Hydrological model calibration plays a crucial role in estimating optimal parameters for accurate simulation. Estimation of parameters is inevitable in hydrological modeling due to the challenge of directly measuring them, as most parameters are conceptual descriptions of physical processes. Modelers commonly employ optimization algorithms for calibrating hydrological models. However, these algorithms often pose computational challenges, especially when dealing with complex physics-based and distributed models. To address these challenges, our study introduces a novel approach called hydroCNN+DDS. By leveraging the strengths of Convolutional Neural Networks (CNN) and the Dynamically Dimensioned Search (DDS) algorithm, hydroCNN+DDS simplifies the model calibration process in complex physics-based models. This approach enables to capture the general patterns and relationships between discharge time series and parameters without compromising the underlying physics. We use hydroCNN+DDS to estimate parameters in the highly parameterized hydrological model, Structure for Unifying Multiple Modeling Alternatives (SUMMA) using hourly observed discharge. Notably, hydroCNN quickly generates sub-optimal parameters, serving as a good initial solution for DDS. This initialization aids DDS in converging faster towards an optimal solution. One of the notable advantages of the hydroCNN+DDS approach is its potential for spatial and temporal transferability. This feature proves valuable in dynamic systems and regions with limited historical data, expanding the applicability of the methodology. Furthermore, our proposed methodology is versatile and can be applied to any simple or complex models, accommodating any variables of interest. The best practices of good model calibration are followed in our approach.

中文翻译：

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使用卷积神经网络引导的动态维度搜索方法优化水文模型中的参数估计


水文模型校准在估计最佳参数以进行精确模拟方面起着至关重要的作用。由于直接测量参数的挑战，参数的估计在水文建模中是不可避免的，因为大多数参数都是对物理过程的概念性描述。建模者通常使用优化算法来校准水文模型。然而，这些算法通常会带来计算挑战，尤其是在处理复杂的基于物理的分布式模型时。为了应对这些挑战，我们的研究引入了一种称为 hydroCNN+DDS 的新方法。通过利用卷积神经网络 （CNN） 和动态维度搜索 （DDS） 算法的优势，hydroCNN+DDS 简化了基于物理的复杂模型中的模型校准过程。这种方法能够在不影响底层物理场的情况下捕获放电时间序列和参数之间的一般模式和关系。我们使用 hydroCNN+DDS 来估计高度参数化的水文模型中的参数，即使用每小时观测流量的统一多种建模备选方案的结构 （SUMMA）。值得注意的是，hydroCNN 会快速生成次优参数，是 DDS 的良好初始解决方案。此初始化有助于 DDS 更快地收敛到最佳解决方案。hydroCNN+DDS 方法的显着优势之一是其空间和时间可转移性的潜力。事实证明，此功能在历史数据有限的动态系统和区域中很有价值，从而扩展了该方法的适用性。此外，我们提出的方法是通用的，可以应用于任何简单或复杂的模型，适应任何感兴趣的变量。 我们的方法遵循良好模型校准的最佳实践。