkalinity hazard (SAR)
< 10	Ex
10–18	***
18–26	**
> 26	*
EC (dS/m)
0.100–0.250
0.250–0.750
0.750–2.250
> 2.250	Not suitable
%Na
> 20	Ex
20–40	***
40–60	Acceptable
60–80	**
> 80	Not suitable
RSC (mEq/l)
< 1.25	Good
1.25–2.5	**
> 2.5	Unsafe
SSP
0–20	Ex
20–40	***
40–60	Acceptable
60–80	**
> 80	Not suitable
< 200	Suitable
> 200	Unsuitable
< 50	Good
> 50	Unsuitable
KR
< 1	Suitable
1–2	Marginally suitable
> 2	Not suitable
PI
Class I (>75%)	***
Class II (25–75%)	Suitable
Class III (<25%)	Not suitable for irrigation
< 80	Good
80–100	Moderate
100–120	Poor

Irrigation indices .	Water class .
Alkalinity hazard (SAR)
< 10	Ex
10–18	***
18–26	**
> 26	*
EC (dS/m)
0.100–0.250
0.250–0.750
0.750–2.250
> 2.250	Not suitable
%Na
> 20	Ex
20–40	***
40–60	Acceptable
60–80	**
> 80	Not suitable
RSC (mEq/l)
< 1.25	Good
1.25–2.5	**
> 2.5	Unsafe
SSP
0–20	Ex
20–40	***
40–60	Acceptable
60–80	**
> 80	Not suitable
< 200	Suitable
> 200	Unsuitable
< 50	Good
> 50	Unsuitable
KR
< 1	Suitable
1–2	Marginally suitable
> 2	Not suitable
PI
Class I (>75%)	***
Class II (25–75%)	Suitable
Class III (<25%)	Not suitable for irrigation
< 80	Good
80–100	Moderate
100–120	Poor

Note: Ex = excellent, *** = good, ** = doubtful, * = unsuitable.

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Based on statistical analysis, understanding the link and changes between the physico-chemical properties and ion concentration of groundwater samples, as well as interpreting the data and interaction, could be carried out (Meireles et al. 2010). A score between 0.5 and 0.7 indicates a moderate correlation between two geochemical parameters, whereas a value of 0.7 indicates a high correlation (Giridharan et al. 2008). The correlation matrix of the 11 analysed variables is given in Table 3 for PRM and POM seasons. The EC and TDS show a significant and positive correlation with ⁠, ⁠, ⁠, and during the PRM and POM periods, respectively. It is claimed that the presence of sodium in the research area significantly impacts TDS and EC. EC and TDS also show a positive correlation with sulphate, ⁠, and during the PRM period. Strong correlations were found between and ⁠, which shows that water–rock interaction, and mineral dissolution may be the main hydrogeochemical processes driving groundwater chemistry in the research area (Feng et al. 2020).

Table 3

Correlation matrix for different water quality parameters

Variables .	pH .	.	.	.	EC .	TDS .	.	.	.	.	.
Pre-monsoon season
pH	1
	0.235*	1
	−0.052	−0.141	1
	0.146	0.146	− 0.265**	1
EC	0.161	0.263*	− 0.358**	0.614**	1
TDS	0.142	0.255*	− 0.231*	0.514**	0.880**	1
	0.078	0.083	−0.056	0.091	0.230*	0.338**	1
	−0.024	0.080	−0.051	−0.027	0.202*	0.314**	0.683**	1
	−0.232*	0.041	−0.044	−0.112	0.094	0.275**	0.452**	0.467**	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
Post-monsoon season
pH	1
	0.202*	1
	0.005	0.014	1
	−0.053	0.102	0.175	1
EC	−0.144	− 0.206*	0.246*	0.420**	1
TDS	−0.075	−0.040	0.438**	0.484**	0.544**	1
	−0.030	−0.021	0.448**	0.527**	0.547**	0.909**	1
	−0.106	0.098	0.207*	0.403**	0.486**	0.788**	0.830**	1
	−0.188	0.227*	0.141	0.265*	0.452**	0.509**	0.469**	0.515**	1
	−0.149	0.146	0.126	0.082	0.125	0.085	−0.003	0.067	0.330**	1
	0.043	−0.042	0.015	−0.108	0.026	−0.146	−0.056	−0.108	−0.151	−0.023	1

Variables .	pH .	.	.	.	EC .	TDS .	.	.	.	.	.
Pre-monsoon season
pH	1
	0.235*	1
	−0.052	−0.141	1
	0.146	0.146	− 0.265**	1
EC	0.161	0.263*	− 0.358**	0.614**	1
TDS	0.142	0.255*	− 0.231*	0.514**	0.880**	1
	0.078	0.083	−0.056	0.091	0.230*	0.338**	1
	−0.024	0.080	−0.051	−0.027	0.202*	0.314**	0.683**	1
	−0.232*	0.041	−0.044	−0.112	0.094	0.275**	0.452**	0.467**	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
Post-monsoon season
pH	1
	0.202*	1
	0.005	0.014	1
	−0.053	0.102	0.175	1
EC	−0.144	− 0.206*	0.246*	0.420**	1
TDS	−0.075	−0.040	0.438**	0.484**	0.544**	1
	−0.030	−0.021	0.448**	0.527**	0.547**	0.909**	1
	−0.106	0.098	0.207*	0.403**	0.486**	0.788**	0.830**	1
	−0.188	0.227*	0.141	0.265*	0.452**	0.509**	0.469**	0.515**	1
	−0.149	0.146	0.126	0.082	0.125	0.085	−0.003	0.067	0.330**	1
	0.043	−0.042	0.015	−0.108	0.026	−0.146	−0.056	−0.108	−0.151	−0.023	1

Note: Significant values are given in bold. **Correlation is significant at the p < 0.01 level (two tailed). *Correlation is significant at the p < 0.05 level (two tailed).

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The mineral composition of irrigation water has a substantial effect on crop productivity. The presence of an excessive amount of dissolved ions in irrigation water may inhibit crop output. Rainfall inconsistency increased farmers' reliance on groundwater for irrigation. As a result, proper irrigation water management should be prioritized. Several indices and chemical characteristics are used to examine the process that controls groundwater chemistry and to assess irrigation water quality.

Table 4 shows the groundwater categorization based on EC data (Richards 1954) and the percentage of samples that fall into a particular class. Salinity hazard (EC) presented in Table 4 revealed that about 9.28% of the total study area had good-quality water in the POM period. About 40.17% area in PRM and 90.48% in the POM period show water quality in the doubtful range. Further, 59.83 and 0.24% of areas are unsuitable for irrigation in PRM and POM periods, respectively.

In the present research, values of collected samples were found under excellent categories in PRM and POM periods, respectively (Table 5). Barua et al.(2021) revealed similar results for Dakshin, West Bengal, India; the study region had SAR values in the range of 0–5, representing that all of the water has less SAR and is thus appropriate for irrigation. Bhange et al. (2018) also reported that all samples have low-sodium hazards for the Konkan region of Maharashtra based on the SAR values. Kumar & Maurya (2023) reported the same results of the SAR index in their study, which also confirms the findings of our study.

Table 5

Classes of sodium risk based on USSL classification

SAR .	Sodium hazard class .	Water class .	PRM samples .	POM samples .
<10			0.891–8.384 (all 95 samples)	0.149–4.415 (all 95 samples)
10–18		***	–	–
18–26		**	–	Nil
>26		*	–	–

SAR .	Sodium hazard class .	Water class .	PRM samples .	POM samples .
<10			0.891–8.384 (all 95 samples)	0.149–4.415 (all 95 samples)
10–18		***	–	–
18–26		**	–	Nil
>26		*	–	–

Note: Ex = excellent, *** = good, ** = doubtful, * = unsuitable.

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From Table 4, it can be seen that KR is <1 (good for irrigation) for 97.62 and 98.23% of the total research area in the PRM and POM seasons, respectively. In contrast, KR is >1 (unsuitable for irrigation) for 2.38 and 1.77% of the total research area in the PRM and POM seasons, respectively, due to alkali hazards. These results are in close confirmative with the results of Kumar et al. (2018a).

Table 6 shows that all samples in the research area have RSC values less than 1.25 (except 15 samples which are in a doubtful category during PRM and eight samples are in unsuitable class during POM), pointing that the complete study area is under the safe limit for agriculture use during irrigation, both PRM and POM, respectively. RSC in groundwater ranges from −29.24 to 1.50 and −22.33 to 3.33 in the PRM and POM periods, respectively. A negative RSC suggests that sodium build-up is unlikely due to high calcium and magnesium carbonates. A positive RSC implies a risk of salt build-up in the soil.

Nag & Das (2017) revealed similar findings; it was discovered that during the PRM period, most of the water samples in the research area were within the safe zone during the POM period, and half of the groundwater samples were in the unsafe zone.

Table 4 shows that %Na values below 20 are 0.47 and 19.34% for PRM and POM periods, respectively. This indicates that the water in the research area falls in the excellent class for agriculture use. %Na values between 20 and 40 are 22.20 and 78.52% for PRM and POM periods, respectively. This states that the water in the research area is safe for irrigation use. %Na values between 40 and 60 are 76.86 and 2.14% for the PRM and POM periods, respectively. This shows that the water in this region falls in the permissible class for agriculture use. %Na values between 60 and 80 are 0.47% for the PRM period. This suggests that the water of research area falls in the doubtful class for agriculture use. Due to long residence time of water, dissolution of minerals from lithological composition, and the addition of chemical fertilisers by the irrigation waters, the Na% was greater during PRM than POM (Subba Rao et al. 2002; Vasanthavigar et al. 2010).

As a life-sustaining component, groundwater is of the utmost importance. Most irrigation suitability indicators and graphs revealed that POM samples are more suited for irrigation than PRM ones. Salinity hazard (EC) data of the study show that about 40.17% area in PRM and 90.48% in the POM period show water quality in the doubtful range. A total of 98.23% area falls in good water quality according to KR values during POM seasons. Similarly, %Na, PI, and SSP indices show that the 78.45, 97.45, and 95.93% falls under the good water quality for irrigation use during the POM season. The PRM samples exhibit poor quality in greater percentage when compared with POM due to overexploitation of groundwater, direct discharge of effluents, and agricultural impact. The Piper's diagram and Gibbs plot illustrated that rain contributes significantly to POM groundwater and that its chemistry is dominated by salt dissolving in the unsaturated zone. The US Salinity Laboratory figure illustrated that the maximum groundwater samples collected during the PRM season were classified as C3S1 or C4S1, indicating groundwater safe for irrigation. Collected water samples fall in C3S1 and C3S2 (during the POM season) classes are of moderate quality and suitable for irrigating semi-tolerant crops. Piper's diagram and Chadha's plot of groundwater geochemical properties demonstrate that POM groundwater has an important input from rain and that the chemistry is dominated by salt dissolution in the unsaturated zone. Water–rock interaction and ion-exchange mechanisms were more dominant during the PRM period. The %Na and RSC data also indicated that the groundwater is safe for agricultural use. The quality assessment for agricultural suitability reveals that the area's groundwater falls into the good to moderate category and may be used for irrigation. At some locations, elevated salinity, RSC, KR, SSP, and sodium percent limit the usefulness of groundwater for irrigation purposes and need the development of a particular management plan for the area. The study has limitation that the analysis was done for PRM and most monsoon seasons; however, the water quality could be affected during different months. Also, another study could be taken to find the relationship between the water quality in open wells/tube wells and the river streams. The current study had utilized certain graphical illustration to evaluate the groundwater quality; however, many other methodologies are available in the literature, which can be explored for better assessment. The pollution of the aquifers from the point and non-point sources should be carried out to frame different management strategies.

The first author (Dimple) acknowledges the Department of Science and Technology, INSPIRE (No: DST/INSPIRE Fellowship/[IF180496]), Government of India for this work and Department of Soil and Water Engineering, CTAE, MPUAT, Udaipur, Rajasthan, India.

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

The authors declare there is no conflict.