Hybrid data-driven long-term wave analysis in the southern Coral Sea, Australia
Year: 2024
Type: article
Abstract: This paper investigates the long-term statistical characteristics of waves in the southern Coral Sea, Australia. Measured and simulated datasets from three representative sites including nearshore and offshore locations were utilised. The study focuses on wave conditions with highly frequent observations and the forecast of extreme events. To address data gaps, an innovative artificial neural network model is proposed to fill in missing data points. The uncertainty in the statistical results due to seasonal variability is also assessed. The analysed results indicate that the wave climate in the Southern Coral Sea has distinct seasonal differences, but the stationary stochasticity can still be applied for data analysis. For the studied sites, the lognormal distribution is suitable for describing the frequently observed wave conditions, while a GEV-GP two-part distribution model may provide an improved and flexible fitting for extreme events but with wide confidence intervals. In addition, under a given recurrence period, as the significant wave height Hs approaches the maxima, the variations in the mean zero up-crossing wave periods Tz are concentrated in a narrow range (indicating swell dominance), while the variation of Tz spans a wide range (indicating the presence of both wind wave and swell) when Hs is small. These findings enhance our understanding of wave statistics and extreme events and provide valuable insights for the design of coastal and offshore structures as well as for other research related to wave climate.
Source: Applied Ocean Research
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,080
OpenAlex ID: W4405535985