Groundwater is one of the most important contributors to fresh water for humankind in the world. The knowledge of groundwater distribution can play a significant role in the planning and implementation of groundwater management strategies. The major aim of this study was to assess the groundwater potential zone using geographic information system (GIS)-based multicriteria decision analysis in the Enemor and Ener Woreda, Ethiopia. The factors considered in this study are rainfall, geology, slope, drainage density, land use/land cover (LULC), lineament density, and soil type. As a result of the analytic hierarchy process (AHP), it was determined that rainfall and geology are the most influencing factors based on their scoring higher weights. The groundwater potential zone of the study area was categorized into five classes, namely, very low, low, moderate, high, and very high. On analysis, most portions of the study area are found to be under the moderate situation, which covers around 540.23 km2 (65.6%) of the study area, whereas very low, low, high, and very high potentiality classes cover 0.002, 10.99, 21.93, and 0.55% of the area, respectively. Approximately 92% of the observed boreholes agreed with the developed map. Finally, the output of this study is important to regional as well as local policymakers for the exploitation of groundwater or management purposes.

  • AHP is used for prioritizing the influencing factors.

  • The groundwater potential zone is developed using weighted overlay analysis.

  • The developed groundwater potential map was validated using borehole data.

  • Saaty's scaling was used to order the influencing factors based on their importance.

Groundwater is the largest contributor of fresh water on our planet. It serves as a primary source of fresh water for many communities around the world, particularly in areas where surface water may be scarce or unreliable (Carrard et al. 2019). It is often used for drinking water supplies, either through direct extraction from wells or as a source for public water systems after treatment (Foster 2022). Groundwater is vital for irrigation in agriculture, particularly in regions where rainfall is insufficient to sustain crops. It provides a consistent water source for farming, which is crucial for food production (Glanville et al. 2023). Many industries rely on groundwater for their operations, including manufacturing, energy production, and mining. Groundwater is used for various purposes such as cooling, processing, and cleaning. Groundwater sustains wetlands, springs, and streamflows, which are essential for supporting diverse ecosystems and biodiversity. It contributes to maintaining the habitat for aquatic species and provides water for vegetation (Bhangaonkar & Fennell 2022). During periods of drought or low precipitation, groundwater can serve as a buffer, providing a reliable source of water when surface water supplies are depleted (Toylor & Alley 2001).

The knowledge of groundwater distribution is important to handle the problems related to overuse and contamination of groundwater including depletion of aquifers, land subsidence, saltwater intrusion, and contamination of drinking water supplies. Sustainable management practices are crucial to ensure the long-term availability and quality of groundwater resources (Tuan et al. 2024). They are most important for operational planning and implementation of a fresh water–based economy (Arabameri et al. 2019). All over the world, the demand for groundwater increases due to the increase in population (Ajayi et al. 2022). Today, water crises have developed in several parts of the world especially in semi-arid regions owing to an increase in population and unwise use of groundwater for agriculture and drinking (Najafzadeh et al. 2021a, 2021b). Furthermore, surface water, a vital freshwater resource, often faces threats to both its quantity and quality owing to industrial, agricultural, and urban development (Najafzadeh et al. 2021a; Najafzadeh & Niazmardi 2021). This problem leads to a reduction of aquifer capacity and if these trends are continuous, in the future, the demand of the people will not be met sufficiently. Thus, to reduce such types of problems and their effects on the living standard of the people, both ground and surface water potential have to be managed well and assessed efficiently (Santhanam & Abraham 2018; Dawit et al. 2020; Shabani et al. 2022).

Recently, different methods and tools have become available to assess groundwater potential zones in a specific area. Among these, remote sensing and geographic information systems (GIS) are commonly used and inexpensive tools that need little data and physical work (Magesh et al. 2012; Duan et al. 2016; Arabameri et al. 2019; Gebrie & Getachew 2019; Craddock et al. 2022; Flem et al. 2022; Gao et al. 2022; Githinji et al. 2022; Guduru & Jilo 2022; Wang et al. 2022; Yu et al. 2022; Danso & Ma 2023). The GIS tool in this specific prospect is mainly used for the preparation and overlying of the map of the factors that influence the occurrence and distribution of groundwater. The map overlaying the GIS environment is according to the weights of influencing factors, which are obtained by multicriteria decision analysis (MCDA) through the analytic hierarchy process (AHP) (Jhariya et al. 2021; Duguma & Duguma 2022; Kabeto et al. 2022; Shabani et al. 2022). The GIS-based MCDA is a powerful tool for assessing groundwater potential zones by integrating various spatial datasets and criteria to identify areas with high potential for groundwater occurrence. It provides a systematic and quantitative approach to assessing groundwater potential zones, enabling decision-makers to prioritize areas for groundwater exploration, development, and protection based on their suitability and potential for sustainable groundwater supply (Arabameri et al. 2019).

According to Rajaveni et al. (2017), Ahmed and Sajjad (2018), Bengal (2020), and Li et al.(2021), the occurrence and distribution of groundwater are influenced by different factors such as geology, geomorphology, rainfall, land use/land cover (LULC), drainage density, slope, groundwater level depth, soil texture, soil depth, soil type, and lineament density.

This study is mainly focused on assessing groundwater potential zones in Enemor and Ener Woreda using GIS-based MCDA through weighted overlay analysis. The groundwater potential zoning criteria considered for this study according to data accessibility and their effects on groundwater occurrence in the previous studies are annual rainfall, slope, geology, drainage density, LULC, lineament density, and soil type. The average weight of each influencing factor is obtained by the AHP.

Study area description

Enemor and Ener Woreda is located in the Gurage administrative zone of the regional state of Central Ethiopia, Ethiopia. It is situated between 37.592° to 37.918°E longitude and 7.834° to 8.124°N latitude as shown in Figure 1. The area coverage of Enemor and Ener Woreda is 823 km2 and is bordered on the south by the Hadiya Zone, on the southwest by Yem Special Woreda, on the west by Oromia Region, on the north by Cheha Woreda, on the east by Geta Woreda, and the southeast by Endegagn Woreda. The administrative center of Enemor and Ener Woreda is Gunchire. The population density of Enemor and Ener Woreda according to the 2007 census conducted by the Central Statistical Agency (CSA) is 167,770.
Figure 1

Location map of Enemor and Ener Woreda.

Figure 1

Location map of Enemor and Ener Woreda.

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According to the 30 × 30 -m Digital Elevation Model (DEM), the elevation of the study area ranges from 966 to 2,860 m and it is part of the Omo-gibe Basin. The Gogare, Nakav, Derke, and Anzeche rivers are among the perennial rivers in the study area and there are numerous non-perennial streams that contribute immense quantities of water to the ground as well as Omo-gibe River.

The average annual rainfall as recorded at weather stations ranges from 856 to 1600 mm, with an average annual rainfall of 1389 mm, having a bimodal distribution. The months from June to September are the main rainy season, and from April to May are the shortest rainy season. Generally, the valley parts of the study area receive a low amount of rainfall compared with that of the mountainous parts of the study area, likely implying the influence of mountains on rainfall patterns. The annual mean temperature calculated from the meteorological stations is 19.1 °C, with maximum and minimum values of 22.5 and 6.7 °C, respectively. The LULC of the study area is dominated by agriculture (crop).

Groundwater potential influencing factors

The first task for this study was the selection of the factors affecting the groundwater potential from previous studies conducted in different parts of the world, mostly in Ethiopia. The factors included in most studies are slope, drainage density, lineament density, rainfall, LULC, soil type, and geological formation. The data required for groundwater potential zoning were collected from freely accessible webs, respective sectors in Ethiopia, and on-site supervision. The collected data were prepared for GIS-based MCDA. The general process applied for this study is presented in Figure 2, and the factors selected for this study are elaborated on hereafter.
Figure 2

Methodological flow charts of the study area.

Figure 2

Methodological flow charts of the study area.

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Slope

The slope describes the variation of elevation in a certain area and is an essential parameter in groundwater investigation as infiltration is inversely proportional to land steepness (D'Acunto & Urciuoli 2006; Kabeto et al. 2022). Usually, on a gently sloped land, the speed of surface flow is low and the infiltration rate is high. Conversely, over a steep slope land more surface runoff and low infiltration will be recorded. The slope of the study area was developed from the DEM (30 × 30 -m resolution). The slope percentage of the Enemor and Ener Woreda landscape varies from 0 to 54.3%. Based on the slope, the study area has been reclassified into five slope classes. The major parts of the landscape were found in Slope class 1 (0–4.26%), which covers 42.2% of the study area as presented in Figure 3 and Table 1.
Table 1

Slope classes

Slope (%)Area (km2)Area in percentage
0–4.26 347 42.2 
4.27–8.73 273 33.2 
8.74–14.9 133 16.2 
15–24.7 51 6.2 
24.8–54.3 18 2.2 
Slope (%)Area (km2)Area in percentage
0–4.26 347 42.2 
4.27–8.73 273 33.2 
8.74–14.9 133 16.2 
15–24.7 51 6.2 
24.8–54.3 18 2.2 
Figure 3

Slope map.

Drainage density

Drainage density is defined as the total length of streams/rivers in a drainage basin divided by the total area of the drainage basin. Drainage density is an inverse function of permeability and therefore an essential parameter in assessing the groundwater potential zone (Bengal 2020; Roy et al. 2020; Ajayi et al. 2022). Low drainage density values are favorable for infiltration and allow no way for runoff, hence indicating a high groundwater potential. The drainage density was done under the spatial analyst tool of ArcGIS. The drainage density map (Figure 4) shows that the drainage density value ranged from 0 to 3.32 km/km2. These are reclassified into five classes as presented in Table 2.
Table 2

Drainage density classes

Drainage density (km/km2)Area (km2)Area in percentage
0–0.286 369 44.8 
0.287–0.780 219 26.6 
0.781–1.210 99 12.0 
1.220–1.740 106 12.9 
1.750–3.320 30 3.6 
Drainage density (km/km2)Area (km2)Area in percentage
0–0.286 369 44.8 
0.287–0.780 219 26.6 
0.781–1.210 99 12.0 
1.220–1.740 106 12.9 
1.750–3.320 30 3.6 
Figure 4

Drainage density map.

Figure 4

Drainage density map.

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Lineament density

The lineament is a linear feature including faults, fractures, and geological contacts between different lithologies measured in km/km2, and can also offer a pathway for water to percolate into the floor and flow through the aquifer system, specifically in the lineament density of an area can signify the groundwater potential directly as the water infiltration rate increases with increasing lineament density (Ahmed & Sajjad 2018; Gebrie & Getachew 2019; Li et al. 2021; Guduru & Jilo 2022). In this particular study, lineaments were extracted by creating hill shade for various altitudes and the lines are drawn for visible linear features. Using these lines, the lineament density of the study landscape was developed and reclassified as presented in Figure 5 and Table 3.
Table 3

Lineament density classes

Lineament density (km/km2)Area (km2)Area in percentage
0–0.108 581 70.6 
0.109–0.314 53 6.4 
0.315–0.492 70 8.5 
0.493–0.694 100 12.2 
0.695–1.2 19 2.3 
Lineament density (km/km2)Area (km2)Area in percentage
0–0.108 581 70.6 
0.109–0.314 53 6.4 
0.315–0.492 70 8.5 
0.493–0.694 100 12.2 
0.695–1.2 19 2.3 
Figure 5

Lineament density map.

Figure 5

Lineament density map.

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Annual rainfall

The contribution of rainfall to the hydrologic cycle and groundwater potential is indisputable. Rainfall is the major source of surface and groundwater, and therefore, the intensity of rainfall and its spatial distribution strongly manage the recharge extent of a basin. The possibility of groundwater recharge would be high at the place where annual rainfall is high and is low where the annual rainfall is low (Thomas et al. 2016; Dey et al. 2020; Kavitha & Kumar 2020; He et al. 2021). The rainfall map of this study area was developed from rainfall data obtained from the National Meteorology Agency of Ethiopia from the years 1983–2020 for gauging stations Imdibir, Agena, Gunchire, Gubre, Werabe, Gibe farm, Hossana, and Gimma as shown in Figure 6.
Figure 6

Annual rainfall map.

Figure 6

Annual rainfall map.

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Land use/land cover

LULC has a significant role in the runoff, infiltration, and groundwater recharge capacity of any watershed, and it also provides soil information such as soil moisture content, groundwater, and surface water, and is an indicator regarding groundwater potential prospects (Abdelaziz et al. 2020; Mengistu et al. 2022; Siddik et al. 2022). LULC data were obtained from the Ethiopian Water and Land Resources Center. The data show that the LULC of the study area has been dominated by five LULC categories, as presented in Figure 7. These are as follows: cropland with an area extent of 629 km2 (76.43%), forest land of 172 km2 (20.9%), grassland of 16 km2 (1.94%), wetland of 9 km2 (1.1%), and water bodies of 1 km2 (0.12%).
Figure 7

LULC map.

Soil

The soil map of Enemor and Ener Woreda was generated from the dissolved shapefile of the study area clipped from the Ethiopia soil map shapefile obtained from the Ethiopian water and energy minister. The study area was dominated by five soil groups as shown in Figure 8.
Figure 8

Soil map.

Geology

Geology is the most important factor influencing potential groundwater occurrence as the penetration mostly depends on the permeability or conductivity level of the different rock types (Rajaveni et al. 2017; Faria et al. 2022). The geological data for this study area were obtained from the Geological Survey of Ethiopia (GSE). The major rock type in the study area is sandstone with an area coverage of 537 km2 (65.25%) at the central part of the study area. The remaining portion is covered by ignimbrite, basalt, and quartz with an area coverage of 188 km2(22.84%), 84 km2(10.2%), and 14 km2(1.7%), respectively, as shown in Figure 9.
Figure 9

Geology map.

Multicriteria decision analysis

MCDA is both an approach and a set of techniques or a way of looking at complex problems to deliver an overall ordering of options, from the highest favored to the lowest favored option. The options may differ in the extent to which they achieve several objectives, and no one option will be best in achieving all objectives (Konidari 2009). MCDA problems include five components, as follows: Goal, Decision-maker or group of decision-makers with opinions (preferences), Decision alternatives, Evaluation criteria (interests), and Results or consequences associated with alternative/interest grouping (NRLI 2011).

MCDA has a limitation in analyzing spatial decision problems. To overcome this limitation it is better to integrate it with the GIS (Ehrgott et al. 2010). The GIS-based MCDA was carried out using three procedures: Boolean overlay, Weighted Linear Combination (WLC), and Ordered Weighted Averaging (OWA). For multicriteria and a large number of alternatives, WLC is preferable (Chou 2013). Furthermore, the AHP was implemented for the determination of criteria weights (Romano et al. 2015).

The AHP is a universal theory of extent. It is used to derive ratio scales from both discrete and continuous paired judgments. These judgments may be taken from actual measurements or from a fundamental scale that reflects the relative strength of preferences and feelings (Saaty 1987).

The AHP concedes the major stages to solve a specific problem; decomposition, comparative judgments, and synthesis priorities (Harker & Vargas 1987). The decomposition principle calls for structuring the hierarchy to capture the basic elements or criteria. The principle of comparative judgment calls for setting up a matrix to carry out pairwise comparisons of the relative importance of the criteria immediately above it. This matrix is developed by the Saaty scale value of criteria comparison (Table 4).

Table 4

Saaty scale value of criteria comparison (Saaty 1977)

Intensity of importanceExplanation
Equal importance 
Weak importance of one over another 
Essential or strong importance 
Demonstrated importance 
Absolute importance 
2,4,6,8 Intermediate values between the two adjacent judgments 
Intensity of importanceExplanation
Equal importance 
Weak importance of one over another 
Essential or strong importance 
Demonstrated importance 
Absolute importance 
2,4,6,8 Intermediate values between the two adjacent judgments 

Subjective judgments on the relative importance of each part are represented by assigning numerical values, as selected under Table 4. First of all, a matrix is constructed using each criterion's preference scale value. Then each criterion is compared with the other criteria relative to its importance, as presented in Table 5.

Table 5

Relative importance of potentiality criteria

Criteria[1][2][3][4][5][6][7]
[1] Rainfall 
[2] Geology 0.33 
[3] Slope 0.33 0.33 
[4] Drainage density 0.20 0.33 
[5] LULC 0.20 0.20 0.33 1.00 
[6] Lineament density 0.20 0.20 0.33 0.50 1.00 
[7] Soil 0.14 0.20 0.20 0.33 0.33 
Column total 2.41 5.27 8.87 11.83 16.33 18.00 25.00 
Criteria[1][2][3][4][5][6][7]
[1] Rainfall 
[2] Geology 0.33 
[3] Slope 0.33 0.33 
[4] Drainage density 0.20 0.33 
[5] LULC 0.20 0.20 0.33 1.00 
[6] Lineament density 0.20 0.20 0.33 0.50 1.00 
[7] Soil 0.14 0.20 0.20 0.33 0.33 
Column total 2.41 5.27 8.87 11.83 16.33 18.00 25.00 

The pairwise comparison matrix value and priority matrix normalized vectors are calculated from the pairwise matrix table by dividing each column entry by the sum of column values. Each element represents the weighting value of each criterion and the relative weight for each factor is determined within the range from 0 to 1 (Table 6), which means a higher weight indicates a greater contribution to groundwater potential.

Table 6

Normalized value and corresponding weight of potentiality criteria

Criteria[1][2][3][4][5][6][7]SumAverage Weight
[1] Rainfall 0.415 0.570 0.338 0.423 0.306 0.278 0.280 2.609 0.37 
[2] Geology 0.138 0.190 0.338 0.254 0.306 0.278 0.200 1.704 0.24 
[3] Slope 0.138 0.063 0.113 0.085 0.184 0.167 0.200 0.949 0.14 
[4] Drainage density 0.083 0.063 0.113 0.085 0.061 0.111 0.120 0.636 0.09 
[5] LULC 0.083 0.038 0.038 0.085 0.061 0.056 0.120 0.480 0.07 
[6] Lineament density 0.083 0.038 0.038 0.042 0.061 0.056 0.040 0.358 0.05 
[7] Soil 0.059 0.038 0.023 0.028 0.020 0.056 0.040 0.264 0.04 
Criteria[1][2][3][4][5][6][7]SumAverage Weight
[1] Rainfall 0.415 0.570 0.338 0.423 0.306 0.278 0.280 2.609 0.37 
[2] Geology 0.138 0.190 0.338 0.254 0.306 0.278 0.200 1.704 0.24 
[3] Slope 0.138 0.063 0.113 0.085 0.184 0.167 0.200 0.949 0.14 
[4] Drainage density 0.083 0.063 0.113 0.085 0.061 0.111 0.120 0.636 0.09 
[5] LULC 0.083 0.038 0.038 0.085 0.061 0.056 0.120 0.480 0.07 
[6] Lineament density 0.083 0.038 0.038 0.042 0.061 0.056 0.040 0.358 0.05 
[7] Soil 0.059 0.038 0.023 0.028 0.020 0.056 0.040 0.264 0.04 

Relatively, rainfall has more weight than the other influencing factors, and soil type has less contribution to groundwater occurrence and distribution.

The value of pairwise comparison relies on subjective judgment, which might lead to arbitrary results with bias, and hence an evaluation is needed. A numerical index called consistency ratio (CR) is used for evaluating the consistency of the pairwise comparison matrix. The CR was determined by using the consistency index (CI) and random index (RI). If the CR is greater than 0.1, the weight of the criterion should be revised (Saaty 1987).
formula
(1)
The CI value is obtained from the following equation:
formula
(2)
where is the principal biggest eigenvalue with the value of 7.42, and n is the number of criteria.

Saaty (1987) provides the RI based on the number of criteria. Therefore, RI corresponding to the number of criteria (7) is 1.32 from Table 7.

Table 7

Saaty random index (RI) value

N12345678910
RI 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 
N12345678910
RI 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 

Therefore, .

Groundwater potential map development

The groundwater potential map was developed by overlapping the determinant groundwater contributing factors. A weighted overlay analysis device was used to develop the groundwater potential map and to compute the groundwater potential index values. The reclassified layers of rainfall, geology, slope, drainage density, LULC, lineament density, and soil type are overlapped using overlay analysis in the spatial analysis toolbox of ArcGIS by multiplying the corresponding average weights.

The contributing factors are reclassified into a common assessment class of 1, 2, 3, 4, and 5 for very low, low, moderate, high, and very high, respectively (Jhariya et al. 2021; Guduru & Jilo 2022; Kabeto et al. 2022; Dimple et al. 2023), as provided in Table 8.

Table 8

Relative weight of potentiality criteria and corresponding classes

Criteria mapTypeClassPotentialityLayer weight (%)
Rainfall 1,325.42–1,357.1 Very low 37 
1,357.2–1,384.35 Low 
1,384.36–1,415.14 Moderate 
1,415.15–1,454.72 High 
1,454.73–1,550 Very high 
Geology Ignimbrite Very low 24 
Quartz Low 
Basalt Moderate 
Sandstone High 
Slope 0–4.26 Very high 14 
4.27–8.73 High 
8.74–14.91 Moderate 
14.92–24.71 Low 
24.72–54.32 Very low 
Drainage density 0–0.285 Very high 
0.286–0.777 High 
0.778–1.205 Moderate 
1.206–1.736 Low 
1.737–3.317 Very low 
LULC Settlement Very low 
Shrub land Low 
Woodland Moderate 
Forest land Moderate 
Cropland High 
Grassland High 
Waterbody Very high 
Wetland Very high 
Lineament density 0–0.11 Very low 
0.12–0.314 Low 
0.315–0.494 Moderate 
0.495–0.698 High 
0.699–1.195 Very high 
Soil Chromic combisols Very low 
Chromic luvisols Low 
Eutric combisols Moderate 
Pellic vertisols High 
Eutric nitisols Very high 
Criteria mapTypeClassPotentialityLayer weight (%)
Rainfall 1,325.42–1,357.1 Very low 37 
1,357.2–1,384.35 Low 
1,384.36–1,415.14 Moderate 
1,415.15–1,454.72 High 
1,454.73–1,550 Very high 
Geology Ignimbrite Very low 24 
Quartz Low 
Basalt Moderate 
Sandstone High 
Slope 0–4.26 Very high 14 
4.27–8.73 High 
8.74–14.91 Moderate 
14.92–24.71 Low 
24.72–54.32 Very low 
Drainage density 0–0.285 Very high 
0.286–0.777 High 
0.778–1.205 Moderate 
1.206–1.736 Low 
1.737–3.317 Very low 
LULC Settlement Very low 
Shrub land Low 
Woodland Moderate 
Forest land Moderate 
Cropland High 
Grassland High 
Waterbody Very high 
Wetland Very high 
Lineament density 0–0.11 Very low 
0.12–0.314 Low 
0.315–0.494 Moderate 
0.495–0.698 High 
0.699–1.195 Very high 
Soil Chromic combisols Very low 
Chromic luvisols Low 
Eutric combisols Moderate 
Pellic vertisols High 
Eutric nitisols Very high 

Validation of groundwater maps

The groundwater potential map has been developed by overlay analysis and can be verified by the observed borehole discharge (Gebrie & Getachew 2019; Duguma & Duguma 2022) and geoelectric methods (Jhariya et al. 2021; Shabani et al. 2022). For this particular study, the recorded yield of the borehole obtained from Enemor and Ener Woreda Mining and Energy Office was used for the verification of the developed groundwater potential map.

The yield of the observed borehole was added to the developed groundwater potential map and verifies their potentiality class.

The groundwater potential map of Enemor and Ener Woreda has been developed using GIS-based MCDA through weighted overlay analysis. The average weights of the influencing factors, annual rainfall (37%), geology (24%), slope (14%), drainage density (9%), land use/land cover (7%), lineament density (5%), and soil type (4%) were estimated by AHP.

The groundwater potentiality of the study landscape was classified into five classes, namely, very high, high, moderate, low, and very low. The potentiality map of the study landscape in Figure 10 shows that most portions of the study area are under the moderate situation, which covers around 540.23 km2 (65.6%) of the study landscape. The potentiality classes and their extents have been enumerated in Table 9.
Table 9

Potential classes and respective area coverage

ClassPotentialityArea (km2)Area in percentage
Very low 0.017 0.002 
Low 90.45 10.990 
Moderate 540.23 65.642 
High 180.48 21.930 
Very high 4.53 0.550 
ClassPotentialityArea (km2)Area in percentage
Very low 0.017 0.002 
Low 90.45 10.990 
Moderate 540.23 65.642 
High 180.48 21.930 
Very high 4.53 0.550 
Figure 10

Groundwater potential map of Enemor and Ener Woreda.

Figure 10

Groundwater potential map of Enemor and Ener Woreda.

Close modal

The output model of groundwater potential zones reveals the fact that groundwater potential is strongly determined by the physical parameters like annual rainfall, geology, slope, drainage density, land use/land cover, lineament density, and soil type. The groundwater potential map (Figure 10) reflects that the northwestern part of the study area bears high groundwater potential, because this region is characterized by high rainfall, lower drainage density, gentle slope, the presence of alternating layers of sand, silt, and clay, and the presence of loamy texture, which allows to penetrate groundwater in this part. Aquifer thickness is also high in this part, which influences the greater availability of groundwater. Therefore, all of these factors are very favorable for influencing the groundwater availability in this part of the study area.

Conversely, the eastern part of the study area represents low to very low groundwater potential zones owing to the presence of granite and basaltic hard rock, which have no primary porosity and have little influence on groundwater availability. Only some part of the northeastern region of the study area, where lineament density is high, represents the secondary porosity on granite and basalt rocks and allows groundwater availability to some extent.

The identification of potential groundwater zones in the present day context of high demand for groundwater is primarily very necessary because the potential of any resource gives an opportunity for a rethinking and further development of that resource. Hence, demarcation of potential groundwater zones in Enemor and Ener Woreda gives an idea to the regional planner about how the available water can be used optimally and how it can help in sustainable development.

But there are some limitations of this study. The AHP method is basically based on the expert advice, therefore there may be error in their decision. Error in the decision means error in the potential zone. Some parameters like the amount of draft of water for irrigation and domestic purpose have not been taken into consideration, but they have a significant impact on groundwater potential.

The developed groundwater potential map was validated by the recorded yield of boreholes that existed in the study landscape. The recorded yield of the observed boreholes can be classified according to their potentiality relatively (Jhariya et al. 2021; Shabani et al. 2022). The relative potentiality classes of the observed boreholes are very low (<3 l/s), low (3–5 l/s), moderate (5–7 l/s), high (7–9 l/s), and very high (>9 l/s). Therefore, most of the observed borehole yields are in agreement with the delineated map except the Gidosay borehole as presented in Table 10.

Table 10

The yield of boreholes and their relative potentiality verification

NameYield (l/s)Relative potentialityRemark
Shebraber 6.92 Moderate Agee 
Eraye 6.5 Moderate Agree 
Kuneber 4.6 Low Agree 
Ebaragne 5.3 Moderate Agree 
Gardashe 6.89 Moderate Agree 
Terhogne_1 High Agree 
Terhogne_2 9.2 Very high Agree 
Gidosay 4.3 Moderate Disagree 
Agata 3.86 Low Agree 
Laka 6.65 Moderate Agree 
Ewetera 5.87 Moderate Agree 
Terede 6.34 Moderate Agree 
NameYield (l/s)Relative potentialityRemark
Shebraber 6.92 Moderate Agee 
Eraye 6.5 Moderate Agree 
Kuneber 4.6 Low Agree 
Ebaragne 5.3 Moderate Agree 
Gardashe 6.89 Moderate Agree 
Terhogne_1 High Agree 
Terhogne_2 9.2 Very high Agree 
Gidosay 4.3 Moderate Disagree 
Agata 3.86 Low Agree 
Laka 6.65 Moderate Agree 
Ewetera 5.87 Moderate Agree 
Terede 6.34 Moderate Agree 

In this study, the application of GIS-based MCDA helped to evaluate and identify the probable groundwater potential zone in the Enemor and Ener Woreda. Several steps were seriously formulated to handle this study including developing of the raster map of groundwater influencing factors with their average weights through AHP and lastly overlaying these influencing factor maps to potential groundwater zone demarcations. The factors influencing groundwater potential zones considered in this study are rainfall, geology, slope, drainage density, LULC, lineament density, and soil type. The AHP result reveals that rainfall, geology, and slope have a major role or higher weightage in the study area.

The groundwater potentiality of the study landscape was classified into five types, namely, very low, low, moderate, high, and very high. As the result of overlying analysis, the spatial distribution and area extent of each class are 0.017 km2 (0.002%), 90.45 km2 (10.99%), 540.23 km2 (65.642%), 180.48 km2 (21.93%), and 4.53 km2 (0.55%) for very low, low, moderate, high, and very high, respectively. Furthermore, the result reveals that the central and southwest areas of the study landscape are categorized under the moderate potential zone and the eastern part is a low potential zone.

This study was validated using the recorded yield of 12 boreholes presented in different portions of the study area and the result of validation was very good agreement except one borehole disagreement. The overall findings indicated that GIS-based multicriteria analysis has the potential to give a good platform for examining groundwater potential zones and developing appropriate groundwater exploitation plans for many objectives. The developed map may have benefits for various developmental activities, including ensuring sustainable groundwater development in the study area and prioritizing locations for groundwater exploitation for the society as well as groundwater conservation schemes.

The authors would like to thank the Ministry of Water, Irrigation, and Energy (MoWIE) of Ethiopia and the Enemor and Ener Woreda Mining and Energy Office for providing the necessary data for the research work.

There is no external funding for this study

A.M. and M.B. contributed toward the conceptualization and methodology, participated in formal analysis and data curation, wrote the original draft, reviewed and edited the manuscript, and visualized the published work; A.M. contributed toward software development; M.B. validated the work; A.M. was involved in resources preparation and prepared the original draft. Both authors agreed to the published version of the manuscript.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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