Evaluating approximations of river channel shape using a national cross section database

Many hydrologic applications require basic information on the size and shape of river channels, but measuring cross section (XS) geometry in the field or via remote sensing can be costly and often provides only partial coverage. Given these challenges, we capitalized upon an existing data set of 46,971 XS from gaging stations to evaluate various approximations of channel shape. After screening and pre-processing these data, we fit four model types to each XS, including a new approach that involves Stacking PDFs (probability density functions) to Approximate River Channel Shapes (SPARCS). This framework produced depth estimates that closely matched field measurements, with typical cross-sectional area errors