One of the most significant events in hydrology is flooding and frequency analysis of the past events is a method to study it. In typical procedures of flood frequency analysis, only flood peak flow is considered, and it is assumed that peak flow conform with special statistical distributions. These assumptions have limitations and cannot lead to precise information about flood risk. On the other hand, climate change impact on water resources is an important challenge so that, variations of meteorological parameters average values, particularly temperature and precipitation, may influence water resources in the future. In this study, two methods (frequency analysis and climate change) have been investigated for peak flow estimation. Base period in this study is 1971-2000 and for the future, two statistical periods have been considered, including 2020-2049 and 2050-2079. Meteorological data for this study were achieved from Chelgerd meteorological station located in Zayanderood upstream watershed and flow data are obtained from Ghale Shahrokh hydrometry station located in the same location. For determining flood risk by using frequency analysis methods, distribution functions of Freq software have been used. Annual peak flows data were fitted by using moments method and in the next step, the best distribution function was selected by using good fit test and finally peak flow values were estimated for different return periods. In flow estimation method by using climate change models to predict future data, fifth IPCC report and five GCM models (IPCC distribution data center website) have been used. These data were used after changing them to microscale data by using LARS-WG model. Average flow was simulated by using IHACRES model and then efficiency of the model carried out and finally the model was calibrated and validated for the watershed. After analyzing and estimating flow by using two methods (frequency analysis and climate change), flow values were predicted for future and calculated-flow conformation for different return periods for different months were determined and the best conformation was observed in September with 0.1% and the worst likeness in November with 32.27%. Comparison of observed temperature and precipitation data in base period with predicted data for the future indicated that temperature increases and it is considered that autumn season is more sensitive to temperature increasing and precipitation regardless of fluctuations in some seasons and months has generally a decreasing trend and the highest value of precipitation decreasing happened in the summer. Keywords: Flood risk, Frequency analysis, Climate change, Freq, IHACRES, LARS-WG