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Identification of groundwater potential sites in the drought-prone area using geospatial techniques at Fafen-Jerer sub-basin, Ethiopia
Identification of groundwater potential sites in the drought-prone area using geospatial...
Seifu, Tesema Kebede; Ayenew, Tenalem; Woldesenbet, Tekalegn Ayele; Alemayehu, Taye
GEOLOGY, ECOLOGY, AND LANDSCAPES INWASCON https://doi.org/10.1080/24749508.2022.2141993 RESEARCH ARTICLE Identification of groundwater potential sites in the drought-prone area using geospatial techniques at Fafen-Jerer sub-basin, Ethiopia a,b c b b Tesema Kebede Seifu , Tenalem Ayenew , Tekalegn Ayele Woldesenbet and Taye Alemayehu a b Haramaya Institute Technology, Haramaya University, Diredawa, Ethiopia; Ethiopian Institute of Water Resources, Addis Ababa University, Addis Ababa, Ethiopia; School of Earth Sciences, Addis Ababa University, Addis Ababa, Ethiopia ABSTRACT ARTICLE HISTORY Received 4 May 2022 Analyzing the groundwater potential zone is a fundamental first step in investigating ground- Accepted 26 October 2022 water resources in arid and semi-arid regions. This study examined the groundwater potential zone of the Fafen-Jerer sub-basin by applying geographic information system (GIS) and remote KEYWORDS sensing (RS). The study used ten influencing factors, including geology, geomorphology, slope, Geospatial; thematic layers; soil, lineament density, drainage density, land use, land cover, topographic wetness index, analytical hierarchical topographic roughness index, and rainfall, to identify potential sites. Based on their effect on process; groundwater groundwater recharge, the sub-class of each influencing factor was identified and evaluated. potential zone; Fafen-Jerer sub-basin The weights were determined using the multi-criteria decision-making method’s analytical hierarchy process technique. At the conclusion of the investigation, the region was divided into four potential zones: low, moderate, high, and extremely high. The study region comprises 84% moderate potential zones, 14% high groundwater potential zones, and 2% low and extremely high potential zones. area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the groundwater potential zone, and the findings show extremely good performance (AUC = 0.87). The study provides recommendations for stakeholders and water management professionals to develop a strategy for water resource management based on the conjunctive use of surface and groundwater. 1. Introduction particularly in recent years. The literature estimates the groundwater potential range from 2.6 to According to the United Nations Educational, 1000 billion cubic meter (Berhanu et al., 2014; Kebede, Scientific, and Cultural Organization (UNESCO), 2013a). This demonstrates that there is still substantial water resources are crucial environmental compo- uncertainty surrounding Ethiopia’s hydrogeological nents as well as a key driver of social and economic investigation. In most parts of Ethiopia, the main chal- development. Policies and management options with lenge in investigating water resources is a lack of the best decision-making choices are required to guar- required meteorological, hydrological, and geomorpho- antee its quality, renewal, and sustainable use in view logical data. The problem varies by region, but it is more of its availability and scarcity (Unesco, World Water severe in arid and semi-arid regions (Ayenew et al., Assessment Programme (United Nations), n.d.). 2008; Berehanu et al., 2017). Some well-known and Several literatures indicated that groundwater is the previously used groundwater investigation procedures, main source of domestic water supply globally such as drilling and stratigraphic analysis, are now used (Chávez García Silva et al., 2020; Liu et al., 2020). As exclusively worldwide (Campo et al., 2020; Regenspurg a result, groundwater becomes the primary driving et al., 2018). However, these methods require a huge factor for development and food security in arid and time and financial investment. The other method of semi-arid regions (Hoogesteger, 2022). The investiga- groundwater investigation is numerical modeling, tion of groundwater resources in these regions, how- which requires uniformly distributed ground truth ever, is hampered by a lack of data availability and data for validation (Singh, 2014). To address these accessibility. issues, researchers used emerging technology to inves- Ethiopia is a developing country where about 80% of tigate the groundwater resource (Prasad et al., 2020). To the population is dependent on agriculture, which is the map the groundwater potential, numerous research backbone of the economy (Baye, 2017; Zerssa et al., used models such as frequency ratio (Razandi et al., 2021). Groundwater is the main source of domestic 2015), statistical models (Azma et al., 2021), ensemble water supply in many parts of the country. models, and logistic regression (Farzin et al., 2021). Nonetheless, estimates of Ethiopian groundwater Machine learning models were used recently to map resource potential vary significantly in the literature, CONTACT Tesema Kebede Seifu email@example.com Ethiopian Institute of Water Resources, Addis Ababa University, PO Box 1176, Addis Ababa, Ethiopia © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 T. K. SEIFU ET AL. the potential of the groundwater in a region (Yariyan 2. Materials and methods et al., 2022). However, the majority of these techniques 2.1. Description of the study area require for substantial amounts of ground truth data, making them unsuitable in cases when there is a lack of The Fafen-Jerer sub-basin is located in the southeast- data. The most efficient method for studying ground- ern part of Ethiopia. The sub-basin is a part of the water in places with a lack of data is to use a geographic Wabi Shebelle river basin (Figure 1). Geographically, information system (GIS) and remote sensing (RS) the region covers an area of 17,623 sq km and is technology with an analytical hierarchy process situated between 42° 23’ and 44° 11’ E and 7° 34’ to (AHP). There hasn’t been much groundwater research 9° 35’ N (Kebede et al., 2017). The climatic condition done in Ethiopia despite the availability of the best of the area is arid and semi-arid (Berhane, 2013). The techniques and methods for assessing groundwater rainfall pattern is bimodal, there are two rainy seasons potential. (April-May and July-September). The area has a mean The study area has an arid and semi-arid climate, annual rainfall of 582.5 mm and an annual average with the main challenges being a highly variable temperature of (21.0°C). The sub-basin has a widely climate and recurrent droughts. The seasonality, varied topography, with altitudes ranging from 761 m small amount, and extreme variability of rainfall are a.m.s.l. at the outlet to about 2972 m a.m.s.l. on the inadequate to meet the amount of water required for escarpment. The topography along the Karamara agriculture. Deep well groundwater is the main water ridge in the northwest is extremely rough, with source in the study area. Concerning the ground- a slope of more than 30 degrees. The dominant feature water in the study area, little research has been con- in the northern part of the sub-basin is Karamara ducted. The sole existing study on groundwater Mountain, which is volcanic terrain formed of ter- exploration and assessment in the region was con- tiary-aged basalts. In the northern region, between ducted in 2013 by Amer and colleagues (Amer et al., the Fafen and Jerer river valleys, the Karamara moun- 2013). Despite the fact that the study reveals newly tain acts as a barrier for both surface- and ground- undiscovered resources and provides a variety of water flows (Amer et al., 2013). There are insufficient hydrogeology-related facts, the amount of data it water resources in the study region to sustain daily life included was constrained to just the wells that had (Abebe & Foerch, 2006; Kebede, 2013a). The Fafen- already been drilled. No published work has been Jerer subbasin is primarily composed of alluvial done in the area where groundwater potential is deposits, fan deposits from seasonal floods, and stream a concern, yet thousands of people in the research beds. The alluvial deposits in the eastern part of the area are affected by periodic drought events. By ana- study region (Jigjig plain) are composed of silty-clay lyzing various thematic layers that affect groundwater deposits generated from basement rocks and lime- resources, the current study applied the geospatial stone (Abebe & Foerch, 2006). method of identifying groundwater potential zones. Remote sensing has emerged as the ideal tool to use 2.2. Data collection and thematic layers for the study due to the difficulties of inaccessibility preparation and the security issues in the study region. The selec- tion of the geospatial method was also influenced by The groundwater potential of a specific location is the scant prior research and the dearth of hydrogeo- influenced by a variety of parameters. Since there is logical data in the studied area. no set of standards by which to select the influencing A groundwater potential investigation is crucial for parameters, the choice of thematic parameters differs the study area to fill gaps in the knowledge and estab- from study to study. In the current study, a wide range lish a benchmark for subsequent research in the of potential affecting factors were combined. region. Following that, emphasis is given to the rela- The thematic layers include soil, slope, topographic tionship between geology, geomorphology, and cli- wetness index (TWI), lineament density, drainage mate factors and how that affects the availability of density, land use land cover (LULC), geology, geomor- groundwater. The current study also identifies and phology, and rainfall (TRI). These variables affect the evaluates ten influencing factors that affect the groundwater condition, and they can be used to iden- groundwater potential of the study region. For verifi - tify a possible site in the study area with accuracy. For cation of the model multicollinearity analysis and this study, a variety of data were gathered from gov- borehole investigations are performed. The study of ernmental agencies and the internet. The geology groundwater potential delineation is very important in parameter for the study area was created using the region where groundwater is the main source of Ethiopia’s geological map (Ethiopian Ministry of water supply. Therefore, this study aims to explore and Mines Geological Survey of Ethiopia, n.d.). Thematic delineate the groundwater resources potential with layer for soil texture created using Ethiopia’s most GIS and remote sensing technology using the AHP recent soil group classification (Berhanu et al., 2013). technique. Maps of the geology and soil were georeferenced, GEOLOGY, ECOLOGY, AND LANDSCAPES 3 Figure 1. Map of the study area. digitalized, and converted to raster files using ArcGIS section elaborates on the thematic layers that software. The rainfall map of the study region was were utilized for mapping groundwater potential created using the weather data collected from the zones. National Metrological Agency (NMA). A rainfall map was produced using daily rainfall data from ten 2.3.1. Geology (Ge) meteorological stations for the period 1985 and 2019. Groundwater availability and movement are influ - Sentinel 2 imagery datasets and a digital elevation enced by the local geology. The subsurface flow sys- model (DEM) of the shutter radar topographic mis- tems are governed by factors such as aquifer thickness, sion (STRM) with a resolution of 30 meters were permeability, and concentration of dissolved sub- downloaded from the website of the United Nations stances (Wirth et al., 2020). The basement of the geological survey (USGS) at https://earthexplorer. Fafen-Jerer catchment is dominated by Precambrian usgs.gov. The drainage density, slope, topographic granite and metamorphic rocks (Amer et al., 2013). wetness index, and topographic roughness index The majority of the western layer of the subbasin is were all calculated using DEM data. To create maps composed of sedimentary rocks from the Mesozoic of land use, land cover, and lineament density, era. Quaternary sediment covers the central region, Sentinel 2 imagery was used. ArcGIS, PCI whereas tertiary sedimentary rocks underlie the east- Geomatica, and Rockwork tools were used to create ern region (Berhanu et al., 2014). To produce the the maps. The overall workflow of the study procedure geological map of the study area, the geologic map of is shown in Figure 2. Ethiopia produced in 1996 was used (Ethiopian Ministry of Mines Geological Survey of Ethiopia, n. 2.3. Thematic layers influencing the groundwater d.). The primary geologic units that cover the study region are the Urandab formation (37.68%), alluvial An understanding of the processes behind subsur- and lacustrine deposits (19.71%), Jessoma formation face infiltration, flow, and factors that affect this (18.29%), and Hamanlei Formation (17.43%). Other system is crucial for managing groundwater geologic formations exist as well, but with minor cov- resources. In order to examine the groundwater erage (Figure 3a). The characteristics of the geological potential sites, the study employed ten thematic features in the study region are shown in Table A2 of layers, each of which has an effect on the potential for groundwater in the region. The following the appendix. 4 T. K. SEIFU ET AL. Figure 2. Framework of the study. 2.3.2. Geomorphology (Gm) shallow valley, plains, hill and ridge, and mountains Geomorphological factors determine an area’s poten- (Figure 3b). tial for groundwater. Through changing the infiltra - tion rate, the topography of the earth’s surface has an 2.3.3. Slope impact on how surface water resources are moved. Land suitability for infiltration and surface runoff is The study area has diverse topographical features, strongly influenced by the slope of the surface. The from high mountains to plains. In the upper catch- slope of the study area is determined from the USGS ment of the study area, there is a ridge and mountains. SRTM digital elevation model and ranges from 0 to Northwestern strips contain quite large rolling areas. 64.4 degree. The slope is reclassified as level sloping From a geomorphological perspective, the research (0 − 3°), gentle sloping (3 − 10°), strong sloping area was categorized as having a u-shaped valley, (10 − 15°), moderate steep sloping (15 − 20°) and Figure 3. (a) Geological map and (b) Geomorphological map of the study area. GEOLOGY, ECOLOGY, AND LANDSCAPES 5 very steep sloping (>20°). High groundwater recharge groundwater movement, is represented by the number occurs on flat surfaces, whereas high runoff generation of lineaments on a given region (Sarikhani et al., occurs on steep slopes. 2014). The lineament density of a region is measured as the ratio of lineament lengths to the catchment’s overall area, expressed in km/km2. Figure 5 shows the 2.3.4. Soil texture lineament distribution and rose diagram map, while The main controlling factor for sub-surface infiltra - Figure 6b shows the lineament density map. tion is soil texture. It affects the surface’s ability to hold water and the rate of infiltration (Das & Pal, 2020). The soil data of the study area were collected from the 2.3.7. Drainage density (DD) Ministry of Water and Energy soil data base (Berhanu The drainage density is a term used to define how et al., 2013). In the study region, there are four differ - close a stream is to another in a watershed. An area ent types of soil: clay, loam, loamy sand, and sandy with a high drainage density has a high rate of surface loam (Figure 4a). Larger areas are covered by loam runoff and a low rate of infiltration. The groundwater and loamy sand. Sandy soil has a high rate of infiltra - potential area is related to high infiltration area, so tion, while clay soil has the lowest rate. drainage density opposes the groundwater potential zone (Mukherjee & Singh, 2020). The drainage density is defined as the ratio of the total length of streams to 2.3.5. Land use and land cover (LULC) the watershed area, expressed in km/km2. In the stu- Surface runoff and groundwater recharge in a region died area, the drainage density (km/km2) value ranges are impacted by changes in land use and land cover. from 0 to 0.898 (Figure 6a). Vegetated land has very low surface runoff and higher infiltration capacity and vice versa. In comparison to barren land, recharge areas have high groundwater 2.3.8. Rainfall (RF) availability (Mukherjee & Singh, 2020). The 10 m Rainfall is the main source of subsurface storage and band composition of Sentinel 2 satellite imagery was recharge. The total amount of precipitation has a long- used to produce a LULC map of the area. A supervised term impact on groundwater zonation. The study area is image classification algorithm was utilized to produce characterized by arid and semi-arid climates having low the land use and cover of the area. The identified land- annual rainfall. In the research area, the elevation varia- use types include built-up areas, rock outcrops, shrub- tion is closely associated with the point rainfall data. The land, grassland, and agricultural land (annual, peren- spatial rainfall map was created using the kriging tech- nial; Figure 4b). nique. The kriging map of the mean annual rainfall of the study area is presented in the appendix (Figure A1). 2.3.6. Lineaments and Lineament density (LD) Lineaments in the current study refer to a linear struc- 2.3.9. Topographic wetness index (TWI) ture, fold-axes, faults, joints, and other surface struc- The topographic wetness index is a physical metric tural features. The porosity, which acts as a conduit for that shows how topography affects runoff, soil Figure 4. (a) Soil texture map and (b) Land use land cover map. 6 T. K. SEIFU ET AL. Figure 5. (a) Lineaments distributions map and (b) Rose diagram map. Figure 6. (a) Drainage density ma p and (b) Lineament density map. moisture, and water accumulation. With the release of and vice versa. Figure A2 shows the map of topo- TOPMODEL, the idea of topographic wetness was graphic wetness index. first proposed (Beven, 1997). TWI determines how much water can flow through a certain place and 2.3.10. Topographic roughness index (TRI) illustrates the impact of topographic factors Roughness is defined as the microrelief, or the irregu- (Ballerine, 2017). The TWI is defined as: larities in a land surface that cause changes in eleva- tion (Lindsay et al., 2019). Surface roughness TWI ¼ ln (1) influences the infiltration of water into the sub- tanβ surface. Topography can have a flat, irregular, or Where: smooth texture. TRI is used to express the elevation difference between a given cell and its neighboring α ¼ upslope contributing area; cells. TRI calculated from focal statistics of ArcGIS β ¼ topgraphic gradientðslopeÞ neighborhood toolbox with the formula: All TWI calculations were performed using the ðSmooth areas Min RÞ TRI ¼ (2) SRTM DEM of the research area in the ArcGIS tool. ðMax R Min RÞ Higher TWI values enhance the infiltration capacity, GEOLOGY, ECOLOGY, AND LANDSCAPES 7 � � CI Where: Min R minimum focal statistic, Max Consistency RatioðCRÞ ¼ ; Consistency IndexðCIÞ RCI R maximum focal statistics, and the smooth area � � λ n max mean focal statistic of a surface. A rough surface is n 1 considered to have low groundwater potential zone (3) because it promotes surface runoff rather than infiltra - tion (Figure A2). Where λ is eigenvalue, RCI is the random consis- max tency index and n is the matrix size (the number of criteria/thematic layers). In this particular case, n = 10 2.4. Assigning weight to the parameters and λ is the average of ratios in the weighted sum max value with the criteria weight of the thematic layers The thematic parameters are given weight using the (λ = 10.549). As a result, the consistency index max multi-criteria decision-making (MCDM) method. value is now (CI = 0.061). The random consistency Saaty’s analytical hierarchy method (AHP) was index value from the table is 1.49 for a n of equal to 10 used to determine the relative importance of the (Tummala & Ling, 1996). Consequently, the analysis’s parameters (Saaty & Katz, 1994). According to consistency ratio (CR) is equal to 0.041. The analysis is Saaty’s scale for relative importance, the pairwise consistent, as indicated by the value consistency ratio comparison matrix table (Table 1) displays the (CR = 0.041), which is CR<0.1 (Saaty, 2002). relative impolrtance of each parameter in the for- mation of the groundwater potential zone. The normalized comparison matrix table is prepared 2.6. Multicollinearity analysis by dividing each value in the pairwise comparison table by the sum of the weights assigned to each Multicollinearity is a technique for detecting the line- criterion (Table 2). The average value of the row in arity of influencing factors and quantifying data the normalized comparison matrix table provides redundancy between factors. One of the requirements the weight of each criterion for GIS overlay for model evaluation is to examine the multicollinear- analysis. ity of the influencing factors. By using one factor as a dependent and the other as an independent variable in a linear regression analysis, the multicollinearity of 2.5. Checking the reliability of the analysis the factors is examined. The following formulas for the tolerance (TOL) and variance inflation factor (VIF) of The study uses consistency index and consistency the model parameters were used to determine the ratio values to check the consistency rate in ranking multicollinearity of all factors: and assigning weights for the thematic layer (Elubid et al., 2020; Mukherjee & Singh, 2020). TOL ¼ 1 R (4) Table 1. Pairwise comparison matrix table. RF Gm Ge Slope LD LULC DD Soil TWI TRI RF 1 1 2 3 3 5 5 7 5 7 Gm 1/1 1 3 3 5 5 5 6 7 7 Ge 1/2 1/3 1 1 3 3 5 5 5 7 Slope 1/3 1/3 1/1 1 1 2 3 3 5 5 LD 1/3 1/5 1/3 1/1 1 1 3 3 5 5 LULC 1/5 1/5 1/3 1/2 1/1 1 1 3 3 5 DD 1/5 1/5 1/5 1/3 1/3 1/1 1 1 3 3 Soil 1/7 1/6 1/5 1/3 1/3 1/3 1/1 1 3 3 TWI 1/5 1/7 1/5 1/5 1/5 1/3 1/3 1/3 1 1 TRI 1/7 1/7 1/7 1/5 1/5 1/5 1/3 1/3 1/1 1 Note: RF: rainfall; Gm: Geomorphology; Ge: geology; LD: Lineament Density; LULC: Land use/Land cover; DD: Drainage Density; TWI: topographic wetness index; and TRI: topographic roughness index Table 2. Normalized comparison matrix. RF Gm Ge Slope LD LULC DD Soil TWI TRI Aveg. Weight (%) RF 0.247 0.269 0.238 0.284 0.199 0.265 0.203 0.236 0.132 0.159 0.223 22.309 Gm 0.247 0.269 0.357 0.284 0.332 0.265 0.203 0.202 0.184 0.159 0.250 25.014 Ge 0.123 0.090 0.119 0.095 0.199 0.159 0.203 0.169 0.132 0.159 0.145 14.466 Slope 0.082 0.090 0.119 0.095 0.066 0.106 0.122 0.101 0.132 0.114 0.103 10.258 LD 0.082 0.054 0.040 0.095 0.066 0.053 0.122 0.101 0.132 0.114 0.086 8.576 LULC 0.049 0.054 0.040 0.047 0.066 0.053 0.041 0.101 0.079 0.114 0.064 6.437 DD 0.049 0.054 0.024 0.032 0.022 0.053 0.041 0.034 0.079 0.068 0.045 4.550 Soil 0.035 0.045 0.024 0.032 0.022 0.018 0.041 0.034 0.079 0.068 0.040 3.966 TWI 0.049 0.038 0.024 0.019 0.013 0.018 0.014 0.011 0.026 0.023 0.024 2.352 TRI 0.035 0.038 0.017 0.019 0.013 0.011 0.014 0.011 0.026 0.023 0.021 2.072 8 T. K. SEIFU ET AL. 1 Table 3. Multi-collinearity assessment result. VIF ¼ (5) Influencing Factors VIF TOL Tollerance Rainfall 1.026 0.974 If a TOL < 0.10 or VIF ≥ 10 the analysis shows that the Geomorphology 1.113 0.899 Geology 1.014 0.986 multicollinearity problem and the associated influen - Drainage density 1.038 0.963 cing factors should be removed from groundwater Lineament density 1.361 0.735 LULC 1.148 0.871 potential prediction models due to multicollinearity Slope 1.453 0.688 (Mukherjee & Singh, 2020). Soil 1.113 0.899 TRI 1.022 0.979 TWI 1.171 0.854 2.7. GIS Overlay analysis 3. Results and discussion The weights were assigned based on how each criter- ion affected groundwater potential zonation. In 3.1. Multi‑collinearity test ArcGIS, every thematic layer was divided into sub- A multicollinearity test was performed for the ten classes based on the type of layer. Based on their influencing factors. Table 3 displays the outcomes of impact on groundwater recharge, the sub-classes of the multicollinearity test verification. For the multi- thematic layers are assigned weight calculation ranks collinearity analysis, 362 randomly chosen points from 1 through 5 in the groundwater potential zone. The the study region were selected, and using ArcGIS groundwater potential index (GWPI) was calcu- tools, the values of the 10 influencing factors from lated as: these locations were extracted. The outcome reveals GWPI ¼ ððRF ÞðRF ÞþðGe ÞðGe ÞþðGm Þ w wi w wi w that the lowest TOL value is 0.688 and the highest VIF ðGm ÞþðSl ÞðSl ÞþðDd ÞðDd Þ wi w wi w wi value is 1.453. These results demonstrate that there is þðLULC ÞðLULC ÞþðLd ÞðLd Þ w wi w wi no collinearity among the ten influencing factors that þðSo ÞðSo ÞþþðTWI ÞðTWI Þ w wi w wi were selected. þðTRI ÞðTRI Þ w wi Where: (RF) rainfall, (Ge) geology, (Gm) geomor- 3.2. Groundwater potential zone phology, (Sl) slope, (Dd) drainage density, (LULC) land use land cover, (Ld) lineament density, (So) The final output map of groundwater potential zones soil, (TWI) topographic wetness index and (TRI) was produced using ten thematic layers. Low, moder- topographic roughness index. While subscript w is ate, high, and very high potential areas with 2.15%, normalized weight main criteria and subscript wi 83.77%, 14.06%, and 0.018% coverage, respectively, is normalized weight of sub criteria (classes; were identified by the ArcGIS overlay analysis. The Fashae et al., 2014). The weight calculated for map of groundwater potential zone in the research thematic layers presented in the appendix area are shown in Figure 7. Zones with high and (Table A1). extremely high potential are mainly found in the upper catchments. Alluvial and lacustrine sediments predominate in these locations, with clay and loam soils as the predominant soil textures and agriculture 2.8. Model validation as the primary activities. The average annual rainfall is In scientific research, validating a model is a crucial also relatively high in the upper catchments. This stage. Several studies used borehole and spring data to demonstrated that the area’s groundwater potential is verify the correctness of the groundwater potential mostly influenced by rainfall and geological character- zone definition (Mukherjee & Singh, 2020). Receiver istics. Since moderate potential comprises up the operating characteristic (ROC) analysis was used in majority of the study area, it may be assumed that this study to validate the model. ROC was used by the groundwater potentiality is also moderate. associating the research area’s existing well data with By identifying suitable locations for well drilling the predicted potential zone map (Pourghasemi et al., and other groundwater resource management strate- 2012). Using the ROC curve, the model prediction gies, the current research has a number of signifi - capacity can be determined using the area under the cances for the study’s stakeholders. It is also highly curve (AUC) which ranges from 0.5 to 1.0. According helpful for local practitioners and policy makers to the value of AUC, predictions range from 0.5 to 0.6 because it provides first-hand information on the (poor), 0.6 to 0.7 (average), 0.7 to 0.8 (good), 0.8 to 0.9 groundwater resources in the area. Only ten ground- (very good), and 0.9 to 1.0 (excellent; Ahmed & water affecting factors were used in the study. To Pradhan, 2019). demarcate precise groundwater potential zonation, GEOLOGY, ECOLOGY, AND LANDSCAPES 9 Figure 7. Groundwater potential zone map of the study area. Figure 8. ROC curve used for validation. however, additional criteria like curvature, the thick- was developed by comparing field data with predicted ness of the aquifers, vicinity to the river, etc., need be groundwater potential zones. The ROC’s area under taken into account. To fully understand the area’s the curve (AUC) is 0.87, which is an excellent perfor- potential for groundwater, additional detailed field - mance (Figure 8). Groundwater potential mapping in work and other related investigations should be the study area can be predicted more effectively using conducted. GIS and remote sensing. 3.3. Validation 4. Conclusion The groundwater potential of the studied region was The identification of the groundwater potential is verified using receiver operating characteristic (ROC) the first step in the groundwater investigation stu- curve analysis based on the borehole data. The ROC dies. This study used GIS and remote sensing 10 T. K. SEIFU ET AL. methodologies together with analytic hierarchy pro- Ballerine, C. (2017). Topographic Wetness Index Urban flooding awareness act action support will and DuPage cess approaches to identify potential groundwater Counties, illinois topographic wetness Index Urban flood - sites. The method works effectively in arid and semi- ing awareness act action support will & DuPage Counties, arid regions of developing countries where field- Illinois. Illinois State Water Survey. based geophysical techniques are difficult to imple- Baye, T. G. (2017). Poverty, peasantry and agriculture in ment. The ROC curve was used to test the accuracy, Ethiopia. Annals of Agrarian Science, 15(3), 420–430. https://doi.org/10.1016/j.aasci.2017.04.002 and it produced extremely good results Berehanu, B., Ayenew, T., & Azagegn, T. (2017). Challenges (AUC = 87.0%). The results showed that regions in of groundwater flow model calibration using the upper catchment with alluvial and lacustrine MODFLOW in Ethiopia: With particular emphasis to sediments and high rainfall amounts were identified the upper awash river basin. Journal of Geoscience and as having potential for groundwater availability. To Environment Protection, 5(3), 50–66. https://doi.org/10. combat the occurrence of recurrent drought in the 4236/gep.2017.53005 Berhane, M. (2013). Estimation of monthly flow for study area brought on by climate variability, more ungauged catchment (case study baro. Akobo Basin). research on the availability of groundwater resources http://edt.aau.edu.et/handle/123456789/9849 is required. Droughts that occur frequently can be Berhanu, B., Melesse, A. M., & Seleshi, Y. (2013). GIS-based devastating, but they can also be reduced with effi - hydrological zones and soil geo-database of Ethiopia. cient management and full understanding of the Catena (Amst), 104, 21–31. https://doi.org/10.1016/j. catena.2012.12.007 water resources. The current study’s finding pro- Berhanu, B., Seleshi, Y., & Melesse, A. M. (2014). Surface vides firsthand information for identifying ground- water and groundwater resources of Ethiopia: Potentials water drilling and dug well locations, which is and challenges of water resources development (Vol. crucial for the area where surface water resources 9783319027203, pp. 97–117). Ecohydrological are limited. Challenges, Climate Change and Hydropolitics. https:// doi.org/10.1007/978-3-319-02720-3_6 Beven, K. (1997). Topmodel: A critique . Hydrological Acknowledgments Process, 11. http://doi.org/10.1002/(SICI)169-1085 (199707)11:9<1069 . The author thanks all governmental organizations, for pro- Campo, B., Bohacs, K. M., & Amorosi, A. (2020). Late viding the necessary data for this research work. I want to Quaternary sequence stratigraphy as a tool for ground- give deep thanks to an anonymous reviewer for their sup- water exploration: Lessons from the Po River Basin portive and constructive reviews, which significantly (northern Italy). AAPG Bulletin, 104, 681–710. https:// improved the quality of the paper. doi.org/10.1306/06121918116 Chávez García Silva, R., Grönwall, J., van der Kwast, J., Danert, K., & Foppen, J. W. (2020, 15). Estimating Disclosure statement domestic self-supply groundwater use in urban continen- tal Africa. Environmental Research Letters, 15(10), No potential conflict of interest was reported by the 1040b2. https://doi.org/10.1088/1748-9326/ab9af9 author(s). Das, B., & Pal, S. C. (2020). Assessment of groundwater recharge and its potential zone identification in groundwater-stressed Goghat-I block of Hugli District, References West Bengal, India. Environment, Development and Sustainability, 22(6), 5905–5923. https://doi.org/10. Abebe, A., & Foerch, G. Catchment characteristics as pre- 1007/s10668-019-00457-7 dictors of base flow index (BFI) in Wabi-shebele river Elubid, B. A., Huang, T., Peng, D. P., Ahmed, E. H., & basin, East Africa. Conference on International Babiker, M. M. (2020). Delineation of groundwater Agricultural Research for Development 2006:1–5. potential zones using integrated remote sensing, gis and Ahmed, J. B., & Pradhan, B. (2019). Spatial assessment of multi-criteria decision making (Mcdm). termites interaction with groundwater potential condi- DESALINATION AND WATER Treatment, 192, tioning parameters in Keffi, Nigeria. J Hydrol (Amst), 578. 248–258. https://doi.org/10.5004/dwt.2020.25761 https://doi.org/10.1016/j.jhydrol.2019.124012 Ethiopian Ministry of Mines Geological Survey of Ethiopia. Amer, S., Gachet, A., Belcher, W. R., Bartolino, J. R., & n.d. Hopkins, C. B. (2013). United State Geological Survey. Farzin, M., Avand, M., Ahmadzadeh, H., Zelenakova, M., & Groundwater exploration and assessment in the Eastern Tiefenbacher, J. P. (2021). Assessment of ensemble mod- Lowlands and associated Highlands of the Ogaden Basin els for groundwater potential modeling and prediction in Area, Eastern Ethiopia: Phase 1 final technical Report. a karst watershed. Water, 13, 2540. https://doi.org/10. Ayenew, T., Demlie, M., & Wohnlich, S. (2008). 3390/W13182540 Hydrogeological framework and occurrence of ground- Fashae, O. A., Tijani, M. N., Talabi, A. O., & Adedeji, O. I. water in the Ethiopian aquifers. Journal of African Earth (2014). Delineation of groundwater potential zones in the Sciences, 52(3), 97–113. https://doi.org/10.1016/j. crystalline basement terrain of SW-Nigeria: An integrated jafrearsci.2008.06.006 GIS and remote sensing approach. Applied Water Science, Azma, A., Narreie, E., Shojaaddini, A., Kianfar, N., 4(1), 19–38. https://doi.org/10.1007/s13201-013-0127-9 Kiyanfar, R., Alizadeh, S. M. S. & Davarpanah, Hoogesteger, J. (2022). Regulating agricultural groundwater A. (2021). Statistical modeling for spatial groundwater use in arid and semi-arid regions of the Global South: potential map based on gis technique. Sustainability Challenges and socio-environmental impacts. Curr Opin (Switzerland), 13. https://doi.org/10.3390/su13073788 GEOLOGY, ECOLOGY, AND LANDSCAPES 11 Environ Sci Health, 27. https://doi.org/10.1016/j.coesh. well for aquifer thermal energy storage in Berlin 2022.100341 (Germany). Environ Earth Sci, 77. https://doi.org/10. Kebede, S. (2013a). Groundwater in Ethiopia: Features, 1007/s12665-018-7696-8 numbers and opportunities. Springer. https://doi.org/10. Saaty, T. L. (2002). Decision making with the analytic hier- 1007/978-3-642-30391-3 archy process. Scientia Iranica, 9, 215–229. https://doi. Kebede, A., Meko, T., Hussein, A., & Tamiru, Y. (2017). Review org/10.1504/ijssci.2008.017590 on opportunities and constraints of fishery in Ethiopia. Saaty, T. L., & Katz, J. M. (1994). Theory and methodology International Journal of Poultry and Fisheries Sciences, 1(1), highlights and critical points in the theory and applica- 1–4. https://doi.org/10.15226/2578-1898/1/1/00104 tion of the analytic hierarchy process. 74. Lindsay, J. B., Newman, D. R., & Francioni, A. (2019). Scale- Sarikhani, R., Kamali, Z., Dehnavi, A. G., & Sahamieh, R. Z. optimized surface roughness for topographic analysis. (2014). Correlation of lineaments and groundwater qual- Geosciences (Switzerland), 9. https://doi.org/10.3390/ ity in Dasht-e-Arjan Fars, SW of Iran. Environmental geosciences9070322 Earth Sciences, 72(7), 2369–2387. https://doi.org/10. Liu, J., Gao, Z., Wang, Z., Xu, X., Su, Q., Wang, S., & Xing, 1007/s12665-014-3146-4 T. (2020). Hydrogeochemical processes and suitability Singh, A. (2014). Groundwater resources management assessment of groundwater in the Jiaodong Peninsula, through the applications of simulation modeling: A China. Environ Monit Assess, 192(3), 192. https://doi. review. Science of the Total Environment, 499, 414–423. org/10.1007/s10661-020-08356-5 https://doi.org/10.1016/j.scitotenv.2014.05.048 Mukherjee, I., & Singh, U. K. (2020). Delineation of ground- Tummala, V. M. R., & Ling, H. (1996). Sampling distribu- water potential zones in a drought-prone semi-arid tion of the random consistency index of the analytic region of East India using GIS and analytical hierarchical hierarchy process (AHP). Journal of Statistical process techniques. Catena (Amst), 194. https://doi.org/ Computation and Simulation, 55(1–2), 121–131. https:// 10.1016/j.catena.2020.104681 doi.org/10.1080/00949659608811754 Pourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2012). Unesco, World Water Assessment Programme (United Application of fuzzy logic and analytical hierarchy pro- Nations). (n.d.). Facing the challenges : Case studies and cess (AHP) to landslide susceptibility mapping at Haraz indicators : UNESCO’s contribution to the United Nations watershed, Iran. Natural Hazards, 63(2), 965–996. world water development report 2015. https://doi.org/10.1007/s11069-012-0217-2 Wirth, S. B., Carlier, C., Cochand, F., Hunkeler, D., & Prasad, P., Loveson, V. J., Kotha, M., & Yadav, R. (2020). Brunner, P. (2020). Lithological and tectonic control on Application of machine learning techniques in ground- groundwater contribution to stream discharge during water potential mapping along the west coast of India. low-flow conditions. Water (Switzerland), 12. https:// GIScience & Remote Sensing, 57(6), 735–752. https://doi. doi.org/10.3390/w12030821 org/10.1080/15481603.2020.1794104 Yariyan, P., Avand, M., Omidvar, E., Pham, Q. B., Razandi, Y., Pourghasemi, H. R., Neisani, N. S., & Linh, N. T. T., & Tiefenbacher, J. P. (2022). Rahmati, O. (2015). Application of analytical hierarchy Optimization of statistical and machine learning hybrid process, frequency ratio, and certainty factor models for models for groundwater potential mapping. Geocarto groundwater potential mapping using GIS. Earth Science International, 37(13), 3877–3911. https://doi.org/10. Informatics, 8(4), 867–883. https://doi.org/10.1007/ 1080/10106049.2020.1870164 s12145-015-0220-8 Zerssa, G., Feyssa, D., Kim, D. G., & Eichler-Löbermann, B. Regenspurg, S., Alawi, M., Blöcher, G., Börger, M., Kranz, S., (2021). Challenges of smallholder farming in Ethiopia Norden, B., & Vieth, H. (2018). Impact of drilling mud and opportunities by adopting climate-smart onchemistry and microbiology of an Upper Triassic agriculture. Agriculture (Switzerland), 11, 1–26. https:// groundwater after drilling and testing an exploration doi.org/10.3390/agriculture11030192 12 T. K. SEIFU ET AL. Appendix Table A1. Calculated weight of thematic layers. Criteria Weight Sub-classes Ranks 0verall weightage 1 Rainfall 22 281–341 1 22 342–445 2 44 446–585 3 66 586–720 4 88 721–884 5 110 2 Geomorphology 25 Mountains 1 25 hills and high ridge 2 50 plain area 4 100 Shallow valley 5 125 U-shaped valley 5 125 3 Geology 15 Alluvial and lacustrine deposits(Q) 5 75 Jessoma Formation (Pj) 2 30 Hamanlei Formation (Jh) 3 45 Adigrat Formation (Ja) 2 30 Ashangi Formation(P2a) 4 60 Gabredarre Formation (Jg) 3 45 Urandab Formation (Ju) 3 45 Alghe Group (ARI) 1 15 4 Lineaments Density 9 0–0.146 1 9 0.147–0.292 2 18 0.293–0.438 3 27 0.439–0.584 4 36 0.585–0.73 5 45 5 Slope 10 Level slope 5 50 Gentle sloping 4 40 Strong slope 3 30 Moderate steep 2 20 Very steep 1 10 6 Soil texture 4 Clay 1 4 Loam 3 12 Loamy sand 5 20 Sandy loam 4 16 7 Land use land cover 6 Forest Land 5 30 Agricultural land 5 30 Shrub land 3 18 Build up area 2 12 Rock out crop 1 6 Grass Land 4 24 8 Drainage Density 5 0–0.18 5 25 0.181–0.359 4 20 0.36–0.539 3 15 0.54–0.719 2 10 0.72–0.898 1 5 9 Topographic Wetness Index 2 2.67–7.02 1 2 7.03–8.79 2 4 8.8–11.3 3 6 11.4–14.7 4 8 14.8–25.3 5 10 10 Topographic Roughness Index 2 0.111–0.364 5 10 0.365–0.471 4 8 0.472–0.566 3 6 0.567–0.675 2 4 0.678–0.889 1 2 GEOLOGY, ECOLOGY, AND LANDSCAPES 13 Table A2. Descriptions of the geological features in the study area (Amer et al., 2013; Geological Survey of Ethiopia, 1996; Getnet Tsegaye et al., 2018). Symbol Geologic units Description Period Comments Q Alluvial and Sand, silt, clay, diatomite, limestone and beach sand. quaternary Major lacustrine undifferentiated deposits P2a Ashangi Deeply weathered alkaline and transitional basalt flows Eocene Formation: with rare intercalations of tuff, often tilted (includes Akobo Basalts of SW Ethiopia). Pj Jessoma Late Cretaceous-Paleocene sandstone. Eocene May contain a lower confining unit; Formation serves as a major recharge area on the eastern edge of study area Jg Gabredarre Kimmeridgia -Tithonian; (Jg2) Upper unit and (Jg1) Lower Late Jurassic Minor Formation unit: Limestone with shaly and gypsiferous units. Ju Urandab Oxfordian-Kmmerdgian marl and shaly limestone Main confining unit above the Formation Hamanlei Formation aquifers Jh Hamanlei Oxfordian limestone and shale. Early – Late High-quality aquifers due to Formation Jurassic karstification; surface exposures act as recharge zones Ja Adigrat Triassic-Middle Jurassic sandstone Early – Late Good-quality aquifers; surface Formation Jurassic exposures act as recharge zones in the northern part of the survey area ARI Alghe Group Biotite and hornblende gneisses, granulite and migmatite Archean with minor metasedimentary gneisses. Figure A1. (a) Slope map and (b) Mean annual rainfall map. Figure A2. (a) Topographic wetness index and (b) Topographic roughness index map.
Geology Ecology and Landscapes
Taylor & Francis
Identification of groundwater potential sites in the drought-prone area using geospatial techniques at Fafen-Jerer sub-basin, Ethiopia
Seifu, Tesema Kebede
Woldesenbet, Tekalegn Ayele
Geology Ecology and Landscapes
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Nov 12, 2022
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