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Flood risk curves and uncertainty bounds

机译：洪水风险曲线和不确定性界限

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摘要

Although flood risk assessments are frequently associated with significant uncertainty, formal uncertainty analyses are the exception rather than the rule. We propose to separate two fundamentally different types of uncertainty in flood risk analyses:aleatory and epistemic uncertainty. Aleatory uncertainty refers to quantities that are inherently variable in time, space or populations of individuals or objects. Epistemic uncertainty results from incomplete knowledge and is related to our inability to understand, measure and describe the system under investigation. The separation between aleatory and epistemic uncertainty is exemplified for the flood risk analysis of the city of Cologne, Germany. This flood risk assessment consists of three modules:(1) flood frequency analysis, (2) inundation estimation and (3) damage estimation. By the concept of parallel models, the epistemic uncertainty of each module is quantified. The epistemic uncertainty associated with the risk estimate is reduced by introducing additional information into the risk analysis. Finally, the contribution of different modules to the total uncertainty is quantified. The flood risk analysis results in a flood risk curve, representing aleatory uncertainty, and in associated uncertainty bounds, representing epistemic uncertainty. In this way, the separation reveals the uncertainty (epistemic) that can be reduced by more knowledge and the uncertainty (aleatory) that is not reducible.
机译：尽管洪水风险评估通常与重大不确定性相关联，但形式上的不确定性分析是例外而不是规则。我们建议在洪水风险分析中将两种根本不同的不确定性类型分开：临时不确定性和认知不确定性。不确定性是指在时间，空间或个体或物体的人口方面固有地可变的数量。认识论上的不确定性源于不完全的知识，这与我们无法理解，测量和描述所研究的系统有关。偶然性和认知不确定性之间的分离以德国科隆市的洪水风险分析为例。该洪水风险评估包括三个模块：（1）洪水频率分析，（2）淹没估计和（3）破坏估计。通过并行模型的概念，可以量化每个模块的认知不确定性。通过将其他信息引入风险分析，可以减少与风险估计有关的认知不确定性。最后，量化了不同模块对总不确定度的贡献。洪水风险分析会得出洪水风险曲线，代表偶然不确定性，而相关不确定性范围代表认知不确定性。通过这种方式，分离揭示出可以通过更多的知识减少的不确定性（流行病）和无法减少的不确定性（初步）。

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