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

The calibration results on mean monthly flow show that SWAT model is able to capture the observed streamflow with R2, NSE, PBIAS and RSR of 0.76, 0.75, 1.02, and 0.49 respectively. The average monthly flow validation indicates R2, NSE, PBIAS and RSR of 0.82, 0.77, 9.4 and 0.47 respectively. This indicates that the performance of the SWAT model in the validation is good enough to simulate the stream flow in the Gilgel Gibe catchment. Furthermore, p-factor and R-factor statistics showed good agreement with 0.87 and 0.79 during calibration and 0.82, and 0.76 during validation, respectively. Overall, the statistics for goodness fit shows good agreement between the observed and simulated average monthly flow. 3.2 Climate change projections of the RCMs The projection of each RCM individually as well as the ensemble mean of the RCMs were examined to show how well each model predicted temperature and precipitation. The distribu- tion mapping of the bias-corrected precipitation and temperature for the future scenarios is compared to the baseline data sets (1991–2021). Further, the hydrological responses of the catchment are analyzed by quantifying and comparing the future water balance components (surface runoff, groundwater flow, total water yield and evapotranspiration). 3.2.1. Rainfall. The individual and ensemble mean of the six RCMs were used to assess climate change in two future scenarios; intermediate term (2041–2070) and far future term (2071–2099) with respect to the baseline period (1991–2021) under RCP4.5 and RCP8.5. The

results of the projected precipitation by the individual RCMs show a different degree of precip- itation changes. Except for HIRHAM5 in far future term, all the RCMs show precipitation decline under both scenarios. According to CCLM4-8, precipitation decreases by -15.87% under RCP4.5 in intermediate term and -30.67% under RCP8.5 scenarios in a far future term. Table 2. Sensitivity analysis and calibrated parameters. No Parameter Code Parameter Description Parameter Value Range Sensitivity t-stat p-value Sensitivity Rank 1 CN2 Curve number II -0.23–0.16 -17.54 0.00 1 2 SOL_AWC Available soil water capacity (mm H2O mm-1 soil) 0–60.26 -4.04 0.00 2 3 RCHRG_DP Deep aquifer percolation fraction -0.04–0.12 -1.90 0.04 3 4 CH_K2 Effective hydraulic conductivity in main channel alluvium (mm h-1) 25.18–95.07 -2.02 0.05 4 5 ALPHA_BF Base flow recession constant 0.39–1.08 1.61 0.07 5 6 ESCO Soil evaporation compensation factor 0.84–1.02 -1.08 0.09 6 7 CANMX Maximum canopy storage (mm) -0.04–0.33 -3.12 0.11 7 8 SOL_BD Moist bulk density (Mg/m3) 0.02–1.06 1.38 0.13 8 9 GW_REVAP Ground water re-evaporation coefficient 0.08–0.69 2.3 0.19 9 https://doi.org/10.1371/journal.pone.0287314.t002 Table 3. Changes in precipitation (%) across the four RCMs in RCP4.5 and RCP8.5 scenarios. No RCMs RCP4.5 RCP8.5 2041–2070 2071–2099 2041–2070 2071–2099 1 CCLM4-8 -15.87 -18.68 -18.84 -30.67 2 HIRHAM5 -6.61 -4.09 2.37 7.62 3 RACMO22T -12.51 -20.2 -9.59 -7.62 4 RCA4 -10.77 -15.16 -13.48 -14.42

5 CRCM5 -11.66 -14.32 -8.69 -10.22 6 REMO2009 -9.96 -13.15 -6.90 -4.81 Ensemble -11.23 -14.27 -9.19 -10.02 https://doi.org/10.1371/journal.pone.0287314.t003 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 15 / 28