Abstract Quantifying uncertainty in projecting climate change impact on flood frequency analysis is particularly relevant for long-term water resources planning and management. This study examines uncertainties arising from (1) a climate model, with and without accounting of intermodel similarities, (2) a hydrological model with two different error model definitions, which have previously received less attention in studies propagating uncertainty through climate change impacts on flood response. Through a Bayesian modeling framework, a proposed statistical framework is utilized to explore various definitions of sources of uncertainty and to develop a series of nested formulations that can evaluate the leverage of specific uncertainty sources in quantiles of future streamflow. To be specific, climate model similarity, hydrologic prediction error, and frequency analysis are formally modeled with an appropriate likelihood function. The quantiles inferred by each formulation are compared in a case study of the Yongdam Basin in Korea. Results indicate that variance in hydrologic response is underestimated if climate model similarities are ignored, and in many cases, the inferred quantiles of streamflow projection are biased accordingly. Furthermore, a simple error model used in defining hydrologic uncertainty may create incorrect information in determining the quantiles in streamflow projection. The approach presented here of quantifying uncertainty has the potential to better depict overall multi-source uncertainties in projections of climate change impacts on hydrologic response. Finally, our results also indicate that careful flood planning management may be required for the Yongdam Basin in future summers.

中文翻译：

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结合气候模型相似性和水文误差模型来量化气候变化对未来河流洪水风险的影响

摘要 量化预测气候变化对洪水频率分析影响的不确定性与长期水资源规划和管理特别相关。本研究检查了由 (1) 气候模型引起的不确定性，有和没有考虑模型间的相似性，(2) 具有两种不同误差模型定义的水文模型，这些模型以前在通过气候变化对洪水的影响传播不确定性的研究中受到的关注较少回复。通过贝叶斯建模框架，提议的统计框架用于探索不确定性来源的各种定义，并开发一系列嵌套公式，以评估特定不确定性来源在未来流量分位数中的杠杆作用。具体来说，气候模型相似性、水文预测误差、和频率分析用适当的似然函数正式建模。在韩国龙潭盆地的案例研究中比较了每个公式推断的分位数。结果表明，如果忽略气候模型的相似性，会低估水文响应的差异，并且在许多情况下，流量预测的推断分位数会相应地出现偏差。此外，用于定义水文不确定性的简单误差模型可能会在确定流量预测中的分位数时产生不正确的信息。这里提出的量化不确定性的方法有可能更好地描述气候变化对水文响应影响的预测中的整体多源不确定性。最后，