Mapping of Groundwater Recharge Zones in Hard Rock Aquifer through Analytic Hierarchy Process in Geospatial Platform

Abstract

Extensive use of groundwater is a result of the growing population; in relation to this, studies have focused on groundwater conservation measures. This study identified groundwater artificial recharge zones (GWARZs) in the upper Manimuktha sub-basin through the application of remote sensing and GIS. A spatial analysis using the analytical hierarchical process (AHP) and weighted overlay analysis (WOA) was employed by integrating several spatial thematic layers such as geology, geomorphology, aquifer thickness, lineament density (LD), drainage density (DD), soil, slope, rainfall, and land use/land cover (LULC) in order to classify the GWARZs. The geomorphology along with lithology, higher aquifer thickness, low lineament densities, higher drainage densities, and gentle slope regions, were identified as suitable areas for artificial recharge zones. The study area was divided up into five classifications based on the integration analysis: excellent (41.1 km²), good (150.6 km²), moderate (123.9 km²), bad (125.5 km²), and very poor (57.7 km²). Excellent and good GWARZs were identified in the eastern and central regions of the study area. The final outcomes of this research were evaluated with seasonal electrical conductivity (EC) variations. The majority of samples with minor seasonal EC variations were observed in the excellent and good GWARZ categories. The results showed that the spatial analysis tool is useful for GWARZ delineation and sustainably managing groundwater resources.

1. Introduction

Water comes in several forms (i.e., surface water, groundwater, snow, meteorological water) and is one of the foremost resources that sustains living beings globally [1,2]. India is one of the most significant consumers of groundwater and roughly 80% of it is utilized for domestic/drinking and agricultural activities [3]. The escalating water demand has led to an overexploitation of groundwater, and the aggressive withdrawal or extraction of these groundwater resources leads to a drop in water levels [4]. Hence, groundwater recharge (GWR) and management studies have been more helpful in addressing water scarcity [5,6]. The fundamental components of a hydrological system are the storage and recharge of groundwater. The nature of the terrain, soil profile, lithology, rainfall, temperature, and humidity are the controlling factors that determine the amount of recharge, which varies depending on the location. Consequently, groundwater potential assessment techniques vary regionally. Relatively low availability of surface water resources is observed in most arid/semiarid regions. Groundwater forms the basic source for domestic and agricultural uses in these regions [7,8]. Open spaces play a crucial role in naturally replenishing water supplies, as the amount of land being converted to urban areas has significantly decreased natural recharge. Attaining a balance between present and future social, economic, and environmental demands requires groundwater sustainability. Strain exerted on existing water supplies to fulfill human and ecological demands is referred to as water stress, as opposed to shortage. There are groundwater indices that policymakers can use to gauge groundwater sustainability, stress, and scarcity, but choosing the correct index can be challenging. Indicator 6.4.2 of the SDGs measures the level of water stress by comparing water use with availability, but there is room for improvement in incorporating various elements like net abstraction, environmental flow requirements, and alternative water resources. Analyzing groundwater fluctuations can help sense water stress levels in specific regions [9,10,11]. There are significant variations in groundwater quantity and quality in hard rock environments. In such conditions, the artificial recharge (AR) of groundwater plays a key role in enriching resources, and managing the increasing demand and groundwater supplies has become inevitable. Channeling the surface runoff to recharge structures like wells, surface spreading, or modification of existing natural recharge entities all encourage the infiltration and replenishment of an aquifer. Hence, AR is a potential technique to store groundwater to meet demand [12]. Additionally, land subsidence due to depletion of groundwater levels can be controlled, base flow in certain streams can be maintained, seawater intrusion in coastal aquifers can be controlled, and groundwater pumping costs can be decreased by AR [13,14,15]. Several studies have focused on replenishing groundwater resources through AR techniques in India [16,17,18]. The selection of appropriate locations for AR technologies is crucial for successful implementation. Hence, determination of recharge in arid/semiarid regions using several parameters spatially requires analysis of the data with GIS and remote sensing (RS) platforms [3,19,20]. The integration of GIS and remote sensing techniques along with field data is essential for addressing the environment sustainability of a region [21,22]. RS has been a prominent platform for the spatial analysis of surficial features and also in water resource management [23,24,25]. On the other hand, GIS has proved an effective technique for analyzing the geographical data of different themes [5,6,26]. It is utilized by a wide range of researchers for quick strategy decisions involving hydrogeological and environmental aspects. Since groundwater is a versatile and ever-changing resource, managing groundwater resources can greatly benefit from the usage of remote sensing and GIS platforms. Numerous researchers have mapped the likely GWR zones throughout the world using these powerful techniques to encourage artificial GWR activities [3,20,22]. After determination of various factors related to subsurface water, different thematic layer maps are generated. Later, their raster images are assessed, and the analytic hierarchy process (AHP) is adopted by assigning the proper weights to each thematic layer based on their levels of influence in the region relating to GWR for mapping the suitable zone for AR [27,28]. Approaches to this process (AHP) have drawn the greatest interest in groundwater management and sustainable development research, such as groundwater potential and AR site suitability mapping [29]. Earlier studies in the region [30,31] revealed the geochemical nature of the groundwater and pointed out the water level depletion in the area, with serious concern regarding suitability for different utility purposes [30]. Current global water management research mainly focuses on microlevel management [32] to enhance recharge and sustainability. In this regard, there has been no study reported to identify the suitable region of recharge in the current study area. Hence, the current effort aims to accomplish the goal of mapping the groundwater AR zones in the Manimuktha sub-basin with different thematic layers relating to groundwater occurrence along with field data, traditional maps, and satellite imagery. The study results were also compared to the seasonal conductivity variation in groundwater samples.

2. Study Area

The proposed investigated area is positioned in between 78°42′ and 78°59′ E longitude, and between 11°42′ and 11°59′ N latitude (Figure 1). The sub-basin spreads around 497.11 km², which includes 62.34% of plain area (309.92 km²) and 37.66% of hilly terrain (187.19 km²). Kalvarayan Hill, which divides the Villupuram and Salem districts, is situated along the western part of the sub-basin. Geologically, the study area is composed of Archaean rocks, including the retrograded Precambrian Peninsular Gneissic complexes [33]. In the sub-basin, there is an average of 1115 mm of rainfall annually, with almost 70% of that amount received between October to December, referred to as the northeast monsoon (NEM) period. There is a drastic decline in river flow from February to June in this region. The water table fluctuates between 8 and 18 m, dug well depths range between 15 and 20 m, and boreholes range between 28 and 46 m [30]. The study region is largely utilized for agricultural activities, such as sugarcane, paddy, and groundnuts.

3. Methodology

SOI (Survey of India) toposheets (58I—9 and 58I—13) were utilized to produce the Manimuktha sub-basin base map with a 1:50,000 scale range. ArcGIS 10.8v was applied to create thematic layers such as geology, geomorphology, slope percentage, land use/land cover (LULC), drainage and lineament density, etc., using the abovementioned toposheets, satellite imagery, and certain auxiliary data.

The geology map was scanned, traced, and imported into a GIS platform to digitize the lithological units. Indian Remote Sensing (IRS) satellite Geocoded IRS-1D LISS III digital data were used for the digitization and analysis of the geomorphological units. The image was analyzed based on supervised classification through the image processing software program ERDAS Imagine 9.1. The various geomorphic features were outlined and digitized for further evaluation in ArcGIS 10.8. The standard visual interpretation technique was the main process that delineated the geomorphological units. Lineament density is a length-dependent factor of surface linear features that is usually measured by dividing the total length of the lineaments by total area, as in the following expression:

$$\mathrm{L}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{a}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{D}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{y}=\sum _{i=1}^n\mathrm{L}\mathrm{i}/\mathrm{A}\left(m^{-1}\right)$$

Drainage density (DD) was calculated using the following equation:

$$\mathrm{D}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{g}\mathrm{e}\mathrm{D}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{y}=\frac{\mathrm{L}\mathrm{W}\mathrm{S}}{\mathrm{A}\mathrm{W}\mathrm{S}}$$

The slope (in %) of the present research area was estimated from the digital elevation model (DEM), which depended on the SRTM platform. The Thiessen polygon (TP) method was adopted to create the rainfall spatial map. The polygons were calculated by bisector lines (perpendicular) between the rain gauge stations. And, the average rainfall of each polygon was measured with the help of the exact precipitation of the given rain gauge station, the area of the particular polygon, and the overall areal extent [34]. A thorough process implemented for the present proposed research is displayed in Figure 2.

The Analytic Hierarchy Process (AHP)

The AHP technique of assigning weights and integration of thematic maps [35] was utilized to confirm the weights allotted to several aspects and their individual attributes used in interpreting the geographical analysis. Weighted index overlay analysis (WIOA) is an easy-to-understand technique for combining multi-class layer analysis [36,37]. Assigning weights to each factor and class within a factor supports its importance. From the center to the right, the number goes up from 1 to 9, and from the center to the left, it goes down from 1 to 1/9 [38].

The GWARZs categorization was carried out by using nine potential thematic spatial layers that include land use/land cover, rainfall, slope, soil, drainage density, lineament density geomorphology, lithology, and aquifer thickness. These selected potential layers were classified depending on the weightage classification and integrated using the spatial analysis tool. Each component was given a weighted hierarchy depending on expertise, ranging from 1 to 9 (more importance) and their fractions 1/3 to 1/9 (less importance). They were distributed according to importance in terms of their relation to AR. This weighting scheme classified a groundwater AR location as excellent zones if it was 9 (extremely important), and its fraction 1/9 (extremely unimportant) represented the least favorable zone.

Table 1. The weightage (assigned and normalized) of influencing factors based on AHP [38].

S. No.	Aspects	Assigned Weightage	Normalized Weightage
1	Lithology	9	0.2
2	Geomorphology	8	0.18
3	Aquifer thickness	7	0.16
4	Lineament density	6	0.1
5	Drainage density	5	0.1
6	Soil	4	0.09
7	Slope	3	0.07
8	Rainfall	2	0.04
9	Land use/land cover	1	0.02

provides the weight influence for locating GWAR regions based on a study in the adjoining region with similar lithology, climate, and land use [38].

In the end, the GWARZs for the research area were created by integrating nine distinct thematic maps with ArcGIS 10.8v software. The index was calculated using the following equation, which combined the total standardized weights of several polygons. This approach is linked to the investigation of geologically observable facts of the region, as well as their collective spatial aspect and associated geographical features [39].

GWARZ Index = (G_w × G_wi) + (GM_w × GM_wi) + (AT_w × AT_wi) + (LD_w × LD_wi) +
(DD_w × DD_wi) + (S_w × S_wi) + (SL_w × SL_wi) + (RF_w × RF_wi) + (LULC_w × LULC_wi)

where ‘G’ denotes geology, ‘GM’ indicates geomorphology, ‘LULC’ represents land use/land cover, ‘DD’ specifies drainage density, ‘SL’ designates the slope, ‘LD’ signifies the lineament density, ‘S’ relates to soil type, ‘RF’ represents rainfall, ‘AT’ is the aquifer thickness, and ‘w’ and ‘wi’ represent the normalized weight of the thematic layers and relative weightage of their individual classes. Consequently, the integrated final GWARZ of the study region was created using raster calculator in the ArcGIS platform, which served as the base for the development of the GWARZ map. Ultimately, there were five categories based on quartile classification for the GWARZ map: excellent (>0.244), good (0.222 to 0.244), medium (0.205 to 0.222), poor (0.194 to 0.205), and very poor (<0.194). The electrical conductivity (EC) was measured from 48 different wells for two seasons (pre- and post-monsoon) to evaluate the final outcomes by correlating the GWARZ index.

4. Result and Discussion

The GWARZ determination was obtained based on the relative weightages considered for each thematic category, as indicated in

Table 2. Assigned weight according to AHP for the nine different attributes considered for this study.

S. No.	Influence Factor	Categories	Weightages	Relative Weightages
1	Geology	Syenite/nepheline syenite, corundum syenite	1	0.7
		Garnet sillimanite–graphite gneiss	2	0.13
		Hornblende biotite gneiss	3	0.20
		Fissile hornblende biotite gneiss	4	0.27
		Charnockite	5	0.33
2	Geomorphology	Dome-type residual hills	1	0.02
		Hilltop weathered	1	0.02
		Inselberg	1	0.02
		Linear ridge/dyke	2	0.05
		Moderate buried pediment	3	0.07
		Moderately weathered/moderately buried pediplain	7	0.16
		Pediment—valley floor	6	0.14
		Pediplain canal command	5	0.12
		Ridge-type structural hills (large)	1	0.02
		Shallow buried pediment	3	0.07
		Shallow weathered/shallow buried pediplain	6	0.14
		Upper piedmont slope	3	0.07
		Water body	4	0.09
3	Aquifer thickness	Low thickness (<29.7 m)	1	0.10
		Medium thickness (29.7–56.0 m)	2	0.20
		High thickness (56–96.3 m)	3	0.30
		Very high thickness (>96.3 m)	4	0.40
4	Lineament density	Very high lineament density (>3.6 km/km2)	4	0.40
		High lineament density (2.4–3.6 km/km2)	3	0.30
		Medium lineament density (1.2–2.4 km/km2)	2	0.20
		Low lineament density (<1.2 km/km2)	1	0.10
5	Drainage density	Very low drainage density (<2 km/km2)	4	0.40
		Low drainage density (2–4 km/km2)	3	0.30
		Medium drainage density (4–6 km/km2)	2	0.20
		High drainage density (6 km/km2)	1	0.10
6	Soil	Hill soils	1	0.06
		Inceptisols	2	0.12
		Reserve forest	2	0.12
		Entisols	3	0.18
		Alfisols	4	0.24
		Vertisols	5	0.29
7	Slope	Mountainous (>16%)	1	0.09
		Moderately steep (12–16%)	1	0.09
		Strongly sloping (8–12%)	2	0.18
		Sloping (4–8%)	3	0.27
		Flat (<4%)	4	0.36
8	Rainfall	1006	1	0.10
		1073	2	0.20
		1226	3	0.30
		1431	4	0.40
9	LU/LC	Hilly region	1	0.04
		Built-up land	1	0.04
		Upland	1	0.04
		Wasteland	5	0.21
		Barren land	1	0.04
		Deciduous forest	2	0.08
		Fallow land	4	0.17
		Plantation	3	0.13
		Agricultural land	3	0.13
		Waterbodies	3	0.13

. The relative weights assigned to each category was based on their relationship to recharge and the highest value of the feature indicated greater probability of groundwater recharge.

4.1. Geology

The study area is predominantly—except the eastern part—composed of a charnockite complex of 435 km² (87.53%). Hornblende biotite gneissic complexes cover the eastern part with non-fissile-type in the southwestern region, and fissile type in the northeastern region. These non-fissile- and fissile-type hornblende biotite gneissic rocks cover an area of 52.11 km² (10.48%). Syenite/nepheline syenite/corundum syenite types are scattered in the central-to-eastern direction with an areal extent of 7.3 km² (1.47%). A minor portion of garnet sillimanite–graphite gneiss is represented with 2.6 km² areal coverage (0.52%) in the central portion near the foothill of ‘Kalvarayan Hill’ (Figure 3). Groundwater flow and its occurrences are primarily controlled by two hydrological properties of the rock formations: permeability and porosity [40]. Diverse weights were allotted to each lithological unit based on the on these two parameters. As groundwater could be retained in the aquifer medium, which is weathered and fractured, charnockite received a higher score [41]. The next highest scores were given to the fissile and non-fissile varieties of hornblende biotite gneissic complexes. Lithological characteristics play a significant role in GWR, with different lithologies impacting permeability and productivity potentials [42,43]. Lithological variations impact groundwater storage capacity by influencing storativity, which is a function of water release from the matrix. Different lithological formations and land surface structures in aquifers can impact local micro-scale infiltration patterns, affecting GWR [44].

4.2. Geomorphology

One of the most crucial elements in assessing the potentiality of groundwater and prospects of any province is its geomorphology [33]. The study area was classified as twelve geomorphological units (Figure 4). These were the ridge-type structural hill (127.8 km²), followed by shallow weathered/shallow buried pediplain (117.27 km²), moderately weathered/moderately buried pediplain (114.51 km²), dome-type residual hills (2 km²), hilltop weathered (33 km²), inselberg (0.8 km²), linear ridge/dyke (0.44 km²), valley floor (22.3 km²), pediplain canal command (26.9 km²), shallow buried pediment (30.26 km²), upper pediment slope (12.57 km²), and water body (8.53 km²). These geomorphological parameters influenced the groundwater through slope and soil and lithology relating to surface runoff and infiltration potential [45]. The groundwater potentiality of these different geomorphic units could be varied due to the different infiltration capacities [46]. In terms of AR in the study area, the weathered buried pediplain, pediment valley floor, and pediplain canal command were designated as higher score categories. The relationship between recharge and different geomorphological units is crucial for determining appropriate areas for AR. Geomorphological units such as alluvial fans and pediment units are considered suitable regions for AR [47]. The geomorphological characteristic of a region is a key in determining GWR, with local scale variability impacting the final assessment.

4.3. Aquifer Thickness and Unsaturated Zone Thickness

The aquifer thickness of the study area was derived from geophysical surveys, and it varied from 12.1 m to 192 m. The high and very high aquifer thickness categories predominantly covered the eastern part and a few minor pockets in the central portion. The eastern region has high potential to hold groundwater as it showed higher aquifer thickness. Most of the central region is covered by the medium thickness category. Low thickness showed as small patches in the central part and foothill areas (Figure 5). Further, the study area mainly divided into two categories based on the unsaturated zone thickness, that is, <3 m and >3 m, as the region favorable for groundwater recharge is considered to be >3 m of unsaturated thickness zone. The unsaturated zone categories presented as two different textures that were superimposed with aquifer thickness zones (Figure 5).

Storativity is one of the dimensionless aquifer parameters that increase as aquifer thickness increases. Furthermore, the thickness and storativity of an aquifer determine its specific storage. Fractures in hard rocks, especially at shallow depths near the water table, play a crucial role in determining storativity. The degree and nature of fracturing are significant factors affecting groundwater storage capacity [44,48,49]. The excellent GWARZ category is characterized by a deep water table, high storativity, and strong water holding capacity. Thus, considering these parameters, the weights were assigned to develop the aquifer thickness map.

Except for recharging via injection wells into an aquifer, all artificially replenished water must traverse the unsaturated zone. An important factor in deciding if a site is suitable for artificial recharge is the hydrogeology of the unsaturated zone. Subterranean recharge storage is available in the unsaturated zone, and its characteristics aid in pinpointing the best spot for artificial recharge. This increase in unsaturated storage leads to a lag time between percolation and recharge, emphasizing the need for more realistic simulation of unsaturated and saturated storage to accurately estimate spatiotemporal variability in recharge [50].

4.4. Lineament and Lineament Density

Lineaments are reliable indicators of groundwater, as they signify fracturing and faulting zones, which increase secondary porosity. Lineament density has a direct relationship with groundwater levels, as areas with higher lineament density tend to have higher groundwater yields. In comparison to boreholes situated away from lineaments, for those on or near lineaments, as well as those at the junction of lineaments, greater yield is observed in the boreholes located at the intersection of the lineaments. Among various lithological units, quartz schist and quartzite have the greatest average groundwater production [51,52]. Lineament density can affect aquifer permeability, as shown in studies evaluating borehole yield and groundwater productivity in fractured aquifers [53]. It is relatively simple to examine the lineament in various spectral bands using remote sensing techniques. Lineaments were predominantly observed in the NW–SE (northwest-to-southeast) and NE–SW (northeast-to-southwest), as shown in Figure 6.

The structural features of lineament density networks result in an increase in the infiltration capacity of the rocks. Hence, this is a vital influencing factor in GWR. The high lineament frequencies indicated a good recharge potential due to the existence of recharge routes. Nevertheless, the low frequency of lineaments implied very low recharge potential [23,54,55]. The eastern low flat terrain exhibited low lineament density, measuring less than 1.2 km/km². The medium category (1.2 to 2.4 km/km²) covered roughly 123.23 km² and was the second most prevalent lineament density type. The third and fourth were the high (2.4 to 3.6 km/km²) and very high (>3.6 km/km²) categories, which covered approximately 101.5 km² (Figure 7). As the densities increased, the areal coverage of each lineament density type decreased. The hilly area gradually displayed a medium-to-extremely high lineament density type from the top of the hills to the foothills. This demonstrated the effects of drainage dispersion throughout the research region.

Geological structures, such as fault barriers and hydrostratigraphic subdivisions, have a significant influence on recharge [56]. Fault lineaments can affect artificial recharge by influencing the flow of water through the subsurface, potentially creating preferential pathways for recharge or hindering the movement of water to recharge zones. Understanding the geological structures and characteristics of fault lineaments is crucial in assessing their impact on artificial recharge processes [57]. Fault lineaments can impact recharge rates by either acting as flow channels or obstacles to groundwater movement, based on the kinematics and trend of the fault in the litho unit. The deformation processes within fault zones can either reduce or enhance permeability, affecting the flow of groundwater. One important part of hydrogeological investigations is the effect of fault systems in groundwater movement, especially in groundwater management issues [58]. Remote sensing and geographic information systems were used to study the relationship between lineaments and borehole specific capacity in limestone areas [59].

4.5. Drainage Density

The history of the evolution of the surface alluvial landforms can be depicted by drainage patterns. Drain density, open-water level, and channel size are three variables that affect the link between drainage density and groundwater levels. The height difference between the bottom depth, the open-water level, and the phreatic surface determines the strength of groundwater flow and open-water flow in undulating areas with high groundwater tables [60]. Drainage characteristics of an area offer a crucial link to hydrogeology [61]. The higher number of drainages in the present study region are derived from the inselbergs and charnockite hills in the western portion. The drainage patterns are typically sub-dendritic to dendritic, which are characteristics of hard rock terrain.

Drainage density and permeability are inversely proportional and thus considered an important factor in designing AR zones for groundwater. A low value of drainage density indicates a suitable medium for AR since it promotes groundwater percolation [20]. The drainage density estimated for the study area (Figure 8) indicated the very low (VL) type of drainage density with <2 km/km² in the easternmost portion, which could be more favorable to the GWARZ. Low-to-moderate infiltration/recharge potentials of the research area are indicated by moderate-to-high drainage densities. The type of vegetation can impact drainage density by influencing sediment transport rates, which in turn affect the stability of channel-forming processes. Vegetation can alter the slope, leading to different scaling behaviors in landscapes, thus resulting in variations in drainage density based on the presence or absence of vegetation [62].

4.6. Soils

The particle size distribution of soils and sediments also plays a crucial role in hydraulic properties, affecting water movement, retention, consistency, tilth, and shrinkage capacity [63]. Understanding the distribution of soil properties and their relationship with terrain attributes is essential for predicting soil–water transmissivity and GWR rates in specific regions [64]. The ability of soils to promote or inhibit GWR is crucial [31]. Different locations have different soil textures due to variations in the local geology and topography. Soil condition is the primary determinant of potential groundwater zones. Soil permeability and profile type greatly affect percolation rate and water-holding capacity [65,66].

There were five major soil types, namely, Alfisols, Entisols, Inceptisols, Vertisols, and hill soils, other than the reserve forest cover in the study area (Figure 9). Hill soils span the whole western region; vertisols were found in the southeast; Entisols were found in the northeast; and both Alfisols and Inceptisols were found across the center region. Numerous geomorphological agents frequently regarded Entisols and Inceptisols as youthful, immature soils with recently formed sediments. They could therefore be able to seep more groundwater. Conversely, vertisols were thought to belong to the heavy clay soil type, which increased runoff and limited recharge. In low rainfall regions, vertisols have low prospects of GWR and availability, with runoff harvesting being a more viable option for water management [67]. Clay shale deposits, like Vertisols, can experience seasonal variations in permeability due to cracking and volume changes, influencing GWR processes [68]. As Alfisol soil profiles have a lower water-holding capacity than Vertisol, GWR in Alfisols is higher than in Vertisols and hence the prospects of groundwater availability is greater [67,69]. GWR in Entisols can vary widely based on land use and land cover settings, with paddy fields, orchards, and crop plantations contributing to higher rates of infiltration and preventing excess runoff [70]. Soil texture affects GWR rates in Inceptisols by influencing hydraulic conductivity, which determines water availability for plants and GWR. Different land management practices, such as cultivation or tillage operations, cropping systems, mulches, and land use/cover, can affect soil physical properties like hydraulic conductivity and infiltration rate, impacting the rate of water flow in the soil [71].

4.7. Slope Gradient

One of the key variables governing the surface runoff and infiltration rate is the slope gradient or slope angle [72,73]. The flat surfaces can give more time for the subsurface migration of water, resulting in an increase in GWR, while steep slopes enhance runoff and reduce surface water infiltration [31].

Five classes of slopes have been identified based on the percentage of slope (<4% to >16%): flat, slopping, moderately sloping, strong sloping, and mountainous region (Figure 10). The spatial map revealed two different major types: a slopy western region and a flat eastern terrain.

Land use change and soil texture are both affected by the slope, which in turn affects the rate of recharge and, ultimately, GWR. GWR rates on hillslopes are typically lower compared to flat landscapes, with soil texture being a dominant control factor. Types of vegetation and additional changes in land use can also have an effect on GWR. Unfortunately, our understanding of the numerical link between vegetation and GWR is still limited [74,75,76]. Hydraulic conductivity, bedrock permeability, and soil depth all have a role in determining the distribution of bedrock GWR hotspots, which in turn affect GWR variations across the slopes. These hotspots can contribute significantly to overall recharge, with 30% of annual GWR occurring in just 10% of the hillslope area. These hotspot locations are mostly determined by the dynamic factors that drive the recharge pattern spatially, such as the size of rainstorms in relation to the water storage capacity of the soil [77,78].

4.8. Rainfall

In all climatic regions, rainfall is recognized as the primary source of GWR [72]. A number of publications show evidence that the rainfall controls the groundwater quantity, and qualities such as water level rises, recharge, discharge, contamination, and transportation [79]. For the rainfall analyses, the Thiessen polygon method was adopted for this study area. The rainfall of the upper Manimuktha sub-basin was estimated based on the four rain gauge stations located at Ariyalur, Manimuktha reservoir, Gomuki dam, and Sankarapuram in the Villupuram district. Ten years of rainfall was collected from the Public Works Department (PWD), and the rainfall deviations of the upper Manimuktha sub-basin are shown in Figure 11. The rainfall data of Ariyalur station showed the maximum yearly rainfall of 1432 mm, with Sankarapuram recording the lowest amount at 1001.1 mm. On a yearly basis, 1184.7 mm of precipitation was recorded on the upper Manimuktha sub-basin. Rainfall in the study region was highest during the northeast monsoon season, compared to the southwest monsoon season.

The amount of rainfall affects GWR, especially in arid regions with minimal recharge. Heavy precipitation events in dry regions are a common trigger for GWR, and even slight variations in the intensity of precipitation can affect GWR. The episodic nature of GWR in arid environments makes precipitation intensity a crucial factor in GWR [80]. Rainfall intensity can affect GWR by influencing the infiltration rate. Studies have shown that as rainfall intensity increases, the recharge coefficient may decrease, impacting the quantity of GWR [81]. Additionally, changes in precipitation intensity can lead to alterations in the ratio of GWR to precipitation, affecting groundwater continuity in arid regions [80]. Occasional recharge from infrequent, high-intensity precipitation events are crucial in GWR in arid environments [80].

4.9. Land Use and Land Cover

The variation in GWR is brought about by modifications to vegetation and land use as a result of groundwater level fluctuations. Remote sensing and GIS techniques offer trustworthy information for the purpose of mapping land use and cover. Land usage and land cover provide further information about the nature, characteristics, and coverage of the land [82,83]. Evapotranspiration, surface runoff, and recharge potential are highly controlled by LULC in any region [84]. The LULC map shows ten different land covers, agricultural land, barren land, built-up land, deciduous forest, fallow land, hilly region, plantation, upland, wasteland, and water bodies (Figure 12). Most of the study area is covered by forest area (forest and deciduous forest), covering an area of 167.2 km², followed by wasteland (46.3 km²), upland (5.2 km²), built-up land (29.9 km²), barren land (68.8 km²), fallow land (85.4 km²), plantation (7.0 km²), agricultural land (58.0 km²), and waterbodies (29.3 km²). The land covers such as built-up land, wasteland, and barren land were considered to be lower GWR potential classes than the other land uses [85]. Hence, these attribute characteristics were accounted for by assigning weightage to the different land use classes.

As witnessed in Ho Chi Minh City, the increase in urbanization scenarios resulted in a 52% decrease in annual average recharge, indicating that land use patterns can have a substantial impact on GWR, especially changes in urban built-up regions [86,87,88]. Thus, changes in GWR rates can be quite noticeable in urban regions. The decrease in evapotranspiration and the increase in impermeable surfaces cause GWR rates to rise with the expansion of metropolitan areas, according to studies. Urbanization and population growth can also lead to higher groundwater exploitation [89]. An important factor influencing GWR potential in urban areas is land use zoning, especially in relation to urbanization. Studies have shown that urbanization can decrease average GWR, leading to reduced groundwater levels and base flow. Further, urban expansion has been widely linked to a decline in GWR and water levels. Assessing the influence of land use changes on groundwater systems relies largely on precise land cover maps. Uncertainties in land use maps can affect hydrological processes, including GWR. To ensure sustainable water usage and the conservation of recharge zones, water authorities can utilize the zoning of GWR potential as a significant tool [90,91].

4.10. Discussion

Aquifer recharge depends on several factors, like lithology, geomorphology, aquifer thickness, lineament density, drainage density, soil type, slope, rainfall, and land cover. All of these variables have significant effects on the rate of recharge. Higher rates of GWR are more likely to occur in regions that receive considerable amounts of precipitation and that have lesser drainage density [92]. Similarly, land cover, such as forests or wetlands, can help facilitate subsurface infiltration and ultimately recharge the aquifer [93]. A study on mountain block contribution to riparian aquifer in hard rock aquifers integrated remote sensing and GIS methods to assess groundwater potential regions [94]. Recharge processes in crystalline rock terrain have been investigated using stable isotopes and hydrochemical controls [95]. Lithology can affect GWR by influencing the hydrochemistry of the aquifer, with factors such as evaporation, mineral weathering/dissolution, and anthropogenic activities having an impact on the chemistry of groundwater along the groundwater course from recharging to discharging zones [96]. The lithological variation in a basin may not have a significant impact on hydrochemistry in hard rock aquifers, as recharge/discharge relations and flow scale control the hydrochemistry in regional groundwater basins [49]. Terrestrial alluvium deposits act as perfect aquifers and store the slowly infiltrated water through the fault planes. In the western part of the study area, the rocks are predominantly charnockite, and in the eastern part, the region is represented by gneiss. The gneiss is relatively weathered compared to the charnockite, which favors the recharge of the groundwater. Further, charnockite is relatively resistant to weathering and hence has less influence of the seasonal geochemistry of water resulting due to natural processes. The accumulated infiltration in experiments indicates that fractures can enhance water infiltration. The impact of fractures on groundwater infiltration is significant, especially during heavy rainfall events [97]. In terms of GWR, faults are very significant. These fault planes/lineaments provide permeable pathways for aquifer recharge [98]. As it pertains to groundwater flow, fault zones can either act as a barrier to the flow or as a preferred pathway. Permeable channels for recharge are more likely to be activated during overbank flooding during intense precipitation events [58,96]. Structures like faults and fractures affect the flow of groundwater near the surface [99]. Drainage distance and density also perform an essential function in the possibility of recharging groundwater, with areas having greater drainage distances and preferential drainage densities being prioritized for recharge [100]. In this regard, lower drainage density along with the existence of surface water bodies favors the recharge. The eastern portion of the research region has lesser lineament intensity, which indicates lesser structural influence. Soil texture is a key component in GWR, with sandy soils allowing for more water to percolate through than clayey soil. In terms of GWR, sandy soils are preferable to clayey soil since water can penetrate them more quickly. Factors like the roots systems of vegetation and the interception capability of canopies contribute to the increasing GWR that results from the conversion of forests or native vegetation to controlled land use systems [101]. The canopy impact and increased infiltration capacity of forest systems cause surface runoff to be lower than that of controlled land use types. Vertisols and Entisols are the major soil types in the eastern part. These Entisols along with the predominance of peneplains in the region favor the recharge in the northwestern portion of the study area. Transforming forested areas into regulated agricultural areas can affect the GWR rates, driven by factors like vegetation root systems and the interception capacity of canopies. Forest ecosystems are less effective in recharging because their evapotranspiration rates are higher than those of controlled land use categories [25]. Native vegetation is more efficient in extracting soil moisture compared to managed LULC like crops and grasses, resulting in increased GWR [101]. After a forest is converted to another LULC type, the GWR is chiefly affected by soil texture, with sand-textured soils showing a more pronounced effect. The aridity index determines GWR changes after LULC change, with more arid environments experiencing stronger recharge changes [101]. Several thematic maps were created using toposheets from the Survey of India and imagery from the Indian Remote Sensing-1C. These layers include lithology, lineament, land use, slope, drainage, soil, and rainfall. Subjective weights were assigned to these layers and overlaid in a GIS to categorize potential zones as ‘low’, ‘moderate’, and ‘high’ based on weightage [93]. Alterations to land cover have an effect on groundwater levels, which in turn affects the water balance of a basin. The simulation models used indicated that different land cover scenarios led to variations in GWR values. It was observed that groundwater levels are likely to decrease due to increased water abstraction, emphasizing the significance of considering the effects of shifting land use practices on available water supplies for sustainable management [102]. GWR rates are susceptible to both beneficial and adverse effects of urbanization. Some studies show that urbanization can result in a reduction in recharge rates due to surface sealing preventing infiltration [89]. However, other studies indicate that urbanization can actually increase recharge rates through mechanisms like water mains leakages, urban landscaping, and runoff infiltration. Overall, the effects of urbanization on recharge rates are conditional and can differ from one metropolis to another [103]. Different land uses impact GWR rates differently. In arid areas, replacing grassland with woodland and cropland can decrease recharge rates by 70% and 250%, respectively, while in humid climates, reforestation and agricultural use of areas originally designated as grassland results in reductions of 20% and 60% [101]. The hard rock aquifers are significantly governed by the geological structures, especially the infiltration, specific capacity, yield flow, water level, thickness of unsaturated/saturated zone, etc. Estimating aquifer thickness can be performed using multiple pumping tests on unconfined aquifers with notable changes in water table levels throughout the year. This technique estimates aquifer thickness by deducing it from the transmissivity–water level connection. In various geological contexts, this method is useful for identifying the aquifer’s active hydraulic zones [104]. In hard rock regions, geological structures can influence GWR by creating pathways for water to flow through, such as fractures or faults [105]. For better comprehension of the interplay between hydrogeology and groundwater ecology, it is important to examine groundwater ecosystems, which shed light on groundwater-related biodiversity and ecosystem services [106]. A fracture network increases the average depth of water infiltration, and high precipitation intensity decreases the uniformity of the percolation process, thereby increasing the aquifer thickness [107]. The eastern part of the study area indicated the presence of high-to-very high aquifer thickness, reflecting the availability of a relatively sustainable aquifer. In addition to the improvement in vertical infiltration, the spatial distribution of the fracture zone can increase the potential and strengthen flow channels, favoring preferential flow, which also indicates that the eastern part is favorable for recharge, with a greater extent of the region with higher thickness and presence of surface water features to facilitate recharge. The presence of fractures increases soil water infiltration [108].

Aquifer thickness can affect GWR, as it is dependent on various considerations like the type of aquifer and its geographic location. The deep aquifers, mainly in areas of crystalline structure that have thick saturated zones of groundwater, or aquifers with extended unsaturated zones, show attenuated changes in groundwater temperatures over long periods. Variations in GWR mechanisms as a function of time, especially in river-fed aquifers, can play a crucial role during times of heavy runoff, enhancing GWR, especially during intense rain events. Climate change adaptation strategies and anthropogenic influences in urban areas can also impact groundwater temperatures significantly [109]. During storms with transient saturation at the soil–bedrock interface, locations with high bedrock permeability or those with topographical characteristics that sustain increased subsurface saturation act as GWR hotspots. Bedrock permeability and soil depth are crucial controls to govern the spatial pattern of bedrock GWR [78]. Rainfall intensity has a significant impact on GWR. Although an increase in rainfall intensity as a result of climate change may encourage GWR in certain regions, more intense storms may have the opposite effect and lead to more runoff and less GWR [81]. Studies have shown that GWR is less in semiarid regions with a yearly precipitation below 300 mm and is facilitated during intense rain events and flood events along gentle terrains [43]. Groundwater supplies are susceptible to fluctuations in precipitation, and the correlation between precipitation and recharge is not linear [110]. It is possible to estimate GWR using modeling and monitoring techniques and water levels, with the accuracy of modelling results depending on aquifer permeability and information accuracy [111]. The eastern part of the study area is observed to have the influence of the higher rainfall compared to other regions. Further, the eastern part has a gentle slope, favoring recharge. Groundwater follows the same pattern as the drainage, which is east to west and takes advantage of the region’s slope. In addition, the rainfall from the western regions flows to the east, adding to the amount of rainfall received in the eastern regions with a gentle slope and more water bodies favoring the recharge of the region. According to previous studies, the central portion of the catchment should be the primary target of groundwater potential zone management [112,113]. Hence, this study recommends the recharge structures in the following regions: Palarambattu, Alagapuram, Paramanatham, Vadasettiyandal and Viriyur. Electrical conductivity is closely related in determining GWR sources. Aside from being a natural indicator of water origin and migration, electrical conductivity values are proportional to the concentration of dissolved ions in water [114]. The lesser values of electrical conductivity in groundwater reflects higher rates of GWR. The relationship between these parameters vary in leaching depth, and soil texture [115]. Soil texture has a greater impact on the recharge–electric conductivity relationship than solute leaching [116]. Researchers also identified an increase in groundwater conductivity during recharge due to the dissolution of salts along the infiltration/migratory pathways [117] and, on the contrary, the dilution leads to a decrease in groundwater conductivity after rain events [118].

The thematic layers were integrated with the cumulative weight for each considered factor and sub-factor to create the GWARZ map. For spatial analysis, the inverse distance weighted (IDW) approach was adopted for thematic layers such as geology, geomorphology, aquifer thickness, lineament density, drainage density, soil, slope, rainfall, and land use/land cover, based on a weighted overlay analysis (WOA) (Gnanachandrasamy et al. 2018). Based on the WOA, the study area is classified into five classes: excellent (41.1 km²), represented in pink; good (150.6 km²), represented in blue; moderate (123.9 km²), represented in green; poor (125.5 km²), represented in grey; and very poor (57.7 km²), represented in brown. The final integration map revealed that a major portion of the study area was represented by the good (150.6 km²) and excellent (41.1km²) GWARZ categories (Figure 13). This information was crucial in creating the GWARZ map, which provided an immediate evaluation of the research region’s groundwater reserves.

The basic AHP technique is limited in its ability to deal with situations that are both consistent and inconsistent. In this study, an enhanced version of the AHP was used, which improved the dependability of the analytical results. However, there were several concerns with the geophysical survey. The complete length of the survey or a specific measurement of the survey was skipped due to topography undulation and land use patterns. This type of issue can be addressed in future studies by increasing the number of survey locations [119].

Though there are other methods available to determine the GWARZ, the availability of data and applicability of the technique are the key deciding factors. 1. Groundwater artificial recharge zones can be determined using environmental isotopes, such as ³H, ¹⁸O, and D. Isotope techniques provide more accurate recharge values than conventional methods and can estimate recharge from artificial structures [120]. 2. Geophysical resistivity surveys can be used to map GWR zones by correlating resistivity values with the lithological properties of the aquifer. The method involves conducting resistivity profiles of deeper sections and inverting pseudo-sections to obtain real resistivity sections [121]. 3. Machine learning methods, such as the LSTM models, are successfully applied for estimating GWR. These models have proven high precision in assessing GWR, with important predictors including evapotranspiration, minimum temperature, and rainfall intensity. These influential factors could be considered as monitoring requirements and to improve groundwater management strategies [122,123]. 4. Artificial intelligence was used to identify suitable artificial GWR areas, with a high accuracy of 97% [124]. 5. Modeling the location-specific and temporal variability of groundwater levels over relatively brief periods is possible with geostatistics, showing fluctuations throughout the year due to factors like rainy season recharge and extraction for irrigation [125,126,127]. 6. In order to identify GWR zones using geochemical methods, it is necessary to assess the recharge from various sources in order to determine the magnitude of each source, the risks of contamination, and the geochemical processes at play. 7. Groundwater flow and transport modeling are essential for a comprehensive assessment of recharge sources [116]. Quantifying recharge sources is challenging, with direct measurements limited to a few cases. Recharge sources in urban areas differ from natural systems due to impervious surfaces and new sources like losses in sewage and water distribution systems [128,129].

4.11. Evaluation

The recharge of fresh groundwater lowers the EC values, which generally occurs after the monsoon/rainy season (post-monsoon). Hence, the difference between the pre- and post-monsoon EC values of groundwater would help in determining the recharge zones. So, the current finding is evaluated by determining the relationship between seasonal variation of conductivity values and GWARZ index (Figure 13). In general, it is inferred that the regions with low values serve as excellent recharge regions. The major limitation of this study is the non-availability of monitoring wells to correlate with water level data and the conductivity in both the periods. The EC values were generally lower after the rainfall (post-monsoon), except for very few samples (N = 5). The higher values indicate a drastic lowering in EC values after monsoon season, indicating the probability of dissolution and precipitation processes. The regions with lesser variation would be more suitable, as the quality of groundwater would be consistent with a lower probability of contamination. Further, higher variation in EC was observed in samples with higher EC values the PRM season and lower variations were mainly noted in samples with lower EC values in both the seasons (Figure 14). Thus, the GWARZ index was found to have an independent correlation with changes in EC values before and after the monsoon. The relationship between EC values of both the seasons and the GWARZ index is depicted in Figure 15. The good and excellent category are indicated based on the GWARZ values, and it was observed that most of the samples (44%) with lower EC values represented these categories.

5. Conclusions

A GWARZ can be determined by evaluating recharge in different environments, with several recharge sources identifiable using limited available data. A decrease in evapotranspiration can offset a decrease in direct infiltration, and urban areas may see the emergence of new recharge sources from sewage and water distribution networks. This study adopted a cost-effective method of GWARZ determination. The GWARZ studies carried out in the upper Manimuktha sub-basin employing GIS and remote sensing identified groundwater AR zones in hard rock aquifers. The location of the AR structures was determined with the help of a GWARZ suitability analysis. This spatial analytical methodology can be used to delineate GWARZs. Nine potential thematic layers were used in order to evaluate the AR zone identification. Both non-fissile and fissile biotite gneissic complexes were selected as a higher priority for AR due to their capacity to retain water. The pediment valley floor, pediplain canal command representation, and buried pediplain are potential suitable sites for the AR structure. The aquifer thickness is a key factor in determining AR zones; thicker aquifers are higher in the eastern region with a significantly higher capacity to store water. The steep western section had higher density of lineament, while the eastern part had minimal density. Hence, inferences from drainage density also indicated that the eastern area would be optimal for AR. The soil profile map indicates that AR was found to be beneficial in the central region. Based on historical data, the region had experienced excellent rainfall, most notably in the east and center. The entire eastern section had a relatively gentle slope, rendering it a suitable area for AR. Thus, combining all the stated parameters, the GWARZs were identified. There were five groups established for the study area according to GWARZ: good (150.6 km²), moderate (123.9 km²), poor (125.5 km²), very poor (57.7 km²), and excellent (41.1 km²). A relatively larger area representing a good GWARZ was observed, and the remaining areas were represented by the poor, medium, very poor, and excellent GWARZs in the relative sequence. An artificial GWR zone can be considered in the good and excellent GWARZ categories. These two categories support the GWARZ of the research area, which primarily covered the eastern portion. These data were evaluated using the difference in electrical conductivity of the seasonal samples. About 44% of samples represented the excellent and good categories and they were closely related to the samples with lower EC values in both seasons. Thus, the proposed GWARZ map will play an essential role in facilitating the effective administration of the area’s water resources.

Thus, the GWARZ approach helps in understanding anthropogenically influenced groundwater recharge processes in semiarid regions especially in the regions affected by climate change. Furthermore, the identified zones are helpful to policymakers in devising sustainable water management strategies and probing the integration potential of different suitable techniques at identified hotspots in detail.