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More than 10 % of the world's population lives in coastal areas that are less than 10 m above sea level (also known as the low-elevation coastal zone – LECZ). These areas are of major importance for local economy and transport and are home to some of the richest ecosystems. At the same time, they are quite susceptible to extreme storms and sea level rise. During the last few years, numerous open-access global datasets have been published, describing different aspects of the environment such as elevation, land use, waves, water levels, and exposure. However, for coastal studies, it is crucial that this information is available at specific coastal locations and, for regional studies or upscaling purposes, it is also important that data are provided in a spatially consistent manner. Here we create a Global Coastal Characteristics (GCC) database, with 80 indicators covering the geophysical, hydrometeorological, and socioeconomic environment at a high alongshore resolution of 1 km and provided at ∼ 730 000 points along the global ice-free coastline. To achieve this, we use the latest freely available global datasets and a newly created global high-resolution transect system. The geophysical indicators include coastal slopes and elevation maxima, land use, and presence of vegetation or sandy beaches. The hydrometeorological indicators involve water level, wave conditions, and meteorological conditions (rain and temperature). Additionally, socioeconomic indices related to population, GDP, and presence of critical infrastructure (roads, railways, ports, and airports) are presented. While derived from existing global datasets, these indicators can be valuable for coastal screening studies, especially for data-poor locations. The GCC dataset can be accessed at https://doi.org/10.5281/zenodo.8200199 (Athanasiou et al., 2024). [ABSTRACT FROM AUTHOR]

Around 10 percent of the world's population live in coastal areas that are less than 10 meters above sea level (also known as the low elevation coastal zone – LECZ). Coastal zones are therefore of major importance for local economy, transport and are home to some of the richest ecosystems. This makes coastal zones quite susceptible to extreme storms and sea level rise due to climate change. During the last few years numerous open access global datasets have been published, describing different aspects of the environment such as elevation, land-use, waves, water-levels and exposure. However, for coastal studies it is crucial that this information is available at specific coastal locations and, for regional studies or upscaling purposes, it is also important that data is provided in a spatially consistent manner. Here we create a Global database of Coastal Characteristics (GCC) with 80 indicators covering the geophysical, hydrometeorological and socioeconomic environment, at a high alongshore resolution of 1 km and provided at ~730,000 points along the global ice-free coastline. To achieve this, we use the latest freely available global datasets and a newly created global high-resolution transect system. The geophysical indicators include coastal slopes and elevation maxima, land-use, presence of vegetation or sandy beaches. The hydro-meteorological indicators involve water level, wave conditions and meteorological conditions (rain and temperature). Additionally, socioeconomic indices related to population, GDP and presence of critical infrastructure (roads, railways, ports and airports) are presented. While derived from existing global datasets, these indicators can be valuable for coastal screening studies, especially for data-poor locations. [ABSTRACT FROM AUTHOR]

Climate change and anthropogenic pressures are widely expected to exacerbate coastal hazards such as episodic coastal flooding. This study presents global-scale potential coastal overtopping estimates, which account for not only the effects of sea level rise and storm surge, but also for wave runup at exposed open coasts. Here we find that the globally aggregated annual overtopping hours have increased by almost 50% over the last two decades. A first-pass future assessment indicates that globally aggregated annual overtopping hours will accelerate faster than the global mean sea-level rise itself, with a clearly discernible increase occurring around mid-century regardless of climate scenario. Under RCP 8.5, the globally aggregated annual overtopping hours by the end of the 21st-century is projected to be up to 50 times larger compared to present-day. As sea level continues to rise, more regions around the world are projected to become exposed to coastal overtopping. As sea levels rise, coasts are being increasingly threatened by overtopping caused by the combination of sea level rise, storm surge and wave runup. Here the authors find that global coastal overtopping has increased by over 50% in the last two decades, and under a RCP 8.5 scenario this could increase up to 50 times by 2100 compared to today. [ABSTRACT FROM AUTHOR]

Sea level rise (SLR) will cause shoreline retreat of sandy coasts in the absence of sand supply mechanisms. These coasts have high touristic and ecological value and provide protection of valuable infrastructures and buildings to storm impacts. So far, large-scale assessments of shoreline retreat use specific datasets or assumptions for the geophysical representation of the coastal system, without any quantification of the effect that these choices might have on the assessment. Here we quantify SLR driven potential shoreline retreat and consequent coastal land loss in Europe during the twenty-first century using different combinations of geophysical datasets for (a) the location and spatial extent of sandy beaches and (b) their nearshore slopes. Using data-based spatially-varying nearshore slope data, a European averaged SLR driven median shoreline retreat of 97 m (54 m) is projected under RCP 8.5 (4.5) by year 2100, relative to the baseline year 2010. This retreat would translate to 2,500 km2 (1,400 km2) of coastal land loss (in the absence of ambient shoreline changes). A variance-based global sensitivity analysis indicates that the uncertainty associated with the choice of geophysical datasets can contribute up to 45% (26%) of the variance in coastal land loss projections for Europe by 2050 (2100). This contribution can be as high as that associated with future mitigation scenarios and SLR projections. [ABSTRACT FROM AUTHOR]

Nearshore slope, defined as the cross-shore gradient of the subaqueous profile, is an important input parameter which affects hydrodynamic and morphological coastal processes. It is used in both local and large-scale coastal investigations. However, due to unavailability of data, most studies, especially those that focus on continental or global scales, have historically adopted a uniform nearshore slope. This simplifying assumption could however have far-reaching implications for predictions/projections thus obtained. Here, we present the first global dataset of nearshore slopes with a resolution of 1 km at almost 620 000 points along the global coastline. To this end, coastal profiles were constructed using global topo-bathymetric datasets. The results show that the nearshore slopes vary substantially around the world. An assessment of coastline recession driven by sea level rise (SLR) (for an arbitrary 0.5 m SLR) with a globally uniform coastal slope of 1 : 100, as carried out in previous studies, and with the spatially variable coastal slopes computed herein shows that, on average, the former approach would underestimate coastline recession by about 40 %, albeit with significant spatial variation. The final dataset has been made publicly available at 10.4121/uuid:a8297dcd-c34e-4e6d-bf66-9fb8913d983d (Athanasiou, 2019). [ABSTRACT FROM AUTHOR]

An increase of the global mean sea level has already been observed and it is expected to continue with even higher rates during the second half of the present century. This can lead to sandy shoreline retreat and permanent and increased episodic inundation of parts of the coastal zone. In order to protect human lives and assets, adaptation measures are necessary. Therefore, in recent years, large scale risk assessments are considered useful tools for the guidance of policy makers to identify high risk hotspots. Reliable input data at this scale are required in order to make useful estimations. Among others, crucial data to assess the impact of sea level rise on coastal recession are the detection of different coastal types and, in particular, of sandy erodible beaches, and the coastal slope, which is usually assumed to be uniform.The important issue of input data uncertainty and spatial variation and consequent impact on predictions has been so far ignored in most large scale studies. Estimates of coastal erosion are however very sensitive to the variation in these inputs. In this study, we present sea level rise (SLR) related coastal recession estimates at the European scale, using different datasets for the location of sandy beaches and their coastal slope. Our results demonstrate how the choice of input data can affect the land loss predictions. For the estimation of the coastal recession, the Bruun Rule is used, which offers a suitable approach for a first approximation of erosion impacts at large scales. Sea level rise projections associated with the moderate-emission- mitigation-policy (RCP4.5) and the high-end, business-as-usual scenario (RCP8.5) are used as boundary conditiions. The location of sandy beaches is determined from two different datasets. One is based on manual visual estimation from satellite images and the other on automatic detection from satellite images using machine learning techniques. The differences of the sandy beach locations between the two datasets used are quite pronounced at some EU countries, with an overall agreement across Europe of almost 60%. This is due to the different methodologies used to describe sandy coastlines. For coastal slopes we apply the commonly used constant slope assumption (1/100) and a newly produced global dataset which captures the spatial variation of coastal slopes. This is expected to give much improved estimates as the shoreline retreat predicted by the Bruun Rule varies linearly with the coastal slope.With this approach, we create four different combinations for each SLR scenario, for which we estimate and compare land loss at EU, country and NUTS3 regional level. We find that the land loss estimations for each scenario can differ significantly, especially at the regional and local level. At the European or country level, even though differences in total land loss projections can be significant, they can be concealed by the spatial aggregation of the results. Additionally, there is a distinct spatial variability across the NUTS3 regions with respect to the year when the SLR scenario uncertainty becomes higher than then input data uncertainty. [ABSTRACT FROM AUTHOR]