Wave height forecasting in Dayyer, the Persian Gulf

Forecasting of wave parameters is necessary for many marine and coastal operations. Different forecasting methodologies have been developed using the wind and wave characteristics. In this paper, artificial neural network (ANN) as a robust data learning method is used to forecast the wave height for the next 3, 6, 12 and 24 h in the Persian Gulf. To determine the effective parameters, different models with various combinations of input parameters were considered. Parameters such as wind speed, direction and wave height of the previous 3 h, were found to be the best inputs. Furthermore, using the difference between wave and wind directions showed better performance. The results also indicated that if only the wind parameters are used as model inputs the accuracy of the forecasting increases as the time horizon increases up to 6 h. This can be due to the lower influence of previous wave heights on larger lead time forecasting and the existing lag between the wind and wave growth. It was also found that in short lead times, the forecasted wave heights primarily depend on the previous wave heights, while in larger lead times there is a greater dependence on previous wind speeds

Investigation on wave energy in Amirabad seaport of Caspian Sea using SWAN model results

In this study, SWAN numerical model used to modeling waves and obtain the significant wave height in range of Amirabad seaport of Caspian Sea. To do this, first, a general model to modeling the wave height in the entire Caspian Sea was built. Then the boundary conditions obtained from the general model, by using the NEST operation of SWAN model, modeling the local with higher magnification in the area Amirabad Seaport was used. The local models built in the Amirabad, was calibration and verification with waves profile data recorded by buoys deployed in that area. Comparison the results with data measured by the Amirabad buoy shows that modeling done in this area had a good accuracy. Then running the SWAN model for three years and Obtained significant wave height in the desired location. Finally the wave energy obtained from significant wave height

Climate change impact on wave energy in the Persian Gulf

Excessive usage of fossil fuels and high emission of greenhouse gases have increased the earth’s temperature, and consequently have changed the patterns of natural phenomena such as wind speed, wave height, etc. Renewable energy resources are ideal alternatives to reduce the negative effects of increasing greenhouse gases emission and climate change. However, these energy sources are also sensitive to changing climate. In this study, the effect of climate change on wave energy in the Persian Gulf is investigated. For this purpose, future wind data obtained from CGCM3.1 model were downscaled using a hybrid approach and modification factors were computed based on local wind data (ECMWF) and applied to control and future CGCM3.1 wind data. Downscaled wind data was used to generate the wave characteristics in the future based on A2, B1, and A1B scenarios, while ECMWF wind field was used to generate the wave characteristics in the control period. The results of these two 30-yearly wave modelings using SWAN model showed that the average wave power changes slightly in the future. Assessment of wave power spatial distribution showed that the reduction of the average wave power is more in the middle parts of the Persian Gulf. Investigation of wave power distribution in two coastal stations (Boushehr and Assalouyeh ports) indicated that the annual wave energy will decrease in both stations while the wave power distribution for different intervals of significant wave height and peak period will also change in Assalouyeh according to all scenarios.Full Tex