Lookup NU author(s): Dr Francesco Serinaldi,
Professor Chris Kilsby
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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 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 ten 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 inter-annual persistence, and thus magnitude and frequency of annual proxy flood losses aggregated at a basin-wide scale, while copulas allow the preservation of the mutual dependence of losses at weekly and annual time scales.
Author(s): Serinaldi F, Kilsby CG
Publication type: Article
Publication status: Published
Journal: Risk Analysis
Print publication date: 01/10/2017
Online publication date: 29/12/2016
Acceptance date: 04/10/2016
ISSN (print): 0272-4332
ISSN (electronic): 1539-6924
Data Source Location: http://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html;jsessionid=E7BC7B7DE7C30DD641FFFA277294F518.live2051
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