Computational efficiency is a major obstacle imposed in the automatic calibration of numerical, high-fidelity surface water quality models. To surpass this obstacle, the present work formulated a metamodeling-enabled algorithm for the calibration of surface water quality models and assessed the computational gains from this approach compared to a benchmark alternative (a derivative-free optimization algorithm). A radial basis function was trained over multiple snapshots of the original high-fidelity model to emulate the latter's behavior. This data-driven proxy of the original model was subsequently employed in the automatic calibration of the water quality models of two water reservoirs and, finally, the computational gains over the benchmark alternative were estimated. The benchmark analysis revealed that the metamodeling-enabled optimizer reached a solution with the same quality compared to its benchmark alternative in 20–38% lower process times. Thereby, this work manifests tangible evidence of the potential of metamodeling-enabled strategies and sets out a discussion on how to maximize computational gains deriving from such strategies in surface water quality modeling.

计算效率是数字高保真地表水水质模型自动校准的主要障碍。为了克服这一障碍，本工作制定了一种用于地表水水质模型校准的元模型启用算法，并与基准方法（无导数优化算法）相比，评估了该方法的计算收益。在原始高保真模型的多个快照上训练了径向基函数，以模拟后者的行为。该原始模型的数据驱动代理随后被用于两个水库水质模型的自动校准中，最后，估算了基准替代方案的计算收益。基准分析表明，启用了元建模的优化程序与其基准替代程序相比，可在质量上降低20-38％的处理时间。因此，这项工作为支持元建模的策略的潜力提供了切实的证据，并对如何最大程度地从地表水质量建模中从此类策略中获得的计算收益进行了讨论。