Natural World Heritage Sites (NWHS), which are of Outstanding Universal Value, are increasingly threatened by natural and anthropogenic pressures. This is especially true for coastal NWHS, which are additionally subject to erosion and flooding. This paper assesses shoreline change from 1984 to 2016 within the boundaries of 67 designated sites, providing a first global consistent assessment of its drivers. It develops a transferable methodology utilising new satellite-derived global shoreline datasets, which are classified based on linearity of change against time and compared with global datasets of geomorphology (topography, land cover, coastal type, and lithology), climate variability and sea-level change. Significant shoreline change is observed on 14% of 52 coastal NWHS shorelines that show the largest recessional and accretive trends (means of -3.4 m yr^-1 and 3.5 m yr^-1, respectively). These rapid shoreline changes are found in low-lying shorelines (<1 m elevation) composed of unconsolidated sediments in vegetated tidal coastal systems (means of -7.7 m yr^-1 and 12.5 m yr^-1), and vegetated tidal deltas at the mouth of large river systems (means of -6.9 m yr^-1 and 11 m yr^-1). Extreme shoreline changes occur as a result of redistribution of sediment driven by a combination of geomorphological conditions with (1) specific natural coastal morphodynamics such as opening of inlets (e.g. Río Plátano Biosphere Reserve) or gradients of alongshore sediment transport (e.g. Namib Sea) and (2) direct or indirect human interferences with natural coastal processes such as sand nourishment (e.g. Wadden Sea) and damming of river sediments upstream of a delta (e.g. Danube Delta). The most stable soft coasts are associated with the protection of coral reef ecosystems (e.g. Great Barrier Reef) which may be degraded/destroyed by climate change or human stress in the future. A positive correlation between shoreline retreat and local relative sea-level change was apparent in the Wadden Sea. However, globally, the effects of contemporary sea-level rise are not apparent for coastal NWHS, but it is a major concern for the future reinforcing the shoreline dynamics already being observed due to other drivers. Hence, future assessments of shoreline change need to account for other drivers of coastal change in addition to sea-level rise projections. In conclusion, extreme multi-decadal linear shoreline trends occur in coastal NWHS and are driven primarily by sediment redistribution. Future exacerbation of these trends may affect heritage values and coastal communities. Thus shoreline change should be considered in future management plans where necessary. This approach provides a consistent method to assess NWHS which can be repeated and help steer future management of these important sites.

Estimating the current risk of coastal flooding requires adequate information on extreme sea levels. For over a decade, the only global data available was the DINAS-COAST Extreme Sea Levels (DCESL) dataset, which applies a static approximation to estimate extreme sea levels. Recently, a dynamically derived dataset was developed: the Global Tide and Surge Reanalysis (GTSR) dataset. Here, we compare the two datasets. The differences between DCESL and GTSR are generally larger than the confidence intervals of GTSR. Compared to observed extremes, DCESL generally overestimates extremes with a mean bias of 0.6 m. With a mean bias of −0.2 m GTSR generally underestimates extremes, particularly in the tropics. The Dynamic Interactive Vulnerability Assessment model is applied to calculate the present-day flood exposure in terms of the land area and the population below the 1 in 100-year sea levels. Global exposed population is 28% lower when based on GTSR instead of DCESL. Considering the limited data available at the time, DCESL provides a good estimate of the spatial variation in extremes around the world. However, GTSR allows for an improved assessment of the impacts of coastal floods, including confidence bounds. We further improve the assessment of coastal impacts by correcting for the conflicting vertical datum of sea-level extremes and land elevation, which has not been accounted for in previous global assessments. Converting the extreme sea levels to the same vertical reference used for the elevation data is shown to be a critical step resulting in 39–59% higher estimate of population exposure.