Applying two methodologies of an integrated coastal vulnerability index (ICVI) to future sea-level rise. Case study: southern coast of the Gulf of Corinth, Greece

Climate change is an issue of concern and is expected to cause various adverse impacts on human societies in the near and long-term future. Sea-level rise, which is caused by global warming and melting continental ice sheets, in combination with the rising global population and evolution of human activities in coastal areas, tends to make coastal societies more prone to coastal hazards. The Gulf of Corinth in Greece with its diverse coastal landforms and tectonic complexity makes the region unique when considering an assessment of coastal vulnerability. In this study we apply an Integrated Coastal Vulnerability Index (ICVI) to a potential sea-level rise for the southern coastline of the Gulf of Corinth (Greece) consisting of physical and socio-economic parameters. Among multiple different methodologies that have been developed over the recent years, we decided to apply two of the mathematical approaches we believe are best suited for the protection of human activities in our study area. The first one, ICVI_1, is based on the Coastal Vulnerability Index (CVI) by Thieler and Hammar-Klose (1999) with variables of equal relative importance, whereas the second one, ICVI_2, uses the Analytic Hierarchic Process (AHP) with the assignment of relative weight values to each parameter. The parameters were identified and ranked into a vulnerability index with a scale from 1 to 5. The results reveal that both approaches depict more or less the same coastal sections of high or very high vulnerability, but differ in the distribution of extreme values. ICVI_1 shows that 18.3% of the total coastline features very high vulnerability (score 5), while ICVI_2 shows 9.1%. The coastal sections with the highest scores of vulnerability are mostly represented in the eastern part of the studied coastline with low-lying regions of gentle slope and concentrated human activity

Applied Hydrological Modeling with the Use of Geoinformatics: Theory and Practice

Water resource management and catchment analysis are crucial aspects of the twenty-first century in hydrological and environmental sciences. Linked directly with studies and research about climate change effects in global resources (e.g., diminution of rainfall dynamic), as well as continuously growing extreme natural phenomena with catastrophic results (e.g., floods and erosion), hydrological modeling has become a key priority in modern academic research goals. On a national or lower administrative level, the need for coping with natural disasters—affecting mainly human life, property, local economy, infrastructure, etc.—and the need to design management plans and projects for sustainable exploitation of natural resources set hydrological modeling in high demand by government organizations and local authorities. Thus, hazard assessment and risk evaluation modeling have become a strategic aim and an extremely useful tool for stakeholders, decision-makers, and scientific community

Integrating expert knowledge with statistical analysis for landslide susceptibility assessment at regional scale

Abstract: In this paper, an integration landslide susceptibility model by combining expert-based and bivariate statistical analysis (Landslide Susceptibility Index—LSI) approaches is presented. Factors related with the occurrence of landslides—such as elevation, slope angle, slope aspect, lithology, land cover, Mean Annual Precipitation (MAP) and Peak Ground Acceleration (PGA)—were analyzed within a GIS environment. This integrated model produced a landslide susceptibility map which categorized the study area according to the probability level of landslide occurrence. The accuracy of the final map was evaluated by Receiver Operating Characteristics (ROC) analysis depending on an independent (validation) dataset of landslide events. The prediction ability was found to be 76% revealing that the integration of statistical analysis with human expertise can provide an acceptable landslide susceptibility assessment at regional scale

Late quaternary rates of stream incision in northeast Peloponnese, Greece

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Coastal Hazard Vulnerability Assessment Based on Geomorphic, Oceanographic and Demographic Parameters: The Case of the Peloponnese (Southern Greece)

Today low-lying coastal areas around the world are threatened by climate change-related hazards. The identification of highly vulnerable coastal areas is of great importance for the development of coastal management plans. The purpose of this study is to assess the physical and social vulnerability of the Peloponnese (Greece) to coastal hazards. Two indices were estimated: The Coastal Vulnerability Index (CVI) and the Social Vulnerability Index (SVI). CVI allows six physical variablesto be related in a quantitative manner whilethe proposed SVI in this studycontains mainly demographic variables and was calculated for 73 coastal municipal communities. The results reveal that 17.2% of the shoreline (254.8 km) along the western and northwestern coast of the Peloponnese, as well as at the inner Messiniakos and Lakonikos Gulfs, is of high and very high physical vulnerability. High and very high social vulnerabilities characterize communities along the northwestern part of the study area, along the coasts of the Messinian and Cape Malea peninsulas, as well as at the western coast of Saronikos Gulf

Use of morphometric variables and self-organizing maps to identify clusters of alluvia fans and catchments in the north Peloponnese, Greece

Abstract: We applied a computational method to aid in clustering 41 alluvial fans along the southern coast of the Gulf of Corinth, Greece. The morphology of the fans and their catchments was quantitatively expressed through 12 morphometric parameters estimated using geographical information system techniques and the relationships among the geomorphometric features of the fans and their catchments were examined. Self-organizing maps were used to investigate the clustering tendency of fans based on morphometric variables describing both the fans and their corresponding catchments. The results of unsupervised classification through the self-organizing maps method revealed correlations among the morphometric parameters and five groups of alluvial fans were identified. These groups had a clear physical explanation, showed a preferred geographical distribution and reflected the processes related to the development of the fans. The geographical distribution of the fan catchment groups was partially controlled by variations in the relative tectonic uplift rate, which was the main control on the accommodation space for the development and accretion of the fans. The smaller fans were located in the central part of the study area, where the uplift rates were higher, whereas larger fluvial-dominated fan deltas formed to the east and west of the central group, where the uplift rates were lower

Tectonics and Sea-Level Fluctuations

Global sea level has fluctuated significantly over geologic time as a result of changes in the volume of available water in the oceans and changes in the shape and volume of the ocean basins [...

Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale

In this paper, an integration landslide susceptibility model by combining expert-based and bivariate statistical analysis (Landslide Susceptibility Index—LSI) approaches is presented. Factors related with the occurrence of landslides—such as elevation, slope angle, slope aspect, lithology, land cover, Mean Annual Precipitation (MAP) and Peak Ground Acceleration (PGA)—were analyzed within a GIS environment. This integrated model produced a landslide susceptibility map which categorized the study area according to the probability level of landslide occurrence. The accuracy of the final map was evaluated by Receiver Operating Characteristics (ROC) analysis depending on an independent (validation) dataset of landslide events. The prediction ability was found to be 76% revealing that the integration of statistical analysis with human expertise can provide an acceptable landslide susceptibility assessment at regional scale