Background: The current study conducted to analysis the bottom water potential zones in Odiyo watershed. The study relies on the secondary data, which is collected from concern department and through internet. Totally nine parameters are consider for the study like drainage density, elevation, geology, geomorphology, land use and land cover, lineaments, rainfall pattern, slope gradient and soil texture. The chosen parameters are prepared and classified in GIS environment, then weightage for every parameter and its classes are assigned using Analytical Hierarchical Process, and eventually, weighted overlay analysis in ArcGIS accustomed discover the result. Results: The result relived that, about 183.87ha (0.613%) areas are having very high, 4846.23ha (16.18%) area are having high, 19229.4 ha (64.19%) having moderate, 5645.7ha (18.855) having low and 48.6ha (0.16%) area are having very low potential of well water. Conclusions: The knowledge on strength of ground water supported ground water zones help in management and development of the groundwater within the study area.

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This research was administered to spatially predict the soil loss rate of kaffa zone using model estimate and GIS. Revised Universal Soil Loss Equation (RUSLE) adapted to Ethiopian conditions was accustomed estimate potential soil losses by utilizing information on rainfall erosivity (R) using interpolation of rainfall data, soil erodibility (K) using DSMW soil map, vegetation cover (C) using Sentinel-2A satellite images, topography (LS) using Digital Elevation Model (DEM) and conservation practices (P ) using DEM and satellite images. supported the analysis, the mean and total annual soil loss potential of the study area was 30 tons ha-1 year-1 and 36264.5tons ha-1 year-1, respectively. The result also showed that about 2.89, 8.02, 15.31 and 73.78% of the study area were classified a slight, moderate, high and very high with values ranging 0 to 15 ,15 to50,50 to 200, and > 200 tons ha-1 year-1, respectively. The study demonstrates that the RUSLE using GIS and RS provides great advantage to spatially analyze multi-layer of knowledge. The expected amount of soil loss and its spatial distribution could facilitate sustainable land use and management.