There has been a growing interest in model-wise uncertainty decomposition, which quantifies contribution of individual models such as emission scenarios, global circulation models, bias correction techniques and hydrological models, to the total uncertainty of a hydrological projection. However, little research has been conducted for model-wise uncertainty decomposition in spite of its usefulness. In this paper, we propose a novel method for decomposing the total uncertainties into model-wise uncertainties. The proposed model-wise uncertainty decomposition method can be applied with general uncertainty measures, which include mean absolute deviation and variance measures. Moreover, the proposed method provides an intuitive interpretation of the quantified model-wise uncertainties. The results of analyzing real data by the proposed method are presented.

人们对模型不确定性分解越来越感兴趣，它量化了单个模型（如排放情景、全球环流模型、偏差校正技术和水文模型）对水文预测总不确定性的贡献。然而，尽管模型很有用，但对模型不确定性分解的研究很少。在本文中，我们提出了一种将总不确定性分解为模型不确定性的新方法。所提出的模型不确定性分解方法可以应用于一般的不确定性度量，包括平均绝对偏差和方差度量。此外，所提出的方法提供了量化模型不确定性的直观解释。