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Effective catchment hydrologic representations are low-dimensional but interaction-dependent
Hydrologic models must reconcile predictive flexibility with physical interpretability, yet the complexity required to reproduce catchment dynamics remains uncertain. Here we compare conceptual hydrologic models and neural networks built from mass-conserving computational units across 513 catchments
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