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 immensely important for society (23, 64). Therefore, understanding 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). 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 had a disastrous effect on 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 (71, 65). However, the proportion varies between country to country depending upon the level of development and the needs of the population. Of the Indian population, 85% relies on groundwater for drinking purposes. And about 40-60 % of water supply provisions are met with groundwater (5, 6). In rural Bangladesh, tube-wells are the primary source of potable water (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 (25, 12). 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 (43, 34). 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 among world’s largest water consumers. In China, around 9 million hectares of land are irrigated using groundwater (30).

In all over the India around five million springs are reported among those three millions are in the Indian Himalayan regions, and these are experiencing depletion or seasonal fluctuation, attributed to diverse factors such as human interventions and the consequences of climate change (71,54). The decline in spring water availability 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. Reports from Uttarakhand indicate a significant reduction in spring flow (4,29). Research conducted by (33), as well as (63), highlights severe water shortages due to the declining discharge of the springs, particularly during dry seasons. (8) observed a decline of over 30% in spring discharge over the past 30 years in Nepal’s mid-hill region. Additionally, about 50% of perennial springs in the Indian Himalayas have either turned out to be dry or become seasonal (53). In addition, the transformation of about 82% of the spring fed streams of the upper Kosi river from perennial to non-perennial 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. Therefore, Evaluating the capacity and productivity of springs in the Himalayan region is essential given the rugged terrain, considerable variations in elevation, and dense forest cover (54). The potential of springs indicates the probability of their presence in regions with adequate water resources, which is essential for estimating groundwater productivity. And in order to access the potentiality of springs, potentiality of groundwater has to be assessed, as it is the major source of water for continuous flowing of springs. For this purpose, remote Sensing (RS) and Geographic Information System (GIS) methods serve as efficient and cost-effective tools for generating valuable data on different geological 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 then overlaid with additional datasets (46, 72).

Many investigators have utilized RS and GIS techniques to recognize potential groundwater zones by employing diverse thematic layers, including lineaments, geology, drainage density, geomorphology, and soil maps (10,16,32,38,40,47,61). However, the validations of groundwater potential zone maps with geology, landuse/cover, spring discharge and lineaments in the watershed scale are not explored sufficiently.

Therefore, the present study attempts to delineate groundwater potential zones with the help of Remote sensing and GIS techniques in the micro watershed of the Kosi basin. The results have been evaluated with the field-observed spring flow data and density of springs in different parts of the study area. The coherence of the results of the identified potential zones with spring flow reveals that the remote sensing and GIS tool can be used to find out potential zones of spring and would be helpful to prepare strategies for the poor zones.

2. Study Area

The Khulgad catchment is situated within the Kosi watershed, located in the Lesser Himalayan terrain of India’s Almora district, within Uttarakhand State (Figure 1). Covering an area of approximately 33 km², the catchment’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 catchment 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 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 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 catchment 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, form 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 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 primarily the 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.

$$\mathrm{L}\mathrm{D}=\sum _{i=1}^{i=n}Li/A(km-1)$$

(1)

where ΣLi 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).

$$\mathrm{D}\mathrm{D}=\sum _{i=1}^{i=n}Di/A(km-1)$$

(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 weightages were assigned to each lithology in 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 in 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 groundwater influencing factors. 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)

Each thematic layer map underwent classification, with ranks assigned to various features within each theme. The normalized weights of these features are detailed in Table 1 (9,31).

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 natural spring water discharge within the study area. The locations of these observed springs are then compared with the groundwater potential map to assess its 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) equation (4,5) (41,49). This validation process helps ensure that the map provides reliable information for groundwater resource management and decision-making purposes.

$$TPR=\frac{TruePositives}{TruePositives+FalseNegatives}$$

(4)

$$FPR=\frac{FalsePositives}{FalsePositives+TrueNegatives}$$

(5)

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

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, making them more conducive to groundwater recharge and infiltration. On the other hand, chernozems cover around 47% of the area and luvisols are present in a very small area around 0.5%. Therefore, understanding the soil types is essential for understanding the groundwater potentiality of the study area.

Figure 3. Soil type map of the study area.

Figure 3. Soil type map 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 4). 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

Lineament density map of the Khulgad watershed has been grouped into five categories (Table 1), each representing the intensity of fractures or joints in the region. 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. The lineament density between rank 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.

Figure 5. Lineament density map of the study area.

Figure 5. Lineament density map of the 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. The highest drainage density has been given to rank five, covering an area of 2.0 km², while the least groundwater availability area has been assigned to the weighted rank of one, consisting of an area of 8.26 km². However, 48.7% of the study area shows evidence of moderate to very high groundwater capability, while the remaining 51.3% indicates very low to low groundwater potential based on the density of the drainage. The majority of the groundwater potential zone is located in the southwestern and central parts of the watershed.

Figure 6. Shows the drainage density map of the study area.

Figure 6. Shows the drainage density map of the study area.

5.5. Land Use and Land Cover

The map of land use/ land cover 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 area. Each type of land use has been given a weightage based on its capacity to retain groundwater. The highest weighted ranks of five have been assigned to dense and semi-dense forests, respectively. On the other hand, the lowest ranking has been given to roads and barren land, as per Table 1. The total area covered by forested area 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. However, the entire 17.2 km² area of forested land in the southwestern part of the study area shows great potential for groundwater (Figure 7).

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 elevation decreases from the northern part to the central and eastern parts of the area, with slopes ranging from 0º to 10º 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 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, as illustrated in (Figure 8).

5.7. Rainfall Map

The map shows the annual average rainfall of the study area, with a weighted rank of one to five assigned for low to high rainfall. The highest rainfall is observed in the southwestern part of the study area, with an annual average of 970 to 980mm. The lowest annual average rainfall has been identified in the eastern part of the study area, with only 960mm and a comparatively low groundwater potential zone. Based on the annual average rainfall (as shown in Figure 9), the highest groundwater potential zone has been identified in the southwestern part of the study area.

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. In this study, among the total springs the 50% of the perennial springs 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. Among the 35%, 10% are in the poor groundwater potential zone with conclusive evidence like the presence of 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. The springs that appear seasonal flowing are mostly found in areas with fair to poor groundwater potential zones this zones are covered around 29% of the study area remaining 71 % of the study area are 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 the model performance is good with the observed spring water in the study area.

Figure 10. Groundwater potential zone map with Spring discharge.

Figure 10. Groundwater potential zone map with Spring discharge.

Figure 11. The model validation using ROC curve with observed spring and potential map.

Figure 11. The model validation using ROC curve with observed spring and potential map.

Table 2. Various thematic layers showing the percentage of groundwater potential area and its respective rank.

Themes	Potentiality for Groundwater Storage	Assigned Rank	% Area
Land use and land cover	Very High (Dense forest)	5	45.1
	High (Forested land)	5	6.5
	High (Agricultural land)	4	19.0
	Low (Buildup area)	2	10.0
	Very low (Road)	1	1.4
	Moderate (Barren land)	3	16.3
	High (Water body)	4	1.7
Lineament density (km2)	Very low	1	26.3
	Low	2	17.9
	Moderate	3	30.1
	High	4	16.8
	Very high	5	8.8
Drainage density (km2)	Very low	1	25.0
	Low	2	26.2
	Moderate	3	23.3
	High	4	19.2
	Very high	5	6.2
Slope (degrees)	Very high	5	13.9
	High	4	29.0
	Moderate	3	30.3
	Low	2	20.0
	Very low	1	6.9
Geology	Moderate (Augen gneiss)	3	16.4
	Low (Schist)	2	0.9
	Moderate (Daili phyllite)	3	8.1
	Low(Garnetiferous schist)	2	28.2
	Very low (Quartzite)	1	27.4
	Very low (Devolikhan quartzite)	1	6.2
	Kathpuria schist	2	10.3
Soil	Very High (Leptosole)	5	52.8
	Moderate (Luvisole)	3	0.5
	Moderate (Chernozems)	3	46.5
Rainfall (mm)	Very low	1	25.2
	Low	2	14.9
	Moderate	3	17.4
	High	4	18.4
	Very high	5	24.0

Table 2. Various thematic layers showing the percentage of groundwater potential area and its respective rank.

Themes	Potentiality for Groundwater Storage	Assigned Rank	% Area
Land use and land cover	Very High (Dense forest)	5	45.1
	High (Forested land)	5	6.5
	High (Agricultural land)	4	19.0
	Low (Buildup area)	2	10.0
	Very low (Road)	1	1.4
	Moderate (Barren land)	3	16.3
	High (Water body)	4	1.7
Lineament density (km2)	Very low	1	26.3
	Low	2	17.9
	Moderate	3	30.1
	High	4	16.8
	Very high	5	8.8
Drainage density (km2)	Very low	1	25.0
	Low	2	26.2
	Moderate	3	23.3
	High	4	19.2
	Very high	5	6.2
Slope (degrees)	Very high	5	13.9
	High	4	29.0
	Moderate	3	30.3
	Low	2	20.0
	Very low	1	6.9
Geology	Moderate (Augen gneiss)	3	16.4
	Low (Schist)	2	0.9
	Moderate (Daili phyllite)	3	8.1
	Low(Garnetiferous schist)	2	28.2
	Very low (Quartzite)	1	27.4
	Very low (Devolikhan quartzite)	1	6.2
	Kathpuria schist	2	10.3
Soil	Very High (Leptosole)	5	52.8
	Moderate (Luvisole)	3	0.5
	Moderate (Chernozems)	3	46.5
Rainfall (mm)	Very low	1	25.2
	Low	2	14.9
	Moderate	3	17.4
	High	4	18.4
	Very high	5	24.0

6. Discussion

The mapping of groundwater potential zones is crucial to understanding the extent of groundwater resources in a given area. The parameters considered for mapping depend on specific conditions in the particular region. Therefore, in this study, different thematic layers such as slope, lineament density, lithology, drainage density, rainfall, soil, and land use-land cover have been prepared to identify the groundwater potential zone mapping as used by the earlier workers (3,26,70,69,7,54,74). Each thematic layer is designated a weight based on the local field conditions. In the Khulgad micro watershed, seven thematic layers were used to identify the groundwater potential zone. For instance, understanding the lithology is crucial for determining the water-holding capacity of rocks in the secondary porosity (28). In the study area, the gneissic rock in the southwestern part exhibits the majority of secondary porosity, as confirmed by field observations. Therefore, it has been assigned the highest rank for this study. In the Himalayan regions, groundwater potentiality is influenced by secondary porosity (39,17). Linear features in the watershed suggest that hard rock terrains transmit more water due to the presence of secondary porosity. In this study, lineaments are digitized from the satellite imagery (IRS-P6 LISS-3 FCC), for the lineament analysis however, we used combined lineaments of both hill shade lineaments and field surveyed lineaments to understand the groundwater potentiality of the area (Figure 5.)

The rainfall is also an important parameter in this study, as several researchers consider it crucial for groundwater potential zone mapping (10,24,59). Therefore, the higher rainfall in the study area is concentrated in the southwestern part. The potentiality of the groundwater zone depends not only on rainfall, but also on the aquifer system’s holding capacity, slope, and soil. In the Khulgad micro watershed, the slope is considered vulnerable and has less residence time to infiltrate rainfall into the aquifer system. About 13.9% of the area has a very high degree of slope (>30 degree), indicating low groundwater potential compared to other regions. However, the lowest slopes (0-10°, 10-17°, 17-23°) are mainly located in the southwestern part of the study area, which has the highest potential. However, the northern part of the watershed (Kathpuria, Kurchon, Kaneli) regions show higher steep slopes >30 degrees indicates that less groundwater potential. Similar studies explored the impact of slopes on groundwater recharge in the Himalayas. It 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 potential (52,55). The soil, land use/land cover, and drainage density, confirm the groundwater potential area has been spread across the western part of the Khulgad micro watershed based on the frequency of the drainage, soil porosity and permeability, and intensity of the forest.

The validation of the groundwater potential zones using RS and GIS with field observed data is always challenging for researchers. In a study conducted by (54), observed perennial and seasonal springs were used to confirm the groundwater potential area of the Eastern Himalayas. Our current study followed similar approaches to substantiate groundwater potential zones. However, we considered the occurrence, distribution, discharge of the spring, and density of the perennial springs to validate the groundwater potential zones. The validation revealed that about 50% of the perennial springs in the study area are located in excellent and very good groundwater potential zones. Hence, this part of the basin is made up of fractured rocks of Gneiss, Quartzite, thick soil cover, and forested land use land cover. 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). The groundwater potential-related works are typically validated using observed well yield and other available water resources of those particular study regions (60). Groundwater potential studies are increasingly valuable for the sustainable development of groundwater, particularly in headwater regions that heavily rely on groundwater resources.

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 watershed has excellent to very good groundwater potential, preeminently in the highly forested, low slope with a higher frequency of fracture areas where most springs are perennial. However, 23.5 % of the seasonal or dried-up springs match with poor to fair groundwater potential zones. The lineaments are mostly found in areas with very good to excellent groundwater potential, and the springs found along these lineaments are showing perennial with excellent discharge. The AHP method has predicted values with an 88% accurate ROC curve, indicating good model performance. Overall, the mapping of the groundwater potential zone in the micro-watershed of the Kosi basin in the lesser Himalayan regions is accurate based on observed springs. This study will be valuable for policymakers and government agencies for managing spring water and groundwater availability in the Himalayan regions.