Ground water potential area delineation
To assess ground water potential zones, important factor maps were considered, and the maps were prepared for each layer. Each factor determining the ground water potential zones in the study area is classified into five classes .Theses criteria maps were altered to raster data sets having the same pixel size (Resolution) and different weightage were assigned using AHP (Analytical Hierarchical process) as per their groundwater potential controlling capacity within the study area and reclassification of each map was done based on the weight values produced. The combination method follows the conventional scheme for GIS‐based MCDA (Malckzewiski, 1999). It involves three main phases. First, the criterion maps were standardized/ reclassified using Spatial Analyst’s Reclassify tool. This approach is important since the criterion maps include the ordinal values (very high, high, moderate, low and very low) indicate the level of potential area of ground water. Secondly, derivation of the weights of relative criterion importance using the pair wise comparison method (cf. assigning criterion weights‐section). The criterion weights are automatically calculated once the pair wise comparison matrix is entered in the AHP weight derivation module. Third, the criterion weights and the standardized criterion maps were combined/ aggregated by means of the weighted overly analysis (WOA) operations. Finally the expected groundwater potential areas for the study area were delineated.
Presence of Ground water is strongly associated Lithology, Rainfall, Geomorphology, slope, soil type, Lineament density, drainage density and land use land. To assess potential ground water potential area using GIS and remote sensing, weighted overlay analysis were used. MCE is a procedure which needs several criteria to be evaluated to meet a specific objective.
Groundwater recharge is controlled by various factors with rainfall playing a key role since it represents the main source of groundwater recharge ( Gebhardt, H.; Glaser, R.; Radtke, U.;2011 Reuber, P;Lakshmi, S.; Reddy, Y.2018; ). The annual mean rainfall for the period from 2011 to 2020 in the study area was obtained from national meteorological agency (NMA) .The rainfall data were converted to a raster layer using the conversation Tools . We then converted the raster layer to points using the tools Conversion Tools > From Raster> Raster to point. These points were interpolated through the tools Spatial Analyst Tools >Interpolation > Kriging to obtain a rainfall contour map. The rainfall map for the study area was extracted using the tools Spatial Analyst Tools > Extraction > Extract by Mask. The rainfall map of the study area is shown in Fig.3. The northwest part of the study area receives rainfall of around 1076.5–1,250.4 mm/year; the eastern part receives rainfall of around 749.2–1,167.1 mm/year. In the southern part, the recorded rainfall is around 1,167.1–1,364.5 mm/ year, and in the northeast part, the recorded rainfall is around 1076.7–1,167.161 mm/year. The rainfall influence on groundwater occurrence likely depends on the southwest and central rainfall, which has a high amount of rainfall from June to September, about 1,250.5–1,364.5 mm/ year. The rainfall distribution along with the southwest part directly affects the infiltration rate and hence increases the possibility of groundwater potential zones in the study area.
It is a topographic setting relates to the local and regional relief situation and gives an idea about the general direction of groundwater flow and its influence on groundwater recharge and discharge. The slope is one of the crucial variables which is predictable to groundwater revive. It controls the ratio of penetration and surface spill-over. It gives awareness about the amount of groundwater recover dependent on the slope angles. Steeper the slope, greater will be the runoff and thus lesser is the groundwater recharge. Digital Elevation model (DEM) is derived using contour information from the topographical map for estimation of slope in degree. The identified slope category varies from 0° to 300 degree in the study area and area classified in to five classes like 0-3° (flat), 3 -6° (gentle), 6-10° (moderate), and 10.1-15° (steep) 15.1°-30° (very steep). flat slope (0–3°) indicates the presence of very high groundwater potential zones where as steep slope (>15°) shows the presence of poor groundwater potential zones as water runs rapidly off the surface and does not have sufficient time to infiltrate the surface, keeping other parameters constant.
The Geomorphology of the area
Geomorphology reflects many land form and structural landscapes and several topographies are inspiring the event of subsurface water and categorized in terms of groundwater possibility. Geomorphology i.e. landforms of the study area developed from SRTM 30m digital elevation model using land form mapping as presented by Morgan et al (2005) procedure in ArcGIS’s spatial analyst tool. Land from is a combination of slope, relief and profile. The classification and description is adopted from Dikau’s landform codes (Dikau, R etal., 1995). Technically a landform map is not complete without the, geologic history and the process that resulted in for the landforms presented in the maps. In this case, however, the map is satisfactory for the intended purpose.
The identification and characterization of various landforms and structural features in the study area are very important from geomorphological study point of view. Which are mandatory for groundwater potential zone mapping (Shifaji and Nitin, 2014).The geomorphology reclassified in terms of groundwater recharge and potential the geomorphology of the study area classified in to five units main geomorphic units identified in the area are flat plain, smooth plain, irregular plain, Escarpment, low Hill, Hills ,low mountain ,
Small Mountain and hills are the cover small area types of geomorphological class in the area; see Fig. 4.2.Groundwater occurrence map, those suitable areas are found with geomorphic class of Flat plain and irregular plain because of high infiltration rate: Escarpment, low hills, hill, low mountain landform, ground water is low. Locational, small hills to the north and south western part of the study area.. There is a maximum runoff associate with Landforms which characterized by hills slope. This shows poor Potentiality for groundwater potential and recharge possibility. However, there is a small portion of land, which has high elevation compared to local surrounding land.The rank assigned to the individual landform classification according to its respective influence of groundwater occurrence, holding and recharge, as presented in figure 5: Geomorphology and its rank as per suitable for groundwater potential and recharge.
The characteristics, types and distribution of soil for a certain area depend on geomorphology, geology, relief, time, and other factors. Soil properties influence the relationship between runoff and infiltration rates which in turn controls the degree of permeability, the principal factor in hydrogeology that determines the groundwater potential (Kumaret etal., 2016; Magesh et al., 2012;Tesfaye 2010). Soil type is a medium that controls the groundwater exposure which is an important in determining the intrinsic vulnerability. In line with FAO and according to the Ethiopian Ministry of Water Resource Soil Classification, the prevailing soil types in the study area were classified into six major groups, namely, loam( pellic vertisols, orthic solonchaks)soil which accounts for 57.12% area coverage, Clay(vitric Andosols, Eutric vertisols) which accounts for 57.12% area coverage , sandy Clay loam(Chromic luvisols) which accounts for 57.12% area coverage , clay Loam(Calcic xerosols, Eutric nitisols) which accounts for 57.12% area coverage as shown in Fig..6 The result showed that pellic vertisols and orthic solonchaks soils are found the most dominant in terms of area coverage with area coverage of 34.34%, 8.09%,and 0.45%, respectively and more determinant in groundwater occurrence and movement as compared to Calcic xerosols and vitric Andosols These soil types are characterized by poor to good infiltration property. Figure 6 shows the soil map of the study area.
A lineament is usually defined as structural lines such as faults, which often represent zones of fracturing and increased secondary porosity and permeability, and therefore of enhanced groundwater occurrence and movement. In hard rock terrain the storage and movement of groundwater is controlled by the secondary porosity i.e. presence of lineaments and fractures. Lineaments for lineament density computation are extracted from SRTM (DEM) which downloaded from path 169, row 55. Lineaments of the study area from remotely sensed data provides important information on subsurface fractures that may control the movement and storage of the groundwater. The distribution of the lineaments is observed to be high on the escarpment and rift floor (Figure 7). These are normal faults having a NW-SE orientation. Faults may act either as pathways for water movement or as flow barriers. At the foot of some of the fault scarps which bound the basin there exist springs indicating that these faults act as drains. The faults in the escarpment areas which comprises the older undifferentiated rocks of Nazret Group and Dino Formation down faulted towards the rift floor resulted in the development of in the study area. High density of the faults is observed near northwest and southeast. Those, faults on the floor may possibly be filled with a weathered glassy volcanic ash. In such cases the faults could act as barriers (Nedaw, 1997). Most of the lineaments are identified Classified into lineament density map in to five categories, i.e. 0.902 -1.51(very high), 0.588 -0.901(high), 0.375 – 0.587(medium), 0.131-0.374(Low) and 0- 0.13 (very low) in the study area (Figure 7)
Drainage density is one of the important indicators of groundwater recharge (Magesh et al., 2012) and groundwater occurrence (Sener et al., 2005). In fact, it is linked with water percolation properties of underlying lithology, consequently having close relation with groundwater mapping. The drainage density is an inverse function of permeability. An area with low permeable surface prone to high drainage density and water comes from precipitation goes to a high runoff as well and vice versa. As a result, high drainage density implies low groundwater potential. The Drainage density was delineated using SRTM(DEM with30m)resolution data of the study area after consecutive processes such as Importing of SRTM data, filled sinks for undefined values, created Flow Direction, Created Flow Accumulation, created Stream network, generated Stream Order and finally converted Stream Order to drainage density using a spatial analysis tool in ArcGIS 10.8. Afterwards, a drainage density map is produced using a kernel density analysis tool (Figure 8). As per the definition of preceding studies (Greenbaum, 1985; Magesh et al., 2012), drainage density (DD) is the total length of the stream segments divided by the unit area .The stream order values were regrouped to produce a drainage density map that was reclassified into five categories based availability of potential of Ground water i.e., namely “very good” (0–0.00005148 km/km2), “good” (0.00005149–0.0001214 km/km2), “moderate” (0.0001215–0.0001809 km/km2), “poor” (0.000181–0.0002482 km/km2), and very poor (0.0002483 –0.0003366 km/ km2). In the study area, 1.73% and 25.07% of landscape were found in 0–0.00005148km/km2 and 0.00005149–0.0001214 km/km2drainage density class, respectively. This implies the availability of good groundwater potential zones. Moreover, 61.18% was entitled under drainage class with good potentiality for groundwater storage. (figure.8).
Land Use/Land Cover
LULC is an important factor affecting groundwater recharge, groundwater occurrence, and availability (Hussein et al., 2016; Kumar et al., 2016; Pande, Khadri, Moharir, & Patode, 2017; Yeh, Cheng, Lin, & Lee, 2016). Supervised image classification was conducted to classify and identify the type of LULC, where Landsat 8 (OLI) satellite image of 2020 with 30 m spatial resolution used. to increase visibility Image pre-processing are conducted in order to analyse remotely sensed images, the different images representing different bands must be stacked, that is, band 1 to 7 LULC 2020 satellite images and classified in ERDAS imagine 2014. Image classification the LULC change studies usually need the development and the definition of homogeneous LULC units before the analysis started. It is differentiated using the available data source such as remote sensing, Google earth, ground control points and the previous local knowledge. Following this, the tool, ERDAS imagine 2014 software was used for classification of the LULC image of the area. In remote sensing, there are various image classification methods, that is, supervised and unsupervised. For this study, we used the most common type of classification technique, supervised classification type. First, Google earth was taken as a signature for the classification. Second, we performed the classification using the maximum likelihood classifier. Lastly, the accuracy assessment was performed using Google earth image for the LULC 2021, 100 random points were generated in Arc GIS10.5. Following these procedures, random points were converted to KML to layer (Hengl et al., 2015). Whereas, the accuracy assessment of 2020 LULC map was used ground truth points as a reference and 100 points were taken to validate the classification; which was built in 12/05/2020. The analysis result was performed using confusion/ error matrix. The land use/cover map of the area was readily interpreted from Landsat image of the year 2020 by using visual interpretation, supervised classification using ERDAS 2014 software. On the result of Classification of land use/cover for analysis is used for identification of the ground potential area. The study area consists of five types of LULC (Figure 5) namely; agricultural area 203.88 (51.13%), forest cover area 112.27 (28.15%), grazing land area 21.64 (5.43%), shrub land area 33.28 (8.35%), urban area 27.72 (6.95%) total area of Land use/ Land cover map of the area398.79 km2
The way of Geologic formation and genetic type is essential condition for ground water flow, transport and mineral composition. Types of rocks determine peculiarities of hydrological crosssection structure, type of porosity values, the nature of permeability, geological structure geomorphology and character of spatial heterogeneity of flow and transport parameters. Lithology has a principal impact on the occurrence and movement of groundwater as it highly controls the infiltration and flow processes (Tolche, A, 2020) stated that the rock type can substantially influence the groundwater recharge potential. Similarly, (El-Baz, F.; Himida, I.; 1995) found that lithology affects the recharge by governing the water percolation. Some investigations have neglected the lithology parameter in ground water potential zoning by considering the drainage features and lineament density as a measure of primary and secondary porosity; however, we followed (Yeh, H etal,;2016)by including the lithology in our analysis to minimize the uncertainty in estimating drainage and lineament densities. The Geological map was then added to ArcGIS, and the study area was extracted using the tools Analysis Tools (Extract tool). Lithological categories classification was done according to the classes available in the lithology classification for Ground water potential zone assessment.
Mapping Ground water potential Areas
In this stage, the AHP results were integrated into a GIS system to map Ground water potential areas using Weighted Linear Combination (WLC). The WLC / simple additive weighting rely on the concept of a weighted average where continuous criterions are standardized to a collective numeric range, and then combined by means of a weighted average (Drobne and Lisec, 2009). The WLC technique can be carried out using any type of GIS system possessing the overlay. The output of this WLC method gave a map the most potential flood susceptible areas. To compute the groundwater potential areas, a weight linear combination was applied as shown in Equation (3).
GWPZM = (27 * RRf) +( 16 *RGm) +(17*RSl)+( 12 *RSt) +( 9 *R Ld )+ (8 * R Dd )+( 6 * RLulc) + (5 * R Lith)………………………… (Equation 7)
Where, RRf: Reclassified Rainfall Map, RGm: Reclassified Geomorphology Map, RSl: Reclassified Slope map, Rst: Reclassified Soil Texture Map, RLd: Reclassified Lineament density Map, RDd: Reclassified Drainage density Map, RLulc: Reclassified Land-use/land-cover Map and RLith: Reclassified Lithology Map.
Rainfall, geomorphology, slope, soil type and lineament density holds the highest value relative to the other parameters. The weight assigned for Rainfall were greater than the weight of other, which influence the occurrence of groundwater potential and recharge zone than others parameters (Mwega, 2013; Kamal et al., 2016). The result for groundwater potential zones was classified in to very high, high, moderate, low and very low Figure 11
The potential area is divided in to five classes. Those were, very high, high, moderately, low and very low groundwater potential areas (Figure 11). In the study area by very high and high ground water area potential areas covers 14.04% and 20.34%. The very high and the high ground water potential areas are found in the South west and Central part of the study area and very low ground water potential areas area a found at upper parts of the study area specifically in the Northern and North eastern part. Moderately ground water potential areas are found in the major near to urban area and included settlements as classified in the land use map.
As can be seen in Table 4.9, means more than 14.04% and 20.34% of the area was found in very high and high ground water potential areas and the remaining as moderately to very low ground water area potential areas. The Very High ground water potential areas a total coverage of 5385.51Ha (53.8551 km2) of the total area. “High” ground water potential areas have total area of the district covers 7803.36 Ha(78.0336km2) ,the “Moderately” ground water potential areas, covers 10240.11 Ha(102,4011km2) of the total area , “low” ground water potential areas covers 10939.59Ha(109.3959 km2) and very low ground water potential areas covers 4005.19 Ha(40.0519km2) (see table 14).