Temporal Variation of the Wave Energy Flux in Hotspot Areas of the Black Sea

Akpınar, Adem; Jafali, Halid; Rusu, Eugen

doi:10.3390/su11030562

Cited by 24 publications

(12 citation statements)

References 24 publications

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“…In this context, the global assessment of the wave power has been the topic of several studies based on the sea state conditions provided either by numerical models [14][15][16][17][18] or satellite measurements [19]. Such a global assessment of the wave power can indicate regional areas with high potential, but they may not provide an exact local picture of where the places with energy concentrations, the so-called "hot spots", are encountered [20][21][22]. These local energy concentrations are determined especially by the local bathymetry characteristics; to find them, simulations with wave models implemented at various levels (from regional to local levels) using high-resolution bathymetry are necessary.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Worldwide Wave Energy Distribution Based on ERA5 Data and Altimeter Measurements

Rusu

2021

Energies

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There is an increasing necessity in reducing CO2 emissions and implementing clean energy technologies, and over the years the marine environment has shown a huge potential in terms of renewable energy. From this perspective, extracting marine renewable energy represents one of the most important technological challenges of the 21st century. In this context, the objective of the present work is to provide a new and comprehensive understanding concerning the global wave energy resources based on the most recent results coming from two different databases, ERA5 and the European Space Agency Climate Change Initiative for Sea State. In this study, an analysis was first made based only on the ERA5 data and concerns the 30-year period of 1989–2018. The mean wave power, defined as the energy flux per unit of wave-crest length, was evaluated at this step. Besides the spatial distribution of this parameter, its seasonal, inter, and mean annual variability was also assessed on a global scale. As a second step, the mean wave energy density per unit horizontal area was analyzed for a 27-year period (1992–2018) with both ERA5 and the satellite data from the European Space Agency being considered. The comparison indicates a relatively good concordance between the results provided by the two databases in terms of mean wave energy density, although the satellite data indicate slightly higher energy values.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the Worldwide Wave Energy Distribution Based on ERA5 Data and Altimeter Measurements

Rusu

2021

Energies

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“…Even though seawater density depends on salinity and temperature, which vary in time and space, an average value is set for this work, ρ w = 1025 kg m −3 , as also used by Iglesias et al [34]. Considering deep waters, the wave energy flux, expressed in kW per meter of wave crest length (kW m −1 ), is approximately estimated following Equation (2) ( [35][36][37][38][39] for total WEF):…”

Section: Methodsmentioning

confidence: 99%

Characterization of Wind-Sea- and Swell-Induced Wave Energy along the Norwegian Coast

et al. 2020

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The necessity to reduce C O 2 emissions in combination with the rising energy demand worldwide makes the extensive use of renewable energy sources increasingly important. To that end, countries with long coastlines, such as Norway, can exploit ocean wave energy to produce large amounts of power. In order to facilitate these efforts as well as to provide quantitative data on the wave energy potential of a specific area, it is essential to analyze the weather and climatic conditions detecting any variabilities. The complex physical processes and the atmosphere-wave synergetic effects make the investigation of temporal variability of wave energy a challenging issue. This work aims to shed new light on potential wave energy mapping, presenting a spatio-temporal assessment of swell- and wind-sea-induced energy flux in the Nordic Seas with a focus on the Norwegian coastline using the NORA10 hindcast for the period 1958–2017 (59 years). The results indicate high spatial and seasonal variability of the wave energy flux along the coast. The maximum wave energy flux is observed during winter, while the minimum is observed during summer. The highest coastal wave energy flux is observed in the Norwegian Sea. The majority of areas with dominant swell conditions (i.e., in the Norwegian Sea) are characterized by the highest coastal wave energy flux. The maximum values of wave energy flux in the North Sea are denoted in its northern parts in the intersection with the Norwegian Sea. In contrast to the Norwegian Sea, areas located in the North Sea and the Barents Sea show that wind sea is contributing more than swell to the total wave energy flux.

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“…Wind and current speeds were estimated as the magnitude of the extracted zonalmeridional components for wind and ocean currents, respectively. Water temperature was estimated as the average of water temperatures at all layers from the water surface down to a depth of 50 m. Given that wave energy flux is proportional to mean wave period and significant wave height squared (Christakos et al, 2020;Akpınar et al, 2019;Waters, 2008;Falnes, 2007), the wave energy index was defined as:…”

Section: Probabilistic Model Developmentmentioning

confidence: 99%

A probabilistic model for fracture events of Petermann ice islands under the influence of atmospheric and oceanic conditions

Torbati

Turnbull

Taylor

et al. 2021

Preprint

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Abstract. Four calving events of Petermann Glacier happened in 2008, 2010, 2011, and 2012, which resulted in the drift and deterioration of numerous ice islands, some reaching as far as offshore Newfoundland. The presence of these ice islands in the eastern Canadian Arctic increases the risk of interaction with offshore operations and shipping activities. This study used the recently developed Canadian Ice Island Drift, Deterioration and Detection database to investigate the fracture events that these ice islands experienced, and presented a probabilistic model for the conditional occurrence of such events by analyzing the atmospheric and oceanic conditions that drive the causes behind the ice island fracture events. Variables representing the atmospheric and oceanic conditions that the ice islands were subjected to were extracted from reanalysis datasets and then interpolated to evaluate their distributions for both fracture and non-fracture events. The probability of fracture event occurrence for different combinations of input variable conditions were quantified using Bayes theorem. Out of the seven variables analyzed in this study, water temperature and ocean current speed were identified as the most and least important contributors, respectively, to the fracture events of the Petermann ice islands. It was also revealed that the ice island fracture probability increased to 75 % as the ice islands encountered extreme (very high) atmospheric and oceanic conditions. A validation scheme was presented using cross-validation approach and Pareto principle, and an average error of 13–39 % was reported in the fracture probability estimations. The presented probabilistic model has a predictive capability for future fracture events of ice islands and could be of particular interest to offshore and marine activities in the eastern Canadian Arctic. Future research, however, is necessary for model training and testing to further validate the presented ice island fracture model.

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Temporal Variation of the Wave Energy Flux in Hotspot Areas of the Black Sea

Cited by 24 publications

References 24 publications

Evaluation of the Worldwide Wave Energy Distribution Based on ERA5 Data and Altimeter Measurements

Evaluation of the Worldwide Wave Energy Distribution Based on ERA5 Data and Altimeter Measurements

Characterization of Wind-Sea- and Swell-Induced Wave Energy along the Norwegian Coast

A probabilistic model for fracture events of Petermann ice islands under the influence of atmospheric and oceanic conditions

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