GIS-Based Hydro-Geo-Chemical Study of the Groundwater and Its Multivariate Statistical Assessment for Drinking and Irrigation Suitability in Dumka Region, Jharkhand, India

Abstract

The present study deals with the state-of-art analysis of hydro-geo-chemistry along with the multivariate statistical analysis of the groundwater in Dumka, Jharkhand. Sixty-one samples were assembled for the month of pre-precipitation (Feb–May) and post-precipitation (Oct–Dec) each to examine the water's compliance for drinking and irrigation purposes. An organised framework has been utilized for the study of water standards and its impact evaluation. This includes the multivariate analysis of physicochemical parameters. Arc-GIS Inverse Weighted Method has been utilized for spatial interpolation. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) was used for detailed analysis of the water dynamics. The results from the descriptive statistics shows average value of TDS and EC ranges between 182–183 ppm and 366.88–358.67 µS/cm respectively. It indicates the groundwater is majorly govern by rainwater chemistry for both pre and post precipitation. The slightly acidic value of pH i.e. 6.9 in the post precipitation seasons supports this inference. Gibbs plot informs about the rock-water interaction dominance in the region. In Piper plot, major-cation follow the sequence as Ca⁺² > Mg⁺² > Na⁺ > K⁺ while the anions follow the trend as Cl⁻ > HCO₃⁻ > SO₄⁻² > CO₃^-2. Mostly the samples fall under the Ca-Mg-Cl₂ type. The ratios obtained and ionic patterns shows the carbonate weathering and selective ion exchange, as the main drivers for this ionic composition in water. HCA classifies 6 and 5 clusters for both seasons respectively. PCA plot reveals longer arrows for Na, SAR contributing significantly to PC1 along with Cl and F. Correlation matrix revealed strong correlation between TDS, EC and Cl which is quite alarming for the area, signifying the degradation of the water quality. The Entropy Weighted—Water quality Index for Jarmundi, Shikaripara zone and jamua was extremely poor.

Article Highlights

The present study deals with the state-of-art analysis of hydro-geo-chemistry along with the multivariate statistical analysis of the groundwater in the Dumka district of Jharkhand.

It indicates the groundwater is majorly govern by rainwater chemistry for both pre and post precipitation.

The major-cation follow the sequence as Ca⁺² > Mg⁺² > Na⁺ > K⁺ while the anions follow the trend as Cl⁻ > HCO₃⁻ > SO₄⁻² > CO₃^-2. Mostly the samples fall under the Ca–Mg–Cl₂

The ratios obtained and ionic patterns shows the carbonate weathering and selective ion exchange, as the main drivers for this ionic composition in water.

Correlation matrix revealed strong correlation between TDS, EC and Cl which is quite alarming for the area as it signifies the degradation of the water quality.

Entropy Weighted—Water quality Index was found to be from an excellent range to extremely poor.

Introduction

Water is one of the most important parameters for the development of human resources. As, groundwater is the key to preserve the natural ecosystem (Ren et al. 2022). Thus, monitoring water quality lays the foundation for not only water quality security. But also identifies secure, long-term sustainable water resources. Ion exchange mechanisms, interaction of aquifer materials, reactivity of water with rocks and sediments, land use patterns, and groundwater chemistry. Elements like these contributes to the dynamic nature of water. India, an agricultural hub with a leading position in global population. It is the world's highest consumer of groundwater with the annual extractions of 230 km³, and increasing (Karmakar et al. 2021). The exponential population growth, climate change, and urbanization have led to fierce competition for fresh water. Also, due to intensive irrigation there is enhancement in percolation of return flow, leading to accumulation of salts in the irrigation flow paths (Vengosh and Rosenthal 1994; Cruz-Fuentes et al. 2014), and contamination with higher nitrogen contents.

Both natural and anthropogenic factors are responsible to exploit these governing elements of groundwater resulting degradation. Firstly, through geo-chemical reactions in phreatic water and soils. It includes oxidation, chemical speciation, and the dissolution of chemical agents in water-bearing rock formations (granite, gneiss) and minerals (Diédhiou et al. 2023). Secondly, during irrigation when water is routed through improper drainage systems. Like indiscriminate dumping of industrial wastewater, sewage sludge, and solid waste without any pre-treatment. Water distribution drainage networks are susceptible to intrusion due to large spatial distribution, ageing pipes and branched structure) (Tomaz  et al. 2020). These factors not only govern the source of water, its elemental constituents. But also, the soil type, salinity tolerance, attributes of plants, and the soil's drainage features. Hence, groundwater becomes an important factor for freshwater sustenance, agricultural production and in food security (Rivas et al. 2020).

Previous studies done in India (Bharat) does not provide a complete view on the groundwater quality study. For instance, study done by Bajwa et al. (2015) utilising the correlation matrix which helps understand the groundwater chemistry with the uranium distribution throughout the area. Prajapati and Bilas (2018) utilised Water Quality Index (WQI) for assessing the water quality of the Holy River Ganga. The study concluded poor quality water in Pindra and Cholapur stations due to bad sanitation and cleanliness. Research carried out in Nalbari, Assam by Jain and Vaid (2018), assess the groundwater for drinking and irrigation purposes. Another study done by Kaur et al. (2019) identifies the dominant hydro chemical facies of groundwater with the inference of supersaturation of water samples with carbonate minerals. Thus, concluding water to be unsuitable for drinking purposes. Chakravarty and Gupta (2021) utilises the multivariate analysis for the assessment of water quality of a hilly river of south Assam. Many other researches have been done which majorly focuses on fewer objectives. But to understand water quality and the processes that affect groundwater. These include both geogenic and anthropogenic processes. Researchers should use both qualitative and quantitative approaches. Moreover, there is very little data availability for the remote districts emphasizing groundwater study. No proper systemic hydro-geochemical approach is undertaken to evaluate and interpolate the groundwater in these regions. It creates a huge gap in analysing the problem which blurs reality and delays solutions.

The present study aims to address these research void. Given that, it is essential to have a profound integrated understanding of hydrogeology and hydro-geo-chemistry of the area. This help in effectively managing groundwater resources and reducing land loss. To overcome the difficulty of the integration, interpretation and representation of the large amount of data set. The best-suited route is the study of hydro-geochemistry accompanied by multivariate statistical analysis (MSA) of groundwater parameters (Steinhorst and Williams 1985) (Adams et al. 2001; Cloutier et al.  2004, 2008). The combination of graphical and statistical techniques offers clear advantages in evaluating chemical data. Numerous studies have used these hydro-geo-chemical approach to pinpoint the evident regulating factors: (i) hydro geochemistry. (ii) rock types and (iii) mineralogy (Murray 1996; Rawat et al. 2017; Ukah et al. 2019; Egbueri et al. 2021). It provides an objective way to cluster and interpret large data sets. This technique is popularly used in the study of soil characteristics, surface and groundwater quality to elucidates the geochemical evolution of the groundwater. Further, the use of advanced entropy-WQI with correlation matrix allows grouping of the groundwater samples and making correlations between chemical parameters (Ganiyu et al. 2021). After classification of different subgroups of data sets to identify the geochemical processes controlling groundwater chemistry. For the spatial interpolation of the region Arc GIS is the best tool that enables to combine geographic data with laboratory sample evaluations in an innovative manner. It is an effective technique for water quality monitoring (Saraf and Choudhary 1998; Latha et al. 2010; Ilayaraja and Ambica., 2015; Oseke et al. 2021). The spatial analyst’s tool Inverse Distance Weighted (IDW) method of ArcGIS (version 10.8) is mostly employed.

After reviewing the existing literature and identifying gaps in the understanding of groundwater quality. This research presents a comprehensive and critical analysis of the groundwater in Dumka, a remote district in Jharkhand, India. The study focuses on the comparative discussion of pre and post precipitation season taking in consideration of all the parameters discussed i.e.

1. multivariate statistical analysis of the physico-chemical parameters,

2. Elaborative discussion on hydrogeochemical facies and Irrigation water indices,

3. advanced Entropy Weighted-Water Quality Index (E-WQI) with the correlation matrix.

A quantitatively independent approach for groundwater classification offers a novel perspective for this research. The urgent need to secure, sustainable and safe water resources drive the motivation for the research. From comprehending various processes to identifying factors influencing groundwater quality. This study provides measurable information to stakeholders and the scientific community. This study, like all studies, depicts a changing condition. It must be monitored to address the issues of climate change and sustainable development.

Study area

The research study was conducted in Dumka district of Jharkhand (Fig. 1). Located in the north-eastern region, overseen by the Santhal Pargana Commissionaire. It extends from North Latitude 23°47′20″ and 24°38′57″ with East Longitude 86°28′25″ and 86°42′16″. The total area of the district is 3716.02 km² with a total of ten blocks. The population of about 1,321,442 out of which 668,514 are male and 652,928 are female (UADAI, Government of India 2011). The topography, nature of soil, and climate in India varies across the nation. The crops grown and nutrient requirement also differ. Soil area is the result of in situ weathering of the crystalline basement which contains alluvial scrap and laterite soil (Sathi Planners Private Limited 2018). Hence, the quality of the soil also influences the groundwater. Area exploration becomes an important factor.

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Fig. 1

A India map showing Jharkhand. B Jharkhand map highlighting districts. C Dumka district showing the sampling points

Climate and Physiography

The köppen climate is of “cwa” type. In this monsoon is influenced with humid subtropical climate. Three distinct months are prevalent, (1) dry winters (Nov–Mar), (2) hot summer (lasts until the middle of Jun), (3) Precipitation month (Middle of Jun–Sep). In summer, the average temperature ranges from 42 to 46 °C. Evaporation rates are impacted by solar radiation and factors like temperature, wind, and atmospheric pressure. The evaporation potential is considerably high in summers. Majorly due to evapotranspiration, raindrops and water surfaces like seas and lakes can also experience evaporation. This can also occur when water settles on vegetation, soil, rocks, or snow. Many times, human activities also contribute to evaporation. Even the surfaces of heated buildings undergo evaporation of settled water. While in winter temperature drops to about 4 °C. Due to low humidity the evaporation is very low. The rainfall is primarily due to the south-west monsoon and about 50–60% is the relative humidity. 80% of the rainfall occurs in the monsoon period of (May–Sep). Block-wise the rainfall is diverse ranging from 776.12 to 1351.07 mm. The typical slope of the geographical region stretches from North West to South East. The valleys incorporate small villages encircled by cultivated clearing. The hills in most areas are heavily forested. Long undulating ridges give rise to a rough, coarsely dissected topography. Wherein the drainage channels flow, characterizes the landscape of the district. Geographically, three divisions can be made in the district based on physical and genial units: the Peneplain flat country, hilly areas, undulating terrains, and the valleys. Nearly 15% of the total geographical area, 3716 km², is considered as net sown area. Forests have 13%, and the remainder accounts for cultivable waste, pasture, barren, and other agricultural uses. The total cultivable and arable waste land accounts for 2325.35 hectare (Soil analysis, Dumka 2009).

Soil Type and Drainage

In-situ weathering of the crystalline basement is responsible for the formation of the soils in the Dumka district. Soil formation is influenced by climate, topography, and vegetation. Four types of soils are found namely, alluvial soil, eroded grey scrap soil, laterite, and Forest soil. The alluvial soil is deposited in the crystalline bed rocks, spread over the river channels. Alluvial material is primarily composed of silt and sand mixed with clay. The entire area is covered in eroded grey scarp soil, forming an inconspicuous cover over granitic/gneissic rocks. Pleistocene laterites can be identified as patches in laterite, which is pellet-like. The lateralization of the weathering minerals produces the lateritic soil. They have a low holding capacity and are highly permeable. The laterites are typically brownish-red, porous, and pitted in nature. It serves as an excellent reservoir for groundwater. The reserve forest region is the sole location where the forest soil is accessible. It has an organic matter surface layer. The pH value of all the soils present, varies in the range of 5 to 6.5, indicating their acidic nature. The Mayurakshi Reservoir Project is the only noteworthy irrigation project in the district. Rivers of various sizes cut deeply through the Dumka district. The Brahmani, Bansloi, and Mayurakshi rivers, as well as multiple of their tributaries, compose the district's primary drainage system. Dendritic drainage pattern is present which resembles the branches of tree. In this, the tributaries join the main river like branches of tree, forming a network which efficiently drains water from the watershed. (Valjarević 2024). During summer the river’s surface flow dries up. Yet, the sub-surface flow indicates its outflow nature (Soil analysis, Dumka 2009; Sathi Planners Private Limited 2018).

Geomorphology

The Dumka district is predominantly formed by the Archean age group. It comprises the Chotanagpur Granite-Gneiss, the Proterozoic and Eastern Ghats (super Group). They are curtained by the Gondwana Super Group, Basaltic lava flows of Rajmahal and at places Laterites (DEIAA 2016) (Fig. 2). A major lithological unit in the area is represented by the Chotanagpur Gneissic complex. The biotite gneisses and granite gneisses give rise to small hillock mounds with porphroblastic texture. At various sides traversed by pegmatite and quartz vein. Amphibolites and mica schist enclaves can be found. The patches of acidic to basic charnockite and khondalite/Garnet Sillimanite-Biotite Gneiss represents the Eastern Ghats Supergroup. The Precambrian formations are overlain by Gondwana formations. The strip running in the NW–SE direction is the Barakar, Talchir, and Dubrajpur formations. The Talchir and Dubrajpur formations consist of sandstone and shales. Barakar formation consist of white to fawn colored sandstone, grit, and carbonaceous shales, thin lenticels, and streaks of coal are found in this formation. The Rajmahal traps consist of basaltic lava flows with sedimentary intertrappeans further overlapping the Precambrian formations/Gondwana formations. Some of the lava flows are vesicular and cavities are filled by calcite and chalcedony. The laterites are found in patches over Rajmahal traps. The rocks in the area have undergone extensive metamorphosis. Granite and other nearby rocks that have been exposed demonstrate complex folding. According to a morphotectonic study, the region has undergone several of tectonic deformation stages. It may have resulted in a variety of sets of fractures, fissures, faults, etc. that lead to lineaments (Soil analysis, Dumka, 2009).

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Fig. 2

A Resource map of Dumka; B geological map Dumka, Jharkhand

Hydrogeology

The main source of groundwater in the district is rain. Water depth and level changes depend on rainfall, land shape, and rock type. Other factors also play a role. Geological frameworks are the traits of rock formations, like porosity and permeability. They control groundwater's occurrence and movement in the region (Moritz et al. 2006). A hydraulic system in the rock and a fracture system has a similar relationship. A rock with two-phase brittle deformation can act as one hydraulic system. This is true for well-connected fracture systems. Hydrologists may handle fractured rocks as porous media in hydrological calculations (Larsson 1984). The district is divided into three hydrogeological units. The morphogenetic, geology, and aquifer potential form the basis for this. They are: 1. Merged or fissured formations. 2. Semi-consolidated and unconsolidated formations or porous formations. 1. Rajmahal Trap and Consolidated or Fissured Formations from the Precambrian. 2. Gondwanan semi-consolidated formations. 3. Laterites and alluvium are porous or unconsolidated deposits. Most aquifers in the district are fissured or hard rock. They are the Chotanagpur Granite Gneissic Complex and Rajmahal Traps. But, in remote areas, laterites, valley fill, and Gondwana rocks can create aquifers (Bonyadi et al. 2011).

Methodology

Sampling and preservation

The groundwater was sampled for the pre- and post-precipitation month in Feb–May and Oct–Dec respectively. A total of 122 samples were collected from entire region. The physicochemical analysis was conducted following APHA guidelines (APHA, Federation 2005). Random sampling technique was adopted for the selection of water sources. After arriving at the location, it was more desirable to collect water samples from those living in the vicinity. The water used for irrigation and domestic consumption was taken. Garmin GPS has been used to locate the latitude and longitude of the sample. The sample was taken after 4–5 times washing sampling bottles from sample water. Majority of samples were taken from hand pumps while others were from wells up to 120 ft. Wells and handpumps were in typically good condition. But all wells were mostly open.

Each sample was collected in a high-density polyethylene bottle. Afterwards, nitric acid was used to preserve the samples (pH < 2) by filtering 100 ml using a 0.45 µm filter. The samples were then stored at 4 °C to avoid major chemical alteration. The basic parameter like a pH and EC were measured on-site. It was further verified with pH meter (Cole Parmer P200) and a Conductivity meter (Elico CM180). The remaining analysis were done within two weeks of the sample collection (Table 1). The distilled water used for drinking in the local area was taken as blank. The samples were kept in dry, cool place away from the sunlight.

Table 1. Method of measurement of various water quality parameters

Parameters	Measuring techniques (method/instruments)
pH	Cole Parmer P200
TDS (ppm)	Elico CM180
EC (µS/cm)	Elico CM180
ORP (mV)	Cole Parmer P200
DO (ppm)	Winkler’s method
Total hardness (mg/l)	Titrimetric method
Salinity (mg/l)	Portable salinity sensor
Ca+2 (mg/l)	Complexometric EDTA method
Mg+2 (mg/l)	Complexometric EDTA method
K+ (mg/l)	Flame photometry
Na+ (mg/l)	Flame photometry
F− (mg/l)	Ion selective sensors
Cl− (mg/l)	Mohr’s technique
HCO3− (mg/l)	Acid titration technique
NO3− (mg/l)	Spectrophotometric technique
SO4−2 (mg/l)	Gravimetric technique
PO4−3 (mg/l)	Spectrophotometric technique

The study presents a comparative yet comprehensive picture of the two seasons. An organised framework study is undertaken for the research work. The laboratory examination of the water sample employed conventional diagrams for data interpretation. The graphical representation utilizes the geochemical data from the sample collected. The information regarding the hydro-geo-chemical facies, Irrigation Water indices have been provided in supplementary file 1 (Singh et al. 2020).

The analytical accuracy for major ions in all water samples was computed according

1

$$ICE\left(\%\right)=\sum _i^m{Z}^{+}-\sum _i^m{Z}^{-}/\sum _i^m{Z}^{+}+\sum _i^m{Z}^{-}$$where ICE% is the ionic charge error percentage between − 5 and + 5% which is compatible with the accepted value of uncertainty. All the concentrations were taken in meq/l.

Multivariate Statistical Analysis and Data Preparation

Multivariate Statistical Analysis is a quantitatively independent approach for groundwater classification which allows grouping and correlations between chemical variables and sample source. This helps in identifying the important factor contributing to the similarities between them. An attempt was made to solve the hydrogeochemical study with the help of multivariate analysis. Thus, each sampling site was characterized with numerous physicochemical variables.

The descriptive analysis of parameters include minimum, maximum, standard deviation, mean, median, mode, skew and kurtosis. Skew measures the degree of asymmetry in the samples. Kurtosis explains about the flatness or degree of peaked-ness in the samples. The negative–positive values signify left and right incline of the data respectively.

Multivariate statistical study is popularly used in the hydrological study as it helps to understand the soil properties along with groundwater and surface water quality. The principal component analysis and hierarchical cluster was performed 122 groundwater samples of the pre and post precipitation period. Hierarchical cluster analysis clusters the objects according to the properties. The Ward technique was employed to find commonalities in the chemistry of groundwater, the joining rule. It assesses the separation between clusters after applying the analysis of variance method. A distance can be expressed by the difference between the analytical values from the two samples, and the squared Euclidean distance often indicates how similar two samples are to one another.

Principal component analysis (PCA) reduces data sets while preserving as much information as possible (Singh et al. 2017). They offer the relationships between the groundwater's hydro-chemical constituents. The remaining variance in the data set is then explained by the other components, with the first PCA accounting for the majority of the total variance. Before interpretation, only the eigen value > 1 was chosen, and it was then rotated by varimax.

For 8 samples some data were missing. In MSA, the missing value for any parameters gets automatically removed from the analysis. Hence, in order to avoid the reduction of sample size, loss of information and bias in the result. The imputation method was employed. Imputation method preserves the data by replacing the missing data with an estimated value of other available information. Average values were taken for the missing data. And a total of 122 samples were finalised and evaluated for 13 parameters. Further the data was standardized, after log transforming. The value obtained was subtracted with mean and divided with standard deviation (2). This is done to eliminate effects from differences in units before analysis. Some parameters were not undertaken in account like the addictive parameters were EC, TDS, and some parameters which show slight variation in range like pH (Cloutier et al. 2004, 2008). The parameters include Ca, Mg, Na, K, Cl, HCO₃, SO₄, PO₄, F, NO₃, TH, SAR, Na%.

2

$$\text{Standardization}\left[\text{S}\right]=\left(\text{Xi}-\text{x}\right)/\mathrm{\varphi }$$where Xi is ith value, x is the mean value and ɸ is standard deviation.

Arc-GIS-(IDW)

The excel file of the experimental data was transformed into a shape file. Then each interpolated cell was calculated using excess data for each parameter. The district network was used to create mask. The Arc GIS 10.8 spatial anaysts tool, IDW extension was used. The IDW utilises linear combination of weighs at known points to estimate unknown locations. That is, values at unknown locations $\widehat{M}(S_0)$ were determined by the weighting value α_i(S₀) and values at known locations M(S_i) expressed mathematically as shown in Eq. (3) (ESRI 2015) (Ijumuluna et al. 2020).

3

$$\widehat{M}\left(S_0\right)=\sum _{i=1}^n{\alpha }_{\text{i}}\left(S_0\right).M(S_{\text{i}})$$However, the weights α_i(D₀) were estimated through inverse distance from all points to the new points by applying Eq. (4),

4

$$M_i\left(S_0\right)=\frac{\frac{1}{\beta d\left(S_0,S_1\right)}}{\frac{1}{{\sum }_{i=0}^n\beta d\left(S_0,S_1\right)}};\alpha >1$$where α_i is the weight for neighbour i (the sum of weights must be unity to ensure an unbiased interpolator), d (S₀,S₁) is the distance from the new point to a known sample point, α is the coefficient used to adjust the weights, and is the total number of points in the neighbourhood analysis.

Water Quality Index-Entropy Weighted (EWQI)

The water quality index, which is frequently used to determine whether groundwater is suitable for domestic use, has been enhanced and expanded upon by the entropy-weighted water quality index. This expansion was done by the Shannon, who measured the uncertainty, which is missed in the other indices (Shannon 1948). The EWQI can be calculated as:

(1) Obtaining the eigenvalue matrix Y with the normalized physicochemical data that can be obtained

5

$$\mathit{Y}=\left[\begin{array}{ccc}{\mathit{y}}_{11}& \cdots & {\mathit{y}}_{1\mathit{j}}\\ ⋮& \ddots & ⋮\\ {\mathit{y}}_{\mathit{i}1}& \cdots & {\mathit{y}}_{\mathit{ij}}\end{array}\right]$$

6

$${\mathit{y}}_{\mathit{kl}}={\mathit{x}}_{\mathit{kl}}-{({\mathit{x}}_{\mathit{kl}})}_{\mathit{min}}/[{\left({\mathit{x}}_{\mathit{kl}}\right)}_{\mathit{max}}-{\left({\mathit{y}}_{\mathit{kl}}\right)}_{\mathit{min}}$$where i is the number of samples and j is the number of index, respectively, x_kl signifies the value of parameter k of sample l and, (x_kl) _max and (x_kl) _min present the maximum and minimum of sample parameters, respectively.

(2) The entropy weight of each parameter is to be calculated as:

7

$$M_{\mathit{kl}}=y_{\mathit{kl}}/\sum _{l=1}^i{y}_{\mathit{kl}}$$

8

$$E_J=-\frac{1}{\mathit{lni}}\sum _{k=1}^i{M}_{\mathit{kl}}\ast ln{P}_{\mathit{kl}}$$

9

$$W{e}_J=\left(1-E_J\right)/\sum _{k=1}^j(1-E_J)$$where M_kl is the parameter ratio k of sample l, E_J signifies the parameter information k, and We_J is the entropy of the weight of parameter l.

(3) The quality rating scale Q_J of every sample is obtained by

10

$${\text{Q}}_{\text{J}}=100\ast {\text{S}}_{\text{J}}/{\text{P}}_{\text{J}}$$where S_J signifies the value of a parameter of each sample and P_J is the permissible limit of each parameter recommended by the national standard or World Health Organization (WHO).

(4) EWQI value computing: Wej*Qj

11

$$\text{EWQI =}\sum _{l=1}^m{W}_J\ast Q_J$$The values obtained can be further classified into five categories based on the ranges-Excellent quality (EWQI≤ 50), good quality (50–100), Medium quality (100–150), Poor Quality (150–200), Extremely Poor Quality (200 and above).

Results and Discussion

Physico-Chemical Analysis

The descriptive analysis of data for the pre- and post-precipitation period is given in Table 2A, B (Standard I 2012; Federation 2005). It includes minimum, maximum, skew, kurtosis, standard deviation with mean, median and mode. The temperature of the samples was found to be around 28 °C showing the average temperature of the study area. The value of pH ranges from 7.1 to 8.6 with 7.89 as the mean value for the pre-precipitation month (Fig. 3a). Whereas the post-precipitation value ranges from 6.1 to 8.4 and the average comes from about 7.66 (Fig. 4a) (Table 2) The value comes slightly alkaline for the pre-precipitation when compared with post precipitation. The pH value is in range of irrigation water i.e. between 6.5 and 8.4. Thus, maintaining nutritional balance (Gautam et al. 2015). 

Table 2. Descriptive analysis of data for the pre-precipitation and post-precipitation

	Min	Max	Mean	Median	Mode	Skewness	SD	Kurtosis
(A) Descriptive statistical analysis of pre-monsoon Dumka
pH	7.1	8.6	7.893443	8	8	− 0.47829	0.346828	0.023391
TDS (ppm)	90	420	183.4426	170	140	1.068069	68.45644	1.137422
EC (µS/cm)	180	840	366.8852	340	280	1.068069	136.9129	1.137422
Salinity (ppm)	81	378	165.0984	153	126	1.068069	61.6108	1.137422
Total hardness (ppm)	118	524	254.2459	228	228	0.706341	102.4654	− 0.29756
Ca hardness	74	344	158.8525	140	80	0.785156	69.84741	− 0.25903
Mg hardness	2	308	95.39344	86	70	1.310828	56.60574	2.576568
Na (mg/l)	10.07	142.6	49.08934	34.76	40	1.4593	38.86263	0.952687
K (mg/l)	0.12	12.01	2.823934	1.97	2.87	1.861833	2.650975	3.090118
Bicarbonate	29	210.9	99.11967	90.2	124.3	0.425966	35.09932	0.130342
Fluoride (ppm)	0.15	1.2	0.503279	0.48	0.46	0.54698	0.236197	0.408267
Chloride(ppm)	12	194	113.6066	114	124	− 0.00663	32.26313	1.833338
Nitrate (mg/l)	1.8	26.67	15.72016	17.32	14.3	− 0.55556	5.987173	− 0.13929
Sulphate (mg/L)	1.71	210.64	58.41656	46.89	84.24	1.475606	41.5797	3.047366
Phosphate (mg/l)	0.1	0.97	0.390328	0.3	0.3	1.002544	0.192847	0.810417

	Min	Max	Mean	Median	Std. dev	Kurtosis
(B) descriptive statistical analysis of post monsoon dumka
pH	6.9	8.4	7.665574	7.7	0.365096	− 0.73218
TDS (ppm)	84	415	183.918	166	69.10169	0.849485
EC (µS/cm)	175	832	358.6721	340	129.5323	1.651612
Salinity (ppm)	65	325	166.8689	156	57.43793	− 0.2152
Total Hardness(ppm)	52	785	254.1475	225	131.5644	4.382856
Ca hardness (ppm)	55	424	158.7049	135	78.17104	1.547846
Mg hardness (ppm)	1	452	99.93443	81	77.21051	7.525924
Na (mg/l)	9.56	140.45	46.96508	32.89	38.12481	1.08015
K (mg/l)	0.09	11.78	2.412131	1.72	2.419172	4.768287
Bicarbonate (ppm)	29	245	105.1295	112.5	39.09575	2.2851
Fluoride (ppm)	0.1	1.02	0.43623	0.41	0.245596	− 0.22254
Chloride (ppm)	10	192	108.5574	105	34.49905	0.760301
Nitrate(mg/l)	4.02	24.56	15.14	16.35	5.386721	− 0.30233
Sulphate (mg/l)	1.71	210.01	56.65246	46.52	40.10478	2.091083
Phosphate (mg/l)	0.02	0.87	0.399016	0.36	0.208308	− 0.495

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Fig. 3

IDW representation of pre-precipitation

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Fig. 4

IDW representation of post-precipitation

In the present study, average TDS value for the pre-precipitation is 183.44 ppm (90–330 ppm) (Fig. 3b). While it shows a range from minimum of 80 ppm to a maximum of 270 ppm with an average of 173.77 ppm for the post-precipitation month (Fig. 4b). The value of TDS comes in good range indicating that most of groundwater is influenced by rainwater chemistry. High salt concentration lying above 800 ppm in irrigation water contributes to acceleration in the salinization of land. Whereas TDS above 300 ppm is not considered good for drinking (Sellamuthu et al. 2022; Srivastava et al. 2022, 2023). EC value is around an average of 366.88 µS/cm for pre-precipitation (200–650 µS/cm) (Fig. 3c). For post-precipitation comes about 358.67 µS/cm ranging from 150 to 600 µS/cm (Fig. 4c). The sampling sites 9, 20, 24, and 59 show increased concentration. As Dumka falls under the ‘cwa’ category of weather where a good amount of rainfall is experienced in the pre-precipitation season i.e. Feb-May. Being a North-Eastern state, Dumka experiences rainfall of about 15–35 mm in the month of Feb–May when compared to Oct-Dec is 116–15 mm (CEIC) (National Weather Service 2020). The value of TDS and EC comes in good range indicating that most of groundwater is derived primarily from the rainwater. The reason behind the EC of the area is slightly higher in the pre-precipitation month than the post-precipitation can be attributed to anthropogenic activities and geo-chemical processes prevailing in the region (Patil et al. 2014). The average value of ORP is 178.78 (mV) for pre-precipitation and 178 mV for post precipitation Salinity impacts the plant when there is less availability of water and more water stress, as evident by the leaf water potential (Kundu  2013). TDS and EC also show the same pattern like salinity. The average value for salinity is 165.09 mg/l for pre-precipitation (min 90–max293 mg/l) (Fig. 3d). It is 156.67 mg/l for post-precipitation (min 65–max 232 mg/l) (Fig. 4d) (Srivastava et al., 2022).

The current study area shows a predominance of Calcium and Magnesium ions. Ca⁺² and Mg⁺² constitute 43.85% in pre-precipitation to almost 43.20% and 43% in post-precipitation respectively. Na⁺ is the third dominant ion as the second largest amount of potassium feldspar is found in these rocks. Na⁺ concentration is 11% in both the months whereas K⁺ forms 0.39% in the pre-precipitation to 0.33% in the post precipitation. The dominance of the Gabbro-Anorthosite is the main reason for the huge concentration of Ca⁺² and Mg⁺² in the region of Dumka. A part of the Chotanagpur Plateau which has an abundance of plagioclase feldspar. Ca⁺² and Mg⁺² deposits in the region has dominancy of granodiorites. It contains more plagioclase i.e. Ca⁺² and Na⁺ feldspar than the potassium feldspar (Bhattacharjee et al. 2012; Haldar et al. 2020). The average total hardness comes to around 254.24 mg/l for pre-precipitation and 253.91 mg/l for post-precipitation. The water can be considered from soft to very hard type of water. The major ion chemistry is Ca⁺² > Mg⁺² > Na⁺ > K⁺.

For Cl⁻ the mean value is 113.60 mg/l for pre precipitation (Fig. 3j) and 108.55 mg/l for post precipitation (Fig. 4j). The mean value of anions F-concentration for pre-precipitation is 0.50 mg/l (Fig. 3) whereas for the post-precipitation is 0.43 mg/l (Fig. 4). The anions of sulphate, nitrate, and phosphate were found to be in 58.41, 15.65, and 0.39 mg/l in the pre-precipitation months. While for the post precipitation, the mean value changed to 56.62, 15.14, 0.39 mg/l respectively (Figs. 3k–m, 4k–m) (Monjerezi 2011). The dominant anion of the region is found to be Cl⁻ following HCO₃⁻. In the pre-precipitation the Cl⁻ comprises 39.54% of the anions and 34.50% HCO₃⁻. The concentration of Cl⁻ > 100 ppm is observed for over 10 sampling sites 2, 3, 4, 6,8, 9,10, 47, 56, 58. But in the post-precipitation month the value changes to 37.91% and 36.71% respectively. Other anions SO₄⁻², NO₃⁻, and F⁻ followed the concentration 20.33%, 5.44%, 0.17 in pre-precipitation to 19.78%, 5.28%, and 0.15% in post-precipitation respectively. The anions follow the trend Cl ⁻ > HCO₃ > SO₄⁻² > CO₃⁻².

Chloride in drinking water sources is typically caused by effluents from chemical companies, sewage, irrigation drainage, synthetic fertilizer leaching in soil and seawater intrusion in coastal areas (Hoque et al., 2018; Hendricks et al., 2019). Also, the Cl⁻ concentration in groundwater generally increases due to “low flow” periods when evaporation exceeds precipitation. F⁻ a few regions were found to exceed the limit at sites 25, 50, 55. This may be due to the dissolution and weathering of rocks like granite and gneiss in the north-eastern region of Jharkhand (Sect. "Soil Type and Drainage" geomorphology). The fluoride concentration was found to be somewhat constant in post-monsoon seasons (Pacheco et al., 2017). Generally, the high value of synthetic fertilizers such as superphosphate, NPK, and cyhalothrin are regarded for the exceeding concentrations of these anions. (Carey et al 1979; Sellamuthu et al. 2022. Sulphate and phosphate are present in a slightly increased amount. Drinking water with a high concentration of nitrates can lead to blue baby syndrome (Meride and Ayenew., 2016).

As supported by the Piper plot for the pre-precipitation states that 27.86% of the samples fall under the ‘a’ region denoting the Mg⁺² dominating. And 52.45% of the samples fall under the ‘b’ region i.e. Ca⁺² dominant facies. Around 19.69% of the sample falls in the ‘d’ region of ‘no dominant type.’ For the anions, 39.34% of the samples fall under the ‘g’ region representing Cl⁻ dominant anion. The remaining 52.45% lie in the ‘h’ region i.e. no dominant type, 3.27% lies in the ‘e’ region signifying SO₄⁻² dominant type. 73.77% of the sample are Calcium chloride dominant type 26.22% of the samples lies in the region of no mixed type. The diamond-shaped trilinear diagram reveals that all groundwater samples belong to Calcium chloride.

For the post-precipitation type 26.22% lies in the ‘a’ region representing the Mg⁺² dominant type. 32.78% fall under the ‘b’ region for dominant Ca⁺² type, and 40.98% lie in the ‘d’ region for no dominant type. The anions 39.34% of the samples fall under the ‘g’ region of the 57.46% fall under no dominant zone. Remaining 3.2% constitutes the SO₄ type. 19.67% of the samples are in mixed type region or else 80.63% of the samples are found in the region Calcium chloride types. The maximum samples fall under the category of CaCl₂ types (Fig. 5a).

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Fig. 5

a Piper plot, b gibbs plot, c CAI value distribution, d Na% plot, e USSL plot, f permeability Index

Hydro-geo Chemical Study

For the Gibbs plot the samples were majorly found to be distributed in the weathering region (rock dominance). It signifies that the majority of ions (cations and anions) in groundwater are governed by the chemical weathering of rocks. However, geochemical processes such as oxidation–reduction, precipitation-dissolution, and ion exchange governs the water chemistry of the region (Fig. 5b). The weathering and dissolution of minerals like anorthite and augite increase the concentration of the Ca⁺² and Mg⁺².

The relatively high contribution of Ca⁺² + Mg⁺² vs TZ⁺ to the total cations. The high Ca⁺² + Mg⁺²/Na⁺ K⁺ ratio 4.89, and the low Na⁺ + K⁺/TZ + ratio 0.16 indicate carbonate weathering as the major source of Ca⁺² & Mg⁺² ions in water samples. The Na⁺ was normally liberated from silicate weathering when the Na/Cl molar ratio was larger than 1 (Bhattacharjee et al. 2012) The Na/Cl ratio in the current investigation is often less than 1 (0.43). This leading to the conclusion that the ion exchange mechanism may be responsible for the large decrease in Na⁺ ion concentration. The overall ionic composition indicates that carbonate weathering plays a more dominant role than silicate weathering in influencing the water chemistry.

12

$${\text{CaAl}}_2{\text{Si}}_2{\text{O}}_8+{\text{2H}}_2{\text{CO}}_3\to {\text{CaCO}}_3+{\text{Al}}_2{\text{O}}_3+{\text{2SiO}}_2+{\text{2H}}_2\text{O}$$

13

$$\text{Ca}\left(\text{Mg},\text{Fe}\right){\text{Si}}_2{\text{O}}_8+{\text{4H}}_2{\text{CO}}_3\to {\text{CaCO}}_3+{\text{MgCO}}_3+{\text{2SiO}}_2+{\text{4H}}_2\text{O}$$In the previous study done in Dumka, by Singh A.K. and team (2021) concludes for the district of Jamatara and Dumka show groundwater has dominance of Ca–Mg–HCO₃ type. But the present study states Cl⁻ dominancy in the water. This can primarily be due to the salt used in fertilizer to promote soil fertility which is potassium chloride. The recent increases may be the result of anthropogenic activities such as the usage of potash-based fertiliser or salts mined from the earth. As not many natural sources were reported for groundwater chloride concentrations other than the dissolution of halite minerals or paleo salinity (Vasanthavigar 2012; Kumar et al. 2013). The free movement of Cl in the groundwater environment is because it is not absorbed by the soil. Chloro–Alkaline Index talks about the ion exchange processes occurring the region. The positive values signify the exchange of Na⁺ and K⁺ in water with Mg⁺² and Ca⁺² indicating base exchange phenomenon. The negative value signifies disequilibrium having cation–anion exchange reaction. For pre-precipitation CAI has 87% have positive values 13% have negative values. The post-precipitation 78% of samples have positive CAI value and 22% have negative value more i.e. − 5 which is most negative in both seasons (Fig. 5c)

Irrigation Water Indices

The samples collected for pre-precipitation month give the mean value of the percentage of 16.67% in pre-precipitation month. For the post-precipitation month, the value is 16.21%. The samples come all in the S1 region with a value lower than 10 which is considered as the excellent range. For salinity hazard 63% of the sample falls under the C2 class remaining falls under C1 class. For post precipitation the S1 values and for salinity hazard, the value lies the same as pre-precipitation being 73% in C2 to 37% in the C1 class (Fig. 5d), resulting in good values for the USSL plot. Wilcox Plot shows that for the pre-precipitation months the % Na mostly falls under the region of good—permissible. A lesser number of samples fall in the doubtful—unsuitable region. A similar pattern was observed in the post-precipitation months. Few samples lie in the region of unsuitable regions. However, the overall results suggest that the groundwater is suitable for irrigation purposes (Fig. 5e). The most popular Alkali hazard leads to problems of water infiltration and Nutrient deficiencies where the Na in exceeding amounts reduces the other nutrients available Ca/K/Mg. (Karmakar et al. 2021). Residual Sodium Carbonate (RSC) values were calculated for both the months and are recorded. The value for the pre-precipitation ranges from min − 35.7 to − 4.7 meq/l. The post-precipitation ranges from − 53.01 to − 3.94 meq/l. All the values calculated were negative. It can be inferred from the data obtained have no residual carbonate is found. The data lies in a good range for both months. Permeability Index is a parameter that depends on all the concentrations of ions. From the plot, certainly all 85% of the samples both from the pre-precipitation. The post precipitation belongs to Class I and the rest fall under Class II of the permeability index. The data shows that the water used in irrigation has a good range value for the permeability index (Fig. 5f). Magnesium Hardness of the samples for pre-precipitation ranges from 0.96 meq/l to 73.73 meq/l. The post-precipitation the value comes from 0.75 meq/l to 70.12 meq/l respectively (Wang et al. 2022).

Multivariate Statistical analysis

The hierarchical cluster of the data was obtained using the squared Euclidean distance and Ward Method was used to classify the samples with similar properties (Fig. 6A, B). The distance measure was done by the Euclidean distance. The more similar groups of sampling sites are first linked. The groups are joined with the linkage rule and repeated until all observations are classified. For the geochemical dataset, Ward’s method is popularly used as they form clusters using the variance method. They form clusters with homogenous and geochemically distinct from other clusters, when compared with weighted pair groups average. The dendrograms obtained for both seasons shows different set of clusters. The cluster classification of samples is done by visual observation of dendrogram. The phenon line was drawn at linkage distance 6 which classifies the pre-precipitation season into 6 cluster having samples as 11 in Mg–Cl₂,13 in Ca–Cl_2, 3 in Na–Cl, 7 in is Ca–HCO₃, 4 in Mg–HCO₃ and 20 in mixed type. For the post precipitation seasons the cluster get reduced to 5 which include 9 samples in Mg–Cl₂ 3 in Na–Cl, 22 in Ca–Cl₂, 26 in mixed type and 2 in Na–HCO₃.

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Fig. 6

A Pre-precipitation-HCA. B Post precipitation-HCA

The Pearson correlation matrix ® checks the linear relationship between the parameters. It gives the dependency coefficient. The significant two-tailed correlation at 0.05 and 0.01 is evaluated. For the pre-and post-precipitation month, presented in Tables 3 and 4. A very strong relation was seen in TDS, EC, and salinity having (r = 1) (r = 0.8); (r = 0.58), (r = 0.5) for the pre-post precipitation respectively. Cl⁻ have strong positive relation with EC, TDS. It is revealing the dominancy of the ion in water also alarming about the various sources. Including natural mineral dissolution, agricultural runoff (from fertilizers and pesticides), industrial discharge, or seawater intrusion in coastal areas. The positive correlation between Na⁺ and F⁻ (0.333) suggest their release in groundwater from dissolution of minerals like sodic feldspar NaAlSi₃O₈. Also, when the ion exchange in soil having clay rich soils the release of sodium is accompanied with fluoride. Common fertilizers and industrial discharge can be one anthropogenic possible reason. Strong correlation between PO₄⁻³ and SO₄⁻² (0.457) is due to agricultural run-off of fertilizer found in the area. Other parameters show weak positive correlation with each other. This reduction in linearity may be attributed to precipitation in the area which increases the solvent concentration.

Table 3. Correlation table for pre-precipitation

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Table 4. Correlation table for post precipitation

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The 13 data variable has been used for 61 observations for factor analysis. In the principal component analysis, the number of components of the data was based on Kaiser criterion. Where only values > 1 are retained. Variance of higher values are kept and varimax rotation for evaluation has been utilized (Tables 5 and 6). The values of first four eigen values comes greater than 1 for both the seasons. The principal component loading of the for the for components are factors account for the strong loading of the parameters for SAR and %Na for both the seasons. PC2 represents other water quality parameters like TH, Ca, Mg but are less dominant. The F, Cl also have noticeable influence on the component.

Table 5. PCA for pre-precipitation

S. no.	Eigenvalue	% Variance	% Cumulative
1	4.08012	31.39	31.39
2	2.01537	15.50	46.89
3	1.44998	11.15	58.04
4	1.15169	8.86	66.90
5	0.96731	7.44	74.34
6	0.88046	6.77	81.11
7	0.70999	5.46	86.58
8	0.65169	5.01	91.59
9	0.59033	4.54	96.13
10	0.46205	3.55	99.68
11	0.0379	0.29	99.98
12	0.00312	0.02	100.00
13	0	0.00	100.00

Parameter	Coefficients of PC1	Coefficients of PC2
F	0.13306	0.31563
Cl	0.09439	0.19284
NO3	0.04797	− 0.01693
SO4	− 0.0555	− 0.10666
PO4	− 0.14459	− 0.33744
TH	− 0.32802	0.47221
Ca	− 0.21869	0.5036
Mg	− 0.28747	0.16049
Na	0.43355	0.19577
K	0.22338	0.40458
HCO3	− 0.11289	− 0.16654
SAR	0.47313	0.05641
Na%	0.48433	− 0.04889

Table 6. PCA for post− precipitation

	Eigenvalue	%Variance	%Cumulative
1	3.4015	26.17	26.17
2	2.27779	17.52	43.69
3	1.59067	12.24	55.92
4	1.23111	9.47	65.39
5	0.96329	7.41	72.80
6	0.90351	6.95	79.75
7	0.78303	6.02	85.78
8	0.62575	4.81	90.59
9	0.58428	4.49	95.08
10	0.48398	3.72	98.81
11	0.14252	1.10	99.90
12	0.01208	0.09	100.00
13	4.92E−4	0.00	100.00

	Coefficients of PC1	Coefficients of PC2
F	0.22484	− 0.18434
Cl	0.10379	− 0.19723
NO3	− 0.18191	− 0.04418
SO4	− 0.2125	0.01919
PO4	− 0.16156	0.09621
TH	− 0.21053	0.53749
Ca	− 0.08481	0.48069
Mg	− 0.17136	0.29311
HCO3	− 0.07178	0.03297
Na	0.45018	0.31467
K	0.16152	0.43292
SAR	0.50474	0.14831
Na%	0.51457	− 0.01246

Entropy Weighted-WQI

The EWQI is calculated for the different sampling points for pre- and post-precipitation months as per WHO standards. The region-wise value of EWQI (Fig. 7) shows conclusive data (Table 8 supplementary file). The post-precipitation month has a good value of EWQI when compared to pre-precipitation. It is revealed from the figure that majority of the samples have good—suitable value for EWQI. But there are certain samples which exceeds the limit of EWQI especially in the region Jarmundi, jamua and Shikaripara. This exceed in value can be attributed to high EC, TDS and Cl⁻ values which makes the water both unsuitable for drinking and irrigation. For the pre precipitation the Jarmundi and jamua have bad EWQI. In the post precipitation season the Shikaripara zone was seen to has such quality. Figure 8 reveals that the post precipitation has comparatively good water quality then pre precipitation period.

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Fig. 7

EWQI—pre-precipitation and post precipitation

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Fig. 8

Graph depicting EWQI value of pre- and post-precipitation with respect to number of sites

Conclusions

The research presents a quantitatively independent approach for groundwater classification which offers a novel perspective to the study. The use of multivariate analysis with hydrogeochemical approach has resulted in the attaining clear picture of the study area. The descriptive analysis of the parameters shows EC and TDS range of 376 µS/cm and 188 ppm respectively may be due to the rainwater dominance in the region. The groundwater chemistry is majorly governed by the rainwater; hence the range is quite controlled. The dominance of Ca, Mg ions in the ionic composition in the region advocates that carbonate weathering is predominant over silicate weathering. This can be due to presence of gabbro-anorthosite, augite in the region. The HCA revealed 6 cluster for pre-precipitation and 5 cluster for post precipitation. Majority of the samples in Ca-Mg-Cl₂ type followed by NaCl and then Ca-Mg-HCO₃. The high values of the Cl indicate the alarming situation. The strong positive correlation between TDS, EC and Cl⁻ is concerning as Cl⁻ in water basically comes from anthropogenic factors including agriculture, industries, synthetic fertilizers and domestic waste. This degrades the land quality and needs to be checked with other factors governing this increase.

For the irrigation, the classification based on SAR and EC lies in the range of C1S1 and C2S1 which falls in the excellent to good category for irrigation water quality. Various indices such as Piper, Gibbs, MH, RSC, CAI, and PI values for both the months are appreciable. EWQI of the region falls in the range of excellent to extremely poor in certain areas. Jarmundi, Shikaripara zone and jamua have bad EWQI. There is need to trace the anthropogenic activities which are unnoticed/unaccountable.

The sampling points includes majorly wells and handpumps. One prime challenge in sample collection is that water is hidden in the subsoil pores and fractures. This reduces the range of investigations in space and time. From the various criteria discussed, it can be concluded that the water quality of the sample collected from different blocks is fairly good for the use of drinking and irrigation in both the months. To enhance the production of agriculture, to ensure good yield the suggested improvements should be undertaken by the specific regions. For the sustainability of groundwater resources, the data should be analysed for decision-making.

Funding

Not applicable.

Availability of Data and Materials

All data generated or analysed during this study are included in this manuscript.

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Conflict of interest

The authors declare no competing interests.

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