  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 properly selecting and designing the training metrics
based on information-theoretic foundations that are better suited to extracting
information across the full range of flow dynamics. This study sets 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.
