Climate change impact assessment on water resources in spatiotemporal scales in arid and semi-arid regions is of crucial importance in long-term water resources planning and management. In this study, we use Soil and Water Assessment Tool (SWAT) model in combination with the sequential uncertainty fitting program (SUFI-2) to calibrate and validate a hydrologic model to predict the effects of climate changes on water resources components in the Zayandeh-Rud watershed in central Iran. The water availability is analyzed based on the changes in blue water flow, green water flow, and green water storage for short and long-term periods (i.e., 2060-2100 and 2020-2100) relative to the historic period (i.e., 1992-2009). The models are calibrated and validated by considering sensitivity and uncertainty analysis. To determine the future climate change impacts on different components of water resources, the GCMs outputs from the newest version of the Fifth Assessment Report (AR5) of Intergovernmental Panel on Climate Change (IPCC) are statically downscaled using machine-learning and dimensionality reduction methods and fed into the SWAT model. The results demonstrate that at sub-basin level, green water is tended to decrease, while green water flow increase in the future in the watershed. The persistence-trend of green water storage in the region indicates that the historic trends will continue in the future as well. At the monthly temporal resolution, the upper and the lower bounds of the 95% prediction uncertainties for different hydrological components, especially, blue water and green water flow show a wide range of changes in the watershed. The calculated uncertainties in historic period for the green water flow and green water storage are lower than the green water flow. Different climate change scenarios are also within a narrow range in green water storage, while heterogeneous conditions exist in the blue and the green water flows. This study provides a strong basis for water supply-demand analyses in central Iran; however, the proposed analytical framework could be applied to other regions with similar challenges.
