The quantification of climate change impacts on hydrology is subjected to multiple uncertainty sources. Large ensembles of hydrological simulations based on multimodel ensembles (MMEs) have been commonly applied to represent overall uncertainty of hydrological impacts. However, as increasing numbers of global climate models (GCMs) are being developed, how many GCMs in MMEs are sufficient to characterize overall uncertainty is not clear. Therefore, this study investigates the influences of GCM quantity on quantifying overall uncertainty and uncertainty contributions of multiple sources in hydrological impacts. Large ensembles of hydrological simulations are obtained through the permutation of 3 greenhouse gas emission scenarios, 22 GCMs, 6 downscaling techniques, 5 hydrological models (HMs), and 5 sets of HM parameters, which enables to decompose uncertainty components using analysis of variance. The influences of GCM quantity are investigated by repeatedly conducting uncertainty decomposition for hydrological simulations from subsets with different numbers of GCMs. The results show that GCMs are the leading uncertainty sources in evaluating changes in annual and peak streamflows, while for changes in low flow, other uncertainty sources except HM parameters also have large contributions to overall uncertainty. Furthermore, on the condition of using no more than five GCMs, there are large possibilities that the overall uncertainty and GCMs' uncertainty contribution are underestimated. Using around 10 GCMs can ensure that the median of different combinations generates similar uncertainty components as the whole ensemble. Therefore, it is recommended to use at least 10 GCMs in studies of climate change impacts on hydrology to thoroughly quantify uncertainty.

中文翻译：

---

量化气候变化的水文影响中多种来源的GCM不确定性贡献的框架

气候变化对水文影响的量化有多种不确定性来源。基于多模型集成（MME）的大型水文模拟集成已普遍应用于表示水文影响的总体不确定性。然而，随着越来越多的全球气候模型（GCM）的开发，尚不清楚MME中有多少个GCM足以表征总体不确定性。因此，本研究调查了GCM量对量化总体不确定性和水文影响中多个来源的不确定性贡献的影响。通过对3种温室气体排放情景，22种GCM，6种降尺度技术，5种水文模型（HMs）和5组HM参数进行置换，可以获得大型的水文模拟集合，使用方差分析可以分解不确定性分量。通过对具有不同数量GCM的子集反复进行不确定性分解以进行水文模拟，研究了GCM数量的影响。结果表明，GCM是评估年流量和峰值流量变化的主要不确定性来源，而对于低流量变化，除了HM参数之外，其他不确定性来源也对整体不确定性有很大贡献。此外，在使用不超过五个GCM的条件下，整体不确定性和GCM的不确定性贡献很可能被低估。使用大约10个GCM可以确保不同组合的中位数产生与整个集合相似的不确定性分量。因此，