Appropriate quantification and identification of the groundwater distribution in a hydrological basin may provide necessary information for effective management, planning and development of groundwater resources. Groundwater potential assessment and delineation in a highly heterogeneous environment with limited Spatiotemporal data derived from Gelana watershed of Abaya Chamo lake basin is performed, using integrated multi-criteria decision analysis (MCDA), water and energy transfer between soil and plant and atmosphere under quasi-steady state (WetSpass) models. The outputs of the WetSpass model reveal a favorable structure of water balance in the basin studied, mainly using surface runoff. The simulated total flow and groundwater recharge are validated using river measurements and estimated baseflow at two gauging stations located in the study area, which yields a good agreement. The WetSpass model effectively integrates a water balance assessment in a geographical information system (GIS) environment. The WetSpass model is shown to be computationally reputable for such a remote complex setting as the African rift, with a correlation coefficient of 0.99 and 0.99 for total flow and baseflow at a significant level of p-value<0.05, respectively. The simulated annual water budget reveals that 77.22% of annual precipitation loses through evapotranspiration, of which 16.54% is lost via surface runoff while 6.24% is recharged to the groundwater. The calibrated groundwater recharge from the WetSpass model is then considered when determining the controlling factors of groundwater occurrence and formation, together with other multi-thematic layers such as lithology, geomorphology, lineament density and drainage density. The selected five thematic layers through MCDA are incorporated by employing the analytical hierarchy process (AHP) method to identify the relative dominance in groundwater potential zoning. The weighted factors in the AHP are procedurally aggregated, based on weighted linear combinations to provide the groundwater potential index. Based on the potential indexes, the area then is demarcated into low, moderate, and high groundwater potential zones (GWPZ). The identified GWPZs are finally examined using the existing groundwater inventory data (static water level and springs) in the region. About 70.7% of groundwater inventory points are coinciding with the delineated GWPZs. The weighting comparison shows that lithology, geomorphology, and groundwater recharge appear to be the dominant factors influence on the resources potential. The assessment of groundwater potential index values identify 45.88% as high, 39.38% moderate, and 14.73% as low groundwater potential zones. WetSpass model analysis is more preferable in the area like Gelana watershed when the topography is rugged, inaccessible and having limited gauging stations.

Characteristics of ungauged catchments can be studied from the hydrological model parameters of gauged catchments. In this research, discharge prediction was carried out in ungauged catchments using HEC-HMS in the central Omo-Gibe basin. Linear regression, spatial proximity, area ratio, and sub-basin mean were amalgamated for regionalization. The regional model parameters of the gauged catchment and physical characteristics of ungauged catchments were collated together to develop the equations to predict discharge from ungauged catchments. From the sensitivity analysis, crop coefficient (CC), storage coefficient (R), constant rate (CR), and time of concentration (TC) are found to be more sensitive than others. The model efficiency was evaluated using Nash–Sutcliffe Efficiency (N_SE) which was greater than 0.75, varying between −10% and +10% and the coefficient of determination (R²) was approximated to be 0.8 during the calibration and validation period. The model parameters in ungauged catchments were determined using the regional model (linear regression), sub-basin mean, area ratio, and spatial proximity methods, and the discharge was simulated using the HEC-HMS model. Linear regression was used in the prediction where p-value ≤ 0.1, determination coefficient (R²) = 0.91 for crop coefficient (CC) and 0.99 for maximum deficit (MD). Constant rate (CR), maximum storage (MS), initial storage (IS), storage coefficient (R), and time of concentration (TC) were obtained. The result is that an average of 30 m³/s and 15 m³/s as the maximum monthly simulated flow for ungauged sub-catchments, i.e. Denchiya and Mansa of the main river basin.