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

Abstract

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.

Unsurprising Surprises: The Frequency of Record‐breaking and Overthreshold Hydrological Extremes Under Spatial and Temporal Dependence

“…To mitigate this effect, we did not apply monthly or seasonal stratification in the analysis of CONUS temperature and precipitation, and spatiotemporal dependence structures were estimated on the parent process X and then suitably deflated by equation (27) to obtain those of Y. Moreover, it is known that correlation can influence the statistics of extreme values yielding, for instance, spatiotemporal clustering of RB, POT, or block maxima (e.g., Bogachev & Bunde, 2012;Eichner et al, 2011;Serinaldi & Kilsby, 2016b) . On the other hand, it is generally difficult to retrieve the underlying correlation structures only from extreme events, which often appear to be approximately independent because of downsampling effects of data selection and consequent removal of nonextreme data providing information on correlation (e.g., Serinaldi et al, 2018).…”

“…This property can result in observed sequences of low and high summary statistics (e.g., block minima, block maxima, and POT values) that appear, however, approximately uncorrelated because these statistics do not provide enough information to assess the actual dependence of the underlying process. Lack of apparent dependence can lead to interpret low and high regimes as lack of stationarity, while they can be explained by the underlying dependence, which is however concealed by the sampling procedure (Serinaldi & Kilsby, 2016b;Serinaldi et al, 2018). In this respect, focusing on monthly, seasonal, and annual values, as done in the literature mentioned above, and using resampling methods that preserve only approximately a fraction of the actual correlation can invalidate preliminary analysis and modeling efforts.…”

Modeling the spatial dependence of floods using the Fisher copula

“…A larger dataset would also be desirable for reliable estimation of the dependence structure. One possibility of increasing the dataset would be to exploit the whole content of a continuous runoff series instead of only using a flood event sample as suggested by Serinaldi and Kilsby (2017).…”

Generation of Spatially Heterogeneous Flood Events in an Alpine Region—Adaptation and Application of a Multivariate Modelling Procedure

“…Section 3.1 (iii)), it should be noted that the presented work is embedded in a flood risk project with a (re)insurance background. In this industry, a time span of 72 h ('hours clause') is often used for event definition [47,48]. Based on the three criteria, a time interval with length τ = 3 days was chosen.…”