Ethiopia was hit by an El Nino induced drought during 2015/2016 that left millions across the country without access to adequate water supply. More than 220 districts throughout the country faced water supply emergencies. These areas are characterized by frequent shortage/shifting of rainy season and resulting water supply problems. During normal rainy years, the communities in these areas rely on rainwater harvesting, traditional ponds, hand dug wells excavated along wadis and a few shallow groundwater wells. The United Nations Children's Fund (UNICEF) has a programme of rural water supply and is also the Emergency WASH cluster lead in Ethiopia. Hence it supports the government and other partners in the rehabilitation, maintenance and construction of new water supply systems in response to the drought. This report presents the first part of a joint hydrogeological study of UNICEF and the European Commission Joint Research Centre (JRC) in nine selected woredas in the lowland areas of Afar, Somali, SNNPR, and Oromia regions in Ethiopia. The study aims to improve groundwater sector knowledge and the access to safe water in these woredas. Specifically, the main goal of the project is to locate drilling sites with a substantial increase in drilling success rate. In order to improve the success rate, it is vital to undertake reliable groundwater investigations. However, conventional methods to generate detailed hydrogeological maps require a huge amount of time, manpower, logistical and financial resources. Besides hydrogeological field studies, remote sensing (RS) based analysis can be carried out to identify the most feasible sites for drilling. The advantage of using RS data is their large spatial coverage and homogeneous data acquisition. Although there is no satellite-based sensor to measure the occurrence and amount of groundwater directly, different RS-based parameters (e.g., elevation, precipitation, evapotranspiration, vegetation) can be utilized in the assessment of groundwater potential as several studies have demonstrated that spatial variability in groundwater levels is controlled by surface and subsurface topography, vegetation, soil- and bedrock properties. The use of RS data does not eliminate or exclude the in situ data collection, which is needed to verify the accuracy of RS data and to aid their interpretation, but RS data helps to minimize field data collection.
