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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. Flooding may cause loss of life and economic damage, therefore temporal changes need assessment. Here, the authors show that since 1870 there has been an increase in area inundated by floods in Europe, but a reduction in fatalities and economic losses, although caution that smaller floods remain underreported.

Understanding the influence of climate change on past extreme weather impacts is a vital research task. However, the effects of climate change are obscured in the observed impact data series due to the rapid evolution of the social and economic circumstances in which the events occurred. The HANZE v2.0 (Historical Analysis of Natural HaZards in Europe) dataset presented in this study quantifies the evolution of key socioeconomic drivers in Europe since 1870, namely land use, population, economic activity and assets. It consists of algorithms to reallocate baseline (2011) land use and population for any given year based on a large collection of historical subnational- and national-level statistics, and then disaggregate data on production and tangible assets by economic sector into a high-resolution grid. Raster datasets generated by the model enable reconstructing exposure within the footprint of any extreme event both at the time of occurrence and anytime between 1870 and 2020. This allows the separation of the effects of climate change from the effects of exposure change.

Are countries at a low level of socio-economic development catching up with developed countries over time or rather falling further behind? Existing work on the subject is not conclusive, partially due to methodological differences. The aim of the paper is to carry out a broader analysis with longer time series and a more diverse set of indicators. The study divides countries of the world into 21 developed \"benchmark\" countries and 156 developing countries. The distance between the benchmark and developing countries is measured using the \"time lags\" method, applied here to nine indicators covering topics such as the economy, health, education and the environment. The study further utilizes a probabilistic approach to extrapolate missing historical data for developing countries, so that the analysis can cover a full century starting in 1920 and ending with short-term projections to year 2020. The study finds that a majority of developing countries, and the population-weighted developing world as a whole, has reduced its lag in most indicators between 1920 and 2020. Progress was unevenly distributed, with East Asian and European countries converging the most with the benchmark, while most African countries have diverged along with some American ones. Catch-up in education attainment and life expectancy has been more successful than in infant survival rate, GDP per capita or technology adoption. The findings are put in context of United Nations' Sustainable Development Goals, showing how the time lag method could improve setting targets for some of the goals. Further, time lags are used to analyze the current demographic, economic and political situation of developing countries, identifying opportunities and risks for future catch-up with developed countries.

Climate change is expected to result in rising seas, exacerbating coastal floods and erosion. Remote islands are projected to be among the most challenged regions, due to their geographic isolation and fragile economies. While, Small Island Developing States have been attracting the attention of scientists and policy makers, Europe’s Outermost Regions (ORs) and Overseas Countries and Territories (OCTs) remain poorly studied in terms of their impacts from Sea Level Rise (SLR). Here we carry out a data-modelling framework to comprehensively study risks of flooding, the submergence of flat regions, and coastal erosion along coastlines of ORs and OCTs. Our study shows that under a high emissions scenario by 2150 annually nearly 3000 km 2 is expected to be flooded, one third of which by tidal flooding, while 150 km 2 of land will be lost by coastal erosion. This translates into an annual exposure to coastal inundation of up to half a million of people and an economic damage of 5.9 € billion per year - a 40-fold increase from today. Our study shows the increasing benefits in time of stringent climate mitigation, which could nearly halve these impacts in the long run. However, sea levels will continue to rise long after net zero carbon is reached, and so will the consequent impacts, highlighting the critical importance of proactive efforts to increase the resilience of these vulnerable regions against rising seas. This work examines future coastal risks in Europe’s Outermost Regions and Overseas Countries and Territories. By 2150, 3,000 km² could flood yearly, with €5.9 bn in annual damages and 0.5 million people exposed. Mitigation cuts impacts nearly in half, but long-term resilience strategies are needed.

Residential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model’s ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model’s performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework.

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.

The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism [1]. [...]a novel quantitative approach for coastal areas containing both sea and land surface sections was developed. [...]a moisture estimation model was developed that eliminated the effects of the incidence angle and distance. Different geomatic techniques, such as: orthophotography, photogrammetric flights, LiDAR surveys, Unmanned Aerial Vehicle (UAV) surveys, and terrestrial laser scanner datasets, were used to find volumetric differences in the beach and sea cliff, attributing them to storms.

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.

Flood losses have steadily increased in the past and are expected to grow even further owing to climate and socioeconomic change. The reduction of flood vulnerability, for example, through adaptation, plays a key role in the mitigation of future flood risk. However, lacking knowledge about vulnerability dynamics, which arise from the interaction between floods and the ensuing response by society, limits the scope of current risk projections. We present a socio‐hydrological method for flood risk assessment that simulates the interaction between society and flooding continuously, including changes in vulnerability through collective (structural) and private (non structural) measures. Our probabilistic approach quantifies uncertainties and exploits empirical data to chart risk dynamics including how society copes with flooding. In a case study for the commercial sector in Dresden, Germany, we show that increased adaptation is necessary to counteract the expected four‐fold growth in flood risk due to transient hydroclimatic and socioeconomic boundary conditions. We further use our holistic approach to identify solutions for effective long‐term adaptation, demonstrating that integrated adaptation strategies (i.e., combined structural and non structural measures) can reduce the average risk by up to 60% at the study site. Ultimately, our case study highlights the benefit of the model for robust flood risk assessment as it can capture unintended, adverse feedbacks of adaptation measures such as the levee effect. Consequently, our socio‐hydrological method contributes to a more systemic and reliable flood risk assessment that can inform adaptation planning by exploring the possible system evolutions comprehensively including unlikely futures. Plain Language Summary The rise in flood losses due to climate and societal changes calls for effective strategies to reduce risks. Understanding how floods interact with society and affect vulnerability is crucial in addressing this challenge. However, current flood risk assessments lack this comprehensive insight. We have developed a novel method that integrates floods and society into a single model, enabling us to comprehend how society's vulnerability to floods changes over time. Our approach examines how communities respond to floods, considering both collective (like constructing levees) and private actions (such as individual property precautions). By factoring in uncertainties and utilizing real‐world data, we improve our understanding of societal flood adaptation. Using the commercial sector in Dresden, Germany, as a case study, we reveal a potential four‐fold increase in future flood risk due to climate and socioeconomic shifts. We propose a combination of collective and private measures, potentially reducing flood risk by up to 60% at the study site. In summary, our method is capable of simulating a wide range of potential futures and uncovering unforeseen challenges that may arise when societies attempt to shield themselves from floods. This aids in robust flood risk management and facilitates better planning for adaptation. Key Points We present a socio‐hydrological model for continuous flood risk projection that captures vulnerability dynamics and quantifies uncertainty Our case study highlights the need for effective adaptation to intensifying flood risk and the potential of integrated flood risk management The robust flood risk assessment method explores the possibility space comprehensively including adverse feedbacks such as the levee effect

Advanced, multidimensional models are typically applied when researching processes occurring in the nearshore. Relatively simple, empirical equations are commonly used in coastal engineering practice in order to estimate extreme wave run-up on beaches and coastal structures. However, they were mostly calibrated to the characteristics of oceanic coasts, which have different wave regime than a semi-enclosed basin like the Baltic Sea. In this paper we apply the formulas to the Polish Baltic Sea coast. The equations were adjusted to match local conditions in two test sites in Międzyzdroje and Dziwnówek, where beaches are under continuous video surveillance. Data from WAM wave model and coastal gauge stations were used, as well as precise measurements of the beaches' cross-sections. More than 600 run-up events spanning from June to December 2013 were analysed, including surges causing dune erosion. Extreme wave run-up R2% was calculated and presented as a percentage value indicating what part of the beach was inundated. The method had a root-mean-square error of 6.1 and 6.5 percentage points depending on the test site. We consider it is a fast and computationally undemanding alternative to morphodynamic models. It will constitute a part of the SatBałtyk Operating System-Shores, delivering forecasts of wave run-up on the beaches for the entire Polish coastline.