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

The distribution mapping uses a transfer function to adjust the cumulative distribution of estimated data to the cumulative distribution of rain gauges [39, 40]. applied seven precipita- tion bias correction methods and five methods for temperature. The results showed that distri- bution mapping reproduces precipitation and temperature very well. A similar study by [41] compared five bias correction methods, using CMhyd, and found distribution mapping per- formed best for climate change impact study on streamflow dynamics of two rivers in North- ern lake Erie basin, Canada. The study by [42] used CMhyd for extraction of CORDEX-NetCDF, and bias correction of minimum and maximum temperature to predict climate change-induced temperature changes in Finchaa catchment. The study compared dis- tribution mapping with other methods such as the delta change, scaling factor and quantile mapping, and demonstrated that distribution mapping was more effective in improving the accuracy of predictions by adjusting the distribution of temperature and precipitation values. This was achieved through the utilization of historical data to calibrate the simulation or by employing statistical methods to modify the distribution of values. Owing to all the above find- ings, distribution mapping served as a basis for the precipitation and temperature corrections before using CORDEX-RCMs outputs for a climate impact study. 2.3 Hydrological modelling The hydrological modelling component of this study used Soil and Water Assessment Tool

(SWAT), Soil and Water Assessment Tool (SWAT) is a physically-based semi-distributed model that operates on a continues time scale [43]. SWAT model operates on a daily time step and predicts the impact of land management in large complex watersheds with varying soils, land use and management conditions. Major model components include DEM, weather, hydrology, soil properties and land management [44]. In SWAT, a watershed is divided into multiple sub-watersheds, which are then further subdivided into Hydrologic Response Units (HRUs) that comprise homogeneous land use, slope and soil characteristics. The SWAT inter- face used an ArcSWAT, a SWAT application within ArcGIS. The hydrologic cycle as simulated by SWAT is based on the water balance equation as developed by [44]. Swt ¼ Swo þ Xt i¼1ðRday Qsurf Ea Wseep QgwÞ ð1Þ Where Swt is the final soil water content (mm), Swo is the initial soil water content (mm), t is time (days), Rday is the amount of precipitation on day i (mm), Qsurf is the surface runoff on day i (mm), Ea is the amount of evapotranspiration on day i (mm), Wseep is the amount of per- colation and bypass flow exiting the soil profile bottom on day i (mm), and Qgw is the amount of return flow on day i (mm). The ArcSWAT evolved from ArcSWAT2012 is an ArcGIS extension developed for an ear- lier version of SWAT. It provides a graphical user interface that allows for GIS data to be easily formatted for use in SWAT model simulations. In the SWAT model, the simulation of the

hydrological process begins with watershed delineation and generating streamflow networks. Using a 30 m resolution Digital Elevation Model (DEM), the Gilgel Gibe catchment was divided into 23 sub-watersheds. To further characterize the catchment, a hydrologic response unit (HRU) was established, taking into account different attributes. Specifically, classes repre- senting 10% land use, 20% soil, and 10% slope were assigned. As a result, a total of 428 hydro- logic response units (HRUs) were established based on these attribute percentages. The weather data inputs from nine stations (Sekoru, Jimma, Chora, Dedo, Shebe, Omo Nada, Kersa, Seka, and Tiro Afeta) was utilized. Among these stations, Sekoru and Jimma were cho- sen as weather generators using an ArcSWAT weather generator (WGEN) due to their com- prehensive coverage of all climate variables required for the SWAT model setup. 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 8 / 28