Automatic calibration is widely used to estimate parameters in hydrological models. The main idea is to use optimization algorithms to minimize the discrepancy between field data and simulation prediction. This process involves iterative exchanges of a parameter set chosen by the optimization and simulation. However, for computationally expensive models, such as groundwater flow and transport models, the calibration process may be extremely computationally demanding. In this study, we introduce and demonstrate a new asynchronous parallel surrogate-assisted optimization algorithm with an early truncation feature and different knowledge extraction strategies (SO-AET-k). Results show that our asynchronous algorithm performs significantly better than the synchronous analogue (SO-SP), requiring only 40%–70% of the computation time to achieve the same averaged results. In addition, various knowledge extraction strategies of the asynchronous SO-AET-k are tested. In comparisons with two other algorithms, SCE-UA and APPSPACK, asynchronous SO-AET-k shows significantly better performance in terms of both efficiency and robustness.

自动校准广泛用于估计水文模型中的参数。主要思想是使用优化算法来最小化现场数据和模拟预测之间的差异。该过程涉及由优化和模拟选择的参数集的迭代交换。然而，对于计算成本高的模型，例如地下水流和传输模型，校准过程可能对计算要求极高。在这项研究中，我们介绍并演示了一种新的异步并行代理辅助优化算法，该算法具有早期截断特征和不同的知识提取策略 (SO-AET-k)。结果表明，我们的异步算法的性能明显优于同步模拟（SO-SP），只需要 40%–70% 的计算时间即可获得相同的平均结果。此外，还测试了异步 SO-AET-k 的各种知识提取策略。与其他两种算法 SCE-UA 和 APPSPACK 相比，异步 SO-AET-k 在效率和鲁棒性方面表现出明显更好的性能。