Research Article
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Year 2025, Volume: 14 Issue: 2, 80 - 93, 30.06.2025
https://doi.org/10.33714/masteb.1649969

Abstract

References

  • Ahn, S. (2021). Modeling mean relation between peak period and energy period of ocean surface wave systems. Ocean Engineering, 228, 108937. https://doi.org/10.1016/j.oceaneng.2021.108937
  • Al-Towayti, F. A. H., Teh, H. M., Ma, Z., Jae, I. A., & Syamsir, A. (2025). Hydrodynamic performance assessment of emerged and sub-merged semicircular breakwaters under random waves: An experimental and empirical study. PLoS One, 20(2), e0313955. https://doi.org/10.1371/journal.pone.0313955
  • Andriolo, U., Mendes, D., & Taborda, R. (2020). Breaking wave height estimation from timex images: Two methods for coastal video monitoring systems. Remote Sensing, 12(2), 204. https://doi.org/10.3390/rs12020204
  • Balas, E. A., Uğurlu, A., & Balas, C. E. (2024a). A hybrid probabilistic design model of riverine jetties incorporating three-dimensional numerical simulations of transport phenomena in the context of emerging climate change. Journal of Coastal Research, 113(sp1). https://doi.org/10.2112/JCR-SI113-044.1
  • Balas, E. A., Genç, A. N., & Balas, C. E. (2024b). Strategic adaptation to climate change through monte carlo-based multi-criteria decision model in marine spatial planning. Journal of Coastal Research, 113(sp1). https://doi.org/10.2112/JCR-SI113-034.1
  • Buyruk, T., Balas, E. A., Genç, A. N., & Balas, L. (2024). Exploring renewable energy on the coastline of Türkiye: Wind and wave power potential. Journal of Coastal Research, 113(sp1). https://doi.org/10.2112/JCR-SI113-155.1
  • Chondros, M. K., Metallinos, A. S., & Papadimitriou, A. G. (2024). Integrated modeling of coastal processes driven by an advanced mild slope wave model. Modelling, 5(2), 458–482. https://doi.org/10.3390/modelling5020025
  • De Vita, F., Verzicco, R., & Iafrati, A. (2018). Breaking of modulated wave groups: Kinematics and energy dissipation processes. Journal of Fluid Mechanics, 855, 267–298. https://doi.org/10.1017/jfm.2018.619
  • Dionísio António, S., van der Werf, J., Horstman, E., Cáceres, I., Alsina, J., van der Zanden, J., & Hulscher, S. (2023). Influence of beach slope on morphological changes and sediment transport under irregular waves. Journal of Marine Science and Engineering, 11(12), 2244. https://doi.org/10.3390/jmse11122244
  • Domala, V., Lee, W., & Kim, T. (2022). Wave data prediction with optimized machine learning and deep learning techniques. Journal of Computational Design and Engineering, 9(3), 1107–1122. https://doi.org/10.1093/jcde/qwac048
  • Durap, A. (2023). A comparative analysis of machine learning algorithms for predicting wave runup. Anthropocene Coasts, 6(1), 17. https://doi.org/10.1007/s44218-023-00033-7
  • Durap, A. (2024a). Data-driven models for significant wave height forecasting: Comparative analysis of machine learning techniques. Results in Engineering, 24, 103573. https://doi.org/10.1016/j.rineng.2024.103573
  • Durap, A. (2024b). Mapping coastal resilience: a GIS-based Bayesian network approach to coastal hazard identification for Queensland’s dynamic shorelines. Anthropocene Coasts, 7(1), 23. https://doi.org/10.1007/s44218-024-00060-y
  • Durap, A. (2025a). Interpretable machine learning for coastal wind prediction: Integrating SHAP analysis and seasonal trends. Journal of Coastal Conservation, 29(3), 24. https://doi.org/10.1007/s11852-025-01108-y
  • Durap, A. (2025b). Machine learning-based wind speed prediction using random forest: A cross-validated analysis for renewable energy applications. Turkish Journal of Engineering, 9(3), 508–518. https://doi.org/10.31127/tuje.1624354
  • Durap, A., & Balas, C. E. (2024). Towards sustainable coastal management: a hybrid model for vulnerability and risk assessment. Journal of Coastal Conservation, 28(4), 66. https://doi.org/10.1007/s11852-024-01065-y
  • Husemann, P., Romão, F., Lima, M., Costas, S., & Coelho, C. (2024). Review of the quantification of aeolian sediment transport in coastal areas. Journal of Marine Science and Engineering, 12(5), 755. https://doi.org/10.3390/jmse12050755
  • Kwon, D.-S., Kim, S.-J., Jin, C., & Kim, M. (2025). Parametric estimation of directional wave spectra from moored FPSO motion data using optimized artificial neural networks. Journal of Marine Science and Engineering, 13(1), 69. https://doi.org/10.3390/jmse13010069
  • Leatherman, S. P., Leatherman, S. B., & Rangel-Buitrago, N. (2024). Integrated strategies for management and mitigation of beach accidents. Ocean & Coastal Management, 253, 107173. https://doi.org/10.1016/j.ocecoaman.2024.107173
  • Morley, C., Grimwood, K., Maloney, S., & Ware, R. S. (2018). Meteorological factors and respiratory syncytial virus seasonality in subtropical Australia. Epidemiology and Infection, 146(6), 757–762. https://doi.org/10.1017/S0950268818000614
  • Pascual, C. V., García, J. P., & García, R. G. (2021). Wind energy ships: Global analysis of operability. Journal of Marine Science and Engineering, 9(5), 517. https://doi.org/10.3390/jmse9050517
  • Petropoulos, A., Kapsimalis, V., Evelpidou, N., Karkani, A., & Giannikopoulou, K. (2022). Simulation of the nearshore sediment transport pattern and beach morphodynamics in the semi-enclosed Bay of Myrtos, Cephalonia Island, Ionian Sea. Journal of Marine Science and Engineering, 10(8), 1015. https://doi.org/10.3390/jmse10081015
  • Premus, V. E., Abbot, P. A., Kmelnitsky, V., Gedney, C. J., & Abbot, T. A. (2022). A wave glider-based, towed hydrophone array system for autonomous, real-time, passive acoustic marine mammal monitoring. The Journal of the Acoustical Society of America, 152(3), 1814–1828. https://doi.org/10.1121/10.0014169
  • Schmelz, W. J., Spector, A., Neitzke‐Adamo, L., & Miller, K. G. (2025). Semi‐empirically modelling barrier sediment transport in response to hydrodynamic forcing using UAV‐derived topographical data (Holgate, New Jersey). Earth Surface Processes and Landforms, 50(1), e6052. https://doi.org/10.1002/esp.6052
  • Tang, Y., Shi, W., Ning, D., You, J., & Michailides, C. (2020). Effects of spilling and plunging type breaking waves acting on large monopile offshore wind turbines. Frontiers in Marine Science, 7, 427. https://doi.org/10.3389/fmars.2020.00427
  • Thompson, M., Zelich, I., Watterson, E., & Baldock, T. E. (2021). Wave peel tracking: A new approach for assessing surf amenity and analysis of breaking waves. Remote Sensing, 13(17), 3372. https://doi.org/10.3390/rs13173372
  • Tian, X., Xie, T., Liu, Z., Lai, X., Pan, H., Wang, C., Leng, J., & Rahman, M. M. (2023). Study on the resistance of a large pure car truck carrier with bulbous bow and transom stern. Journal of Marine Science and Engineering, 11(10), 1932. https://doi.org/10.3390/jmse11101932
  • Tran, H. Q., Ayala Cruz, F., McCarroll, J., & Babanin, A. (2024). Non-linear surges and extreme wind-waves in Port Phillip Bay under existing and future mean sea levels. Frontiers in Marine Science, 11, 54. https://doi.org/10.3389/fmars.2024.1480054
  • Trizna, D. B. (2001). Errors in bathymetric retrievals using linear dispersion in 3-D FFT analysis of marine radar ocean wave imagery. IEEE Transactions on Geoscience and Remote Sensing, 39(11), 2465–2469. https://doi.org/10.1109/36.964983
  • Uğurlu, A., & Balas, C. (2024). Integrative probabilistic design of river jetties by 3D numerical models of transport phenomena: The case study of Kabakoz River jetties. Marine Science and Technology Bulletin, 13(2), 151–167. https://doi.org/10.33714/masteb.1414048
  • Vieira, B. F. V., Pinho, J. L. S., & Barros, J. A. O. (2024). Semicircular coastal defence structures: Impact of Gap spacing on shoreline dynamics during storm events. Journal of Marine Science and Engineering, 12(6), 850. https://doi.org/10.3390/jmse12060850
  • Xie, W., Shimozono, T., & Tajima, Y. (2024). Wave-gravity-induced sediment transport on steep shoreface. Coastal Engineering Journal, 66(2), 312–331. https://doi.org/10.1080/21664250.2024.2319396
  • Yu, Q., Peng, Y., Du, Z., Wang, L., Wang, Y., & Gao, S. (2024). Movement of sediments across a gently sloping muddy coast: Wave‐ and current‐supported gravity flows. Earth Surface Processes and Landforms, 49(5), 1590–1605. https://doi.org/10.1002/esp.5788
  • Zhong, Y., Luo, F., Li, Y., Lin, Y., He, J., Lin, Y., Shu, F., & Zheng, B. (2024). Influence of tropical cyclones and cold waves on the eastern guangdong coastal hydrodynamics: Processes and mechanisms. Journal of Marine Science and Engineering, 12(12), 2148. https://doi.org/10.3390/jmse12122148
  • Zhongbiao Chen, Yijun He, Biao Zhang, Zhongfeng Qiu, & Baoshu Yin. (2014). A new algorithm to retrieve wave parameters from marine X-band radar image sequences. IEEE Transactions on Geoscience and Remote Sensing, 52(7), 4083–4091. https://doi.org/10.1109/TGRS.2013.2279547

Thresholds and trends in wave steepness: A data-driven study of coastal wave breaking risk

Year 2025, Volume: 14 Issue: 2, 80 - 93, 30.06.2025
https://doi.org/10.33714/masteb.1649969

Abstract

Wave steepness plays a crucial role in coastal engineering, sediment transport, and maritime safety, as steeper waves exert stronger forces on coastal structures, enhance sediment mobilization, and increase risks for vessels and swimmers. Despite its importance, previous studies have often treated wave steepness in generalized contexts, lacking region-specific evaluations or failing to account for temporal variability and localized wave dynamics. Moreover, many analyses have not sufficiently linked wave steepness to practical risk indicators such as wave breaking potential. To address these gaps, this study presents a comprehensive analysis of wave steepness and its association with breaking risk on the Gold Coast, Australia, using data collected throughout 2023. Wave steepness, a dimensionless parameter defined as the ratio of wave height to wavelength, serves as a critical indicator for assessing wave stability and potential for breaking in coastal environments. Using the formula S≈(2πH_s)/(gT_p^2 ), we analyzed 17,520 observations of significant wave height (H_s) and peak period (T_p) to categorize waves into four distinct stability classes: gentle, moderate, steep, and breaking risk. Results indicate that only 0.34% of observations exceeded the critical breaking threshold of S>0.04, with the maximum steepness of 0.0564 recorded on December 1, 2023. Significant seasonal variations were observed, with October exhibiting the highest mean steepness (0.0127) and June the lowest (0.0052). A strong negative correlation (r=-0.78) between peak period and wave steepness confirms the theoretical relationship between these parameters. The study also revealed that 69% of waves were classified as gentle (S<0.01), 28% as moderate (0.01≤S<0.025), 2.3% as steep (0.025≤S<0.04), and only 0.3% posed a breaking risk. These findings provide valuable insights for coastal management, maritime safety, and engineering applications by establishing quantitative thresholds for wave breaking risk assessment in similar coastal environments.

References

  • Ahn, S. (2021). Modeling mean relation between peak period and energy period of ocean surface wave systems. Ocean Engineering, 228, 108937. https://doi.org/10.1016/j.oceaneng.2021.108937
  • Al-Towayti, F. A. H., Teh, H. M., Ma, Z., Jae, I. A., & Syamsir, A. (2025). Hydrodynamic performance assessment of emerged and sub-merged semicircular breakwaters under random waves: An experimental and empirical study. PLoS One, 20(2), e0313955. https://doi.org/10.1371/journal.pone.0313955
  • Andriolo, U., Mendes, D., & Taborda, R. (2020). Breaking wave height estimation from timex images: Two methods for coastal video monitoring systems. Remote Sensing, 12(2), 204. https://doi.org/10.3390/rs12020204
  • Balas, E. A., Uğurlu, A., & Balas, C. E. (2024a). A hybrid probabilistic design model of riverine jetties incorporating three-dimensional numerical simulations of transport phenomena in the context of emerging climate change. Journal of Coastal Research, 113(sp1). https://doi.org/10.2112/JCR-SI113-044.1
  • Balas, E. A., Genç, A. N., & Balas, C. E. (2024b). Strategic adaptation to climate change through monte carlo-based multi-criteria decision model in marine spatial planning. Journal of Coastal Research, 113(sp1). https://doi.org/10.2112/JCR-SI113-034.1
  • Buyruk, T., Balas, E. A., Genç, A. N., & Balas, L. (2024). Exploring renewable energy on the coastline of Türkiye: Wind and wave power potential. Journal of Coastal Research, 113(sp1). https://doi.org/10.2112/JCR-SI113-155.1
  • Chondros, M. K., Metallinos, A. S., & Papadimitriou, A. G. (2024). Integrated modeling of coastal processes driven by an advanced mild slope wave model. Modelling, 5(2), 458–482. https://doi.org/10.3390/modelling5020025
  • De Vita, F., Verzicco, R., & Iafrati, A. (2018). Breaking of modulated wave groups: Kinematics and energy dissipation processes. Journal of Fluid Mechanics, 855, 267–298. https://doi.org/10.1017/jfm.2018.619
  • Dionísio António, S., van der Werf, J., Horstman, E., Cáceres, I., Alsina, J., van der Zanden, J., & Hulscher, S. (2023). Influence of beach slope on morphological changes and sediment transport under irregular waves. Journal of Marine Science and Engineering, 11(12), 2244. https://doi.org/10.3390/jmse11122244
  • Domala, V., Lee, W., & Kim, T. (2022). Wave data prediction with optimized machine learning and deep learning techniques. Journal of Computational Design and Engineering, 9(3), 1107–1122. https://doi.org/10.1093/jcde/qwac048
  • Durap, A. (2023). A comparative analysis of machine learning algorithms for predicting wave runup. Anthropocene Coasts, 6(1), 17. https://doi.org/10.1007/s44218-023-00033-7
  • Durap, A. (2024a). Data-driven models for significant wave height forecasting: Comparative analysis of machine learning techniques. Results in Engineering, 24, 103573. https://doi.org/10.1016/j.rineng.2024.103573
  • Durap, A. (2024b). Mapping coastal resilience: a GIS-based Bayesian network approach to coastal hazard identification for Queensland’s dynamic shorelines. Anthropocene Coasts, 7(1), 23. https://doi.org/10.1007/s44218-024-00060-y
  • Durap, A. (2025a). Interpretable machine learning for coastal wind prediction: Integrating SHAP analysis and seasonal trends. Journal of Coastal Conservation, 29(3), 24. https://doi.org/10.1007/s11852-025-01108-y
  • Durap, A. (2025b). Machine learning-based wind speed prediction using random forest: A cross-validated analysis for renewable energy applications. Turkish Journal of Engineering, 9(3), 508–518. https://doi.org/10.31127/tuje.1624354
  • Durap, A., & Balas, C. E. (2024). Towards sustainable coastal management: a hybrid model for vulnerability and risk assessment. Journal of Coastal Conservation, 28(4), 66. https://doi.org/10.1007/s11852-024-01065-y
  • Husemann, P., Romão, F., Lima, M., Costas, S., & Coelho, C. (2024). Review of the quantification of aeolian sediment transport in coastal areas. Journal of Marine Science and Engineering, 12(5), 755. https://doi.org/10.3390/jmse12050755
  • Kwon, D.-S., Kim, S.-J., Jin, C., & Kim, M. (2025). Parametric estimation of directional wave spectra from moored FPSO motion data using optimized artificial neural networks. Journal of Marine Science and Engineering, 13(1), 69. https://doi.org/10.3390/jmse13010069
  • Leatherman, S. P., Leatherman, S. B., & Rangel-Buitrago, N. (2024). Integrated strategies for management and mitigation of beach accidents. Ocean & Coastal Management, 253, 107173. https://doi.org/10.1016/j.ocecoaman.2024.107173
  • Morley, C., Grimwood, K., Maloney, S., & Ware, R. S. (2018). Meteorological factors and respiratory syncytial virus seasonality in subtropical Australia. Epidemiology and Infection, 146(6), 757–762. https://doi.org/10.1017/S0950268818000614
  • Pascual, C. V., García, J. P., & García, R. G. (2021). Wind energy ships: Global analysis of operability. Journal of Marine Science and Engineering, 9(5), 517. https://doi.org/10.3390/jmse9050517
  • Petropoulos, A., Kapsimalis, V., Evelpidou, N., Karkani, A., & Giannikopoulou, K. (2022). Simulation of the nearshore sediment transport pattern and beach morphodynamics in the semi-enclosed Bay of Myrtos, Cephalonia Island, Ionian Sea. Journal of Marine Science and Engineering, 10(8), 1015. https://doi.org/10.3390/jmse10081015
  • Premus, V. E., Abbot, P. A., Kmelnitsky, V., Gedney, C. J., & Abbot, T. A. (2022). A wave glider-based, towed hydrophone array system for autonomous, real-time, passive acoustic marine mammal monitoring. The Journal of the Acoustical Society of America, 152(3), 1814–1828. https://doi.org/10.1121/10.0014169
  • Schmelz, W. J., Spector, A., Neitzke‐Adamo, L., & Miller, K. G. (2025). Semi‐empirically modelling barrier sediment transport in response to hydrodynamic forcing using UAV‐derived topographical data (Holgate, New Jersey). Earth Surface Processes and Landforms, 50(1), e6052. https://doi.org/10.1002/esp.6052
  • Tang, Y., Shi, W., Ning, D., You, J., & Michailides, C. (2020). Effects of spilling and plunging type breaking waves acting on large monopile offshore wind turbines. Frontiers in Marine Science, 7, 427. https://doi.org/10.3389/fmars.2020.00427
  • Thompson, M., Zelich, I., Watterson, E., & Baldock, T. E. (2021). Wave peel tracking: A new approach for assessing surf amenity and analysis of breaking waves. Remote Sensing, 13(17), 3372. https://doi.org/10.3390/rs13173372
  • Tian, X., Xie, T., Liu, Z., Lai, X., Pan, H., Wang, C., Leng, J., & Rahman, M. M. (2023). Study on the resistance of a large pure car truck carrier with bulbous bow and transom stern. Journal of Marine Science and Engineering, 11(10), 1932. https://doi.org/10.3390/jmse11101932
  • Tran, H. Q., Ayala Cruz, F., McCarroll, J., & Babanin, A. (2024). Non-linear surges and extreme wind-waves in Port Phillip Bay under existing and future mean sea levels. Frontiers in Marine Science, 11, 54. https://doi.org/10.3389/fmars.2024.1480054
  • Trizna, D. B. (2001). Errors in bathymetric retrievals using linear dispersion in 3-D FFT analysis of marine radar ocean wave imagery. IEEE Transactions on Geoscience and Remote Sensing, 39(11), 2465–2469. https://doi.org/10.1109/36.964983
  • Uğurlu, A., & Balas, C. (2024). Integrative probabilistic design of river jetties by 3D numerical models of transport phenomena: The case study of Kabakoz River jetties. Marine Science and Technology Bulletin, 13(2), 151–167. https://doi.org/10.33714/masteb.1414048
  • Vieira, B. F. V., Pinho, J. L. S., & Barros, J. A. O. (2024). Semicircular coastal defence structures: Impact of Gap spacing on shoreline dynamics during storm events. Journal of Marine Science and Engineering, 12(6), 850. https://doi.org/10.3390/jmse12060850
  • Xie, W., Shimozono, T., & Tajima, Y. (2024). Wave-gravity-induced sediment transport on steep shoreface. Coastal Engineering Journal, 66(2), 312–331. https://doi.org/10.1080/21664250.2024.2319396
  • Yu, Q., Peng, Y., Du, Z., Wang, L., Wang, Y., & Gao, S. (2024). Movement of sediments across a gently sloping muddy coast: Wave‐ and current‐supported gravity flows. Earth Surface Processes and Landforms, 49(5), 1590–1605. https://doi.org/10.1002/esp.5788
  • Zhong, Y., Luo, F., Li, Y., Lin, Y., He, J., Lin, Y., Shu, F., & Zheng, B. (2024). Influence of tropical cyclones and cold waves on the eastern guangdong coastal hydrodynamics: Processes and mechanisms. Journal of Marine Science and Engineering, 12(12), 2148. https://doi.org/10.3390/jmse12122148
  • Zhongbiao Chen, Yijun He, Biao Zhang, Zhongfeng Qiu, & Baoshu Yin. (2014). A new algorithm to retrieve wave parameters from marine X-band radar image sequences. IEEE Transactions on Geoscience and Remote Sensing, 52(7), 4083–4091. https://doi.org/10.1109/TGRS.2013.2279547
There are 35 citations in total.

Details

Primary Language English
Subjects Water Resources and Water Structures
Journal Section Research Article
Authors

Ahmet Durap 0000-0002-6218-0129

Publication Date June 30, 2025
Submission Date March 3, 2025
Acceptance Date May 30, 2025
Published in Issue Year 2025 Volume: 14 Issue: 2

Cite

APA Durap, A. (2025). Thresholds and trends in wave steepness: A data-driven study of coastal wave breaking risk. Marine Science and Technology Bulletin, 14(2), 80-93. https://doi.org/10.33714/masteb.1649969
AMA Durap A. Thresholds and trends in wave steepness: A data-driven study of coastal wave breaking risk. Mar. Sci. Tech. Bull. June 2025;14(2):80-93. doi:10.33714/masteb.1649969
Chicago Durap, Ahmet. “Thresholds and Trends in Wave Steepness: A Data-Driven Study of Coastal Wave Breaking Risk”. Marine Science and Technology Bulletin 14, no. 2 (June 2025): 80-93. https://doi.org/10.33714/masteb.1649969.
EndNote Durap A (June 1, 2025) Thresholds and trends in wave steepness: A data-driven study of coastal wave breaking risk. Marine Science and Technology Bulletin 14 2 80–93.
IEEE A. Durap, “Thresholds and trends in wave steepness: A data-driven study of coastal wave breaking risk”, Mar. Sci. Tech. Bull., vol. 14, no. 2, pp. 80–93, 2025, doi: 10.33714/masteb.1649969.
ISNAD Durap, Ahmet. “Thresholds and Trends in Wave Steepness: A Data-Driven Study of Coastal Wave Breaking Risk”. Marine Science and Technology Bulletin 14/2 (June2025), 80-93. https://doi.org/10.33714/masteb.1649969.
JAMA Durap A. Thresholds and trends in wave steepness: A data-driven study of coastal wave breaking risk. Mar. Sci. Tech. Bull. 2025;14:80–93.
MLA Durap, Ahmet. “Thresholds and Trends in Wave Steepness: A Data-Driven Study of Coastal Wave Breaking Risk”. Marine Science and Technology Bulletin, vol. 14, no. 2, 2025, pp. 80-93, doi:10.33714/masteb.1649969.
Vancouver Durap A. Thresholds and trends in wave steepness: A data-driven study of coastal wave breaking risk. Mar. Sci. Tech. Bull. 2025;14(2):80-93.

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