Using a coastal storm hazard index to assess storm impacts in lisbon

Abstract

Coastal areas are among the most dynamic earth systems as they are exposed to powerful agents. Nearshore wave energy is one of the most important triggering factors for erosion and flooding and is often neglected for severe infrastructure damaging, property losses and loss of life. These consequences are amplified with high population density and heavy infrastructure implantation as it happens in Lisbon (Portugal). In this context, it is of great importance for coastal stakeholders, decision-makers and civil protection entities to estimate precisely the spatial distribution of storm hazard for prevention and mitigation purposes, as well as to design adjusted answers for calamity responses. We apply a coastal storm hazard index (CSHI) considering triggering and conditioning variables involved in the effects of an extreme storm, namely: 100-year return period of SWAN modelled H s , and its spatial distribution across the study area, land use, number of buildings, height, slope, geology, geomorphology, erosion/ accretion rates, width of the systems, exposure of the coastline, bathymetry and legally protected areas. The variables were weighted according to a hierarchical analysis process and classified into five classes of exposure. A validation process was then implemented by comparing the occurrences identified in the last two decades newspapers and the storm hazard classification, showing a satisfactory validation results. The results show a classified storm hazard map that identifies the most and the less exposed areas. High values of CSHI occur in areas with excessive human pressure, low heights sandy systems with significant costal erosion rates. The main type of consequences identified are associated with inland flooding and erosion, resulting in the destruction of coastal protection infrastructures, and population displacement leading to great economic and social impacts and loss of life.

Combining Artificial Neural Networks and GIS Fundamentals for Coastal Erosion Prediction Modeling

“…The Costa da Caparica coastal zone is part of a coastal cell that extends from the Tagus river estuary to the Cabo Espichel, which is over almost 30 km, and is mainly characterized by low altitude/slope sandy beach-dune systems [23]. Morphologically, the study area has an arc shape with an orientation south/southeast and north/northwest (SSE-NNW).…”

“…The relationships between humans and the environment and the hierarchical structures are characterized by nonlinearity; the underlying processes are either unknown or hard to understand. Several approaches to quantify human-erosion interaction have been proposed in recent years, including hazard/vulnerability exposures and dependencies [18][19][20][21][22][23]. These methods are mainly physical-based and lack anthropogenic variables.…”

Coastal Erosion Hazard and Vulnerability: Case Study of Porticello, South Calabria, Italy

“…In recent years, the methodologies for the coastal erosion hazard and vulnerability assessment have increasing importance [39]- [43]. These methodologies allow to effectively manage the risk areas by allocating the funds intended for coastal protection with a certain order of priority [44], [45].…”

Risk Mapping of Coastal Flooding Areas. Case Studies: Scilla and Monasterace (Italy)

“…Until recently, most of the legislations and of the scientific research focused only on river and urban flooding or coastal erosion [26][27][28][29] but in the last 15 years the coastal flooding risk has significantly increased due to climate changes which have caused an increase in the sea level, and in the frequency of extreme events. As a consequence, in recent years, scientific researchers and legislators have devoted more attention to coastal flooding [30][31][32][33][34][35][36][37][38][39].…”