Natural hazards affect many types of tangible assets, the most valuable of which are often residential assets, comprising buildings and household contents. Yet, information necessary to derive exposure in terms of monetary value at the level of individual houses is often not available. This includes building type, size, quality, or age. In this study, we provide a universal method for estimating exposure of residential assets using only publicly available or open data. Using building footprints (polygons) from OpenStreetMap as a starting point, we utilized high-resolution elevation models of 30 European capitals and pan-European raster datasets to construct a Bayesian-network-based model that is able to predict building height. The model was then validated with a dataset of (1) buildings in Poland endangered by sea level rise, for which the number of floors is known, and (2) a sample of Dutch and German houses affected in the past by fluvial and pluvial floods, for which usable floor space area is known. Floor space of buildings is an important basis for approximating their economic value, including household contents. Here, we provide average national-level gross replacement costs of the stock of residential assets in 30 European countries, in nominal and real prices, covering the years 2000-2017. We either relied on existing estimates of the total stock of assets or made new calculations using the perpetual inventory method, which were then translated into exposure per square metre of floor space using data on countries' dwelling stocks. The study shows that the resulting standardized residential exposure values provide much better coverage and consistency compared to previous studies.

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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

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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