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、可解释的集总式水文模型中的适用性，该模型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。本研究的重点在于单一地点的架构复杂度（深度），而非跨大规模汇水流域样本的通用适用性（广度）。目标是发现能够解释特定流域输入-状态-输出行为主导过程的极小化表征（细胞状态数与流路径数），特别强调对全范围（高、中、低）流动态的模拟。研究发现，包含三种细胞状态和两条主要流路径的HyMod类架构可在研究地点实现此类表征，但额外加入输入旁路机制可显著改善水文过程线的时间与形态，而纳入双向地下水质量交换则能显著增强基流模拟效果。总体而言，我们的结果表明采用多种诊断指标进行模型评估的重要性，同时突显了设计更适合提取全范围流动态信息的训练指标的必要性。此外，该研究通过神经架构搜索确定不同水文气候区流域的合适极小化表征，为基于MCP的可解释区域尺度水文建模（利用大样本数据）奠定基础。