Different sources of uncertainty exist in climate change impacts projection. This study aims to propose a framework to deal with the various sources of uncertainties involved in hydro-climate projections of Zayandeh-Rud River Basin with area of 26,917 km² in Central Iran. The Bayesian model averaging (BMA) was here used through two distinct approaches for weighting the hydrologic outputs (App. I) as well as the global climate models (GCMs) (App. II) based on their abilities to simulate the baseline period. The results showed that different GCMs have different abilities in estimating the hydro-climatic variables and the application of uncertainty analysis is necessary for climate change studies. Application of the BMA can significantly reduce the errors in historical runoff prediction. Although App. I showed a better performance of generating the stream flow time series during the baseline period, the App. II approach has an acceptable ability in different months. The findings of flow duration curves under both approaches revealed that App. II is more appropriate to deal with uncertainty of hydro-climate projection especially in arid and semi-arid regions.

气候变化影响预测中存在不同的不确定性来源。这项研究旨在提出一个框架，以处理涉及面积为26,917 km ²的Zayandeh-Rud流域水文气候预测的各种不确定性来源在伊朗中部。贝叶斯模型平均法（BMA）是通过两种不同的方法对水文产出（附录I）以及全球气候模型（GCM）（附录II）加权的基础上使用的，这两种方法均基于其模拟基线期的能力。结果表明，不同的GCM具有不同的估算水文气候变量的能力，不确定性分析的应用对于气候变化研究是必要的。BMA的应用可以大大减少历史径流预测中的误差。虽然应用程序。我展示了在基线时段App生成流时间序列的更好的性能。II方法在不同月份中具有可接受的能力。在两种方法下的流动持续时间曲线的发现揭示了该应用。