The riverbed is a physical interface that connects stream water and subsurface sediments and microbial communities. Complex hydro-biogeochemical interactions and strong gradients occur at the interface driven by interactions among shear stress, pressure gradients, and turbulent mixing of solutes mediated by microbial reactions. Among those interactions, sediment size heterogeneity and flow intermittency play a fundamental role as defining geological and hydrological control factors. Despite widely recognized importance, the role of sediment heterogeneity and flow intermittency in hydro-biogeochemical parameterization is rarely studied due to the scarcity of sediment size data across sediment to river reach scales. We shed lights on this issue by first introducing AI4GSD, an end-to-end modular AI Python toolkit, capable of continually mapping sediment sizes from millimeters to a hundred meters scales along a river shoreline and then evaluating how this sediment size heterogeneity affects streamed hydrobiogeochemical parameterization using water depth and velocity simulated from a large-scale computational fluid dynamics modeling along the same shoreline. Our results showed that fine scale sediment texture contributes to 15-52% mean absolute percent biases in hydro-biogeochemical parameters compared to values estimated using reach-average grain sizes and such impact is further enhanced by up to several folds when flow shifts from perennial to intermittent stage.