Influence of Local Factors on Coastal Erosion: The Case of Vagueira Beach in Portugal

Abstract

Vagueira Beach, on the central Portuguese coast, is known as one of the places in Europe most affected by coastal erosion. The area has suffered more than 156 m of coastline retreat from the period 1958 to 2001. With the aim of evaluating the influence of local factors on coastal erosion, this paper assesses the anthropogenic and natural factors that are related to the retreat of the coastline by adopting statistical correlation and regression analyses. Through Pearson’s correlation coefficient (r), it was observed that local factors, such as annual dredging at the Aveiro Port entrance (r = 0.93), the total length of groins in the Espinho–Vagueira section (r = 0.89), and storm events (r = 0.52), are directly related to coastline retreat in the area. A multiple linear regression model was developed in which coastline retreat is explained by these same factors over the period 1980–2006. With a coefficient determination of R² = 0.91, it was observed that the length of groins (significant at the 1% level), the dredging of the port entrance (significant at the 5% level), and precipitation (as a proxy for storm events; significant at the 10% level) are significantly correlated with coastline retreat. Hence, it is shown that anthropogenic factors are the main drivers of coastline retreat in Vagueira Beach. This study provides an innovative approach for the assessment of coastal erosion, resulting in important information that could be used for decision-making related to coastal zone management as it allows us to understand in greater detail the main drivers of coastal erosion.

Application of Geographic Information System to Sensing Strength and Potentiality of Ecological Industrialization in China’s Huai River Ecological Economic Belt

“…We constructed an overall development model to analyze the factors and their roles in unified development between ecological industrialization strength and potentiality from an overall perspective based on a multiple linear regression model. (29)(30)(31)(32) The constructed model is…”