**2.3 Future climate change data**

The statistically downscaled Regional Climate Model (RCM) Bias-corrected Coordinated Regional Climate Downscaling Experiment (CORDEX) precipitation, min/mean/max temperature for Ethiopia, under RCP 4.5 and RCP 8.5, downloaded from(https://dataservices.gfzpotsdam.de/pik/showshort.php?id=escidoc:3124935) is provided as input data for hydrological modeling of this study. This dataset contributes bias-corrected daily precipitation, min/mean/max air temperature of ten CORDEX RCM runs covering the country of Ethiopia for historical (1970–1999) and over the 21st century for RCP 4.5 and RCP 8.5 [24]. For this study, daily rainfall and maximum & minimum temperature data of historical (1990–2013) of eight climatic stations were obtained from the Meteorological Agency of Ethiopia, and other eight climatic stations were from the global database of Climate Forecast System Reanalysis (CFSR) after filling missing data, consistency, and outlier checked [1].

Accordingly, all the bias correction has improved the simulation of precipitation and temperature before using CORDEX-RCM outputs for any climate impact modeling. The study used climate model data for hydrological modeling CMhyd to extract CORDEX-NetCDF and bias correction of precipitation, minimum and maximum temperature to predict climate change-induced temperature changes in the Genale catchment.

**Figure 2.** *Steps of the implementation of the SWAT model for the study area. Analysis of SWAT input data.*

#### *Evaluation of Climate Change-Induced Impact on Streamflow and Sediment Yield of Genale… DOI: http://dx.doi.org/10.5772/intechopen.98515*

SWAT model was used to simulate water yield using the RCM under the future emission scenarios of two representative concentration pathways (RCPs) (medium emission scenario (RCP4.5) and high emission scenario (RCP-8.5)). Climate data for different periods are input into the SWAT model with the other components unchanged. The period of 1990–2013 is set as the baseline period.

Digital elevation model was downloaded from USGS Earth Explorer (http:// earthexplorer.usgs. gov/) SRTM (Shuttle Radar Topography Mission) 90 m\*90 m and used for watershed delineation, sub-basin, slope calculation/ classification, and extract stream networks. The spatial land use/cover collected from the Ethiopian Ministry of Water, Irrigation, and Electricity (MoWIE) GIS department of the year 2013 used for SWAT input, and the dominant land use/cover is range brushland (RNGB) accounts for about 71% of the area. This study's soil map/type is from the Food and Agricultural Organization (FAO) Digital Soil Map of the World (http:// www.fao.org/geonetwork/srv/en/metadata) the scale of 1/5000000 for 2007. The soil data that integrated into the SWAT model are: the available water content, the texture, the hydraulic conductivity, the apparent density of the different soil layers, and the dominant soil type in the study watershed was Rc19-bc-204 (Calcaric Regosols), and it accounts about 40% of the catchment.

The climate data required for this paper has been taken from the National Meteorological Agency of Ethiopia, http://www.ethiomet.gov.et/etms. These data subsist of precipitation, max and min temperatures, wind energy, solar radiation, and relative humidity daily and covered the period from 1990 to 2013 for sixteen stations. The discharge data from the Ethiopian Ministry of Water, Irrigation, and Electricity (MoWIE) Hydrology department Genale @ Halwen gauging station a bit upstream of the outlet, and then transferred to the outlet, and arranged for SWAT language for the period from 1990 to 2013.

**Figure 3**, shows different 16 (sixteen) meteorological stations were distributed in the watershed, hydrological gauging station, watershed outlet, stream reach, and basin mark of the study area. The stations which were designated as; GMS1- Gridded Meteorological station-1, GMS2- Gridded Meteorological station-2, GMS3- Gridded Meteorological station-3, 4,5,6,7, & 8 respectively (**Figure 3**).

**Figure 4** shows the distribution of rainfall in the study area for the selected different gauge stations.

From **Figure 5b**, the details of maximum, average, and minimum yearly temperature of the study area were pinpointed as 24.6, 19, & 12.93 °C, respectively, for 1990–2013 (**Figure 6**).

#### **2.4 SWAT-CUP(SUFI-2) description**

The automatic calibration and validation adjustment in the SWAT model achieved using the SWAT-CUP (SUFI-II) public user software developed by [25]. The SWAT-CUP has interfaced with five algorithms: (1) sequential uncertainty fitting (SUFI-2, (2) generalized likelihood uncertainty estimation (GLUE), (3) parameter solution (ParaSol), (4) Markov chain Monte Carlo (MCMC), and (5) particle swarm optimization (PSO) [26].

In this study, the analysis of uncertainty, calibration, validation was conducted using the SUFI-2 optimization algorithm; this algorithm needs less simulation number, faster, and one of the most used in the automatic calibration of model for several basins the semi-arid region like Genale Basin.

Assessment of performance criteria for the model is; Nash-Sutcliffe Efficiency (NSE), PBIAS, and Coefficient of Determination (R<sup>2</sup> ) has been used as the efficiency criteria to evaluate the performance of models in the Genale watershed.

#### **Figure 3.**

*Meteorological and hydrological gauging station distribution sub-basin wise for Genale River.*

**Figure 4.**

*Mean monthly rainfall for selected stations in the study area over 1990–2013.*

The first three objective functions are mainly used for daily and monthly streamflow /sediment calibration–validation uncertainty analysis.

Coefficient of Determination (R2 )

$$R^2 = \frac{\left[\sum\_{i=1}^{n} (Q\_{si} - Q\_{sm})(Q\_{oi} - Q\_{om})\right]^2}{\sum\_{i=1}^{n} (Q\_{si} - Q\_{sm})^2 \sum\_{i=1}^{n} (Q\_{oi} - Q\_{om})^2} \tag{6}$$

*Evaluation of Climate Change-Induced Impact on Streamflow and Sediment Yield of Genale… DOI: http://dx.doi.org/10.5772/intechopen.98515*

#### **Figure 5.**

*Daily and yearly average maximum, minimum, and average daily temperatures in the study area, respectively.*

**Figure 6.** *DEM, LULC, soil type, and slope classes of the Genale watershed, respectively.*

Where, *Qsi* is the simulated value, *Qoi* is the measured value, *Qom*, is the average observed value and Qsm is an average simulated value? Nash Sutcliffe Efficiency (NSE).

$$\text{NSE} = \mathbf{1} - \frac{\sum\_{i=1}^{n} \left(Q\_{oi} - Q\_{si}\right)^2}{\sum\_{i=1}^{n} \left(Q\_{oi} - Q\_{om}\right)^2} \tag{7}$$

where, *Qoi* is the observed, *Qsi* is the simulated, and Qom is the observed discharge.

$$\text{Percent Bias (PBIAS)}; \text{PBIAS} = 100\text{\%} \times \frac{\sum\_{i=1}^{n} (Q\_{oi} - Q\_{si})}{\sum\_{i=1}^{n} Q\_{oi}} \tag{8}$$
