The impacts of climate change on hydrological processes of Gilgel Gibe catchment, southwest Ethiopia

(RCP 4.5) and an extreme scenario (RCP 8.5) to detect the possible impact of climate change. A summary of RCMs projections is presented in Table 1. 2.2.3 Climate change scenarios. The performance of the Regional Climate Models (RCMs) was evaluated using the root mean square error (RMSE) and the Nash-Sutcliffe effi- ciency (NSE), which measure the average error and the model’s ability to reproduce the observed variability and distribution of the values, respectively [35, 36]. The evaluation fol- lowed the guidelines of the Coordinated Regional Climate Downscaling Experiment (COR- DEX) and showed that the RCMs performed reasonably well, with the ensemble mean of the models performing better than the individual models. The RMSE and NSE for the ensemble mean were all within acceptable ranges, indicating that the models were able to capture the main features of the observed climate. These results are consistent with those of other studies that have used RCMs for climate change impact assessment. Following an evaluation of Regional Climate Models (RCMs) as part of the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa, six RCMs that demonstrated better performance were selected. This is consistent with a similar study by [35] in the central Rift valley basin where the ensemble mean of five RCMs showed better performance than individual RCMs. The use of multi-RCMs for climate modelling helps to reduce uncertainties compared to the use of a single RCM. However, despite the reasonable performance of the RCMs, including the ensem-

ble mean, all RCMs displayed noticeable biases. Therefore, it is necessary to correct these biases before conducting a climate change impact study. In this study, six Regional Climate Models (RCMs) were used, namely CCLM4-8, HIR- HAM5, RACMO22T, RCA4, CRCM5, and REMO2009. The future scenarios were evaluated for the intermediate (2041–2070) and far future (2071–2099) periods, while the period from 1991 to 2021 was used as the historical baseline to evaluate climate changes. Other weather var- iables, such as solar radiation, relative humidity, and wind speed, were considered in the future scenarios without modification, as changes in these variables may not significantly impact the modeling of climate change scenarios on local hydrology. 2.2.4 Bias correction. The climate model data for hydrological modelling (CMhyd) [37], obtained from https://swat.tamu.edu/software/, was used to process the precipitation and tem- perature bias correction. [38] have provided a full review of the bias correction techniques. According to [38] all the bias correction techniques have improved the simulation of precipita- tion and temperature. However, there are differences between the correction methods in the daily precipitation series, standard deviations, and percentiles. In our analysis, we evaluated multiple bias correction methods, including the distribution mapping method, delta change, scaling factor, quantile mapping and empirical quantile mapping. We compared the perfor- mance of these methods for both temperature and precipitation corrections based on mean

absolute error ranking. After carefully considering these various bias correction methods, we concluded that the distribution mapping method provided the most accurate results. Table 1. Description of the climate models (RCMS) from which data is obtained for this study. No RCMs Model Center 1 CCLM4-8 Climate Limited Area modelling Community (CLMcom), USA 2 HIRHAM5 Denmarks Meteorologiske institute (DMI), Denmark 3 RACMO22T Koninklijk Nederlands Meteorologisch Instituut (KNMI), Netherlands 4 RCA4 Sveriges Meteorologiska och Hydrologiska institute (SMHI), Sweden 5 CRCM5 Universite du Quebec a Montreal (UQAM), Canada 6 REMO2009 Max Planck Institute for Meteorology-Climate Service centre (MPI-CSC), Germany Ensemble Ensemble mean of all RCMs https://doi.org/10.1371/journal.pone.0287314.t001 PLOS ONE The impacts of climate change on hydrological processes of gilgel gibe catchment PLOS ONE | https://doi.org/10.1371/journal.pone.0287314 June 28, 2023 7 / 28