Belete, Behanu (PhD)Dawit, Girma2020-11-252023-11-112020-11-252023-11-112020-07http://etd.aau.edu.et/handle/12345678/23558Precipitation data is the most important input parameter to simulate rainfall-runoff processes, since it's strongly hooked into the accuracy of the spatial and temporal representation of the precipitation. In regions where rainfall stations are scarce, additional data sources could also be needed. Satellite platforms has provided a satisfactory alternative because of its global coverage. Although a good range of satellite-based estimations of precipitation is out there, not all the satellite products are suitable for all regions. Moreover, in data-scarce regions in which interpolation schemes are applied, it becomes difficult to have an accurate performance assessment; another comparison tool is required as rainfall-runoff models. Remotely-sensed estimates need to generate realistic and reliable data to be used in water resource assessments. Therefore there is a need to evaluate the accuracy of remote sensing techniques. This study investigated the reliability of the following satellite-derived rainfall estimates; Tropical Applications of Meteorology using SATellite (TAMSAT) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) in Genale Dawa river basin, Ethiopia where climate data scarcity problem extremely high. Besides, the study evaluated the performance of satellite precipitation estimates with ground observations from the most representative rain gauge for nine stations at daily, monthly and yearly timescale. Intercomparison between Satellite rainfall product and observed data were done using point to grid method selecting nine representative metrological stations namely, Bore, Robe, Delomena, Ginir, Moyale, Finchawa, KibreMengist, Negele, and Filtu. TAMSAT shows unacceptable linear correlation coefficient with rain gauges while CHRIPS shows a good linear correlation coefficient with rain gauges. Therefore bias correction was done for TAMSAT. The average correlation R is 0.45 and the average NS is 0.028 for Raw TAMSAT. After bias correction, this value was improved to the average value of R=0.87 and NS =0.764. Considering four Categorical index POD, FAR, FB and HSS, the average value were (0.49, 0.4, 0.84 and 0.41) respectively before Bias correction and improved to (0.71, 0.22, 0.92 and 0.66) respectively after bias correction. For CHRIPS average R and NS are 0.88 and 0.755 respectively and categorical index POD, FAR, FB and HSS were (0.8, 0.05, 0.85 and 0.81) respectively The study model streamflow using both CHRIPS and TAMSAT rainfall products by using the SWAT model from 1983-2017). The model is calibrated from 1998 to 2003 and validated from 2004 to 2007 using SUFI-2 algorithm embodied in the SWAT-CUP. Comparisons of the simulations to the observed streamflow for the four discharge gauging stations namely Dawa at Melka Guba, Welme at Melka Amana, Dimtu Nr. Bore and Genale Nr. Halwen. The Nash-Sutcliffe Efficiency (NSE), linear correlation coefficient (R) and BIAS indices were used to benchmark the model performance and shows very good result (having R and NS=0.71-0.95 during calibration and 0.72-0.97 during validation).en-USCHRIPSModelTAMSATSatellite rainfall productsSUFI-2SWATSWAT-CUPThesis