Since early history, humans have been attracted to coastal areas. This can be related to the economic benefits of these areas due to access of ocean navigation, coastal fisheries, tourism and recreation (Seas and Plans, 2011). Around 40% of the world’s population lives within 100 km of the coast (Seas and Plans, 2011). People are drawn to sandy beaches in particular because of its aesthetics and value for specific economic amenities (Luijendijk et al., 2018). Nevertheless, as these beaches are dynamic both in time and space, proper coastal management is required to prevent loss of land and secure future coastal life.
Up till now, studies into coastal erosion have been conducted locally, resulting in site specific observations. However, the promising results of using satellite imagery in the field of coastal engineering allowed studies to be performed at larger spatial scales. This can lead to the identification of areas with similar characteristics, resulting in methodological standardization of approaching a specific problem. A first step toward this new approach of studying shoreline evolution was taken by Luijendijk et al. (2018) who presented a global dataset of annual shoreline positions for sandy beaches over the period 1984-2016 using satellite derived shorelines
(SDS). However, the drivers (causes) of shoreline evolution on a global scale were still unknown, making it only suitable for identifying areas of structural shoreline change, but less suitable for deriving coastal management solutions. Therefore, the research objective in this study is to identify and characterize drivers of shoreline evolution on a global scale using SDS.
This study focused on dynamic sandy beaches, or hotspots, extracted using a method developed by Kras (2019). In this method, using a 2.5-kilometer moving window, transects showing structural shoreline changes and similar characteristics both in space and time were grouped. The small size of the moving window led to locally created hotspots, 95% of which had a spatial extent of less than 10 kilometers, allowing to study shoreline drivers with small to moderate spatial scales (∼10 kilometer). Therefore, the main focus in this study lies on seasonality as a natural driver of shoreline evolution and three anthropogenic drivers: reclamations, nourishments and littoral drift barriers. As seasonality shows inter-annual variability, the temporal resolution of the SDS is increased from annual to monthly.
Using time series decomposition methods, different parameters are extracted that can be used to link the drivers to the SDS. Besides temporal parameters, also parameters related to spatial characteristics are considered. These parameters can be split into identification parameters, used for identifying a driver, and informative parameters, providing knowledge on the behavior of the driver. These parameters were developed and tested using local case studies. Results from these local case studies showed that the identification parameters showed similar behavior along the case studies. This implies that the identification parameters correctly reflect a driver’s behavior. Next, identification of the drivers was verified on a larger scale, all transects within hotspots on West-European coastlines. Verification was done on hundreds of samples using literature or manual inspection of satellite images. Using precision scores, the fraction of true positives to the total identified cases, optimal settings were derived for identification of the drivers. These settings resulted in a pattern of driver identification and characterization along the West-European coastline that is supported by literature.
With the optimal settings for identification determined, the methods were deployed on a global scale. The global dataset consisted out of 3033 prograding and 2121 retreating hotspots containing over 58 thousand transects in total. For these hotspots, SDS were generated over the period 1984-2021 with a monthly temporal resolution. This resulted in a global dataset of more than 26 million monthly shoreline positions. Two other processes, in addition to a seasonal change in wave height, were found to be able to generate seasonal variations in coastline positions from this global dataset. At the Red Sea, even though the wave climate is low in energy (Langodan et al., 2017), the coast is characterized by seasonal behavior. However, in this basin, seasonal variations in water levels rather than the wave climate best described this pattern. In addition to varying wave height and water level, seasonal beach morphology can also be caused by a shift in wave direction. This was observed in southern and western parts of Australia. Furthermore, non-seasonal beaches were primarily seen in low-energetic wave regions where neither of these other two processes occurred, as is the case in the Mediterranean. In regions where seasonal shoreline fluctuations are caused by differences in wave energy, minimum shoreline positions were found at the start of the summer. However, the period in which minimal shoreline positions are observed may be observed at a different time of the year in regions where seasonal shoreline behavior is driven by water level variations or a shift in wave direction. The identification of reclamations pointed out that this driver was especially linked to shoreline evolution in the Middle East and East-Asia. Furthermore, while the amount of constructions of reclamations remained
constant on a global level over the period 1987-2017, in these two areas an increase was observed. Opposite behavior was found by the identification of nourishments, as this driver was identified more often in the period 2007-2017 compared to the two decades before that. Moreover, nourishments were mostly observed in Western countries, for example the USA and the Netherlands. Nevertheless, also in Non-Western countries, an increment over time in the amount of nourishments could be detected. This indicates that throughout the entire world the use of nourishments as a measure to prevent coastal erosion is increasing. Shoreline evolution linked to littoral drift barriers was mostly observed in North-America, Europe and Africa. Downdrift (erosive) hotspots were mostly observed in Africa while in North-America and Europe mostly updrift (accreting) hotspots were linked to littoral drift barriers. On a global level, a combination of an updrift and downdrift hotspots (a pair) was observed in only 2% of all hotspots.
The outcomes above can support local-scale studies by identifying the drivers of shoreline evolution, describing their characteristics and even create standardization by analyzing areas with similar behavior. Hence, it can be concluded that spatiotemporal parameters describing the behavior of a driver can be used to identify and characterize drivers on a global scale using SDS. Nevertheless, not all drivers of shoreline evolution were included in this study. Therefore, to include drivers with larger spatial scales, hotspots should be extracted by using a larger moving spatial window. Furthermore, by increasing the spatiotemporal resolution on which this extraction is based, accuracy of the spatial extent of the hotspots is expected to increase. The small proportion of pairs identified for littoral drift barriers can be partly explained by the erroneous spatial extent of some hotspots. Finally, drivers are identified independently from each other neglecting their interactions. Even though interactions might be complex, drivers should not be identified independently as this will rather
require local studies than support them. Still, even though refinement and further development of the methods is required, this research has shown that identifying the drivers of shoreline development on a global scale using SDS has great potential for sustainable coastal management in the face of future challenges