A sudden surge of data has created new challenges in water management, spanning quality control, assimilation, and analysis. Few approaches are available to integrate growing volumes of data into interpretable results. Process-based hydrologic models have not been designed to consume large amounts of data. Alternatively, new machine learning tools can automate data analysis and forecasting, but their lack of interpretability and reliance on very large data sets limits the discovery of insights and may impact trust. To address this gap, we present a new approach, which seeks to strike a middle ground between process-, and data-based modeling. The contribution of this work is an automated and scalable methodology that discovers differential equations and latent state estimations within hydrologic systems using only rainfall and runoff measurements. We show how this enables automated tools to learn interpretable models of 6 to 18 parameters solely from measurements. We apply this approach to nearly 400 stream gaging sites across the US, showing how complex catchment dynamics can be reconstructed solely from rainfall and runoff measurements. We also show how the approach discovers surrogate models that can replicate the dynamics of a much more complex process-based model, but at a fraction of the computational complexity. We discuss how the resulting representation of watershed dynamics provides insight and computational efficiency to enable automated predictions across large sensor networks.

中文翻译：

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从数据自动生成可解释的降雨径流模型


数据的突然激增给水资源管理带来了新的挑战，包括质量控制、同化和分析。很少有方法可以将不断增长的数据整合到可解释的结果中。基于过程的水文模型并非设计为消耗大量数据。或者，新的机器学习工具可以自动进行数据分析和预测，但它们缺乏可解释性并且依赖于非常大的数据集，这限制了见解的发现，并可能影响信任。为了解决这一差距，我们提出了一种新方法，该方法试图在基于过程的建模和基于数据的建模之间找到一个中间地带。这项工作的贡献在于一种自动化和可扩展的方法，该方法仅使用降雨和径流测量来发现水文系统中的微分方程和潜在状态估计。我们展示了这如何使自动化工具仅从测量中学习 6 到 18 个参数的可解释模型。我们将这种方法应用于美国近 400 个河流测量站点，展示了如何仅从降雨和径流测量中重建复杂的集水区动态。我们还展示了该方法如何发现替代模型，这些模型可以复制更复杂的基于过程的模型的动态，但计算复杂性只是其中的一小部分。我们将讨论流域动力学的结果表示如何提供洞察力和计算效率，以实现跨大型传感器网络的自动预测。