Groundwater Potential Zone Mapping to Understand the Sustainability of Springs in a Micro Watershed of Kosi River, Kumaun Lesser Himalaya, Uttarakhand, India

1. Introduction

Groundwater proves to be an invaluable and priceless natural resource essential for various human activities, such as drinking water supply, industrial processes, and irrigation. Accounting for about 34% of the Earth’s freshwater, it forms the largest freshwater resource in the world and is immensely important for society [23,64]. Therefore, understanding the hydrological processes of this precious resource in a particular territory is necessary for supporting sustainable development.

The Sustainable Development Goal Six (SDG-6) of the United Nations aims to ensure universal access to clean drinking water, adequate sanitation, and improved hygiene, recognizing them as essential rights for all individuals by 2030 [66]. Setting up this kind of target is a dire necessity in the ongoing unsustainable practices in the world, which are not only leading to quantitative reduction of surface water but also have made direct use of this water harmful for human beings, inviting various diseases. Further, the pace of rapid modernization, industrialization, and intensive agricultural practices induced by high population growth has also led to the shortage and increase in the demand for clean drinking water in both rural and urban areas [18,85]. Organic and heavy metal contamination has accumulated in surface waterways due to excessive and uncontrolled use, lowering the water’s quality. Meanwhile, global problems like drought and climate change have disastrously affected surface water, sometimes entirely destroying it.

These above-mentioned factors have led to humans’ dependency on groundwater to satisfy their daily needs. Subsequently, the agriculture sector accounts for the largest utilization of the groundwater resource in the world, i.e., around 42% of the world’s available groundwater, followed by household consumption for safe drinking water supply ranging between 25 % to 50% globally [65,71]. However, the proportion varies from country to country depending on the development level and the population’s needs. In India, over 90% of people in rural areas and around 30% in urban areas rely on groundwater for drinking and domestic purposes, causing over-exploitation in certain regions of the country [5, 6, 78]. In rural Bangladesh, tube wells are the primary source of drinking and domestic needs [22]. In several rapidly developing sub-Saharan African nations, including Burkina Faso, the Central African Republic, South Sudan, Ethiopia, Chad, Nigeria, Somalia, and Uganda, groundwater serves as the primary water source for drinking around 70% to 90% of the local people [12,25]. In the United States, 38% of the population counts on groundwater for the purpose of drinking, sourced either from private wells or public systems, with self-supply wells catering to nearly 13% of the population [34,43]. In Nigeria, the dependency on groundwater is so high that only 20% of the Lagos population are provided with a piped utility water supply [15]. With respect to agricultural consumption, approximately half of India’s irrigation activities rely on groundwater, making it one of the world’s largest water consumers. In China, around 9 million hectares of land are irrigated using groundwater [30].

Around five million springs have been reported throughout India. Among those, around three million are in the Indian Himalayan regions and are experiencing depletion or seasonal fluctuation, attributed to diverse factors such as human interventions and the consequences of climate change [54,71]. The decline in spring water discharge can also be linked to rising temperatures, heightened rainfall intensity, altered temporal distribution, and a notable decrease in winter precipitation [58].

The drying of springs is increasingly recognized as a critical issue in the Himalayan region, with a notable decline in spring discharge being documented across various areas [71]. Research has highlighted severe water shortages due to the declining discharge of the springs, particularly during dry seasons [33,63]. A decline of over 30% in spring discharge over the past 30 years has been observed in Nepal’s mid-hill region [8]. Additionally, about 50% of perennial springs in the Indian Himalayas have either turned out to be dry or become seasonal [53]. Similarly, reports from the Himalayan state of Uttarakhand indicate a significant reduction in spring flow [4,29]. In addition, the transformation of about 82% of the spring fed streams of the upper Kosi river from perennial to ephemeral over the last twenty-five years is a critical issue [57]. This drastic change has significantly reduced the lean flow of these rivers, creating serious challenges for the sustainability of the rivers and the communities that depend on them. Consequently, the local inhabitants are experiencing water shortages for drinking and irrigation, putting their livelihoods and the well-being of their livestock at risk.

The declining rate of ephemeral springs is notably faster than that of perennial springs in the Himalayan area [51]. Consequently, understanding and managing the Himalayan region’s spring systems is challenging and crucial due to its accessibility. However, evaluating the capacity and productivity of springs in the Himalayan region is essential to understanding groundwater availability [54]. The potential of springs indicates the probability of groundwater presence in regions with adequate water resources. In order to assess the potentiality of springs, the availability of groundwater has to be assessed, as it is the major source of water for the springs. For this purpose, remote Sensing (RS) and Geographic Information Systems (GIS) serve as efficient and cost-effective tools for generating valuable data on different thematic layers, aiding in the interpretation of potential groundwater zones [36,42,45,46,47,48]. RS techniques serve as a significant source of information regarding the correlation among slopes, landforms, land use, and lineaments. This data can act as an attribute in GIS and overlaid with additional datasets [46,72].

Recent studies have utilized various AI models to map groundwater prospect zones across different regions. In one study, Explainable Artificial Intelligence (XAI) was employed to assess fifteen groundwater conditioning factors using models like k-Nearest Neighbors (KNN), Neural Networks (NNET), Gradient Boosting Machines (GBM), Random Forest (RF), and Support Vector Machines (SVM). This study identified five zones with varying potential, ranging from very low suitability (14.95 to 300.24 km²) to very high suitability (969.49 to 1958.85 km²) [80]. The study in Kanchipuram district, Tamil Nadu, India, used Landsat data and seven thematic layers to delineate groundwater potential zones, validating their map with high accuracy using the ROC method [83]. A study conducted in Marathwada used frequency ratio (FR) and statistical index (SI) models across eight factors, with the SI model proving more effective for groundwater management and planning in the region [84]. These studies highlight diverse methodologies and their application in enhancing groundwater resource management strategies globally.

However, investigators have utilized RS and GIS techniques to recognize potential groundwater zones by employing diverse thematic layers and field-observed data to validate their findings [10,16,32,38,40,47,61]. However, the validations of groundwater potential zone maps with observed spring water discharge and perennial nature in a micro-watershed scale have not been explored sufficiently.

Therefore, the present study aims to delineate groundwater potential zones based on spring water discharge using RS and GIS techniques in the Khulgad micro watershed of the Kosi basin, Kumaun Lesser Himalaya. The demarcation of the potential zone mapping in a micro-watershed would be significantly useful for villagers, policymakers, and local government authorities to manage the potential zone of groundwater and conserve it at the micro-watershed level to meet the drinking water demand of the villagers.

2. Study Area

The Khulgad watershed is situated within the Kosi basin, located in the Lesser Himalayan terrain of India’s Almora district, within Uttarakhand State (Figure. 1). Covering an area of approximately 33 km², the watershed’s elevation ranges from 1080 m to 2140 m. The Syahidevi peak, locally known as the "water tower" of the Khulgad watershed, represents the highest point, contributing significantly to the area’s water resources. The drainage pattern of the watershed trails a dendritic pattern, with some abrupt diversions noted in major drainage channels due to faults traversing the watershed [50]. Geomorphologically, the area comprises 24 types of fluvial, pluvial, and tectonic landforms [50,56].

The predominant land use/land cover in the area encompasses dense and semi-dense forests, primarily located in the southern and western parts of the watershed. Forested areas, mainly comprising oak and pine, cover approximately 52.2% of the total area, while barren land occupies 30.2%, agricultural land 13.7%, and urban areas 3.9%. The majority of the Khulgad micro watershed features shallow soil depths ranging from 25 to 55 cm, with sandy loam covering 50% of the area and the basin’s outlet identified as sandy clay loam.

The annual average rainfall in the basin ranges from approximately 980 mm in August to around 8.2 mm in November. Summer monsoon rains contribute approximately 75% of the total annual rainfall. The Khulgad micro watershed falls within a cool temperate climate region, with an annual average temperature of 20.3°C. The average maximum temperature was recorded as 26.7°C, and the minimum average temperature recorded was 15.3°C, respectively.

3. Hydrological and Geological Setting

The Khulgad watershed is situated within the northeast-dipping southern limb of the synclinal Almora Nappe, characterized by a thick folded sheet of Precambrian metamorphic rocks and associated granites [50]. Extensively studied by various researchers [20,35,50,67], the geological map reveals six lithological units within the basin.

Augen gneisses formation covers approximately 18.8% of the area, predominantly comprising sheared and fractured gneiss formations in the southwestern part of the watershed [50]. These gneisses pushed southward upon underlying schist and quartzite, have undergone extensive crushing, resulting in the development of secondary porosities that enhance permeability and form aquifers.

Garnetiferous mica schist, jointed quartzite, and friable quartzite, covering 18.8%, 28%, and 8% of the region respectively, from the underlying geological layers. Biotite schist and phyllite, constituting 15% and 8% of the watershed respectively, comprise the bottom layers (Figure 1b). Hydrogeologically, the area is characterized by aquifers of secondary porosity, primarily consisting of faults, fractures, and joints. Structural geology plays a crucial role in understanding the characteristics of springs and groundwater in the region. The Sitalakhet thrust passing over the Salla, Naula, and Dhamas schist and quartzite formations is particularly significant, as it contributes to the formation of secondary porosity, making this zone a potential aquifer. The gneissic rock in the thrust region has become highly permeable due to the formation of fractures and joints, underlain by gougy clay acting as an impermeable layer. Consequently, the thrust areas of Sitalakhet, Salla, Champa, Naula, and Syahidevi hills form clusters of springs. The discharge of these springs is primarily dependent on rainfall and its variability, with climate change emerging as a crucial factor influencing the quantity and quality of Himalayan springs, sustaining their flow throughout the year.

4. Materials and Methods

The remote sensing and GIS techniques are implemented to understand the spring water potential zone mapping in the micro watershed of Khulgad, for that a detailed methodology was developed (Figure 2). Various maps, such as lineament density, slope, drainage density, rainfall, and lithology map, were clubbed to create a groundwater potential map. The occurrence of groundwater is significantly influenced by hydrological conditions, which are primarily determined by these thematic layers. These layers serve as a dependable foundation for accurately predicting the groundwater potential of a given area [68].

4.1. Preparation of Thematic Maps

4.1.1. Lineaments Extractions

The linear features are lineaments that are straight structural features visible on the surface of the earth, often referred to as significant "lines of landscape" [21]. The geological or geomorphic processes are the primary cause of these discontinuities [11]. The methodology employed in this study involves the analysis of various geospatial datasets to assess groundwater potential and hydrological characteristics within the study area. Firstly, linear features on the Earth’s surface are identified using hillshade techniques based on the digital elevation model (DEM) with azimuth angles of 30^o, 35^o, 40^o, and 45^o. On the other hand, the linear features were automatically generated using Geomatica software with a sentinel-2B multispectral images dataset. In this, we utilize automated feature extraction tools available in Geomatica software to detect linear features in the imagery. These tools typically use algorithms based on edge detection, pattern recognition, or spectral analysis to identify potential lineaments. The lineaments generated from the remote sensing techniques were compared with an existing lineament map (NRSC-IRS-P6 LISS-3 FCC) of the area to validate the linear features in the study region.

The Lineament Density (LD) is calculated using Equation (1) by dividing the total length of all identified lineaments by the area of interest.

$$\mathrm{L}\mathrm{D}={\displaystyle \frac{{\sum }_{i=1}^n{L}_i}{A}}$$

(1)

where ΣLi is the total length of the entire lineaments in km and the “A” is the grid area in km².

The total length of all lineaments (ΣLi) in kilometers is divided by the area of each grid in square kilometers to compute the lineament density value for all grids. The calculated value is then outlined at the corresponding center of the grid, and these values aid in generating the lineament density map using interpolation.

4.1.2. Drainage Density Analysis

Drainage density is a crucial metric used in hydrology to understand the distribution and characteristics of stream channels within a given area, typically measured in kilometers of channel length per square kilometer of drainage basin area (km/km²). It serves as an indicator of how closely spaced the stream channels are and provides insights into the nature of the surface materials. The drainage density is calculated based on the Equation (2).

$$\mathrm{D}\mathrm{D}={\displaystyle \frac{{\sum }_{i=1}^n{D}_i}{A}}$$

(2)

where Σ Di is the overall stream length (km), and “A” is the area of the grid (km²).

Drainage density serves as a crucial indicator inversely related to permeability, thus holding significance in assessing groundwater potential [60]. In regions characterized by low drainage density, there is a higher potential for infiltration, as highlighted by [62], leading to the formation of favorable groundwater zones [13,32] compared to areas with high drainage density.

4.1.3. Slope and Land Use Analysis

The Slope, defined as the rate of change of elevation across a surface, finds a significant role in determining the movement of superficial water flow, primarily influenced by gravitational forces [14]. A slope map of the area under study was created from Advanced Land Observing Satellite Phased array–type L-band synthetic aperture radar (ALOS PASAR) DEM with 12 m resolution using ArcGIS 10.5 software. In this study, the slopes are characterized into five classes, very low, low, moderate, high, and very high in degree.

The land use/land cover (LULC) mapping holds great importance in remote sensing as it plays a crucial role in the development of groundwater resources. LULC directly influences various hydrogeological processes within the water cycle, such as infiltration, evapotranspiration, and surface runoff. Additionally, surface cover characteristics, like roughness, have a significant impact on discharge rates, with rougher surfaces generally leading to increased infiltration and reduced runoff.

For this study, land use and land cover are created based on the sentinel-2b optical imagery having 10m spatial resolution, each land use is created based on supervised classification with field verification.

4.1.4. Lithology and Soil Analysis

The lithology map was generated through field surveys and validated according to Rai, 1993 Ph.D. thesis. The study area encompasses various geological formations, including augen gneiss, garnetiferous schist, schist, quartzite, and phyllite. Each lithological unit possesses unique characteristics influencing its ability to facilitate water infiltration from precipitation. Consequently, specific weights were assigned to each lithology in the weighted overlay method in the arc GIS based on its infiltration capabilities.

The Soil map was downloaded from the (https://soilgrids.org/) website with a 200cm standard depth. The study area consists of three types of soil classes such as chernozems, leptosols, and luvisols. Among chernozems and luvisols, the leptosols are generally considered to be the most porous soil. Leptosols are characterized by their shallow depth and often have a high proportion of coarse fragments, such as gravel or rock fragments, within the soil profile. This coarse texture and shallow nature typically result in good drainage and high porosity, allowing water to infiltrate easily into the soil. Therefore, Leptosols tend to have higher porosity compared to Chernozems and Luvisols, making them more conducive to groundwater recharge and infiltration. Consequently, specific weights were given to each soil using the weighted overlay method in the arc GIS based on its infiltration capacity.

4.1.5. Rainfall Map Preparation

Rainfall being the primary source of recharge, as indicated by previous studies [32,60], governs the volume of water accessible for infiltration into the groundwater system, thus holding significant importance in this study’s hydrological analysis [2]. Daily rainfall data spanning 14 years (2010–2023) were obtained from the Indian Meteorological Department (IMD) for all existing weather stations in and around the study area. The precipitation data is interpolated with two stations in the study area using Arc GIS 10.5.

4.1.6. Delineation of Groundwater Potential Zones

The groundwater potential zone map (GPM) for the study area was created by combining five different thematic maps using ArcGIS 10.5. The integration was conducted by summating the overall factors that influence groundwater. The following formula (3), derived from previous studies [27,37,47], was employed:

$$\begin{gathered} \mathrm{GPM}=(\mathrm{MC}1\mathrm{w} \times \mathrm{SC}1\mathrm{r}) + (\mathrm{MC}2\mathrm{w} \times \mathrm{SC}2\mathrm{r}) \\ + (\mathrm{MC}3\mathrm{w} \times \mathrm{SC}3\mathrm{r}) + (\mathrm{MC}4\mathrm{w} \times \mathrm{SC}4\mathrm{r})+(\mathrm{MC}5\mathrm{w}\times \mathrm{SC}5\mathrm{r}) \end{gathered}$$

(3)

Here, GPM represents the groundwater potential map, MC1 to MC5 denote the chief criteria from the thematic layer maps, (w) signifies the weight assigned to each thematic map, SC1–SC5 indicate the sub-criteria of each thematic layer map, and (r) represents the ranking of the sub-criteria.

4.2. Analytic Hierarchy Process (AHP)

The Analytic Hierarchy Process (AHP) was utilized to assign weights to the thematic layers according to their significance in influencing groundwater potential based on a literature review and expert opinions [76]. The expert opinions were gathered from hydrogeologists, geologists, geomorphologists, and local officials involved with groundwater resources through a questionnaire-based field survey. Each thematic layer map underwent classification, with ranks assigned to various features within each theme in Table 5 [9,31,75].

The major eigenvalue and consistency index within the Analytic Hierarchy Process (AHP) encompass judgmental uncertainty [76]. Therefore, Table 1 provides a pairwise comparison matrix for the thematic layers with strength assessments. Additionally, Table 2 calculates the normalized pairwise matrix for criteria weightage to calculate the consistency index (CI) and consistency ratio (CR). The consistency index (CI) was utilized to assess the accuracy of pairwise comparisons, while the consistency ratio (CR) was employed to validate judgment consistency (Table 3). The CR controls the balance at which weights are assigned in Equation (5). It is essential for the weights to be as accurate as possible and not be overestimated or underestimated, given that they are determined by field surveys and literature reviews. The acceptable CR level is < 0.10. However, the saaty’s provides a measure of consistency known as the Consistency Index (CI), calculated using Equation (5). The consistency indices of RI values in Table 4 were 1.32 for the seven factors.

$$CI={\displaystyle \frac{ \lambda \mathrm{m}\mathrm{a}\mathrm{x}-\mathrm{n}}{n-1}}$$

(4)

$$CR={\displaystyle \frac{\mathrm{C}\mathrm{I}}{\mathrm{R}\mathrm{I}}}$$

(5)

Where the λmax was the eigenvalues of the consistency index, n was the number of factors used. The Equation (5) was calculated with the help of Equation (4) Where the CR was the consistency ratio and the CI was the consistency index, RI was the ratio index. The consistency index (CI) was calculated as 0.05 which is less than 0.1. The consistency ratio was found that 0.037 which is acceptable for the consistency judgement.

4.3. Validation of the Analysis

The validation process of the data involves several steps to ensure its accuracy and reliability. For the lineament data, field observations play a crucial role in validating the detected features. Field survey was carried out to verify the presence and characteristics of lineaments identified through remote sensing techniques. Additionally, lineament maps are utilized as reference documents to cross-validate the detected lineaments with known geological features and structures. This comprehensive validation approach helps confirm the accuracy of the lineament data and enhances confidence in its interpretation.

Regarding the groundwater potential map, validation is primarily conducted through field observations of observed spring discharge. Field surveys are conducted to locate and document perennial and ephemeral springs within the study area (Figure 3). The locations of these observed springs are then compared with the groundwater potential map to assess their reliability in categorizing areas with high groundwater potential. If the mapped groundwater potential zones correspond well with the observed spring with higher discharge in the field, it provides validation of the accuracy and effectiveness of the groundwater potential map. On the other hand, the groundwater potential zone map was assessed using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) metrics. The ROC curve, which ranges from 0.5 to 1.0, is used to estimate the model’s accuracy. Specifically, the AUC value stipulates a single measure of overall model performance. An AUC value of 0.5 indicates that the model performs no better than random chance, while values closer to 1.0 indicate higher model accuracy. It plots the True Positive Rate (TPR) against the False Positive Rate (FPR) Equations (6) and (7) [41,49]. This validation process helps ensure that the map provides reliable information for groundwater resource management and decision-making purposes.

$$TPR={\displaystyle \frac{True Positives}{True Positives+False Negatives}}$$

(6)

$$FPR={\displaystyle \frac{False Positives}{False Positives+True Negatives}}$$

(7)

This validation process helps ensure that the map provides reliable information for groundwater resource management and decision-making purposes.

Table 5. Weights, both assigned and normalized, are attributed to various features within seven thematic layers.

Themes	Feature/ Classes	Category of Groundwater Potential Storage	Rank assigned	Weight %
Land use/land cover (LULC)	Dense forest Semi dense forest Agricultural land Buildup area Road Barren land Waterbody	Very High Very High High Low Very low Moderate High	5 5 4 2 1 3 4	6.9
Lineament density (km2)	0-1.3 1.3-2.5 2.5-3.7 3.7-5.0 5.0-6.5	Very low Low Moderate High Very high	1 2 3 4 5	5.1
Drainage density (km2)	0-2.0 2.0-4.0 4.0-6.0 6.0-8.0 8.0-10.0	Very High High Moderate Low Very Low	5 4 3 2 1	8.9
Slope (degrees)	0-10 10-20 20-30 30-40 40-60	Very high High Moderate Low Very low	5 4 3 2 1	13.4
Geology	Gneiss Schist Phyllite Garnetiferous schist Quartzite	Low Low Moderate Low Very low	1 1 3 2 1	24.4
Soil	Leptosol Luvisol Chernozems	Very High Moderate Moderate	5 3 3	3.9
Rainfall (mm)	956-959 960-962 963-966 967-969 970-973	Very low Low Moderate High Very high	1 2 3 4 5	37.4

Table 5. Weights, both assigned and normalized, are attributed to various features within seven thematic layers.

Themes	Feature/ Classes	Category of Groundwater Potential Storage	Rank assigned	Weight %
Land use/land cover (LULC)	Dense forest Semi dense forest Agricultural land Buildup area Road Barren land Waterbody	Very High Very High High Low Very low Moderate High	5 5 4 2 1 3 4	6.9
Lineament density (km2)	0-1.3 1.3-2.5 2.5-3.7 3.7-5.0 5.0-6.5	Very low Low Moderate High Very high	1 2 3 4 5	5.1
Drainage density (km2)	0-2.0 2.0-4.0 4.0-6.0 6.0-8.0 8.0-10.0	Very High High Moderate Low Very Low	5 4 3 2 1	8.9
Slope (degrees)	0-10 10-20 20-30 30-40 40-60	Very high High Moderate Low Very low	5 4 3 2 1	13.4
Geology	Gneiss Schist Phyllite Garnetiferous schist Quartzite	Low Low Moderate Low Very low	1 1 3 2 1	24.4
Soil	Leptosol Luvisol Chernozems	Very High Moderate Moderate	5 3 3	3.9
Rainfall (mm)	956-959 960-962 963-966 967-969 970-973	Very low Low Moderate High Very high	1 2 3 4 5	37.4

5. Results

5.1. Soil Map

The study area exhibits three types of soil classes, namely chernozems covering 15.3km², leptosols covering 17.4km², and luvisols covering only 0.17km². Among these three types, leptosols are considered to be the most porous. They are characterized by their shallow depth and high proportion of coarse fragments, such as gravel or rock fragments within the soil profile. This results in good drainage, high porosity, and easy water infiltration into the soil. Consequently, 53% of the study area is covered by leptosols in the northern half of the study area, making them more conducive to groundwater recharge and infiltration. On the other hand, chernozems cover around 47% of the area, mainly in the southern parts, and luvisols are present in a very small area, around 0.5% in certain pockets in northcentral and northeast (Figure 4 and Table 7). Therefore, understanding the soil types is essential for understanding the groundwater potentiality of the study area.

5.2. Lithology Map

The lithology map delineates several geological formations within the study area, including Augen gneiss covering an area of 5.4 km², Devolikhan quartzite spanning 2.04 km², Garnetiferous schist extending over 9.29 km², Dhaili phyllite occupying 2.68 km², Dhamas quartzite encompassing 9.02 km², Muscovite schist covering 0.31 km², and Kathpuria schist spanning 0.77 km² (Figure 5 and Table 7). These geological formations exhibit secondary porosity, facilitating the transmission of groundwater (Figure 10). The southwestern region of the area hosts the gneissic rock, characterized by chernozems (organic black soil) in the topsoil layer and dense and semi-forested vegetation cover, suggesting a favorable environment for groundwater availability. Garnetiferous schist predominates below the gneissic rock, which covers 28.2% of the study area with a rank of two due to its low porous and permeable nature; however, these rocks have higher secondary porosity, such as fractures, which can hold a good amount of water [50]. While Dhamas quartzite is the second most abundant rock type found in the eastern part of the region covering an area of 27.4% with very low porosity nature, on the other hand this area stabilizes the groundwater potentiality by the influence of secondary porosity, soil type, and land use.

5.3. Lineament Density Map

The lineament density map of the Khulgad micro watershed has been grouped into five categories (Table 5), each representing the intensity of fractures or joints in the region (Figure 6). Rank one indicates a very low intensity of lineaments, while rank five represents a very high intensity. The smallest area covered is rank one with 8.66 km², followed by rank two with 5.9 km². Rank three has the highest area of coverage with 9.94 km², while rank four constitutes 5.55 km² (Table 7). The lineament density between ranks three and five has a higher chance of groundwater potential than one and two. However, it is completely dependent on the aquifer properties. Based on the potentiality of groundwater, the very high groundwater potential area covers around 8.8% of the total area, the high groundwater potential area covers about 16.8% of the total area, the moderate groundwater potential area covers approximately 30.1%, the low groundwater potential area covers 17.9%, and the very low groundwater potential area covers around 26.3% of the total study area.

5.4. Drainage Density Map

The drainage density of the watershed is determined by the availability of a maximum number of channels present in that area. In this particular study, the drainage density has been categorized into five ranks based on the density of the drainages, similar to the lineament density (Figure 7). The highest drainage density has been given to rank one, covering an area of 10 km², while the least drainage density area has been assigned to the weighted rank of five, consisting of an area of 2 km². However, 74.5% of the study area shows evidence of moderate to very high groundwater capability, while the remaining 25.5% indicates very low to low groundwater potential based on the density of the drainage (Table 7). The majority of the groundwater potential zone is located in the southwestern and central parts of the watershed.

5.5. Land Use and Land Cover (LULC)

The map of LULC comprises seven different categories of land use, which are dense forest, semi-dense forest, agricultural land settlements, roads, barren land, water bodies, and built-up areas. Each type of land use has been given a weightage based on its capacity to retain groundwater (Table 5). The highest weighted ranks of five have been assigned to dense and semi-dense forests, respectively, as they have the maximum capacity to store water for infiltration and significantly contribute to groundwater recharge. On the other hand, the lowest ranking has been given to roads and barren land, as they hardly provide a suitable environment for water infiltration, and most of the water entering its surface area drains away as surface run-off. The LULC analysis of the study area revealed that the total area covered by forested is 17.1 km²; out of which 2.1 km² is dense forest, 3.3 km² is agricultural land, 10.3 km² is barren land, 0.6 km² is water body, 1.2 km² is roads, and 0.4 km² is built-up area (Table 7). However, the entire 17.2 km² area of forested land in the southwestern part of the study area shows great potential for groundwater (Figure 8).

5.6. Slope Map

The slope forms a crucial topographic parameter that indicates how steep or gentle the terrain is [14]. The spacing between the contours can help determine the slope. The contours are closely spaced in a terrain, indicating a steeper slope, whereas broadly spaced contours suggest a gentle slope. In an elevation output raster, each cell is assigned a slope value. Lower slope angle values signify flatter terrain, while higher slope angle values denote steeper areas. The slope is measured by determining the maximum rate of change in elevation from each cell to its neighboring cells. These slope values can be expressed either in percentages or degrees and can be calculated in both vector and raster formats.

The slope analysis reveals that the slope of the relief features decreases from the northern part to the central and eastern parts of the area, with slopes ranging from 0° to 10° in both flat and mountainous regions, respectively. In areas with nearly level slopes (0-10 degrees), surface runoff is slow, giving more time for rainwater to infiltrate and percolate, thus creating zones with good groundwater potential. Conversely, in regions with steep slopes (31-60 degrees), runoff is rapid, reducing the time rainwater stays on the surface and consequently bringing about lower infiltration rates. This results in poor groundwater potential, particularly in the northeastern part of the study area, which is marked by high to very high slopes. The slope map is categorized into five distinct classes (Figure 9).

5.7. Rainfall Map

The map shows the annual average rainfall of the study area (Figure 9), with a weighted rank of one to five assigned for low to high rainfall. The highest rainfall is recorded in the southwestern part of the study area, with an annual average of 970 to 980mm. Hence, this zone has been attributed to the highest weighted rank, i.e., 5 (Table 5). The lowest annual average rainfall has been recorded in the eastern part of the study area, i.e., 960mm only, leading to comparatively lesser water availability for groundwater recharge. Hence, the lowest weighted rank of 1 has been assigned to this zone for the current analysis. The spatial distribution of rainfall pattern in the study area reveals the highest rainfall (973 mm) occurring in the southwestern parts of the study area with the highest potential of groundwater recharge, and this trend decreases in the north and east directions (Figure 10).

5.8. Validation of Groundwater Potential Zones

The potential groundwater zone has been verified through field observations of the springs and the existing lineament map (NRSC-IRS-P6 LISS-3 FCC) in order to validate the groundwater potential zones in the study area. Measurements of the discharge of spring water were taken in the field to validate the groundwater potential zone in the area. 50% of the perennial springs of the study area are located in the excellent and very good groundwater potential area, 15% of the springs are in the good groundwater potential zone, and the remaining 35% are in the fair to poor groundwater potential area (Table 6). Among the 35% and 10% are in the poor groundwater potential zone, with conclusive evidence that ephemeral and low discharge springs are clustered together. Apart from that, the springs are primarily connected to lineament features or fractures, and those with higher discharge are more closely associated with these features (Figure 11). The springs that appear seasonal are mostly found in areas with fair to poor groundwater potential zones covering around 29% of the study area. The remaining 71 % of the study area is in the good to excellent groundwater potential zone. This indicates that the analysis made in this study is supported by the available field evidence. The AUC 0.88 value indicates that the model performance is good with the observed spring water in the study area (Figure 12).

6. Discussion

The identification of groundwater potential zones helps to understand the availability of groundwater resources in a specific area. The parameters for mapping depend on the physical conditions of the region. In this study, seven thematic layers, such as slope, lineament density, lithology, drainage density, rainfall, soil, and land use land cover, were prepared to identify the groundwater potential zones [3,7,26,54,69,70,74]. Each thematic layer was assigned a weight from a 1-5 scale, the 1 indicates low value, and 5 indicates high value based on the hydrological conditions of the area [54].

The rainfall is one of the primary inputs for the mountainous regions. Hence, the relationship between rainfall and groundwater potential zone mapping is a crucial aspect, especially the high rainfall was concentrated in the southwestern part of the study area, varying from 970 to 973 mm. However, it’s important to note that groundwater potential is not solely determined by rainfall, other factors, such as the water-holding capacity of the aquifer material, lineaments, geology, slope, soil, land use, and drainage density also play significant roles [75,79].

The lithology and secondary porosity directly impact the water-holding capacity of rocks in the study area [56]. The field observations confirm that the gneissic rock in the southwestern part of the study area shows the highest amount of secondary porosity. Therefore, it has been assigned 5, the highest rank. The linear features in the watershed suggest that hard rock terrains transmit more water due to the presence of inter-connected secondary porosity [17,39,79]. For this study, lineaments are digitized from satellite imagery (IRS-P6 LISS-3 FCC). However, both digitized lineaments and field-surveyed lineaments were used to understand the groundwater potentiality of the area.

In the Khulgad micro watershed, the steep slopes prevent the infiltration of rainfall into the aquifer system, particularly in areas with a very high degree of slope (>30 degrees), representing lower groundwater potential compared to other regions. Conversely, the southwestern part of the study area, characterized by lower slopes (0-10°, 10-17°, 17-23°), exhibits the highest groundwater potential. However, the northern part of the watershed (Kathpuria, Kurchon, Kaneli) shows higher steep slopes >30 degrees, indicating less groundwater potential, which was ranked as 1 and 2. Similarly, other researchers have explored the impact of slopes on groundwater potential in different terrains [52,55]. These studies revealed that steep slopes (>30 degrees) were linked to lower groundwater potential because of quick runoff and limited infiltration. On the other hand, slopes less than 15 degrees were associated with higher groundwater recharge [55].

In the study area, soil has been ranked based on the texture and water-holding capacity, and the highest rank-5 has been assigned to the leptosol soil, and the moderate rank of 3 was assigned to the luvisol and chernozems [86]. Similarly, for LULC, the highest rank of 5 was assigned to the forested land and low to very low for the buildup regions and roads in the study area [87]. The drainage density of the watershed is also important to understand groundwater availability. The drainage density has been ranked 5 for very low drainage dense regions due to the less runoff and higher chance of infiltration, and the rank 1 has been assigned to high drainage dense regions due to the sudden runoff [79,82].

The current study utilized seven thematic layers integrated using the Analytical Hierarchy Process (AHP). However, the AHP has some limitations due to the subjective nature of assigning weights to the layers. To address this, we conducted an extensive literature review and field observations and sought expert opinions to determine the weights for each thematic layer. The consistency of the result can be checked by the consistency ratio (CR) value for the analysis. The CR value <10% (0.1) is accepted, and the weight and rank assigned to the parameters are justified and suitable for the AHP analysis [75,77]. In the present study, the CR value is 4% for seven parameters, which suggests the pairwise comparison is consistent.

The validation of the groundwater potential zones using RS and GIS with field observed data is always challenging in the absence of observed well yield and other available water resources information, particularly in the Himalayan regions [54,60]. In the current study, we followed similar approaches to substantiate groundwater potential zones. The occurrence, distribution, discharge of the spring, and density of the perennial springs are used to validate the groundwater potential area (Figure 12). The validation revealed that about 50% of the perennial springs in the study area are located in excellent and very good groundwater potential zones. Geologically, this part of the basin is made up of fractured rocks of Gneiss and Quartzite, which is covered with thick soil and densely forested land. All these conditions are favorable for the high recharge in this part of the basin. Additionally, the ROC curve was employed further to justify the groundwater potential areas. Similarly, ROC curve methods were used by various researchers to validate the groundwater potential zone mapping [19,44].

This study has significant importance for local villagers regarding better groundwater utilization. This study provides a valuable resource for targeted water resource management and strategic planning. The policymakers can use this information to prioritize regions for groundwater extraction, implement sustainable water usage practices, and plan for infrastructure development, such as rainfall harvesting structures and spring water storage tanks to reduce the runoff. Additionally, the identified low potential zones in the eastern parts of the study area allow for better drought preparedness and management strategies, ensuring that water resources are allocated efficiently during periods of scarcity. Overall, this study helps policymakers and local villagers for implementing long-term strategies for sustainable water management.

7. Conclusions

The Khulgad micro-watershed in the Kosi basin has been analyzed to identify potential groundwater available zones. The results show that 48% of the area in the southwestern part of the Khulgad watershed has excellent to very good groundwater potential, preeminently in the highly forested, low slope areas with a higher frequency of fractures where 76.9% of the springs are perennial. The lineaments are mostly found in areas with very good to excellent groundwater potential, and the springs found along these lineaments are perennial with excellent discharge. However, 23.1% of the seasonal or dried-up springs match with poor to fair groundwater potential zones due to the steep slope, less forest cover, and more barren land, triggering the low groundwater potential in the southeastern part of the study area. The groundwater potential modeling with the AHP method has predicted values with an 88% accurate ROC curve, indicating good model performance is valid based on observed springs. The study will be valuable for policymakers and stakeholders in sustaining spring water and groundwater at the village level of the Khulgad micro-watershed through engineering and biological interventions in the low groundwater potential zone. Overall, the study reveals that it can be replicated in a similar micro-watershed of the Himalayas.