Introduction

Water as a resource is being utilized for multiple purposes including drinking, agriculture, industrial, and domestic all over the globe (Pal, Ghosh et al., 2020; Pal, Kundu et al., 2020; WWDR, 2018). Uneven distribution of water and its injudicious usage, pollution, population growth, rapid urbanization, and erratic rainfall due to climate change make availability of usable water a scarcity (Nabi et al., 2019; Morote & Hernández, 2016; Yan et al., 2019; Zubaidi et al., 2020). Available water resource manifests itself in the form of surface and groundwater. The demand of water increases with economic development while its quality and quantity are decreasing due to anthropogenic interventions (Wilopo et al., 2021a). The global groundwater system accounts for about 30% of the total freshwater resources (UN WWDR, 2021). Groundwater caters to the 50% of irrigation needs and 90% of drinking water requirements in rural areas around the world. In fact, some major cities rely entirely on groundwater to meet their water requirements (UNESCO, 2021). Groundwater is among the many natural resources that are facing the problems of overexploitation due to its increasing demands (Bierkens, & Wada, 2019). This exploitation is prominent around the world, especially in cities which has resulted in the form of falling water table and decreased surface water flows (Gleeson & Richter, 2016; Roy et al., 2020; Sutanudjaja, 2020). India has also been witnessing tremendous increase in demand for usage across sectors of domestic, agriculture, and industrial (Bhattacharya et al., 2014). The consumption of groundwater for irrigation exceeds 90% of the total groundwater utilization in the country (Schneider, 2018), besides groundwater caters to about 85% rural and 30% urban need of drinking water in India (CGWR, 2017). However, rapid growth in population, increased industrialization and urbanization, and increase in agricultural activities are further accelerating the demand in which availability of groundwater quantity and quality becomes of paramount importance (Arulbalaji et al., 2019; Duan et al., 2016; Jha et al., 2010). Likewise, shift in the cropping pattern, uncertainty of monsoon, scarcity of surface water, and pollution put more stress on groundwater which results in its unsustainable use (Hussein et al., 2017; Patel et al., 2014). As a result, according to World Wide Fund for Nature, more than 30 Indian cities are expected to face an acute shortage of water by 2050. Besides depletion of groundwater, India has been found to be associated with seawater intrusions (Prusty & Farooq, 2020), land subsidence (Kadiyan et al., 2021), climate change (Ferrantet al., 2014), and contamination by arsenic (Shaji et al., 2021), urbanization (Roy et al., 2020), and nitrates (Jayarajan & Kuriachan, 2021). So, managing the usage of the existing groundwater resources and planning its future usage is critical for its sustainable development in a country like India in which groundwater potential (GWP) mapping is crucial (Anbazhagan, & Jothibasu, 2016). Proper delineation of GWP zones can be utilized to find the location for new abstraction wells to meet the rising demand for water (Ibrahim-Bathis & Ahmed, 2016). Drilling wells at the location where the probability is maximum will significantly reduce the time and cost associated with it. Thus, mapping of underground water can help in the sustainable development and management of groundwater in the country (Suganthi et al., 2013; Andualem & Demeke, 2019).

Groundwater is not uniformly distributed in space and time throughout the country (Mukherjee et al., 2015). This distribution of groundwater is governed by several factors such as distribution of rainfall, vegetation, geology, slope, geomorphology, land use/land cover (LU/LC), lineament features, and the relationship between these factors (Jha et al., 2010; Andualem & Demeke, 2019, Nowreen et al., 2021). The amount of rainfall that recharges groundwater is attributed to several factors, namely soil type, groundwater depth, vegetation, and weather patterns (Wilopo et al., 2021b). As such, many researchers have used criteria like drainage density, geology, geomorphology, lineament, rainfall, soil type, LU/LC, lithology, DEM, and slope for the zonation of GWP zones (Jaiswal et al., 2003; Greenbaum, 1985; Gupta & Srivastava, 2010; Gdoura et al., 2015; Mallick et al., 2021). Remote sensing and GIS-based multi-criteria decision model (MCDM) have been widely used for the identification and mapping of these GWP zones (Lakshmi & Reddy, 2018; Allafta et al., 2021; Benjmel et al., 2020; Kolli et al., 2020; Tolche, 2021). Besides, overlay analysis in GIS has been widely used to integrate the different criteria/thematic maps (Kumar et al., 2016; Tolche, 2021). One of the important MCDM method is analytical hierarchy process (AHP) proposed by Saaty (1980). Apart from AHP technique, scholars have also used principal component analysis (Helena et al., 2000), decision tree algorithms (Duan et al., 2016; Razavi-Termeh et al., 2019), machine learning techniques (Lee et al., 2020; Sarkar et al., 2021), ensemble machine learning algorithms (Mallick et al., 2021), etc. to delineate the GWP zones. However, for the present study, AHP has been used as it is easy, authentic, and efficient technique for GWP analysis (Celik, 2019). AHP technique is used to evaluate the weights of different criteria after a pair-wise comparison of each criterion with the rest of the criteria (Chen et al., 2013). It reduces the subjectivity and biasness in the decision-making process (Saaty, 1990).

Jammu district is blessed with many perennial rivers, viz., Chenab, Tawi, Munawar Tawi, and Basantar. But still, the region is facing water shortages (Hakhoo, 2019) especially the Kandi belt which is even experiencing shortage for the drinking purposes (Kumar et al., 2004). On one hand, Shiwalik hills bounding North and Northeastern parts does not allow water to percolate and most of the rainfall goes waste as overland flow (Sharma & Arora, 2015). And on the other, groundwater contamination due to improper disposal of domestic and industrial solid wastes is adding the already water scarcity (Ramshoo et al., 2017). Though the quantity and quality of groundwater resource are adequate and good in Jammu (CGWR, 2018–19) but considering the rapid expansion of impervious surface in the region due to increasing urban population and migration, overexploitation of groundwater and its reduced recharge can threaten the very existence of groundwater aquifers (Murtaza et al., 2020). Thus, scientific and systematic usage and development of groundwater is required to utilize the optimum capacity of the resource (Velis et al., 2017). For this, mapping of the GWP of various segments of a region becomes essential (Pham et al., 2019). However, to the best of our knowledge, there have been relatively few studies that map the GWP zones in India’s Jammu area. As a result, in order to cover this research gap, this study will use twelve GWP conditioning characteristics to evaluate the probable groundwater zones in the Tawi River basin in Jammu district.

Study area

Jammu district is flanked to the north and northeast by the Udhampur and Reasi districts, to the east and southeast by Udhampur and Samba districts, to the south and southwest by Rawalpindi (Pakistan), and to the northwest by Rajauri district. The district is located between 74° 24″ to 75° 18″ E and 32° 50″ to 33° 30″ N (Fig. 1). It covers an area of 2342 km2 (Ministry of Home Affairs, 2011). River Tawi, known as Surya Putri in ancient literature, is among the important rivers of Jammu district. It has its source from the Kali Kundi glacier in Doda district. The total length of the river is 141 km and its catchment area is 2168 km2 which comes under Jammu, Udhampur, and Doda districts (Karmakar, 2019). The catchment area of Tawi River within Jammu district is 928.94 km2 and the elevation ranges between 234 and 1057 m. The physiography of the region mainly consists of Indo-Gangetic plain southernmost part of Shiwaliks. The sub-tropical type of climate characterized by the seasonal reversal of wind direction is found in the study area (CGWR, 2018–19, CGWR, 2017). The average annual rainfall ranges between 113 and 175 cm. During summer season, groundwater table reaches at low level due to consistent higher temperature. With the increase in temperature, water resources are under profound stress due to increasing rate of evaporation and decrease in available quantity of fresh water. As a result, mountain springs and groundwater resources are immensely reduced (CGWR, 2018–19; Farooq, 2016).

Fig. 1
figure 1

Location of the Tawi River basin in Jammu and Kashmir

Full size image

Materials and methods

Data sources

The study used digital elevation model (DEM) of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite with a spatial resolution of 30 m, obtained from the United States Geological Survey (USGS) website to prepare elevation, slope, curvature, roughness, and topographic wetness indexes (TWI). Linear Imaging Self-Scanning Sensor 3 (LISS III) data from the Indian Remote Sensing (IRS) satellite Resourcesat-2 was utilized to create the LU/LC map of the Tawi basin. Furthermore, the soil map was obtained from the Jammu and Kashmir Department of Soil Survey (DSS), while the geology and geomorphology maps were taken from the Geological Survey of India (GSI). The data of groundwater depth was taken from the Central Ground Water Board (CGWB), Jammu. The details of the datasets used in the present study are given in Table 1.

Table 1 Sources of the datasets used in this study
Full size table

Methods

The present study uses AHP for mapping GWP zones in Tawi basin as it is efficient, reliable, and simple method (Celik, 2019). AHP can handle a variety of characteristics and the consistency ratio can be used to assess the subjectivity of weights (Shekhara & Pandey, 2014; Saranya & Saravanan, 2020). Besides, AHP is a useful tool for determining a result’s consistency and, as such, helps in reducing the bias in decision-making process (Saaty, 1990). The methods of the study involve the preparation of the twelve GWP conditioning parameters using ArcGIS software. Pair-wise matrix was prepared after comparing each parameter with rest of the parameters using AHP technique. While comparing the layers, numeric value based on Saaty’s scale of relative importance was used and then the normalized weight of each layer was evaluated. The accuracy of the pair-wise matrix was determined after computing the consistency ratio. The layers were then integrated using overlay analysis to obtain the GWP index (GWPI). The GWPI was classified into 4 categories to obtain the GWP zones of the study area. At last, the results were validated using average groundwater depth of the monitoring dug wells obtained from Central Ground Water Board. The detailed methodology is shown in Fig. 2.

Fig. 2
figure 2

Flowchart of the methodology for delineating groundwater potential zones

Full size image

Satellite data pre-processing and delineation of watershed

Firstly, the atmospheric and radiometric corrections of the downloaded satellite data were done using the ERDAS Imagine software (version 2014). For this, the haze and noise reduction tools were utilized. The watershed boundary of the Tawi River basin was prepared using stream gauge (pour point) method from the ASTER DEM with the help of flow direction and flow accumulation function of spatial analyst tool of ArcGIS 10.5.1 software. The extracted watershed boundary was then utilized for the preparation of the thematic layers of GWP conditioning parameters like slope, curvature, and soil map.

LU/LC classification

Numerous techniques have been developed for the LU/LC classification using the satellite datasets since the beginning of Landsat era in 1970s (Phiri & Morgenroth, 2017). The most popular techniques for the LU/LC classification from satellite data are maximum likelihood classifier (MLC), K-means clustering, random forest, artificial neural network (ANN), etc. (Kumari et al., 2021; Naikoo et al., 2022; Talukdar et al., 2020). In this study, the supervised classification technique using MLC was utilized for the LU/LC classification from the IRS LISS III data by using level I classification scheme of National Remote Sensing Centre (NRSC, 1995). The level I LU/LC classification scheme of NRSC has a total of 8 LU/LC classes of which six LU/LC classes were identified in the study area, i.e., forest, agricultural land, river bed, scrubland, built-up area, and water bodies.

Preparation of the groundwater conditioning parameters

Generally, nine to thirteen GWP conditioning parameters are used for the mapping of GWP zones (Mallick et al., 2021). However, in this study, twelve GWP conditioning parameters were used, viz., soil, slope, rainfall, elevation, LU/LC, lineament density, drainage density, geology, geomorphology, topographic wetness index (TWI), roughness, and curvature. These layers play an important role in groundwater recharge and hence become important in the delineation of GWP of the study area.

Different methods were used to prepare these layers. ASTER DEM was used to prepare elevation map, drainage density map, TWI map, roughness map, curvature map, and slope map. The geo-referenced soil map obtained from the Department of Soil Survey was geo-rectified and digitized using ArcGIS software. The rainfall map was created in ArcGIS 10.5.1 by interpolating the average yearly rainfall data from several meteorological stations using the inverse distance weighting (IDW) interpolation method (Wen & Wuing, 2012). The geomorphology map was created using a dataset taken from the Bhukosh web page (Geological survey of India, 2021). Geology map of northwestern Himalayas was used to digitize the geology map of the study area. Lineament map downloaded from Bhukosh was used to prepare the lineament density map using linear density tool in ArcGIS (Suganthi et al., 2013).

Weighting procedure using AHP

AHP model involves the use of expert’s knowledge to evaluate the weight of different layers. It involves the construction of pair-wise matrix using Saaty’s scale (Saaty, 1987) as shown in Table 2. Numeric values have been assigned while comparing two criteria based on the relative importance (Rajaveni et al., 2017; Andualem & Demeke, 2019).

Table 2 Saaty’s scale of relative importance (Saaty, 1980)
Full size table

After comparing each layer with the rest of the layers based on their relative importance, pair-wise matrix has been constructed (Machiwal et al., 2011). Using pair-wise matrix under AHP model, the normalized weights of each individual layer have been calculated (Table 3).

Table 3 Pairwise comparison matrix
Full size table

To evaluate the accuracy of the applied AHP model in pair-wise comparison, the consistency ratio (CR) has been calculated using Eq. (1) and Eq. (2) (Saaty, 1980).

$$Consistency\;Ratio\;(CR)=\frac{\mathrm{CI}}{RI}$$
(1)

where CI is consistency index and RI is random index.

$$Consistency\;Index\;\left(CI\right)=\frac{\lambda max-1}{n-1}$$
(2)

Applying Eqs. 1 and 2, the consistency ratio (CR) calculated is 0.049 which is within the permissible limit of 0.01. Thus, it reflects a good accuracy in pair-wise comparison of different thematic layers, i.e., GWP conditioning parameters.

After evaluating weights using AHP model, the classes of each GWP conditioning parameters have been assigned a rank ranging from 1 to 9. Higher rank has been assigned to the class which is expected to have high GWP (Table 5).

Mapping of groundwater potential (GWP) zones

Weighted overlay analysis tool in ArcGIS software has been used to integrate the different thematic layers, i.e., GWP conditioning parameters. The GWPI has been evaluated after the integration of all the parameters using Eq. (3) (Malczewski, 1999).

$${GWPI}=\sum_{{w}=1}^{{m}}\sum_{{j}=1}^{{n}}({Wj}*{Xi})$$
(3)

where Wj is the normalized weight of the jth parameter and Xi is the normalized weight of ith feature of the parameter.

Then, the computed GWPI values have been classified into four categories, viz., high, moderate, low, and very low. Based on these classes, the map of GWP zones of Tawi River basin has been developed.

Validation of GWP zones

The GWP zone map was validated using groundwater depth data from monitoring wells collected from the Central Ground Water Board. There are about 94 monitoring wells in Jammu district out of which 25 monitoring wells lie in the study area. Groundwater depths of pre-monsoon and post-monsoon months of 2018 and 2019 have been used for calculating the overall groundwater depth of these monitoring wells and classified into three classes, i.e., low depth, moderate depth, and high depth. Average groundwater depth classes of monitoring wells were represented using triangle of various size for each class in ArcGIS software and compared with GWP zones for the validation.

Results

Description of the GWP conditioning parameters

Geomorphology

Geomorphology is defined as the study of topographic features of the earth’s surface (Britannica, 2021). In our study area, seven geomorphic features are identified which are alluvial plain, flood plain, highly dissected hills and valleys, low dissected hills and valleys, moderately dissected hills and valleys, river, and other water bodies (Fig. 3a). Higher rank was given to water bodies, alluvial land, and flood plain, while lower rank was assigned to dissected hills and valleys. Hussein et al. (2017), Soumen (2014), and Arulbalaji et al. (2019) also indicated that water body, alluvial plain, and flood plain have more impact in groundwater occurrence whereas mountains and dissected hills and valleys have less impact in controlling groundwater.

Fig. 3
figure 3

Groundwater controlling factors thematic map; a geomorphology, b geology, c drainage density, d land use land cover, e slope, f rainfall

Full size image

Geology

Geology plays an important role in the GWP as the percolation of water is directly controlled by the permeability of underlying rocks (Charon, 1974; Nampak et al., 2014). In our study area, two types of sediments were identified which are tertiary sediments and quaternary sediments. Tertiary sediments are found in the hilly areas while Quaternary deposits were found in outer plains (Fig. 3b). Water infiltration capacity is more as in quaternary sediments as compared to tertiary sediments. Arulbalaji et al. (2019) also indicated that quaternary deposits have more contribution in increasing the GWP as compared to tertiary deposits.

Drainage density

Drainage density is the ratio between the total length of all the river in a drainage basin and the total area of that drainage basin (Suganthi et al., 2013). Drainage density has inverse relation with permeability of aquifers. Areas having high drainage density depict low water infiltration whereas high water infiltration is found in the areas having low drainage density. The obtained results have been categorized into five classes (Fig. 3c). Higher ranks were assigned to the areas having low drainage density while areas having high drainage density are assigned low ranks.

Land use/land cover (LU/LC)

LU/LC is an important factor in the recharge of groundwater. Rate of percolation and water runoff largely depends upon the type of land use (Ibrahim & Ahmed, 2016; Saranya & Saravanan, 2020). Low infiltration rate occurs in the built-up area due to lack of impermeable spaces, while agricultural land, forest, and water body have higher rate of water percolation (Arulbalaji et al., 2019; Celik, 2019). LU/LC map has been prepared according to NRSC scheme of LU/LC classification into six classes which were forest, agricultural land, water body, riverbed, built-up area, and scrub forest (Fig. 3d). Higher ranks were assigned to water body, riverbed, agricultural land, and forest whereas lower ranks were assigned to built-up area and scrub forest.

Slope

Slope is an important factor influencing the infiltration of water to the ground. Areas having higher slope gradient have relatively lower water infiltration owing to higher runoff. On the other hand, higher infiltration is found in the areas having lower slope gradient (Singh et al., 2013). The slope has been classified into five categories ranging from 0 to 5°, 5 to 11°, 11 to 18°, 18 to 27°, and > 27°. Higher rank was assigned to the areas having gentle slope, while lower weight was given to the areas having relatively steep slope (Fig. 3e).

Rainfall

Rainfall is an essential factor that influences the groundwater recharge as it is the primary source of water available for percolation in the study area. Rate of water recharge depends upon the intensity and rainfall duration (Ibrahim & Ahmed, 2016). Average annual rainfall is classified into five classes ranging from 113 to 125 cm, 125 to 138 cm, 138 to 150 cm, 150 to 163 cm, and 163 to 175 cm (Fig. 3f). Higher weight has been assigned to the areas having high rainfall, whereas areas receiving lesser rainfall are given lower weight.

Lineament density

Lineament density is defined as linear property which shows the characteristics of underlying structural features such as faults, fractures, cleavages, and discontinuity surfaces (Leary et al., 1976). It is an important indicator that contributes in water infiltration to the ground. Areas having high lineament density have high water infiltration, while low water infiltration was shown by the areas having lower lineament density (Koch & Mather, 1997; Shekhara & Pandey, 2014; Sreedevi et al., 2001). In our study, lineament density has been classified into five classes, which are 0–14 km−1, 14–40 km−1, 40–63 km−1, 63–84 km−1, and 84–114 km−1 (Fig. 4a). Higher rank was given to areas having high lineament density, while lower weight was given to the areas having lower lineament density.

Fig. 4
figure 4

Groundwater controlling factors thematic map; a lineament density, b elevation, c curvature, d soil, e TWI, f roughness

Full size image

Elevation

It refers to the altitude of a place above the mean sea level. It is one of the most important factors that influence the GWP. Lower topography is more favorable for water storage than higher topography (Ramu & Vinay, 2014). The elevation map has been classified into five classes which are 234–330 m, 330–430 m, 430–532 m, 532–659 m, and 659–1057 m (Fig. 4b). The higher rank has been assigned to the areas having low altitude, while lower weight was assigned to the areas having high altitude.

Curvature

Curvature is a numerical representation of the presence of a surface profile, which might be concave or convex upwards. In a convex and concave contour, water tends to slow down and gather (Nair et al., 2017). Curvature map has been prepared from ASTER DEM and the obtained values are categorized into four classes, which are − 14 to − 1.8, − 1.8 to − 0.65, − 0.65 to 0.037, 0.037 to 1.1, and 1.1 to 13 (Fig. 4c). High ranks were assigned to areas having high curvature, while low ranks are given to the areas having low curvature.

Soil

Soil is a significant component influencing groundwater, because the rate of water penetration is determined by the soil’s water holding capacity and permeability (Gupta et al., 2018; Saranaya, & Sarwanan, 2020). In our study area, soil has been classified into fifteen types based on drainage class, groundwater depth, and level of erosion which are shown in Fig. 4d and Table 4.

Table 4 Characteristics of different soil types
Full size table

Topographic wetness index (TWI)

The TWI is an index or measure of impact on flow and accumulation of water because of local topography (Ballerine, 2017). It is widely used to measure the topographic influence on surface and sub-surface hydrology and reflects the potential for water infiltration caused by topographic effects (Arulbalaji et al., 2019; Mokarram et al., 2015). TWI has been classified into five classes which are − 8.62 to − 6, − 6 to − 5.9, − 5.9 to − 4.7, − 4.7 to − 0.14, and − 0.14 to 12 (Fig. 4e).

Roughness

Roughness index depicts the difference in elevation of the adjacent cells of digital elevation model (Riley, 1999). It measures the undulation in the topography. Areas having higher roughness index have more undulation in the topography and vice versa (Nair et al., 2017; Arulbalaji et al., 2019). Roughness index of the study area has been classified into five classes which are 0.01–0.35, 0.35–0.44, 0.44–0.51, 0.51–0.6, and 0.6–0.88 (Fig. 4f).

Analysis of the GWP zones

The total of 12 GWP conditioning parameters has been prepared using ArcGIS. Normalized weight for each thematic layer has been calculated through AHP method and ranks to sub-parameters of each conditioning parameter have been assigned based on their relative importance in GWP (Table 5). Subsequently, overlay analysis tool in ArcGIS 10.5 has been used for the delineation of GWP zones. The derived result has been categorized into 4 categories, viz., high, moderate, low, and very low (Fig. 5).

Table 5 Assigned weight and ranks to different thematic layers
Full size table
Fig. 5
figure 5

Groundwater potential zones of Tawi River basin

Full size image

The high GWP zone covers 204.19 km2 accounting for 21.98% of the total land area (Table 6). High GWP lies in the western part of the study area mostly comprising of flood plain and alluvium plains. Areas having high lineament density, gentle slope, and areas in the vicinity of water bodies also have high GWP. The moderate GWP zone covers 376.58 km2, accounting for 40.54% of the total land area. Moderate GWP was found mainly in the area of alluvium plain and moderately dissected hills and valleys. Areas having low lineament density, gentle slope, and areas having coarse loamy and clayey skeletal type of soil coincide with moderate GWP. Low and very low GWP zones were spread in the area of 324.31 km2 and 23.86 km2 which accounts for 34.91% and 2.57% of the total area, respectively. Low and very low GWP zones were found in the central and eastern part of the study area comprising mostly of highly dissected hills and valleys. Areas having high drainage density, high altitude, low lineament density, and steep slope also coincide with low and very low GWP zones.

Table 6 Area under groundwater potential zones
Full size table

Validation of the GWP zones

The GWP zone map was validated using the existing average groundwater depth data from 25 monitoring dug wells collected from CGWB Jammu (Fig. 6). Groundwater depths of the monitoring dug wells have been classified into 3 classes, i.e., low depth (0 to 4 m), moderate depth (4 to 8 m), and high depth (> 8 m), as depicted in Table 6.

Fig. 6
figure 6

Location of monitoring dug wells

Full size image

Table 7 and Fig. 7 illustrate that monitoring wells of low depth coincide with high GWP zone except monitoring dug well of Muthi which lies within the moderate GWP zone. Similarly, monitoring dug wells of moderate depth calibrate with moderate water potential zone except Miran Sahib and Chatha and lastly monitoring wells of high depth coincide with low GWP zone except Purkhoo and Patta khu. No monitoring well was found in very low GWP zone.

Table 7 Water depth of monitoring wells and their respective level of potential
Full size table
Fig. 7
figure 7

Validation of groundwater potential zone with water depth of monitoring dug wells

Full size image

Hence, groundwater depth classes of 20 monitoring wells out of 25 coincide with their respective GWP zones, which show that the obtained results have a good correlation with 80% accuracy after comparing with actual field data. The average depths of monitoring wells which coincide with high GWP zone, moderate GWP zone, and low GWP zones are 2.45 m, 6.81 m, and 13.93 m, respectively. Thus, there is an indirect correlation between the average groundwater depth and GWP.

Discussion

GIS-based multi-criteria decision-making is a strong tool for decision makers to detect GWP. Pal, Ghosh, et al. (2020), Pal, Kundu, et al. (2020)) used geospatial techniques and the MCDA method to investigate GWP in the Purba Bardhhaman area of West Bengal. Owolabi et al. (2020) in a similar study used AHP technique for analyzing GWP for semi-arid environment region of South Africa. Similarly, we applied the AHP technique with pair-wise comparison in our study and then combined the data using overlay analysis. This method is particularly effective for establishing outcome consistency and decreasing decision bias. In the current study, agricultural land, forests, and water bodies were given higher rankings than scrub forest and built-up areas. These areas aid in groundwater recharge because they have a higher rate of water percolation (Rajaveni et al., 2017). In contrast, built-up areas have low infiltration rates and capacities due to the dominance of impermeable surface in this type of land use (Jat et al., 2009). The study area has moderate to high drainage density, implying a negative impact on GWP. Since the permeability of aquifers is inversely proportional to the density of drainage. Water infiltration is minimal in areas having high drainage density, whereas areas having water low drainage density have high water infiltration (Allafta et al., 2021). Kumar et al. (2020) reported that areas with high lineament density depict high permeability of water and thus his results coincide with our study. Tesfaye (2010) shows in his research that soil qualities impact the link between runoff and infiltration rates, which in turn regulates the degree of permeability, which determines GWP. Similarly, in our study, areas having coarse loamy and clayey skeletal type of soil have shown moderate GWP.

Geology is important in GWP because the permeability of underlying rocks directly controls water percolation. The flow of water is aided by unconsolidated and fractured rocks (Balamurugan et al., 2020). According to Arulbalaji et al. (2019), quaternary deposits contribute more to increasing GWP than tertiary deposits. Similarly, in our study, quaternary sediments were found to be more favorable for groundwater prospect than tertiary sediments. Because of the longer stagnation period for water to percolate, GWP rises with gentle slope and low topographic elevation locations (Mogaji et al., 2016). Hence, in the study area, higher rank was assigned to the areas having gentle slope, while lower weight was given to the areas having relatively steep slope (Fig. 3e). Thapa et al. (2017) as well as Maity and Mandal (2017) reported that waterbody, alluvial plain, and flood plain have a greater impact on groundwater occurrence, while mountains, dissected hills, and valleys have a lesser impact on groundwater control and thus assigned higher and lower ranks, respectively. The intensity and duration of rainfall determine the rate of water recharge (Katchoni et al., 2019). In our study, areas with high rainfall lie in higher elevation leading to higher runoff and low GWP, while areas with low rainfall lie in Kandi belt. Curvature, roughness, and TWI add significant impact on the groundwater recharge. Previous studies suggested areas having convex and concave profile, higher roughness, and TWI showed greater accumulation of water (Nair et al., 2017; Mokarram et al., 2015; Arulbalaji et al., 2019). Likewise, in the current study, roughness, curvature, and TWI showed similar results.

The findings of the present study show that high GWP zone accounts for 21.98% of the entire study area. This zone is particularly noticeable in the areas with high lineament density, alluvial plain, mild slope, flood plain, and places near water bodies in the study area. The findings of the current study are consistent with similar studies conducted in the Himalayan regions. Qadir et al. (2020) found that alluvial plain and flood plains have higher GWP. Similarly, Jasrotia et al. (2016) found that moderate GWP zones were confined in residual hills and valley fill deposits with moderate to moderately steep slopes in Devak and Rui watersheds of Jammu. The results of the study also show similar results for Tawi basin where moderate GWP zones are concentrated in areas with low lineament density, gentle slopes, and moderately dissected hills. The areas with high drainage density, high elevation, highly dissected hills, and valleys with steep slopes were found to have low GWP in the study area. Singh et al. (2018) show that lower GWP is found along Shiwalik ridge and along steeper slopes.

The final GWP map was validated using CGWB data related to depth of 25 monitoring dug wells which were classified into 3 categories, viz., high depth (0.56–4 m), moderate depth (4–8 m), and low depth (8–22 m). Similar approach has been used by Jasrotia et al. (2016) and Yildrim (2021) in their studies for the validation of GWP. Variation in the water depth of different monitoring dug wells is due to the number of conditioning factors. For example, monitoring wells of high water depth lie in the areas of high elevation, high drainage density, highly dissected hills, and valleys and steep slopes. Similarly, areas of high lineament density, alluvium plains, gentle slope, flood plains, and places near water bodies have monitoring dug wells with low water depth. Our results are consistent with the results of similar studies done by Qadir et al. (2020) and Singh et al. (2018) in Jammu region.

The MCDM approach was utilized to analyze GWP, which is purely logical and beneficial for determining the GWP of large-scale basins, since conventional methods of drilling observational wells are expensive and time consuming. The GWP zone map prepared in this study provides insights to planners and decision makers that can be used for watershed management and planning. Planning is to be done in such a way that in low GWP zones, groundwater usage should be limited to household purposes, whereas in high GWP zones, where water availability is enormous, groundwater could be used for large-scale agricultural and commercial purposes.

Conclusion

Depletion of groundwater is a serious threat for sustainability of hydrological cycle. Therefore, it is the need of the hour to estimate the available groundwater for better planning and management of this precious resource. In the present, an attempt has been made for the mapping of GWP zones in the Tawi River basin of Jammu district, which is characterized of having both hilly and plain topographies. Geospatial techniques and the analytical hierarchy process were proven to be a cost-effective tool for defining GWP zones. Based on the study, the GWP in the region is being controlled by combination of many factors among which lineament, topography, slope, rainfall, LU/LC, and soil condition were the most important ones. The main findings of the present study show that high GWP zone is located in the southwestern and western part of the study area. The high GWP zone accounts for 21.98% of the study area and is characterized by plain terrain and gentle slope in the flood plain and alluvial plain. Around 40.54% of the land area is found to be lying under moderate GWP zone, characterized by alluvial and moderately dissected hills and valleys. Thus, it is very important to use the available groundwater judiciously with proper planning by constructing dams and check dams for proper recharge in this zone so that this zone may not slip towards low GWP zone. Low and very low GWP zones cover around 37.48% of the total area and were associated with highly dissected hills and valleys governed by steep mountainous slopes which were found predominantly in the central and eastern parts of the study area. Approximately 78% of the land is classified as having moderate to very low GWP, indicating that there is lack of favorable condition for high GWP. The observed results have been validated with groundwater depth of monitoring dug wells with 80% field validity. The outcome of the present study will be helpful for planner and decision makers for proper management of available groundwater in a judicious manner. Similar studies could be carried out for better planning especially in the hilly and foothill regions where the groundwater is crucial for sustainable development.