This study suggests a stochastic Bayesian approach for calibrating and validating morphodynamic sediment transport models and for quantifying parametric uncertainties in order to alleviate limitations of conventional (manual, deterministic) calibration procedures. The applicability of our method is shown for a large‐scale (11.0 km) and time‐demanding (9.14 hr for the period 2002–2013) 2‐D morphodynamic sediment transport model of the Lower River Salzach and for three most sensitive input parameters (critical Shields parameter, grain roughness, and grain size distribution). Since Bayesian methods require a significant number of simulation runs, this work proposes to construct a surrogate model, here with the arbitrary polynomial chaos technique. The surrogate model is constructed from a limited set of runs (n=20) of the full complex sediment transport model. Then, Monte Carlo‐based techniques for Bayesian calibration are used with the surrogate model (10⁵ realizations in 4 hr). The results demonstrate that following Bayesian principles and iterative Bayesian updating of the surrogate model (10 iterations) enables to identify the most probable ranges of the three calibration parameters. Model verification based on the maximum a posteriori parameter combination indicates that the surrogate model accurately replicates the morphodynamic behavior of the sediment transport model for both calibration (RMSE = 0.31 m) and validation (RMSE = 0.42 m). Furthermore, it is shown that the surrogate model is highly effective in lowering the total computational time for Bayesian calibration, validation, and uncertainty analysis. As a whole, this provides more realistic calibration and validation of morphodynamic sediment transport models with quantified uncertainty in less time compared to conventional calibration procedures.

中文翻译：

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大规模时限泥沙输送模型的贝叶斯定标和验证

这项研究提出了一种随机贝叶斯方法，用于校准和验证形态动力学沉积物传输模型以及量化参数不确定性，以减轻常规（手动，确定性）校准程序的局限性。我们的方法适用于大规模（11.0 km）和时间要求（2002-2013年为9.14 hr）下游萨尔察赫河的二维地貌动力沉积物输运模型以及三个最敏感的输入参数（关键Shields参数，晶粒粗糙度和晶粒尺寸分布）。由于贝叶斯方法需要大量的模拟运行，因此本文建议使用任意多项式混沌技术构建替代模型。替代模型由一组有限的运行（n= 20）的全复杂沉积物传输模型。然后，将基于蒙特卡洛的贝叶斯校准技术与代理模型结合使用（4小时内实现10 ^5个实现）。结果表明，遵循贝叶斯原理和代理模型的迭代贝叶斯更新（10次迭代）可以识别三个校准参数的最可能范围。基于最大后验参数组合的模型验证表明，替代模型可以准确地复制沉积物输送模型的形态动力学行为，以进行校准（RMSE = 0.31 m）和验证（RMSE =0.42 m）。此外，结果表明，替代模型在减少贝叶斯校准，验证和不确定性分析的总计算时间方面非常有效。总体而言，与传统的校准程序相比，这可在更短的时间内以更定量的不确定性提供形态动力学沉积物迁移模型的更现实的校准和验证。