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A Blueprint for Full Collective Flood Risk Estimation: Demonstration for European River Flooding

机译：全面集体洪水风险估算的蓝图：欧洲河流洪水示范

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摘要

Floods are a natural hazard evolving in space and time according to meteorological and river basin dynamics, so that a single flood event can affect different regions over the event duration. This physical mechanism introduces spatio-temporal relationships between flood records and losses at different locations over a given time window that should be taken into account for an effective assessment of the collective flood risk. However, since extreme floods are rare events, the limited number of historical records usually prevents a reliable frequency analysis. To overcome this limit, we move from the analysis of extreme events to the modeling of continuous stream flow records preserving spatio-temporal correlation structures of the entire process, and making a more efficient use of the information provided by continuous flow records. The approach is based on the dynamic copula framework, which allows for splitting the modeling of spatio-temporal properties by coupling suitable time series models accounting for temporal dynamics, and multivariate distributions describing spatial dependence. The model is applied to 490 stream flow sequences recorded across 10 of the largest river basins in central and eastern Europe (Danube, Rhine, Elbe, Oder, Waser, Meuse, Rhone, Seine, Loire, and Garonne). Using available proxy data to quantify local flood exposure and vulnerability, we show that the temporal dependence exerts a key role in reproducing interannual persistence, and thus magnitude and frequency of annual proxy flood losses aggregated at a basin-wide scale, while copulas allow the preservation of the spatial dependence of losses at weekly and annual time scales.
机译：洪水是根据气象和流域动力学在时空上演变的自然灾害，因此单个洪水事件可以在事件持续时间内影响不同地区。这种物理机制在给定的时间范围内，在不同位置的洪水记录和损失之间引入了时空关系，为有效地评估集体洪水风险，应将其考虑在内。但是，由于极端洪水是罕见事件，因此数量有限的历史记录通常会妨碍可靠的频率分析。为了克服这一限制，我们从对极端事件的分析转向对连续流记录的建模，以保留整个过程的时空相关结构，并更有效地利用连续流记录提供的信息。该方法基于动态copula框架，该框架允许通过耦合考虑时间动态的适当时间序列模型和描述空间依赖性的多元分布，来拆分时空属性模型。该模型应用于记录在中欧和东欧10个最大流域（多瑙河，莱茵河，易北河，奥得河，奥泽河，默兹河，罗纳河，塞纳河，卢瓦尔河和加龙河）的490个水流序列。使用可用的代理数据来量化局部洪水的暴露和脆弱性，我们表明时间依赖性在再现年际持久性方面起着关键作用，因此在整个流域范围内，年度代理洪水损失的数量和频率合计，而copulas可以保存在每周和每年的时间尺度上损失的空间依赖性。

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