Abstract:
Groundwater is considered as the first most source of pure water and represents 30% of the
world’s fresh water contained in aquifers, a geological formation or part of the earth consisting
of permeable material capable to store/yield in the layers beneath the surface. at very high
depths, helps protecting it from contamination and preserve its quality and with significant
quantities of water. However, with increasing of anthropogenic activities and geological
formations coupled with climate change, its quality and quantity is deteriorating at alarming
rate. Hence, closely monitoring the potential of groundwater intended to be used for domestic
purposes is very important. Accordingly, the objective of current study focused on the
delineation of Ground water Potential and recharge area of Kaffa Zone using remote sensing
and ArcGIS 10.4 software analysis techniques. Different data from all over the world and
Ethiopia were used to identify groundwater potential and recharge zone influencing factors.
Eight main factors influencing groundwater potential zone were identified in this study; these
were rainfall, slope, geology, lineament density, geomorphology, drainage density,
Landuse/land-cover and soil texture. Likewise, six main influencing factors (rainfall, slope, land
use/cover, lineaments, and drainage density and lithology) are selected for groundwater
recharge zone mapping. Thematic maps were scanned; geo-referenced and classified as per
suitable for groundwater using ArcGIS 10.4. The methods to assess the potential zone was using
weight overlay analysis and Analytical Hierarchy Process (AHP) algorithm. The result depicted
the groundwater potential and recharge zones into four categories, those are; very good, good,
and moderate and low that can be used for better planning and management of groundwater
resources. Accordingly, low, Moderate, High and Very High groundwater potentials area cover
1664.1 km2
,7682.9 km2
, 958.27km2
and 192.78 km2
respectively. The prediction accuracy was
checked based on borehole yield observed and predicted data of respective location. The
prediction accuracy obtained (68.42%) reflects that the method applied for present study
produced significantly reliable and precise results.
