In this study four experiments were conducted to investigate uncertainty in future longshore sediment transport (LST) projections due to: working with continuous time series of CSIRO CMIP6-driven waves (experiment #1) or sliced time series of waves from CSIRO-CMIP6-Ws and CSIRO-CMIP5-Ws (experiment #2); different wave-model-parametrization pairs to generate wave projections (experiment #3); and the inclusion/exclusion of sea level rise (SLR) for wave transformation (experiment #4). For each experiment, a weighted ensemble consisting of offshore wave forcing conditions, a surrogate model for nearshore wave transformation and eight LST models was used. The results of experiment # 1 indicated that the annual LST rates obtained from a continuous time series of waves were influenced by climate variability acting on timescales of 20-30 years. Uncertainty decomposition clearly reveals that for near-future coastal planning, a large part of the uncertainty arises from model selection and natural variability of the system (e.g., on average, 4% scenario, 57% model, and 39% internal variability). For the far future, the total uncertainty consists of 25% scenario, 54% model and 21% internal variability. Experiment #2 indicates that CMIP6 driven wave climatology yield similar outcomes to CMIP5 driven wave climatology in that LST rates decrease along the study area’s coast by less than 10%. The results of experiment #3 indicate that intra- and inter-annual variability of LST rates are influenced by the parameterization schemes of the wave simulations. This can increase the range of uncertainty in the LST projections and at the same time can limit the robustness of the projections. The inclusion of SLR (experiment #4) in wave transformation, under SSP1-2.6 and SSP5-8.5 scenarios, yields only meagre changes in the LST projections, compared to the case no SLR. However, it is noted that future research on SLR influence should include potential changes in nearshore profile shapes.@en

This study determines the qualities of atmospheric wind field data in comparison with wind measurements at five locations along the Black Sea coast. For this purpose, four different wind fields were obtained from three different weather centers (NCEP, NASA, and ECMWF). Three of these are reanalyzed winds (Climate Forecast System Reanalysis CFSR, Modern-Era Retrospective analysis for Research and Applications MERRA, ECMWF reanalyses ERA-Interim), and one is an operational dataset (ECMWF operational). Their performances were determined using the wind measurements from 2000 to 2014 at five coastal locations along the southern coastline of the Black Sea (Kumköy, Amasra, Sinop, Giresun, Hopa) and from 2006 to 2009 at the offshore location (Gloria) off the coast of Romania. The performances of these wind fields were determined based on statistical characteristics (mean, standard deviation and variation coefficient, etc.), the statistical error analysis for all data and for different wind speed intervals, the wind roses and the probability distributions. Additionally, long-term variations of the yearly error values (SI and bias) of wind speeds from wind data sources during 2000 - 2014 were discussed. Finally, it was concluded that the CFSR wids give the best performance at most stations. The ECMWF datasets yield better results along the western side but the CFSR wind fields have shown better performances along the eastern side of the Black Sea coast and at the Gloria offshore location.

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This study presents the south-west Black Sea wave climatology based on a downscaling approach of a long-term 31-year SWAN model wave hindcast using telescoping nested grids. At all domains the SWAN model is forced with the CFSR winds. Sensitivity tests are conducted on domain size, computational resolutions, the physical formulations and their adjustable coefficients for deep water source terms, time step of non-stationary calculation, and wind forcing for all domains. For each nested grid the physical and numerical settings were determined separately by calibration against wave buoy measurements at six locations (Gelendzhik, Hopa, Sinop, Gloria, Filyos, and Karaburun) in appropriate domains. Model validation is also conducted for the long-term data using the unused measurements in the calibration. Using the calibrated nested models, a 31-year long-term wave hindcast is conducted. Two-hourly sea state parameters of significant wave height (Hm0), wave energy period (Tm-10), spectral period (Tm01), zero-crossing period (Tm02), peak period (Tp), wind speed components, and mean wave direction (DIR) were collected over three sub-grid domains. Using this database normal and extreme wave conditions in the three sub-grid domains were determined. Finally, extreme waves with different return periods were determined and compared with those presented in the Wind and Deep Water Wave Atlas of Özhan and Abdalla (2002). The present study demonstrates the sensitivity of the SWAN model towards different GEN3 physics options and its adjustable whitecapping parameter Cds and time step of the non-stationary calculations. It is shown that the developed wave model set-up with a nested grid system performs quite satisfactorily and storms are also well-captured. This study yields higher extreme waves in the western part of our area of interest and lower extremes in the eastern part in comparison with those of the presently used Wind and Deep Water Wave Atlas.@en

A high-resolution SWAN wind wave hindcast model was implemented for the Sea of Marmara. For this, we focused firstly on the quality of two data sources for the wind forcing, viz., the ERA-Interim winds from the ECMWF and CFSR winds from the NOAA/NCEP. These were compared against wind measurements for 2013 collected at the Silivri offshore buoy in the north of the Sea of Marmara. A sensitivity analysis was performed to find the optimal numerical settings and wind source. This analysis showed that the CFSR winds are most suited for wave modeling in the Sea of Marmara. As the Sea of Marmara can practically be considered as deep water, we calibrated the SWAN model for different combinations of wind input and whitecapping source terms. The calibration was performed by varying the whitecapping coefficient for different combinations. The model setting giving the lowest errors and highest correlation via sensitivity analysis was determined as the calibrated model. Thirdly, the calibrated model was validated against measurements at the Silivri buoy for the years 2014, 2015 and 2016. This validation confirmed that the calibrated SWAN model with CFSR wind forcing performed better than the default settings. Lastly, the performance of the calibrated SWAN model was assessed for different wave height ranges, wind sources, annually and per season, their directional properties using wind and wave roses, their distribution function and Quantile – Quantile plots along with extreme waves. The calibrated model offers almost the same extreme waves with different recurrent periods as the measurements.

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This study analyzes the wave energy potential in the Black Sea based on long-term model simulations. A dataset covering the period of 1979–2009 is produced using a calibrated numerical wave prediction model (SWAN). This dataset was analyzed in detail to determine the wave energy potential to enable a reliable and optimal design of wave energy conversion devices in the Black Sea. This analysis provides information on the long-term variability as well as on the annual, seasonal and monthly averages. The analysis of the hindcast results is conducted on a spatial and a location scale. The spatial analysis provides information for the entire Black Sea on; the averaged mean wave energy flux over the period 1979–2009, and the decades 1980–1989, 1990–1999, and 2000–2009, seasonal and monthly averages of wave energy flux during 31 years, variability indices for the 1979−2009 period, and variabilities on monthly and seasonality basis based on inter-annual averages during 31 years. The location scale considered nine locations providing information on; wave power roses, probabilities of occurrence and cumulative distribution functions of wave power in different power ranges, variation and trend of yearly average wave power, seasonal average wave power and its annual variations, and quantities of wave energy flux for different H_m0 and T_m-10 ranges. Results show that areas with the highest wave energy potential are located in the south-western part of the Black Sea. These areas are; Burgas – Rezovo (BR) with an average annual total energy of 43.9 MW h/m followed by Dolni Chiflik – Shkorpilovtsi (DCS) with 37.3 MW h/m and Istanbul – Alacali (IA) with 36.1 MW h/m.

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Wind and wave characteristics and their long-term variability in the Black Sea over a period of 31 years are investigated in this study. The state-of-the art spectral wave model SWAN is implemented to perform a 31- year wave hindcast in the area of interest. The simulation results are used to assess the inter-annual variability and long-term changes in wind and wave climate in the Black Sea for the period 1979–2009. The SWAN model is forced with the Climate Forecast System Reanalysis (CFSR) winds. The model is calibrated and validated against available wave measurements at six offshore and near-shore locations spread over a large region in the Black Sea. The calibration was performed by tuning parameters in the white-capping and wind input formulations against available measurements for 1996 at three offshore locations (Gelendzhik, Hopa, and Sinop). The validation was carried out using measured data, at Gelendzhik, Hopa, and Sinop offshore locations, Gloria drilling platform and Karaburun and Filyos near-shore locations. From the 31-year simulation results, the long-term spatial distributions and changes of the mean wind and mean and maximum wave characteristics and their inter-annual variabilities were determined. The calibration improved SWAN model performance by 11.6% for H_m0 and 3.3% for T_m02 on average at three locations. The mean annual significant wave height (H_m0) and mean wind speed (WS) indicate the occurrence of higher wave heights and wind speeds in the western Black Sea compared to the south eastern coasts of the Black Sea. The coefficient of variation over the Black Sea for H_m0 and WS shows that the variability for H_m0 is higher than that of WS. It is also observed that the variability for H_m0 is higher in areas (such as offshore Gelendzhik, Russia) where the variability of WS is high. Besides, the storms mentioned in the previous studies (such as Galabov and Kortcheva, 2013; Tarakcioglu et al., 2015) are observed in four interesting characteristic areas with maximum H_m0 determined in this study over the Black Sea.

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This study aims to assess wave energy potential and its long-term spatial and temporal characteristics in the Black Sea within the TUBITAK research project (Akpınar et al., 2015). With this purpose, a wave model (SWAN model version 41.01 driven by the CFSR winds) over the entire Black Sea was constructed. The model was calibrated using buoy data from 1996 at three offshore locations (Gelendzhik, Hopa, and Sinop) obtained within NATO TU-WAVES Project. The calibrated model was also validated using buoy data unused in calibration at five locations (Gelendzhik, Hopa, Gloria, Filyos, and Karaburun). Using this model a database including many of integral wave parameters (such as Hm0, Tm-10 etc.) was produced. Long-term variability of wave energy in the Black Sea basin over a period of 31 years was determined. Finally, hot-spot areas for harvesting wave energy in the Black Sea were identified.@en

The swell climate of the Black Sea has been determined using a long-term 31-year wave hindcast with the thirdgeneration spectral wave model SWAN in combination with spectral partitioning. This technique enables decomposing wave spectra into individual wave systems representing wind seas or swells and computing integral wave parameters of each partition. Results are presented of the partition technique and of spatial and seasonal characteristics of wind sea and swell systems. In addition, the average amount of swell energy and the occurrence probability of dangerous crossing sea states are determined.@en