High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization

Sediment size regulates flow resistance, sediment transport, stream–groundwater exchange and microbial respiration, yet its distributions remain poorly mapped at scales relevant to hydrobiogeochemical (HBGC) processes. Existing methods are labor-intensive, site-limited and difficult to reproduce, leaving the role of sediment heterogeneity in HBGC dynamics poorly constrained. Here we present a computer-vision artificial intelligence (AI) framework that uses drone and smartphone videos to map centimeter-resolution grain size percentiles with automated uncertainty quantification. Three models, i.e., Scale AI, Anthro AI and Grain AI, are developed to estimate pixel size and uncertainty, remove anthropogenic objects, and measure individual grains, respectively. Working together, they achieved 3–4% error and 4.9% reproducibility error in median grain size mapping and sampling efficiencies up to 173 square-meters per second, nearly five orders of magnitude faster than traditional surveys. Incorporating the AI-derived heterogeneous grain-size fields into established HBGC relationships shows that accounting for centimeter-scale heterogeneity changes HBGC metrics by 15-52% in terms of mean absolute relative errors and up to 60-220% relative errors in extreme conditions, when compared to estimates using reach-averaged grain size, with the largest effects at fine scales and in non-perennial streams. Our results reveal a spatiotemporal scale- and flow intermittency-dependent role of sediment heterogeneity in HBGC function and provide a high-throughput approach to map it across river networks in a reproducible and quality-controlled manner.