Abstract
Storm-induced waves threaten ship traffic and offshore infrastructures, yet observing water surfaces remains challenging because of complex air-water interactions and limited spatial coverage. We used distributed acoustic sensing measurements from a telecom fiber-optic cable in Lake Ontario, one of the world’s largest lakes, to analyze wind-wave evolution at tens-of-meter scales along a 43-km-long array. By combining observations and modeling, we found that chaotic waves induced by local wind forcing and wave-wave interactions generate high-frequency microseisms (1–4 Hz), whereas frequency variations in low-frequency microseisms (0.2–1 Hz) are strongly controlled by wind speed and fetch evolution. We tracked changes in frequency and energy throughout the full life cycle of wind waves, from chaotic conditions to organized gravity waves formed under steady winds, followed by dissipation as fetch decreases. These results are particularly relevant for fetch-limited water bodies and highlight the potential of distributed acoustic sensing for real-time monitoring of wind waves, with implications for coastal hazards, ecosystem dynamics, and wave-energy development.
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Data availability
We used weather data from the Great Lakes Observing System, which can be accessed through the NOAA’s National Data Buoy Center at https://www.ndbc.noaa.gov/. Water level collected by the NOAA’s National Water Level Observation Network is available at https://tidesandcurrents.noaa.gov/. The wind field data from the National Digital Forecast Database, used to force the Great Lakes Operational Wave Model based on WAVEWATCH III, as well as the wave data, can be found on the Great Lakes Coastal Forecasting System website at https://www.glerl.noaa.gov/emf/waves/WW3/. The Great Lakes bathymetry data used in this paper63,64 are available from the National Centers for Environmental Information website at https://www.ncei.noaa.gov/products/great-lakes-bathymetry/. The weather, water level, model data, and 10-Hz DAS data from channels 2435, 2835, and 3235 used in the moderate wind and winter storm event analyses are available in a public data repository65. The complete DAS dataset is available from the corresponding author upon request.
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Acknowledgements
We thank Crosslake Fiber for their assistance during the experiment. We thank Yang Li, Jing Ci Neo, and Marcelle Collares for supporting the installation and retrieval of the DAS integrator. We thank Bryan Mroczka and Dan Titze for accessing the wind and wave data archived at NOAA Great Lakes Environmental Research Laboratory. We thank the editor and anonymous reviewers for their valuable comments and constructive suggestions. C.-F.Y. expresses gratitude to Dr. Wu-Cheng Chi for the valuable discussions and to the postdoctoral fellowship program at CIGLR for awarding the NOAA grant NA22OAR4320150. Y.M. was partially supported by the U.S. Geological Survey award G23AP00498. This research (CIGLR contribution number 1274) was supported by the CIGLR Seed Funding under the same NOAA grant NA22OAR4320150. Data was processed using ObsPy (version 1.4.0; URL: http://docs.obspy.org), NumPy (version 1.26.4; URL: https://numpy.org/), and SciPy (version 1.12.0; URL: https://scipy.org/). Maps and figures were plotted with Matplotlib graphic tool (version 3.4.3; URL: http://matplotlib.org) for Python (version 3.9.7).
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C.-F.Y. initiated this study, performed the formal analysis, and led the writing of the manuscript. Z.S. conceived and initiated the project and secured funding. A.F.-M. processed the wave, wind, and bathymetry data. Y.M. processed the raw DAS data. All authors reviewed and commented on the manuscript.
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Yang, CF., Spica, Z., Fujisaki-Manome, A. et al. Fiber-optic observations capture wind wave evolution in Lake Ontario. Commun Earth Environ (2026). https://doi.org/10.1038/s43247-026-03182-y
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DOI: https://doi.org/10.1038/s43247-026-03182-y
