School of Civil Engineering and Geosciences, Newcastle University, Newcastle Upon Tyne, UK.
Willis Research Network, London, UK.
Risk Anal. 2017 Oct;37(10):1958-1976. doi: 10.1111/risa.12747. Epub 2016 Dec 29.
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.
洪水是一种随气象和流域动力而在时空上演变的自然危害，因此单次洪水事件会在事件持续时间内影响不同地区。这种物理机制在给定时间窗口内将洪水记录与不同位置的损失之间产生了时空关系，这些关系应在对集体洪水风险进行有效评估时加以考虑。然而，由于极端洪水是罕见事件，历史记录的数量通常有限，从而无法进行可靠的频率分析。为了克服这一限制，我们从极端事件分析转向连续水流记录的建模，保留整个过程的时空相关结构，并更有效地利用连续水流记录提供的信息。该方法基于动态 Copula 框架，该框架允许通过耦合合适的时间序列模型来划分时空特性的建模，这些模型考虑了时间动态和描述空间相关性的多元分布。该模型应用于记录在中欧和东欧 10 个最大流域中的 490 个水流序列（多瑙河、莱茵河、易北河、奥得河、瓦尔瑟河、默兹河、罗纳河、塞纳河、卢瓦尔河和加龙河）。利用可用的代理数据来量化当地洪水暴露和脆弱性，我们表明，时间依赖性在重现年际持续性方面发挥了关键作用，从而再现了流域范围内年度代理洪水损失的规模和频率，而 Copula 则允许在每周和年度时间尺度上保留损失的空间相关性。
