While many modern studies are dedicated to ML-based large-sample hydrologic modeling, these efforts have not necessarily translated into predictive improvements that are grounded in enhanced physical-conceptual understanding. Here, we report on a CONUS-wide large-sample study (spanning diverse hydro-geo-climatic conditions) using ML-augmented physically-interpretable catchment-scale models of varying complexity based in the Mass-Conserving Perceptron (MCP). Results were evaluated using attribute masks such as snow regime, forest cover, and climate zone. Our results indicate the importance of selecting model architectures of appropriate model complexity based on how process dominance varies with hydrological regime. Benchmark comparisons show that physically-interpretable mass-conserving MCP-based models can achieve performance comparable to data-based models based in the Long Short-Term Memory network (LSTM) architecture. Overall, this study highlights the potential of a theory-informed, physically grounded approach to large-sample hydrology, with emphasis on mechanistic understanding and the development of parsimonious and interpretable model architectures, thereby laying the foundation for future models of everywhere that architecturally encode information about spatially- and temporally-varying process dominance.

翻译：尽管许多现代研究致力于基于机器学习的大样本水文建模，但这些努力未必转化为基于增强的物理概念理解的预测改进。本文报告了一项覆盖全美国大陆的大样本研究（涵盖多种水文-地质-气候条件），采用基于质量守恒感知机（MCP）的机器学习增强型物理可解释流域尺度模型（复杂度各异），并使用雪情、森林覆盖、气候区等属性掩膜对结果进行了评估。我们的结果表明，根据过程主导性随水文情势的变化选择适当复杂度模型架构的重要性。基准比较显示，基于物理可解释质量守恒MCP的模型能够达到与基于长短期记忆网络（LSTM）架构的数据驱动模型相当的预测性能。总体而言，本研究强调了基于理论指导、物理基础的大样本水文学方法的潜力，注重机理理解与简约可解释模型架构的开发，从而为未来能够通过架构编码空间与时间变化过程主导性的普适模型奠定基础。