Evaluating watershed hydrological responses to climate changes at Hangar Watershed, Ethiopia

The aim of this study is to model the responses of Hangar Watershed hydrology to future climate changes under two representative concentration pathway (RCP) scenarios. Future changes in precipitation and temperature were produced using the output of dynamically downscaled data of a regional climate model (RCM) 0.44 (cid:1) resolution under RCP 4.5 and 8.5 scenarios for 2025 – 2055 and 2056 – 2086. The future projection of the RCM model of precipitation and temperatures showed an increasing trend relative to thebaseperiod(1987 – 2017).At2025 – 2055averageannualprecipitationincrementsof þ 15.7and þ 19.8% were expected for RCP 4.5 and RCP 8.5, respectively. For 2056 – 2086 of RCP 4.5 and 8.5, a similar trend was also shown as average annual precipitation may increase by þ 20.1 and þ 23.4%, respectively. The changes of climate parameters were used as input into the SWAT hydrological model to simulate the future runoff at Hangar Watershed. The increment in precipitation projection resulted in a positive magnitude impact on average runoff ﬂ ow. The average annual change in runoff at 2025 – 2055 of both RCP 4.5 and 8.5 may increase by þ 24.5 and þ 23.6%, respectively. In 2056 – 2086, a change in average annual runoff of þ 73.2 and þ 73.2% for RCP 4.5 and 8.5 may be expected, respectively.


INTRODUCTION
The issues related to climate change are of prime concern for every nation around the globe in general and Africa in There are two major reasons for global climate change: the Earth's magnetic field changes and greenhouse gases in the lower levels of Earth's atmosphere (Mikhaylov et al. ). According to Hussain et al. (), the primary cause of global climate change is greenhouse gases () reported that fossil CO 2 emissions are the largest source of global GHG emissions, with a share of about 72%, followed by CH 4 (19%), N 2 O (6%) and fluorinated gases, so called F-gases (3%). The foremost sources of rise in the GHG emissions are human activities through fossil fuel combustion, industrial production processes, agriculture and forestry, human society, and vehicle usage (Hussain et al. ). Global climate change caused by increasing concentrations of GHGs is likely to cause intensification of the global hydrologic cycle (Abeysingha et al. ). Any changes to the land and how it is used can effect exchanges of water, energy, and GHGs between the land and the atmosphere (IPCC ).
The other factor that affects climate is land use and land cover change (LULCC). According to Brovkin et al. (), LULCC affects climate in two different pathways. First, through biogeophysical pathways which affect climate through alteration of the physical characteristics of the land surface such as albedo, soil moisture, and roughness; second, through biogeochemical pathways which consider alterations of the atmospheric concentrations of greenhouse gases in response to changes land-atmosphere fluxes of these trace gases. Changes in land conditions, either from land-use or climate change, affect global and regional climate. At the regional scale, changing land conditions can reduce or accentuate warming and affect the intensity, frequency and duration of extreme events. The magnitude and direction of these changes vary with location and season (IPCC ).
Climate change can be a significant driver of desertification and land degradation and can affect food production, thereby influencing food security (Mbow et al. ) and it has a direct impact on the hydrological cycle which in turn starts a chain reaction impacting agriculture, energy and ecology, to name a few (Mehan et al. ). Climate change will exacerbate the impending water resources management challenge in some regions by reducing total physical water availability and altering the river flow regime (Ferguson et al. ). Evaluating the hydrological response to an increased climate change is critical for the proper management of water resources within agricultural systems. Consequently, such impacts of climate change have been widely studied, mainly using water balance models coupled with climate models (Musau et al. ).
Given the vital role of water resources in socio-economic development, the potential hydrological impacts of climate change pose a significant challenge for water resource planning and management (Musau et al. ).
Climate models facilitate the understanding of how a warming climate may affect the distribution of freshwater globally. Global climate models (GCMs) can give insight into the possible future climate of an area based on emission scenarios known as representative concentration pathways (RCPs). GCMs are based on mathematical representations of the physical laws governing the Earth's climate (Hidalgo & Alfaro ). However, GCMs cannot accurately reproduce certain features of regional and global climate due to their coarse spatial resolution (Hidalgo & Alfaro ; Shiru et al. ). Therefore, the downscaling technique, which involves the transfer of large-scale changes in atmospheric variables called predictors which are simulated reliably from GCMs to local weather series, have been popularly used in their applications. The downscaling technique is broadly divided into statistical and dynamical methods.
The dynamical downscaling method (DDM), which was used in this study, involves the use of limited area models (LAMs) or regional climate models (RCMs) in the production of outputs of higher resolutions, by using largescale and lateral boundary conditions from GCMs.
Hydrological models are mainly used for predicting system behavior and understanding various hydrological processes (Devia et al. ). SWAT is one of the most widely used models for simulating basin hydrology and assessing the effect of land use and climate change at basin scale (Abeysingha et al. 

Study area description
The study area is located at the upper reaches of the Didessa River basin (Figure 1). The river is fed by tributaries flowing from the north-western slopes of Jardaga Jarte district. It  It is the best among the different hydrological models due to its capability for application to large-scale watersheds (>100 km 2 ), interface with a Geographic where SW t is the final soil water content in mm H

SWAT input data used
The SWAT model requires spatial and temporal data. Spatial data include a digital elevation model (DEM), land-use/land cover and soil data. The temporary data include hydrological data (stream flow and sediment yield) and climatic data (precipitation, temperature, solar radiation, relative humidity, and wind speed) (Welde & Gebremariam ).
The details of the data needed to simulate the runoff in the study basin and their sources are presented in Table 1.

Climate scenario data
A climate scenario is a representation of future climate conditions (temperature, precipitation and other climatological phenomena). Regional climate models (RCMs) provide a new opportunity for climate change effects analysis since they have a higher spatial resolution and more reliable results on a regional scale compared to general circulation models (GCMs) (Turco et al. ). CORDEX-Africa, initiated by the World Climate Research Program (WCRP), provides an opportunity for the generation of high-resolution regional climate projections over Africa that is used to assess future impacts of climate change at regional and local scales.
In this research, climate change scenarios data from the newly available CMIP5 ( (2)). Temperature was corrected by the additive correction approach under linear scaling. The mean monthly difference of the model and observed data was calculated and added to the model data at each time step (Equation (3)). Hence, the projected values of climate variables after bias correction were used in the Soil and Water Assessment Tool (SWAT) hydrological model to estimate the runoff in the basin. The linear scaling approach can be defined as: where P cor is corrected precipitation, P unc is uncorrected precipitation, P obs and P rcm are the mean value of observed and simulated precipitation, respectively, and T corrected temperature ,  Table 2.
Hence, for this study, the performance of the SWAT model was checked using values of coefficients of determination (R2), Nash-Sutcliffe efficiency (NSE) and percent bias (PBIAS) based on their performance rating (Table 2).
These statistics were calculated using Equations (4)-(6): In the above equations, Qm is the measured discharge, Qs is the simulated discharge, Qm is the average measured discharge, and Qs is the average simulated discharge (Dibaba et al. ).

Sensitivity analysis
The SWAT model generated an output which was processed  (Table 3).

SWAT model calibration and validation
The model generated output using model input parameters which remained within a realistic uncertainty range (Arnold et al. ). Therefore, to obtain the physical knowledge of the watershed, calibration was carried out using

Base period climate projection
In addition to the observed metrological data, the down- During dry and short rainy seasons different parts of the country gain rainfall varying in volume that made changes greater. In contrast, the months from June to September are known as the rainy season in Ethiopia when the study area received an almost uniform amount of precipitation that

Minimum temperature
The trend of minimum temperature showed an increase in both scenario projections where the maximum percentage change is expected in the months of February and December. Relatively, the minimum percentage change of  for RCP 8.5 ( Figure 5).

Maximum temperature
The projected percentage change of average monthly maximum temperature also showed an increasing trend. In  (Figure 6).

Future impacts of climate change on runoff
A change in climate parameters, especially temperature and precipitation, have had significant impacts on the amount of runoff. Figure 7 shows the projected average monthly runoff increases varying from þ2.3 to þ75.6%, and from -0.4 to Average annual runoff may increase by þ24.5 and þ23.6%