This study aims to assess the impact of climate change on the water balance component of the Katar and Meki watersheds of the Central Rift Valley Lakes Basin, Ethiopia. The semi-distributed soil and water assessment tool hydrological model and multiple regional climate model outputs were used to assess climate change impacts on water balance components and stream flow. Future climate scenarios were developed under a representative concentration pathway (RCP 4.5 and 8.5) for the 2040s (2021–2050) and 2070s (2051–2080). The study found that future annual and seasonal rainfall will show increasing and decreasing trends but that they are statistically insignificant. Furthermore, future temperatures show a significant increase in the subbasins. For the applied scenarios, an increasing and decreasing trend of future rainfall and increased temperatures would decrease the water yield by 4.9–15.3% at the Katar subbasin and 6.7–7.4% at the Meki subbasin. Furthermore, annual water yields will increase in the range of 0.38–57.1% and 6.57–49.9% for the Katar and Meki subbasins, respectively. The findings of this study will help basin planners, policymakers, and water resource managers develop appropriate adaptation strategies to mitigate the negative effects of climate change in the rift-bound lake system.

  • The baseline and future rainfall show increasing and decreasing trends but are statistically not significant.

  • The selected bias correction methods were significantly improved and add value to the RCMs outputs.

  • Changes in the climate variables reduce the annual water yield and stream flow under all climate scenarios except RCP 4.5 by 2070.

Graphical Abstract

Graphical Abstract
Graphical Abstract

There is no doubt that climate change is the most important environmental challenge facing the world in the 21st century, owing to its wider-reaching impacts on human society. There are two causes of global climate change. Natural processes, such as changes in the sun's energy, variations in Earth's orbit, and volcanic eruptions and human activities, change the climate of the earth (US EPA 2016). Since the industrial revolution, humans added large amounts of greenhouse gases (GHGs) into the atmosphere by burning fossil fuels, heating, and cooling buildings and vehicles as well as land use land cover change, and environmental degradation (Oroud 2008; US EPA 2016; Chakilu et al. 2022). Human activities significantly increase the release of large amounts of carbon dioxide (CO2) and other GHGs into the atmosphere. For example, the global CO2 concentration in the atmosphere increased from 288 parts per million in 1750 to 415 parts per million in 2021 (Jin et al. 2021) and causing for warming of the Earth's atmosphere (Chakilu et al. 2022). According to IPCC (2007), the global surface temperature increased by 0.74 °C over the last 100 years. The rise of global and regional temperatures changes the hydrological cycle which is likely to enhance the frequency and severity of extrema climate events of floods and droughts (Machiwal & Jha 2012; Ye et al. 2013; Dowlatabadi & Ali Zomorodian 2015). As temperatures increase, more water evaporated into the atmosphere and intensifies the hydrologic cycle which results in severe showers in the coming years (Bao et al. 2012; Ye et al. 2013; Zhao et al. 2014; Xin et al. 2019). Many studies have shown the impacts of climate change and variability (Chien et al. 2013; Kundu et al. 2017) and the rate of potential evapotranspiration on the hydrology cycle (Wang et al. 2017). As a result of the cycling effects of climate change, the spatial and temporal distribution of runoff is altered, finally which is resulting in the most significant consequences of alteration of access to water, increase dry spells, and food shortage in many parts of the world (Yuan et al. 2016). This is more common in regions where the population is heavily dependent on natural resources and has a limited capacity to adapt to climate change impacts (Wagesho et al. 2012).

Understanding the current and future water resources under various management scenarios could provide a solid foundation for the sustainability of water resources. One of the most fundamental aspects of climate change research is the ability of global climate model output to simulate local climate conditions and downscaling techniques. A coordinated regional climate downscaling experiment (CORDEX) has been planned to simulate the climate of Africa based on a variety of climate regimes (Geleta et al. 2022). Regional climate models (RCMs) at their finer resolution allow the simulation of local climate conditions in detail and provide future predictions. As the study indicated, East African countries are vulnerable to future climate change and Ethiopia is often cited as one of the most extreme examples (Bekele et al. 2021). The availability of fresh water is expected to be among the most significant consequences of the projected climate change scenarios (Du et al. 2021; Galata et al. 2021). Over the last few decades, hydrological and general circulation models (GCMs) have been coupled to extensively study the climate change impact on water resources under different scenarios (Galata et al. 2021). The potential impact of climate change on hydrologic processes is widely investigated using the soil and water assessment tool (SWAT) developed by the US Department of Agricultural Service (Arnold et al. 2012a, 2012b), and the dynamical downscale RCMs data (Kuma et al. 2021; Emiru et al. 2022). Many studies have been conducted in Ethiopian basins on climate change's impact on water resources, and much significant work has been done on stream flow responses to climate change (Nigatu et al. 2016; Abraham et al. 2018; Worqlul et al. 2018; Bekele et al. 2021; Chakilu et al. 2022; Takele et al. 2022).

All these mentioned studies have used one or more GCM-RCM and emission scenarios, various downscaling methods (statistical or dynamical), and either bias-corrected or raw (uncorrected) data without evaluation of the ability of the climate model to simulate local climate conditions. These methodological differences can have a significant impact on the outcome of an impact study. For example, Chakilu et al. (2022) investigated the impact of climate change on the streamflow the Upper Nile of the basin of Gumera watershed and they conclude that there will be an increase of streamflow in future scenarios, while Mengistu et al. (2021) concluded that there would be a decreasing stream flow up to 22.7% in the same basin. This suggests that there is no common agreement on the effects of climate change on water resources result may result due to uncertainty of the GCMs and RCMs inherited from model structure, parametrization of the climate system, initial and boundary conditions, spatial resolution, emission scenarios (Taylor et al. 2012; Kim et al. 2014), and availability of multiple climate model outputs. For example, Balcha et al. (2022a) have evaluated the ability of five (5) RCMs to downscale 23 GCMs outputs over the study regions and concluded that each GCM is simulating differently the climate condition of the study regions and there was uncertainty between the downscaling institutions while downscaled the same GCM outputs. The details are found in Balcha et al. (2022a).

Additionally, the utilization of water resources has proceeded without knowing the hydrologic components and climate variability, which is a critical problem in water resource management. The growing demand for irrigation water, food security, policy enforcement for import substitution of wheat, expansion of horticulture crops, the establishment of a new agro-processing industrial zone (Bulbula and Hawassa), and population pressure will create concerns about the sustainability of water, as predicted by unsettling future climate conditions (Goshime et al. 2021). The supply of a more accurate explanation regarding the timing of the development of stream flow, lake level, and precipitation in connection with climate change could help to convey critical information to water resources planners, decision-makers, and the local community. The main objective of this study was to assess the impact of climate change on the water resources of the Central Rift Valley (CRV) using the SWAT model and best RCMs. This study creates climate change scenarios for 2021–2050 (the 2040s) and 2051–2070 (the 2070s) and examines climate change trends of rainfall and temperature changes. The methodology presented in this document could be a useful approach for studying climate change's impact on water resources. Section 2 describes the research topic, data sets, and methods, while Section 3 presents the findings and related discussions. Section 4 concludes with conclusions.

Description of the study area

The study was conducted in the Katar and Meki subbasins, located in the central part of Ethiopia's Rift Valley Lakes Basin. Geographically, the Katar subbasin is located between 38.88°–39.41°E longitude and 7.360–8.18°N latitude, with an altitudinal range of 1,630–4,188 m above mean sea level (a.m.s.l.). Similarly, the Meki subbasin is located between 38.22°–39.00°E longitude and 7.830–8.46°N latitude, with an altitudinal range of 1,686–3,614 m a.m.s.l. Ethiopia's climate condition is classified into three seasons: summer season (June to September), spring season (October to January), and winter season (February to May) (Seleshi & Zanke 2004; Ademe et al. 2020). The climate conditions of the study areas are influenced by altitude, wind direction, and complex topographic characteristics. Mean annual rainfall ranges between 749 and 1,276 mm in the Katar subbasin and 712–1,150 mm in the Meki subbasins. The average monthly maximum temperatures vary in the range of 27.5–28.7 °C and the minimum temperatures are 13.4–14.2 °C in the Katar subbasin. In the Meki subbasin, the maximum temperature varies between 27.5 and 30 °C and the minimum temperature varies between 13.4 and 14.2 °C (Figure 1).
Figure 1

Map of the study area.

Figure 1

Map of the study area.

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Observed data

The Ethiopian National Meteorological Agency (NMA) provided daily climate data of precipitation and maximum and minimum temperatures for 16 stations from 1997 to 2017. The data were prescreened for unrepresentative or wrongly entered values and checked for negative rainfall with a minimum temperature greater than the maximum temperature. For stations that do not have temperatures (maximum or minimum or both), values from the nearest stations were used. Additionally, Climate Hazards Group Infrared Precipitation with Station (CHIRPS) was downloaded from http://climateserv.servirglobal.net/ website from 1983 to 2017. The spatial correlation between observed and CHIRPS datasets was greater than 0.8, which falls in the range of very good. Hence, the CHIRPS datasets were used for SWAT model setup, calibration, validation, and bias correction (BC) of climate model output.

RCMs data

The output of the Coordinated Regional Downscaling Experiment (CORDEX) of the African domain was used to simulate the study regions’ past and future climate conditions. Three CMIP5 GCMs’ outputs were selected after extensive evaluation for their performance to simulate the climate condition of the study regions. To address the uncertainty in future concentrations of GHGs and emissions of aerosols, we used two representative concentration pathways (RCPs), which compress diverse possible future greenhouse gas and aerosol emissions scenarios (RCP 4.5 and RCP 8.5). The RCP 4.5 is similar to the lowest scenarios (B1) of ERES of IPCC AR4 and it represents a ‘medium’ emissions scenario where total greenhouse gas emissions are reduced by 2050s, while the RCP 8.5 is similar to with high emission scenarios of A1F1 of SRES of IPCC AR4 and characterized by increasing gas emissions greenhouse with more of a ‘business as usual’ scenario until the end of 2100 (San José et al. 2016). Additionally, according to Ethiopia's medium-term and long-term for sustainable development (FDRE 2011) and the word developing level, we select mid and high typical standard RCP scenarios, which were RCP 4.5 and RCP 8.5. The historical and future runs of middle and high (RCP 4.5 and RCP 8.5) datasets of rainfall and temperatures were downloaded from https://esgf-node.llnl.gov/search/esgf-llnl/ and used for climate change impact studies on water balance components. The datasets are available in a curvilinear or rotated projection with a horizontal resolution of 0.44°. The datasets were re-projected bilinearly into latitude and longitude and units of rainfall were converted from kg/m2 into mm/day and the temperature from Kelvin to degrees Celsius using the Climate Data Operator (CDO) on the Ubuntu operating system. Then, the datasets were extracted for each station considered in this study using the Python programming language version 3.9. Furthermore, the datasets were grouped as baseline run from 1983 to 2005; future runs from 2021 to 2050 and 2051 to 2080, referred to as baseline, and future scenarios of the 2040s and 2070s, respectively.

Hydrological modeling

Following the development of the computing power of the computer, various hydrological models have been developed to investigate the changes in climate, land use land cover, and soil properties on the hydrological cycles (Islam 2011; Devi et al. 2015; Ghonchepour et al. 2021). Hydrological models are classified based on the physical processes involved in modeling (Refsgaard & Abbott 1996). Although event-based models were developed in the 1930s, the first hydrological models for continuous simulation of rainfall-runoff processes appeared in the 1960s, when computing power allowed for a simplified ‘conceptual’ representation of the relevant land-phase processes. Later, in the 1970 and 1980s, ‘physically based’ hydrological models could be developed (Ghonchepour et al. 2021). According to Islam (2011), physically based hydrological models are based on known scientific principles of energy and water fluxes.

Nowadays, various hydrological models are ranging from lumped models such as the unit hydrograph concept to semi or fully distributed physical models, which include: Hydrologiska Byråns Vattenbalansavdelning (HBV; Bergström & Lindström 2015), Systeme Hydrologique Européen (MIKE SHE; Abbott et al. 1986), hydrologic engineering center-hydrologic modeling system (HEC-HMS; HEC-HMS 2022), and soil and water assessment tool (SWAT; Neitsch et al. 2011) are some of the widely used hydrological models. Hydrologic modelers are challenged to determine which model best is suited to a specific catchment for a given modeling exercise. According to Marshall et al. (2005), there is no single model that can be identified as ideal over the range of possible hydrological situations. There is a range of criteria to select the best hydrological model (Marshall et al. 2005; Ghonchepour et al. 2021). However, user preferences and their ability to use a particular model, the aim of the modeling, and the available time to develop and execute a model are a few of them. Additionally, the quantity and quality of hydrometeorological data as well as data on physical basin properties are associated with the selection of a hydrological model (Hughes et al. 2010). Based on this, the SWAT model was selected for this particular study of the climate change impact on the water balance of the Katar and Meki subbasins.

SWAT model

The SWAT is a physically semi-distributed model that is computationally highly effective and capable of modeling continuous simulation over a long period. The SWAT model was developed to predict the impact of land management practices on water, sediment, and agricultural chemicals in large and intricate watersheds with varying soils, land use, and management (Kim et al. 2008; Gassman et al. 2010; Winchell et al. 2010; Arnold et al. 2012a, 2012b). The SWAT model divides a given watershed into multiple sub-watersheds and further subdivides into the smallest unit called Hydrologic Response Units (HRUs) constituting homogeneous land use or management, soil, and slope distributions. Empirical and physical equations are embedded into the SWAT model to calculate surface runoff, evapotranspiration, infiltration, percolation, and flow of shallow and deep aquifers from each HRU and finally route the surface runoff through channels, ponds, and/or reservoirs and flow into the watershed outlet (Arnold & Fohrer 2005; Zhang et al. 2015). The SWAT model has been used for over three decades to simulate various hydrologic and environmental simulations. Nowadays, it is extensively recognized as one of the top hydrological models applied in research focused on hydrologic assessment, soil erosion and sediment transport, water quality analyses, climate, and land use changes, and watershed management impact studies (Arnold et al. 2012a, 2012b; Akoko et al. 2021). The water balance equation of the SWAT model includes precipitation, surface runoff, actual evapotranspiration (AET), lateral flow, percolation, base flow, and deep groundwater components (Neitsch et al. 2011). The model uses the modified Soil Conservation Service Curve Number (SCS-CN) method (USDA-NRCS 1986) to determine the surface runoff based on the combination of soil hydrologic group, land use, and slope and the antecedent moisture content for each HRU. In this study, the SCS-CN method for surface runoff simulations, the Hargreaves method for estimation of potential evapotranspiration, and the variable storage routing method for monthly stream flow routing were used.

Model setup

The SWAT model was built based on the geospatial data of the Digital Elevation Model (DEM), soil and land use maps, physical and chemical properties of the soil, and hydrological and meteorological data. Using DEM, the watershed and sub-watershed were delineated after various steps passed through, including DEM setup, stream definition, watershed outlet selection, definition, and calculation of subbasin parameters in the SWAT model. Furthermore, the resulting sub-watersheds were divided into HRUs based on a combination of land use, soil, and slope based on the threshold values of 20% for land use, 10% for soil, and 10% for slope, which was recommended for the Ethiopian case study by Setegn (2008). Due to high topographic variability in the study regions, five slope classes of 0–5%, 5–10%, 10–15%, 15–20%, and >20% were defined.

Model sensitivity analysis, calibration, and validation

In hydrological scientific research, the uncertainty is summarized into input data uncertainty, model uncertainty, and parameter uncertainty (Abbaspour et al. 2015). Any source of uncertainty is a bottleneck to the accuracy of the hydrological model. Several studies have shown that the ability of hydrological models to produce satisfactory predictions is based on an adequate sensitivity analysis followed by model calibration and uncertainty analysis (Lenhart et al. 2002; van Griensven & Meixner 2003; White & Chaubey 2005; van Griensven et al. 2006; Saltelli et al. 2008). The Sequential Uncertainty Fitting Version 2 (SUFI-2) algorithm can map all sources of uncertainties within the 95%PPU and is used for SWAT model calibration, validation, sensitivity, and uncertainty analysis (Abbaspour et al. 2007, 2015). In this study, the global sensitivity analysis method was used to identify the most sensitive parameters, and the significance of the sensitivity was judged by the t-value and p-value (Khalid et al. 2016). Calibration is the process of estimating model parameters by comparing the model simulation results with observed or measured data for the same conditions (Moriasi et al. 2007).

After the entire watersheds were simulated for the period 1997–2014, the first simulation was compared with the observed stream flow and the parameter range was adjusted manually. Then, automatic model calibration techniques were used until the model's performance met the rating criteria specified by Moriasi et al. (2007) for Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), and percent of bias (PBIAS). Additionally, the P-factor and R-factor were used to measure the strength of SWAT to simulate monthly stream flow in both subbasins. The P-factor can measure how much the data is bracketed within the 95PPU and the values are varied from 0 to 1, where 1 is 100% bracketed by the measured variables within the model prediction uncertainty. The R-factor is another metric that is calculated as the ratio of the average width of the 95PPU to the standard deviation of the measured variables (Abbaspour et al. 2015). In addition to model performance metrics, these two uncertainty measures were used until the values were within the acceptable range recommended by Abbaspour et al. (2015) for flow simulation. After achieving the objective function through calibration, validation of the model followed to check the calibrated model for estimating the effectiveness of future potential management practices by testing the model against an independent set of measured data from the 2008–2014 periods.

Performance of multiple outputs of RCMs

The historical datasets of five RCMs available in the Coordinated Regional Downscaling Experiment (CORDEX)-Africa database are evaluated against ground-based observed rainfall in the Katar and Meki subbasins. The goal of the evaluation is to see how successfully RCMs recreate climate conditions in the research region, such as monthly, seasonal, and yearly rainfall cycles, and to quantify the uncertainty in downscaling the same global climate model outputs between RCMs. The ability of the RCM outputs was assessed using the normalized root mean square (NRMSE), bias, and correlation coefficient (CC). In addition to evaluating the performance, a multi-criteria decision approach of compromise programming was applied to weight the assessment metrics and rank the GCMs for each station. The details are found in Balcha et al. (2022a).

Bias correction of future climate

Dynamic downscaling using RCMs can eliminate some of these biases due to their smaller resolutions, which allows topography to be more accurately represented, and these models are currently considered convection-permitting at the highest resolutions. Considerable biases can persist in many circumstances, either from the driving GCM or from the RCM itself (Sørland et al. 2018). According to Mehrotra et al. (2018), simulations of climate models require BC before use in impact assessments or for statistical or dynamic downscaling to finer scales. Several BC methods are available in the literature, which outperform differently under different conditions. The performance of BC methods has been evaluated by Teutschbein & Seibert (2013), Mehrotra et al. (2018), and Enayati et al. (2021). In this study, distribution mapping and power transformation for rainfall and distribution mapping and linear scaling for temperature BC were compared for their performances, with the best being selected to correct future climate conditions.

Trend analysis of future climate

The Mann-Kendall Trend (MK) test is a non-parametric test that is used to determine the presence or absence of monotonic trends in the time series data of a candidate station (Kocsis et al. 2020). The null hypothesis (Ho) of MK is that there is no trend, and the alternative hypothesis (Ha) is that the time series of a candidate station follows a monotonic trend over time. Equation (1) is used to calculate the Mann-Kendall test statistic.
formula
(1)
where Xi and Xj are the sequential data in the series and n is the size of the data series.
Where j > I and i = 1, 2, 3…, n − 1, k = 2, 3, 4…, n, and n is the number of data sign (XjXi) is calculated by (Equation (2))
formula
(2)
Equation (3) was used to calculate the variance of S
formula
(3)
where q is the number of tied groups in the datasets, tp is the number of data in the pth tied group, and n is the total number of data in the time series.
A positive value of S indicates that an increasing and negative value of S is a decreasing trend of time series data of the candidate station. Equation (4) is used to calculate the standardized Mann-Kendall test statistics.
formula
(4)
To estimate the magnitude or rate of change, the Thiel–Son slope method was used. Equation (5) is used to calculate the Theil–Sen slope (β).
formula
(5)

Performance of the SWAT model

The ability of the SWAT model to simulate monthly discharge was tested against measured discharges at Abura (Katar subbasin) and Meki Village (Meki subbasin) stations from 1997 to 2014. To optimize the SWAT model, a sensitivity analysis was performed using the global sensitivity analysis in the SWAT-CUP of the SUFI-2 algorithm (Abbaspour 2012). The most sensitive parameters with a larger absolute t-stat value and a smaller p-value (0.05) were selected for calibration of the streamflow. After identifying these parameters, streamflow calibration and validation were performed for each subbasin separately. The model performance was evaluated using Nash–Sutcliff efficiency (NSE), coefficient of determination (R2), and percent of bias (PBIAS) during the calibration and validation periods. The simulated streamflow results show good agreement with the observed streamflow, as per the statistical indices suggested by Moriasi et al. (2007) for hydrological model performances. Specifically, for the calibration period, the values of NSE, R2, and PBIAS were 0.68, 0.73, and −17.5% at the Katar subbasin and 0.83, 0.85, and −1.6% at the Meki subbasin, respectively. Furthermore, for the validation period, the values of NSE, R2, and PBIAS were 0.67, 0.72, and −22.7% at the Katar subbasin and 0.75, 0.75, and −1.9% at the Meki subbasin, respectively. The details of the model setup, calibration, and validation processes are found in Balcha et al. (2022b).

Performance of RCMs

The performance of five RCMs, which were downscaled from 22 GCM outputs, was evaluated by Balcha et al. (2022a) for stations in the Katar and Meki subbasins. Three GCMs, two of them downscaled by RCA4 and one by CRCM5, the top-ranked, were used for climate change impact assessment on the surface water resources of the study region (Katar and Meki subbasins). The details of the evaluations were presented by the authors, and the top five of the GCMs–RCMs are summarized in Tables 1 and 2 for the Katar and Meki subbasins, respectively. Three GCMs/RCMs such as MPI-ESM-LR/CRCM5, EC-EARTH/RCA4, and MIROC5/RCA4 were common for the two watersheds selected for climate change impact assessment on surface water (Tables 1 and 2).

Table 1

Top five GCMs/RCMs identified for the Katar subbasin

RCMsGCMsArataAsellaIteyaKatarKulumsaOgolchoSagureWeighted rank
Individual rank at each station
CRCM5 MPI-ESM-LR 11 10 46 
RCA4 EC-EARTH 10 26 
MIROC5 11 38 
HadGEM2-ES 12 11 42 
REMO2009 MIROC5 39 
RCMsGCMsArataAsellaIteyaKatarKulumsaOgolchoSagureWeighted rank
Individual rank at each station
CRCM5 MPI-ESM-LR 11 10 46 
RCA4 EC-EARTH 10 26 
MIROC5 11 38 
HadGEM2-ES 12 11 42 
REMO2009 MIROC5 39 
Table 2

Top five GCMs/RCMs identified for the Meki subbasin

RCMsGCMsAdami-TBuiButajeraEjerse-LKosheMekiZiwayWeighted rank
Individual rank at each station
CRCM5 MPI-ESM-LR 12 14 10 50 
RCA4 CSIRO-Mk3 − 6 − 0 41 
EC-EARTH 29 
MIROC5 13 54 
HadGEM2-ES 15 42 
RCMsGCMsAdami-TBuiButajeraEjerse-LKosheMekiZiwayWeighted rank
Individual rank at each station
CRCM5 MPI-ESM-LR 12 14 10 50 
RCA4 CSIRO-Mk3 − 6 − 0 41 
EC-EARTH 29 
MIROC5 13 54 
HadGEM2-ES 15 42 

Performances of the BC methods

A 23-year (1983–2005) rainfall and a 9-year (1997–2005) temperature (maximum and minimum) data series were used to correct the historical and future rainfall and temperature output of the selected climate models. The spatial cycle of rainfall over the study region shows that considerable biases exist between the observed and climate model simulated (Figure 2). The rainfall characteristics of the study region are bimodal and are influenced by topography and wind direction; the ability of the GCM-RCM to reproduce the observed rainfall largely depends on it. The climate models over or under-simulated annual and seasonal rainfall over the study regions during the baseline period. Therefore, to address the limitations of GCM-RCMs, BC is crucial to increase the applicability of the climate model outputs, which are used as input for the assessment of the climate change impact. Figure 3 shows, the average annual rainfall after BC. As the result has shown that the employed BC methods were significantly improved and the rainfall data simulated by each climate model is close to the annual observed rainfall. Both methods were performed very well, but the power transformer is more powerful than the distribution mapping method to correct the rainfall in the study regions (Figure 4). Several studies have been done on the performance of BC methods. Each BC method significantly reduced the bias between the observed and GCM-RCM rainfall (Rica 2020; Tumsa 2022). As shown in Figures 5 and 6, all climate models underestimated the monthly average maximum and minimum temperatures over most stations for the baseline period. After the BC was applied, the difference between observed and simulated temperatures was significantly reduced, and the simulated maximum and minimum temperatures were exactly close to the observed temperatures over all stations. The linear scaling and distribution mapping methods were performed equally, and there was no significant difference between the results. For this study, the linear scaling method was used to correct the temperature under future climate scenarios. A similar study has been conducted on how BC can be used to improve the climate model simulation results in the Awash and Upper Blue Nile basins of Ethiopia (Sitotaw et al. 2022; Tumsa 2022), respectively.
Figure 2

Long-term average annual rainfall of all climate models simulation before bias correction.

Figure 2

Long-term average annual rainfall of all climate models simulation before bias correction.

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Figure 3

Long-term average annual rainfall of all climate models simulation after bias correction.

Figure 3

Long-term average annual rainfall of all climate models simulation after bias correction.

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Figure 4

Monthly summary of the performance of bias correction methods (1983–2005).

Figure 4

Monthly summary of the performance of bias correction methods (1983–2005).

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Figure 5

Average monthly maximum temperature at °C before and after bias correction for baseline.

Figure 5

Average monthly maximum temperature at °C before and after bias correction for baseline.

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Figure 6

Average monthly minimum temperature in °C before and after bias correction for baseline.

Figure 6

Average monthly minimum temperature in °C before and after bias correction for baseline.

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Spatial and temporal variability of future rainfall and temperatures

This section presented the spatial and temporal variability of rainfall and temperature by comparing future simulation scenarios of RCP 4.5 and RCP 8.5 to the baseline or historical. As Figure 7 shows, all climate models simulated the annual rainfall of the study region with different magnitudes of change, and there is no single climate model that consistently outperforms. For the RCP 4.5 emission scenario, EARTH and MPI-ESM-LR climate models predicted that future rainfall amounts would increase by 40% at a few stations by 2040 (Figure 7). However, the MIROC5 model is projected to cause the annual rainfall to decrease by 20% over most stations by 2040 and 2070 under RCP 4.5. Likewise, EARTH and MIROC5 climate models project that annual rainfall will decrease over most stations in the range of 0.37–29% by 2040 and 2070 under RCP 8.5 (Figure 7).
Figure 7

Annual rainfall change in percentage for RCP 4.5 and 8.5 for the times of the 2040 and 2070s.

Figure 7

Annual rainfall change in percentage for RCP 4.5 and 8.5 for the times of the 2040 and 2070s.

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Seasonal rainfall is used to identify the temporal variability of rainfall in the study regions. Summer (Kiremt) is the main rainy season, which starts in June and ends in September. EARTH and MPI-ESM-LR are projected as the rainfall increases in the range of 0.87–47% for RCP 4.5 (Figure 8). However, MIROC5 projects that the summer season rainfall will decrease in the range of 2–29% over the stations (Figure 8). During the winter season, MIROC5 under RCP 4.5 and EC-EARTH and MIROC5 under RCP 8.5 are projected to decrease in the range of 7–70% overall stations by 2040 (Figure 9). Furthermore, MPI-ESM-LR simulated that the spring season rainfall will increase in the range of 7.16–60.71%, but EC-EARTH and MIROC5 will show a decrease in rainfall in the range of 3–24% for RCP 4.5 by 2040. By the 2070s, all climate models simulate the spring season rainfall to be decreased in the range of 2–40% over most stations’ scenarios under the RCP 4.5 emission scenario (Figure 10). Similarly, for the RCP 8.5 emission scenario, all climate models project that the spring season rainfall will decrease in the range of 0.24–48.8% by the 2040 and 2070s. This result agrees with other studies conducted in Ethiopia as a whole (NMA 2007; Mcsweeney et al. 2010; Adem & Bewket 2011).
Figure 8

Summer rainfall changes in percentage for RCP 4.5 and 8.5 for the times of the 2040 and 2070s.

Figure 8

Summer rainfall changes in percentage for RCP 4.5 and 8.5 for the times of the 2040 and 2070s.

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Figure 9

Winter rainfall changes in percentage for RCP 4.5 and 8.5 for the times of the 2040 and 2070s.

Figure 9

Winter rainfall changes in percentage for RCP 4.5 and 8.5 for the times of the 2040 and 2070s.

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Figure 10

Spring season rainfall change in percentage for RCP 4.5 and 8.5 for the times of the 2040 and 2070s.

Figure 10

Spring season rainfall change in percentage for RCP 4.5 and 8.5 for the times of the 2040 and 2070s.

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Tables 3 and 4 show the average change in monthly maximum and minimum temperatures in the study regions for the RCP 4.5 emission scenario. Monthly maximum temperatures in the Katar subbasin will rise by 0.32–1.55 °C by the 2040s and by 0.97–2.35 °C by the 2070s. Similarly, for the Meki watershed, the maximum temperature will increase in the range of 0.28–1.57 °C by 2040 and in the range of 0.66–1.94 °C by 2070. Over the Katar subbasin, average monthly minimum temperatures will rise by 1.05–1.74 °C by 2040 and by 1.28–2.59 °C by 2070. In the Meki subbasin, the future minimum temperature will increase in the range of 1.01–2.00 °C by 2040 and 1.31–2.06 °C by 2070, respectively. For the RCP 8.5 emission scenario, monthly average maximum and minimum temperatures will increase over Katar and Meki watersheds for the periods of 2040 and 2070 (Tables 5 and 6). Averagely, monthly maximum temperatures will increase in the range of 1.29–3.8 °C by 2040 and in the range of 3.46–5.6 °C over the Katar subbasin. The monthly maximum temperature in the Meki subbasin will rise to 2.72–3.56 °C by 2040 and 3.97–5.55 °C by 2070 (Table 5). Similarly, monthly average minimum temperatures will increase in the range of 1.31–2.02 °C by 2040 and in the range of 2.69–3.5 °C by 2070 over the Katar subbasin. As well, for the Meki subbasin, the monthly average temperature will increase in the range of 0.85–2.20 by 2040 and in the range of 2.32–3.85 by 2070 (Table 6). The study result has been confirmed by similar studies by NMA (2007) and Adem & Bewket (2011) specifically as indicated by Mcsweeney et al. (2010), the average temperature will be expected to increase to 5.1 °C by 2090. Notwithstanding the increasing trend of both climatic variables, the increase in rainfall seems obscured by increases in temperature.

Table 3

Average monthly maximum temperature and change from the baseline in °C during 2040 and 2070 for RCP 4.5 of Katar and Meki subbasins

MonthsJanFebMarAprMayJunJulAugSepOctNovDec
Katar subbasin The 2040s (2021 − 2050) for RCP 4.5 
 Baseline 22.24 23.53 23.67 22.70 23.00 21.96 19.99 19.70 20.59 21.75 21.88 21.54 
 Ensemble 23.28 24.50 25.20 24.26 23.31 21.83 20.39 20.19 21.15 22.36 22.50 22.34 
 Change (Ensemble – Baseline) 1.04 0.97 1.54 1.55 0.32 − 0.13 0.40 0.49 0.56 0.61 0.61 0.8The0 
The 2070s (2051–2080) for RCP 4.5 
 Ensemble 23.94 25.41 25.89 25.05 24.37 22.93 21.33 20.78 21.61 22.73 23.00 22.87 
 Change (Ensemble – Baseline) 1.69 1.88 2.22 2.35 1.37 0.97 1.34 1.09 1.03 0.98 1.12 1.33 
Meki subbasin The 2040s (2021–2050) for RCP 4.5 
 Baseline 24.83 26.21 26.36 26.10 26.45 25.19 23.01 22.66 23.92 24.65 24.62 24.38 
 Ensemble 25.71 27.07 27.78 27.67 27.02 25.47 23.41 23.22 24.48 25.28 25.44 25.38 
 Change (Ensemble – Baseline) 0.88 0.86 1.42 1.57 0.57 0.28 0.40 0.56 0.56 0.63 0.83 1.00 
The 2070s (2051–2080) for RCP 4.5 
Ensemble 25.86 27.52 27.96 28.04 27.24 26.03 24.08 23.47 24.58 25.40 25.68 25.53 
Change (Ensemble – Baseline) 1.02 1.31 1.60 1.94 0.79 0.83 1.08 0.81 0.66 0.75 1.06 1.14 
MonthsJanFebMarAprMayJunJulAugSepOctNovDec
Katar subbasin The 2040s (2021 − 2050) for RCP 4.5 
 Baseline 22.24 23.53 23.67 22.70 23.00 21.96 19.99 19.70 20.59 21.75 21.88 21.54 
 Ensemble 23.28 24.50 25.20 24.26 23.31 21.83 20.39 20.19 21.15 22.36 22.50 22.34 
 Change (Ensemble – Baseline) 1.04 0.97 1.54 1.55 0.32 − 0.13 0.40 0.49 0.56 0.61 0.61 0.8The0 
The 2070s (2051–2080) for RCP 4.5 
 Ensemble 23.94 25.41 25.89 25.05 24.37 22.93 21.33 20.78 21.61 22.73 23.00 22.87 
 Change (Ensemble – Baseline) 1.69 1.88 2.22 2.35 1.37 0.97 1.34 1.09 1.03 0.98 1.12 1.33 
Meki subbasin The 2040s (2021–2050) for RCP 4.5 
 Baseline 24.83 26.21 26.36 26.10 26.45 25.19 23.01 22.66 23.92 24.65 24.62 24.38 
 Ensemble 25.71 27.07 27.78 27.67 27.02 25.47 23.41 23.22 24.48 25.28 25.44 25.38 
 Change (Ensemble – Baseline) 0.88 0.86 1.42 1.57 0.57 0.28 0.40 0.56 0.56 0.63 0.83 1.00 
The 2070s (2051–2080) for RCP 4.5 
Ensemble 25.86 27.52 27.96 28.04 27.24 26.03 24.08 23.47 24.58 25.40 25.68 25.53 
Change (Ensemble – Baseline) 1.02 1.31 1.60 1.94 0.79 0.83 1.08 0.81 0.66 0.75 1.06 1.14 
Table 4

Average monthly minimum temperature and change from the baseline in °C for RCP 4.5 of Katar and Meki subbasins

MonthsJanFebMarAprMayJunJulAugSepOctNovDec
Katar Watershed The 2040s (2021–2050) for RCP 4.5 
 Baseline 7.40 7.79 9.08 10.20 10.10 9.93 10.33 10.15 9.69 8.64 6.82 6.05 
 Ensemble 8.47 9.12 10.83 11.71 11.26 11.67 11.87 11.77 11.19 9.71 7.98 7.09 
 Change (Ensemble – Baseline) 1.07 1.33 1.74 1.51 1.16 1.73 1.54 1.62 1.51 1.07 1.16 1.05 
The 2070s (2051–2080) for RCP 4.5 
 Ensemble 9.12 9.66 11.67 12.34 12.03 12.29 12.50 12.34 11.90 10.60 8.31 7.32 
 Change (Ensemble – Baseline) 1.72 1.86 2.59 2.14 1.94 2.36 2.17 2.18 2.22 1.95 1.49 1.28 
Meki Watershed The 2040s (2021–2050) for RCP 4.5 
 Baseline ave. 10.19 10.88 12.25 12.67 12.30 12.65 12.35 12.14 11.80 10.60 9.88 9.49 
 Ensemble 11.92 12.38 14.25 14.19 13.67 13.75 13.36 13.28 12.97 12.27 11.64 11.18 
 Change (Ensemble – Baseline) 1.73 1.50 2.00 1.52 1.37 1.11 1.01 1.14 1.16 1.68 1.76 1.70 
The 2070s (2051–2080) for RCP 4.5 
 Ensemble 12.16 12.36 14.31 14.34 13.99 13.99 13.67 13.45 13.36 12.60 11.65 11.29 
 Change (Ensemble – Baseline) 1.97 1.48 2.06 1.67 1.69 1.35 1.32 1.31 1.56 2.00 1.77 1.80 
MonthsJanFebMarAprMayJunJulAugSepOctNovDec
Katar Watershed The 2040s (2021–2050) for RCP 4.5 
 Baseline 7.40 7.79 9.08 10.20 10.10 9.93 10.33 10.15 9.69 8.64 6.82 6.05 
 Ensemble 8.47 9.12 10.83 11.71 11.26 11.67 11.87 11.77 11.19 9.71 7.98 7.09 
 Change (Ensemble – Baseline) 1.07 1.33 1.74 1.51 1.16 1.73 1.54 1.62 1.51 1.07 1.16 1.05 
The 2070s (2051–2080) for RCP 4.5 
 Ensemble 9.12 9.66 11.67 12.34 12.03 12.29 12.50 12.34 11.90 10.60 8.31 7.32 
 Change (Ensemble – Baseline) 1.72 1.86 2.59 2.14 1.94 2.36 2.17 2.18 2.22 1.95 1.49 1.28 
Meki Watershed The 2040s (2021–2050) for RCP 4.5 
 Baseline ave. 10.19 10.88 12.25 12.67 12.30 12.65 12.35 12.14 11.80 10.60 9.88 9.49 
 Ensemble 11.92 12.38 14.25 14.19 13.67 13.75 13.36 13.28 12.97 12.27 11.64 11.18 
 Change (Ensemble – Baseline) 1.73 1.50 2.00 1.52 1.37 1.11 1.01 1.14 1.16 1.68 1.76 1.70 
The 2070s (2051–2080) for RCP 4.5 
 Ensemble 12.16 12.36 14.31 14.34 13.99 13.99 13.67 13.45 13.36 12.60 11.65 11.29 
 Change (Ensemble – Baseline) 1.97 1.48 2.06 1.67 1.69 1.35 1.32 1.31 1.56 2.00 1.77 1.80 
Table 5

Average monthly maximum temperature and change from the baseline in °C for RCP 8.5 of Katar and Meki subbasins

JanFebMarAprMayJunJulAugSepOctNovDec
Katar watershed The 2040s (2021–2050) of RCP 8.5 
 Baseline 22.24 23.53 23.67 22.70 23.00 21.96 19.99 19.70 20.59 21.75 21.88 21.54 
 Change (Ensemble – Baseline) 3.10 3.14 3.19 3.80 2.12 1.29 1.85 2.00 2.51 2.64 3.20 3.24 
The 2070s (2051–2080) of RCP 8.5 
 Ensemble 26.71 27.83 28.55 28.22 27.07 25.75 24.08 23.61 24.05 25.30 26.15 26.59 
 Change (Ensemble – Baseline) 4.47 4.30 4.88 5.52 4.07 3.79 4.09 3.91 3.46 3.55 4.27 5.05 
Meki watershed The 2040s (2021–2050) of RCP 8.5 
 Baseline 24.83 26.21 26.36 26.10 26.45 25.19 23.01 22.66 23.92 24.65 24.62 24.38 
 Ensemble 27.60 29.14 29.39 30.24 29.17 27.80 25.93 25.66 27.48 27.68 28.00 27.80 
 Change (Ensemble – Baseline) 2.77 2.93 3.03 4.14 2.72 2.61 2.92 3.00 3.56 3.03 3.38 3.42 
The 2070s (2051–2080) of RCP 8.5 
 Ensemble 29.17 30.18 31.07 31.65 31.18 30.22 28.21 27.59 28.31 28.82 29.15 29.47 
 Change (Ensemble – Baseline) 4.34 3.97 4.71 5.55 4.73 5.03 5.20 4.93 4.39 4.17 4.53 5.09 
JanFebMarAprMayJunJulAugSepOctNovDec
Katar watershed The 2040s (2021–2050) of RCP 8.5 
 Baseline 22.24 23.53 23.67 22.70 23.00 21.96 19.99 19.70 20.59 21.75 21.88 21.54 
 Change (Ensemble – Baseline) 3.10 3.14 3.19 3.80 2.12 1.29 1.85 2.00 2.51 2.64 3.20 3.24 
The 2070s (2051–2080) of RCP 8.5 
 Ensemble 26.71 27.83 28.55 28.22 27.07 25.75 24.08 23.61 24.05 25.30 26.15 26.59 
 Change (Ensemble – Baseline) 4.47 4.30 4.88 5.52 4.07 3.79 4.09 3.91 3.46 3.55 4.27 5.05 
Meki watershed The 2040s (2021–2050) of RCP 8.5 
 Baseline 24.83 26.21 26.36 26.10 26.45 25.19 23.01 22.66 23.92 24.65 24.62 24.38 
 Ensemble 27.60 29.14 29.39 30.24 29.17 27.80 25.93 25.66 27.48 27.68 28.00 27.80 
 Change (Ensemble – Baseline) 2.77 2.93 3.03 4.14 2.72 2.61 2.92 3.00 3.56 3.03 3.38 3.42 
The 2070s (2051–2080) of RCP 8.5 
 Ensemble 29.17 30.18 31.07 31.65 31.18 30.22 28.21 27.59 28.31 28.82 29.15 29.47 
 Change (Ensemble – Baseline) 4.34 3.97 4.71 5.55 4.73 5.03 5.20 4.93 4.39 4.17 4.53 5.09 
Table 6

Average monthly Minimum temperature and change from the baseline in °C for RCP 8.5 of Katar and Meki subbasins

JanFebMarAprMayJunJulAugSepOctNovDec
Katar watershed The 2040s (2021–2050) of RCP 8.5 
 Baseline 7.4 7.79 9.08 10.2 10.1 9.93 10.3 10.2 9.69 8.64 6.82 6.05 
 Ensemble 8.78 9.40 11.10 11.78 11.96 11.21 11.57 11.46 10.84 9.90 8.24 7.41 
 Change (Ensemble – Baseline) 1.38 1.61 2.02 1.58 1.86 1.28 1.24 1.31 1.15 1.26 1.42 1.3The 6 
The 2070s (2051–2080) of RCP 8.5 
 Change (Ensemble – Baseline) 2.94 2.84 3.37 3.11 3.53 2.94 2.68 2.71 2.68 3.40 3.12 2.69 
Meki watershed The 2040s (2021–2050) of RCP 8.5 
 Baseline 10.19 10.88 12.25 12.67 12.30 12.65 12.35 12.14 11.80 10.60 9.88 9.49 
 Ensemble 11.60 12.03 14.45 14.54 14.39 13.50 13.27 13.21 12.96 12.39 10.99 10.59 
 Change (Ensemble – Baseline) 1.41 1.15 2.20 1.87 2.09 0.85 0.92 1.07 1.16 1.79 1.11 1.10 
The 2070s (2051–2080) of RCP 8.5 
 Ensemble 13.22 13.39 15.88 16.08 16.15 15.11 14.67 14.63 14.47 14.36 12.76 12.17 
 Change (Ensemble – Baseline) 3.03 2.51 3.63 3.41 3.85 2.46 2.32 2.49 2.67 3.76 2.88 2.68 
JanFebMarAprMayJunJulAugSepOctNovDec
Katar watershed The 2040s (2021–2050) of RCP 8.5 
 Baseline 7.4 7.79 9.08 10.2 10.1 9.93 10.3 10.2 9.69 8.64 6.82 6.05 
 Ensemble 8.78 9.40 11.10 11.78 11.96 11.21 11.57 11.46 10.84 9.90 8.24 7.41 
 Change (Ensemble – Baseline) 1.38 1.61 2.02 1.58 1.86 1.28 1.24 1.31 1.15 1.26 1.42 1.3The 6 
The 2070s (2051–2080) of RCP 8.5 
 Change (Ensemble – Baseline) 2.94 2.84 3.37 3.11 3.53 2.94 2.68 2.71 2.68 3.40 3.12 2.69 
Meki watershed The 2040s (2021–2050) of RCP 8.5 
 Baseline 10.19 10.88 12.25 12.67 12.30 12.65 12.35 12.14 11.80 10.60 9.88 9.49 
 Ensemble 11.60 12.03 14.45 14.54 14.39 13.50 13.27 13.21 12.96 12.39 10.99 10.59 
 Change (Ensemble – Baseline) 1.41 1.15 2.20 1.87 2.09 0.85 0.92 1.07 1.16 1.79 1.11 1.10 
The 2070s (2051–2080) of RCP 8.5 
 Ensemble 13.22 13.39 15.88 16.08 16.15 15.11 14.67 14.63 14.47 14.36 12.76 12.17 
 Change (Ensemble – Baseline) 3.03 2.51 3.63 3.41 3.85 2.46 2.32 2.49 2.67 3.76 2.88 2.68 

Trends of future rainfall and temperature

A non-parametric Mann-Kendall test and Sen's slope estimator are used to locate the trend and quantify the magnitude of change for observed rainfall and temperatures. From 16 stations of rainfall data, stations at Dagaga, Iteya, Bui, and Butajera exhibited decreasing trends, while stations found at Kulumsa and Ejerese-Lele presented an increasing trend in monthly rainfall. However, the rest stations have shown no significant increasing or decreasing trends in the monthly observed rainfall. Similarly, as with observed rainfall, the trend of climate model outputs was examined for the baseline and future climate conditions under RCP 4.5 and RCP 8.5. The trends of future rainfall and temperatures were tested using the MK test (Zs) and the magnitude of the trend was quantified using the Sone's slope estimator (β) for the time series of the 2040s (2021–2050) and 2070s (2051–2080). Table 7 shows the values of Zs statistics and the magnitude of change β for baseline and future climate scenarios. Based on the Zs statistics, the baseline and future rainfall show increasing and decreasing trends in the annual and seasonal rainfall. However, the trend was statistically insignificant at 95% of the confidence interval except for summer rainfall under RCP 4.5 by 2070 for the EARTH climate model. Similar to rainfall, the Zs statistics of all climate models show an increasing trend in the projected temperature under RCP 4.5 and RCP 8.5 by 2040 and 2070. Annual maximum temperatures in the Katar and Meki watersheds increased by 0.02–0.07 °C and 0.02–0.0.09 °C, respectively, by 2040 and 2070. In the summer season, maximum temperatures will increase in the range of 0.04–0.13, in winter in the range of 0.03–0.06, and in spring in the range of 0.02–0.09 °C (Table 8). The annual minimum temperature will significantly increase in the range of 0.02–0.09 °C for the climate scenarios of RCP 4.5 and RCP 8.5 by 2040 and 2070 over both watersheds. As well, the seasonal minimum temperature significantly increased during the summer season in the range of 0.02–0.06 °C, the winter season in the range of 0.01–0.06 °C, and the spring season in the range of 0.02–0.07 °C by 2040 and 2070 (Table 9).

Table 7

Annual and seasonal rainfall trend analysis for the Katar and Meki watersheds of the baseline and future climate scenario under RCP 4.5 and RCP 8.5 and the level of significance is α = 0.05

SubbasinGCMs/RCMsScenariosAnnual
Summer
Winter
Spring
ZsβZsβZsβZsβ
Katar watershed EARTH/RCA4 Baseline 0.60 6.21 0.61 6.22 0.21 0.57 0.60 0.60 
RCP 4.5 (2040) 0.93 4.32 0.75 3.01 −0.39 −0.91 0.39 1.37 
RCP 4.5 (2070) 0.93 4.52 0.79 2.56 0.21 0.52 0.14 0.12 
RCP 8.5 (2040) −1.03 −5.95 −0.29 −0.98 −0.75 −1.38 −1.50 −3.58 
RCP 8.5 (2070) 0.64 3.85 0.04 0.25 2.25 4.70 −0.39 −0.40 
MIROC5/RCA4 Baseline 0.85 4.85 0.53 1.64 −0.32 −1.43 1.07 3.04 
RCP 4.5 (2040) 0.75 2.65 0.14 0.31 −0.29 −0.26 0.54 0.64 
RCP 4.5 (2070) −0.11 −0.27 −0.39 −1.64 −0.89 −1.03 −0.07 −0.18 
RCP 8.5 (2040) 0.04 0.29 0.75 2.23 −0.82 −1.38 −0.57 −0.88 
RCP 8.5 (2070) 0.57 2.05 1.28 2.86 0.93 1.36 −1.53 −1.80 
MPI-ESM-LR/CRCM5 Baseline −1.21 −7.79 0.00 −0.00 −1.00 −5.00 −0.90 −3.30 
RCP 4.5 (2040) 0.07 0.54 1.21 3.23 −0.82 −1.46 −0.11 −0.39 
RCP 4.5 (2070) 0.61 6.22 0.54 1.36 0.21 0.57 0.61 0.69 
Meki watershed EARTH/RCA4 Baseline 1.27 9.75 0.05 0.71 −0.21 −0.51 1.43 5.47 
RCP 4.5 (2040) 0.18 0.59 0.43 1.42 −0.11 −0.50 −0.36 −0.55 
RCP 4.5 (2070) 1.64 8.39 2.32* 6.29 −0.46 −0.54 0.00 0.03 
RCP 8.5 (2040) −1.32 −6.13 0.00 −0.02 −1.14 −1.79 −1.75 −4.47 
RCP 8.5 (2070) 0.86 3.69 0.25 0.97 1.85 3.10 −0.53 −0.99 
MIROC5/RCA4 Baseline 0.74 3.65 0.26 1.65 0.11 0.11 0.89 2.37 
RCP 4.5 (2040) −0.82 −2.52 −0.49 −1.17 −0.25 −0.38 −0.35 −0.89 
RCP 4.5 (2070) 0.75 3.49 0.49 1.90 −0.82 −1.21  1.18 1.82 
RCP 8.5 (2040) 0.64 2.95 1.78 4.59 −1.18 −2.35 −0.07 −0.11 
RCP 8.5 (2070) 0.43 2.33 1.39 4.43 0.99 1.84 −2.60 −3.53 
MPI-ESM-LR/CRCM5 Baseline −1.16 −7.81 0.00 0.05 −0.84 −3.07 −0.89 −2.75 
RCP 4.5 (2040) −0.04 −0.20 0.61 2.06 −1.11 −2.40 −0.85 −1.83 
RCP 4.5 (2070) 0.00 −0.02 −1.21 −2.28 −0.28 −1.60  0.07 0.34 
SubbasinGCMs/RCMsScenariosAnnual
Summer
Winter
Spring
ZsβZsβZsβZsβ
Katar watershed EARTH/RCA4 Baseline 0.60 6.21 0.61 6.22 0.21 0.57 0.60 0.60 
RCP 4.5 (2040) 0.93 4.32 0.75 3.01 −0.39 −0.91 0.39 1.37 
RCP 4.5 (2070) 0.93 4.52 0.79 2.56 0.21 0.52 0.14 0.12 
RCP 8.5 (2040) −1.03 −5.95 −0.29 −0.98 −0.75 −1.38 −1.50 −3.58 
RCP 8.5 (2070) 0.64 3.85 0.04 0.25 2.25 4.70 −0.39 −0.40 
MIROC5/RCA4 Baseline 0.85 4.85 0.53 1.64 −0.32 −1.43 1.07 3.04 
RCP 4.5 (2040) 0.75 2.65 0.14 0.31 −0.29 −0.26 0.54 0.64 
RCP 4.5 (2070) −0.11 −0.27 −0.39 −1.64 −0.89 −1.03 −0.07 −0.18 
RCP 8.5 (2040) 0.04 0.29 0.75 2.23 −0.82 −1.38 −0.57 −0.88 
RCP 8.5 (2070) 0.57 2.05 1.28 2.86 0.93 1.36 −1.53 −1.80 
MPI-ESM-LR/CRCM5 Baseline −1.21 −7.79 0.00 −0.00 −1.00 −5.00 −0.90 −3.30 
RCP 4.5 (2040) 0.07 0.54 1.21 3.23 −0.82 −1.46 −0.11 −0.39 
RCP 4.5 (2070) 0.61 6.22 0.54 1.36 0.21 0.57 0.61 0.69 
Meki watershed EARTH/RCA4 Baseline 1.27 9.75 0.05 0.71 −0.21 −0.51 1.43 5.47 
RCP 4.5 (2040) 0.18 0.59 0.43 1.42 −0.11 −0.50 −0.36 −0.55 
RCP 4.5 (2070) 1.64 8.39 2.32* 6.29 −0.46 −0.54 0.00 0.03 
RCP 8.5 (2040) −1.32 −6.13 0.00 −0.02 −1.14 −1.79 −1.75 −4.47 
RCP 8.5 (2070) 0.86 3.69 0.25 0.97 1.85 3.10 −0.53 −0.99 
MIROC5/RCA4 Baseline 0.74 3.65 0.26 1.65 0.11 0.11 0.89 2.37 
RCP 4.5 (2040) −0.82 −2.52 −0.49 −1.17 −0.25 −0.38 −0.35 −0.89 
RCP 4.5 (2070) 0.75 3.49 0.49 1.90 −0.82 −1.21  1.18 1.82 
RCP 8.5 (2040) 0.64 2.95 1.78 4.59 −1.18 −2.35 −0.07 −0.11 
RCP 8.5 (2070) 0.43 2.33 1.39 4.43 0.99 1.84 −2.60 −3.53 
MPI-ESM-LR/CRCM5 Baseline −1.16 −7.81 0.00 0.05 −0.84 −3.07 −0.89 −2.75 
RCP 4.5 (2040) −0.04 −0.20 0.61 2.06 −1.11 −2.40 −0.85 −1.83 
RCP 4.5 (2070) 0.00 −0.02 −1.21 −2.28 −0.28 −1.60  0.07 0.34 

Zs is Mann-Kendall test statistics and β is Son's slope estimator (magnitude of change).

∗Significance at a 95% confidence level with the corresponding critical value of 1.96 and bold shows the magnitude of change.

Table 8

Annual and seasonal maximum temperature trend analysis for the Katar and Meki watersheds of the baseline and future climate scenario under RCP 4.5 and RCP 8.5 and the level of significance is α = 0.05

SubbasinGCMs/RCMsScenariosAnnual
Summer
Winter
Spring
ZsβZsβZsβZsβ
Katar watershed EARTH/RCA4 Baseline −0.31 −0.11 1.77 0.33 1.15 0.13 −0.31 −0.11 
RCP 4.5 (2040) 1.53 0.03 0.39 0.01 2.64* 0.03 1.53 0.03 
RCP 4.5 (2070) 0.03 0.00 0.61 0.02 0.85 0.01 0.03 0.00 
RCP 8.5 (2040) 3.46* 0.07 2.10* 0.05 3.60* 0.06 3.46* 0.07 
RCP 8.5 (2070) 3.28* 0.05 2.53* 0.09 2.32* 0.04 3.29* 0.05 
MIROC5/RCA4 Baseline 0.31 0.07 0.73 0.08 0.73 0.05 0.31 0.07 
RCP 4.5 (2040) 1.07 0.02 1.61 0.05 3.17 0.04 1.03 0.02 
RCP 4.5 (2070) 1.28 0.03 0.25 0.01 0.60 0.00 1.14 0.03 
RCP 8.5 (2040) 1.39 0.02 2.18* 0.05 2.89* 0.05 1.39 0.02 
RCP 8.5 (2070) 3.00* 0.07 4.17* 0.13 0.28 0.00 2.92* 0.08 
MPI-ESM-LR/CRCM5 Baseline 0.00 0.00 0.52 0.31 0.00 0.00 −0.31 −0.03 
RCP 4.5 (2040) 1.78 0.03 1.61 0.04 2.03* 0.03 1.71 0.03 
RCP 4.5 (2070) 1.18 0.02 1.46 0.04 0.85 0.02 1.17 0.02 
Meki watershed EARTH/RCA4 Baseline 0.94 0.10 0.93 0.21 1.35 0.14 0.73 0.09 
RCP 4.5 (2040) 2.14* 0.03 0.82 0.02 3.28* 0.04 2.14* 0.03 
RCP 4.5 (2070) 0.43 0.01 1.07 0.03 0.96 0.02 0.43 0.01 
RCP 8.5 (2040) 2.71* 0.09 2.21* 0.05 4.06* 0.07 3.03* 0.09 
RCP 8.5 (2070) 1.71 0.03 2.85* 0.08 2.39* 0.04 1.78 0.04 
MIROC5/RCA4 Baseline 0.93 0.12 0.31 0.08 1.35 0.09 0.93 0.11 
RCP 4.5 (2040) 1.50 0.04 1.25 0.04 4.14* 0.04 1.50 0.04 
RCP 4.5 (2070) 1.32 0.03 0.14 0.01 1.28 0.03 0.03 0.00 
RCP 8.5 (2040) 2.71* 0.06 1.64 0.03 2.35* 0.05 2.71* 0.06 
RCP 8.5 (2070) 2.92* 0.06 3.71* 0.10 1.57 0.03 2.64* 0.05 
MPI-ESM-LR/CRCM5 Baseline 0.31 0.04 1.56 0.39 −0.52 −0.05 0.31 0.04 
RCP 4.5 (2040) 2.71* 0.06 1.28 0.04 1.71 0.03 2.64 0.05 
RCP 4.5 (2070) 2.21* 0.02 1.57 0.05 0.99 0.02 2.21* 0.02 
SubbasinGCMs/RCMsScenariosAnnual
Summer
Winter
Spring
ZsβZsβZsβZsβ
Katar watershed EARTH/RCA4 Baseline −0.31 −0.11 1.77 0.33 1.15 0.13 −0.31 −0.11 
RCP 4.5 (2040) 1.53 0.03 0.39 0.01 2.64* 0.03 1.53 0.03 
RCP 4.5 (2070) 0.03 0.00 0.61 0.02 0.85 0.01 0.03 0.00 
RCP 8.5 (2040) 3.46* 0.07 2.10* 0.05 3.60* 0.06 3.46* 0.07 
RCP 8.5 (2070) 3.28* 0.05 2.53* 0.09 2.32* 0.04 3.29* 0.05 
MIROC5/RCA4 Baseline 0.31 0.07 0.73 0.08 0.73 0.05 0.31 0.07 
RCP 4.5 (2040) 1.07 0.02 1.61 0.05 3.17 0.04 1.03 0.02 
RCP 4.5 (2070) 1.28 0.03 0.25 0.01 0.60 0.00 1.14 0.03 
RCP 8.5 (2040) 1.39 0.02 2.18* 0.05 2.89* 0.05 1.39 0.02 
RCP 8.5 (2070) 3.00* 0.07 4.17* 0.13 0.28 0.00 2.92* 0.08 
MPI-ESM-LR/CRCM5 Baseline 0.00 0.00 0.52 0.31 0.00 0.00 −0.31 −0.03 
RCP 4.5 (2040) 1.78 0.03 1.61 0.04 2.03* 0.03 1.71 0.03 
RCP 4.5 (2070) 1.18 0.02 1.46 0.04 0.85 0.02 1.17 0.02 
Meki watershed EARTH/RCA4 Baseline 0.94 0.10 0.93 0.21 1.35 0.14 0.73 0.09 
RCP 4.5 (2040) 2.14* 0.03 0.82 0.02 3.28* 0.04 2.14* 0.03 
RCP 4.5 (2070) 0.43 0.01 1.07 0.03 0.96 0.02 0.43 0.01 
RCP 8.5 (2040) 2.71* 0.09 2.21* 0.05 4.06* 0.07 3.03* 0.09 
RCP 8.5 (2070) 1.71 0.03 2.85* 0.08 2.39* 0.04 1.78 0.04 
MIROC5/RCA4 Baseline 0.93 0.12 0.31 0.08 1.35 0.09 0.93 0.11 
RCP 4.5 (2040) 1.50 0.04 1.25 0.04 4.14* 0.04 1.50 0.04 
RCP 4.5 (2070) 1.32 0.03 0.14 0.01 1.28 0.03 0.03 0.00 
RCP 8.5 (2040) 2.71* 0.06 1.64 0.03 2.35* 0.05 2.71* 0.06 
RCP 8.5 (2070) 2.92* 0.06 3.71* 0.10 1.57 0.03 2.64* 0.05 
MPI-ESM-LR/CRCM5 Baseline 0.31 0.04 1.56 0.39 −0.52 −0.05 0.31 0.04 
RCP 4.5 (2040) 2.71* 0.06 1.28 0.04 1.71 0.03 2.64 0.05 
RCP 4.5 (2070) 2.21* 0.02 1.57 0.05 0.99 0.02 2.21* 0.02 

Zs is Mann-Kendall test statistics and β is Son's slope estimator (magnitude of change).

∗Significance at a 95% confidence level with the corresponding critical value of 1.96 and bold shows the magnitude of change.

Table 9

Trend analysis of annual and seasonal minimum temperature for the Katar and Meki watersheds of the baseline and future climate scenario under RCP 4.5 and RCP 8.5 and the level of significance is α = 0.05

SubbasinGCMs/RCMsScenariosAnnual
Summer
Winter
Spring
ZsβZsβZsβZsβ
Katar watershed EARTH/RCA4 Baseline −0.31 −0.10 1.56 0.04 −0.52 −0.06 0.73 0.15 
RCP 4.5 (2040) 3.39* 0.07 1.92 0.02 2.92* 0.06 1.03 0.03 
RCP 4.5 (2070) 1.89 0.04 1.71 0.01 2.03* 0.04 1.43 0.05 
RCP 8.5 (2040) 2.57* 0.05 3.35* 0.04 2.64* 0.05 2.21* 0.07 
RCP 8.5 (2070) 3.32* 0.08 4.56* 0.06 3.67* 0.10 1.46 0.04 
MIROC5/RCA4 Baseline −0.73 −0.21 0.31 0.04 −0.73 −0.21 0.73 0.13 
RCP 4.5 (2040) 1.61 0.05 3.14* 0.04 1.53 0.05 1.35 0.06 
RCP 4.5 (2070) 1.43 0.04 3.21* 0.03 1.43 0.04 −0.07 0.00 
RCP 8.5 (2040) 1.39 0.02 2.18* 0.05 2.89* 0.05 1.39 0.02 
RCP 8.5 (2070) 1.21 0.03 4.35* 0.05 1.14 0.04 1.68 0.06 
MPI-ESM-LR/CRCM5 Baseline 0.00 0.00 2.51* 0.10 0.00 0.00 1.56 0.27 
RCP 4.5 (2040) 1.28 0.05 3.07* 0.04 1.25 0.05 1.21 0.06 
RCP 4.5 (2070) 1.21 0.04 2.89* 0.03 1.28 0.05 −0.57 −0.02 
Meki watershed EARTH/RCA4 Baseline 0.31 0.03 2.81* 0.09 0.72 0.08 0.00 0.01 
RCP 4.5 (2040) 2.32* 0.02 2.43* 0.02 0.96 0.01 2.350.03 
RCP 4.5 (2070) 1.99* 0.03 2.25* 0.02 1.53 0.02 1.82 0.02 
RCP 8.5 (2040) 2.89* 0.05 4.21* 0.05 2.14* 0.03 2.89* 0.05 
RCP 8.5 (2070) 3.10* 0.04 4.82* 0.07 4.42* 0.06  3.10* 0.04 
MIROC5/RCA4 Baseline −0.73 −0.09 −0.52 −0.08 −0.10 −0.04 0.10 0.06 
RCP 4.5 (2040) 2.10* 0.04 2.60* 0.04 1.75 0.05 1.78 0.04 
RCP 4.5 (2070) 0.25 0.01 1.64 0.02 1.07 0.04 0.75 0.02 
RCP 8.5 (2040) 2.46* 0.05 2.60* 0.04 2.57* 0.06 2.46* 0.05 
RCP 8.5 (2070) 3.92* 0.07 3.14* 0.05 3.38* 0.05 3.89* 0.07 
MPI-ESM-LR/CRCM5 Baseline 1.56 0.20 1.56 0.20 1.77 0.19 1.14 0.10 
RCP 4.5 (2040) 1.32 0.03 2.89* 0.04 2.25* 0.05 1.14 0.03 
RCP 4.5 (2070) 1.28 0.02 1.82 0.03 0.28 0.01 1.28 0.02 
SubbasinGCMs/RCMsScenariosAnnual
Summer
Winter
Spring
ZsβZsβZsβZsβ
Katar watershed EARTH/RCA4 Baseline −0.31 −0.10 1.56 0.04 −0.52 −0.06 0.73 0.15 
RCP 4.5 (2040) 3.39* 0.07 1.92 0.02 2.92* 0.06 1.03 0.03 
RCP 4.5 (2070) 1.89 0.04 1.71 0.01 2.03* 0.04 1.43 0.05 
RCP 8.5 (2040) 2.57* 0.05 3.35* 0.04 2.64* 0.05 2.21* 0.07 
RCP 8.5 (2070) 3.32* 0.08 4.56* 0.06 3.67* 0.10 1.46 0.04 
MIROC5/RCA4 Baseline −0.73 −0.21 0.31 0.04 −0.73 −0.21 0.73 0.13 
RCP 4.5 (2040) 1.61 0.05 3.14* 0.04 1.53 0.05 1.35 0.06 
RCP 4.5 (2070) 1.43 0.04 3.21* 0.03 1.43 0.04 −0.07 0.00 
RCP 8.5 (2040) 1.39 0.02 2.18* 0.05 2.89* 0.05 1.39 0.02 
RCP 8.5 (2070) 1.21 0.03 4.35* 0.05 1.14 0.04 1.68 0.06 
MPI-ESM-LR/CRCM5 Baseline 0.00 0.00 2.51* 0.10 0.00 0.00 1.56 0.27 
RCP 4.5 (2040) 1.28 0.05 3.07* 0.04 1.25 0.05 1.21 0.06 
RCP 4.5 (2070) 1.21 0.04 2.89* 0.03 1.28 0.05 −0.57 −0.02 
Meki watershed EARTH/RCA4 Baseline 0.31 0.03 2.81* 0.09 0.72 0.08 0.00 0.01 
RCP 4.5 (2040) 2.32* 0.02 2.43* 0.02 0.96 0.01 2.350.03 
RCP 4.5 (2070) 1.99* 0.03 2.25* 0.02 1.53 0.02 1.82 0.02 
RCP 8.5 (2040) 2.89* 0.05 4.21* 0.05 2.14* 0.03 2.89* 0.05 
RCP 8.5 (2070) 3.10* 0.04 4.82* 0.07 4.42* 0.06  3.10* 0.04 
MIROC5/RCA4 Baseline −0.73 −0.09 −0.52 −0.08 −0.10 −0.04 0.10 0.06 
RCP 4.5 (2040) 2.10* 0.04 2.60* 0.04 1.75 0.05 1.78 0.04 
RCP 4.5 (2070) 0.25 0.01 1.64 0.02 1.07 0.04 0.75 0.02 
RCP 8.5 (2040) 2.46* 0.05 2.60* 0.04 2.57* 0.06 2.46* 0.05 
RCP 8.5 (2070) 3.92* 0.07 3.14* 0.05 3.38* 0.05 3.89* 0.07 
MPI-ESM-LR/CRCM5 Baseline 1.56 0.20 1.56 0.20 1.77 0.19 1.14 0.10 
RCP 4.5 (2040) 1.32 0.03 2.89* 0.04 2.25* 0.05 1.14 0.03 
RCP 4.5 (2070) 1.28 0.02 1.82 0.03 0.28 0.01 1.28 0.02 

Zs is Mann-Kendall test statistics and β is Son's slope estimator (magnitude of change).

∗Significance at a 95% confidence level with the corresponding critical value of 1.96 and bold shows the magnitude of change.

Future climate change scenarios

Annual water balance

Under this section, the main water balance component such as AET, PERC, and WYLD were described under future climate conditions. Table 10 summarizes the climate change scenarios of RCP 4.5 and RCP 8.5 to evaluate the sensitivity of water balance components to future anticipated climate changes. In this study, it was assumed that future land use change and all other variables would be constant to investigate the impact of climate on SWAT water balance components. In general, regardless of the applied scenarios, the predicted annual average water balance components decreased and increased in comparison to the baseline for Katar and Maki subbasins. The AET revealed a relative decrease or increase depending on the applied scenarios. As a result, the annual water yield (WYLD) is expected to fall in the future for EARTH and MIROC5 climate models and increase for the climate MPI-ESM-LR model over the Katar subbasin under RCP 4.5 and RCP 8.5. However, for the Meki subbasin, the annual water yield (WYLD) is likely to increase due to a decrease in AET, and deep percolation loss (PERC).

Table 10

Annual water balance components simulated for Katar and Meki subbasins using the SWAT model under future climate conditions

BasinsModelsScenariosTimePr (mm)AET (mm)PERCWYLD (mm)
Katar EARTH Baseline 2000s 1,159 450 370 670 
RCP 4.5 2040s 1,129 ( − 2.57%) 455 ( + 0.95%) 339 ( − 8.17%) 638 ( − 4.9%) 
2070s 1,148 ( − 0.95%) 438 ( − 2.69%) 342 ( − 7.4%) 673 ( + 0.37%) 
RCP 8.5 2040s 1,092 ( − 5.71%) 450 ( − 0.19%) 340 ( − 8.1%) 607 ( − 9.4%) 
2070s 1,083 ( − 6.54%) 447 ( − 0.72%) 310 ( − 16.3%) 599 ( − 10.6%) 
MIROC5 RCP 4.5 2040s 959 ( − 9.31%) 459 ( + 0.03%) 294 ( − 10.1%) 467 ( − 17.2%) 
2070s 1,023 ( − 3.27%) 457 ( − 0.53%) 295 ( − 9.8%) 533 ( − 5.39%) 
RCP 8.5 2040s 1,063 (0.45%) 454 ( − 1.21%) 332 ( + 1.51%) 574 ( + 1.8%) 
2070s 982 ( − 7.19%) 470 ( − 2.38%) 271 ( − 17.2%) 477 ( − 15.3%) 
MPI-ESM-LR RCP 4.5 2040s 1,228 ( + 21%) 507 ( + 6.44%) 385 ( + 33.7%) 683 ( + 35.4%) 
2070s 1,332 ( + 31%) 496 ( + 4.1%) 409 ( + 41.7%) 795 ( + 57.1%) 
Meki EARTH Baseline 2000s 793 130 581 632 
RCP 4.5 2040s 828 ( + 4.4%) 109 ( − 16%) 506 ( − 13%) 697 ( + 10.4%) 
2070s 1,068 ( + 34.7%) 108 ( − 17.1%) 570 ( − 1.9%) 947 ( + 49.9%) 
RCP 8.5 2040s 851 ( + 7.4%) 111 ( − 14.8%) 539 ( − 7.3%) 721 ( + 14.2%) 
2070s 840 (5.96%) 107 ( − 17.2%) 535 ( − 7.9%) 722 ( + 14.4%) 
MIROC5 RCP 4.5 2040s 713 ( − 10%) 107 ( − 17.6%) 442 ( − 24%) 589 ( − 6.7%) 
2070s 701 ( − 11.6%) 106 ( − 18.9%) 430 ( − 26%) 585 ( − 7.4%) 
RCP 8.5 2040s 797 ( + 0.5%) 106 ( − 18.8%) 479 ( − 17.7%) 673 ( + 6.57%) 
2070s 814 ( + 2.6%) 107 ( − 17.6) 495 ( − 14.9%) 694 ( + 9.9%) 
MPI_ESM-LR RCP 4.5 2040s 933 ( + 17.7%) 105 ( − 19.2%) 507 ( − 12.8%) 806 ( + 27.6%) 
2070s 1,035 ( + 29.3%) 104 ( − 16.5%) 657 (12.5%) 906 ( + 43.2%) 
BasinsModelsScenariosTimePr (mm)AET (mm)PERCWYLD (mm)
Katar EARTH Baseline 2000s 1,159 450 370 670 
RCP 4.5 2040s 1,129 ( − 2.57%) 455 ( + 0.95%) 339 ( − 8.17%) 638 ( − 4.9%) 
2070s 1,148 ( − 0.95%) 438 ( − 2.69%) 342 ( − 7.4%) 673 ( + 0.37%) 
RCP 8.5 2040s 1,092 ( − 5.71%) 450 ( − 0.19%) 340 ( − 8.1%) 607 ( − 9.4%) 
2070s 1,083 ( − 6.54%) 447 ( − 0.72%) 310 ( − 16.3%) 599 ( − 10.6%) 
MIROC5 RCP 4.5 2040s 959 ( − 9.31%) 459 ( + 0.03%) 294 ( − 10.1%) 467 ( − 17.2%) 
2070s 1,023 ( − 3.27%) 457 ( − 0.53%) 295 ( − 9.8%) 533 ( − 5.39%) 
RCP 8.5 2040s 1,063 (0.45%) 454 ( − 1.21%) 332 ( + 1.51%) 574 ( + 1.8%) 
2070s 982 ( − 7.19%) 470 ( − 2.38%) 271 ( − 17.2%) 477 ( − 15.3%) 
MPI-ESM-LR RCP 4.5 2040s 1,228 ( + 21%) 507 ( + 6.44%) 385 ( + 33.7%) 683 ( + 35.4%) 
2070s 1,332 ( + 31%) 496 ( + 4.1%) 409 ( + 41.7%) 795 ( + 57.1%) 
Meki EARTH Baseline 2000s 793 130 581 632 
RCP 4.5 2040s 828 ( + 4.4%) 109 ( − 16%) 506 ( − 13%) 697 ( + 10.4%) 
2070s 1,068 ( + 34.7%) 108 ( − 17.1%) 570 ( − 1.9%) 947 ( + 49.9%) 
RCP 8.5 2040s 851 ( + 7.4%) 111 ( − 14.8%) 539 ( − 7.3%) 721 ( + 14.2%) 
2070s 840 (5.96%) 107 ( − 17.2%) 535 ( − 7.9%) 722 ( + 14.4%) 
MIROC5 RCP 4.5 2040s 713 ( − 10%) 107 ( − 17.6%) 442 ( − 24%) 589 ( − 6.7%) 
2070s 701 ( − 11.6%) 106 ( − 18.9%) 430 ( − 26%) 585 ( − 7.4%) 
RCP 8.5 2040s 797 ( + 0.5%) 106 ( − 18.8%) 479 ( − 17.7%) 673 ( + 6.57%) 
2070s 814 ( + 2.6%) 107 ( − 17.6) 495 ( − 14.9%) 694 ( + 9.9%) 
MPI_ESM-LR RCP 4.5 2040s 933 ( + 17.7%) 105 ( − 19.2%) 507 ( − 12.8%) 806 ( + 27.6%) 
2070s 1,035 ( + 29.3%) 104 ( − 16.5%) 657 (12.5%) 906 ( + 43.2%) 

Pr, precipitation; AET, actual evapotranspiration; WYLD, total amount of water that contributes to streamflow (surface runoff contribution to streamflow + lateral flow + groundwater contribution to streamflow − transmission losses); PERC, amount of water entering deep aquifer from the root zone (Arnold et al. 2011).

As can be seen from the table, the annual water yield for the baseline at Katar and Meki subbasins is 670 and 632 mm, respectively. It was very clear that an increase in future rainfall and temperatures would lead to a decreasing trend of the WYLD in the range of 4.9–15.3% at the Katar subbasin and 6.7–7.4% at the Meki subbasin for applied scenarios. Furthermore, the annual water yield will be increased in the range of 0.38–57.1% and 6.57–49.9% for Katar and Meki subbasins, respectively, for the RCP 4.5 and RCP 8.5 climate scenarios. As far as the knowledge of the authors, there are no studies on climate change impacts on water balance components in the study area. However, the findings of this study were consistent with previous research at the Upper Blue Nile Basin by Sitotaw et al. (2022) and in the Awash basin by Taye et al. (2018) which predicted that the annual water yield would decrease the climate scenarios used. For example, the result in Table 10 indicated a 2.57% change in rainfall (Pr), resulting in a 4.9% change in WYLD for the EARTH under RCP 4.5 and a scenario period of the 2040s at the Katar subbasin. Similarly, for the Meki subbasin, a 4.4% change in rainfall results in a 10.4% change in WYLD for the same conditions. In general, the change in rainfall depth is directly correlated with the change in the depth of water yield of the subbasins.

Monthly water balance

The monthly water balance components responded differently to seasonal changes based on the scenario used. In the RCP 4.5 scenario, a maximum decrease of WYLD of up to 83% in February in the Katar subbasin and by 63% in April in the Meki subbasin is predicted. Furthermore, the WYLD component exhibits an increasing and decreasing trend compared with the baseline period (Figure 11). A more pronounced change was observed during the spring season (February to May), indicating that water balance components are more sensitive to changes in rainfall (Figure 11) at both subbasins for the RCP 4.5 and RCP 8.5 scenarios. Regardless of the direction of the seasonal rainfall change, monthly water yield consistently decreases for the RCP 8.5 scenario for the Katar subbasin. This may be due to a greater decrease in rainfall during the winter season (October to January) and an increase in AET during the winter season. This shows that our impact assessment can provide useful information on water balance perturbation without accounting for long-term climate change projections despite data scarcity.
Figure 11

Monthly change of water yield (WYLD) at Katar (a and b) and Meki (c and d) subbasins under future climate scenarios.

Figure 11

Monthly change of water yield (WYLD) at Katar (a and b) and Meki (c and d) subbasins under future climate scenarios.

Close modal

Predicted stream flow

In the preceding section, we have discussed the spatial and temporal variability of rainfall and temperature in the study regions. Furthermore, the trends and their magnitude of change were investigated using the MK test and Sen's slope estimator for future scenarios under RCP 4.5 and RCP 8.5. Future climate change impacts on stream flow are investigated as follows. Under RCP 4.5 of the future climate scenario, the average annual stream flow will decrease by 14.1% in the 2040s and increase by 1.0% in the 2070s for the Katar subbasin. In the Meki subbasin, the average annual stream flow may decrease by 1.9% in the 2040s and increase by 6.5% in the 2070s in comparison to the baseline period. About the RCP 8.5 climate scenario, the average annual stream flow may decrease by 10.7 and 23.8% in 2040 and 2070, respectively, for the Katar subbasin, and by 2.4 and 9.5% in 2040 and 2070, respectively, for the Meki subbasin.

The values in parentheses represent the minimum and maximum annual values of the three GCM simulations (Table 11). Regarding the seasonal stream flow, during the summer season, the stream flow will vary in the range of 4.7% to 26.9% under RCP 4.5 and −10.2 to 15.9% under RCP 8.5. For the winter season, the average stream flow may vary in the range of −48.2% to 28.8% under RCP 4.5 and will reduce by 31.7% to 4.1% under RCP 8.5. Likewise, the average stream flow during the spring season will decrease by 12.7–72.4 under RCP 4.5 and 18.6–47.8% under RCP 8.5 at Katar and Meki subbasins. The annual and seasonal flow reduction or increment is directly associated with the rainfall condition of corresponding climate models (look at the preceding section of this study). Furthermore, Figures 12 and 13 display the monthly average stream flow predicted for Katar and Meki subbasins under RCP 4.5 and RCP 8.5 climate scenarios during the 2040 and 2070s, respectively. Generally, the future stream flow under both climate scenarios was predicted with a different range of values over the study regions. This may be associated with the difference in radiative forcing between the two climate scenarios. Except for annual stream flow in the 2070s and summer seasons for all climate models, the findings show a general decrease in annual and seasonal (spring and winter) stream flow through the Katar and Meki subbasins in the future (Table 11). Changes in stream flow at both subbasins are related to changes in precipitation during the same seasons. The result of the study is comparatively consistent with earlier studies in Katar and Meki subbasins focused on Ziway lake (Chimdesa 2016; Abraham et al. 2018; Gadissa et al. 2018), which projected decreases in annual, spring, and winter seasonal streamflow in the future except for the wet (summer) season. Emiru et al. (2022) also pointed out a decreasing trend of future stream flow in the Awash basin, which is adjacent to the Katar and Meki subbasins. Also, other studies show the impacts of climate change on stream flow in Ethiopian basins, such as in the Nile basin (Taye et al. 2018; Worqlul et al. 2018; Bekele et al. 2021; Takele et al. 2022).
Table 11

Projected annual and seasonal stream flow change under RCP 4.5 and RCP 8.5 climate scenarios at Katar and Meki subbasins

SubbasinsClimate scenariosTimeAnnual change in %Seasonal change in %
Summer (wet)Winter (dry)Spring
Katar RCP 4.5 2040s −3.3 ( − 45.8–20.4) 11.9 ( − 26.9–52.1) −13.9 ( − 66.1–13.9) −12.7 ( − 41.4–39.6) 
 2070s 15.4 ( − 43.0–45.8) 26.9 ( − 8.6–78.4) 28.8 ( − 70.9–28.8) −39.4 ( − 55.4–21.2) 
RCP 8.5 2040s −11.2 ( − 22.8–1.5) 15.9 (21.7–22.8) −31.7 ( − 55.4–33.5) −24.4 ( − 46.8–33.2) 
 2070s −22.8 ( − 35.8–11.7) −10.2 ( − 8.5–1.9) −23.8 ( − 53.1–18.9) −47.8 ( − 56.6–18.3) 
Meki RCP 4.5 2040s −4.7 ( − 24.6–10.9) 4.7 ( − 21.6–27.2) −48.2 ( − 59.3–42.9) −72.3 ( − 73.5–64.8) 
 2070s 3.2 ( − 19.4–28.2) 8.8 ( − 13.0–33.2) −37.1 ( − 58.0–21.7) −72.4 ( − 73.2–63.9) 
RCP 8.5 2040s −4.2 ( − 15.6–10.8) 5.7 ( − 12.9–29.6) −12.3 ( − 18.9–11.1) −18.6 ( − 18.9–5.8) 
 2070s −9.5 ( − 22.8–3.8) −6.9 ( − 26.2–17.9) −4.1 ( − 9.1–2.6) −34.5 ( − 33.8–24.4) 
SubbasinsClimate scenariosTimeAnnual change in %Seasonal change in %
Summer (wet)Winter (dry)Spring
Katar RCP 4.5 2040s −3.3 ( − 45.8–20.4) 11.9 ( − 26.9–52.1) −13.9 ( − 66.1–13.9) −12.7 ( − 41.4–39.6) 
 2070s 15.4 ( − 43.0–45.8) 26.9 ( − 8.6–78.4) 28.8 ( − 70.9–28.8) −39.4 ( − 55.4–21.2) 
RCP 8.5 2040s −11.2 ( − 22.8–1.5) 15.9 (21.7–22.8) −31.7 ( − 55.4–33.5) −24.4 ( − 46.8–33.2) 
 2070s −22.8 ( − 35.8–11.7) −10.2 ( − 8.5–1.9) −23.8 ( − 53.1–18.9) −47.8 ( − 56.6–18.3) 
Meki RCP 4.5 2040s −4.7 ( − 24.6–10.9) 4.7 ( − 21.6–27.2) −48.2 ( − 59.3–42.9) −72.3 ( − 73.5–64.8) 
 2070s 3.2 ( − 19.4–28.2) 8.8 ( − 13.0–33.2) −37.1 ( − 58.0–21.7) −72.4 ( − 73.2–63.9) 
RCP 8.5 2040s −4.2 ( − 15.6–10.8) 5.7 ( − 12.9–29.6) −12.3 ( − 18.9–11.1) −18.6 ( − 18.9–5.8) 
 2070s −9.5 ( − 22.8–3.8) −6.9 ( − 26.2–17.9) −4.1 ( − 9.1–2.6) −34.5 ( − 33.8–24.4) 
Figure 12

Projected the average monthly stream flow of the Katar and Meki subbasins under the RCP 4.5 climate scenario.

Figure 12

Projected the average monthly stream flow of the Katar and Meki subbasins under the RCP 4.5 climate scenario.

Close modal
Figure 13

Projected the average monthly stream flow the of Katar and Meki subbasins under the RCP 8.5 climate scenario.

Figure 13

Projected the average monthly stream flow the of Katar and Meki subbasins under the RCP 8.5 climate scenario.

Close modal

This study investigated the climate change on water balance components and surface water availability in the Katar and Meki subbasins, which are important river basins of Lake Ziway of the Central Rift Valley Lakes Basin of Ethiopia. After the performance evaluation of RCMs, the three best climate model outputs were selected to assess future climate change impact studies. The bias-corrected RCMs have been integrated into a semi-distributed SWAT model to assess the future climate and water resources of the Katar and Meki subbasins for the periods of 2021–2050 (the 2040s) and 2051–2070 (2070s). The results show that rainfall and temperatures in the study region are anticipated to be increased by the 2040 and 2070s under the climate scenarios of RCP 4.5 and RCP 8.5. However, the average annual stream flow will decrease by 2040s and increase by 2070s for the Katar subbasin under the RCP 4.5 scenario. Also in the Meki subbasin, the average annual stream flow may decrease by the 2040s and increase by the 2070s in comparison to the baseline period under the RCP 4.5. About the RCP 8.5 climate scenario, the average annual stream flow may decrease for both subbasins by the 2040 and 2070s.

The average annual streamflow could decrease in future periods compared with the baseline period. In the dry season, however, the possibility of evaporation in the basin is high, the amount of rainfall is small, and the stream flow is small, resulting in a small total amount of water. Overall, the declining trend of water resources in the study area suggests that water resources are insufficient to support the development of the subbasin. As a result, strategies for managing the basin's water resources are required to avoid future impacts of climate change on water availability. Climate change scenarios and simulated impacts are used to develop water resource management strategies. The study's limitation is that it does not account for future land-use and land-cover changes or the impact of socioeconomic conditions in the subbasins on water resources. Therefore, future studies should consider the combined effects of climate change, land-use/land-cover change, and socioeconomic conditions in the subbasins.

The authors would like to thank GCRF, the Ministry of Water and Electricity (MoWE), Water and Land Resource Center (WLRC), National Meteorological Service Agency (NMSA), Rift Valley Lakes Basin Authority, towns’ water supply offices (Asella, Bokoji, Sagure, Abura, Arata, and Meki), Silte, East and West Arsi zone Agriculture, and Natural Resources offices for providing relevant data.

This work was supported by the Water Security and Sustainable Development Hub funded by the UK Research and Innovation's Global Challenges Research Fund (GCRF) [grant number: ES/S008179/1].

All authors made substantial contributions to the development of this manuscript. S.K.B. oversaw the conceptualization, data collection, data analysis, and writing of the original draft. T.A.H. and G.A. reviewed, edited, and improved the manuscript. A.A.A. and A.B. supervised the overall research work of this study. All authors read and approved the final manuscript.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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