Abstract:
e focus of this study was to evaluate the performance of the regional climate models with regard to simulating stream ow,
sediment yield, precipitation, and temperatures. It is recognized that RCMs are not free of bias and uncertainty when simulating
climate variables. e evaluation was about simulating annual climatology, annual cycles, and annual variability of climate
variables by statistical tools and stream ow and sediment yield by SWAT model output. e study used observed and CORDEX
Africa-44 meteorological data for RACMO22T, RCA4, CCLM4-8-17, and HIRHAM5 models using grid points. is analysis of
the mean annual rainfall cycle in the summer season shows that all RCMs were underestimated. However, RACMO22Tand RCA4
are better suited for simulating climate variables. e higher errors were associated with the simulations of maximum and
minimum temperatures in the highest terrain area of the catchment. e statistical analysis with climatology indicates that all
RCM was performed in much the same way, except for the seasonal perspective. In this case, RACMO22Twas best able to simulate
stream ow and sediment yield with PBIAS of 0.14, NSE of 0.91, R2 of 0.82, R2 of 0.72, NSE of 0.78, and PBIAS of −2.61%,
respectively. RCA4 simulated stream ow better, but it underestimated the simulated sediment yield. e result proved that
RACMO22T and RCA4 performed better in the upper oodplain area. e performance of the climate model varied with
catchments, locations, and terrains. e output of this statistical and SWAT model shows that climate models do not accurately
simulate hydro-climatological variables. Finally, this study showed that climate models were better at simulating the rainy season
than the dry season. is integration of statistical tools and the SWAT model to analyze the RCM’s performance is a unique
method to improve the quality of the output for its implementation in maintaining water balance and sediment load reduction
