Stream Flow Response to Climate Change on Tikur Wuha Catchment, Rift Valley of Ethiopia

Brook Legese


Climate changes alter regional hydrologic conditions and results in a variety of impacts on water resource systems. This study aims to assess the streamflow response to Climate Change: the case of Tikur Wuha Sub-watershed, Rift Valley Basin of Ethiopia. In the study, the daily hydro-meteorological data values for the baseline period of 1981-2005 were used. Historical Representative Concentration Pathway (RCPs) data along with observed data of precipitation and temperature were used for extraction and bias correction using CMhyd tool. After evaluation of bias correction methods using RMSE, MAE and RE, the downscaled climate data such as, RCP4.5 and RCP8.5 scenarios was used for the future period assessment. Soil water assessment tool (SWAT) model were used to assess the streamflow response to Climate Change. The model calibration and validation results, using SWAT-CUP(SUFI-2), shows a good agreement with the observed flow with the coefficient of determination 0.79 and 8.6, and a Nash Sutcliffe efficiency was 0.56 and 0.64, respectively. The dynamically downscaled daily climate variables (precipitation and temperature) were used to simulate future projections of streamflow. Streamflow projections for future time periods showed that mean annual streamflow may increase by 15.43, 23.48, and 25.42% in 2020s, 2050s, and 2080s, respectively, from the baseline period for RCP 4.5 scenario, whereas for RCP 8.5 scenario, it will be expect to increase by 29.58, 34.20, and 38.72% in 2020s, 2050s, and 2080s, respectively. The model simulations considered only future climate change scenarios assuming all spatial data constant. Therefore, future study need to consider impact of land use/cover change on the sub-watershed for future sustainable development plan.

Keywords: Climate Change, Streamflow, RCPs, Bias Correction, CMhyd, SWAT, SWAT-CUP

DOI: 10.7176/JEES/9-8-01

Publication date: August 31st 2019

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ISSN (Paper)2224-3216 ISSN (Online)2225-0948

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