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.

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In this paper we discuss PyBanshee, which is a Python-based open-source implementation of the MATLAB toolbox BANSHEE. PyBanshee constitutes the first fully open-source package to quantify, visualize and validate Non-Parametric Bayesian Networks (NPBNs). The architecture of PyBanshee is heavily based on its MATLAB predecessor. It presents the full implementation of existing tools and introduces new modules. Specifically, PyBanshee allows for: (i) choosing fully parametric one-dimensional margins, (ii) choosing different sample sizes for the model-validation tests based on the Hellinger distance, (iii) drawing user-defined sample sizes of the NPBN, (iv) sample-based conditioning sampling (similarly to the closed-source proprietary package UNINET by LightTwist Software) and (v) visualizing the comparison between the histograms of the unconditional and conditional marginal distributions. New detailed examples demonstrating new features are provided.@en

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.

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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.

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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.

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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.

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Cliff coasts are dynamic environments that can retreat very quickly. However, the
short-term changes and factors contributing to cliff coast erosion have not received as much attention as dune coasts. In this study, three soft-cliff systems in the southern Baltic Sea were monitored with the use of terrestrial laser scanner technology over a period of almost two years to generate a time series of thirteen topographic surveys. Digital elevation models constructed for those surveys allowed the extraction of several geomorphological indicators describing coastal dynamics. Combined with observational and modeled datasets on hydrological and meteorological conditions, descriptive and statistical analyses were performed to evaluate cliff coast erosion. A new statistical model of short-term cliff erosion was developed by using a non-parametric Bayesian network approach. The
results revealed the complexity and diversity of the physical processes influencing both beach and cliff erosion. Wind, waves, sea levels, and precipitation were shown to have different impacts on each part of the coastal profile. At each level, different indicators were useful for describing the conditional dependency between storm conditions and erosion. These results are an important step toward a predictive model of cliff erosion.@en

Adverse consequences of floods change in time and are influenced by both natural and socio-economic trends and interactions. In Europe, previous studies of historical flood losses corrected for demographic and economic growth (‘normalized’) have been limited in temporal and spatial extent, leading to an incomplete representation of trends in losses over time. Here we utilize a gridded reconstruction of flood exposure in 37 European countries and a new database of damaging floods since 1870. Our results indicate that, after correcting for changes in flood exposure, there has been an increase in annually inundated area and number of persons affected since 1870, contrasted by a substantial decrease in flood fatalities. For more recent decades we also found a considerable decline in financial losses per year. We estimate, however, that there is large underreporting of smaller floods beyond most recent years, and show that underreporting has a substantial impact on observed trends.@en

Coastal flood maps covering the whole European continent have become available in recent years. However, their ability to complement or replace high-resolution local flood maps was not investigated so far. In this paper we compare pan-European estimates of extreme sea levels and coastal flood extents at given return periods with observations and high-resolution reference maps. The analysis is done for two pan-European assessments and one global study. We find that whereas the models have good accuracy in estimating storm surge heights, large disparities exist between the large-scale flood maps and four local maps of flood extents from England, the Netherlands, Poland and France. Moreover, the accuracy of the underlying digital elevation model and assumptions about flood protection existing in a given area influence significantly the results. Additionally, the first pan-European projection of temporal trends in the size of flood zones is presented, with and without assuming flood protection levels.@en

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.

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The influence of social and economic change on the consequences of natural hazards has been a matter of much interest recently. However, there is a lack of comprehensive, high-resolution data on historical changes in land use, population or assets available to study this topic. Here, we present HANZE database, or ‘Historical Analysis of Natural Hazards in Europe’, which contains two parts: (1) HANZE-Exposure with maps for 37 countries and territories from 1870 to 2020 in 100 m resolution and (2) HANZE-Events, a compilation of past disasters with information on dates, locations and losses, currently limited to floods only. The database was constructed using high-resolution maps of present land use and population, a large compilation of historical statistics, and relatively disaggregation techniques and rule-based land-use reallocation schemes. Data encompassed in HANZE allow to ‘normalize’ information on losses due to natural hazards by taking into account inflation as well as changes in population, production and wealth. Database of past events currently contains 1564 records (1870–2016) of flash, river, coastal and compound floods. HANZE database is freely available at https://doi.org/10.4121/collection:HANZE@en

The interaction between storm surges and hydrometeorological phenomena on the land has been gaining increasing attention recently, especially after the severe flooding during the Harvey storm. In Europe, several compound events were recorded in the past century in western Europe and the Mediterranean region. Here, we investigate the joint occurrence of storm surges, precipitation, river discharges and waves through a statistical analysis based on copulas. We use several datasets covering most of Europe, including observations and data from the European Flood Awareness System (EFAS), ERA-Interim climate reanalysis and a regional climate model within CORDEX framework. The results show considerable regional differences in dependency structures and resulting joint probability of extreme surge, precipitation and river discharge events.We also compare correlations obtained from modelled data with those calculated from observational datasets. Finally, we synthesize the joint probability of occurrence of extreme compound events, and intensity of their components, in the form of a composite index, thus identifying areas where compound floods could be of most concern. @en

In the past decade, there has been growing interest in analysing flood hazard and risk on European scale. Such studies allow assessment of climate change impacts, can be used at EU-level policymaking and provide information on countries where local flood maps are not available. In this thesis, some innovative methodologies that contribute to improvement of pan-European flood mapping are explored. The topics covered include: (1) using Bayesian statistics to reduce time needed to map river flood hazard compared to rainfall-runoff models; (2) computing extreme sea levels and coastal flood hazard zones under present and future climate; (3) adjusting historical flood losses for changes in exposure to reveal true long-term trends in flood losses in Europe; (4) utilizing dependency modelling for assessing the hazard of compound flood occurrence.@en

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.

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Large-scale hydrological modelling of flood hazards requires adequate extreme discharge data. In practise, models based on physics are applied alongside those utilizing only statistical analysis. The former require enormous computational power, while the latter are mostly limited in accuracy and spatial coverage. In this paper we introduce an alternate, statistical approach based on Bayesian networks (BNs), a graphical model for dependent random variables. We use a non-parametric BN to describe the joint distribution of extreme discharges in European rivers and variables representing the geographical characteristics of their catchments. Annual maxima of daily discharges from more than 1800 river gauges (stations with catchment areas ranging from 1.4 to 807 000 km2) were collected, together with information on terrain, land use and local climate. The (conditional) correlations between the variables are modelled through copulas, with the dependency structure defined in the network. The results show that using this method, mean annual maxima and return periods of discharges could be estimated with an accuracy similar to existing studies using physical models for Europe and better than a comparable global statistical model. Performance of the model varies slightly between regions of Europe, but is consistent between different time periods, and remains the same in a split-sample validation. Though discharge prediction under climate change is not the main scope of this paper, the BN was applied to a large domain covering all sizes of rivers in the continent both for present and future climate, as an example. Results show substantial variation in the influence of climate change on river discharges. The model can be used to provide quick estimates of extreme discharges at any location for the purpose of obtaining input information for hydraulic modelling.
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Currently there is no internationally accepted framework for assessing the readiness of innovations that reduce disaster risk. To fill this gap, BRIGAID is developing a standard, comprehensive Testing and Implementation Framework (TIF). The TIF is designed to provide innovators with a framework for innovation and guidelines for assessing an innovation’s technical effectiveness, its social acceptance, and its impact on key socio-economic and environmental sectors. The vision is that the TIF will become the standard framework used to assess the effectiveness of climate adaptation innovations and the European quality label for testing.@en

In this paper two methodologies are investigated that contribute to better assessment of risks related to extreme rainfall events. Firstly, one-parameter bivariate copulas are used to analyze rain gauge data in the Netherlands. Out of three models considered, the Gumbel copula, which indicates upper tail dependence, represents the data most accurately for all 33 stations in the Netherlands. Seasonal variability is noticeable, with rank correlation reaching maximum in winter and minimum in summer as well as other temporal and spatial patterns. Secondly, an expert judgment elicitation was undertaken. The experts’ opinions were combined using Cooke’s classical method in order to obtain estimates of future changes in precipitation patterns. Experts predicted mostly an approximate 10% increase in rain amount, duration, intensity and the dependence between amount and duration. The results were in line with official national climate change scenarios, based on numerical modelling. Applicability of both methods was presented based on an example of an existing tunnel in the Netherlands, contributing to better estimates of the tunnel’s limit state function and therefore the probability of failure.

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This report focuses on the methodological development of the testing and implementation framework (TIF) for increasing the socio-technical readiness of climate adaptation innovations and assessing their impact on different socio-economic and environmental sectors. It is designed to be read by innovators and used as a supporting document for the application of different toolboxes made available through BRIGAID. In this report, Chapter 2 provides an overview of the different components of the TIF, including an overview of the planned testing phases. Definitions for the Performance Indicators (PI) are provided in Chapter 3, which also includes a description of how the test results which are to be integrated into the Climate Innovation Window (CIW) (in WP7). Elaborated guidelines for testing are provided in Chapters 4-6. Specifically, guidelines for assessing the technical effectiveness of innovations are provided in Chapter 4; guidelines for assessing the impact of an innovation on the environment and socio-economic sectors that will feel direct consequences of climate change are provided in Chapter 5; guidelines for assessing the societal acceptance of innovations in Chapter 6.@en

Polish coastal zone is thought to be one of the most exposed to sea level rise in Europe. With mean sea levels expected to increase between 28 and 98 cm by the end of the century, and storms increasing in severity, accurate estimates of the consequences of those phenomena are needed. Recent advances in quality and availability of spatial data in Poland made possible the reassessment of previous estimates of inundation caused by sea level rise. Up-to-date, detailed information on land use, population and buildings was used here to calculate their exposure to floods at a broad range of scenarios. Inclusion of a high-resolution digital elevation model contributed to a further improvement in estimates. The results revealed that even by using a static ``bathtub fill'' approach, the amount of exposed land, population or assets is significantly smaller than indicated in previous assessments. In the perspective of the twenty-first century, direct damages caused by sea level rise will be small and adaptation costs will not be significant. However, the increase in the frequency of storm surges could elevate the risk to the population and economy, but cost-effective flood protection measures would be able to mitigate the risk. The exposure of different kinds of assets and sectors of the economy varies to a large extent, though the structural breakdown of potential losses is remarkably stable between scenarios.@en

Objectives Since the beginning of the second industrial revolution in the second half of the 19th century, Europe’s society and economy has been profoundly transformed. The population doubled in the last 150 years, together with more than fourfold increase in number of dwellings and 30-fold increase in production value in real terms. At the same time, rural population dropped, and share of agriculture in production declined from 30% to a mere 2%. Cities that once have been small and very densely populated evolved into less cramped, but quickly sprawling metropolitan areas. All those trends were not without effect on flood exposure and vulnerability, two crucial components of flood risk. The study aims to reevalute reported flood losses (population killed or affected, monetary value of losses, inundated area) so that for each flood event that occurred since 1870, flood losses relative to potential damage given the size of the flood event could be calculated. Methods In order to be able to calculate potential losses during any flood event within the study’s timeframe, a set of high-resolution maps of land use, population, production and assets distribution is needed. Firstly, such detailed maps of population and land use at 100 m resolution was compiled for year 2011/2012. From this ‘baseline’ other maps for other time points (decenially 1870–1970 and five-yearly 1975–2020) could be calculated. However, for those other time points we only know the total population and land use at regional level. Hence, for each time step, the population and the different land use classes had to be redistributed inside each region in order to match the regional totals. Several methodologies were used in order to provide the best approximation for each land use class and population. Most effort was put to estimate past and future residential urban areas (where most population lives) and lands used by agriculture and infrastructure. A database of population, land use and economy at NUTS 3 regions was compiled for this study. Estimates of production and assets were disaggregated from regional or national level to a 100 m grid based on population and land use. Information on flood events, each with a flood extent defined using NUTS 3 regions, was also collected. Finally, the exposure maps were intersected with flood zones taken from pan-European flood hazard models. Results@en