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Groundwater is fresh water that is stored in an underground zone, which is less vulnerable to pollution than surface water and communally used for economic, social, and ecological purposes. This study aimed to evaluate groundwater recharge potential by using a geographic information system (GIS) and remote sensing in the Ziway Abijata sub-basin, Central Rift Valley of Ethiopia. In the present study, the six parameters of soil drainage, slope, lineament density, drainage density, soil texture, and land use land cover were used. ArcGIS 10.3, ERDAS Imagine 2015, IDRISI Selva 17, Arc SWAT 10.3, and Google Earth Pro were applied. The results revealed that about 58 km2 (2.1%) and 1,442.5 km2 (52.9%) were ranked as excellent and good for the groundwater recharge potential. Consequently, about 1,183.2 km2 (43.4%) was covered by a moderate groundwater recharge zone. Larger parts of the study area were dominated by good and moderate groundwater recharge potential. Based on the results, we recommended that decision-makers, environmentalists, geologists, and other stakeholders will have a critical role in the improvements of the future sustainability and proper management of groundwater in the study area. Further researchers can investigate other ecological parameters and socio-economic data that were not included in the present study. HIGHLIGHTS Evaluating a groundwater recharge potential zone plays a crucial role in the drought-vulnerable area.; Geospatial technology was applied for groundwater recharge potential zone mapping.; The study area was dominated by excellent (2.1%) and good (52.9%) groundwater recharge zones.;

ABSTRACTAnthropogenic land use and land cover (LULC) change was the most serious problem in worldwide. This study attempted to investigate spatiotemporal LULC changes in the Upper Anger watershed, Western Ethiopia. In the present study, Landsat TM of 1990, Landsat ETM+ of 2003, and Landsat OLI/TIRS of 2020 were used to analyze the LULC change and Normalized Difference Vegetation Index (NDVI). The supervised classification with maximum likelihood algorithm was employed for classification of the LULC. The result shows that the agricultural land was increased by 707.4 km2, whereas forest land was decreased by 179.9 km2. Consequently, agricultural land and bare land were increased with rate of 23.6 km2/year and 0.5 km2/year, while grassland and forest cover decreased by rate of 18.2 km2/year and 6 km2/year, respectively, from 1990 to 2020. The maximum NDVI value was declined with 0.19 from 1990 to 2020 due to expansion of agricultural land, bare land, and settlement in the study area. About 530 km2 of grassland and 241.5 km2 of forest cover were converted into other land cover classes between 1990 and 2020. Thus, appropriate land use planning and environmental education should be promoted to minimize the rapid loss of forests and grasslands in the study area.