Climate is one of the main drivers of hydrological processes, and climate change has caused worldwide effects such as water scarcity, frequent floods and intense droughts. The purpose of this study was to analyze the effects of climate change on the water balance components, high flow and low flow stream conditions in a semi-arid basin in Iran. For this reason, the climate outputs of the CanESM5 model under Shared Socioeconomic Pathways (SSP) scenarios SSP126, SSP245, and SSP585 were spatially downscaled by the Statistical Downscaling Model (SDSM). The hydrological process was simulated by the Soil and Water Assessment Tool (SWAT) model. Key findings include a 74% increase in evapotranspiration, a reduction by up to 9.6% in surface runoff, and variations in discharge by up to 53.6%. The temporal analysis of snow melting changes revealed an increase in the volume of snow melting during winter months and a reduction in the volume during spring. The projected climate change is expected to cause notable variations in high and low flow events, particularly under the SSP585 scenario, which anticipates significant peaks in flow rates. This comprehensive analysis underscores the pressing need for adaptive strategies in water resource management to mitigate the anticipated impacts of climate variability.

Effective watershed management requires anticipating environmental changes and implementing strategies to reduce soil erosion and sediment yield. However, the effectiveness of structural and biological best management practices (BMPs) under combined climate and land use/land cover (LULC) changes remains poorly understood in semi-arid mountainous regions, where implementation is limited by site-specific factors such as soil depth, slope, rainfall, and LULC. This study quantified hydrological and sediment dynamics in the Taleghan Watershed, Iran (823 km²), dominated by rangeland, for baseline (1990–2010) and future (2020–2040) periods. Future climate scenarios were derived from downscaled CanESM5 projections (SSP2-4.5), and 2038 LULC was simulated using a Cellular Automata Markov model. SWAT was coupled with the Non-dominated Sorting Genetic Algorithm II (NSGA II) to optimize the selection and spatial allocation of BMPs including seeding, pile seeding, contour farming and check dams within critical source areas (CSAs) to reduce sediment yield and implementation costs under feasibility constraints. Results showed a rise in maximum (21.5 °C to 22.2 °C) and minimum (7.2 °C to 7.3 °C) temperatures, a 4–11 % increase in mean annual precipitation, and rangeland improvement, with moderate rangeland increasing from 31 % to 42 %. Soil erosion increased by 32 % under climate scenarios but decreased by 7.5 % under land use change, whereas their combined effects led to 23.8 % higher erosion and 6.5 % greater sediment yield. Among biological measures, pile seeding was most widely applicable, though feasibility constraints limited sediment control with reductions of 3.8 to 4.8 % along the Pareto front. Integrating feasibility constraints with climate and land use projections provides conservative yet practical insights for sustainable watershed management in semi-arid mountainous regions.