Precipitation data is the most important input parameter to simulate rainfall-runoff processes,rnsince it's strongly hooked into the accuracy of the spatial and temporal representation of thernprecipitation. In regions where rainfall stations are scarce, additional data sources could alsornbe needed. Satellite platforms has provided a satisfactory alternative because of its globalrncoverage. Although a good range of satellite-based estimations of precipitation is out there, notrnall the satellite products are suitable for all regions. Moreover, in data-scarce regions in whichrninterpolation schemes are applied, it becomes difficult to have an accurate performancernassessment; another comparison tool is required as rainfall-runoff models. Remotely-sensedrnestimates need to generate realistic and reliable data to be used in water resource assessments.rnTherefore there is a need to evaluate the accuracy of remote sensing techniques. This studyrninvestigated the reliability of the following satellite-derived rainfall estimates; TropicalrnApplications of Meteorology using SATellite (TAMSAT) and Climate Hazards Group InfraRedrnPrecipitation with Stations (CHIRPS) in Genale Dawa river basin, Ethiopia where climate datarnscarcity problem extremely high. Besides, the study evaluated the performance of satelliternprecipitation estimates with ground observations from the most representative rain gauge forrnnine stations at daily, monthly and yearly timescale. Intercomparison between Satellite rainfallrnproduct and observed data were done using point to grid method selecting nine representativernmetrological stations namely, Bore, Robe, Delomena, Ginir, Moyale, Finchawa, KibreMengist,rnNegele, and Filtu. TAMSAT shows unacceptable linear correlation coefficient with rain gaugesrnwhile CHRIPS shows a good linear correlation coefficient with rain gauges. Therefore biasrncorrection was done for TAMSAT. The average correlation R is 0.45 and the average NS isrn0.028 for Raw TAMSAT. After bias correction, this value was improved to the average value ofrnR=0.87 and NS =0.764. Considering four Categorical index POD, FAR, FB and HSS, thernaverage value were (0.49, 0.4, 0.84 and 0.41) respectively before Bias correction and improvedrnto (0.71, 0.22, 0.92 and 0.66) respectively after bias correction. For CHRIPS average R and NSrnare 0.88 and 0.755 respectively and categorical index POD, FAR, FB and HSS were (0.8, 0.05,rn0.85 and 0.81) respectively The study model streamflow using both CHRIPS and TAMSATrnrainfall products by using the SWAT model from 1983-2017). The model is calibrated from 1998rnto 2003 and validated from 2004 to 2007 using SUFI-2 algorithm embodied in the SWAT-CUP.rnComparisons of the simulations to the observed streamflow for the four discharge gaugingrnstations namely Dawa at Melka Guba, Welme at Melka Amana, Dimtu Nr. Bore and Genale Nr.rnHalwen. The Nash-Sutcliffe Efficiency (NSE), linear correlation coefficient (R) and BIAS indicesrnwere used to benchmark the model performance and shows very good result (having Rrn andrnNS=0.71-0.95 during calibration and 0.72-0.97 during validation).