Automatic calibration of numerical models and systematic quantification of the uncertainties associated with their use is not yet a widespread practice in coastal engineering. This paper proposes the use of a Bayesian algorithm for the automatic calibration and uncertainty quantification of a wave model, in the framework of the dynamical downscaling of off-shore waves to a nearshore project site, where a set of wave measurements is available. A spectral error was defined and used for the definition of the likelihood function used by the algorithm; this allows for improvements in terms of the wave spectra and not only in terms of a restricted set of wave parameter. In addition to the calibration of the model parameters, the methodology also addresses errors coming from boundary conditions; to this end it distinguishes between different wave systems and a set of parameters are defined for the correction of each wave system, reducing the errors introduced into the model by the boundary conditions. A case study in the South Atlantic coast showed the ability of the methodology to calibrate the model, resulting in simulations that properly fit the available measurement, while also providing an estimation of the uncertainties associated with the model results that can be straightforwardly used for probabilistic analysis in the coastal environment.

数值模型的自动校准和与其使用相关的不确定性的系统量化在沿海工程中还不是普遍的做法。本文提出了使用贝叶斯算法对波浪模型进行自动校准和不确定性量化，在将离岸波浪动态缩小到近岸项目现场的框架内，在那里可以使用一组波浪测量值。定义了一个谱误差并将其用于定义算法使用的似然函数；这允许在波谱方面进行改进，而不仅仅是在一组受限的波参数方面。除了模型参数的校准之外，该方法还解决了来自边界条件的错误；为此，它区分了不同的波浪系统，并为每个波浪系统的校正定义了一组参数，减少了边界条件引入模型的误差。南大西洋沿岸的一个案例研究显示了该方法校准模型的能力，从而产生适合可用测量的模拟，同时还提供了与模型结果相关的不确定性的估计，可直接用于概率分析在沿海环境中。