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.

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 the Historical Analysis of Natural Hazards in Europe (HANZE) database, 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 simple disaggregation techniques and rule-based land use reallocation schemes. Data encompassed in HANZE allow one to "normalize" information on losses due to natural hazards by taking into account inflation as well as changes in population, production, and wealth. This database of past events currently contains 1564 records (1870–2016) of flash, river, coastal, and compound floods. The HANZE database is freely available at https://data.4tu.nl/repository/collection:HANZE.

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. In addition, the first pan‐European projection of temporal trends in the size of flood zones is presented, with and without assuming flood protection levels.

Compound flooding in coastal regions, i.e., the simultaneous or successive occurrence of high sea levels and high river flows, is expected to increase in a warmer world. To date, however, there is no robust evidence on projected changes in compound flooding for northwestern Europe. We combine projected storm surges and river floods with probabilistic, localized relative sea‐level rise (SLR) scenarios to assess the future compound flood hazard over northwestern coastal Europe in the high (RCP8.5) emission scenario. We use high‐resolution, dynamically downscaled regional climate models (RCM) to drive a storm surge model and a hydrological model, and analyze the joint occurrence of high coastal water levels and associated river peaks in a multivariate copula‐based approach. The RCM‐forced multi‐model mean reasonably represents the observed spatial pattern of the dependence strength between annual maxima surge and peak river discharge, although substantial discrepancies exist between observed and simulated dependence strength. All models overestimate the dependence strength, possibly due to limitations in model parameterizations. This bias affects compound flood hazard estimates, and requires further investigation. While our results suggest decreasing compound flood hazard over the majority of sites by 2050s (2040‐2069) compared to the reference period (1985‐2005), an increase in projected compound flood hazard is limited to around 34% of the sites. Further, we show the substantial role of SLR, a driver of compound floods, which has frequently been neglected. Our findings highlight the need to be aware of the limitations of the current generation of Earth system models in simulating coastal compound floods.

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.