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As industrial and economic growth intensifies, greenhouse gases are released into the atmosphere, leading to a shift in global warming and climate change patterns. The Mille watershed faces significant challenges such as flooding, drought, irrigation, and water supply scarcity, as well as health issues stemming from climate change within the community. Thus, this study aims to assess the impact of climate change on hydrology in the Mille River, Awash River Basin, Ethiopia, utilizing multiple hydrological and climate models. The study examines three global circulation models (MIROC‐6, CMCC, and MRI) operating under two shared socioeconomic pathways emission scenarios (SSP2‐4.5 and SSP5‐8.5) for both mid‐term (near future) (2041–2070) and long‐term (far future) (2071–2100) periods. Precipitation and temperature scenarios data were obtained using the CMhyd Tool and then bias‐corrected using various methods based on the base time period (1985–2014). The projected annual rainfall is expected to increase by 8.91‐18.68% and 8.09‐18.65%, while the average temperature is projected to increase by 1.08–3.04°C and 1.59–4.05°C in the 2050s (2041–2070) and 2080s (2071–2100), respectively. The SWAT model shows daily responses with NSE (Nash–Sutcliffe efficiency) values of 0.77 for calibration and 0.79 for validation, R2 (coefficient of determination) values of 0.80 for calibration and 0.81 for validation, and PBIAS (percent bias) values of −10.6 for calibration and −8.6 for validation. Similarly, the HBV model shows NSE values of 0.683 for calibration and 0.706 for validation, R2 values of 0.705 for calibration and 0.71 for validation, and PBIAS values of −4.25 for calibration and −6.669 for validation. The results indicate a decrease in average annual streamflow ranging from −5.95% to −39.29% for SWAT and from −12.28% to −35.04% for HBV in the near future (2050s) and Far future (2080s) compared to the base period (1985–2014). The significance of this study lies in its contribution to understanding climate‐hydrology interactions in a vulnerable region, providing actionable insights for adaptation planning, policy formulation, and sustainable resource management in the face of climate change. Extreme high and low changes in flow were used to quantify this impact. Therefore, based on the observed trends of decreased streamflow volume, recommendations for the study area include the development of water sources such as microdams, ponds, and water wells, implementation of water harvesting techniques, improvement of land use and land cover practices, proper utilization and management of available discharge, drought assessment, and environmental impact assessment. These measures are crucial for mitigating the adverse effects of climate change and ensuring the resilience of the region's water resources. [ABSTRACT FROM AUTHOR]

Accurate prediction of stream flow is essential for effective water resource management, particularly in regions with complex hydrological dynamics. The Mille River, which flows through this basin and has a watershed area of 4,862.3 km², plays a crucial role in the region's water resources. However, selecting the most suitable hydrological model for this task remains a challenge due to varying model performances. This study addresses the problem of identifying the most effective hydrological model for predicting stream flow in the Mille River by evaluating three models: Soil and Water Assessment Tool (SWAT), Hydrologiska Byråns Vattenbalansavdelning (HBV-IHMS), and Artificial Neural Network (ANN). The objectives are to compare these models’ abilities to simulate daily stream flow and to determine their accuracy. The significance of this evaluation lies to guide water management decisions by identifying which model provides the most reliable predictions. The methodology involves using performance metrics, Nash-Sutcliffe Efficiency (NSE) values, to assess the accuracy of each model. The results show that SWAT achieved the highest NSE values of 0.81 and 0.82, indicating the best performance in simulating stream flow. ANN followed with NSE values of 0.79 and 0.81, while HBV-IHMS had lower NSE values of 0.73 and 0.74. Although all models demonstrated potential, SWAT and ANN outperformed HBV-IHMS in capturing the complex hydrological processes of the Mille River. The findings from this study indicate that the choice of model significantly impacts the accuracy and reliability of streamflow predictions. The SWAT and ANN models show strong potential for use in operational streamflow forecasting in the Mille River and similar basins. In conclusion, further research is recommended to refine model calibration and validation processes. Incorporating additional data sources, such as remote sensing and climate projections, could further enhance model accuracy and provide a more comprehensive understanding of the region's hydrological dynamics. [ABSTRACT FROM AUTHOR]