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This study aims to present an evaluation and implementation of a high-resolution SWAN wind wave hindcast model forced by the CFSR wind fields in the west Mediterranean basin, taking into account the recent developments in wave modelling as the new source terms package ST6. For this purpose, the SWAN model was calibrated based on one-year wave observations of Azeffoune buoy (Algerian coast) and validated against eleven wave buoys measurements through the West Mediterranean basin. For the calibration process, we focused on the whitecapping dissipation coefficient C-ds and on the exponential wind wave growth and whitecapping dissipation source terms. The statistical error analysis of the calibration results led to conclude that the SWAN model calibration corrected the underestimation of the significant wave height hindcasts in the default mode and improved its accuracy in the West Mediterranean basin. The exponential wind wave growth of Komen et al (1984) and the whitecapping dissipation source terms of Janssen (1991) with C-ds = 1.0 have been thus recommended for the western Mediterranean basin. The comparison of the simulation results obtained using this calibrated parameters against eleven measurement buoys showed a high performance of the calibrated SWAN model with an average scatter index of 30% for the significant wave heights and 19% for the mean wave period. This calibrated SWAN model will constitute a practical wave hindcast model with high spatial resolution ((similar to)3 km) and high accuracy in the Algerian basin, which will allow us to proceed to a finer mesh size using the SWAN nested grid system in this area.

This paper aims to examine the temporal variation of wave energy flux in the hotspot areas of the Black Sea. For this purpose, a 31-year long-term wave dataset produced by using a three-layered nested modelling system was used. Temporal variations of wave energy were determined at hourly, monthly, seasonal, and yearly basis at seventeen stations. Based on the results obtained, it can be concluded that the stations have very low fluctuations in mean wave power during the day. Mean wave power in the summer months shows a low difference between the stations, but in the winter months, there is a higher difference in wave power between the stations. This difference is more at the stations in the southwestern part of the Black Sea and much lower in the eastern Black Sea stations around Sinop, being in the middle of the southern coast of the Black Sea. In addition, it is concluded that mean wave energy flux presents a decreasing trend at all stations, but maximum wave power offers an increasing trend at most of the stations. Romanian Executive Agency for Higher Education, Research, Development and Innovation Funding (PN-III-P4-IDPCE-2016-0017)

This paper evaluates the impact of using different wind field products on the performance of the third generation wave model SWAN in the Black Sea and its capability for predicting both normal and extreme wave conditions during 1996. Wind data were obtained from NCEP CFSR, NASA MERRA, JRA-25, ECMWF Operational, ECMWF ERA40, and ECMWF ERA-Interim. Wave data were obtained in 1996 at three locations in the Black Sea within the NATO TU-WAVES project. The quality of wind fields was assessed by comparing them with satellite data. These wind data were used as forcing fields for the generation of wind waves. Time series of predicted significant wave height (H-mo), mean wave period (T-m02), and mean wave direction (DIR) were compared with observations at three offshore buoys in the Black Sea and its performance was quantified in terms of statistical parameters. In addition, wave model performance in terms of significant wave height was also assessed by comparing them against satellite data. The main scope of this work is the impact of the different available wind field products on the wave hindcast performance. In addition, the sensitivity of wave model forecasts due to variations in spatial and temporal resolutions of the wind field products was investigated. Finally, the impact of using various wind field products on predicting extreme wave events was analyzed by focussing on storm peaks and on an individual storm event in October 1996. The numerical results revealed that the CFSR winds are more suitable in comparison with the others for modelling both normal and extreme events in the Black Sea. The results also show that wave model output is critically sensitive to the choice of the wind field product, such that the quality of the wind fields is reflected in the quality of the wave predictions. A finer wind spatial resolution leads to an improvement of the wave model predictions, while a finer temporal resolution in the wind fields generally does not significantly improve agreement between observed and simulated wave data. NATO Science for Stability Program

The main objective of the present study was to apply the ANN (artificial neural network) model with the TLBO (teaching-learning-based optimization) algorithm to estimate energy consumption in Turkey. Gross domestic product, population, import, and export data were selected as independent variables in the model. Performances of the ANN-TLBO model and the classical back propagation-trained ANN model (ANN-BP (teaching learning-based optimization) model) were compared by using various error criteria to evaluate the model accuracy. Errors of the training and testing datasets showed that the ANN-TLBO model better predicted the energy consumption compared to the ANN-BP model. After determining the best configuration for the ANN-TLBO model, the energy consumption values for Turkey were predicted under three scenarios. The forecasted results were compared between scenarios and with projections by the MENR (Ministry of Energy and Natural Resources). Compared to the MENR projections, all of the analyzed scenarios gave lower estimates of energy consumption and predicted that Turkey's energy consumption would vary between 142.7 and 158.0 Mtoe (million tons of oil equivalent) in 2020.

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.

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. NATO (North Atlantic Treaty Organisation) National Oceanic and Atmospheric Administration Turkish Ministry of Transport NIMRD NCEP CFS

Spatial and temporal variations of significant wave height (H m0) and wind speed (WS) at selected locations over the Black Sea are studied based on 31-year long-term SWAN simulations forced with Climate Forecast System Reanalysis dataset. The objective was to investigate whether or not there is a possible increase in wind and wave conditions along the Black Sea shelves. Wind and wave parameters are obtained at 33 locations enclosing the Black Sea coast line from SWAN simulations and annual mean and maximum H m0 and WS values as the climatological variables are computed for these locations. Using these data, long-term trends and their significance at these locations are investigated based on Mann–Kendall trend test. To quantify the trends, Sen’s slope estimator and least square linear regression (the slope of the linear best-fit curve) are used. Variation of monthly mean H m0 and WS values at these locations are also discussed. Besides, decadal variations of these four climatological variables at 33 locations are studied. The results show that higher wind speeds and wind wave heights are monitored in the winter season in all locations, while during the summer months, there is a significant drop in both H m0 and WS. In the western Black Sea, average H m0 is highest (about 1.02 m) at locations 23 and 25. During the period of 1979 and 2009, it is determined that mean WS has a weak significant increasing trend (maximum 1.29 cm/s/year) along the north-eastern coasts of Turkey and the Crimean peninsula, while there is no statistically significant H m0 trend in the Black Sea except at location 11, offshore Sochi in the north-eastern part of the Black Sea. A weak decreasing trend (maximum 0.24 cm/year) in mean H m0 is seen along the north-western coasts of Turkey, while maximum H m0 and WS show no statistically significant increasing or decreasing trend except location 2, which has a weak significant increasing trend for maximum WS. All the trends at other locations for four variables are statistically insignificant, and they have no trend. The most significant difference is observed in maximum WS as 6.14 m/s in different decades in the north-western part of the Black Sea. The difference in the decades is very low in mean H m0 at all locations. Mean wind and wave conditions at all locations have almost negligible difference, whereas decadal variations of maximum H m0 and WS show high differences. This may be probably due to storms and cyclones conditions.

This study aims at an assessment of wind re-analyses for modelling storms in the Black Sea. A wind-wave modelling system (Simulating WAve Nearshore, SWAN) is applied to the Black Sea basin and calibrated with buoy data for three recent re-analysis wind sources, namely the European Centre for Medium-Range Weather Forecasts Reanalysis-Interim (ERA-Interim), Climate Forecast System Reanalysis (CFSR), and Modern Era Retrospective Analysis for Research and Applications (MERRA) during an extreme wave condition that occurred in the north eastern part of the Black Sea. The SWAN model simulations are carried out for default and tuning settings for deep water source terms, especially whitecapping. Performances of the best model configurations based on calibration with buoy data are discussed using data from the JASON2, TOPEX-Poseidon, ENVISAT and GFO satellites. The SWAN model calibration shows that the best configuration is obtained with Janssen and Komen formulations with whitecapping coefficient (C-ds) equal to 1.8e-5 for wave generation by wind and whitecapping dissipation using ERA-Interim. In addition, from the collocated SWAN results against the satellite records, the best configuration is determined to be the SWAN using the CFSR winds. Numerical results, thus show that the accuracy of a wave forecast will depend on the quality of the wind field and the ability of the SWAN model to simulate the waves under extreme wind conditions in fetch limited wave conditions. NATO Science for Stability Program

The primary objective of this study was to apply the ANN (artificial neural network) model with the ABC (artificial bee colony) algorithm to estimate annual hydraulic energy production of Turkey. GEED (gross electricity energy demand), population, AYT (average yearly temperature), and energy consumption were selected as independent variables in the model. The first part of the study compared ANN-ABC model performance with results of classical ANN models trained with the BP (back propagation) algorithm. Mean square and relative error were applied to evaluate model accuracy. The test set errors emphasized positive differences between the ANN-ABC and classical ANN models. After determining optimal configurations, three different scenarios were developed to predict future hydropower generation values for Turkey. Results showed the ANN-ABC method predicted hydroelectric generation better than the classical ANN trained with the BP algorithm. Furthermore, results indicated future hydroelectric generation in Turkey will range from 69.1 to 76.5 TWh in 2021, and the total annual electricity demand represented by hydropower supply rates will range from 14.8% to 18.0%. However, according to Vision 2023 agenda goals, the country plans to produce 30% of its electricity demand from renewable energy sources by 2023, and use 20% less energy than in 2010. This percentage renewable energy provision cannot be accomplished unless changes in energy policy and investments are not addressed and implemented. In order to achieve this goal, the Turkish government must reconsider and raise its own investments in hydropower, wind, solar, and geothermal energy, particularly hydropower.