ABSTRACT
The expected influences of climate alteration on hydrological responses in the Gibe Gojeb catchment, southwestern Ethiopia, were evaluated using the Soil and Water Assessment Tool (SWAT) model and regional climate models (RCMs). This study emphasizes the evaluation of the climate change impact on hydrological responses. The RCM data were downloaded from CORDEX-Africa. Power transformation and delta change methods were used for bias correction of precipitation and temperature, respectively. The calibration and validation of the SWAT model showed good agreement, and climate change impact was simulated. Accordingly, precipitation, soil water content, percolation, water yield, and groundwater are projected to decrease by 28.18, 7.5, 9.1, 5.4, and 9.2%, respectively, under RCP4.5 in the future (2021–2050). Besides, surface runoff and potential and actual evapotranspiration are projected to increase by 17.4, 11.32, and 5.06%, respectively. Soil water content, percolation, water yield, and precipitation are projected to decrease by 10.51, 5.63, 2.76, and 21.57%, respectively, under RCP8.5 in the future. Rain-fed crop production and hydropower generation in the catchment would be vulnerable to the effects of climate change. The results help to develop effective adaptation measures to reduce the impacts of climate change and draft long-term water resource management plans in the Gibe Gojeb catchment.
HIGHLIGHTS
The study employed regional climate and SWAT models.
The change in temperature and rainfall impacted the hydrological responses.
The baseline and forthcoming (2030s) hydrological components were compared to indicate the change.
INTRODUCTION
Climate change is a thoughtful risk, and its significance impacts various aspects of environmental, ecological, and hydrological systems. Climate change emphasizes that hotter temperatures raise the intensity of heatwaves, which can increase health risks (Vose et al. 2017); changes in the trends and quantity of rainfall can disturb water supplies and quality and the production of hydroelectricity (Lall et al. 2018), while altering ecosystems impact many plant and animal species and their lifecycle events (Lipton et al. 2018). Climate change emphasizes the incidence and strength of risky weather events, such as droughts and floods, which can increase losses to property (Ebi et al. 2018). According to the conditions upheld by Lindwall (2022), widespread shifts in weather systems occur due to climate change, making events like droughts, hurricanes, and floods more intense and unpredictable. Besides, climate change, accentuated by increasing sea levels, threatens coastal communities and ecosystems (Fleming et al. 2018).
These impacts can interrupt human and animal lives, increase natural disturbances, and alter the distribution of water resources. Zhang et al. (2017) reported that climate change has a considerable influence on hydrological responses. Climate change is viewed as a significant factor affecting the timing and magnitude of hydrological processes. Some studies have shown the influences of climate alteration on the hydrology of the watershed. For example, Talib & Randhir (2017) reported that climate change caused changes in surface runoff, actual evapotranspiration, infiltration, and active groundwater flow in the SuAsCo River watershed in MA, USA.
Similarly, surface runoff, actual evapotranspiration, and stream flow are expected to rise by 51.8, 12.2, and 34.3%, respectively, in the Gumara watershed, northern Ethiopia (Teklay et al. 2020). Besides, the runoff expected to decrease further intensifies the current shortage of irrigation water demand and requires effective application of adaptation measures to reduce susceptibility in the Gilgel Abbay Watershed, Nile Basin, Ethiopia (Ayele et al. 2016). Mengistu et al. (2021) found that climate alteration in the Nile Basin showed an increase in potential evapotranspiration of up to 27% and a decrease in water yield of 10.7–22.7%. Similarly, climate dynamics impacted hydrological responses in many catchments in Ethiopia (Ayalew et al. 2022, 2023a, 2023b). Climate change has impacted the hydrological processes in different parts of the world. For example, in the Mago River basin located in the Eastern Himalayan region of India, an increase in the average annual precipitation (+23.18%), maximum temperature (+2.4 °C), and minimum temperature (+2.64 °C) increased streamflow (+31.62%), water yield (+31.81%), actual evapotranspiration (+6.37%), and groundwater flow (+33.41%), while soil water (–2.0%) was projected to decrease under RCP4.5 scenario, with higher changes anticipated under RCP8.5 by the end of the 21st century (Chiphang et al. 2022). Similarly, in a data-starved Upper Baitarani River basin of eastern India, projections indicated a reduction in the surface runoff ranging from 2.5 to 11% by changing the temperature from 1 to 5 °C by the end of the 21st century, whereas an increase in rainfall by 2.5–15% suggested an increase in surface runoff by 6.67–43.42% from the baseline condition (Uniyal et al. 2015). Changes in climate will impact future hydrological responses, especially considering the growing water demand and agricultural activities (Abbaszadeh et al. 2023). All these changes in the hydrologic series have major implications for water resource management.
In the Gibe Gojeb catchment, the Gibe One and Gibe Two cascade dams along the river were constructed with a total installed capacity (power) of 184 MW and 420 MW, respectively. Gibe One reservoir was constructed along the Gilgel Gibe River with a magnitude of 840 million cubic meters (MCM), and Gibe Two dam was constructed along the Omo Gibe River. In the Gibe watershed, intensive agricultural activities by a large number of smallholding farmers dishonored land and water resources, which made the catchment prone to soil erosion and siltation in Gibe One reservoir (Nebiyu 2011). Anose et al. (2021) reported that inconsistent rainfall and increasing temperature in the Omo Gibe River Basin, in which the Gibe Gojeb catchment is located in the northern part, suggest that the basin water resource is at risk. Similarly, mean annual rainfall exhibited a decreasing tendency, while air temperature showed an increasing tendency in the upper Omo Gibe Basin (Jawesso et al. 2019). Significant declining trends in annual and Kiremt (main rainy season) rainfall amounts, common in the Omo Gibe River basin (Degefu & Bewket 2014), have an effect on rain-fed agriculture. Substantial warming and erratic rainfall have made the Omo Gibe River basin vulnerable to drought events, causing adverse effects on the water availability of downstreams (Anose et al. 2022). Besides, Orkodjo et al. (2022) reported an announcement by the Ministry of Water and Energy of Ethiopia in 2019 regarding a shortage of 476-MW power as a result of climate change's effects on the Omo Gibe cascading dams in the Omo Gibe River basin.
Likewise, many recent studies indicate that there is a substantial impact of climate change on hydrologic components in the northern part of the Gibe catchment (Demissie 2023; Tilahun et al. 2023), but there is no comprehensive scientific evidence on the impact of climate change on the western and southern parts of Gojeb catchment (Gebremichael et al. 2021; Anteneh et al. 2022). Water demand is rising in the Gibe Gojeb catchment due to large numbers of smallholders' subsistence and rain-fed agricultural activities and cascade hydropower dams. To address these research gaps and alleviate the problems, water resource modeling and planning are imperative in the catchment. Therefore, this study aimed to evaluate the climate change impact on hydrological responses in the Gibe Gojeb catchment.
METHODS AND MATERIALS
Study area
Climate change modeling
The association between climate change and hydrological responses, established by temperature and rainfall variations, differs with future periods under greenhouse gas emissions. The Representative Concentration Pathways (RCPs) represent the range of greenhouse gas emissions, including an intermediate greenhouse gas emission (RCP4.5) and one with a very high greenhouse gas emission (RCP8.5). These RCPs were named according to the radiative forcing target level for 2100. RCP4.5 describes the stabilization without overshoot pathway to 4.5 W/m2 (∼650 ppm CO2) at stabilization after 2100 (Clarke et al. 2007). RCP8.5 corresponds to the rising radiative forcing pathway leading to 8.5 W/m2 (∼1,370 ppm CO2) by 2100 (Riahi et al. 2011). Several studies employed regional climate models (RCMs) for climate change and its impacts on hydrological responses due to their improved simulations at local and regional levels (Mengistu et al. 2022; Namara et al. 2022; Demissie 2023).
Regional climate model data were downscaled from CORDEX-Africa through representative concentration pathways (RCP4.5 and RCP8.5) on the Earth Systems Grid Federation (ESGF) website (https://esg-dn1.nsc.lpu.se/projects/esgf.liu/cordex). The downscaling was accomplished at a horizontal resolution of latitude 0.44° and latitude 0.44°, with their driving GCMs providing the boundary conditions (Nikulin et al. 2012; Mascaro et al. 2015). The projection of the future period employed the ensemble mean of three RCMs’ data. Multi-Regional Climate Models Ensemble Mean helps to reduce uncertainties compared with the use of a single regional climate model; however, bias correction is required. This study has three RCMs, namely, CCLM4-ICHE, CCLM4-CNMR, and REMO2009-MPI. To evaluate future climate change, the period from 1992 to 2016 was used as the baseline and the period from 2021 to 2050 was used as the 2030 scenario.
The climate models were further evaluated using statistical parameters to check their association with observed data from the three weather stations. The evaluation was performed by using the root mean square (RMSE) and Pearson correlation coefficient (r) (Chai & Draxler 2014). The RMSE and r between models and observed data of rainfall, maximum temperature, and minimum temperature are 43.3–14.2 and 0.65–0.89, 4.3–11.1 and 0.59–0.96, and 2.6–7.10 and 0.52–0.76, respectively.
Bias correction of climate model data of rainfall and temperature was performed by using the Climate Model tool for Hydrological Modeling (CMhyd) (Rathjens et al. 2016). The bias correction of rainfall and temperature employed power transformation and delta change methods, respectively.
Hydrological modeling
SWAT model inputs
Land use/land cover
A 10-m resolution land use and cover data for the Gibe Gojeb catchment in 2017 was downloaded from Sentinel-2 (https://www.arcgis.com/home/item.html?id=cfcb7609de5f478eb7666240902d4d3d). The images covering the basin were mosaicked. The land use and cover classes were substantiated using Google Earth for validation. Nine land use/cover classes are provided to the SWAT requirement: water bodies (WATR), wetlands (WETL), urban and built-up areas (URBN), forest lands/scattered (FRST), plantations (FRSE), barren lands (BARR), range land (RNGE), crops (AGRL), and cultivation lands (AGRC). The land use and cover of the periods are presented in Figure 2(a).
Digital Elevation Model (DEM)
A 30-m resolution DEM of the Gibe Gojeb catchment was downloaded from the USGS website (https://earthexplorer.usgs.gov) (Figure 2(b)). Delineation of basin boundary, sub-basins, sub-basin size and distribution, and channel network was performed by using the DEM in ArcSWAT. The Gibe Gojeb catchment was divided into 61 sub-basins and 559 hydrologic response units (HRUs).
Soil
The soil of the Gibe Gojeb catchment was extracted from the Food and Agricultural Organization (FAO 2002). In the catchment, the main soils include Pellic Vertisols, Eutric Cambisols, Dystric Nitissols, Orthic Acrisols, Dystric Fluvisols, Eutric Nitisos, Eutric Fluvisols, Chromic Vertisols, Orthic Luvisols, Lepthosols, and Orthic Solonchacs, rating at 42.24, 15.68, 9.29, 7.13, 6.12, 5.15, 3.39, 3.68, 2.58, 1.29, and 1.07%, respectively. In contrast, the minor soils include Dystric Gleysols, Calcic Fluvisols, Gypsic Yemosols, Chromic Cambisols, Cambisols, and Calcaric Fluvisols, rating at 0.91, 0.89, 0.28, 0.14, 0.07, and 0.04%, respectively (Figure 2(c)).
Weather data
Observed daily rainfall, temperature, humidity, wind, and daily sunshine hours from 1992 to 2016 were collected from the National Meteorological Agency of Ethiopia. The Jimma weather station was used for analyzing the weather data in the catchment. The missing values of historical daily data were filled by using the Weather Generator in the SWAT model. The projected rainfall and maximum and minimum temperature data were used to indicate the impacts of climate change on hydrological responses in the Gibe Gojeb catchment, keeping others the same.
Stream flow data
Gibe River flow data (2000–2014) measured at Abelti gauge station were obtained from the Ministry of Water and Energy of Ethiopia. The data were used to accomplish the standardization and justification of the SWAT model.
Sensitivity analysis, calibration, validation, and model performance evaluation
The SWAT model requires identifying the most sensitive parameters to improve model calibration. The adjustment of model parameters within the endorsed arrays improves the simulated output so that it matches the observed data performed during model calibration. Testing the calibrated parameters against an independent set of observed data with no further changes to the parameters was executed during validation. The Sequential Uncertainty Fitting Version 2 (SUFI-2) algorithm set in SWAT-CUP 2012 was used during calibration and validation (Abbaspour et al. 2015).
RESULTS AND DISCUSSION
Flow simulation
SWAT model warmup, calibration adjustment, and validation justification were accomplished from 2003–2005, 2006–2011, and 2012–2014, respectively. Model sensitivity analysis was executed using SWAT-CUP through the SUFI-2 algorithm to detect the most sensitive flow parameters for calibration. Parameters related to surface runoff, soil water, groundwater, and evaporation were considered and ten parameters were identified as the most sensitive parameters for calibration (Table 1). Sensitive parameters were selected and measured using the t-stat and p-values because the larger absolute t-stat and p-values are close to zero.
Sensitive flow parameters
Parameter . | Parameter description . | Range . | Fitted value . | t-Stat . | p-value . |
---|---|---|---|---|---|
R_GW_REVAP.gw | Threshold depth of water in the shallow aquifer | 0–1 | 0.35 | 5.75 | 0 |
R_CN2.mgt | Runoff curve number to moisture condition II | 35–98 | 39.56 | 4.69 | 0 |
V_GW_DELAY.gw | Groundwater delay | 0–12 | 5.53 | 5.75 | 0 |
R_SOl_K.sol | Effective hydraulic conductivity in main channel alluvium | 0–2,000 | 1,209.1 | 7.61 | 0 |
R_SOl_AWc.sol | An available water capacity of the soil layer | 0–1 | 0.56 | 5.77 | 0 |
R_ESCO.bsn | Soil evaporation compensation factor | 0.02–0.2 | 0.05 | 9.78 | 0 |
R_CH_K2.rte | Effective hydraulic conductivity in the main channel | 0–500 | 101.72 | 12.82 | 0 |
R_SURLAG.bsn | Surface runoff delay coefficient | 0–12 | 7.62 | 8.73 | 0 |
V_ALPHA-BF.gw | Baseflow recession constant | 0–1 | 0.43 | 10.56 | 0 |
R_CANMX.hru | Maximum canopy storage | 0–100 | 82.77 | 15.64 | 0 |
Parameter . | Parameter description . | Range . | Fitted value . | t-Stat . | p-value . |
---|---|---|---|---|---|
R_GW_REVAP.gw | Threshold depth of water in the shallow aquifer | 0–1 | 0.35 | 5.75 | 0 |
R_CN2.mgt | Runoff curve number to moisture condition II | 35–98 | 39.56 | 4.69 | 0 |
V_GW_DELAY.gw | Groundwater delay | 0–12 | 5.53 | 5.75 | 0 |
R_SOl_K.sol | Effective hydraulic conductivity in main channel alluvium | 0–2,000 | 1,209.1 | 7.61 | 0 |
R_SOl_AWc.sol | An available water capacity of the soil layer | 0–1 | 0.56 | 5.77 | 0 |
R_ESCO.bsn | Soil evaporation compensation factor | 0.02–0.2 | 0.05 | 9.78 | 0 |
R_CH_K2.rte | Effective hydraulic conductivity in the main channel | 0–500 | 101.72 | 12.82 | 0 |
R_SURLAG.bsn | Surface runoff delay coefficient | 0–12 | 7.62 | 8.73 | 0 |
V_ALPHA-BF.gw | Baseflow recession constant | 0–1 | 0.43 | 10.56 | 0 |
R_CANMX.hru | Maximum canopy storage | 0–100 | 82.77 | 15.64 | 0 |
The SWAT model performance presented acceptable agreement between simulated and observed river flows during calibration, with an NSE of 0.72, an R2 of 0.74, and a PBIAS of 2.8%. Similarly, validation was done through an NSE of 0.74, an R2 of 0.77, and a PBIAS of 3.02%.
Previous hydrological responses
The SWAT model simulation was run to show the previous hydrological components in the Gibe Gojeb catchment with respect to 2017 land use/cover. The simulation result depicted that the mean annual (2006 to 2014) potential evapotranspiration (PET), actual evapotranspiration (ET), soil water content (SW), percolation (PER), surface runoff (SUR_Q), lateral flow (LAT_Q), groundwater (GW_Q), water yield (WYLD), and precipitation (PREC) were quantified at 239.39, 183.70, 175.12, 494.70, 318.29, 124.17, 765.57, 1,448.95, and 1,587 mm yr−1, respectively.
Climate change projections
Climate change projection was performed by using the ensemble mean of three RCMs for the 2030s (2021–2050) century under two emission scenarios relative to the baseline (1992–2016) over the Gibe Gojeb catchment. The projected change in monthly rainfall and temperatures was put in the 2030s (2021–2050) century under RCP4.5 and RCP8.5 emission scenarios relative to the base period (1992–2016) in the following sections.
Temperature projection
Changes in mean monthly maximum and minimum temperatures in 2021–2050 under RCP4.5 and RCP8.5 scenarios.
Changes in mean monthly maximum and minimum temperatures in 2021–2050 under RCP4.5 and RCP8.5 scenarios.
The projected temperature change in the Gibe Gojeb catchment is in line with the projected temperature change of the world's mean of 1.5 °C (Climate & Environment 2022). The projected change in temperature, further upheld by Daniel (2023), on climate change profiles in the Omo Gibe River Basin shows that the mean annual temperature is projected to increase by 2.15 °C in the far future under the RCP8.5 scenario. They further elaborated that this incremental change will have an impact on declining annual rainfall up to 27.6% under the RCP8.5 scenario in 2031–2050. Thus, the concerned body integrates its duties with climate change.
Rainfall projection
Percentage change of rainfall in Gibe Gojeb under RCP4.5 and RCP8.5 scenarios.
Decreasing rainfall is more pronounced under two emission scenarios. For example, under RCP4.5, a projected decline in rainfall is featured in January, February, May, June, July, and August. Similarly, under RCP8.5, a projected decline in rainfall is featured in January, February, May, June, and August.
A water shortage is likely to be pronounced in the near future, along with increasing temperatures in the Gibe Gojeb catchment. Ethiopia's heavy dependence on rain-fed and subsistence agriculture increases its vulnerability to the contrasting effects of climate alteration (Kassa 2015; Zerga & Gebeyehu 2018). Studies on Ethiopia's climate trend analysis from 2010 to 2039 revealed rainfall declines ranging from 50 to 150 mm, which are related to lower harvests and poor pastoral rangelands across the south and eastern parts (Funk et al. 2012).
Similarly, Kassie et al. (2013) reported that annual rainfall will change in the range of +10 to −40% by 2080, increasing outside the growing season and declining during the growing seasons. Studies in Ethiopia reported unstable rainfall patterns and magnitudes in the future. For example, the rainfall trend from 2040 to 2050 during the high rainy season (June–September) tends to decrease over the southern part of the country (Li et al. 2015), on which the Omo-Ghibe Basin lies.
The most important climate change effects impacting future hydropower production are expected to change the runoff seasonality and increase the frequency of extreme high- and low-runoff events. Hydropower is vulnerable to the influences of climate alteration, mainly sensitive to variations in stream flow, variations in rainfall and temperature, and extended drought, previously seen in the sub-Saharan African countries (Trace 2019; Wei et al. 2020).
Hydrological responses to climate changes
The projected alteration in climate showed a decrease in rainfall and a rise in temperature in the Gibe Gojeb catchment. Variations in rainfall and temperature in the 2030s (2021–2050) were used to predict the influences of climate alteration on the hydrological responses of the Gibe Gojeb catchment. Variations in rainfall and temperature were associated with the variations in hydrological processes in the catchment. The simulation result showed that the mean annual potential evapotranspiration (PET), actual evapotranspiration (ET), soil water content (SW), percolation (PER), surface runoff (SUR_Q), lateral flow (LAT_Q), groundwater (GW_Q), water yield (WYLD), and precipitation (rainfall) (PREC) were quantified under the RCP4.5 and RCP8.5 scenarios, as presented in Table 2.
Mean annual hydrological responses in the 2030s period
PET (mm) . | ET (mm) . | SW (mm) . | PER (mm) . | SUR_Q (mm) . | LAT_Q (mm) . | GW_Q (mm) . | WYLD (mm) . | PREC (mm) . | Scenario . |
---|---|---|---|---|---|---|---|---|---|
266.4 | 193.7 | 161.9 | 450.0 | 373.6 | 132.9 | 694.9 | 1,370.7 | 1,139.7 | RCP4.5 |
290.1 | 211.7 | 156.7 | 467.1 | 378.8 | 137.9 | 906.4 | 1,425.8 | 1,244.5 | RCP8.5 |
PET (mm) . | ET (mm) . | SW (mm) . | PER (mm) . | SUR_Q (mm) . | LAT_Q (mm) . | GW_Q (mm) . | WYLD (mm) . | PREC (mm) . | Scenario . |
---|---|---|---|---|---|---|---|---|---|
266.4 | 193.7 | 161.9 | 450.0 | 373.6 | 132.9 | 694.9 | 1,370.7 | 1,139.7 | RCP4.5 |
290.1 | 211.7 | 156.7 | 467.1 | 378.8 | 137.9 | 906.4 | 1,425.8 | 1,244.5 | RCP8.5 |
The simulated hydrological processes of the 2030s and previous periods were compared to show their variations (Table 3). The SWAT model simulations in the forthcoming period showed a decrease in soil water content, percolation, groundwater, and water yield, while there was an increase in surface runoff, potential, and actual evapotranspiration under the RCP4.5 scenario.
Percentage changes of the annual hydrological responses under climate change
PET (%) . | ET (%) . | SW (%) . | PER (%) . | SUR_Q (%) . | GW_Q (%) . | WYLD (%) . | PREC (%) . | Temp (°C) . | Scenario . |
---|---|---|---|---|---|---|---|---|---|
11.32 | 5.06 | −7.50 | −9.08 | 17.40 | −9.22 | −5.39 | −28.18 | 1.5 | RCP4.5 |
21.21 | 15.26 | −10.51 | −5.63 | 19.01 | 18.40 | −2.76 | −21.58 | 1.8 | RCP8.5 |
PET (%) . | ET (%) . | SW (%) . | PER (%) . | SUR_Q (%) . | GW_Q (%) . | WYLD (%) . | PREC (%) . | Temp (°C) . | Scenario . |
---|---|---|---|---|---|---|---|---|---|
11.32 | 5.06 | −7.50 | −9.08 | 17.40 | −9.22 | −5.39 | −28.18 | 1.5 | RCP4.5 |
21.21 | 15.26 | −10.51 | −5.63 | 19.01 | 18.40 | −2.76 | −21.58 | 1.8 | RCP8.5 |
Temp = mean temperature change; positive values are increasing, and negative values are decreasing compared to baseline period.
Water yield and groundwater will likely decrease under RCP4.5 and RCP8.5 in the future. These variations follow the path of rainfall changes. Soil water content, potential, and actual evapotranspiration will likely increase under RCP4.5 and RCP8.5 in the future, thus following the path of increase in temperature. The highest likely increase in surface runoff under RCP4.5 and RCP8.5 in the Gibe Gojeb catchment may result in flooding in the lower part of the Omo Gibe River Basin.
CONCLUSIONS
This study evaluated the impacts of climate change on the hydrological responses in the Gibe Gojeb catchment, southwestern Ethiopia. Climate change scenario (2021–2050) was established, and the hydrological responses were replicated using the SWAT model. The climate change for the 2030s (2021–2050) century was projected using the ensemble mean of RCMs under RCP4.5 and RCP8.5 scenarios. The model was calibrated, validated, and used to simulate the forthcoming hydrological responses under two scenarios. The agreement between the measured and simulated river flows indicated that the SWAT model could be used to simulate the hydrological responses to climate variation in the catchment. Besides, the forthcoming hydrological responses were associated with those of the previous period.
In the Gibe Gojeb catchment, the maximum and minimum temperatures in the 2030s century (2021–2050) are projected to increase in the range of 0.3–1.2 °C and 0.4–1.7 °C under RCP4.5, respectively. Similarly, the maximum and minimum temperature ) are projected to increase in the range of 0.3–1.5 °C and 0.8–2.0 °C, respectively, under RCP8.5. The rainfall is likely to decrease in the range of 32.9–1,139.7 mm under RCP4.5; however, the increase in rainfall ranges between 8.3 and 1,578.4 mm under RCP8.5. Surface runoff, potential, and actual evapotranspiration will increase in the future, while soil water content, percolation, and water yield will decrease under RCP4.5 and RCP8.5.
The climate change in the Gibe Gojeb catchment shows that a multidisciplinary approach will be vital to agricultural activity and hydropower project planners. Fluctuating rainfall and increasing temperatures in the catchment highly influence rain-fed crop production and hydropower generation in the catchment. The projected increases in temperature and reductions in rainfall will help to develop effective adaptation measures to reduce the ongoing impacts of climate change and draw up long-term water resource management plans in the Gibe Gojeb catchment.
The study suggests that the evaluation of the impact of climate change on hydrological responses in the Gibe Giojeb catchment is well-represented by the SWAT simulations. Nevertheless, the feature of meteorological and hydrological data and the shortage of gauge stations in the catchment need urgent attention to improve our understanding of the forthcoming differences. If climate-resilient approaches are practiced and soil and water conservation efforts are properly implemented, groundwater recharge, soil water, water yield, and percolation will increase. Accordingly, the surface runoff and actual and potential evapotranspiration will be reduced.
The results provided evidence of the comparative effects of how the hydrological responses in the catchment respond to changes in climate. This could help plan water resources and the climate resilience measures. This study also suggests a further study on soil erosion and sedimentation of a reservoir along the Gilgel Gibe River. Moreover, the result highlights the need for concerned bodies to urge strong climate-resilient management strategies and counteract the climate changes in the Gibe Gojeb catchment.
ACKNOWLEDGEMENTS
The authors thank Wolaita Soddo University for providing materials and resource support for the study. The authors are pleased to thank the Ministry of Water and Energy for providing hydro-meteorological data.
DATA AVAILABILITY STATEMENT
All relevant data are available from an online repository or repositories. Land use/Land cover from: https://www.arcgis.com/home/item.html?id=cfcb7609de5f478eb7666240902d4d3d DEM from: https://earthexplorer.usgs. gov
CONFLICT OF INTEREST
The authors declare there is no conflict.