An Empirical Conditional Model for Estimating Wave Characteristics from Wind Speed, Fetch, and Depth: Application to the Red Sea

An empirical model is developed to predict significant wave height and significant wave period using only wind speed at 10 m height, fetch, and water depth. The model distinguishes between fetch-limited and duration-limited sea states within a conditional empirical framework that incorporates modified empirical exponents and corrections into classical wave formulations. Validation was performed using wind and wave data from the Global Forecast System at 26 coastal and offshore stations distributed across eleven different pilot seas and oceans worldwide, encompassing a broad spectrum of marine environments and climatic conditions. The proposed model was benchmarked against established empirical approaches. Results indicate a mean prediction error of 6.6% for the significant wave height and 9.6% for the significant wave period, substantially outperforming conventional formulations whose errors exceed 50% under comparable conditions. Unlike existing empirical models that are restricted to specific regions or sea-state conditions, the proposed model demonstrated strong predictive performance across diverse seas, oceans, and climatic conditions, enabling more reliable wave predictions in data-scarce and dynamically complex marine environments. The developed model was further applied to the Red Sea, where it successfully reproduced the spatial variability of significant wave height and wave period. From the results, it has been found that the developed model provides a practical and transferable tool for wave forecasting, coastal engineering, and offshore renewable energy applications.

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Publication Details

Journal
Water
Published
2026-06-19
DOI
https://doi.org/10.3390/w18121515
Primary Topic
Ocean Waves and Remote Sensing
Type
article
Field-Weighted Citation Impact
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article

An Empirical Conditional Model for Estimating Wave Characteristics from Wind Speed, Fetch, and Depth: Application to the Red Sea

Mohammed S Almasoudi, Hassan Hemida, Soroosh Sharifi
Water
Ocean Waves and Remote Sensing
article

An Empirical Conditional Model for Estimating Wave Characteristics from Wind Speed, Fetch, and Depth: Application to the Red Sea

Mohammed S Almasoudi, Hassan Hemida, Soroosh Sharifi
article en

Abstract

An empirical model is developed to predict significant wave height and significant wave period using only wind speed at 10 m height, fetch, and water depth. The model distinguishes between fetch-limited and duration-limited sea states within a conditional empirical framework that incorporates modified empirical exponents and corrections into classical wave formulations. Validation was performed using wind and wave data from the Global Forecast System at 26 coastal and offshore stations distributed across eleven different pilot seas and oceans worldwide, encompassing a broad spectrum of marine environments and climatic conditions. The proposed model was benchmarked against established empirical approaches. Results indicate a mean prediction error of 6.6% for the significant wave height and 9.6% for the significant wave period, substantially outperforming conventional formulations whose errors exceed 50% under comparable conditions. Unlike existing empirical models that are restricted to specific regions or sea-state conditions, the proposed model demonstrated strong predictive performance across diverse seas, oceans, and climatic conditions, enabling more reliable wave predictions in data-scarce and dynamically complex marine environments. The developed model was further applied to the Red Sea, where it successfully reproduced the spatial variability of significant wave height and wave period. From the results, it has been found that the developed model provides a practical and transferable tool for wave forecasting, coastal engineering, and offshore renewable energy applications.

WaterVol. 18(12)
King Abdulaziz University (SA), University of Birmingham (GB)
Life below water
Openalex Percentile: Top 8%
Ocean Waves and Remote Sensing
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