Introduction

Groundwater is an important components of the hydrological cycle and influences natural geochemical processes beneath the land surface. Groundwater is also a life sustaining resource catering the daily water requirements of many communities, aids in industrial development, influences agriculture and maintains a healthy ecological balance. This water resource is generally less vulnerable to pollution than surface water bodies, and sustains wetlands and river flows in many regions. However, excessive withdrawal of groundwater can lead to the depletion of aquifers. The consequences of this are lowering of the water table, decreased water availability, high pumping costs, deterioration of water quality, incursion of saline water in coastal zones and adverse effects on groundwater-dependent ecosystems. Both groundwater pollution and depletion are increased by rapid population growth, urbanization, intensification of agricultural land uses, urbanization, poor cultivation techniques, inappropriate sewage disposal practices and poorly managed wastewater management practices by industrial facilities.

As the demand for potable water is increasing throughout the world, there is an increasing need for new methods for identify the spatial distribution and suitability of groundwater resources (Arulbalaji et al. 2019). India is a densely populated, semi-arid country that has a large population that depends on groundwater for their daily water needs. Hence, it is important that the spatial distribution and availability of groundwater is well characterised in this country. Historically, a wide range of hydrogeological, geophysical, geological and photogeological techniques have been used for identifying groundwater availability in a region (McNeill 1988; Fashae et al. 2014). However, with technological advancement and the advent of remote sensing and GIS techniques, new ways of assessing the potential availability of groundwater resources are becoming available. Geographic information systems (GIS) and remote sensing techniques are comprehensive tools for effective assessment of water resources, their management and conservation.

Geospatial tools, are cost-effective methods for rapidly assessing spatial data in various geoscience fields (Arulbalaji et al. 2019). Literature reviews indicate that several index based methods have been applied for assessing groundwater potential zones (Prasad et al. 2008; Rahmati et al. 2015). Some researchers have used the evidential belief function (Nampak et al. 2014; Pourghasemi and Beheshtirad 2015; Mogaji et al. 2016), Shannon entropy (Naghibi et al. 2015; Al-Abadi 2017; Khoshtinat et al. 2019), weight of evidence (Lee et al. 2012a; Al-Abadi 2015; Tahmassebipoor et al. 2016), Fishers Linear Discriminant Function (Wilson et al. 2018; Chen et al. 2019), logistic regression (Ozdemir 2011; Rizeei et al. 2018). Artificial neural network (Nayak et al. 2006; Cho et al. 2011; Lee et al. 2012b), fuzzy logic (Kord and Moghaddam 2014; Kumar et al. 2014; Aouragh et al. 2017), random forest (Baudron et al. 2013; Naghibi et al. 2016; Naghibi et al. 2017), Certainty factor models (Razandi et al. 2015; Hou et al. 2018) and frequency ratio (Manap et al. 2014; Das and Pardeshi 2018) methods for assessing groundwater availability.

The present study was carried out in three groundwater management areas (blocks) in Uttar Dinajpur in India. The purpose of the study was to assess the effectiveness of the multi-criteria decision analysis technique (MCDA) for determining groundwater availability in the region. The Saaty’s (1980) analytic hierarchy process (AHP) is an effective and a well renowned tool largely used in MCDA technique in water resource engineering (Jha et al. 2010; Sargaonkar et al. 2011; Aalianvari et al. 2012; Manap et al. 2013; Kaliraj et al. 2014; Razandi et al. 2015; Mandal et al. 2016; Jenifer and Jha 2017; Pinto et al. 2017; Patra et al. 2018; Kordestani et al. 2019; Pande et al. 2019; Das 2019). A total of 83% of the study area is covered by agricultural land where excessive extraction of groundwater has reduced groundwater levels.

Characteristics of the study area

The present study was conducted on three blocks of Uttar Dinajpur District, West Bengal. These are the Goalpokhar-I, Goalpokhar-II and Karandighi blocks (Fig. 1). The study area lies between the latitudes 25°41′24″ N to 26°11′10″ N and the longitudes 87°48′1″ E to 88°11′23″ E, covering an area of 1084.68 sq km and is connected by railway routes and NH-34. The study area comprises alluvial plains adjacent to the Mahananda river. Agriculture is the primary source of livelihood in the area since the soil is very fertile.

Fig. 1
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Location map of the study area

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Uttar Dinajpur is designated among the least developed districts of West Bengal. The region is experiencing rapid population increases, acute poverty, economic backwardness and a high illiteracy rate. The average annual temperature varies from 17.03–29.63 °C and the annual rainfall of the region varies from 1223 to 1260 mm. The potential evapotranspiration rate ranges from 4.93 to 7.83 mm/day.

Sediments of quaternary age that comprise the Bengal alluvium overly crystalline basement rocks of Precambrian age. The region mainly has a flat-lying topography. Information from the Central Groundwater Board (CGWB) indicates that groundwater occurs under semi confined to unconfined state in deeper aquifers of the Karandighi block. In the Goalpokhar-I and Goalpokhar-II blocks a single unconfined aquifer contain significant groundwater resources at depths within 100 m of the thin soil cover.

Materials and methodology

The groundwater potential of the study area was determined using 10 thematic layers and their respective maps which were generated using ArcGIS 10.0 software. The geological map was procured from Geological Survey of India (GSI) and the Central Groundwater Board (CGWB). The geomorphological map of the area was scanned and digitized. The soil maps were obtained from National Bureau of Soil Survey and Land Use Planning (NBSS & LUP). The rainfall data were collected from Indian Meteorological Department (IMD) and WorldClim-Global Climate Data Version-2 for the years 1970–2000 with spatial resolution of 30 s. The slope and curvature maps were generated using the ASTER DEM having a resolution of 30 m. The DEM was used for generating groundwater flow direction, flow accumulation layers and the resulting map of streams was used for calculating the drainage density. The direction of flow is determined by the direction of steepest descent from each cell (Greenlee 1987; Jenson and Domingue 1988). Flow Accumulation is a raster of accumulated flow to each cell, as determined by accumulating the weight for all cells that flow into each downslope cell (Schäuble et al. 2008; Arnold 2010). The Topographic Wetness Index (TWI) was calculated using the basic terrain analysis tool of the SAGA GIS 7.3.0 software. Mattivi et al. (2019) has thoroughly explained the computational processes of TWI. A flow chart for the assessment process is presented in Fig. 2. The thematic layers and the number of layers chosen for a specific study area for demarking groundwater potential zones by geospatial tools analysis may differ from other study areas. Consequently, a large number of researchers have used personal judgment in allotting weightage to various thematic layers and their characteristic features (Machiwal et al. 2011).

Fig. 2
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Methodological flow chart for GPZ mapping

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In this study, the MNDWI map was prepared from Landsat 8 data. The cropping and irrigation data were collected from District Statistical Handbook, Department of Planning and statistics (2012–2013), WB. The cropping intensity was calculated using the formula

$${\text{Cropping intensity}} = \frac{\text{Net irrigated area}}{\text{Net sown area}} \times 100.$$

For calculating the potential water reserves of the area, a multi decision criteria method (MCDM) technique based on AHP was used. This technique was basically used for assessing the relative weightage of the thematic layers involved. While computing the AHP, as inputs of the program the relative pairwise comparisons of all thematic layers were considered, and outputs were the relative weights. The process hps in decision making and the members can use their previous experiences and knowledge to solve the problem by breaking it into smaller sub parts. For the study area, different thematic layers depending on their extent of influence on the groundwater reserves were assigned respective weights. The assigned weights had ranks ranging from a scale of 1–9 according to the Saaty’s (1980) scale (Table 1), and were normalized using two thematic layers at a time. The final weights were normalized using the eigenvector method. Also to check the consistency and uncertainty of the method, the factors consistency ratio (CR) and Consistency Index (CI) were used:

$${\text{CI}} = { (}\lambda_{ \hbox{max} } - n )/(n - 1); \, n{\text{ being the number of thematic layers,}}$$

where CR = CI/RI; RI stands for Ratio Index (Table 2).

Table 1 Description of scales for pair comparison with AHP
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Table 2 Random index value
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A CR value of ≤ 0.1 is acceptable, in other cases the values need to be reconsidered, revised and re-evaluated. These criteria were analysed accordingly using AHP pairwise comparison matrix (Tables 3, 4, 5).

Table 3 Pairwise comparison matrix for all factors
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Table 4 Pairwise comparison matrix, weights and consistency ratio of the data layers used
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Table 5 Weights assignment for subcategories of all factors through AHP
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Thematic layers

Geomorphology

The geomorphology of an area is considered to be an essential component in delineating groundwater potential zones. This factor gives an insight of distribution of various landform features. The study area is mainly overlain by a relatively flat alluvial plain landform that covers an area of 450.46 sq km. According to the CGWB, the region slopes gently in a southerly direction. The study area comprises the following geomorphic features: alluvial plains, active floodplains, back-swamps, extended floodplains, valley fill and wetlands, and a piedmont/fan region that cover 41.52, 27.74, 14.61, 9.02, 4.97, 2.14% of the study area respectively (Fig. 3). Valley fill deposits comprises unconsolidated sediment such as pebbles, sand, silt, clay which gets deposited at the stream floor. During wet seasons these act as recharge features and hence the groundwater potentiality of these valley fills are moderately high (Chowdhury et al. 2009). Flood is a recurring phenomenon in Uttar Dinajpur (Thakkar 2006; Das et al. 2017). The adjacent banks of the River Sudhani, on the western part of the study area, are highly prone to flooding and inundation and are marked as active flood plains. The extended and active flood plains are characterised by poor to well sorted sediments. Along with yearly monsoonal rainfall and channel flood these active and extended flood plains get recharged amply, therefore these have high ground water potentiality (Pirastru and Niedda 2013; Zhang et al. 2017). The back-swamp area consists of wetlands, which are depressed landforms relying on rainfall as their major source of water, and flood waters often flow in them (Das and Pal 2016). Alluvial plains have moderate groundwater potentiality, Adji and Sejati (2014) had similar observations on their research work.

Fig. 3
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Geomorphological map of study area

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Slope

Slope is a major terrain characteristic, expressing how steep the ground surface is. It plays a dominant role in determining the runoff, rate of infiltration and sedimentation quantity. Regions with high slope are inefficient recharge sources, since a large proportion of the incident rainfall may be lost in surface runoff with limited infiltration. By contrast, water infiltration and groundwater recharge rates are generally higher in areas with more gentle slopes due to the longer residence time of water on the land surface during a rainfall event. The slope map for this study was prepared using a DEM. The extracted slope data were reclassified into 5 classes, namely: flat (0–1.33%), gentle (1.33–3.54%), medium (3.54–6.06%), steep (6.06–9.90%), and very steep (9.90–37.69%) slopes (Fig. 4). Using this classification system, the major portion (35.27%) of the study area has a flat topography and areas with a very steep relief cover only 1.45% of the study area.

Fig. 4
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Slope map of study area

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Geology

The occurrence and distribution of groundwater is largely impacted by the geological setting of the region, since this factor has a large influence on surface runoff, infiltration and seepage. The entire study area is underlain by crystalline rocks of Precambrian age that comprise bedrock in the area. These rocks are overlain by a sequence of sediments that collectively comprise the Bengal Alluvium which range in thickness from 17 to 22 km. (Curray and Munasinghe 1989; Roy and Chatterjee 2015). The stratigraphic succession of these formations have been shown on Table 6. The predominant formations are the Malda Formation and Shaugaon formation. These formations are overlain by Recent Alluvium of Holocene age (GSI 2009; Biswas 2016). Hence the groundwater management blocks are blanketed by contemporaneous deposits. The Malda Formation covers the majority of the study area (that is, 813.50 sq. km), followed by the Shaugaon formation which covers an area of 151.80 sq. km (Fig. 5). Recent alluvial sediments cover 10.97% of the study area. Older alluvium consisting of a well-oxidized reddish argillaceous formation, also referred to as the Barind Tract, covers a portion of the study area (Gayen 2015). These findings and AHP calculations suggest that a high weight can be assigned to the recent alluvium formation followed by the Malda Formation and the Shaugaon formation.

Table 6 Generalized stratigraphic succession of rock unit of the study area
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Fig. 5
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Geology map of study area

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Land use land cover (LULC)

Important information on infiltration, soil moisture, groundwater requirements and surface water dependency can be inferred from a LULC map of the region (Yeh et al. 2016). The LULC map was developed using the NRSC Bhuvan LULC thematic data which was compiled at a scale of 1:50,000. The study area was divided into 6 LULC categories, namely: agricultural land, grasslands, wetlands, plantations, settlement and water bodies that cover 82.73, 0.43, 1.04, 0.47, 13.82, and 1.51% of the study area, respectively (Fig. 6). Agricultural land and grasslands have high groundwater potentiality (Patra et al. 2018; Ghosh et al. 2020). Plantations have been assigned moderately high weightage since these slow down the surface run off flow and increase the rate of infiltration (Bhattacharya et al. 2020). The wetlands and water bodies aid in groundwater recharge in wet seasons, however during the dry seasons these act as discharge features, so, these have moderately low groundwater potentiality (Saranya and Saravanan 2020). Low groundwater potentiality has been assign to the settlement zone for their low rate of infiltration.

Fig. 6
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Land use/land cover map of study area

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Rainfall

Rainfall is the primary source of groundwater recharge in the study area and plays a crucial role in the hydrological cycle. Rainfall feeds the rivers of the regions and farmers rely on rainfall for irrigating crops. The intensity and duration of rainfall influences the infiltration rate. Short duration-high intensity rainfall influences more surface runoff and less infiltration, long duration-lesser intensity rainfall influences high infiltration than runoff (Ibrahim-Bathis and Ahmed 2016). In the present study rainfall data of the years 1970–2000 were used. The annual rainfall ranges from 1584–2086 mm (Fig. 7). The spatial distribution of rainfall map was prepared using IDW interpolation method on Arc GIS 10.0. The rainfall map was reclassified into five classes such as very low (1584–1692 mm), low (1692–1781 mm), moderate (1781–1870 mm), high (1870–1964 mm) and very high (1964–2086 mm).

Fig. 7
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Rainfall distribution Map of study area

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Curvature

The morphology of the regional topography is represented by the curvature values (Vijith and Madhu 2008). A positive curvature is indicative of the fact that the surface is convex, while a negative curvature is indicative of the fact that the surface is concave. The value zero suggests the surface is flat. Soil thickness is generally greater in areas that have concave slopes, consequently soils in these areas generally store more water than soils in areas underlain by convex slopes. By contrast, convex slopes tend to shed more in surface runoff than concave slopes. The study area was categorized into 3 slope-curvature categories. These are: concave (− 6.10 to − 0.475), flat (− 0.475 to 0.4537), and convex (0.4537 to 7.829). The study area is mainly a flat terrain, 15.04 and 18.44% of the study area are covered by concave and convex profiles respectively (Fig. 8).

Fig. 8
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Curvature map of study area

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Modified Normalized Difference Water Index

McFeeters (1996) coined the term “Normalized Difference Water Index” (NDWI) and used this parameter for delineating open water bodies also enhancing their presence in remotely sensed data. This index is used for analysing water bodies and is calculated as follows:

$${\text{NDWI }} = \, \left( {{\text{Green }} - {\text{NIR}}} \right)/\left( {{\text{Green }} + {\text{ NIR}}} \right).$$

NDWI is calculated using the Green band and the Near-infrared band in satellite imagery and is sensitive to built-up land and vegetative cover. Water bodies have positive values while vegetative cover or soil mass have negative values. The term MNDWI stands for “Modified Normalized Difference Water Index” (MNDWI) and the concept was conceived by Xu (2005) which uses the Green and SWIR bands. However, for Landsat-8 Band 3 and 6 were used. The MNDWI is calculated using the formula:

$${\text{MNDWI }} = {\text{ (Green }} - {\text{SWIR)}}/({\text{Green }} + {\text{ SWIR}}).$$

In the study area MNDWI was applied for evaluating the presence of water features and land cover by eliminating atmospheric noise and terrain disturbances and were classified into the following five categories: very low (− 0.36 to − 0.15), low (− 0.15 to − 0.10), moderate (− 0.10 to − 0.03), high (− 0.03 to − 0.05), very high (0.05 to 0.25) (Fig. 9). A high MNDWI value (i.e. a positive value) indicates that an area has a high groundwater potential. A total of 32.03% of the study area falls under the category of a low MNDWI value.

Fig. 9
figure 9

Modified normalized difference water index (MNDWI) map of study area

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Drainage density

Drainage density is an important factor that should be considered while delineating groundwater potential zones. This and is fundamentally controlled by the geological setting of an area (Sener et al. 2005). The closeness of spacing of river channels is referred to drainage density, and is an inversely related to the permeability of underlying rock and sediments (Magesh et al. 2012). Differences in drainage density can also be caused by varying slopes and local changes in relief (Preeja et al. 2011). Drainage density is directly proportional to the runoff rate; a large runoff rate increases the drainage density and vice versa. The drainage density of the study area was classified into the following categories: very low (0–0.54), low (0.54–0.84), moderate (0.84–1.09), high (1.09–1.33) and very high (1.33–1.81). These categories cover 8.87, 13.71, 27.31, 35.19, 14.92% of the study area respectively (Fig. 10).

Fig. 10
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Drainage density map of study area

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Soil

The soil types that cover an area and their characteristics can influence the rainfall infiltration rate and groundwater recharge. In the study area 3 soil types were encountered (Fig. 11). These include: Typic Ustorthents, Typic Ustochrepts and Typic Ustifluvents. Typic Ustochrepts cover the majority of the study area (51.15%). These soils have the characteristics of being very deep and poorly drained with a fine loamy texture. Typic Ustorthents cover 34.14% of the study area. These are very deep and well-drained soils that have a coarse loamy texture. These characteristics make this soil-type suitable for groundwater recharge as these materials have an infiltration rate that exceeds the runoff rate. Typic Ustochrepts, on the other hand, are less suitable for groundwater recharge since the runoff rate far exceeds the infiltration rate. 16.71% of the study area is covered by Typic Ustifluvents which is a very deep, moderately well drained, fine loamy variety.

Fig. 11
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Soil map of study area

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Topographic Wetness Index

Beven and Kirkby (1979) first developed the concept of the Topographic Wetness Index (TWI) as part of the runoff model TOPMODEL (Sørensen and Seibert 2007). This index is a measure of water availability in a region due to the effects of water accumulation caused by effects of topography. The formula of TWI is given by:

$${\text{TWI}} = {\text{ln }}\left( {\frac{{A_{\text{s}} }}{\tan \beta }} \right),$$

where As is the specific catchment area; and tan β is the slope angle of the specific grid, used to replace approximately the local hydraulic gradient under steady state conditions. Regions with steep slopes have lower water availability, higher drainage and low TWI, whereas, lowlands and depressed landforms have high index value and are capable of storing water. Values of TWI in the study area were found to vary from 2.75 to 24.31, and were categorized into five categories, namely: very low (2.75–6.97), low (6.97–9.76), moderate (9.76–12.47), high (12.47–15.68) and very high (15.68–24.31) (Fig. 12). A total of 37.78% of the study area has very low TWI and 2.50% has very high TWI.

Fig. 12
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Topographical Wetness Index map of study area

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Results and discussion

Groundwater potential zones

The groundwater potential mapping was undertaken for the study area due to an urgent need to improve groundwater management practices in the area. On the basis of this mapping, the study area was categorized into five groundwater potential zones (GWPZ) which were: very poor, poor, moderate, high and very high having areal extents of 122, 235, 335, 273 and 114 sq km. respectively (Fig. 13). About 11% of the study area has very poor groundwater potential, 22% has a poor potential, 31% has a moderate potential, 25% has a high potential, and 11% has a very high potential.

Fig. 13
figure 13

Groundwater potential zone of study area

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A number of scatter diagrams were plotted with GWPZ on the Y axis and the respective factors on the X axis to examine their relationships. The diagrams give a graphical insight of the factors that influence potential groundwater reserves in the study area. The scatter graphs suggest the principal factors affecting the groundwater potentiality of a zone are slope, TWI, and rainfall since they have correlation coefficients close to 1 (Fig. 14). From the output map generated of the GWPZ it can be stated that the regions Mahua, Jaingaon, Goalin situated in the north-eastern part of the block Goalpokhar-I, Nizampur, Sukhor, Bagdob, Suripar situated in the mid-south western part of the Goalpokhar-II, and Bhulki, Bagela, Raniganj, Kalibari, Khudargachhi situated in the northern and north-eastern parts of Karandighi block are likely to be highly prospective for groundwater. However, the southern and central parts of the Karandighi groundwater management block, the north eastern part of the Goalpokhar-II block and sparsely located regions in the southern half of the Goalpokhar-I block have poor groundwater prospects.

Fig. 14
figure 14

a–f Relationship between the influencing factors for ground water recharge (grid based) and the Groundwater potential zone (GPZ). g–j Bar graphs represent the mean GPZ values for each influencing (vector based) factors and each sub-category (for g: P-Piedmont, AP-Alluvial plain, EFP-Extended flood plain, BS-Back swamp, AFP-Active flood plain, RW-Rivers and wetland; for h: SF-Silty sand, MF-Malda formation, RA-Recent alluvium, SS-Shaugaon formation; for i: STMT-Settlement, AG-Agricultural land, WET-Wetland; for j: TUC-Typic ustochrepts, TUF-Typic ustifluvents, TUT-Typic ustorthents)

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Validation

Validating the obtained and inferred results helps in checking the accuracy of the model applied. Groundwater fluctuation level (Fig. 15) has been used for validating groundwater potential zone, and its relation with cropping intensity has been shown. Cropping intensity refers to raising of a number of crops from the same field during one agricultural year (Saito et al. 2006; Jain et al. 2013). The cropping intensity map shows that the Karandighi block has a cropping intensity of 219.18%, of which 118 hectares is irrigated by river water, 300 deep tube wells and 18,552 shallow tube wells. This clearly indicates the agricultural activities of this block is highly dependent on intense groundwater extraction and that surface water is used to a lesser extent. Also, the fluctuation map shows this block is marked by high groundwater fluctuation levels (Fig. 16). Therefore, the block has a low groundwater potential. This is because the groundwater fluctuation level has an inverse relationship with groundwater potential zones and have a negative relationship with the mapped groundwater potential factor indicated on the respective scatter plots (Fig. 17). The Goalpokhar 2 and 1 blocks have ample surface water sources to rely on, hence, have low dependency on groundwater. As a result of which these blocks have higher groundwater potential zones.

Fig. 15
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Water level fluctuation map of study area

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Fig. 16
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Groundwater potential zone with water level fluctuation

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Fig. 17
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Relationship between the water level fluctuation and the groundwater potential zone (GPZ)

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Agricultural dependency on groundwater of the study area and its relationship with the groundwater potential zones

Agriculture is the primary economic activity in the study area. Various crops such as paddy, wheat, maize, mustard, potato, sugarcane, jute are sown in the Kharif (from July to October, during the south-west monsoon) and Rabi seasons (October–March, during winter) (Table 7). Paddy rice is mostly cultivated during the monsoon season for maximum productivity. The major rivers of the study area namely Sudana, Bolhakuao, Putano, Sarriano flow through the blocks of Goalpokhar-1, 2. As a result of which these two blocks largely rely on river-lift irrigation.

Table 7 Annual production of major crops in the study area
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However, this source of water is limited in case of Karandighi, and consequently a large amount of groundwater is extracted in the region for irrigation. The findings from the groundwater potential map suggests that the Karandighi block has a low groundwater potential, but a high cropping intensity (Fig. 18). The crops sown in the Rabi season are provided in the Table 7 and these require heavy groundwater supply. The Karandighi block gets its major irrigational supply from the deep tube wells and shallow tube wells which are present in much greater numbers in this block than in the Goalpokhar-1, 2 blocks (Table 8). From Figure-15 it has been obtained that the seasonal fluctuation of groundwater in Karandighi block is very high.

Fig. 18
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Cropping intensity map of study area

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Table 8 Area irrigated by different sources (2010–2011)
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Conclusions

The present study was conducted to delineate the groundwater potential zones in three groundwater management blocks in Uttar Dinajpur, West Bengal. A total of 10 thematic layers were used to compile a groundwater resource potential map for the study area. These are: geology, rainfall, geomorphology, slope, drainage density, land use and cover (LULC), soil type, curvature, the Topographic Wetness Index (TWI) and the Modified Normalized Difference Water Index (MNDWI).

Analysis of the compiled map allowed the study area to be classified into 5 groundwater potential zones, including areas with a very low, low, moderate, high and very high prospectivity. From the output result thus obtained a high groundwater potentiality can be assigned to the following features; extended flood plain and active floodplain (geomorphological features), grassland and agricultural land (landuse features), and recent alluvium (lithological features). By comparison with the Karandighi block, the Goalpokhar-I and II blocks receive a greater amount of rainfall, and are underlain by more permeable lithologies that are suitable for the storage of groundwater.

The area gets inundated by frequent monsoonal floods, this enriches the fertility of the soil, and this fertile land helps farmers to carry out farming effectively. As a result, the cropping intensity of study area is high ranging from 172 to 220%. A sum up of the results suggest, efficient management of groundwater and irrigation via shallow and deep tube wells of the regions can be done in the high GPWZ, leading to higher annual crop productivity especially in the dry seasons. The obtained groundwater potential zones map provides information that can improve the management of groundwater resources in the area.