Coastal flood vulnerability assessment, a satellite remote sensing and modeling approach

Although there are numerous case studies assessing coastal vulnerability, many of these studies have been performed in places where notable efforts have been carried out to provide informa-tion on the different variables that affect the coast. However, this is not the case for most places worldwide given the lack of long-term datasets. This study makes use of information from satel-lite remote sensing and analytical models to derive two vulnerability indices along a 9.5 km stretch of the coast of Langue de Barbarie, Saint Louis, Senegal (Western Africa). The first is a coastal vulnerability index (CVI) to sea level rise due to climate change and results in a five -category classification: Very Low, Low, Moderate, High, and Very High. The second is a flood vul-nerability index (FVI) to coastal flooding due to extreme events and results in a three-category classification: Low, Moderate, and High. Results for the CVI index show that 70% of the coast pre-sents High and Very High vulnerability values, largely located in the most densely populated ar-eas. The FVI is assessed for one of the most energetic storms for the 1979-2021 period which oc-curred in February 2018 using a beach configuration of March 2021. Results show that 29% of the coastline presents High FVI values (i.e., are likely to be overtopped) concentrated in the cen-tral sector of the most-populated districts. This provides relevant tools to improve coastal man-agement when in situ data are not available.