Extended Abstract

Introduction:
Climate change, as one of the main challenges of this century, has significant impacts on the hydrological cycle and water resources management, especially in arid and semi-arid regions. Temperature changes, precipitation fluctuations, and increased occurrence of extreme events such as floods and droughts threaten the sustainability of water systems. In this context, hydrological models such as the Soil and Water Assessment Tool (SWAT) serve as effective tools for simulating watershed responses to various climatic scenarios. At the same time, the new climate scenarios presented in the sixth report of the IPCC and the new generation GCM models have enabled a more accurate assessment of hydrological responses. Numerous studies conducted in regions such as China, India, Iran, and South Korea have demonstrated that climate change can significantly alter surface runoff patterns, typically increasing runoff during wet seasons and reducing flow during drier periods. Therefore, a detailed study of the hydrological response of watershed under different climate change scenarios can play a key role in developing sustainable water resources management strategies in the future.

Material and Methods:
The SWAT model (Soil and Water Assessment Tool) was applied in this study to evaluate the impact of climate change on the hydrologic response of the Golroodbar watershed, Semnan Province, Iran. The input data, which were collected from authoritative sources, included digital elevation models (DEM), land use/land cover maps, soil maps, and climatic and hydrological time series. The SWAT model was calibrated and validated with observed streamflow to simulate surface runoff under the baseline climate conditions. For future climates, outputs of CanESM5 global climate models were used under the Sixth Assessment Report of the IPCC (CMIP6), including three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The Statistical Downscaling Model (SDSM) was used to downscale the GCM daily precipitation and temperature to the basin scale. The downscale future climate data were used as for the inputSWAT model to simulate runoff for different future time periods. Next, seasonal runoff patterns for each climate scenario were viewed and compared with the baseline. This methodological design enables a realistic and holistic hydrological analysis of basin responses to future climate, providing a foundation for adaptive water resource management can be conducted in semi-arid watersheds.

Results and Discussion:
The simulation results demonstrated that the SWAT model performed well in simulating the surface runoff in the Golarudbar watershed. The model accuracy was acceptable with Nash-Sutcliffe Efficiency (NSE) and Coefficient of Determination (R²) values of 0.66 and 0.66 during the calibration period, and 0.55 and 0.79 during the validation period. The p-factor and r-factor values were also within satisfactory ranges in both periods. Future climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were simulated using the SDSM model with CanESM5 data. These scenarios showed significant changes in monthly average precipitation and temperature compared with the historical baseline. According to the results, in the warm months of the year (May to September), precipitation remains very low and almost similar to historical values; although in some months such as June, a slight increase is seen in the SSP5-8.5 scenario. After that, the precipitation trend increases again from October. Overall, the SSP5-8.5 scenario shows the greatest change in the precipitation pattern, which could indicate an increased likelihood of heavy rainfall and flooding in the future. Also, a decrease in runoff is observed in most months compared with the historical period. The largest decrease in runoff is in January in the SSP1-2.6 scenario, which is -64.05 percent, which indicates a significant effect of the decrease in temperature and precipitation in this scenario. In contrast, the largest percentage increase in runoff is in August, July, and November in the SSP5-8.5 scenario, which is 51.30, 33.98, and 33 percent, respectively. This trend indicates that runoff increases due to more intense short-term precipitation caused by global warming conditions.

Conclusion:
This study demonstrated that the SWAT model effectively simulates the hydrological pattern of the semi-arid Golrudbar basin, capturing runoff variation accurately. Climatic and physiographic parameter optimization and statistical index-based validation ensure maximum credibility of the results. Downscaling climate predictions from the CanESM5 model at different greenhouse gas emission scenarios demonstrates that temperature and precipitation variability has significant impacts on runoff patterns. The SSP5-8.5 high-emission scenario predicts greater peak runoff and increased variability in surface flow, suggesting a potential increase in future flood hazards. Lower-emission scenarios like SSP1-2.6 has more symmetric trends in runoff. The findings highlight the need for wise climate policies and adaptive strategies, especially in arid and semi-arid regions, to ensure sustainable water resource management and flood risk reduction under climate change.