The purpose of this study was to investigate the rainfall and temperature changes for the projected periods of (2021–2050) and (2051–2080) under two Representative Concentration Pathways (RCP4.5 and RCP8.5) at selected stations in the Ajora-Woybo watershed of Omo-Gibe basin, Ethiopia. CORDEX-Africa with the Coupled Models Intercomparison Project Phase 5 (CMIP5) has been used to downscale the future climate change (2021–2050 and 2051–2080). The RCPs scenarios RCP4.5 and RCP8.5 were considered for this study. The climate model data for hydrological modeling (CMhyd) were used for extraction of CORDEX-NetCDF and the rainfall and temperature bias correction. The monthly observational and reanalysis rainfall and temperature data were validated with ground observations using statistical measures such as the mean relative error (MRE), the correlation coefficient (CORR), and the Nash-Sutcliffe efficiency coefficient (NSE). The simulation performance evaluation revealed that all of the chosen global circulation models (GCMs) have good simulation capacity over the Ajora-Woybo watershed. The predicted mean annual RF shows a non-significant decline in the ensemble GCMs’ for the two time periods 2021–2050 and 2051–2080. In comparison to the RCP 8.5 emissions scenario, rainfall is expected to decline less under the RCP 4.5 climate change scenario. For ensemble GCMs, it is anticipated that mean annual Tmax and Tmin would both rise in comparison to the baseline at all stations. The Tmax and Tmin trends at the end of the 2040s and 2070s changed more in the highest emission scenario, RCP8.5, than in the RCP4.5 scenario. In order to reduce ongoing effects of climate change and create long-term water resource management plans for the Ajora-Woybo watershed, it will be helpful to consider projected changes in temperature and rainfall.
Return level calculations are widely used to determine the risks that extreme events may pose to infrastructure, including hydropower site operations. Extreme events (e.g., extreme precipitation and droughts) are expected to increase in frequency and intensity in the future, but not necessarily in a homogenous way across regions. This makes localized assessment important for understanding risk changes to specific sites. However, for sites with relatively small datasets, selecting an applicable method for return level calculations is not straightforward. This study focuses on the application of traditional univariate extreme value approaches (Generalized Extreme Value and Generalized Pareto) as well as two more recent approaches (extended Generalized Pareto and Metastatistical Extreme Value distributions), that are specifically suited for application to small datasets. These methods are used to calculate return levels of extreme precipitation at six Alpine stations and high reservoir inflow events for a hydropower reservoir. In addition, return levels of meteorological drought and low inflow periods (dry spells) are determined using a non-parametric approach. Return levels for return periods of 10- and 20- years were calculated using 10-, 20-, and 40- years of data for each method. The results show that even shorter timeseries can give similar return levels as longer timeseries for most methods. However, the GEV has greater sensitivity to sparse data and tended to give lower estimates for precipitation return levels. The MEV is only to be preferred over GPD if the underlying distribution fits the data well. The result is used to assemble a profile of 10- and 20-year return levels estimated with various statistical approaches, for extreme high precipitation/inflow and low precipitation/inflow events. The findings of the study may be helpful to researchers and practitioners alike in deciding which statistical approach to use to assess local extreme precipitation and inflow risks to individual reservoirs.