Abstract Short waves are of key importance for nearshore dynamics, particularly under storms, where they contribute to extreme water levels and drive large morphological changes. Therefore, it is crucial to model accurately the propagation and dissipation of storm waves in the nearshore area. In this paper, field observations collected in contrasted environments and conditions are combined with predictions from a third-generation spectral wave model to evaluate four formulations of wave energy dissipation by depth-induced breaking. The results reveal a substantial over-dissipation of incident wave energy occurring over the continental shelf, resulting in a negative bias on significant wave height reaching up to 50%. To overcome this problem, a breaking coefficient dependent of the local bottom slope is introduced within depth-induced breaking models in order to account for the varying degrees of saturation naturally found in breaking and broken waves. This approach strongly reduces the negative bias observed in the shoreface compared to default parameterizations, yielding significant improvements in the prediction of storm waves. Among the implications of this study, our new parameterization of the breaking coefficient results in systematically increased predictions of the wave setup near the shoreline compared to the default parameterization. This increase reaches a factor 2 for gently sloping beaches.

中文翻译：

---

使用频谱模型模拟近岸地区的风暴波：当前问题和务实的解决方案

摘要 短波对于近岸动力学至关重要，特别是在风暴下，它们会导致极端水位并推动大的形态变化。因此，准确模拟近岸地区风暴波的传播和消散至关重要。在本文中，在对比环境和条件下收集的实地观察与第三代谱波模型的预测相结合，以评估深度诱导破裂引起的波浪能量耗散的四种公式。结果表明，大陆架上发生了大量的入射波能量过度消散，导致显着波高的负偏差高达 50%。为了克服这个问题，在深度诱发的破碎模型中引入了与局部底部坡度相关的破碎系数，以解释破碎波和破碎波中自然发现的不同程度的饱和度。与默认参数化相比，这种方法大大减少了在岸面观察到的负偏差，显着改善了风暴波的预测。在这项研究的意义中，与默认参数化相比，我们对破碎系数的新参数化导致对海岸线附近波浪设置的系统性增加预测。对于平缓倾斜的海滩，这种增加达到了 2 倍。与默认参数化相比，这种方法大大减少了在岸面观察到的负偏差，显着改善了风暴波的预测。在这项研究的意义中，与默认参数化相比，我们对破碎系数的新参数化导致对海岸线附近波浪设置的系统性增加预测。对于平缓倾斜的海滩，这种增加达到了 2 倍。与默认参数化相比，这种方法大大减少了在岸面观察到的负偏差，显着改善了风暴波的预测。在这项研究的意义中，与默认参数化相比，我们对破碎系数的新参数化导致对海岸线附近波浪设置的系统性增加预测。对于平缓倾斜的海滩，这种增加达到了 2 倍。