We investigate the applicability of machine learning technologies to the development of parsimonious, interpretable, catchment-scale hydrologic models using directed-graph architectures based on the mass-conserving perceptron (MCP) as the fundamental computational unit. Here, we focus on architectural complexity (depth) at a single location, rather than universal applicability (breadth) across large samples of catchments. The goal is to discover a minimal representation (numbers of cell-states and flow paths) that represents the dominant processes that can explain the input-state-output behaviors of a given catchment, with particular emphasis given to simulating the full range (high, medium, and low) of flow dynamics. We find that a HyMod-like architecture with three cell-states and two major flow pathways achieves such a representation at our study location, but that the additional incorporation of an input-bypass mechanism significantly improves the timing and shape of the hydrograph, while the inclusion of bi-directional groundwater mass exchanges significantly enhances the simulation of baseflow. Overall, our results demonstrate the importance of using multiple diagnostic metrics for model evaluation, while highlighting the need for designing training metrics that are better suited to extracting information across the full range of flow dynamics. Further, they set the stage for interpretable regional-scale MCP-based hydrological modeling (using large sample data) by using neural architecture search to determine appropriate minimal representations for catchments in different hydroclimatic regimes.

翻译：我们研究了机器学习技术在开发简约、可解释的流域尺度水文模型中的适用性，采用基于质量守恒感知机（MCP）作为基本计算单元的有向图架构。此处，我们重点关注单一站点的架构复杂性（深度），而非跨大量流域样本的普适性（广度）。目标是发现一种最小化表示（单元状态数量与流动路径数量），以表征主导过程并解释特定流域的输入-状态-输出行为，尤其强调模拟高、中、低全范围流量动态。研究发现，在研究对象站点上，采用类似HyMod的三单元状态与两条主流动路径的架构可实现此类表示，但额外引入输入旁路机制显著改善了流量过程线的时序与形态，而双向地下水质量交换则大幅增强了基流模拟效果。总体而言，我们的结果证明了使用多诊断指标评估模型的重要性，同时强调需设计更适用于全范围流量动态信息提取的训练指标。此外，本研究通过利用神经架构搜索确定不同水文气候区流域的适宜最小化表示，为基于MCP的可解释区域尺度水文建模（使用大样本数据）奠定了基础。