Combined Geospatial, Geophysical And Geochemical Studies On Coastal Aquifer At Muttom-Mandaikadu Area, Tamilnadu, India

doi:10.21203/rs.3.rs-731527/v1

A study was conducted in the Muttom-Mandaikadu coastal region, which is among the profitable coastal sectors in Tamil Nadu, to find the groundwater potential as well as its quality by an integrated geophysical, geospatial, and geochemical approach. The GIS-based weighted overlay analysis was used to merge different thematic layers to create the groundwater potential zone map. The geophysical resistivity survey was performed in the study area at 26 stations by applying a Schlumberger vertical electrical sounding technique. The observed data were interpreted using one-dimensional software AGI Earth Imager. The combined vertical electrical sounding result and remote sensing thematic maps have exposed the potential zone of groundwater in the study area. From the inferred results, it was observed that 20.8% of the area has ample groundwater potential, and 7.7 % of the area has scanty groundwater potential. The saltwater intrusion zone has been predicted by validating aquifer resistivity with Dar-Zarrouck (D-Z) parameter. From the geophysical and geochemical interpreted results, it was found that aquifers in 34.6% of the study area are vulnerable to saline contamination. The 4-D model with integrated groundwater quantity and quality suggests that the study area's Western part falls under excellent to good groundwater potential zone and excellent water quality.

Environmental Policy

Remote sensing

Vertical electrical sounding

Geochemical

Groundwater potential

Contamination

Water quality index

Coastal zone.

Groundwater is an ultimate resource of fresh water, most suitable for human consumption. It is also an unconventional source for industries and the agricultural sector. This water, which seeps downward, gets contaminated by a number of pollutants on its way to the aquifers. Especially the coastal aquifer faces the problem of seawater intrusion, which leads to impairment of the water quality in aquifers. The degree of saline water intrusion is determined by hydraulic gradient, groundwater withdrawal, and recharge rate and geological constraints (Choudhury et al. 2001). Today, with the advancement of technology, it is now possible to delineate and analyse the surface and subsurface water, its potential and quality.

The functioning of several mining projects and power plants created in small regions depends on freshwater requirements. However, the identification of potential fresh groundwater in the small region is a challenging task. In recent days geophysical techniques seem to be best for groundwater-related studies as it gives clear information on the unknown subsurface features. Among all other geophysical methods, the surface electrical resistivity method is best for groundwater investigation, such as Schlumberger configuration for locating aquifers is almost exclusively used both for shallow and deeper investigations throughout the world (Patra 2016). The vertical electrical sounding (VES) resistivity data develop a conceptual model for Dar Zarrouk parameters (transverse resistance (R) and longitudinal conductance (S)) for the evaluation of hydrologic properties of aquifers. These parameters give rise to electrical anisotropy (λ), which mainly depends on the lithology of the aquifer, the hydraulic conductivity, and the porosity (Mazáč et al. 1988; Nair et al. 2019). In many studies such as this, various researchers have successfully applied electrical resistivity to delineate groundwater potential zones (Akintorinwa and Okoro 2019; Adeyeye et al. 2019; Gyeltshen et al. 2019; Virupaksha and Lokesh 2019).

Many scientific communities have stated the importance of different hydrogeological factors such as geomorphology, geology, drainage density, soil type, slope, and land use/land cover controlling groundwater potential of any area (Nag 1998; Adiat et al. 2012; Mukherjee et al. 2012; Magesh et al. 2012; Mallick et al. 2015; Kumar et al. 2016; Chakrabortty et al. 2018; Arulbalaji et al. 2019; Nithya et al. 2019). Remote sensing techniques may be useful in finding the probable zone of groundwater potential results in large-scale areas, but its accuracy is found to be low in small-scale regions. Remote sensing (RS) has become an indispensable tool for groundwater mapping of large and inaccessible areas with the features extracted from satellite data products and used in conjunction with the thematic details from appropriate topographic maps (Elbeih 2015; Dasho et al. 2017).

To acquire precise subsurface information, the geospatial technique is to be essentially followed by the geophysical resistivity survey. The combined remote sensing geospatial technique and geophysical techniques give the best result to find out the groundwater potential zones in the study area.

The quality of groundwater varies from place to place. Therefore, the quality of groundwater is equally important as its quantity owing to the suitability of water for various purposes. Hydrogeochemical reactions are controlled by both natural processes and human impacts (Hosono et al. 2009). Groundwater chemistry plays a crucial role in the study of its quality in coastal aquifers (Mondal et al. 2008; Maiti et al. 2013; Srinivas et al. 2017). Several researchers have used hydrochemical parameters and methods to evaluate the seawater intrusion process and one such method is water quality index (WQI), which is useful to assess the quality and viability of water (Yadav et al. 2015; Alfaifi et al. 2019; Ameen 2019; Rajkumar et al. 2019; Maiti and Gupta 2020). The reason behind most hydrogeological exploration is to find potential zones of sufficient amount of fairly great quality groundwater for different utilizations, which relies upon the nearby needs and the financial states of the general population. Various strategies have been generated for measuring this quantity and quality, but combined geophysical, remote sensing, and geochemical data have been the goal of recent studies.

In this study, remotely sensed and geoelectrical data was used to generate the foremost groundwater potential map of Muttom to Mandaikadu coastal region and a detailed investigation was carried out for qualitative analysis of groundwater by combined geophysical and geochemical techniques. This study area comprises of Manavalakurichi region, which is one of the profitable coastal sectors under the authority of Indian Rare Earth Limited (IREL). This study will be helpful in local groundwater management and also be potentially important for the protection and governance of groundwater in many other parts of the world that face similar situations.

The study area Muttom to Mandaikadu coastal zone is located in (lat 8.14423°, long 77.3058°) the Kanyakumari district of Tamil Nadu (Fig. 1). The North and North West region of the district is entirely occupied by means of Western Ghats Mountain with a maximum elevation of 1658 m and the Southern region by the Indian Ocean. Regionally the area is drained by Valliyar River, which is a perennial river flow between Manavalakurichi and Muttom. The West Coast area enjoys a typical monsoonal climate and receives an average annual rainfall of about 1200 mm. The maximum rainfall is received during the period of April, May (Hot whether period), June, July (South West Monsoon) and October, November (North-East Monsoon). The study area is mostly covered by coastal alluvium, which is composed of fine and medium-grained sand and very little clay; the main constituents of the sand being heavy minerals include ilmenite, garnet, monazite, and zircon. Red Teri sand is present in the north of the Muttom region. Teri sand occurs as irregular thin patches in between the outcrops of crystalline rocks on the beach. The Indian Rare Earths Limited (IREL) heavy mineral separation plant is located on the Manavalakurichi beach. Which mainly depends on freshwater resources; the daily requirements are estimated to be about two lakhs gallons per day (CGWB, 1977). The Muttom area has hard massive crystalline rocks that will be encountered at shallow depth. Outcrops of coarse-grained granetiferous charnokites are found on the beach. In our study area, crystalline rock comprises mainly granetiferous-biotite-gneisses and charnockites, of which the former predominates (CGWB, 1980). Alluvium occurs as the upper layer and is characterized by sand, gravel, and sandy clay with thickness ranging from 1 m to 20 m in the study area. This alluvium along with weathered charnockites beneath it functions as an unconfined aquifer system. The depth of groundwater in the monitoring wells varies between the range of 0.5 m to 10 m above MSL and 5 to 11 m below the ground level. The hydraulic conductivity of alluvium is 30.0 m/day. The specific yield value is 0.2 (Perumal et al. 2010). Rainfall forms the only source of aquifer replenishment of these aquifers.

The methodology adopted for this study is illustrated in Fig. 2. The base map of the study area was prepared based on SOI topographic maps (No. 58 H/8) on a 1:50,000 scale. Georectification/geometric correction process was carried out from the ground control points (GCPs) obtained from SOI toposheets using ArcGIS software (version 10.2) and projected to Universal Transverse Mercator (UTM) with reference to WGS-84 datum. During the first stage of this study, five parameters such as geomorphology, soil, slope, drainage density, and land-use have been analyzed to explore the probable groundwater potential zones.

The data collection includes the collection of toposheet maps, field and satellite data. Further, the satellite data was subjected to geometric and atmospheric corrections. From this corrected satellite data, the area of our interest is cropped out. This pre-processed Landsat ETM 7 + satellite 2018 data collected from USGS earth explorer with 30 m resolution have been analyzed to extract geomorphology features. Indian Remote Sensing (IRS) satellite, Linear Imaging Self scanning Sensor (LISS)-III 2018 data with 23.5 m resolution published by Bhuvan (Indian Geoportal website) was used to prepare land use landcover (LULC) map. The spatial distribution of the land use and land cover of the study area has been mapped from IRS LISS-III image using supervised classification (maximum likelihood classifier algorithm) techniques in ArcGIS software (version 10.2). The geomorphology features have been extracted with the same technique as of LULC except with a change that it was from Landsat band (2,3,4) with true color composition, whereas for LULC it was IRS LISS-III from the band (2,3,4) but with false-color composition. Furthermore, these classified images are involved for accuracy assessment using post field verification technique.

A slope and Drainage density map is created by using ALOS PALSAR DEM (12.5 m). Soil data has been referred from the national atlas thematic mapping organization. During multi-influence factor (MIF) analysis, the ranking has been given for different parameters of each thematic map, and credits were allotted based on thematic parameters in terms of good, moderate, and poor zones (Fagbohun, 2018). The weight assignment of each feature class is the most critical in integrated analysis because the output is dependent on the appropriate assignment of weightage. The weights thus assigned to individual features are based on field observation and expertise.

In the second stage, Resistivity measurements were carried out using high accuracy digital signal-enhancement resistivity-meter WDDS-2. DC Resistivity sounding measures the apparent resistivity (ρ_a) of the subsurface. The observed data are inverted to develop a model of the subsurface lithology and its electrical properties by using AGI Earth Imager 1D software. Schlumberger vertical electrical sounding (VES) survey was accomplished at 26 locations with the electrode spacing varying from 2.5 to 100 m and the potential electrode (MN/2) spacing varying from 0.5 to 10 m for each succeeding measurement. Ambiguities in interpretation may occur, and therefore for accurate resistivity values, calibration of the observed DC-sounding data with the available borehole log is mandatory (Fig. 3). The Dar-Zarrouk (D-Z) parameters (Zohdy 1949), namely (i) the total longitudinal unit conductance ‘S’ (Eq. 1), (ii) total transverse unit resistance ‘T’ (Eq. 2) (iii) average longitudinal resistivity ‘ρ_s’ (Eq. 3) utilized to sort out the water aquifers in the seaside locales (iv) average transverse resistivity ρt (Eq. 4) and (v) anisotropy ${\lambda }$ (Eq. 5) are derived from the subsurface layer resistivity and thickness following:

$$S={\sum }_{i=1}^{n}\frac{{h}_{1}}{{\rho }_{1}}=\frac{{h}_{1}}{{\rho }_{1}}+\frac{{h}_{2}}{{\rho }_{2}}+\frac{{h}_{3}}{{\rho }_{3}}+\cdots \cdots \cdots + \frac{{h}_{n}}{{\rho }_{n}} \text{E}\text{q}$$

1

$$T={\sum }_{i=1}^{n}{h}_{1}{\rho }_{1}={h}_{1}{\rho }_{1}+{h}_{2}{\rho }_{2}+{h}_{3}{\rho }_{3}+\cdots \cdots \cdots +{h}_{n}{\rho }_{n} \text{E}\text{q}$$

2

$${\rho }_{s}=\frac{H}{S}=\frac{{\sum }_{i=1}^{n}{h}_{1}}{{\sum }_{i=1}^{n}\frac{{h}_{1}}{{\rho }_{1}}} \text{E}\text{q}$$

3

${\rho }_{t }=\frac{T}{H}=\frac{{\sum }_{i}^{n}{h}_{1}{\rho }_{1}}{{\sum }_{i}^{n}{h}_{1}}$ Eq. (4)

$${\lambda }=\sqrt{\frac{{\rho }_{t}}{{\rho }_{s}}} \text{E}\text{q}$$

5

To understand the quality of groundwater in the study area, a total of 24 water samples was collected from the duct and bore wells around the VES locations for geochemical analysis. Physicochemical parameters like the potential of hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS) were measured using the HANNA instrument. The other parameters like total hardness (TH), calcium (Ca²⁺), chloride (Cl⁻), sulphate (SO₄²⁻), magnesium (Mg²⁺), sodium (Na⁺), potassium (K⁺), bicarbonate (HCO₃⁻) were analyzed using standard analytical methods (Clesceri et al. 1998). The values obtained were compared with the guidelines for drinking purposes defined by the World Health Organization (WHO). To measure WQI, a set of 11 physical and chemical parameters such as pH, EC, TDS, TH, Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃⁻, Cl⁻, and SO₄²⁻ were resolved (Kangabam et al. 2017). The water quality index analysis has been done with the help of the Canadian Water Quality index (CWQI) programmed excel software (John-Mark 2006). CWQI was developed by the Canadian council based on the WQI of British Columbia. The water quality objective depends on three attributes of CWQI (Rosemond et al. 2008; Hurley et al. 2012), and it can be calibrated using Eq. (6) (Lumb 2006).

Scope – F₁
Frequency – F₂
Amplitude – F₃

$\text{C}\text{W}\text{Q}\text{I} = 100-\sqrt{(}{\text{F}}_{1}^{2}+{\text{F}}_{2}^{2}+{\text{F}}_{3}^{2})/1.732$ Eq. (6)

4.1. Groundwater potential assessment using RS, GIS and MIF (Multi influence factor) technique

In order to identify the groundwater potential zones of the study area, five parameters, viz, Geomorphology, Slope, Landuse, Soil, and drainage density, were selected and the thematic maps of these selected parameters were prepared using conventional and remote sensing data in GIS platform (Hussein et al. 2016). These parameters have been analyzed to delineate groundwater potential zones using a multi-influencing factor technique. The main geomorphological landforms identified in the study area are swale, sand dune, rocky shore cliff, alluvial plain, pediment moderate, marshy/swamp, pediment shallow, coastal upland, Teri sand, jetty, beach ridges, coastal plains, beach cusps and beach scarps (Kaliraj et al. 2017). Almost 50 % of the study area has occupied by coastal plain and alluvial plain (Fig. 4). The occurrence of groundwater is higher in marshy/swamp and swale deposits than rocky shore cliffs and sand dunes.

The study area consists of three types of soil classes, such as Inceptisols, Entisols, and alfisols. Among them, Inceptisol is predominantly distributed in most of the study areas (Fig. 5a). Inceptisol has been assigned high weightage, whereas alfisol soil has given low weightage (Suganthi et al. 2013). The drainage density of the study area (Fig. 5b) is classified in to three categories, 0-1.7 km/km2, 1.8–4.5 km/km2 and 4.6–11 km/km2. Since drainage density is an inverse function of permeability, the area with less drainage density is considered a good site for groundwater potential and an area having high drainage density is considered poor site for groundwater potential (Magesh et al. 2012).

The land use map of the study area (Fig. 6a) reveals that the main land-use types are villages, towns, land with scrub, plantations, rivers, tanks, and cropland (Kaliraj et al. 2014). Almost 50% of the study area is encroached by plantations. The areas with a river, tanks, and cropland are considered as good sites for groundwater potential, while towns and villages have poor groundwater potential. The slope in the degree of the study area varies from 0 to 19.5. The study area can be sorted into five divisions in terms of the degree of slope (Kanwal et al. 2016). The area with a slope of 0 to 2 normally falls in the ‘very good’ groundwater potential category due to its flat nature. The areas of 2.1to 4.3 slope are considered good potential zones for groundwater storage. The areas with a slope of 4.31 to 6.8 are categorized as moderate. The slope categories belonging to ranges of 6.9–8.6 and 8.7–19.5 (Fig. 6b) are considered as poor and very poor groundwater occurrence due to higher runoff (Kanagaraj et al. 2019).

The five selected parameters and their individual feature classes were assigned suitable weights according to their influence on groundwater potential (Dar et al. 2020) in Table 1. All the vector thematic layers were converted to raster format and overlaid step by step using overlay analysis in ArcGIS to delineate groundwater potential zones. The groundwater potential zone of this study area was demarcated and divided into five classes, namely excellent, good, moderate, poor, and very poor. The probable groundwater potential map Fig. 7 demonstrates that the excellent groundwater potential zone is 17% of the study area.

Table 1

Weightage and rank assigned for different parameters based on groundwater potential.
S.No	Parameter	Class	Weight (W)	Rank (R)
1	Geomorphology	Swale	4	8
		Sand dune	4	4
		Rocky shore cliff	4	1
		Alluvial plain	4	7
		Pediment moderate	4	5
		Marshy/ Swamp	4	9
		Pediment shallow	4	6
		Coastal upland	4	6
		Teri sand (red)	4	6
		Jetty	4	7
		Beach ridges	4	5
		Coastal plain (younger)	4	7
		Coastal plain (older)	4	6
		Beach cusps	4	5
		Beach scarp	4	5
2	Soil	Alfisols	2	3
		Inceptisols	2	7
		Entisols	2	5
3	Drainage density (Km/Km2)	0-1.7	3	7
		1.8–4.5	3	4
		4.6–11	3	2
4	Land use land cover	Villages (Rural)	3	3
		Towns/cities (Urban)	3	1
		Land with scrub	3	6
		Plantations	3	7
		Water bodies	3	10
		Tanks	3	10
		Cropland	3	9
5	Slope (Degree)	0–1	3	8
		1.1–2.1	3	7
		2.12–3.2	3	6
		3.21–4.3	3	5
		4.31–5.4	3	4
		5.5–6.8	3	3
		6.9–8.6	3	2
		8.7–11.3	3	1
		11.4–19.5	3	1
6	Electrical anisotropy	1-1.1	10	10
		1.11–1.2	10	8
		1.21–1.3	10	6
		1.31–1.5	10	4
		1.51–1.8	10	2
		1.81–2.19	10	1

The ground observation is necessary to validate the features studied by Remote sensing investigations. Therefore GPS was used for ground truth verifications. In the study area, coastal landforms, outer crop crystalline rock, Teri sand, water bodies, settlement, cropland and plantation could be visually confirmed to be matching with the Geospatial information. Some field photos are added in the supplementary information section for reference (SI 1).

4.2. Groundwater potential assessment using geophysical resistivity method

A geophysical survey is carried out to ascertain subsurface geology, hydrogeological conditions and aquifer characteristics following the examinations by aerial photography and satellite remote sensing techniques. The geophysical electrical resistivity method is an effective tool for deducing the subsurface geological framework of an area. Resistivity measurements depend, for the most part, on the lithology, water saturation, porosity, and ionic concentration of the pore liquid (James 2002). The response curves along with interpreted VES layer models are added in supplement information (SI 2). The results of data acquisition are tabulated and given in Table 2. Where ρ is the subsurface layer's resistivity in ohm meter, h is the thickness of the subsurface layer in meter, and H is the total thickness of the VES result.

By interpreting the final output, there are 3 to 6 geoelectrical layers found. Among the 26 vertical electrical soundings, three sites (VES 13, VES 14, and VES 18) are found to have three layers, two sites (VES 2 and VES 11) are found to have six layers, and other sites are found to have four to five geoelectrical layers. The geoelectrical layers obtained from the VES survey reveal various hydrogeological conditions of the subsurface in the study area, varying between highly saline areas and freshwater regions. Such low-resistivity values indirectly confirm the saline water intrusion from Sea (Maiti et al. 2013), and high resistivity values indicate hard rock.

The normal longitudinal resistivity (ρ_s) and transverse resistivity (ρ_t) values were determined in equations 3 & 4 from the results of each arrangement of soundings (Table 2). The longitudinal resistivity (ρ_s) distribution reasoned from each sounding clearly outlined the zones of saline and fresh groundwater aquifer into two distinct entities dependent on their sizes. The saline water zone had a scope of 4.9 to 47.6 Ωm, and freshwater zones had a scope of greater than 50 Ωm (Mondal et al. 2012). There is a risk of false prediction of subsurface clay layer and water-saturated zone instead of groundwater contamination zone during the process of identification using aquifer resistivity. Therefore cross-verification of aquifer resistivity with other relevant data is mandatory. The saltwater intrusion zone can be predicted by validating longitudinal resistivity with aquifer resistivity. Following the identification, integrated geochemical and geophysical techniques have been employed to assess the seawater intrusion status in the study area. The previous studies suggest that the low aquifer resistivity and high EC and Cl⁻ content are connected with saline water intrusion (Srinivas et al. 2015; Senthilkumar et al. 2019). The spatial variation maps of EC and aquifer resistivity (ρ_A) in the study area are shown in Fig. 8. The correlation between aquifer resistivity (from VES1 to VES26 except for VES4, VES8 & VES24) and water sample results of chloride and EC (from WS1 to WS23) has been illustrated in SI 3. The resistivity of the aquifer is strongly negative correlated with the electrical conductivity and chloride ion concentration of the water samples in the study area. The final validated results show that the aquifers in the VES station VES 1, VES 2, VES 3, VES 6, VES 18, VES 20, VES 24, VES 25 and VES 26 are vulnerable to saline contamination.

Table 2

Interpreted VES results with layer parameters.
VES	LAT	LONG	VES-LAYER PARAMETERS												DAR-ZARROUCK PARAMETERS
			ρ₁ Ωm	h₁ m	ρ₂ Ωm	h₂ m	ρ₃ Ωm	h₃ m	ρ₄ Ωm	h₄ m	ρ₅ Ωm	h₅ m	ρ₆ Ωm	ρ_A Ωm	T Ωm²	S Ω⁻¹	ρ_S Ωm	ρ_T Ωm	λ
1	77.307	8.140	168	1.34	84.8	0.7	16.4	6.3	43.7	3.56	123	--		16	543	0.48	24.7	45.66	1.360
2	77.304	8.145	178.5	1.08	97.8	1	322	2	98	2.24	9.6	19	14110	9.6	1335	2.01	12.5	53.04	2.058
3	77.297	8.151	570	1.12	292.8	1.24	10	2.9	19.4	7.86	14	--	--	10	1183	0.70	18.7	90.16	2.195
4	77.292	8.156	256	3.88	207	4.26	77.5	5.51	56.3	3.44	56	--	--	56	2496	0.17	101.8	146.04	1.198
5	77.297	8.159	263	0.7	1209	2	492	6.46	60	--	--	--	--	60	7295	0.02	505.8	585.48	1.076
6	77.276	8.163	42	1.3	23	0.6	19	2.5	41	14	24	--	--	26	742	0.61	33.6	36.37	1.040
7	77.282	8.166	101	2.3	149.6	8	256	19.4	32	--	--	--	--	32	6396	0.15	195.4	215.34	1.050
8	77.288	8.165	253.5	2.7	131	2.49	233	12.1	70	--	--	--	--	70	3851	0.08	212.0	221.57	1.022
9	77.281	8.174	292	1	184	1.6	72	4.2	463	10.3	251	--	--	72	5658	0.09	184.5	330.86	1.339
10	77.315	8.124	35	2	21	1.44	245	9	895		--	--	--	21.5	20205	0.18	175.5	622.85	1.884
11	77.318	8.129	264	1.7	301	1.9	511	3.14	176	5	26	25	970	26	3505	0.05	248.2	298.57	1.097
12	77.302	8.154	158	1	494	2.12	208	3.8	70	7	34	--	--	34	1996	0.03	239.5	288.39	1.097
13	77.302	8.156	56	1.4	34	11	74	--	--	--	--	--	--	32	452	0.35	35.6	36.48	1.013
14	77.298	8.165	149	8	56	3.5	87	--	--	--	--	--	--	56	1388	0.12	99.0	120.70	1.104
15	77.294	8.167	350	2.6	390	3.8	221	6.6	133	--	--	--	--	133	3851	0.05	276.4	296.20	1.035
16	77.294	8.161	672	1.5	586	1.7	733	5	400	9.9	74	--	--	74	9559	0.04	489.2	525.21	1.036
17	77.289	8.157	185	1.5	52	5.5	67	8	28	--	--	--	--	28	1100	0.23	64.3	73.30	1.068
18	77.308	8.144	109	3	12.5	16.3	72.5	--	--	--	--	--	--	12.5	531	1.33	14.5	27.50	1.377
19	77.314	8.144	142	1	35	4	97.3	2.6	10.5	14.2	71	--	--	10.5	535	0.15	51.3	70.39	1.171
20	77.284	8.159	164	1.6	43	1	11	31.6	7.5	--	--	--	--	11	653	2.91	11.8	19.09	1.274
21	77.280	8.169	215	1	133	1.2	55	4	156	28.5	93	--	--	55	5041	0.27	129.0	145.26	1.061
22	77.289	8.173	29	0.4	222	6.7	60	7.6	384	23.5	16	--	--	60	10979	0.23	164.8	287.41	1.321
23	77.296	8.173	245	2.2	79	5	501	12	230	--	--	--	--	79	6946	0.10	199.5	361.77	1.346
24	77.301	8.155	47.3	1	344	1	23	1.5	117	9.26	7	19	--	7	1641	2.90	10.9	51.68	2.173
25	77.308	8.143	34	1	1	0.5	13	27	2162	--	--	--	--	13	386	2.61	10.9	13.53	1.112
26	77.308	8.158	120	1.68	7.36	14.6	12	56	124	--	--	--	--	7.36	981	6.66	10.8	13.57	1.119

The electrical anisotropy in the rocks depends on the packing of grains and the layers based on the resistivity values and is calculated by the Eq. (6). According to the equation of Singh & Singh (1970) and Nair (2019), lower values of λ correspond to higher aquifer potential zones. Since the geological formation of the study area is made of tertiary sandstones and coastal alluvium in meager thickness, the lineaments inferred in the crystalline terrain may offer good scope for groundwater potential. An area with a ‘λ’ value greater than 1.5 is considered to be a scanty zone for groundwater (Gupta et al. 2015). The electrical anisotropy (λ) values varied from 1.01 to 2.2 in the study area. The GIS overlaid maps of electrical anisotropy and remote sensing thematic data result (Table 1) revealed high accurate groundwater potential zone map for the study area (Fig. 9). From the derived results, the groundwater potential zone of the study area can be classified into five potential zones. They are very poor, poor, moderate, good, and excellent, with a percentage of 7.7%, 21.5%, 22.8%, 27.2%, and 20.8%. Figure 9 suggests that Mandaikadu and surrounding areas are filled with an excellent level of groundwater owing to the subsurface lithology. Sankar's (1995) pumping test result shows that saturated thickness near Mandaikadu and Manavalakurichi is 3.7 & 1.6 m and the time of full recovery is 25 & 63 hours, respectively. Yield potential in the West part of the aquifer is more than in the East part. The zones with high yield potential have been determined for future development in the plain and for choosing the drilling sites.

4.3. Groundwater quality assessment

The outcomes of the chemical analysis and the factual parameters of the groundwater specimens are exhibited in the Table in SI 4. The pH values in the study area varied from 5.1–7.4, with an average value of 6.3, indicating the slightly acidic nature of the groundwater within the permissible limit of 6.5–8.5 (WHO, 2011). The EC values ranged from 68–3034 mS/cm with an average of 891mS/cm. Higher EC in the Manavalakurichi region shows the enhancement of salt in the groundwater.

In the study region, the TDS value shifts between at least 43 mg/l and the most extreme of 1942 mg/l (average 570 mg/l). The TDS range in the investigation territory shows that most groundwater samples exist within the permissible limit. As per Davis and De Wiest's (1966) grouping of groundwater based on TDS, 62.5 % of the aggregate groundwater samples are desirable for drinking ( TDS < 500 mg/l), 16.5 % passable for drinking (500–1000 mg/l) and 20.8 % sensible for irrigation needs (TDS > 1000 mg/l). The total hardness (TH) ranges from 29 to 900 mg/l, with an average of 250 mg/l. The TH can be named delicate (< 75 mg/l), reasonably hard (75 to 150 mg/l), hard (150 to 300 mg/l) and very hard (> 300mg/l) (Sawyer and McCarty 1967). According to the classification, 21% of the samples fall under soft, 25 % fall under moderately hard, 25 % fall under hard, and 29 % fall under the very hard category. The hardness of water causes the scaling of pots, boilers, and irrigation pipes and may also cause health problems to humans (WHO, 2008). The mean concentration of cation Na⁺>Ca²⁺> Mg²⁺> K⁺ and anions Cl⁻>HCO₃⁻> SO₄⁻ (in mg/l) were in the order. Sodium and chloride are the major dominant ions in the study area.

4.3.1. Water quality index (WQI)

WQI has been applied worldwide to assess the overall water quality within a particular region quickly and effectively (Ameen 2019). This WQI according to the Guidelines recommended by WHO for the standards of drinking water is used in this study (Shah and Joshi 2015).

According to CWQI, the water can be classified into six types: poor (0–44), marginal (45–64), fair (65–79), good (80–88), very good (89–94), and excellent (95–100). The CWQI value of the water samples is given in table (SI 4). If the CWQI ranking stands poor and marginal, the water is dangerous and must undergo purification. If it is an excellent ranking, the water does not need purification, while the good ranking needed slight purification (Khan et al. 2004; Baghapour et al. 2013). The present study area result shows 62% of the samples under the good to excellent category.

4.4. Integrated approach

The information generated from the vertical electrical sounding result, remote sensing thematic data and geochemical analyses have been used in deriving the aquifer quantity and drinking suitability of groundwater that circulates in the aquifer. Thus the combined 4-D model (Fig. 10) delineates that the Manvalakurichi (B) region has a very poor water potential zone with a poor to marginal water quality index (WQI). Though the area Periyakulam shows an excellent water potential zone, which is also a part of the Manavalakurichi, it still has poor WQI. Muttom (A) near the coastal area is a poor water potential zone with fair WQI. Mandaikadu (C) region obviously has excellent to good groundwater potential zone with excellent WQI except for the two samples WS 5 and WS 18. The best part of the aquifer is in the West portion of the study area, which may be relied upon for future development. In this part, the quality and quantity of groundwater sources were found to be excellent.

Satellite imageries, topographic maps, and secondary data sets are used to prepare five different thematic layers: geomorphology, drainage density, soil type, slope, and land use/land cover. They are integrated with a weighted overlay in GIS to generate a possible groundwater potential zone map of the study area. The combined VES and remote sensing thematic maps have exposed the more realistic groundwater potential zone in the study. The integrated geophysical and hydrogeochemical results show that the VES locations VES 1, VES 2, VES 3, VES 6, VES 18, VES 20, VES 24, VES 25 and VES 26 are vulnerable to saline contamination. Integrated Electrical anisotropy, various thematic layers, and WQI 4-D model clearly suggest that the Mandaikadu region falls under excellent to good groundwater potential zone and excellent to very good WQI excluding sites WS 5 and WS 18. In the study, the Mandaikadu region shows a huge amount of fresh groundwater that acts as a barrier that prevents saltwater from entering the coastal aquifer. Remote sensing, geophysical, and geochemical studies show that the Muttom region has good to very poor groundwater potential and excellent to fair WQI. The Muttom area has hard massive crystalline rocks that are encountered at shallow depth and outcrops of crystalline rocks on the beach, which protects seawater intrusion. Most part of the Manavalakurichi region has the poor potential of groundwater and is vulnerable to saltwater intrusion. The less recharge and more discharge of groundwater is the reason for the profound saline water intrusion in the Manavalakurichi region. From the above observation, it is recommended to limit the groundwater pumping to avoid seawater intrusion due to the over-pumping of freshwater.

Ethical approval All ethical principles have been followed while preparing this manuscript.

Consent to participate Not applicable.

Consent for publication Not applicable.

Data availability All data generated or analyzed during this study are included in this published article and its supplementary information files.

Competing interests The authors declare no competing interests.

Funding The authors would like to acknowledge the University Grants Commission (UGC), New Delhi for the financial help.

Authors' contributions S. Rajkumar: Writing-original draft preparation, conceptualization, methodology, software. Y.Srinivas: supervision and conclusion, reviewed the edited manuscript. Nithya C Nair: Writing-reviewing and editing. S. Arunbose: review and editing. All authors read and approved the final manuscript

Acknowledgement The authors gratefully acknowledge the University Grants Commission (UGC), New Delhi for the financial assistance provided for Major Project No: F.No.41-940/2012 during the period of study.

Adeyeye O, Ikpokonte E, Arabi S (2019) GIS-based groundwater potential mapping within Dengi area, north central Nigeria. The Egyptian Journal of Remote Sensing Space Science 22:175–181. https://doi.org/10.1016/j.ejrs.2018.04.003
Adiat K, Nawawi M, Abdullah K (2012) Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool – A case of predicting potential zones of sustainable groundwater resources. J Hydrol 440–441:75–89. doi:10.1016/j.jhydrol.2012.03.028
Akintorinwa OJ, Okoro OV (2019) Combine electrical resistivity method and multi-criteria GIS-based modeling for landfill site selection in the southwestern Nigeria. Environ Earth Sci 78(5). https://doi.org/10.1007/s12665-019-8153-z
Alfaifi H, Kahal A, Albassam A, Ibrahim E, Abdelrahman K, Zaidi F, Alhumidan S (2019) Integrated geophysical and hydrochemical investigations for seawater intrusion: a case study in southwestern Saudi Arabia. Arab J Geosci 12(12). doi:10.1007/s12517-019-4540-8
Ameen HA (2019) Spring water quality assessment using water quality index in villages of Barwari Bala, Duhok, Kurdistan Region, Iraq. Applied Water Science 9(8). https://doi.org/10.1007/s13201-019-1080-z
Arulbalaji P, Padmalal D, Sreelash K (2019) GIS and AHP techniques based delineation of groundwater potential zones: A case study from southern western Ghats, India. Sci Rep 9(1). https://doi.org/10.1038/s41598-019-38567-x
Baghapour MA, Nasseri S, Djahed B (2013) Evaluation of Shiraz wastewater treatment plant effluent quality for agricultural irrigation by Canadian Water Quality Index (CWQI). Iranian Journal of Environmental Health Science Engineering 10(1):27. doi:10.1186/1735-2746-10-27
CGWB (1977) Report on the water supply investigation for Indian rare earths plant, Manavalakurichi,Tamilnadu
CGWB (1980) Hydrogeological investigation for augmentation of water supply to zomby exports factory at Muttom, Kanyakumari district. Tamil Nadu
Chakrabortty R, Pal SC, Malik S, Das B (2018) Modeling and mapping of groundwater potentiality zones using AHP and GIS technique: A case study of Raniganj block, Paschim Bardhaman, West Bengal. Modeling Earth Systems Environment 4(3):1085–1110. https://doi.org/10.1007/s40808-018-0471-8
Choudhury K, Saha D, Chakraborty P (2001) Geophysical study for saline water intrusion in a coastal alluvial terrain. J Appl Geophys 46(3):189–200. doi:10.1016/s0926-9851(01)00038-6
Clesceri LS, Greenberg AE, Eaton AD (1998) Standard Methods for the Examination of Water and Wastewater, 20th Edition
Dar T, Rai N, Bhat A (2020) Delineation of potential groundwater recharge zones using analytical hierarchy process (AHP). Geology Ecology Landscapes 1–16. https://doi.org/10.1080/24749508.2020.1726562
Dasho OA, Ariyibi EA, Akinluyi FO, Awoyemi MO, Adebayo AS (2017) Application of satellite remote sensing to groundwater potential modeling in Ejigbo area, Southwestern Nigeria. Modeling Earth Systems Environment 3(2):615–633. doi:10.1007/s40808-017-0322-z
Davis SN, De Wiest RJ (1996) Hydrogeology. Wiley, NewYork, p 463
Fagbohun BJ (2018) Integrating GIS and multi-influencing factor technique for delineation of potential groundwater recharge zones in parts of Ilesha schist belt, southwestern Nigeria. Environ Earth Sci 77(3). doi:10.1007/s12665-018-7229-5
Gupta G, Patil SN, Padmane ST, Erram VC, Mahajan SH (2015) Geoelectric investigation to delineate groundwater potential and recharge zones in Suki river basin, north Maharashtra. J Earth Syst Sci 124(7):1487–1501. doi:10.1007/s12040-015-0615-4
Gyeltshen S, Tran TV, Teja Gunda GK, Kannaujiya S, Chatterjee RS, Champatiray PK (2019) Groundwater potential zones using a combination of geospatial technology and geophysical approach: Case study in Dehradun, India. Hydrol Sci J 65(2):169–182. https://doi.org/10.1080/02626667.2019.1688334
Hosono T, Ikawa R, Shimada J, Nakano T, Saito M, Onodera S, Taniguchi M (2009) Human impacts on groundwater flow and contamination deduced by multiple isotopes in Seoul City, South Korea. Science of The Total Environment 407(9):3189–3197. doi:10.1016/j.scitotenv.2008.04.014
Hurley T, Sadiq R, Mazumder A (2012) Adaptation and evaluation of the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) for use as an effective tool to characterize drinking source water quality. Water Res 46(11):3544–3552. doi:10.1016/j.watres.2012.03.061
Hussein A, Govindu V, Nigusse AG (2016) Evaluation of groundwater potential using geospatial techniques. Applied Water Science 7(5):2447–2461. doi:10.1007/s13201-016-0433-0
James GS (2002) Mapping in Engineering Geology. Geological Society of London
John-Mark D (2006) Application and Tests of the Canadian Water Quality Index for Assessing Changes in Water Quality in Lakes and Rivers of Central North America. Lake Reservoir Management 22:308–320. https://doi.org/10.1080/07438140609354365
Kaliraj S, Chandrasekar N, Ramachandran K (2017) Mapping of coastal landforms and volumetric change analysis in the south west coast of Kanyakumari, South India using remote sensing and GIS techniques. The Egyptian Journal of Remote Sensing Space Science 20(2):265–282. doi:10.1016/j.ejrs.2016.12.006
Kaliraj S, Chandrasekar N, Peter TS, Selvakumar S, Magesh NS (2014) Mapping of coastal aquifer vulnerable zone in the south west coast of Kanyakumari, South India, using GIS-based DRASTIC model. Environ Monit Assess 187(1). doi:10.1007/s10661-014-4073-2
Kanagaraj G, Suganthi S, Elango L, Magesh NS (2019) Correction to: Assessment of groundwater potential zones in Vellore district, Tamil Nadu, India using geospatial techniques. Earth Sci Inf. doi:10.1007/s12145-018-0374-2
Kangabam DR, Bhoominathan SD, Kanagaraj S, Govindaraju M (2017) Development of a water quality index (WQI) for the Loktak Lake in India. Applied Water Science 7(6):2907–2918. doi:10.1007/s13201-017-0579-4
Kanwal S, Atif S, Shafiq M (2016) GIS based landslide susceptibility mapping of northern areas of Pakistan, a case study of Shigar and Shyok Basins. Geomatics Natural Hazards Risk 8(2):348–366. doi:10.1080/19475705.2016.1220023
Khan AA, Paterson R, Khan H (2004) Modification and Application of the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) for the Communication of Drinking Water Quality Data in Newfoundland and Labrador. Water Quality Research Journal 39(3):285–293. doi:10.2166/wqrj.2004.039
Kumar P, Herath S, Avtar R, Takeuchi K (2016) Mapping of groundwater potential zones in Killinochi area, Sri Lanka, using GIS and remote sensing techniques. Sustainable Water Resources Management 2(4):419–430. doi:10.1007/s40899-016-0072-5
Lumb A, Halliwell D, Sharma T (2006) Application of CCME Water Quality Index to Monitor Water Quality: A Case Study of the Mackenzie River Basin, Canada. Environ Monit Assess 113(1–3):411–429. doi:10.1007/s10661-005-9092-6
Magesh N, Chandrasekar N, Soundranayagam JP (2012) Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques. Geosci Front 3(2):189–196. doi:10.1016/j.gsf.2011.10.007
Maiti S, Gupta G (2020) Integrated Geoelectrical and Hydrochemical Investigation of Shallow Aquifers in Konkan Coastal Area, Maharashtra, India: Advanced Artificial Neural Networks Based Simulation Approach. Springer Geophysics, pp 39–60. doi:10.1007/978-3-030-28909-6_3
Maiti S, Erram VC, Gupta G, Tiwari RK, Kulkarni UD, Sangpal RR (2013) Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks. Environ Monit Assess 185(4):3445–3465. doi:10.1007/s10661-012-2802-y
Mallick J, Singh CK, Al-Wadi H, Ahmed M, Rahman A, Shashtri S, Mukherjee S (2015) Geospatial and geostatistical approach for groundwater potential zone delineation. Hydrol Process 29(3):395–418. doi:10.1002/hyp.10153
Mazáč O, Císlerová M, Vogel T (1988) Application of geophysical methods in describing spatial variability of saturated hydraulic conductivity in the zone of aeration. J Hydrol 103(1–2):117–126. doi:10.1016/0022-1694(88)90009-1
Mondal NC, Das SN, Singh VS (2008) Integrated approach for identification of potential groundwater zones in Seethanagaram Mandal of Vizianagaram District, Andhra Pradesh, India. J Earth Syst Sci 117(2):133–144. doi:10.1007/s12040-008-0004-3
Mondal NC, Singh VP, Ahmed S (2012) Delineating shallow saline groundwater zones from Southern India using geophysical indicators. Environ Monit Assess 185(6):4869–4886. doi:10.1007/s10661-012-2909-1
Mukherjee P, Singh CK, Mukherjee S (2012) Delineation of Groundwater Potential Zones in Arid Region of India—A Remote Sensing and GIS Approach. Water Resour Manage 26(9):2643–2672. doi:10.1007/s11269-012-0038-9
Nag S (1998) Morphometric analysis using remote sensing techniques in the chaka sub-basin, purulia district, West Bengal. J Indian Soc Remote Sens 26(1–2):69–76. doi:10.1007/bf03007341
Nair NC, Srinivas Y, Rajkumar S, Arun Bose S (2019) Geoelectrical investigation to delineate the groundwater potential aquifers in Chittar river basin, southern India. International Journal of River Basin Management 1–16. https://doi.org/10.1080/15715124.2019.1700514
Nithya CN, Srinivas Y, Magesh N, Kaliraj S (2019) Assessment of groundwater potential zones in Chittar basin, southern India using GIS based AHP technique. Remote Sensing Applications: Society Environment 15:100248. https://doi.org/10.1016/j.rsase.2019.100248
Patra HP, Adhikari SK, Kunar S (2016) Groundwater Prospecting and Management. Springer Hydrogeology. doi:10.1007/978-981-10-1148-1_1
Perumal SB, Thamarai P, Elango L (2010) Hydrogeological studies along the coastal area of Kanyakumari to colachel after Tsunami, South Tamil Nadu. IJEP 30:793–813
Rajkumar S, Srinivas Y, Nair NC, Arunbose S (2019) Groundwater quality and vertical electrical sounding data of the Valliyar river basin, south West Coast of Tamil Nadu, India. Data in Brief 24:103919. https://doi.org/10.1016/j.dib.2019.103919
Rosemond SD, Duro DC, Dubé M (2008) Comparative analysis of regional water quality in Canada using the Water Quality Index. Environ Monit Assess 156(1–4):223–240. doi:10.1007/s10661-008-0480-6
Sankar K, Hyrogeological studies in the Kanyakumari district tamilnadu, University of madras. January 1995
Sawyer CN, McCarty PL (1967) Chemistry for sanitary engineers, 2nd edn. McGraw–Hill, New York
Senthilkumar S, Vinodh K, Johnson Babu G, Gowtham B, Arulprakasam V (2019) Integrated seawater intrusion study of coastal region of Thiruvallur district, Tamil Nadu, South India. Applied Water Science 9(5). https://doi.org/10.1007/s13201-019-1005-x
Shah KA, Joshi GS (2015) Evaluation of water quality index for River Sabarmati, Gujarat, India. Applied Water Science 7(3):1349–1358. doi:10.1007/s13201-015-0318-7
Singh CL, Singh SN (1970) Some geoelectrical investigations for potential groundwater zones in part of Azamgarh area of U.P. Pure appl Geophys 82(1):270–285. doi:10.1007/bf00876184
Srinivas Y, Aghil TB, Hudson OD, Nithya NC, Chandrasekar N (2017) Hydrochemical characteristics and quality assessment of groundwater along the Manavalakurichi coast, Tamil Nadu, India. Applied Water Science 7(3):1429–1438. doi:10.1007/s13201-015-0325-8
Srinivas Y, Hudson OD, Stanley RA, Chandrasekar N (2015) Geophysical and geochemical approach to identify the groundwater quality in AgastheeswaramTaluk of Kanyakumari District, Tamil Nadu, India. Arab J Geosci 8(12):10647–10663. doi:10.1007/s12517-015-1989-y
Suganthi S, Elango L, Subramanian SK (2013) Groundwater potential zonation by Remote Sensing and GIS techniques and its relation to the Groundwater level in the Coastal part of the Arani and Koratalai River Basin, Southern India. Earth Sci Res J 17:87–95
Virupaksha HS, Lokesh KN (2019) Electrical resistivity, remote sensing and geographic information system approach for mapping groundwater potential zones in coastal aquifers of Gurpur watershed. Geocarto International 1–15. https://doi.org/10.1080/10106049.2019.1624986
WHO (2008) Guidelines for drinking water quality: [electronic resource] incorporating 1st and 2nd addenda. Recommendations, 1, 3rd edn... WHO, Geneva, p 515
World Health Organization (WHO) (2011) Guidelines for drinking water quality, 4thedn (978-92-4-154815-1$4. World Health Organization, Geneva
Yadav KK, Gupta N, Kumar K, Sharma S, Arya S (2015) Water quality assessment of Pahuj River using water quality index at UnnaoBalaji, M.P, India. International journal of basic applied research 19:241–250
Zohdy AA (1949) Use of Dar Zarrouk Curves in the Interpretation of Vertical Electrical Sounding Data

supplementaryinformation.docx

Combined Geospatial, Geophysical And Geochemical Studies On Coastal Aquifer At Muttom-Mandaikadu Area, Tamilnadu, India

Status:

Version 1

Abstract

Figures

1. Introduction

2. Study Area

3. Methodology

4. Result And Discussion

4.1. Groundwater potential assessment using RS, GIS and MIF (Multi influence factor) technique

4.2. Groundwater potential assessment using geophysical resistivity method

4.3. Groundwater quality assessment

4.3.1. Water quality index (WQI)

4.4. Integrated approach

5 Conclusion

Declarations

References

Supplementary Files

Status:

Version 1