Predicting fluvial sediment process domains and floodplain (dis)connectivity from high-resolution topography

Fluvial sediment process domains (a.k.a. sediment regimes) conceptualize rivers as discrete segments with characteristic and variable tendencies to source, move, and store sediment, providing a useful categorization of river-network sediment connectivity with implications for river management. However, reaches are traditionally classified by expert assessment of field-collected data, preventing extensions to large domains. Here, we document that hydraulic properties automatically extracted from reach-averaged cross sections derived from high-resolution Digital Elevation Models (DEMs) encode the geomorphic signature of sediment regimes. We leveraged a Vermont, USA, dataset of field-labeled sediment regimes and used supervised machine learning to classify reaches by DEM-derived features. Random forest classifiers attained cross-validated balanced accuracies of 0.73-0.92 on a coarse-to-fine partitioning of the dataset, distinguishing reaches based on valley confinement, channel incision, and regime characteristics, although classification failed in confined settings. Prediction of sediment regimes from DEMs advances regional-scale assessments of river hazards, habitat, and water quality.