The magnitude of flood impacts is regulated not only by hydrometeorological hazard and exposure, but also flood protection levels (primarily from structural flood defenses) and vulnerability (relative loss at given intensity of hazard). Here, we infer the variation of protection levels and vulnerability from data on historical riverine, coastal, and compound floods and associated impacts obtained from the HANZE database, in 42 European countries over the period 1950–2020. We contrast actual damaging floods, which imply flood protection was locally inadequate, with modelled potential floods, i.e. events that were hydrologically extreme but did not lead to significant impacts, which imply that flood protection was sufficient to prevent losses. Further, we compare the reported magnitude of impacts (fatalities, population affected, and economic losses) with potential impacts computed with depth-damage functions. We finally derive the spatial and temporal drivers of both flood protection and vulnerability through a multivariate statistical analysis. We apply vine-copulas to derive the best predictors out of a set of candidate variables, including hydrological parameters of floods, exposure to floods, socioeconomic development, and governance indicators. Our results show that riverine flood protection levels are much lower than assumed in previous pan-European studies. North-western Europe is shown to have better riverine protection than the south and east, while the divide is not so clear for coastal protection. By contrast, many parts of western Europe have relatively high vulnerability, with lowest value observed in central and northern Europe. Still, a strong decline in flood vulnerability over time is also observed for all three indicators of relative losses, suggesting improved flood adaptation. Flood protection levels have also improved since 1950, particularly for coastal floods.

This contribution presents an assessment at a regional (island) scale of the beach erosion due to storm events under Climate Change. The approach adopted to assess beach erosion at the island scale consisted of three modules. First, the location, dimensions and other attributes of the Cypriot beaches were recorded on the basis of widely-available satellite imagery. Secondly, sea levels and waves were modeled along the coast under different climatic scenarios and dates in the 21st century. Finally, using these projections beach retreat due to the relative mean sea level rise (RSLR) and extreme sea levels (ESLs) was estimated using ensembles of analytical and numerical cross-shore morphodynamic models, respectively. Extreme sea levels (ESLs) were projected to (a) increase by up to 60% in 2100 from their baseline (2000) levels, and (b) vary along the coast, with the highest ESLs (and corresponding waves) projected for the southern and western coasts. The mostly narrow Cypriot beaches (91% recorded maximum widths of < 50 m) showed increased exposure to erosion. In 2100, about 47% and 72% (based on the median model estimates) of the 241 unprotected Cypriot beaches will be permanently eroded, due to mean sea level rise (SLR), to 50% of their present maximum width, depending on the scenario. In addition to the long-term erosion due to SLR, severe storm erosion is projected by 2050 even under the RCP4.5 scenario; the 100-year extreme sea level event (ESL100) may overwhelm (at least temporarily) 49% of the currently unprotected Cypriot beaches without effective adaptation responses, with the most exposed beaches located along the northern coast. As the beach carrying capacity and hedonic value will be severely compromised, effective adaptation policies and technical measures will be urgently required.

The interaction between storm surges and inland run-off has been gaining increasing attention recently, as they have the potential to result in compound floods. In Europe, several flood events of this type have been recorded in the past century in Belgium, France, Ireland, Italy and UK. First projections of compound flood hazard under climate change have been made, but no study has so far analysed whether existing, independent climate and hydrodynamic models are able to reproduce the co-occurrence of storm surges, precipitation, river discharges or waves. Here, we investigate the dependence between the different drivers in different observational and modelled data set, utilizing gauge records and high-resolution outputs of climate reanalyses and hindcasts, hydrodynamic models of European coasts and rivers. The results show considerable regional differences in strength of the dependence in surge–precipitation and surge–discharge pairs. The models reproduce those dependencies, and the time lags between the flood drivers, rather well in north-western Europe, but less successfully in the southern part. Further, we identified several compound flood events in the reanalysis data. We were able to link most of those modelled events with historical reports of flood or storm losses. However, false positives and false negatives were also present in the reanalysis and several large compound floods were missed by the reanalysis. All in all, the study still shows that accurate representation of compound floods by independent models of each driver is possible, even if not yet achievable at every location.

Commercial assets comprise buildings, machinery and equipment, which are susceptible to floods. Existing damage models and exposure estimation methods for this sector have limited transferability between flood events and therefore limited potential for pan-European applications. In this study we introduce two methodologies aiming at improving commercial flood damage modelling: (1) disaggregation of economic statistics to obtain detailed building-level estimates of replacement costs of commercial assets; (2) a Bayesian Network (BN) damage model based primarily on post-disaster company surveys carried out in Germany. The BN model is probabilistic and provides probability distributions of estimated losses, and as such quantitative uncertainty information. The BN shows good accuracy of predictions of building losses, though overestimates machinery/equipment loss. To test its suitability for pan-European flood modelling, the BN was applied to three case studies, comprising a coastal flood in France (2010) and fluvial floods in Saxony (2013) and Italy (2014). Overall difference between modelled and reported average loss per company was only 2–19% depending on the case study. Additionally, the BN model achieved better results than six alternative damage models in those case studies (except for one model in the Italian case study). Further, our exposure estimates mostly resulted in better predictions of the damage models compared to previously published pan-European exposure data, which tend to overestimate exposure. All in all, the methods allow easy modelling of commercial flood losses in the whole of Europe, since they are applicable even if only publicly-available datasets are obtainable. The methods achieve a higher accuracy than alternative approaches, and inherently provide confidence intervals, which is particularly valuable for decision making under high uncertainty.

Bayesian Networks (BNs) are probabilistic, graphical models for representing complex dependency structures. They have many applications in science and engineering. Their particularly powerful variant – Non-Parametric BNs – are for the first time implemented as an open-access scriptable code, in the form of a MATLAB toolbox “BANSHEE”.¹ The software allows for quantifying the BN, validating the underlying assumptions of the model, visualizing the network and its corresponding rank correlation matrix, and finally making inference with a BN based on existing or new evidence. We also include in the toolbox, and discuss in the paper, some applied BN models published in most recent scientific literature.

In Miȩdzyzdroje, a coastal town in Poland, significant cliff retreat has been observed in recent times. It used to be considered mainly a response to storm events with particularly high water levels and wave energy. However, morphology of cliff coasts is shaped not only by the most extreme storm surges or by a number of accompanying processes such as precipitation. Much wider effects are now being linked to the occurrence of series of subsequent storms. This research uses a set of five terrestrial LiDAR surveys carried out between November 2016 and April 2017 to determine short-term cliff erosion associated with two major storm surges and several smaller storms. The surveys covered the whole cliff profile as well as the topography of the adjacent beach. Results indicate a considerable reduction in beach levels as a first important effect. Frequency of the storm events prevented the beach from recovering between the surges, allowing the waves to directly attack the cliff base. Consequently, the cliff foot line retreated up to 4.7 m. This resulted in an erosion volume exceeding 25.000 m within 5 sections of the coastal cliff analysed, which are 500 m long in total. This work demonstrates that the development of the coastline is not only directly linked with the rate of erosion at given storm parameters. More importantly, the frequency of extreme events has to be considered.

Flood hazard is currently being researched on continental and global scales, using models of increasing complexity. In this paper we investigate a different, simplified approach, which combines statistical and physical models in place of conventional rainfall-run-off models to carry out flood mapping for Europe. A Bayesian-network-based model built in a previous study is employed to generate return-period flow rates in European rivers with a catchment area larger than 100-km². The simulations are performed using a one-dimensional steady-state hydraulic model and the results are post-processed using Geographical Information System (GIS) software in order to derive flood zones. This approach is validated by comparison with Joint Research Centre's (JRC) pan-European map and five local flood studies from different countries. Overall, the two approaches show a similar performance in recreating flood zones of local maps. The simplified approach achieved a similar level of accuracy, while substantially reducing the computational time. The paper also presents the aggregated results on the flood hazard in Europe, including future projections. We find relatively small changes in flood hazard, i.e. an increase of flood zones area by 2-4-% by the end of the century compared to the historical scenario. However, when current flood protection standards are taken into account, the flood-prone area increases substantially in the future (28-38-% for a 100-year return period). This is because in many parts of Europe river discharge with the same return period is projected to increase in the future, thus making the protection standards insufficient.