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

Rajkumar Stanly Manonmaniam Sundaranar University Srinivas Yasala (  [email protected] ) Manonmaniam Sundaranar University Nithya C Nair Cochin University of Science and Technology Cochin Arunbose Subash Manonmaniam Sundaranar University

Research Article

Keywords: Remote sensing, Vertical electrical sounding, Geochemical, Groundwater potential, Contamination, Water quality index, Coastal zone.

Posted Date: September 1st, 2021

DOI: https://doi.org/10.21203/rs.3.rs-731527/v1

License:   This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License

Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 1/19 Abstract

A study was conducted in the Muttom-Mandaikadu coastal region, which is among the proftable coastal sectors in , to fnd 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.

1. Introduction

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 identifcation 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 confguration 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 scientifc 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 fnding 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 fnd 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; Alfaif et al. 2019; Ameen 2019; Rajkumar et al. 2019; Maiti and Gupta 2020). The reason behind most hydrogeological exploration is to fnd potential zones of sufcient amount of fairly great quality groundwater for different utilizations, which relies upon the nearby needs and the fnancial 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 proftable 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.

2. Study Area

The study area Muttom to Mandaikadu coastal zone is located in (lat 8.14423°, long 77.3058°) the 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 fow 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 alLlouavdiiunmg [M, wathhiJcahx] /ijsa xc/oumtppuot/sCeomd moof nfHnTeM aL/nfdon mts/eTdeiXu/mfon-gtdratian.jesd sand and very little clay; the main constituents of the sand being heavy minerals include ilmenite, Page 2/19 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 unconfned 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 specifc yield value is 0.2 (Perumal et al. 2010). Rainfall forms the only source of aquifer replenishment of these aquifers.

3. Methodology

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. Georectifcation/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 frst stage of this study, fve 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, feld 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 classifcation (maximum likelihood classifer 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 classifed images are involved for accuracy assessment using post feld verifcation 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-infuence 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 feld 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 (ρa) 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 ‘ρs’ (Eq. 3) utilized to sort out the water aquifers in the seaside locales (iv) average transverse resistivity ρt (Eq. 4) and (v) anisotropy λ (Eq. 5) are derived from the subsurface layer resistivity and thickness following:

n h1 h1 h2 h3 hn S = ∑ i = 1 = + + + ⋯⋯⋯ + Eq ρ1 ρ1 ρ2 ρ3 ρn

1

n T = ∑ i = 1h1ρ1 = h1ρ1 + h2ρ2 + h3ρ3 + ⋯⋯⋯ + hnρnEq

2

n H ∑ i = 1h1 ρs = = h Eq S n 1 ∑ i = 1 ρ1

3 n T ∑ i h1ρ1 ρt = H = n Eq. (4) ∑ i h1

Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 3/19 ρt λ = Eq √ρs

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 2+ − 2− 2+ measured using the HANNA instrument. The other parameters like total hardness (TH), calcium (Ca ), chloride (Cl ), sulphate (SO4 ), magnesium (Mg ), + + − 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 defned by the World Health Organization (WHO). To measure WQI, a set of 11 physical and chemical 2+ 2+ + + − − 2− parameters such as pH, EC, TDS, TH, Ca , Mg , Na , K , HCO3 , Cl , and SO4 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).

1. Scope – F1

2. Frequency – F2

3. Amplitude – F3

2 2 2 CWQI = 100 − √(F1 + F2 + F3)/1.732 Eq. (6)

4. Result And Discussion 4.1. Groundwater potential assessment using RS, GIS and MIF (Multi infuence factor) technique

In order to identify the groundwater potential zones of the study area, fve 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-infuencing factor technique. The main geomorphological landforms identifed 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 alfsols. Among them, Inceptisol is predominantly distributed in most of the study areas (Fig. 5a). Inceptisol has been assigned high weightage, whereas alfsol soil has given low weightage (Suganthi et al. 2013). The drainage density of the study area (Fig. 5b) is classifed 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 fve 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 fat 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 fve selected parameters and their individual feature classes were assigned suitable weights according to their infuence 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 fve 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.

Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 4/19 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 Alfsols 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

Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 5/19 S.No Parameter Class Weight (W) Rank (R)

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 verifcations. In the study area, coastal landforms, outer crop crystalline rock, Teri sand, water bodies, settlement, cropland and plantation could be visually confrmed to be matching with the Geospatial information. Some feld 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 fnal 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 fve 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 confrm the saline water intrusion from Sea (Maiti et al. 2013), and high resistivity values indicate hard rock.

The normal longitudinal resistivity (ρs) and transverse resistivity (ρt) values were determined in equations 3 & 4 from the results of each arrangement of soundings (Table 2). The longitudinal resistivity (ρs) 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 identifcation using aquifer resistivity. Therefore cross-verifcation 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 identifcation, 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 (ρA) 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 fnal 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.

Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 6/19 Table 2 Interpreted VES results with layer parameters. VES LAT LONG VES-LAYER PARAMETERS DAR-ZARROUCK PARAMETERS

ρ1 h1 ρ2 h2 ρ3 h3 ρ4 h4 ρ5 h5 ρ6 ρA T S ρS ρT Ωm Ωm m Ωm m Ωm m Ωm m m Ωm Ωm Ωm2 Ω−1 Ωm Ωm

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

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

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

4 77.292 8.156 256 3.88 207 4.26 77.5 5.51 56.3 3.44 56 -- -- 56 2496 0.17 101.8 146.0

5 77.297 8.159 263 0.7 1209 2 492 6.46 60 ------60 7295 0.02 505.8 585.4

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

7 77.282 8.166 101 2.3 149.6 8 256 19.4 32 ------32 6396 0.15 195.4 215.3

8 77.288 8.165 253.5 2.7 131 2.49 233 12.1 70 ------70 3851 0.08 212.0 221.5

9 77.281 8.174 292 1 184 1.6 72 4.2 463 10.3 251 -- -- 72 5658 0.09 184.5 330.8

10 77.315 8.124 35 2 21 1.44 245 9 895 ------21.5 20205 0.18 175.5 622.8

11 77.318 8.129 264 1.7 301 1.9 511 3.14 176 5 26 25 970 26 3505 0.05 248.2 298.5

12 77.302 8.154 158 1 494 2.12 208 3.8 70 7 34 -- -- 34 1996 0.03 239.5 288.3

13 77.302 8.156 56 1.4 34 11 74 ------32 452 0.35 35.6 36.48

14 77.298 8.165 149 8 56 3.5 87 ------56 1388 0.12 99.0 120.7

15 77.294 8.167 350 2.6 390 3.8 221 6.6 133 ------133 3851 0.05 276.4 296.2

16 77.294 8.161 672 1.5 586 1.7 733 5 400 9.9 74 -- -- 74 9559 0.04 489.2 525.2

17 77.289 8.157 185 1.5 52 5.5 67 8 28 ------28 1100 0.23 64.3 73.30

18 77.308 8.144 109 3 12.5 16.3 72.5 ------12.5 531 1.33 14.5 27.50

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

20 77.284 8.159 164 1.6 43 1 11 31.6 7.5 ------11 653 2.91 11.8 19.09

21 77.280 8.169 215 1 133 1.2 55 4 156 28.5 93 -- -- 55 5041 0.27 129.0 145.2

22 77.289 8.173 29 0.4 222 6.7 60 7.6 384 23.5 16 -- -- 60 10979 0.23 164.8 287.4

23 77.296 8.173 245 2.2 79 5 501 12 230 ------79 6946 0.10 199.5 361.7

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

25 77.308 8.143 34 1 1 0.5 13 27 2162 ------13 386 2.61 10.9 13.53

26 77.308 8.158 120 1.68 7.36 14.6 12 56 124 ------7.36 981 6.66 10.8 13.57

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 classifed into fve 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 flled 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. Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 7/19 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 classifcation, 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+> 2+ + − − − Mg > 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 classifed 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 purifcation. If it is an excellent ranking, the water does not need purifcation, while the good ranking needed slight purifcation (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.

5 Conclusion

Satellite imageries, topographic maps, and secondary data sets are used to prepare fve 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.

Declarations

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 fles.

Competing interests The authors declare no competing interests.

Funding The authors would like to acknowledge the University Grants Commission (UGC), New Delhi for the fnancial 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 fnal manuscript

Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 8/19 Acknowledgement The authors gratefully acknowledge the University Grants Commission (UGC), New Delhi for the fnancial assistance provided for Major Project No: F.No.41-940/2012 during the period of study.

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Figures

Figure 1

Study area map.

Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 11/19 Figure 2

Methodology fow chart

Figure 3

Correlation of feld data with available subsurface data. Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 12/19 Figure 4

Geomorphology map of the study area.

Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 13/19 Figure 5 a) Soil b) Drainage density map of the study area.

Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 14/19 Figure 6 a) Land use/ Land cover (LULC) b) Slope map of the study area.

Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 15/19 Figure 7

RS Groundwater potential zone map delineated using a multi infuencing factor technique.

Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 16/19 Figure 8

Map showing the spatial distribution of a) Electrical conductivity of the water sample b) Aquifer resistivity of the VES result.

Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 17/19 Figure 9

Groundwater potential zone map delineated from combined geophysical and remote sensing data.

Loading [MathJax]/jax/output/CommonHTML/fonts/TeX/fontdata.js Page 18/19 Figure 10

Groundwater 4-D Model of combined quantity and quality (A, B and C indicates Muttom, Manavalakurichi and Mandaikard regions respectively).

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