Spatial-based mapping of the groundwater potential zones of Akaki catchment in the surrounding highlands of Addis Ababa, Ethiopia

doi:10.21203/rs.3.rs-4991095/v1

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Spatial-based mapping of the groundwater potential zones of Akaki catchment in the surrounding highlands of Addis Ababa, Ethiopia

https://doi.org/10.21203/rs.3.rs-4991095/v1

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Human activities and climate change are hindering water supply in the Akaki catchment. This issue is aggravated by the reduction in groundwater levels due to excessive withdrawal. Hence, this study investigated the potential groundwater areas within the catchment by considering eight different factors. Eight thematic map layers were created utilizing ArcGIS. The analytical hierarchy process (AHP) was conducted using the geospatial monitoring and modeling system software (i.e., TerrSet- v.19.0.6). Through the analysis, it was determined that lithology (31%), rainfall (23%), lineament density (18%), drainage density (11%), slope (6%), land use (4%), soil (4%), and elevation (3%) exhibit their respective degrees of significance on groundwater potential in the catchment. Pair-wise computations showed an acceptable range, displaying a consistency ratio below 0.1. Subsequently, a weighted overlay analysis was done and it revealed that approximately 41% of the catchment area is situated in the Northeast, Central, and Northwest regions characterized by moderate groundwater potential. Also, about 43% of the catchment, located in the Northwest, Central, and Southeast regions, shows a high groundwater potential. In the southern part, an area covering approximately 15% of the catchment, has been identified as having a very high groundwater potential. Furthermore, single-parameter sensitivity analysis indicated that lithology, rainfall, and lineament were the parameters unveiling the highest sensitivity. Finally, the results were validated by superimposing data from 199 wells onto the categorized groundwater potential regions, indicating that the majority (76%) of the wells aligned with high and very high groundwater potential zones. Additionally, the validation demonstrates excellent groundwater predictability with an overall AUC value of 0.925. Therefore, the study underscores the importance of gaining a deep understanding of the factors influencing groundwater potential within the catchment to plan a sustainable supply of groundwater resources.

Akaki

catchment

groundwater potential

sustainable

Water scarcity is a persistent global concern (Mahato et al., 2022; Elvis et al., 2022; Nigussie et al., 2019). Without effective water management measures, water scarcity is projected to triple by 2050 due to pollution in river basins (Wang et al., 2024). Furthermore, the prevalence of heavy reliance on groundwater resources has arisen due to the restricted and ineffective utilization of surface water (Kumar et al., 2024; Rao et al., 2022; Achu et al., 2020). Consequently, there is a decline in global groundwater, highlighting the necessity for sustainable groundwater management (Jasechko et al., 2024; Gupta et al., 2022; Gorelick & Zheng, 2015).

Urban population growth in developing countries exacerbates water scarcity (Abraha et al., 2022; Ma et al., 2022; He et al., 2021). It is also predicted that there will be a rising water scarcity in African cities (Liu et al., 2024). This is evident in Addis Ababa where the city provides 580,000 m³ of water per day, with 36.5% lost due to leaks, and yet contends with a daily demand of 1.1 million m³ (Beker & Kansal, 2024; AARPO, 2021; Alemu & Dioha, 2020). Factors such as population growth, watershed degradation, and climate change intensified water scarcity in the city (Tefera et al., 2023; Birhanu et al., 2018).

Meanwhile, a tracer investigation indicated that 50% of the city's water is sourced from groundwater (Kebede et al., 2023). However, the unsustainable withdrawal of aquifers in Addis Ababa City leads to the depletion of wells and a decline in water quality (Haileslassie et al., 2024; AARPO,2020; Worku, 2017). Also, the present and future predictions prove that the groundwater level in the Akaki catchment is declining and affecting the constant flow of water (Hailu et al., 2023; Yifru et al., 2022; Birhanu et al., 2021; Muleta & Abate, 2021).

Nevertheless, similar to other predominant volcanic aquifers in central Ethiopia, the Akaki catchment exhibits high groundwater potential due to its considerable depth and remarkable permeability (Abraha et al., 2022; Lin, 2020). However, groundwater in the Akaki catchment relies solely on local recharge, which is diminished during the winter months due to evapotranspiration (Tolera & Chung, 2021; Demlie et al., 2007). Additionally, the city's geographical location between the recharge area and the Akaki wellfield makes the potential for groundwater pollution a significant concern (Mengistu et al., 2019).

Previously, extensive research has been conducted on sub-surface and groundwater dynamics in the Akaki catchment (Hailu et al., 2023; Kebede et al., 2023; Tefera et al., 2023; Tolera & Chung, 2021; Birhanu et al., 2021; Muleta & Abate, 2021; Birhanu et al., 2018; Demlie, 2015). However, comprehensive studies addressing system-level analyses of groundwater potential zones within the catchment are currently lacking. Additionally, hydrogeological investigations and modeling for groundwater exploration are often costly and complex (Paul et al., 2020). Recently, GIS-based Analytical Hierarchy Process (AHP) modeling has gained acceptance for delineating groundwater potential zones in Ethiopia (Manderso et al., 2024; Duguma, 2023; Abrar et al., 2021; Berhanu & Hatiye, 2020; Nigussie et al., 2019). Therefore, applying this approach offers valuable insights for current and future groundwater management and decision-making.

Building upon existing knowledge, this study aimed to delineate the groundwater potential of the Akaki catchment for sustainable groundwater management. The research involved identifying and measuring key factors affecting groundwater potential, conducting a sensitivity analysis to determine the most critical parameters, and applying validation techniques to refine the groundwater potential zones. A GIS-based Analytical Hierarchy Process (AHP) was employed to assess the relative importance of eight thematic factors within the catchment. These factors were then overlaid to create a composite groundwater potential map. The study also performed a sensitivity analysis to pinpoint the most influential parameters and validated the results to ensure the model’s accuracy.

2.1. Study area

The Akaki catchment area includes the Legedadi, Gefersa, Dire, and Aba Samuel reservoirs (Fig. 1). This catchment is positioned in the central highlands of Ethiopia, forming a significant part of the Ethiopian Rift Valley system. It is geographically defined within Latitudes 8^o45’43.49’’ − 9^o13’27.16’’and Longitudes 38^o34’33.38^’’ − 39^o 4’ 6.71’’E. The catchment covers an area of 1444.06 km² and is bordered by the Entoto ridge system to the North, Mount Menagesha and Wechecha volcanic range to the West, Mount Furi to the Southwest, Bilbilo, and Guji to the South, Gara Bushu hills to the Southeast, and Yerer volcanic center to the East (Tamirat et al., 2023; Demlie, 2015). The topography of the region consists of rolling terrain, forming a plateau in the northern, western, and southwestern parts of the area. In contrast, the southern and southeastern sections have milder slopes and flatlands (Argaw & Yohannes, 2024; Feyissa & Gebremariam, 2018; Shibeshi et al., 2019).

Despite its proximity to the equator, the Akaki catchment area exhibits a temperate Afro-alpine climate (Asefa et al., 2020). Monthly temperatures in the region range from a low of 10.8°C to a high of 23°C on average. The annual average rainfall in the Akaki catchment is recorded at 1148 mm (Fig. 2a), with the upper and middle catchment areas receiving above-average annual rainfall. Thiessen polygon was employed using ArcGIS tools to validate meteorological data based on station distribution, and the annual average was found to be 1134 mm (Fig. 2b), close to the arithmetic mean. The rainfall in the catchment has a bimodal pattern, with 76 percent occurring from June to September and the remainder from mid-February to mid-April. In general, a small amount of rainfall is observed from March to June and September, while July and August experience high amounts of rainfall.

Akaki aquifer system is mainly weathered and fractured volcanic rocks with minimal sediment between lava flows (Kebede, 2013: Lulu et al., 2005). Also, scoria and scoriaceous basalt are part of the aquifer system (Demlie et al., 2008). The catchment, drainage pattern follows a dendritic configuration with smaller tributaries merging into the mainstream at acute angles, resembling a tree-like structure. Accordingly, the Akaki River originates from the northern part of Addis Ababa, specifically Entoto Ridge, and eventually joins the Awash River (Negash et al., 2023). The Big Akaki River is connected to the Little Akaki, Bole, and Kebena Rivers, and the water from these rivers ultimately converges at the Aba Samuel artificial reservoir (Argaw & Yohannes, 2024). Whereas, groundwater recharge primarily occurs during the period from June to September, and this water is lost through evapotranspiration from December to May (Demlie, 2015; Demlie et al., 2007).

2.2. Methods

In this study, the assessment of groundwater potential in the catchment was conducted through the consideration of eight distinct factors: lithology, rainfall, lineament density, drainage density, slope, land use, soil, and elevation. The selection of the eight factors is based on previous studies and the characteristics of the study area. Consequently, thematic map layers corresponding to these factors were developed to facilitate the application of the Analytical Hierarchy Process (AHP). This methodology involves establishing interrelations between the eight contributing factors through a matrix calculation, resulting in a percentage value. Furthermore, each factor is assigned a rank based on its contribution to groundwater potential. The flowchart depicted in Fig. 3, outlines the conceptual framework employed in this study includes usage of ALOS (Advanced Land Observation Satellite), Arc Hydro (ArcGIS Hydrology), OTI (Landsat Operational Land Imager), LULC (Land use Land Cover), IDW (Inverse Distance Weighting), RF (Rainfall) map, GWPZ (Groundwater Potential Zone) map.

2.2.1. Input Data Preparation

Lithology. A lithological map for the study area was extracted from the Adaa Becho Geology Map of Ethiopia (WEDSWS,2008), which is a shapefile created by the Ethiopian Ministry of Water Resources. To enable overlay analysis, this extracted shapefile was transformed into a raster format.

Rainfall. In this study, data from five meteorological stations spanning from 2000 to 2020 years were obtained from the Meteorological Agency of Ethiopia. To estimate the spatial variability of rainfall within the catchment, Inverse Distance Weighting (IDW) interpolation was employed. This interpolation method was applied because of the spatial uniformity of rainfall distribution and the representativeness of metrological stations in the catchment. Subsequently, the interpolated data was categorized into four groups using a natural break classification method. Also, the statistical significance of the interpolation shows less a standard deviation (36.01) than other approaches.

Lineament density. The thematic layer representing lineament density (Ld) can be expressed as the ratio of the cumulative length of all documented lineaments to the total area of the analyzed catchment (Edet et al., 1998).

$$\:{L}_{d}=\sum\:_{i=1}^{n}(\frac{{L}_{i}}{A})$$

In this context, "Li" denotes the length of the individual lineament "i," while "ΣLi" signifies the collective length of all lineaments, measured in kilometers. "A" represents the grid's area, expressed in square kilometers.

Lineament density data was extracted from the Adaa Becho Geology Map of Ethiopia using ArcGIS software. The selected lineaments, which were identified and digitized, were sourced from the designated faults and fractures. Then, the lineament density was determined by converting these lineaments into line density. In the end, the resulting raster file was categorized into four distinct groups, thereby creating a spatial representation of the lineaments.

Drainage Density. Drainage density (D_d) is the measure of the total length of the stream segment of all orders per unit area and is calculated in the following equation (Rahmati et al., 2014)

$$\:{D}_{d}=\sum\:_{i=1}^{n}(\frac{{D}_{i}}{A})$$

where, ΣDi is the total length of all streams in stream order i (km) and A is the area of the watershed (km²).

The catchment's drainage system was established using the ArcGIS hydrology tools, employing the Digital Elevation Model (DEM) of the study area. The length of drainage density was computed using the line density function within the spatial analyst tool. Then, the drainage density values were classified into four distinct groups, based on their respective densities.

Slope. The generation of the slope map involved the utilization of ALOS (Advanced Land Observation Satellite) data with a 30-meter digital resolution model (DEM) in ArcGIS. In the end, the slope map was categorized into four classes.

Land use. A 30 x 30 m resolution Landsat 8 Operational Land Imager (OLI) image was obtained from the USGS on February 24, 2022, due to its recency and the absence of cloud cover. The Land Use and Land Cover (LULC) map was generated using the maximum-likelihood algorithm for supervised classification. Seven LULC classes were identified, and an accuracy assessment was performed using 800 ground truth points collected from the field. The optimal accuracy of the classification was confirmed using the Kappa coefficient.

Soil. A soil raster map generated from the ISDASoil (soil and agronomy map for Africa) database at 200 m resolution. Also, the raster data was resampled to 30 m resolution. Finally, five soil textural classes that represent the catchment are identified.

Elevation. The production of the elevation map was accomplished by employing data from the ALOS (Advanced Land Observation Satellite), which provided a digital resolution model (DEM) with a 30-meter granularity. Then, the elevation map was divided into four distinct categories.

2.2.2. Analytical Hierarchy Process (AHP) of pairwise comparison matrix

The occurrence and movements of groundwater are influenced by several factors presented above. It is difficult to bring all factors together to quantify and identify the hierarchies among the factors. However, AHP utilizes the pairwise comparison matrix as the foundation for forming judgments and determining the weight of each factor (Doke et al., 2021). Therefore, the analytical hierarchy process (AHP) is chosen for its systematic identification of influential parameters (Saaty, 1987). AHP emphasizes consistency in breaking down, assessing, and merging priority rankings. The initial stage involves constructing a decision hierarchy by incorporating the decision-maker's knowledge and expertise (Benítez et al., 2012). Thus, pairwise comparisons are vital in AHP, employing a foundational value scale of 1 to 9 (Saaty, 1987; 1995) as shown in Table 1.

Table 1

Pairwise comparison scales
Verbal judgment	AHP numeric value (scale)
Extremely important	9
Very strongly to extremely important	8
Very strongly important	7
Strongly to very strongly important	6
Strongly important	5
Moderately to strongly important	4
Moderately important	3
Equally to moderately important	2
Equally important	1

Consequently, pairwise computations were performed using TerrSet software (v19.0.6) with eight thematic map layers to weigh important factors for groundwater potential. On a scale of 1 to 9, values were given to see the relative importance of one factor to the other in a pairwise comparison. Numerical values for AHP pairwise comparisons were derived from literature, expert knowledge, and experience on hydrogeological, meteorological, and topographic characteristics of the catchment. Also, to eliminate the subjectivity of assigning weights detailed characterizations of the parameter are done. On the other hand, consistency in judgments may have limitations. To address this, iterative pairwise comparisons were conducted, and the resulting consistency ratio is defined below.

$$\:CR=\frac{CI}{RI}$$

Where CR is Consistency Ratio, CI is Consistency Index and RI is Random Index

A consistency ratio should be less or equal to 0.1 to proceed with the AHP analysis. Otherwise, it must be re-corrected by going through the analysis and assigning new judgment values (Saaty, 1990, Malczewski, 1999; Saaty and Vargas,2012). The consistency index is generated from the following formula.

CI= (λ max- n) /(n-1) (4)

Where, λ is max is the Principal Eigenvalue of the pair-wise comparison matrix, and n is the number of parameters involved in the comparison.

Taking into account the number of parameters, a random index is given (Saaty and Vargas, 2012; Saaty,1990). Therefore, due to the eight parameters used in this study, the random index for this study will be 1.41 as indicated below.

Table 2

Random index (RI) corresponding to the number of parameters (n)
N	1	2	3	4	5	6	7	8	9	10
RI	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45	1.49

2.2.3. Ranking of weights and weighted overlay analysis

Ranking the weight of each parameter is a prerequisite for weighted overlay analysis. Both inverse ranking and straight ranking are common methods used to rank features based on their importance in various analytical contexts (Yahaya et al., 2010). Parameters inversely ranked (1 to 4) with "poor groundwater potential" as 1 and "very high groundwater potential" as 4, while 2 and 3 denote moderate and very high, respectively. After ensuring weight consistency, a weighted overlay analysis was done (Kumar et al., 2014). Groundwater Potential Index (GWPI) produced through ArcGIS reveals potential zones (Malczewski, 1999).

$$\:GWPI=\sum\:_{j=1}^{m}\sum\:_{i=1}^{n}({W}_{j}\times\:{X}_{i})$$

(5)

Where, Wj is the normalized weight of the j thematic layer, Xi is the rank/rate value of each class for j, m is the total thematic layers, and n is the total classes in each layer.

Sensitivity analysis, per Mahato et al. (2022), employed a single-parameter method. Effective weights were correlated with empirical AHP weights to ensure consistency.

W = ((Pr Pw) / A) × 100 (6)

Where, "W" signifies the effective weight for each thematic layer, with "Pr" and "Pw" representing rates and weight values assigned to each layer, respectively. "A" denotes the groundwater potential map integrating all thematic layers.

Finally, validation was done by overlaying 199 boreholes and spring locations into an identified groundwater potential zone map. The average yield data of each zone was used as a decision threshold to graphically represent the model using the Receiver Operating Characteristic (ROC) curve, along with the AUC (Area Under the Curve) (Gebrie & Getachew, 2019). Therefore, the overall performance of the classification (AUC) is identified based on its sensitivity (true positively) and specificity (true negatively) (Kumar et al., 2024).

3.1. Identification of parameters for evaluating groundwater potential

Including rainfall, eight parameters were identified and their thematic layers prepared. The lithological characteristics of the area encompass a variety of rock types (Fig. 4a), including Tarma Ber basalt (PNtbB), Addis Ababa Ignimbrite (NadI), Entoto Becho rhyolite (NebRy), Addis Ababa basalt (NadB), Akaki basalt (NakB), and porphyritic trachytic lavas (NcvTy). Lineament density in the Akaki catchment ranges from 0 to 3.5 kilometers per square kilometer, with the majority falling within the 0-0.88 range (Fig. 4b). Meanwhile, drainage density varies across the catchment, with downstream areas exhibiting high drainage density correlated with elevation (Fig. 4d).

The catchment has distinct physiographic features. However, most of the catchment area has slopes below 10 degrees (Fig. 5a). However, the upper catchment area has notably steep slopes. On the other hand, over 70 percent of the catchment area is below 2600 meters above sea level (Fig. 5b). On the other hand, over 86% of the catchment area is primarily composed of clay and clay loam soils (Fig. 5c). Whereas, according to Fig. 5d, the 2022 land use and land cover changes reveal that cultivated land and settlement constitute 85% of the catchment. Finally, the overall land use land cover accuracy is 0.95 Kappa coefficient and equivalent to a previous study done by Tamirat and others (2023).

3.2. Development of Analytical hierarchal process for the catchment

The most significant factors affecting groundwater recharge dynamics in the study area are lithology (31%), rainfall (23%), lineament density (18%), and drainage density (11%). Conversely, slope (6%), land use (4%), soil (4%), and elevation (3%) have relatively lower importance for groundwater potential. The consistency ratio is within the acceptable range of 0.09, meeting the criterion of ≤ 0.10. Furthermore, the calculated weights of each parameter and their sum are normalized, totaling 100%, which confirms the validity of the analysis for advancing to the overlay process.

Table 3

A pairwise matrix of eight parameters
	Rainfall	Lithology	Slope	Drainage	LULC	Lineament	Soil	Elevation	Eigenvector*
Rainfall	1	1/3	5	3	5	3	5	3	23.17
Lithology	3	1	5	3	5	3	5	5	30.65
Slope	1/5	1/5	1	1/3	3	1/5	3	3	6.41
Drainage	1/3	1/3	3	1	3	1/3	5	5	11.35
LULC	1/5	1/5	1/3	1/3	1	1/5	1/2	3	3.85
Lineament	1/3	1/3	5	5		1	5	5	17.51
Soil	1/5	1/5	1/3	1/5	2	1/5	1	3	4.36
Elevation	1/5	1/5	1/3	1/5	1/3	1/5	1/3	1	2.7
									100
* Consistency ratio = 0.09

3.3. Development and validation of groundwater potential zones

The overlay analysis revealed that the catchment area comprises the following groundwater potential levels: 15% very high, 43% high, 41% moderate, and 1% low (Fig. 6). The low groundwater potential zone represents a minor proportion of the total area. Moderate groundwater potential areas are located in the Northwest (Entoto), Central (Addis Ababa), and Northeast (Sendafa). The high groundwater potential zones are situated in the Southeast, Central Addis Ababa, the northern tip, and several pockets in the Northwest. Additionally, the very high groundwater potential area is concentrated in the southern part of the catchment.

Table 4

The single parameter of sensitivity analysis
Thematic Layers	Empirical Weights (%)	Effective weights (%)
Rainfall	23.17	18.12
Lithology	30.65	40.63
Slope	6.41	4.90
Drainage	11.35	8.83
LULC	3.85	5.64
Lineament	17.51	13.8
Soil	4.36	5.84
Elevation	2.7	2.10
Correlation coefficient 0.93

In terms of sensitivity analysis, lithology demonstrates notable significance, surpassing the influence of other factors as indicated in Table 4. Whereas the sensitivity analysis weights were marginally reduced for rainfall, lineament, and drainage density, with no alteration in their respective rankings. However, within the group of the least sensitive parameters, soil (5.8%) exhibits a higher level of sensitivity compared to land use (5.6%), slope (4.9%), and elevation (2.1%), following their respective sensitivity rankings.

Finally, boreholes overlaid data show that the majority (76%) are situated within regions characterized by high and very high groundwater potential (Fig. 7a). Also, as shown in Fig. 7b, the ROC-AUC curve demonstrated that boreholes data are highly correlated to the groundwater potential zones. Thus, the 0.925 AUC value depicts that it was excellent in the predictability of the groundwater potential zone in the catchment (Elvis et al., 2022).

The groundwater potential in the Akaki catchment area is influenced by its geological formation, landscape attributes, and precipitation patterns ( Hailu et al., 2023; Kebede et al., 2023; Tefera et al., 2023; Demlie, 2015, Demlie et al., 2007). However, there has been a lack of comprehensive studies that systematically consider all the crucial factors contributing to groundwater potential. Therefore, this study aims to identify and characterize these parameters, develop an analytical hierarchy process, and validate the results.

In this study, lithology, rainfall, lineament, and drainage density hold significant importance. Similarly, research conducted in the Ethiopian Main Rift Valley found lithology (geology) and rainfall to be the most influential factors (Nigussie et al., 2019). In comparison, another study in the Western Escarpment of the Rift Valley System highlighted lithology as a primary factor while giving relatively less importance to rainfall (Abrar et al., 2021).

Furthermore, a study conducted in the northern part of Ethiopia aligns with the outcomes of this study (Berhanu and Hatiye, 2020). Although it considered lithology as a significant factor, it differed in its assessment of the importance of rainfall. This deviation in the significance of rainfall can be attributed to variations in rainfall patterns and differences in groundwater recharge mechanisms specific to each area (Fattah et al., 2024; Mengistu et al., 2021; Dey et al., 2020).

The Akaki catchment, located within the Main Ethiopian Rift, is characterized by prominent faults and lineaments trending NE-SW, E-W, N-S, and NW-SE (Tadesse et al., 2023; Bretzler et al., 2011; WEDSWS, 2008). This observation is reinforced by the fact that lineaments (faults) are associated with elevated permeability levels (Takorabt et al., 2018; Souissi et al., 2018). Studies in the Awash Basin further support the argument that lineaments are a critical factor for groundwater potential (Anteneh et al., 2022). Conversely, the significance of drainage cannot be overlooked due to the low drainage density within the catchment. This leads to enhanced recharge rates and increased groundwater potential, as lower drainage density is linked to higher water permeability (Hussein et al., 2017; Tolche, 2021).

On the other hand, slope, land use, soil texture, and elevation are also significant due to their respective roles. The upper catchment features steep slopes that contribute to high runoff, as steep gradients lead to a rapid downhill flow of precipitation, limiting the time available for infiltration and the replenishment of the saturated zone (Arulbalaji et al., 2019). This assertion is supported by the elevations within the catchment. Furthermore, unconfined aquifers are significantly influenced by topographical features (Tigabu et al., 2020).

Moreover, land use and land cover play a crucial role in groundwater occurrence and accessibility (Arabameri et al., 2019). Furthermore, alterations in land use impact the base flow within the catchment (Huang et al., 2016). Additionally, the infiltration of surface water into an aquifer system depends on soil type (Souissi et al., 2018). However, the relative importance of soil versus land use varies across locations and study environments (Arefaine et al., 2012; Berhanu & Hatiye, 2020).

Based on the results and justifications, the catchment is predominantly classified as having moderate to high groundwater potential. This finding is supported by the work of Abraha et al. (2022), who conducted a country-level assessment but did not provide a detailed classification at the micro-catchment level. In contrast, this study classifies the catchment into low, moderate, high, and very high groundwater potential zones. The low-potential area, predominantly covered by eucalyptus trees and located in the upstream region of the catchment, affects groundwater availability due to the selection and planting densities of the trees (Adane et al., 2018).

In the moderately groundwater-potential zones, the most common lithological structures are PNtbB, NcvTy, NadI, and NebRy (Demile, 2015; Kebede, 2013). These zones are characterized by moderate slopes, with prevalent clay and loam soils, and some areas of sandy loam and sandy clay loams in the central region. Additionally, they feature moderately elevated terrain and a diverse blend of land uses. Consequently, these factors, in conjunction with the broader environmental context, collectively influence groundwater potential (Ozdemir, 2011).

Previous studies support that Tarma Ber basalt (PNtbB) has moderate permeability and productivity (Hussein et al., 2017). This lithological unit is prevalent in the western and northern plateaus and is characterized by lenticular basalts and abundant scoriaceous lava flows (Abbate et al., 2021). In this basalt, an increase in well depth is associated with a decrease in aquifer productivity (Kebede, 2013). In the western part of the Akaki catchment, porphyritic trachytic lavas (NcvTy) are present, originating from the central volcanic areas of Wechecha, Furi, and Yerer (Kebede, 2013). On the other hand, the Addis Ababa Ignimbrite (NadI) consists of tall, poorly welded pyroclastic deposits found in the Addis Ababa and Legedadi plains and is characterized as a moderate potential zone (Yifru et al., 2022; Muleta & Abate, 2021). In contrast, Entoto Becho rhyolite (NebRy) is considered to have low groundwater potential and is characterized by obsidian-bearing rhyolite outcrops on the Entoto Mountain (Hailu et al., 2023; WEDSWS, 2008).

All lithological units, excluding NebRy, fall into the high groundwater potential category. This area includes the Addis Ababa basalt (NadB), which is characterized by high groundwater potential (Hailu et al., 2023; Mengistu et al., 2019). Addis Ababa basalt (NadB) is primarily composed of alkaline and olivine basalt lava flows that cover the central and southern regions of Addis Ababa (WEDSWS, 2008). In this zone, there are instances of both high and moderate rainfall and lineament density. The predominant characteristic of this area is low to moderate drainage density, which serves as an indicator of high groundwater potential (Hussein et al., 2017). In contrast, the central and southern sections of the catchment exhibit moderate to low slopes and elevations compared to the northwestern regions. Moreover, except for sandy loam and sandy clay loams in the central part, this zone is characterized predominantly by clay and loam soils with a mixed land use system.

A very high groundwater potential area is predominantly characterized by the presence of Akaki basalt formations, which are known for their significant groundwater productivity due to their geological properties (Shibeshi et al., 2019; Demile, 2015; Demlie et al., 2008). Akaki basalt (NakB) is distinguished by scoria and scattered cones along with associated basaltic lava flows extending in a northeast-southeast direction (Lulu et al., 2005; Muleta & Abate, 2021). Although the rainfall in this specific area is comparatively lower than in other parts of the Akaki catchment, it remains sufficient due to the overall high rainfall conditions across the region. The combination of high lineament density with low drainage density, gentle slopes, and low elevation levels provides a distinct advantage that contributes to the very high groundwater potential in this region. Additionally, these areas are primarily composed of clay and loam soils amidst diverse land use patterns.

The study demonstrated that the catchment has moderate to high groundwater potential. The findings indicate that approximately 41% of the catchment area, located in the Northeast, Central, and Northwest regions, possesses moderate groundwater potential. Similarly, 43% of the catchment, situated in the Northwest, Central, and Southeast regions, exhibits high groundwater potential. In the southern portion of the catchment, covering around 15% of the area, a remarkably very high groundwater potential zone was identified. Additionally, the majority of high-yielding wells are found in this very high groundwater potential zone.

Single-parameter sensitivity analysis revealed that lithology, rainfall, and lineament were the most influential factors for groundwater potential. This result is consistent with previous studies conducted in the central highlands of Ethiopia. Moreover, a significant majority (76%) of the wells used for validation were located in areas identified as high to very high groundwater potential zones. Consequently, the ROC-AUC curve demonstrated that the model was excellent at predicting groundwater potential zones in the catchment.

In general, the study emphasizes the critical need for a comprehensive understanding of the factors affecting groundwater potential within the catchment. It establishes important factors for future decision-making to ensure a sustainable and reliable groundwater supply. Future research should focus on water balance and landscape management to further improve groundwater resources in the catchment.

Authors constributions

Getamesay Nigussie collected the data, performed the analysis, and prepared and wrote the manuscript. Mekuria Argaw prepared the study concept and design, and edited the manuscript. Dessie Nedaw drafted the study concept and design, and edited the manuscript. Tsegaye Tadesse edited the study concept, design, and manuscript. Andreas Hartmann edited the study concept and manuscript.

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare no competing interests.

Funding

No funding was received during the study period.

Author Contribution

Acknowledgments

The authors want to express their acknowledgment to the Ethiopian Institute of Agricultural Research, the Centre for Environmental Science of the Addis Ababa University, AMAS (EU intra Africa Academic Mobility), the ABCD (DAAD) Center, Institute of Groundwater Management of the Technical University of Dresden for granting the opportunity to do this research.

Data availability

No datasets were generated or analysed during the current study.

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Spatial-based mapping of the groundwater potential zones of Akaki catchment in the surrounding highlands of Addis Ababa, Ethiopia

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Abstract

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1. Introduction

2. Methodology

2.1. Study area

2.2. Methods

2.2.1. Input Data Preparation

2.2.2. Analytical Hierarchy Process (AHP) of pairwise comparison matrix

2.2.3. Ranking of weights and weighted overlay analysis

3. Results

3.1. Identification of parameters for evaluating groundwater potential

3.2. Development of Analytical hierarchal process for the catchment

3.3. Development and validation of groundwater potential zones

4. Discussion

5. Conclusions

Declarations

Authors constributions

Funding

Author Contribution

Acknowledgments

Data availability

References

Additional Declarations

Supplementary Files

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