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.

使用机器学习技术研究了以质量保持感知器为基本计算单元、基于有向图架构的简明可解释的集水区尺度水文模型的适用性，重点关注单一位置的架构复杂性，而非广泛适用于大样本集水区；研究旨在发现代表主要过程的最小表示（细胞状态和流动路径的数量），以解释给定集水区的输入状态和输出行为，并特别强调模拟流量动态的完整范围（高、中、低）。研究发现，在研究位置上，具有三个细胞状态和两个主要流动路径的类 HyMod 架构实现了这样的表示；然而，额外融入输入绕行机制显著提高了水文图的时序和形状，而双向地下水质量交换的引入则显著提高了基流的模拟。总体而言，我们的结果表明在模型评估中使用多个诊断指标的重要性，同时强调需要设计更适合提取完整流量动态信息的训练指标。此外，通过使用神经架构搜索确定不同水文气候区域集水区的适当最小表示，我们为基于大样本数据的可解释的区域尺度 MCP 水文模拟建立了基础。