Water resource developers and modelers focus on sustainable water resourcesrnmanagement and availability of watercourses into account during design of hydraulicrnstructures such as small dams for use at present and the future. Since most of the rivers inrnEthiopia are ungauged, knowledge of predicting annual streamflow to be used for waterrnresource development and other multiple purposes requires a study on stream flow ofrnungauged catchments. In this study an attempt was made to develop multiple linearrnregression model of mean annual stream flow for ungauged catchments of Awash RiverrnBasin using some statistical tests to secure the performance of the model. First the annualrnrainfall homogeneity test, trend analysis, and monthly and annual rainfall characteristicsrnand variation were analyzed, based on this study the mean monthly rainfall of AwashrnRiver basin for the 19 stations varied from 3.15 mm to 411.8 mm in the period of 19812013rnG.Crnandrnthernmeanrnannualrnrainfallrndistributionrnvariesrnfromrn352.9rnmmrntorn1766.4rnmm,rnrnthernmeanrnannualrnflowrnvariesrnfromrn36.68rnMCMrntorn2026.82rnMCM.rnSecondrndevelopmentrnrnofrnrnequation for each gauged catchments based on different forms of equations (i.e.rnpower, polynomial and linear functions) were performed and only one form of equationrnwas chosen depending on model performance indices like Rrn2rn, NSE and RMSE results. rnThe majority of the results show remarkably good fits with Rrn2rn-values ranging from 0.51 rnto 0.72 and NSE ranging from 0.53 to 0.7. The third and main task of this thesis workrnwas developing a Regression model that could be used for estimation of mean annualrnflow at ungauged catchments, which was trained for Region-I using 9 gauging stationsrnand for Region-II using 8 gauging stations. The performance and model fit was checkedrnby statistical indicators and graphical methods. For Region-I it is found that the modelrnwith prediction variables Catchment area, mean annual rainfall and mean slope result thernbest fit giving (Rrn2rn=0.89), (NSE=0.94) and (RMSE=107.24), Similarly For Region-II it is rnfound that the model with prediction variables Catchment Area, mean annual Rainfall,rnmean Elevation and mean Slope result the best fit giving (NSE=0.88) andrn(RMSE=48.51). These model evaluation indicators were under a Very Goodrnperformances for Region-I and Good performances for Region-II.