European coastal regions host a dense transport network that supports various human activities and well-being. However, global warming is expected to increase coastal flooding risk, whose impact on existing and planned European transport systems remains unknown. Here we present the fully probabilistic assessment of coastal flood risk to Europe’s surface transport infrastructure at different levels of global warming. Under baseline conditions (1980–2020), we find 1,592 km of networks are affected annually, causing expected annual damage of up to €722 million. Roads are projected to be more affected than railways in all countries. Passenger and haulage transport within the low-elevation coastal zone are currently overwhelmingly road dependent, which signals potential for widespread disruptions unless transportation modes change. With 1.5 °C warming, the Europe-wide expected annual damage may reach €1,108 million, and with 4 °C, it is projected to be as high as €1,487 million. Adaptation expenditures will increase with every fraction of global warming in most countries.

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.

Sandy beaches and dune systems have high recreational and ecological value, and they offer protection against flooding during storms. At the same time, these systems are very vulnerable to storm impacts. Process-based numerical models are presently used to assess the morphological changes of dune and beach systems during storms. However, such models come with high computational costs, hindering their use in real-life applications which demand many simulations and/or involve a large spatial-temporal domain. Here we design a novel meta-model to predict dune erosion volume (DEV) at the Dutch coast, based on artificial neural networks (ANNs), trained with cases from process-based modeling. First, we reduce an initial database of 1/41400 observed sandy profiles along the Dutch coastline to 100 representative typological coastal profiles (TCPs). Next, we synthesize a set of plausible extreme storm events, which reproduces the probability distributions and statistical dependencies of offshore wave and water level records. We choose 100 of these events to simulate the dune response of the 100 TCPs using the process-based model XBeach, resulting in 10 000 cases. Using these cases as training data, we design a two-phase meta-model, comprised of a classifying ANN (which predicts the occurrence (or not) of erosion) and a regression ANN (which gives a DEV prediction). Validation against a benchmark dataset created with XBeach and a sparse set of available dune erosion observations shows high prediction skill with a skill score of 0.82. The meta-model can predict post-storm DEV 103-104 times faster (depending on the duration of the storm) than running XBeach. Hence, this model may be integrated in early warning systems or allow coastal engineers and managers to upscale storm forcing to dune response investigations to large coastal areas with relative ease.

The African coast contains heritage sites of ‘Outstanding Universal Value’ that face increasing risk from anthropogenic climate change. Here, we generated a database of 213 natural and 71 cultural African heritage sites to assess exposure to coastal flooding and erosion under moderate (RCP 4.5) and high (RCP 8.5) greenhouse gas emission scenarios. Currently, 56 sites (20%) are at risk from a 1-in-100-year coastal extreme event, including the iconic ruins of Tipasa (Algeria) and the North Sinai Archaeological Sites Zone (Egypt). By 2050, the number of exposed sites is projected to more than triple, reaching almost 200 sites under high emissions. Emissions mitigation from RCP 8.5 to RCP 4.5 reduces the number of very highly exposed sites by 25%. These findings highlight the urgent need for increased climate change adaptation for heritage sites in Africa, including governance and management approaches, site-specific vulnerability assessments, exposure monitoring, and protection strategies.

Dune erosion driven by extreme marine storms can damage local infrastructure or ecosystems and affect the long-term flood safety of the hinterland. These storms typically affect long stretches (∼100 km) of sandy coastlines with variable topo-bathymetries. The large spatial scale makes it computationally challenging for process-based morphological models to be used for predicting dune erosion in early warning systems or probabilistic assessments. To alleviate this, we take a first step to enable efficient estimation of dune erosion using the Dutch coast as a case study, due to the availability of a large topo-bathymetric dataset. Using clustering techniques, we reduce 1,430 elevation profiles in this dataset to a set of typological coastal profiles (TCPs), that can be employed to represent dune erosion dynamics along the whole coast. To do so, we use the topo-bathymetric profiles and historic offshore wave and water level conditions, along with simulations of dune erosion for a number of representative storms to characterize each profile. First, we identify the most important drivers of dune erosion variability at the Dutch coast, which are identified as the pre-storm beach geometry, nearshore slope, tidal level and profile orientation. Then using clustering methods, we produce various sets of TCPs, and we test how well they represent dune morphodynamics by cross-validation on the basis of a benchmark set of dune erosion simulations. We find good prediction skill (0.83) with 100 TCPs, representing a 93% input and associated computational costs reduction. These TCPs can be used in a probabilistic model forced with a range of offshore storm conditions, enabling national scale coastal risk assessments. Additionally, the presented techniques could be used in a global context, utilizing elevation data from diverse sandy coastlines to obtain a first order prediction of dune erosion around the world.

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.

Global warming is expected to drive increasing extreme sea levels (ESLs) and flood risk along the world's coastlines. In this work we present probabilistic projections of ESLs for the present century taking into consideration changes in mean sea level, tides, wind-waves, and storm surges. Between the year 2000 and 2100 we project a very likely increase of the global average 100-year ESL of 34-76 cm under a moderate-emission-mitigation-policy scenario and of 58-172 cm under a business as usual scenario. Rising ESLs are mostly driven by thermal expansion, followed by contributions from ice mass-loss from glaciers, and ice-sheets in Greenland and Antarctica. Under these scenarios ESL rise would render a large part of the tropics exposed annually to the present-day 100-year event from 2050. By the end of this century this applies to most coastlines around the world, implying unprecedented flood risk levels unless timely adaptation measures are taken.