Coastal storms constitute a key factor controlling shoreline position changes. They may deeply modify the beach morphology and contribute to erosive processes. Earth observation data as the images from the Sentinel satellites of ESA's Copernicus program and the Copernicus Contributing Missions offer potential information for characterizing beach changes. SAET (Shoreline Analysis and Extraction Tool) is an open-source tool developed within the framework of the ECFAS project intended to enable the automatic shoreline extraction from optical satellite imagery. SAET is assessed in order to determine the accuracy of the resulting satellite-derived shorelines (SDSs) as well as its capacity to detect and characterise beach changes. The SDSs are employed to define the changes of the shoreline position through 82 km of beaches in the Ebro Delta (E Spain) associated with Storm Gloria. The storm peaked on 22 of January 2020 (significant wave heights over 7 m), heavily affecting the whole of eastern Spain. The accuracy of the SDS extracted using SAET was assessed by comparing its position against the shoreline photo-interpreted on a VHR image. A Spot 7 (1.5 m of spatial resolution) acquired 37 minutes before the Sentinel-2 used for defining the SDS was employed for this purpose. Both images were acquired on 26 of January, four days after the peak of the storm. An average error of 5.18 m (seawards) ± 9.98 m was measured. The comparison of the position of the SDS obtained before (18/01/2020) and after the peak of the storm (26/01/2020) allows to map the retreat of the shoreline position linked to this event. Within the ECFAS project this approach will be extended to a number of other test cases. The ECFAS (European Coastal Flood Awareness System) project (https://www.ecfas.eu/) has received funding from the EU H2020 research and innovation programme under Grant Agreement No 101004211.
SAET (Shoreline Analysis and Extraction Tool) is a tool intended to enable the automatic detection and quantification of the changes experienced by the shoreline position on beaches affected by coastal storms. It is an open-source tool developed within the framework of the ECFAS project which aims to demonstrate the technical and operational feasibility of a European Coastal Flood Awareness System. SAET takes advantage of the freely-available images from the Sentinel satellites of ESA's Copernicus program and the Copernicus Contributing Missions. The tool currently uses the mid-resolution images of the Sentinel 2 and Landsat 8 satellites, although in the future it will allow the use of images from other satellites (as the recently available Landsat 9). In order to characterize the shoreline changes caused by a coastal storm at a certain coastal segment, SAET identifies, downloads, and processes the most suitable satellite images (those closest in time and with low cloud coverage). The shoreline extraction starts by an approximate definition of the shoreline position at pixel level using the AWEINSH water index. Subsequently, the subpixel extraction algorithm is applied over dynamic coastal stretches not affected by clouds operating over the Short-Wave Infrared bands. For each of the analysed images, the process results in the obtention of satellite-derived shorelines in vector format. Analysis of shoreline position changes is intended to offer quantitative data about the state of beaches in terms of erosion/accretion,and about their response subsequent capacity to recover after storm episodes. The ECFAS (European Coastal Flood Awareness System) project (https://www.ecfas.eu/) has received funding from the EU H2020 research and innovation programme under Grant Agreement No 101004211.
This contribution presents the high-resolution Pan-European storm surge (SSL) dataset, ANYEU-SSL, produced with the SCHISM circulation model. The dataset covers 40 years (1979-2018) of SSL data along the European coastline with 3-hour temporal resolution and has been extensively validated for the period spanning from 1979 to 2016, considering the whole time series, as well as for the extreme SSL values. Validation against tidal gauge data shows an average RMSE of 0.10 m, and RMSE below 0.12 m in 75% of the tidal gauges. Comparisons with satellite altimetry data show average RMSE of 0.07 m. SSL trends are estimated as an example of a potential application case of the dataset. The results indicate an overall latitudinal gradient in the trend of the extreme storm surge magnitude for the period 1979-2016. SSLs appear to increase in areas with latitudes >50 °N and to decrease in the lower latitudes. Additionally, a seasonal variation of the extreme SSL, particularly strong in the northern areas, has been observed. The dataset is publicly available and aspires to provide the scientific community with an important data source for the study of storm surge phenomena and consequential impacts, either on large or local scales.
The knowledge of extreme total water levels (ETWLs) and the derived impact, coastal flooding and erosion, is crucial to face the present and future challenges exacerbated in European densely populated coastal areas. Based on 24 years (1993-2016) of multimission radar altimetry, this paper investigates the contribution of each water level component: tide, surge and annual cycle of monthly mean sea level (MMSL) to the ETWLs. It focuses on the contribution of the annual variation of MMSL in the coastal flooding extreme events registered in a European database. In microtidal areas (Black, Baltic and Mediterranean Sea), the MMSL contribution is mostly larger than tide, and it can be at the same order of magnitude of the surge. In meso and macrotidal areas, the MMSL contribution is <20% of the total water level, but larger (>30%) in the North Sea. No correlation was observed between the average annual cycle of monthly mean sea level (AMMSL) and coastal flooding extreme events (CFEEs) along the European coastal line. Positive correlations of the component variance of MMSL with the relative frequency of CFEEs extend to the Central Mediterranean (r = 0.59), North Sea (r = 0.60) and Baltic Sea (r = 0.75). In the case of positive MMSL anomalies, the correlation expands to the Bay of Biscay and northern North Atlantic (at >90% of statistical significance). The understanding of the spatial and temporal patterns of a combination of all the components of the ETWLs shall improve the preparedness and coastal adaptation measures to reduce the impact of coastal flooding.