Work

Coastal Vulnerability Analysis of the Semi-Enclosed Water Systems of the Sea of Marmara, Türkiye using InVEST Model.

Year: 2025
Type: article
Abstract: Coastal vulnerability models serve as decision-support tools to identify high-risk areas and develop ranking systems to assess the relative vulnerability of coastal communities. The InVEST Coastal Vulnerability Model, developed by Stanford University's Natural Capital Project, is a recent approach that evaluates the Exposure Index, demonstrating strong performance in tropical regions. This comprehensive framework considers physical factors and incorporates assessments of social vulnerability and economic impacts. While numerous studies have focused on tropical coral habitats in open ocean systems, coastal vulnerability assessments remain scarce in semi-enclosed water bodies, such as the Black Sea and Sea of Marmara (SoM). These regions, which differ from tropical reef ecosystems, lack in-depth understanding of coastal vulnerability risk. This study concentrates on the SoM and conducts the necessary analyses by considering the different parameters. Obtained results show significant exposure levels, with 28% of the coastline at low risk, 60.9% at intermediate risk, and 8.7% at high risk. The analysis also highlights an uneven distribution of vulnerability, with northern areas like Kocaeli and Tekirdağ exhibiting higher susceptibility to coastal hazards than southern regions. Examining different climate change scenarios, such as SSP1-1.9, SSP4-4.5, and SSP8-8.5, reveals contrasting trends in the risks posed by storm surges and sea level rise. This underscores the importance of thoroughly understanding regional vulnerability differences, which can guide the development of customized adaptation strategies to enhance the resilience of coastal communities around the SoM. Keywords: Coastal Vulnerability Index, Sea of Marmara, Coastal hazard, Climate change, InVEST model
Language: English
Open Access status: green
OpenAlex ID: W6949371632
FWCI:
Cites:
Cited by:
Related to: