LHW has participated in IAHR Europe Congress with four papers:
Advanced Multi-Area Approach for Coastal Vulnerability Assessment
Christina Tsaimou, Georgios Kagkelis, Andreas Papadimitriou, Vasiliki Chalastani, Panagiotis Sartampakos, Michalis Chondros and Vasiliki Tsoukala
Development of a Complex Vulnerability Index for Fishing Shelters – The Case of Cyprus
Vasiliki Chalastani, Andreas Pantelidis, Christina Tsaimou and Vasiliki Tsoukala
Andreas Papadimitriou, Michalis Chondros, Anastasios Metallinos and Vasiliki Tsoukala
Andreas Papadimitriou and Vasiliki Tsoukala
Abstract: Integrated Coastal Zone Management (ICZM) policies require a comprehensive evaluation of the complex coastal spatial and temporal characteristics. Stresses induced by natural hazards, human forces and the ever-changing climate are inherently linked with the coastal vulnerability concept that is elaborated through the employment of multi-faceted ICZM practices. Currently, advances in Geographic Information System (GIS) applications enable detailed examination and visualization of the output produced by coastal vulnerability analyses. The present paper pursues to promote advances for assessing coastal vulnerability by investigating segregation of distinct areas identified along a coastal zone into sub-sections and classification of vulnerability parameters aiming at applying a GIS-based multi-area approach. Towards this, the case study of the Coastal Zone of the Municipality of Thivaion, located at the Northeastern Corinthian Gulf of central Greece is examined. This zone encloses six (6) areas of particular interest where vulnerability assessment is prerequisite in terms of a powerful ICZM program. The study allows for robust vulnerability assessments that help decision makers to undertake ICZM actions.
Abstract: Coastal areas are increasingly threatened both by the impacts of climate change as well as human pressures. Seaports lying at the land-sea interface are considered vulnerable infrastructure, affected by sea level rise, storm surges and increased human activities. In this paper, the 16 existing fishing shelters of Cyprus are used as case study to develop a complex vulnerability index to assess and evaluate the current state of the fishing shelters. This vulnerability index includes physical, environmental, socio-economic and infrastructural indicators which describe the structural and operational components of the shelters in a holistic way. These indicators are scored and ranked to describe the degree of vulnerability of each fishing shelter and allow for comparison among shelters. The novelty of this index is that it is informed by on-site visits; questionnaires answered by local fishermen and targeted interviews with representatives of the port authorities. This study highlights the complex interactions between physical and socio-economic conditions in driving vulnerability. The results can assist decision-makers to prioritize interventions and design adaptation pathways that reduce the shelters’ vulnerability while increasing their resilience.
Abstract: Process-based models have been employed extensively in the last decades for the prediction of coastal bed evolution in the medium term (1-5 years), under the combined action of waves and currents, due to their ability to resolve the dominant coastal processes. Despite their widespread application, they are associated with a high demand in computational resources, rendering the annual prediction of the coastal bed evolution a tedious task. To combat this, various accelaration techniques such as wave input reduction or elimination of lowly-energetic sea-states have been implemented in many practical applications. The purpose of this research is to further expand on the concept of accelerating morpholgical simulations by developing a methodology centered around employing an Artificial Neural Network (ANN), tasked with eliminating wave records unable to initiate sediment motion and hence further reduce computational times. The ANN has been trained on a 2DH idealized plane sloping beach with a robust dataset produced by simulations of three sophisticated numerical models. The proposed methodology has been implemented for a real field case in the coastal area of Rethymno, Greece and the obtained results were deemed very satisfactory by maintaining a balance between accuracy of results and computational efficiency, having strong implications for practical coastal engineering purposes.
Abstract: The morphological coastal bed evolution is of high interest to engineers, scientists and the public due to the vast number of activities concentrated near the shoreline. Traditionally, process-based models have been employed to predict bed level changes in time scales of 1-5 years, however they are associated with prohibitive computational restrictions. To reduce the computational burden, wave Input Reduction methods, aiming to reduce the forcing input and accelerate morphological simulations have been developed. The present paper aims at evaluating the K-Means algorithm as an alternative approach to select wave representatives for morphological simulations. Several alternative configurations were tested in order to enhance and coerce the algorithm to “smartly” select the representative waves. The examined configurations were implemented in the coastal area of Rethymno, Greece for an annual dataset of sea-state wave characteristics and the results were deemed very satisfactory compared to those obtained by the full wave climate, rendering the use of K-Means algorithm a valuable tool for coastal engineers and scientists.
