Assessment of vulnerability to storm induced flood hazard along diverse coastline settings

et al. 2017

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European coasts suffer significantly from hazards caused by low-probability and high-impact hydro-meteorological events. The aim of the study is to assess in probabilistic terms the magnitude of storm-induced flooding and erosion hazards along Varna regional coast (Bulgaria, western Black Sea). The study is performed employing the Coastal Risk Assessment Framework (CRAF) developed within EU FP7 RISC-KIT project. It constitutes a screening process that allows estimation of relevant hazard intensities and extents within predefined sectors. Since total water level was the chief property considered for determination of coastal flooding hazard, the accurate calculation of maximum wave induced run-up is of utter importance. Therefore, a central part of the study is testing the applicability and validation of three empirical models -from which the Holman model was preferred to be applied on sandy beaches, as well as EurOtop formulation for artificial or rocky slopes. As for erosion hazard, the performance of the convolution model was assessed and subsequently employed for evaluation of shoreline retreat. Quantification of hazard intensities allowed hinterland extent to be roughly delineated. As a final result of the study, hazard indicators were obtained for both flooding and erosion, which were subsequently mapped and used for identification of the most susceptible sectors along the regional coast. It was found that their concentration is highest in Varna Bay.

Section: Hazard Intensities and Extentsmentioning

confidence: 99%

Calculation of Maximum Wave Run-Up and Erosion at Varna Regional Coast (Western Black Sea) Using Empirical Models

Eftimova

et al. 2017

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“…The process is described in (Eftimova et al 2017). Subsequently, the calculated TWLs were best fitted to Generalized Extreme Value distribution (Valchev et al 2016). Hazard intensities relevant to return periods of 20, 50 and 100 years were further analyzed since they were considered benchmark values by most of the regional stakeholders.…”

Section: Hazard Assessment Backgroundmentioning

confidence: 99%

“…Ultimately, the CRAF approach combines several hazard and exposure indicators into a single index, thereby allowing a rapid comparison of coastal sectors. Results on application of the framework and identification of 'hotspots' along Varna regional coast are presented in Valchev et al (2016), while the present study mainly focuses on and presents results of assessment of receptors' exposure vulnerability.…”

Section: Introductionmentioning

confidence: 99%

ASSESSMENT OF COASTAL RECEPTORSâ€™ EXPOSURE VULNERABILITY TO FLOOD HAZARD ALONG VARNA REGIONAL COAST

Prodanov

et al. 2017

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Storms and related disasters are phenomena producing coastal hazards and endangering human life and occupation. The study evaluates coastal receptorsâ€™ exposure and their vulnerability to storm-induced flooding hazard along Varna regional coast in Western Black Sea, Bulgaria. The assessment is performed employing the Coastal Risk Assessment Framework (CRAF) developed within EU FP7 RISC-KIT project. It constitutes a screening process that allows determination of susceptible alongshore sectors (hotspots) by assessing relevant hazard intensities, hazard extents and potential receptorsâ€™ exposure vulnerability within them by means of coastal indices approach. The ultimate goal is to evaluate potential risk posed by flooding in support to coastal managers, decision and policy makers. Assessment of coastal receptorsâ€™ exposure vulnerability is done by exposure indicators approach, using hazard intensities and flooding extents relevant to return period of 100 years. The approach consists of combining several indicators into a single index, thereby allowing a rapid comparison of coastal sectors. Five types of receptors are considered to formulate the relevant exposure indicators: Land Use, Population, Transport, Utilities and Business, which subsequently are combined into an Overall Exposure Indicator to evaluate potential direct and indirect impacts. Results show that the most vulnerable to coastal flooding in terms of exposure are coastal sectors located within Varna Bay, comprising port and industry facilities with regional, national and international significance.

“…The selected hotspot -a highly vulnerable coastal sector according to Valchev et al (2016) includes Varna beach and the breakwater of Port of Varna (herein referred to as port wall) ( Fig. 2).…”

Section: Study Sitementioning

confidence: 99%

“…Therefore, main data source was a wave hindcast dataset spanning 63-year period (1949-2012) (Valchev et al 2012). In compliance with thresholds set in Valchev et al (2016), more than 140 individual events were identified each represented by maximum significant wave height (Hs), corresponding peak wave period (Tp), mean wave direction and storm duration. As for peak surge level (h), the historic component of data originating from another modeling study (Vousdoukas et al 2016) was considered.…”

Section: Compilation Of Synthetic Datasetmentioning

confidence: 99%

Application of Bayesian Network as a Tool for Coastal Flooding Impact Prediction at Varna Bay (Bulgaria, Western Black Sea)

Eftimova

et al. 2017

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Storms and related disasters are one of the most important phenomena producing coastal hazards and endangering human life and occupation. While coastal response to extreme forcing conditions can be evaluated using numerical models, there is increasing need for less computationally expensive probability tools that can quickly produce results thus contributing to more effective coastal risk management. A possible response to this demand is a Bayesian Network, which relates near-shore storm conditions to their onshore flood potential and ultimately translates them to relevant impact (consequences) expressed as damage to various receptor groups. Bayesian Network can constitute a module in an early warning system or can be used as a planning tool to evaluate the long-term vulnerability due to multiple coastal hazards, under various climate-related scenarios. The present study describes the application of a Bayesian Network for Varna Bay building on developments made in the framework of the RISC-KIT (Resilience-Increasing Strategies for Coasts â€“ toolKIT) project. Moreover, several alternatives involving disaster risk reduction measures were examined both in present and future climate conditions. It was found that the analysis of results through the prism of the Bayesian Network provides a useful insight of the problems at the study site making it a reliable coastal impact prediction tool.